From 4f5190edbeec40ae3c6cb50a989f1bf3c27930fb Mon Sep 17 00:00:00 2001 From: CritasWang Date: Thu, 9 Jan 2025 15:12:53 +0800 Subject: [PATCH] fix all Dead Link --- src/.vuepress/config_check_links.ts | 1 + .../Data-Model-and-Terminology.md | 4 +- .../Master/Tree/Basic-Concept/Query-Data.md | 2 +- .../IoTDB-Introduction_apache.md | 2 +- .../Tree/Reference/UDF-Libraries_apache.md | 4 +- .../SQL-Manual/Operator-and-Expression.md | 38 +- .../Master/Tree/SQL-Manual/SQL-Manual.md | 38 +- .../Tree/SQL-Manual/UDF-Libraries_apache.md | 4 +- .../Tree/SQL-Manual/UDF-Libraries_timecho.md | 4 +- .../Tree/User-Manual/IoTDB-View_timecho.md | 2 +- .../User-defined-function_apache.md | 2 +- .../User-defined-function_timecho.md | 2 +- src/UserGuide/V1.2.x/QuickStart/QuickStart.md | 4 +- .../V1.2.x/User-Manual/Query-Data.md | 2 +- .../Data-Model-and-Terminology.md | 2 +- .../IoTDB-Package.md | 23 + .../Ecosystem-Integration/Thingsboard.md | 2 +- .../IoTDB-Introduction_apache.md | 2 +- .../V1.3.0-2/Reference/UDF-Libraries.md | 23 + .../Reference/UDF-Libraries_apache.md | 4 +- .../Reference/UDF-Libraries_timecho.md | 4 +- .../V1.3.0-2/SQL-Manual/SQL-Manual.md | 8 +- .../V1.3.0-2/User-Manual/Data-Sync_apache.md | 2 +- .../V1.3.0-2/User-Manual/Operate-Metadata.md | 23 + .../Tree/API/Programming-Java-Native-API.md | 2 +- .../Tree/Background-knowledge/Data-Type.md | 2 +- .../Data-Model-and-Terminology.md | 4 +- .../Tree/Basic-Concept/Operate-Metadata.md | 23 + .../Basic-Concept/Operate-Metadata_apache.md | 2 +- .../Basic-Concept/Operate-Metadata_timecho.md | 2 +- .../V2.0.1/Tree/Basic-Concept/Query-Data.md | 6 +- .../Tree/Basic-Concept/Write-Delete-Data.md | 2 +- .../IoTDB-Package.md | 23 + .../Tree/Ecosystem-Integration/Thingsboard.md | 2 +- .../IoTDB-Introduction_apache.md | 2 +- .../V2.0.1/Tree/Reference/Syntax-Rule.md | 2 +- .../Tree/Reference/UDF-Libraries_apache.md | 5244 ----------------- .../SQL-Manual/Operator-and-Expression.md | 40 +- .../V2.0.1/Tree/SQL-Manual/SQL-Manual.md | 38 +- .../V2.0.1/Tree/SQL-Manual/UDF-Libraries.md | 23 + .../Tree/SQL-Manual/UDF-Libraries_apache.md | 4 +- .../Tree/SQL-Manual/UDF-Libraries_timecho.md | 4 +- .../Tree/User-Manual/Data-Sync_apache.md | 2 +- .../Tree/User-Manual/IoTDB-View_timecho.md | 2 +- .../User-defined-function_apache.md | 2 +- .../User-defined-function_timecho.md | 2 +- .../latest/API/Programming-Java-Native-API.md | 2 +- .../latest/Background-knowledge/Data-Type.md | 2 +- .../Data-Model-and-Terminology.md | 4 +- .../latest/Basic-Concept/Operate-Metadata.md | 23 + .../Basic-Concept/Operate-Metadata_apache.md | 2 +- .../Basic-Concept/Operate-Metadata_timecho.md | 2 +- .../latest/Basic-Concept/Query-Data.md | 6 +- .../latest/Basic-Concept/Write-Delete-Data.md | 2 +- .../IoTDB-Package.md | 23 + .../Ecosystem-Integration/Thingsboard.md | 2 +- .../IoTDB-Introduction_apache.md | 2 +- src/UserGuide/latest/Reference/Syntax-Rule.md | 2 +- .../latest/Reference/UDF-Libraries_apache.md | 5244 ----------------- .../SQL-Manual/Operator-and-Expression.md | 38 +- src/UserGuide/latest/SQL-Manual/SQL-Manual.md | 38 +- .../latest/SQL-Manual/UDF-Libraries.md | 23 + .../latest/SQL-Manual/UDF-Libraries_apache.md | 4 +- .../SQL-Manual/UDF-Libraries_timecho.md | 4 +- .../latest/User-Manual/Data-Sync_apache.md | 2 +- .../latest/User-Manual/IoTDB-View_timecho.md | 2 +- .../User-defined-function_apache.md | 2 +- .../User-defined-function_timecho.md | 2 +- .../IoTDB-Introduction_apache.md | 2 +- .../Data-Model-and-Terminology.md | 4 +- .../Tree/Basic-Concept/Operate-Metadata.md | 23 + .../Master/Tree/Basic-Concept/Query-Data.md | 2 +- .../IoTDB-Introduction_apache.md | 2 +- .../SQL-Manual/Operator-and-Expression.md | 38 +- .../Master/Tree/SQL-Manual/SQL-Manual.md | 18 +- .../Tree/User-Manual/IoTDB-View_timecho.md | 2 +- .../User-defined-function_apache.md | 2 +- .../User-defined-function_timecho.md | 2 +- .../UserGuide/V1.2.x/QuickStart/QuickStart.md | 2 +- .../User-Manual/Database-Programming.md | 2 +- .../V1.2.x/User-Manual/Query-Data.md | 2 +- .../Data-Model-and-Terminology.md | 2 +- .../IoTDB-Introduction_apache.md | 2 +- .../V1.3.0-2/Reference/UDF-Libraries.md | 23 + .../V1.3.0-2/User-Manual/Data-Sync_apache.md | 2 +- .../V1.3.0-2/User-Manual/Operate-Metadata.md | 23 + .../Table/Background-knowledge/Data-Type.md | 2 +- .../IoTDB-Introduction_apache.md | 4 +- .../Tree/API/Programming-Java-Native-API.md | 2 +- .../Data-Model-and-Terminology.md | 4 +- .../Tree/Basic-Concept/Operate-Metadata.md | 23 + .../Basic-Concept/Operate-Metadata_apache.md | 2 +- .../Basic-Concept/Operate-Metadata_timecho.md | 2 +- .../V2.0.1/Tree/Basic-Concept/Query-Data.md | 6 +- .../IoTDB-Introduction_apache.md | 2 +- .../V2.0.1/Tree/Reference/Syntax-Rule.md | 2 +- .../SQL-Manual/Operator-and-Expression.md | 40 +- .../V2.0.1/Tree/SQL-Manual/SQL-Manual.md | 18 +- .../V2.0.1/Tree/SQL-Manual/UDF-Libraries.md | 23 + .../Tree/User-Manual/Data-Sync_apache.md | 2 +- .../Tree/User-Manual/IoTDB-View_timecho.md | 2 +- .../User-defined-function_apache.md | 2 +- .../User-defined-function_timecho.md | 2 +- .../latest/API/Programming-Java-Native-API.md | 2 +- .../Data-Model-and-Terminology.md | 4 +- .../latest/Basic-Concept/Operate-Metadata.md | 23 + .../Basic-Concept/Operate-Metadata_apache.md | 2 +- .../Basic-Concept/Operate-Metadata_timecho.md | 2 +- .../latest/Basic-Concept/Query-Data.md | 12 +- .../IoTDB-Introduction_apache.md | 2 +- .../UserGuide/latest/Reference/Syntax-Rule.md | 2 +- .../SQL-Manual/Operator-and-Expression.md | 38 +- .../UserGuide/latest/SQL-Manual/SQL-Manual.md | 18 +- .../latest/SQL-Manual/UDF-Libraries.md | 23 + .../latest/User-Manual/Data-Sync_apache.md | 2 +- .../latest/User-Manual/IoTDB-View_timecho.md | 2 +- .../User-defined-function_apache.md | 2 +- .../User-defined-function_timecho.md | 2 +- 118 files changed, 687 insertions(+), 10806 deletions(-) create mode 100644 src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/IoTDB-Package.md create mode 100644 src/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md create mode 100644 src/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md create mode 100644 src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md create mode 100644 src/UserGuide/V2.0.1/Tree/Deployment-and-Maintenance/IoTDB-Package.md delete mode 100644 src/UserGuide/V2.0.1/Tree/Reference/UDF-Libraries_apache.md create mode 100644 src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md create mode 100644 src/UserGuide/latest/Basic-Concept/Operate-Metadata.md create mode 100644 src/UserGuide/latest/Deployment-and-Maintenance/IoTDB-Package.md delete mode 100644 src/UserGuide/latest/Reference/UDF-Libraries_apache.md create mode 100644 src/UserGuide/latest/SQL-Manual/UDF-Libraries.md create mode 100644 src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata.md create mode 100644 src/zh/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md create mode 100644 src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md create mode 100644 src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md create mode 100644 src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md create mode 100644 src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata.md create mode 100644 src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries.md diff --git a/src/.vuepress/config_check_links.ts b/src/.vuepress/config_check_links.ts index 537fa449d..7c26e064b 100644 --- a/src/.vuepress/config_check_links.ts +++ b/src/.vuepress/config_check_links.ts @@ -21,6 +21,7 @@ import { defineUserConfig } from 'vuepress'; import config from './config.js'; if(config.plugins === undefined) config.plugins = []; + config.plugins = [...config.plugins,linksCheckPlugin({build: 'error'})]; export default defineUserConfig(config); diff --git a/src/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md b/src/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md index 597c1691a..e1aeb3564 100644 --- a/src/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md @@ -109,7 +109,7 @@ In order to make it easier and faster to express multiple timeseries paths, IoTD ### Timestamp -The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps. For detailed description, please go to [Data Type doc](./Data-Type.md). +The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps. For detailed description, please go to [Data Type doc](../Background-knowledge/Data-Type.md). ### Data point @@ -147,6 +147,6 @@ In the following chapters of data definition language, data operation language a ## Schema Template -In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../User-Manual/Operate-Metadata_timecho.md#Device-Template). +In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../Basic-Concept/Operate-Metadata.md#Device-Template). In the following chapters of, data definition language, data operation language and Java Native Interface, various operations related to schema template will be introduced one by one. diff --git a/src/UserGuide/Master/Tree/Basic-Concept/Query-Data.md b/src/UserGuide/Master/Tree/Basic-Concept/Query-Data.md index 62fc3c9f9..e97b3bcf6 100644 --- a/src/UserGuide/Master/Tree/Basic-Concept/Query-Data.md +++ b/src/UserGuide/Master/Tree/Basic-Concept/Query-Data.md @@ -3002,7 +3002,7 @@ The user must have the following permissions to execute a query write-back state * All `WRITE_SCHEMA` permissions for the source series in the `select` clause. * All `WRITE_DATA` permissions for the target series in the `into` clause. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ### Configurable Properties diff --git a/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md index e783f74b3..94a1a30d0 100644 --- a/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -74,4 +74,4 @@ Timecho provides a more diverse range of product features, stronger performance - Timecho Official website：https://www.timecho.com/ -- TimechoDB installation, deployment and usage documentation：[QuickStart](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB installation, deployment and usage documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/UserGuide/Master/Tree/Reference/UDF-Libraries_apache.md b/src/UserGuide/Master/Tree/Reference/UDF-Libraries_apache.md index ab63a071c..8afe3d514 100644 --- a/src/UserGuide/Master/Tree/Reference/UDF-Libraries_apache.md +++ b/src/UserGuide/Master/Tree/Reference/UDF-Libraries_apache.md @@ -606,14 +606,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md b/src/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md index ff2507a84..1b6fd667f 100644 --- a/src/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md +++ b/src/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md @@ -21,11 +21,11 @@ # Operator and Expression -This chapter describes the operators and functions supported by IoTDB. IoTDB provides a wealth of built-in operators and functions to meet your computing needs, and supports extensions through the [User-Defined Function](../Reference/UDF-Libraries.md). +This chapter describes the operators and functions supported by IoTDB. IoTDB provides a wealth of built-in operators and functions to meet your computing needs, and supports extensions through the [User-Defined Function](../SQL-Manual/UDF-Libraries.md). A list of all available functions, both built-in and custom, can be displayed with `SHOW FUNCTIONS` command. -See the documentation [Select-Expression](../Reference/Function-and-Expression.md#selector-functions) for the behavior of operators and functions in SQL. +See the documentation [Select-Expression](../SQL-Manual/Function-and-Expression.md#selector-functions) for the behavior of operators and functions in SQL. ## OPERATORS @@ -41,7 +41,7 @@ See the documentation [Select-Expression](../Reference/Function-and-Expression.m | `+` | addition | | `-` | subtraction | -For details and examples, see the document [Arithmetic Operators and Functions](../Reference/Function-and-Expression.md#arithmetic-functions). +For details and examples, see the document [Arithmetic Operators and Functions](../SQL-Manual/Function-and-Expression.md#arithmetic-functions). ### Comparison Operators @@ -64,7 +64,7 @@ For details and examples, see the document [Arithmetic Operators and Functions]( | `IN` / `CONTAINS` | is a value in the specified list | | `NOT IN` / `NOT CONTAINS` | is not a value in the specified list | -For details and examples, see the document [Comparison Operators and Functions](../Reference/Function-and-Expression.md#comparison-operators-and-functions). +For details and examples, see the document [Comparison Operators and Functions](../SQL-Manual/Function-and-Expression.md#comparison-operators-and-functions). ### Logical Operators @@ -74,7 +74,7 @@ For details and examples, see the document [Comparison Operators and Functions]( | `AND` / `&` / `&&` | logical AND | | `OR`/ | / || | logical OR | -For details and examples, see the document [Logical Operators](../Reference/Function-and-Expression.md#logical-operators). +For details and examples, see the document [Logical Operators](../SQL-Manual/Function-and-Expression.md#logical-operators). ### Operator Precedence @@ -123,7 +123,7 @@ The built-in functions can be used in IoTDB without registration, and the functi | MAX_BY | MAX_BY(x, y) returns the value of x corresponding to the maximum value of the input y. MAX_BY(time, x) returns the timestamp when x is at its maximum value. | The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE. | / | Consistent with the data type of the first input x. | | MIN_BY | MIN_BY(x, y) returns the value of x corresponding to the minimum value of the input y. MIN_BY(time, x) returns the timestamp when x is at its minimum value. | The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE. | / | Consistent with the data type of the first input x. | -For details and examples, see the document [Aggregate Functions](../Reference/Function-and-Expression.md#aggregate-functions). +For details and examples, see the document [Aggregate Functions](../SQL-Manual/Function-and-Expression.md#aggregate-functions). ### Arithmetic Functions @@ -150,7 +150,7 @@ For details and examples, see the document [Aggregate Functions](../Reference/Fu | LOG10 | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | / | Math#log10(double) | | SQRT | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | / | Math#sqrt(double) | -For details and examples, see the document [Arithmetic Operators and Functions](../Reference/Function-and-Expression.md#arithmetic-operators-and-functions). +For details and examples, see the document [Arithmetic Operators and Functions](../SQL-Manual/Function-and-Expression.md#arithmetic-operators-and-functions). ### Comparison Functions @@ -159,7 +159,7 @@ For details and examples, see the document [Arithmetic Operators and Functions]( | ON_OFF | INT32 / INT64 / FLOAT / DOUBLE | `threshold`: a double type variate | BOOLEAN | Return `ts_value >= threshold`. | | IN_RANGR | INT32 / INT64 / FLOAT / DOUBLE | `lower`: DOUBLE type `upper`: DOUBLE type | BOOLEAN | Return `ts_value >= lower && value <= upper`. | -For details and examples, see the document [Comparison Operators and Functions](../Reference/Function-and-Expression.md#comparison-operators-and-functions). +For details and examples, see the document [Comparison Operators and Functions](../SQL-Manual/Function-and-Expression.md#comparison-operators-and-functions). ### String Processing Functions @@ -179,7 +179,7 @@ For details and examples, see the document [Comparison Operators and Functions]( | TRIM | TEXT STRING | / | TEXT | Get the string whose value is same to input series, with all leading and trailing space removed. | | STRCMP | TEXT STRING | / | TEXT | Get the compare result of two input series. Returns `0` if series value are the same, a `negative integer` if value of series1 is smaller than series2,
a `positive integer` if value of series1 is more than series2. | -For details and examples, see the document [String Processing](../Reference/Function-and-Expression.md#string-processing). +For details and examples, see the document [String Processing](../SQL-Manual/Function-and-Expression.md#string-processing). ### Data Type Conversion Function @@ -187,7 +187,7 @@ For details and examples, see the document [String Processing](../Reference/Func | ------------- | ------------------------------------------------------------ | ----------------------- | ------------------------------------------------------------ | | CAST | `type`: Output data type, INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT | determined by `type` | Convert the data to the type specified by the `type` parameter. | -For details and examples, see the document [Data Type Conversion Function](../Reference/Function-and-Expression.md#data-type-conversion-function). +For details and examples, see the document [Data Type Conversion Function](../SQL-Manual/Function-and-Expression.md#data-type-conversion-function). ### Constant Timeseries Generating Functions @@ -197,7 +197,7 @@ For details and examples, see the document [Data Type Conversion Function](../Re | PI | None | DOUBLE | Data point value: a `double` value of `π`, the ratio of the circumference of a circle to its diameter, which is equals to `Math.PI` in the *Java Standard Library*. | | E | None | DOUBLE | Data point value: a `double` value of `e`, the base of the natural logarithms, which is equals to `Math.E` in the *Java Standard Library*. | -For details and examples, see the document [Constant Timeseries Generating Functions](../Reference/Function-and-Expression.md#constant-timeseries-generating-functions). +For details and examples, see the document [Constant Timeseries Generating Functions](../SQL-Manual/Function-and-Expression.md#constant-timeseries-generating-functions). ### Selector Functions @@ -206,7 +206,7 @@ For details and examples, see the document [Constant Timeseries Generating Funct | TOP_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000 | Same type as the input series | Returns `k` data points with the largest values in a time series. | | BOTTOM_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000 | Same type as the input series | Returns `k` data points with the smallest values in a time series. | -For details and examples, see the document [Selector Functions](../Reference/Function-and-Expression.md#selector-functions). +For details and examples, see the document [Selector Functions](../SQL-Manual/Function-and-Expression.md#selector-functions). ### Continuous Interval Functions @@ -217,7 +217,7 @@ For details and examples, see the document [Selector Functions](../Reference/Fun | ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE` | Long | Return intervals' start times and the number of data points in the interval in which the value is always 0(false). Data points number `n` satisfy `n >= min && n <= max` | | NON_ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE` | Long | Return intervals' start times and the number of data points in the interval in which the value is always not 0(false). Data points number `n` satisfy `n >= min && n <= max` | -For details and examples, see the document [Continuous Interval Functions](../Reference/Function-and-Expression.md#continuous-interval-functions). +For details and examples, see the document [Continuous Interval Functions](../SQL-Manual/Function-and-Expression.md#continuous-interval-functions). ### Variation Trend Calculation Functions @@ -230,7 +230,7 @@ For details and examples, see the document [Continuous Interval Functions](../Re | NON_NEGATIVE_DERIVATIVE | INT32 / INT64 / FLOAT / DOUBLE | / | DOUBLE | Calculates the absolute value of the rate of change of a data point compared to the previous data point, the result is equals to NON_NEGATIVE_DIFFERENCE / TIME_DIFFERENCE. There is no corresponding output for the first data point. | | DIFF | INT32 / INT64 / FLOAT / DOUBLE | `ignoreNull`：optional，default is true. If is true, the previous data point is ignored when it is null and continues to find the first non-null value forwardly. If the value is false, previous data point is not ignored when it is null, the result is also null because null is used for subtraction | DOUBLE | Calculates the difference between the value of a data point and the value of the previous data point. There is no corresponding output for the first data point, so output is null | -For details and examples, see the document [Variation Trend Calculation Functions](../Reference/Function-and-Expression.md#variation-trend-calculation-functions). +For details and examples, see the document [Variation Trend Calculation Functions](../SQL-Manual/Function-and-Expression.md#variation-trend-calculation-functions). ### Sample Functions @@ -250,7 +250,7 @@ For details and examples, see the document [Sample Functions](../SQL-Manual/Func | ------------- | ------------------------------- | ------------------- | ----------------------------- | ----------------------------------------------------------- | | CHANGE_POINTS | INT32 / INT64 / FLOAT / DOUBLE | / | Same type as the input series | Remove consecutive identical values from an input sequence. | -For details and examples, see the document [Time-Series](../Reference/Function-and-Expression.md#time-series-processing). +For details and examples, see the document [Time-Series](../SQL-Manual/Function-and-Expression.md#time-series-processing). ## LAMBDA EXPRESSION @@ -259,7 +259,7 @@ For details and examples, see the document [Time-Series](../Reference/Function-a | ------------- | ----------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------- | ------------------------------------------------------------ | | JEXL | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | `expr` is a lambda expression that supports standard one or multi arguments in the form `x -> {...}` or `(x, y, z) -> {...}`, e.g. `x -> {x * 2}`, `(x, y, z) -> {x + y * z}` | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | Returns the input time series transformed by a lambda expression | -For details and examples, see the document [Lambda](../Reference/Function-and-Expression.md#lambda-expression). +For details and examples, see the document [Lambda](../SQL-Manual/Function-and-Expression.md#lambda-expression). ## CONDITIONAL EXPRESSION @@ -267,7 +267,7 @@ For details and examples, see the document [Lambda](../Reference/Function-and-Ex | --------------- | -------------------- | | `CASE` | similar to "if else" | -For details and examples, see the document [Conditional Expressions](../Reference/Function-and-Expression.md#conditional-expressions). +For details and examples, see the document [Conditional Expressions](../SQL-Manual/Function-and-Expression.md#conditional-expressions). ## SELECT EXPRESSION @@ -322,7 +322,7 @@ Aggregate functions are many-to-one functions. They perform aggregate calculatio > select a, count(a) from root.sg group by ([10,100),10ms) > ``` -For the aggregation functions supported by IoTDB, see the document [Aggregate Functions](../Reference/Function-and-Expression.md#aggregate-functions). +For the aggregation functions supported by IoTDB, see the document [Aggregate Functions](../SQL-Manual/Function-and-Expression.md#aggregate-functions). #### Time Series Generation Function @@ -337,7 +337,7 @@ See this documentation for a list of built-in functions supported in IoTDB. ##### User-Defined Time Series Generation Functions -IoTDB supports function extension through User Defined Function (click for [User-Defined Function](./Database-Programming.md#udtfuser-defined-timeseries-generating-function)) capability. +IoTDB supports function extension through User Defined Function (click for [User-Defined Function](../User-Manual/Database-Programming.md#udtfuser-defined-timeseries-generating-function)) capability. ### Nested Expressions diff --git a/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md b/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md index 9f0967438..2a078041c 100644 --- a/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md +++ b/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md @@ -23,7 +23,7 @@ ## DATABASE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Create Database @@ -105,7 +105,7 @@ IoTDB> SHOW DEVICES ## DEVICE TEMPLATE -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ![img](https://alioss.timecho.com/docs/img/%E6%A8%A1%E6%9D%BF.png) @@ -184,7 +184,7 @@ IoTDB> alter device template t1 add (speed FLOAT encoding=RLE, FLOAT TEXT encodi ## TIMESERIES MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Create Timeseries @@ -364,7 +364,7 @@ The above operations are supported for timeseries tag, attribute updates, etc. ## NODE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Show Child Paths @@ -409,7 +409,7 @@ IoTDB> count devices root.ln.** ### Insert Data -For more details, see document [Write-Delete-Data](../User-Manual/Write-Delete-Data.md). +For more details, see document [Write-Delete-Data](../Basic-Concept/Write-Delete-Data.md). #### Use of INSERT Statements @@ -471,7 +471,7 @@ For more details, see document [Data Import](../Tools-System/Data-Import-Tool.md ## DELETE DATA -For more details, see document [Write-Delete-Data](../User-Manual/Write-Delete-Data.md). +For more details, see document [Write-Delete-Data](../Basic-Concept/Write-Delete-Data.md). ### Delete Single Timeseries @@ -508,7 +508,7 @@ IoTDB > DELETE PARTITION root.ln 0,1,2 ## QUERY DATA -For more details, see document [Query-Data](../User-Manual/Query-Data.md). +For more details, see document [Query-Data](../Basic-Concept/Query-Data.md). ```sql SELECT [LAST] selectExpr [, selectExpr] ... @@ -1090,11 +1090,11 @@ select change_points(s1), change_points(s2), change_points(s3), change_points(s4 ## DATA QUALITY FUNCTION LIBRARY -For more details, see document [Operator-and-Expression](./UDF-Libraries_timecho.md). +For more details, see document [Operator-and-Expression](../SQL-Manual/UDF-Libraries.md). ### Data Quality -For details and examples, see the document [Data-Quality](./UDF-Libraries_timecho.md#data-quality). +For details and examples, see the document [Data-Quality](../SQL-Manual/UDF-Libraries.md#data-quality). ```sql # Completeness @@ -1119,7 +1119,7 @@ select Accuracy(t1,t2,t3,m1,m2,m3) from root.test ### Data Profiling -For details and examples, see the document [Data-Profiling](./UDF-Libraries_timecho.md#data-profiling). +For details and examples, see the document [Data-Profiling](../SQL-Manual/UDF-Libraries.md#data-profiling). ```sql # ACF @@ -1199,7 +1199,7 @@ select zscore(s1) from root.test ### Anomaly Detection -For details and examples, see the document [Anomaly-Detection](./UDF-Libraries_timecho.md#anomaly-detection). +For details and examples, see the document [Anomaly-Detection](../SQL-Manual/UDF-Libraries.md#anomaly-detection). ```sql # IQR @@ -1234,7 +1234,7 @@ select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3 ### Frequency Domain -For details and examples, see the document [Frequency-Domain](./UDF-Libraries_timecho.md#frequency-domain-analysis). +For details and examples, see the document [Frequency-Domain](../SQL-Manual/UDF-Libraries.md#frequency-domain-analysis). ```sql # Conv @@ -1266,7 +1266,7 @@ select envelope(s1) from root.test.d1 ### Data Matching -For details and examples, see the document [Data-Matching](./UDF-Libraries_timecho.md#data-matching). +For details and examples, see the document [Data-Matching](../SQL-Manual/UDF-Libraries.md#data-matching). ```sql # Cov @@ -1287,7 +1287,7 @@ select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 ### Data Repairing -For details and examples, see the document [Data-Repairing](./UDF-Libraries_timecho.md#data-repairing). +For details and examples, see the document [Data-Repairing](../SQL-Manual/UDF-Libraries.md#data-repairing). ```sql # TimestampRepair @@ -1312,7 +1312,7 @@ select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 ### Series Discovery -For details and examples, see the document [Series-Discovery](./UDF-Libraries_timecho.md#series-discovery). +For details and examples, see the document [Series-Discovery](../SQL-Manual/UDF-Libraries.md#series-discovery). ```sql # ConsecutiveSequences @@ -1325,7 +1325,7 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 ### Machine Learning -For details and examples, see the document [Machine-Learning](./UDF-Libraries_timecho.md#machine-learning). +For details and examples, see the document [Machine-Learning](../SQL-Manual/UDF-Libraries.md#machine-learning). ```sql # AR @@ -1340,7 +1340,7 @@ select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0 ## LAMBDA EXPRESSION -For details and examples, see the document [Lambda](./UDF-Libraries_timecho.md#lambda-expression). +For details and examples, see the document [Lambda](../SQL-Manual/UDF-Libraries.md#lambda-expression). ```sql select jexl(temperature, 'expr'='x -> {x + x}') as jexl1, jexl(temperature, 'expr'='x -> {x * 3}') as jexl2, jexl(temperature, 'expr'='x -> {x * x}') as jexl3, jexl(temperature, 'expr'='x -> {multiply(x, 100)}') as jexl4, jexl(temperature, st, 'expr'='(x, y) -> {x + y}') as jexl5, jexl(temperature, st, str, 'expr'='(x, y, z) -> {x + y + z}') as jexl6 from root.ln.wf01.wt01;``` @@ -1348,7 +1348,7 @@ select jexl(temperature, 'expr'='x -> {x + x}') as jexl1, jexl(temperature, 'exp ## CONDITIONAL EXPRESSION -For details and examples, see the document [Conditional Expressions](./UDF-Libraries_timecho.md#conditional-expressions). +For details and examples, see the document [Conditional Expressions](../SQL-Manual/UDF-Libraries.md#conditional-expressions). ```sql select T, P, case @@ -1548,7 +1548,7 @@ CQs can't be altered once they're created. To change a CQ, you must `DROP` and r ## USER-DEFINED FUNCTION (UDF) -For more details, see document [Operator-and-Expression](./UDF-Libraries_timecho.md). +For more details, see document [Operator-and-Expression](../SQL-Manual/UDF-Libraries.md). ### UDF Registration diff --git a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md index a4f786005..8bab853b8 100644 --- a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md +++ b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md @@ -607,14 +607,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md index d96a60b14..d4ee30c76 100644 --- a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md +++ b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md @@ -606,14 +606,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md b/src/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md index 161890722..ba013039d 100644 --- a/src/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md +++ b/src/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md @@ -308,7 +308,7 @@ AS SELECT temperature FROM root.db.* ``` -This is modelled on the query writeback (`SELECT INTO`) convention for naming rules, which uses variable placeholders to specify naming rules. See also: [QUERY WRITEBACK (SELECT INTO)](../User-Manual/Query-Data.md#into-clause-query-write-back) +This is modelled on the query writeback (`SELECT INTO`) convention for naming rules, which uses variable placeholders to specify naming rules. See also: [QUERY WRITEBACK (SELECT INTO)](../Basic-Concept/Query-Data.md#into-clause-query-write-back) Here `root.db.*.temperature` specifies what time series will be included in the view; and `${2}` specifies from which node in the time series the name is extracted to name the sequence view. diff --git a/src/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md b/src/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md index 72325f08e..42413bcad 100644 --- a/src/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md +++ b/src/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md @@ -190,7 +190,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 When users use UDF, they will be involved in the `USE_UDF` permission, and only users with this permission are allowed to perform UDF registration, uninstallation, and query operations. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ## 4. UDF Libraries diff --git a/src/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md b/src/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md index 2b91554ba..63c195ce8 100644 --- a/src/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md +++ b/src/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md @@ -190,7 +190,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 When users use UDF, they will be involved in the `USE_UDF` permission, and only users with this permission are allowed to perform UDF registration, uninstallation, and query operations. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ## 4. UDF Libraries diff --git a/src/UserGuide/V1.2.x/QuickStart/QuickStart.md b/src/UserGuide/V1.2.x/QuickStart/QuickStart.md index cd86ffde8..0cabe9889 100644 --- a/src/UserGuide/V1.2.x/QuickStart/QuickStart.md +++ b/src/UserGuide/V1.2.x/QuickStart/QuickStart.md @@ -244,7 +244,7 @@ The server can be stopped using `ctrl-C` or by running the following script: ``` Note: In Linux, please add the `sudo` as far as possible, or else the stopping process may fail. -More explanations on running IoTDB in a clustered environment are available at [Cluster-Setup](../Deployment-and-Maintenance/Deployment-Guide_timecho.md). +More explanations on running IoTDB in a clustered environment are available at [Cluster-Setup](../Deployment-and-Maintenance/Deployment-Guide.md). ### Administration @@ -260,7 +260,7 @@ ALTER USER SET PASSWORD ; Example: IoTDB > ALTER USER root SET PASSWORD 'newpwd'; ``` -More about administration options：[Administration Management](../User-Manual/Security-Management_timecho.md) +More about administration options：[Administration Management](../User-Manual/Authority-Management.md) ## Basic configuration diff --git a/src/UserGuide/V1.2.x/User-Manual/Query-Data.md b/src/UserGuide/V1.2.x/User-Manual/Query-Data.md index 443fb223f..6df43c3d0 100644 --- a/src/UserGuide/V1.2.x/User-Manual/Query-Data.md +++ b/src/UserGuide/V1.2.x/User-Manual/Query-Data.md @@ -2880,7 +2880,7 @@ The user must have the following permissions to execute a query write-back state * All `READ_TIMESERIES` permissions for the source series in the `select` clause. * All `INSERT_TIMESERIES` permissions for the target series in the `into` clause. -For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Security-Management_timecho.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ### Configurable Properties diff --git a/src/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md b/src/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md index 6cfc76b08..9571fdf11 100644 --- a/src/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md @@ -144,6 +144,6 @@ In the following chapters of data definition language, data operation language a ## Schema Template -In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../User-Manual/Operate-Metadata_timecho.md#Device-Template). +In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../User-Manual/Operate-Metadata.md#Device-Template). In the following chapters of, data definition language, data operation language and Java Native Interface, various operations related to schema template will be introduced one by one. diff --git a/src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/IoTDB-Package.md b/src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/IoTDB-Package.md new file mode 100644 index 000000000..6057ef6a2 --- /dev/null +++ b/src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/IoTDB-Package.md @@ -0,0 +1,23 @@ +--- +redirectTo: IoTDB-Package_apache.html +--- + \ No newline at end of file diff --git a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md index 3afbdd42c..46b37ee77 100644 --- a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md +++ b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md @@ -41,7 +41,7 @@ | **Preparation Content** | **Version Requirements** | | :---------------------------------------- | :----------------------------------------------------------- | | JDK | JDK17 or above. Please refer to the downloads on [Oracle Official Website](https://www.oracle.com/java/technologies/downloads/) | -| IoTDB |IoTDB v1.3.0 or above. Please refer to the [Deployment guidance](../Deployment-and-Maintenance/IoTDB-Package_timecho.md) | +| IoTDB |IoTDB v1.3.0 or above. Please refer to the [Deployment guidance](../Deployment-and-Maintenance/IoTDB-Package.md) | | ThingsBoard
(IoTDB adapted version) | Please contact Timecho staff to obtain the installation package. Detailed installation steps are provided below. | ## Installation Steps diff --git a/src/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md index e783f74b3..2e6b05e48 100644 --- a/src/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -74,4 +74,4 @@ Timecho provides a more diverse range of product features, stronger performance - Timecho Official website：https://www.timecho.com/ -- TimechoDB installation, deployment and usage documentation：[QuickStart](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB installation, deployment and usage documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/V1.3.0-2/QuickStart/QuickStart_timecho.html) diff --git a/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md b/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md new file mode 100644 index 000000000..2867a78eb --- /dev/null +++ b/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md @@ -0,0 +1,23 @@ +--- +redirectTo: UDF-Libraries_apache.html +--- + \ No newline at end of file diff --git a/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_apache.md b/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_apache.md index a4f786005..8bab853b8 100644 --- a/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_apache.md +++ b/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_apache.md @@ -607,14 +607,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_timecho.md b/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_timecho.md index ab7cf77bb..02745cf67 100644 --- a/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_timecho.md +++ b/src/UserGuide/V1.3.0-2/Reference/UDF-Libraries_timecho.md @@ -605,14 +605,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md b/src/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md index f217fae26..bd49782cb 100644 --- a/src/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md +++ b/src/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md @@ -23,7 +23,7 @@ ## DATABASE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata.md). ### Create Database @@ -104,7 +104,7 @@ IoTDB> SHOW TTL ON StorageGroupNames ## DEVICE TEMPLATE -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata.md). ![img](https://alioss.timecho.com/docs/img/%E6%A8%A1%E6%9D%BF.png) @@ -180,7 +180,7 @@ IoTDB> alter device template t1 add (speed FLOAT encoding=RLE, FLOAT TEXT encodi ## TIMESERIES MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata.md). ### Create Timeseries @@ -362,7 +362,7 @@ The above operations are supported for timeseries tag, attribute updates, etc. ## NODE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata.md). ### Show Child Paths diff --git a/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md b/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md index 0913f71eb..883009c42 100644 --- a/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md +++ b/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md @@ -263,7 +263,7 @@ Detailed introduction of pre-installed plugins is as follows (for detailed param -For importing custom plugins, please refer to the [Stream Processing](./Streaming_timecho.md#custom-stream-processing-plugin-management) section. +For importing custom plugins, please refer to the [Stream Processing](./Streaming_apache.md#custom-stream-processing-plugin-management) section. ## Use examples diff --git a/src/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md b/src/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md new file mode 100644 index 000000000..4eb80c594 --- /dev/null +++ b/src/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md @@ -0,0 +1,23 @@ +--- +redirectTo: Operate-Metadata_apache.html +--- + diff --git a/src/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md b/src/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md index 387a9e075..08c0cb1f6 100644 --- a/src/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md +++ b/src/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md @@ -43,7 +43,7 @@ ## Syntax Convention -- **IoTDB-SQL interface:** The input SQL parameter needs to conform to the [syntax conventions](../User-Manual/Syntax-Rule.md#Literal-Values) and be escaped for JAVA strings. For example, you need to add a backslash before the double-quotes. (That is: after JAVA escaping, it is consistent with the SQL statement executed on the command line.) +- **IoTDB-SQL interface:** The input SQL parameter needs to conform to the [syntax conventions](../Reference/Syntax-Rule.md#Literal-Values) and be escaped for JAVA strings. For example, you need to add a backslash before the double-quotes. (That is: after JAVA escaping, it is consistent with the SQL statement executed on the command line.) - **Other interfaces:** - The node names in path or path prefix as parameter: The node names which should be escaped by backticks (`) in the SQL statement, escaping is required here. - Identifiers (such as template names) as parameters: The identifiers which should be escaped by backticks (`) in the SQL statement, and escaping is not required here. diff --git a/src/UserGuide/V2.0.1/Tree/Background-knowledge/Data-Type.md b/src/UserGuide/V2.0.1/Tree/Background-knowledge/Data-Type.md index 846e8067c..bc1f03e1a 100644 --- a/src/UserGuide/V2.0.1/Tree/Background-knowledge/Data-Type.md +++ b/src/UserGuide/V2.0.1/Tree/Background-knowledge/Data-Type.md @@ -40,7 +40,7 @@ The difference between STRING and TEXT types is that STRING type has more statis ### Float Precision -The time series of **FLOAT** and **DOUBLE** type can specify (MAX\_POINT\_NUMBER, see [this page](../SQL-Manual/SQL-Manual.md) for more information on how to specify), which is the number of digits after the decimal point of the floating point number, if the encoding method is [RLE](Encoding-and-Compression.md) or [TS\_2DIFF](Encoding-and-Compression.md). If MAX\_POINT\_NUMBER is not specified, the system will use [float\_precision](../Reference/DataNode-Config-Manual.md) in the configuration file `iotdb-system.properties`. +The time series of **FLOAT** and **DOUBLE** type can specify (MAX\_POINT\_NUMBER, see [this page](../SQL-Manual/SQL-Manual.md) for more information on how to specify), which is the number of digits after the decimal point of the floating point number, if the encoding method is [RLE](../Technical-Insider/Encoding-and-Compression.md) or [TS\_2DIFF](../Technical-Insider/Encoding-and-Compression.md). If MAX\_POINT\_NUMBER is not specified, the system will use [float\_precision](../Reference/DataNode-Config-Manual.md) in the configuration file `iotdb-system.properties`. ```sql CREATE TIMESERIES root.vehicle.d0.s0 WITH DATATYPE=FLOAT, ENCODING=RLE, 'MAX_POINT_NUMBER'='2'; diff --git a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md index 597c1691a..e1aeb3564 100644 --- a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md @@ -109,7 +109,7 @@ In order to make it easier and faster to express multiple timeseries paths, IoTD ### Timestamp -The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps. For detailed description, please go to [Data Type doc](./Data-Type.md). +The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps. For detailed description, please go to [Data Type doc](../Background-knowledge/Data-Type.md). ### Data point @@ -147,6 +147,6 @@ In the following chapters of data definition language, data operation language a ## Schema Template -In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../User-Manual/Operate-Metadata_timecho.md#Device-Template). +In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../Basic-Concept/Operate-Metadata.md#Device-Template). In the following chapters of, data definition language, data operation language and Java Native Interface, various operations related to schema template will be introduced one by one. diff --git a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md new file mode 100644 index 000000000..e0ddf712e --- /dev/null +++ b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md @@ -0,0 +1,23 @@ +--- +redirectTo: Operate-Metadata_apache.html +--- + \ No newline at end of file diff --git a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md index 58c01a886..45becb7bd 100644 --- a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md +++ b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md @@ -612,7 +612,7 @@ IoTDB > create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODI error: encoding TS_2DIFF does not support BOOLEAN ``` -Please refer to [Encoding](../Basic-Concept/Encoding-and-Compression.md) for correspondence between data type and encoding. +Please refer to [Encoding](../Technical-Insider/Encoding-and-Compression.md) for correspondence between data type and encoding. ### Create Aligned Timeseries diff --git a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md index 8d57facb1..8306823fc 100644 --- a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md +++ b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md @@ -613,7 +613,7 @@ IoTDB > create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODI error: encoding TS_2DIFF does not support BOOLEAN ``` -Please refer to [Encoding](../Basic-Concept/Encoding-and-Compression.md) for correspondence between data type and encoding. +Please refer to [Encoding](../Technical-Insider/Encoding-and-Compression.md) for correspondence between data type and encoding. ### Create Aligned Timeseries diff --git a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md index 62fc3c9f9..4f38b287e 100644 --- a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md +++ b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md @@ -440,7 +440,7 @@ The supported operators are as follows: ### Time Filter -Use time filters to filter data for a specific time range. For supported formats of timestamps, please refer to [Timestamp](../Basic-Concept/Data-Type.md) . +Use time filters to filter data for a specific time range. For supported formats of timestamps, please refer to [Timestamp](../Background-knowledge/Data-Type.md) . An example is as follows: @@ -2934,7 +2934,7 @@ This statement specifies that `root.sg_copy.d1` is an unaligned device and `root #### Other points to note - For general aggregation queries, the timestamp is meaningless, and the convention is to use 0 to store. -- When the target time-series exists, the data type of the source column and the target time-series must be compatible. About data type compatibility, see the document [Data Type](../Basic-Concept/Data-Type.md#Data Type Compatibility). +- When the target time-series exists, the data type of the source column and the target time-series must be compatible. About data type compatibility, see the document [Data Type](../Background-knowledge/Data-Type.md#Data Type Compatibility). - When the target time series does not exist, the system automatically creates it (including the database). - When the queried time series does not exist, or the queried sequence does not have data, the target time series will not be created automatically. @@ -3002,7 +3002,7 @@ The user must have the following permissions to execute a query write-back state * All `WRITE_SCHEMA` permissions for the source series in the `select` clause. * All `WRITE_DATA` permissions for the target series in the `into` clause. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ### Configurable Properties diff --git a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Write-Delete-Data.md b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Write-Delete-Data.md index 9c694a616..b445bf3ef 100644 --- a/src/UserGuide/V2.0.1/Tree/Basic-Concept/Write-Delete-Data.md +++ b/src/UserGuide/V2.0.1/Tree/Basic-Concept/Write-Delete-Data.md @@ -31,7 +31,7 @@ Writing a repeat timestamp covers the original timestamp data, which can be rega ### Use of INSERT Statements -The [INSERT SQL statement](../SQL-Manual/SQL-Manual.md#insert-data) statement is used to insert data into one or more specified timeseries created. For each point of data inserted, it consists of a [timestamp](../Basic-Concept/Data-Model-and-Terminology.md) and a sensor acquisition value (see [Data Type](../Basic-Concept/Data-Type.md)). +The [INSERT SQL statement](../SQL-Manual/SQL-Manual.md#insert-data) statement is used to insert data into one or more specified timeseries created. For each point of data inserted, it consists of a [timestamp](../Basic-Concept/Data-Model-and-Terminology.md) and a sensor acquisition value (see [Data Type](../Background-knowledge/Data-Type.md)). **Schema-less writing**: When metadata is not defined, data can be directly written through an insert statement, and the required metadata will be automatically recognized and registered in the database, achieving automatic modeling. diff --git a/src/UserGuide/V2.0.1/Tree/Deployment-and-Maintenance/IoTDB-Package.md b/src/UserGuide/V2.0.1/Tree/Deployment-and-Maintenance/IoTDB-Package.md new file mode 100644 index 000000000..6057ef6a2 --- /dev/null +++ b/src/UserGuide/V2.0.1/Tree/Deployment-and-Maintenance/IoTDB-Package.md @@ -0,0 +1,23 @@ +--- +redirectTo: IoTDB-Package_apache.html +--- + \ No newline at end of file diff --git a/src/UserGuide/V2.0.1/Tree/Ecosystem-Integration/Thingsboard.md b/src/UserGuide/V2.0.1/Tree/Ecosystem-Integration/Thingsboard.md index 072d3c378..c0fdf50e8 100644 --- a/src/UserGuide/V2.0.1/Tree/Ecosystem-Integration/Thingsboard.md +++ b/src/UserGuide/V2.0.1/Tree/Ecosystem-Integration/Thingsboard.md @@ -41,7 +41,7 @@ | **Preparation Content** | **Version Requirements** | | :---------------------------------------- | :----------------------------------------------------------- | | JDK | JDK17 or above. Please refer to the downloads on [Oracle Official Website](https://www.oracle.com/java/technologies/downloads/) | -| IoTDB |IoTDB v1.3.0 or above. Please refer to the [Deployment guidance](../Deployment-and-Maintenance/IoTDB-Package_timecho.md) | +| IoTDB |IoTDB v1.3.0 or above. Please refer to the [Deployment guidance](../Deployment-and-Maintenance/IoTDB-Package.md) | | ThingsBoard
(IoTDB adapted version) | Please contact Timecho staff to obtain the installation package. Detailed installation steps are provided below. | ## Installation Steps diff --git a/src/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md index e783f74b3..94a1a30d0 100644 --- a/src/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -74,4 +74,4 @@ Timecho provides a more diverse range of product features, stronger performance - Timecho Official website：https://www.timecho.com/ -- TimechoDB installation, deployment and usage documentation：[QuickStart](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB installation, deployment and usage documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md b/src/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md index 320fa1464..38dffc6ac 100644 --- a/src/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md +++ b/src/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md @@ -146,7 +146,7 @@ An integer may be used in floating-point context; it is interpreted as the equiv ### Timestamp Literals -The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps in IoTDB. For information about timestamp support in IoTDB, see [Data Type Doc](../Basic-Concept/Data-Type.md). +The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps in IoTDB. For information about timestamp support in IoTDB, see [Data Type Doc](../Background-knowledge/Data-Type.md). Specially, `NOW()` represents a constant timestamp that indicates the system time at which the statement began to execute. diff --git a/src/UserGuide/V2.0.1/Tree/Reference/UDF-Libraries_apache.md b/src/UserGuide/V2.0.1/Tree/Reference/UDF-Libraries_apache.md deleted file mode 100644 index f7b68d4fb..000000000 --- a/src/UserGuide/V2.0.1/Tree/Reference/UDF-Libraries_apache.md +++ /dev/null @@ -1,5244 +0,0 @@ - - -# UDF Libraries - -# UDF Libraries - -Based on the ability of user-defined functions, IoTDB provides a series of functions for temporal data processing, including data quality, data profiling, anomaly detection, frequency domain analysis, data matching, data repairing, sequence discovery, machine learning, etc., which can meet the needs of industrial fields for temporal data processing. - -> Note: The functions in the current UDF library only support millisecond level timestamp accuracy. - -## Installation steps - -1. Please obtain the compressed file of the UDF library JAR package that is compatible with the IoTDB version. - - | UDF libraries version | Supported IoTDB versions | Download link | - | --------------- | ----------------- | ------------------------------------------------------------ | - | UDF-1.3.3.zip | V1.3.3 and above | Please contact Timecho for assistance | - | UDF-1.3.2.zip | V1.0.0～V1.3.2 | Please contact Timecho for assistance| - -2. Place the library-udf.jar file in the compressed file obtained in the directory `/ext/udf ` of all nodes in the IoTDB cluster -3. In the SQL command line terminal (CLI) or visualization console (Workbench) SQL operation interface of IoTDB, execute the corresponding function registration statement as follows. -4. Batch registration: Two registration methods: registration script or SQL full statement -- Register Script - - Copy the registration script (register-UDF.sh or register-UDF.bat) from the compressed package to the `tools` directory of IoTDB as needed, and modify the parameters in the script (default is host=127.0.0.1, rpcPort=6667, user=root, pass=root); - - Start IoTDB service, run registration script to batch register UDF - -- All SQL statements - - Open the SQl file in the compressed package, copy all SQL statements, and execute all SQl statements in the SQL command line terminal (CLI) of IoTDB or the SQL operation interface of the visualization console (Workbench) to batch register UDF - -## Data Quality - -### Completeness - -#### Registration statement - -```sql -create function completeness as 'org.apache.iotdb.library.dquality.UDTFCompleteness' -``` - -#### Usage - -This function is used to calculate the completeness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the completeness of each window will be output. - -**Name:** COMPLETENESS - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. -+ `downtime`: Whether the downtime exception is considered in the calculation of completeness. It is 'true' or 'false' (default). When considering the downtime exception, long-term missing data will be considered as downtime exception without any influence on completeness. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select completeness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+-----------------------------+ -| Time|completeness(root.test.d1.s1)| -+-----------------------------+-----------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.875| -+-----------------------------+-----------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select completeness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------+ -| Time|completeness(root.test.d1.s1, "window"="15")| -+-----------------------------+--------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.875| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+--------------------------------------------+ -``` - -### Consistency - -#### Registration statement - -```sql -create function consistency as 'org.apache.iotdb.library.dquality.UDTFConsistency' -``` - -#### Usage - -This function is used to calculate the consistency of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the consistency of each window will be output. - -**Name:** CONSISTENCY - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select consistency(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+----------------------------+ -| Time|consistency(root.test.d1.s1)| -+-----------------------------+----------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -+-----------------------------+----------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select consistency(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------+ -| Time|consistency(root.test.d1.s1, "window"="15")| -+-----------------------------+-------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+-------------------------------------------+ -``` - -### Timeliness - -#### Registration statement - -```sql -create function timeliness as 'org.apache.iotdb.library.dquality.UDTFTimeliness' -``` - -#### Usage - -This function is used to calculate the timeliness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the timeliness of each window will be output. - -**Name:** TIMELINESS - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select timeliness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+---------------------------+ -| Time|timeliness(root.test.d1.s1)| -+-----------------------------+---------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -+-----------------------------+---------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select timeliness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------+ -| Time|timeliness(root.test.d1.s1, "window"="15")| -+-----------------------------+------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+------------------------------------------+ -``` - -### Validity - -#### Registration statement - -```sql -create function validity as 'org.apache.iotdb.library.dquality.UDTFValidity' -``` - -#### Usage - -This function is used to calculate the Validity of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the Validity of each window will be output. - -**Name:** VALIDITY - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select Validity(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|validity(root.test.d1.s1)| -+-----------------------------+-------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.8833333333333333| -+-----------------------------+-------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select Validity(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------+ -| Time|validity(root.test.d1.s1, "window"="15")| -+-----------------------------+----------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.8833333333333333| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+----------------------------------------+ -``` - - - - - -## Data Profiling - -### ACF - -#### Registration statement - -```sql -create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' -``` - -#### Usage - -This function is used to calculate the auto-correlation factor of the input time series, -which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. - -**Name:** ACF - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. - -**Note:** - -+ `null` and `NaN` values in the input series will be ignored and treated as 0. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| null| -|2020-01-01T00:00:03.000+08:00| 3| -|2020-01-01T00:00:04.000+08:00| NaN| -|2020-01-01T00:00:05.000+08:00| 5| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select acf(s1) from root.test.d1 where time <= 2020-01-01 00:00:05 -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|acf(root.test.d1.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.001+08:00| 1.0| -|1970-01-01T08:00:00.002+08:00| 0.0| -|1970-01-01T08:00:00.003+08:00| 3.6| -|1970-01-01T08:00:00.004+08:00| 0.0| -|1970-01-01T08:00:00.005+08:00| 7.0| -|1970-01-01T08:00:00.006+08:00| 0.0| -|1970-01-01T08:00:00.007+08:00| 3.6| -|1970-01-01T08:00:00.008+08:00| 0.0| -|1970-01-01T08:00:00.009+08:00| 1.0| -+-----------------------------+--------------------+ -``` - -### Distinct - -#### Registration statement - -```sql -create function distinct as 'org.apache.iotdb.library.dprofile.UDTFDistinct' -``` - -#### Usage - -This function returns all unique values in time series. - -**Name:** DISTINCT - -**Input Series:** Only support a single input series. The type is arbitrary. - -**Output Series:** Output a single series. The type is the same as the input. - -**Note:** - -+ The timestamp of the output series is meaningless. The output order is arbitrary. -+ Missing points and null points in the input series will be ignored, but `NaN` will not. -+ Case Sensitive. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s2| -+-----------------------------+---------------+ -|2020-01-01T08:00:00.001+08:00| Hello| -|2020-01-01T08:00:00.002+08:00| hello| -|2020-01-01T08:00:00.003+08:00| Hello| -|2020-01-01T08:00:00.004+08:00| World| -|2020-01-01T08:00:00.005+08:00| World| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select distinct(s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|distinct(root.test.d2.s2)| -+-----------------------------+-------------------------+ -|1970-01-01T08:00:00.001+08:00| Hello| -|1970-01-01T08:00:00.002+08:00| hello| -|1970-01-01T08:00:00.003+08:00| World| -+-----------------------------+-------------------------+ -``` - -### Histogram - -#### Registration statement - -```sql -create function histogram as 'org.apache.iotdb.library.dprofile.UDTFHistogram' -``` - -#### Usage - -This function is used to calculate the distribution histogram of a single column of numerical data. - -**Name:** HISTOGRAM - -**Input Series:** Only supports a single input sequence, the type is INT32 / INT64 / FLOAT / DOUBLE - -**Parameters:** - -+ `min`: The lower limit of the requested data range, the default value is -Double.MAX_VALUE. -+ `max`: The upper limit of the requested data range, the default value is Double.MAX_VALUE, and the value of start must be less than or equal to end. -+ `count`: The number of buckets of the histogram, the default value is 1. It must be a positive integer. - -**Output Series:** The value of the bucket of the histogram, where the lower bound represented by the i-th bucket (index starts from 1) is $min+ (i-1)\cdot\frac{max-min}{count}$ and the upper bound is $min + i \cdot \frac{max-min}{count}$. - -**Note:** - -+ If the value is lower than `min`, it will be put into the 1st bucket. If the value is larger than `max`, it will be put into the last bucket. -+ Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| -|2020-01-01T00:00:01.000+08:00| 2.0| -|2020-01-01T00:00:02.000+08:00| 3.0| -|2020-01-01T00:00:03.000+08:00| 4.0| -|2020-01-01T00:00:04.000+08:00| 5.0| -|2020-01-01T00:00:05.000+08:00| 6.0| -|2020-01-01T00:00:06.000+08:00| 7.0| -|2020-01-01T00:00:07.000+08:00| 8.0| -|2020-01-01T00:00:08.000+08:00| 9.0| -|2020-01-01T00:00:09.000+08:00| 10.0| -|2020-01-01T00:00:10.000+08:00| 11.0| -|2020-01-01T00:00:11.000+08:00| 12.0| -|2020-01-01T00:00:12.000+08:00| 13.0| -|2020-01-01T00:00:13.000+08:00| 14.0| -|2020-01-01T00:00:14.000+08:00| 15.0| -|2020-01-01T00:00:15.000+08:00| 16.0| -|2020-01-01T00:00:16.000+08:00| 17.0| -|2020-01-01T00:00:17.000+08:00| 18.0| -|2020-01-01T00:00:18.000+08:00| 19.0| -|2020-01-01T00:00:19.000+08:00| 20.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select histogram(s1,"min"="1","max"="20","count"="10") from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+---------------------------------------------------------------+ -| Time|histogram(root.test.d1.s1, "min"="1", "max"="20", "count"="10")| -+-----------------------------+---------------------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 2| -|1970-01-01T08:00:00.001+08:00| 2| -|1970-01-01T08:00:00.002+08:00| 2| -|1970-01-01T08:00:00.003+08:00| 2| -|1970-01-01T08:00:00.004+08:00| 2| -|1970-01-01T08:00:00.005+08:00| 2| -|1970-01-01T08:00:00.006+08:00| 2| -|1970-01-01T08:00:00.007+08:00| 2| -|1970-01-01T08:00:00.008+08:00| 2| -|1970-01-01T08:00:00.009+08:00| 2| -+-----------------------------+---------------------------------------------------------------+ -``` - -### Integral - -#### Registration statement - -```sql -create function integral as 'org.apache.iotdb.library.dprofile.UDAFIntegral' -``` - -#### Usage - -This function is used to calculate the integration of time series, -which equals to the area under the curve with time as X-axis and values as Y-axis. - -**Name:** INTEGRAL - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `unit`: The unit of time used when computing the integral. - The value should be chosen from "1S", "1s", "1m", "1H", "1d"(case-sensitive), - and each represents taking one millisecond / second / minute / hour / day as 1.0 while calculating the area and integral. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the integration. - -**Note:** - -+ The integral value equals to the sum of the areas of right-angled trapezoids consisting of each two adjacent points and the time-axis. - Choosing different `unit` implies different scaling of time axis, thus making it apparent to convert the value among those results with constant coefficient. - -+ `NaN` values in the input series will be ignored. The curve or trapezoids will skip these points and use the next valid point. - -#### Examples - -##### Default Parameters - -With default parameters, this function will take one second as 1.0. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| 2| -|2020-01-01T00:00:03.000+08:00| 5| -|2020-01-01T00:00:04.000+08:00| 6| -|2020-01-01T00:00:05.000+08:00| 7| -|2020-01-01T00:00:08.000+08:00| 8| -|2020-01-01T00:00:09.000+08:00| NaN| -|2020-01-01T00:00:10.000+08:00| 10| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select integral(s1) from root.test.d1 where time <= 2020-01-01 00:00:10 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|integral(root.test.d1.s1)| -+-----------------------------+-------------------------+ -|1970-01-01T08:00:00.000+08:00| 57.5| -+-----------------------------+-------------------------+ -``` - -Calculation expression: -$$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 57.5$$ - -##### Specific time unit - -With time unit specified as "1m", this function will take one minute as 1.0. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select integral(s1, "unit"="1m") from root.test.d1 where time <= 2020-01-01 00:00:10 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|integral(root.test.d1.s1)| -+-----------------------------+-------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.958| -+-----------------------------+-------------------------+ -``` - -Calculation expression: -$$\frac{1}{2\times 60}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 0.958$$ - -### IntegralAvg - -#### Registration statement - -```sql -create function integralavg as 'org.apache.iotdb.library.dprofile.UDAFIntegralAvg' -``` - -#### Usage - -This function is used to calculate the function average of time series. -The output equals to the area divided by the time interval using the same time `unit`. -For more information of the area under the curve, please refer to `Integral` function. - -**Name:** INTEGRALAVG - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the time-weighted average. - -**Note:** - -+ The time-weighted value equals to the integral value with any `unit` divided by the time interval of input series. - The result is irrelevant to the time unit used in integral, and it's consistent with the timestamp precision of IoTDB by default. - -+ `NaN` values in the input series will be ignored. The curve or trapezoids will skip these points and use the next valid point. - -+ If the input series is empty, the output value will be 0.0, but if there is only one data point, the value will equal to the input value. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| 2| -|2020-01-01T00:00:03.000+08:00| 5| -|2020-01-01T00:00:04.000+08:00| 6| -|2020-01-01T00:00:05.000+08:00| 7| -|2020-01-01T00:00:08.000+08:00| 8| -|2020-01-01T00:00:09.000+08:00| NaN| -|2020-01-01T00:00:10.000+08:00| 10| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select integralavg(s1) from root.test.d1 where time <= 2020-01-01 00:00:10 -``` - -Output series: - -``` -+-----------------------------+----------------------------+ -| Time|integralavg(root.test.d1.s1)| -+-----------------------------+----------------------------+ -|1970-01-01T08:00:00.000+08:00| 5.75| -+-----------------------------+----------------------------+ -``` - -Calculation expression: -$$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] / 10 = 5.75$$ - -### Mad - -#### Registration statement - -```sql -create function mad as 'org.apache.iotdb.library.dprofile.UDAFMad' -``` - -#### Usage - -The function is used to compute the exact or approximate median absolute deviation (MAD) of a numeric time series. MAD is the median of the deviation of each element from the elements' median. - -Take a dataset $\{1,3,3,5,5,6,7,8,9\}$ as an instance. Its median is 5 and the deviation of each element from the median is $\{0,0,1,2,2,2,3,4,4\}$, whose median is 2. Therefore, the MAD of the original dataset is 2. - -**Name:** MAD - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `error`: The relative error of the approximate MAD. It should be within [0,1) and the default value is 0. Taking `error`=0.01 as an instance, suppose the exact MAD is $a$ and the approximate MAD is $b$, we have $0.99a \le b \le 1.01a$. With `error`=0, the output is the exact MAD. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the MAD. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -##### Exact Query - -With the default `error`(`error`=0), the function queries the exact MAD. - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -............ -Total line number = 20 -``` - -SQL for query: - -```sql -select mad(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -| Time|median(root.test.s1, "error"="0")| -+-----------------------------+---------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -+-----------------------------+---------------------------------+ -``` - -##### Approximate Query - -By setting `error` within (0,1), the function queries the approximate MAD. - -SQL for query: - -```sql -select mad(s1, "error"="0.01") from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -| Time|mad(root.test.s1, "error"="0.01")| -+-----------------------------+---------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.9900000000000001| -+-----------------------------+---------------------------------+ -``` - -### Median - -#### Registration statement - -```sql -create function median as 'org.apache.iotdb.library.dprofile.UDAFMedian' -``` - -#### Usage - -The function is used to compute the exact or approximate median of a numeric time series. Median is the value separating the higher half from the lower half of a data sample. - -**Name:** MEDIAN - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `error`: The rank error of the approximate median. It should be within [0,1) and the default value is 0. For instance, a median with `error`=0.01 is the value of the element with rank percentage 0.49~0.51. With `error`=0, the output is the exact median. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the median. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -Total line number = 20 -``` - -SQL for query: - -```sql -select median(s1, "error"="0.01") from root.test -``` - -Output series: - -``` -+-----------------------------+------------------------------------+ -| Time|median(root.test.s1, "error"="0.01")| -+-----------------------------+------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -+-----------------------------+------------------------------------+ -``` - -### MinMax - -#### Registration statement - -```sql -create function minmax as 'org.apache.iotdb.library.dprofile.UDTFMinMax' -``` - -#### Usage - -This function is used to standardize the input series with min-max. Minimum value is transformed to 0; maximum value is transformed to 1. - -**Name:** MINMAX - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `compute`: When set to "batch", anomaly test is conducted after importing all data points; when set to "stream", it is required to provide minimum and maximum values. The default method is "batch". -+ `min`: The maximum value when method is set to "stream". -+ `max`: The minimum value when method is set to "stream". - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select minmax(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|minmax(root.test.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.100+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.200+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.300+08:00| 0.25| -|1970-01-01T08:00:00.400+08:00| 0.08333333333333333| -|1970-01-01T08:00:00.500+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.600+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.700+08:00| 0.0| -|1970-01-01T08:00:00.800+08:00| 0.3333333333333333| -|1970-01-01T08:00:00.900+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.000+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.100+08:00| 0.25| -|1970-01-01T08:00:01.200+08:00| 0.08333333333333333| -|1970-01-01T08:00:01.300+08:00| 0.08333333333333333| -|1970-01-01T08:00:01.400+08:00| 0.25| -|1970-01-01T08:00:01.500+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.600+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.700+08:00| 1.0| -|1970-01-01T08:00:01.800+08:00| 0.3333333333333333| -|1970-01-01T08:00:01.900+08:00| 0.0| -|1970-01-01T08:00:02.000+08:00| 0.16666666666666666| -+-----------------------------+--------------------+ -``` - - -### MvAvg - -#### Registration statement - -```sql -create function mvavg as 'org.apache.iotdb.library.dprofile.UDTFMvAvg' -``` - -#### Usage - -This function is used to calculate moving average of input series. - -**Name:** MVAVG - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `window`: Length of the moving window. Default value is 10. - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select mvavg(s1, "window"="3") from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -| Time|mvavg(root.test.s1, "window"="3")| -+-----------------------------+---------------------------------+ -|1970-01-01T08:00:00.300+08:00| 0.3333333333333333| -|1970-01-01T08:00:00.400+08:00| 0.0| -|1970-01-01T08:00:00.500+08:00| -0.3333333333333333| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -0.6666666666666666| -|1970-01-01T08:00:00.800+08:00| 0.0| -|1970-01-01T08:00:00.900+08:00| 0.6666666666666666| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 0.3333333333333333| -|1970-01-01T08:00:01.200+08:00| 0.0| -|1970-01-01T08:00:01.300+08:00| -0.6666666666666666| -|1970-01-01T08:00:01.400+08:00| 0.0| -|1970-01-01T08:00:01.500+08:00| 0.3333333333333333| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 3.3333333333333335| -|1970-01-01T08:00:01.800+08:00| 4.0| -|1970-01-01T08:00:01.900+08:00| 0.0| -|1970-01-01T08:00:02.000+08:00| -0.6666666666666666| -+-----------------------------+---------------------------------+ -``` - -### PACF - -#### Registration statement - -```sql -create function pacf as 'org.apache.iotdb.library.dprofile.UDTFPACF' -``` - -#### Usage - -This function is used to calculate partial autocorrelation of input series by solving Yule-Walker equation. For some cases, the equation may not be solved, and NaN will be output. - -**Name:** PACF - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `lag`: Maximum lag of pacf to calculate. The default value is $\min(10\log_{10}n,n-1)$, where $n$ is the number of data points. - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Assigning maximum lag - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 3| -|2020-01-01T00:00:04.000+08:00| NaN| -|2020-01-01T00:00:05.000+08:00| 5| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select pacf(s1, "lag"="5") from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------------------+ -| Time|pacf(root.test.d1.s1, "lag"="5")| -+-----------------------------+--------------------------------+ -|2020-01-01T00:00:01.000+08:00| 1.0| -|2020-01-01T00:00:02.000+08:00| -0.5744680851063829| -|2020-01-01T00:00:03.000+08:00| 0.3172297297297296| -|2020-01-01T00:00:04.000+08:00| -0.2977686586304181| -|2020-01-01T00:00:05.000+08:00| -2.0609033521065867| -+-----------------------------+--------------------------------+ -``` - -### Percentile - -#### Registration statement - -```sql -create function percentile as 'org.apache.iotdb.library.dprofile.UDAFPercentile' -``` - -#### Usage - -The function is used to compute the exact or approximate percentile of a numeric time series. A percentile is value of element in the certain rank of the sorted series. - -**Name:** PERCENTILE - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `rank`: The rank percentage of the percentile. It should be (0,1] and the default value is 0.5. For instance, a percentile with `rank`=0.5 is the median. -+ `error`: The rank error of the approximate percentile. It should be within [0,1) and the default value is 0. For instance, a 0.5-percentile with `error`=0.01 is the value of the element with rank percentage 0.49~0.51. With `error`=0, the output is the exact percentile. - -**Output Series:** Output a single series. The type is the same as input series. If `error`=0, there is only one data point in the series, whose timestamp is the same has which the first percentile value has, and value is the percentile, otherwise the timestamp of the only data point is 0. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+-------------+ -| Time|root.test2.s1| -+-----------------------------+-------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+-------------+ -Total line number = 20 -``` - -SQL for query: - -```sql -select percentile(s0, "rank"="0.2", "error"="0.01") from root.test -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------------+ -| Time|percentile(root.test2.s1, "rank"="0.2", "error"="0.01")| -+-----------------------------+-------------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| -1.0| -+-----------------------------+-------------------------------------------------------+ -``` - -### Quantile - -#### Registration statement - -```sql -create function quantile as 'org.apache.iotdb.library.dprofile.UDAFQuantile' -``` - -#### Usage - -The function is used to compute the approximate quantile of a numeric time series. A quantile is value of element in the certain rank of the sorted series. - -**Name:** QUANTILE - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `rank`: The rank of the quantile. It should be (0,1] and the default value is 0.5. For instance, a quantile with `rank`=0.5 is the median. -+ `K`: The size of KLL sketch maintained in the query. It should be within [100,+inf) and the default value is 800. For instance, the 0.5-quantile computed by a KLL sketch with K=800 items is a value with rank quantile 0.49~0.51 with a confidence of at least 99%. The result will be more accurate as K increases. - -**Output Series:** Output a single series. The type is the same as input series. The timestamp of the only data point is 0. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+-------------+ -| Time|root.test1.s1| -+-----------------------------+-------------+ -|2021-03-17T10:32:17.054+08:00| 7| -|2021-03-17T10:32:18.054+08:00| 15| -|2021-03-17T10:32:19.054+08:00| 36| -|2021-03-17T10:32:20.054+08:00| 39| -|2021-03-17T10:32:21.054+08:00| 40| -|2021-03-17T10:32:22.054+08:00| 41| -|2021-03-17T10:32:23.054+08:00| 20| -|2021-03-17T10:32:24.054+08:00| 18| -+-----------------------------+-------------+ -............ -Total line number = 8 -``` - -SQL for query: - -```sql -select quantile(s1, "rank"="0.2", "K"="800") from root.test1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------+ -| Time|quantile(root.test1.s1, "rank"="0.2", "K"="800")| -+-----------------------------+------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 7.000000000000001| -+-----------------------------+------------------------------------------------+ -``` - -### Period - -#### Registration statement - -```sql -create function period as 'org.apache.iotdb.library.dprofile.UDAFPeriod' -``` - -#### Usage - -The function is used to compute the period of a numeric time series. - -**Name:** PERIOD - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is INT32. There is only one data point in the series, whose timestamp is 0 and value is the period. - -#### Examples - -Input series: - - -``` -+-----------------------------+---------------+ -| Time|root.test.d3.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.001+08:00| 1.0| -|1970-01-01T08:00:00.002+08:00| 2.0| -|1970-01-01T08:00:00.003+08:00| 3.0| -|1970-01-01T08:00:00.004+08:00| 1.0| -|1970-01-01T08:00:00.005+08:00| 2.0| -|1970-01-01T08:00:00.006+08:00| 3.0| -|1970-01-01T08:00:00.007+08:00| 1.0| -|1970-01-01T08:00:00.008+08:00| 2.0| -|1970-01-01T08:00:00.009+08:00| 3.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select period(s1) from root.test.d3 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time|period(root.test.d3.s1)| -+-----------------------------+-----------------------+ -|1970-01-01T08:00:00.000+08:00| 3| -+-----------------------------+-----------------------+ -``` - -### QLB - -#### Registration statement - -```sql -create function qlb as 'org.apache.iotdb.library.dprofile.UDTFQLB' -``` - -#### Usage - -This function is used to calculate Ljung-Box statistics $Q_{LB}$ for time series, and convert it to p value. - -**Name:** QLB - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters**: - -`lag`: max lag to calculate. Legal input shall be integer from 1 to n-2, where n is the sample number. Default value is n-2. - -**Output Series:** Output a single series. The type is DOUBLE. The output series is p value, and timestamp means lag. - -**Note:** If you want to calculate Ljung-Box statistics $Q_{LB}$ instead of p value, you may use ACF function. - -#### Examples - -##### Using Default Parameter - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T00:00:00.100+08:00| 1.22| -|1970-01-01T00:00:00.200+08:00| -2.78| -|1970-01-01T00:00:00.300+08:00| 1.53| -|1970-01-01T00:00:00.400+08:00| 0.70| -|1970-01-01T00:00:00.500+08:00| 0.75| -|1970-01-01T00:00:00.600+08:00| -0.72| -|1970-01-01T00:00:00.700+08:00| -0.22| -|1970-01-01T00:00:00.800+08:00| 0.28| -|1970-01-01T00:00:00.900+08:00| 0.57| -|1970-01-01T00:00:01.000+08:00| -0.22| -|1970-01-01T00:00:01.100+08:00| -0.72| -|1970-01-01T00:00:01.200+08:00| 1.34| -|1970-01-01T00:00:01.300+08:00| -0.25| -|1970-01-01T00:00:01.400+08:00| 0.17| -|1970-01-01T00:00:01.500+08:00| 2.51| -|1970-01-01T00:00:01.600+08:00| 1.42| -|1970-01-01T00:00:01.700+08:00| -1.34| -|1970-01-01T00:00:01.800+08:00| -0.01| -|1970-01-01T00:00:01.900+08:00| -0.49| -|1970-01-01T00:00:02.000+08:00| 1.63| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select QLB(s1) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|QLB(root.test.d1.s1)| -+-----------------------------+--------------------+ -|1970-01-01T00:00:00.001+08:00| 0.2168702295315677| -|1970-01-01T00:00:00.002+08:00| 0.3068948509261751| -|1970-01-01T00:00:00.003+08:00| 0.4217859150918444| -|1970-01-01T00:00:00.004+08:00| 0.5114539874276656| -|1970-01-01T00:00:00.005+08:00| 0.6560619525616759| -|1970-01-01T00:00:00.006+08:00| 0.7722398654053280| -|1970-01-01T00:00:00.007+08:00| 0.8532491661465290| -|1970-01-01T00:00:00.008+08:00| 0.9028575017542528| -|1970-01-01T00:00:00.009+08:00| 0.9434989988192729| -|1970-01-01T00:00:00.010+08:00| 0.8950280161464689| -|1970-01-01T00:00:00.011+08:00| 0.7701048398839656| -|1970-01-01T00:00:00.012+08:00| 0.7845536060001281| -|1970-01-01T00:00:00.013+08:00| 0.5943030981705825| -|1970-01-01T00:00:00.014+08:00| 0.4618413512531093| -|1970-01-01T00:00:00.015+08:00| 0.2645948244673964| -|1970-01-01T00:00:00.016+08:00| 0.3167530476666645| -|1970-01-01T00:00:00.017+08:00| 0.2330010780351453| -|1970-01-01T00:00:00.018+08:00| 0.0666611237622325| -+-----------------------------+--------------------+ -``` - -### Resample - -#### Registration statement - -```sql -create function re_sample as 'org.apache.iotdb.library.dprofile.UDTFResample' -``` - -#### Usage - -This function is used to resample the input series according to a given frequency, -including up-sampling and down-sampling. -Currently, the supported up-sampling methods are -NaN (filling with `NaN`), -FFill (filling with previous value), -BFill (filling with next value) and -Linear (filling with linear interpolation). -Down-sampling relies on group aggregation, -which supports Max, Min, First, Last, Mean and Median. - -**Name:** RESAMPLE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - - -+ `every`: The frequency of resampling, which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. This parameter cannot be lacked. -+ `interp`: The interpolation method of up-sampling, which is 'NaN', 'FFill', 'BFill' or 'Linear'. By default, NaN is used. -+ `aggr`: The aggregation method of down-sampling, which is 'Max', 'Min', 'First', 'Last', 'Mean' or 'Median'. By default, Mean is used. -+ `start`: The start time (inclusive) of resampling with the format 'yyyy-MM-dd HH:mm:ss'. By default, it is the timestamp of the first valid data point. -+ `end`: The end time (exclusive) of resampling with the format 'yyyy-MM-dd HH:mm:ss'. By default, it is the timestamp of the last valid data point. - -**Output Series:** Output a single series. The type is DOUBLE. It is strictly equispaced with the frequency `every`. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -##### Up-sampling - -When the frequency of resampling is higher than the original frequency, up-sampling starts. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2021-03-06T16:00:00.000+08:00| 3.09| -|2021-03-06T16:15:00.000+08:00| 3.53| -|2021-03-06T16:30:00.000+08:00| 3.5| -|2021-03-06T16:45:00.000+08:00| 3.51| -|2021-03-06T17:00:00.000+08:00| 3.41| -+-----------------------------+---------------+ -``` - - -SQL for query: - -```sql -select resample(s1,'every'='5m','interp'='linear') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------------------------+ -| Time|resample(root.test.d1.s1, "every"="5m", "interp"="linear")| -+-----------------------------+----------------------------------------------------------+ -|2021-03-06T16:00:00.000+08:00| 3.0899999141693115| -|2021-03-06T16:05:00.000+08:00| 3.2366665999094644| -|2021-03-06T16:10:00.000+08:00| 3.3833332856496177| -|2021-03-06T16:15:00.000+08:00| 3.5299999713897705| -|2021-03-06T16:20:00.000+08:00| 3.5199999809265137| -|2021-03-06T16:25:00.000+08:00| 3.509999990463257| -|2021-03-06T16:30:00.000+08:00| 3.5| -|2021-03-06T16:35:00.000+08:00| 3.503333330154419| -|2021-03-06T16:40:00.000+08:00| 3.506666660308838| -|2021-03-06T16:45:00.000+08:00| 3.509999990463257| -|2021-03-06T16:50:00.000+08:00| 3.4766666889190674| -|2021-03-06T16:55:00.000+08:00| 3.443333387374878| -|2021-03-06T17:00:00.000+08:00| 3.4100000858306885| -+-----------------------------+----------------------------------------------------------+ -``` - -##### Down-sampling - -When the frequency of resampling is lower than the original frequency, down-sampling starts. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select resample(s1,'every'='30m','aggr'='first') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------+ -| Time|resample(root.test.d1.s1, "every"="30m", "aggr"="first")| -+-----------------------------+--------------------------------------------------------+ -|2021-03-06T16:00:00.000+08:00| 3.0899999141693115| -|2021-03-06T16:30:00.000+08:00| 3.5| -|2021-03-06T17:00:00.000+08:00| 3.4100000858306885| -+-----------------------------+--------------------------------------------------------+ -``` - - - -##### Specify the time period - -The time period of resampling can be specified with `start` and `end`. -The period outside the actual time range will be interpolated. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select resample(s1,'every'='30m','start'='2021-03-06 15:00:00') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-----------------------------------------------------------------------+ -| Time|resample(root.test.d1.s1, "every"="30m", "start"="2021-03-06 15:00:00")| -+-----------------------------+-----------------------------------------------------------------------+ -|2021-03-06T15:00:00.000+08:00| NaN| -|2021-03-06T15:30:00.000+08:00| NaN| -|2021-03-06T16:00:00.000+08:00| 3.309999942779541| -|2021-03-06T16:30:00.000+08:00| 3.5049999952316284| -|2021-03-06T17:00:00.000+08:00| 3.4100000858306885| -+-----------------------------+-----------------------------------------------------------------------+ -``` - -### Sample - -#### Registration statement - -```sql -create function sample as 'org.apache.iotdb.library.dprofile.UDTFSample' -``` - -#### Usage - -This function is used to sample the input series, -that is, select a specified number of data points from the input series and output them. -Currently, three sampling methods are supported: -**Reservoir sampling** randomly selects data points. -All of the points have the same probability of being sampled. -**Isometric sampling** selects data points at equal index intervals. -**Triangle sampling** assigns data points to the buckets based on the number of sampling. -Then it calculates the area of the triangle based on these points inside the bucket and selects the point with the largest area of the triangle. -For more detail, please read [paper](http://skemman.is/stream/get/1946/15343/37285/3/SS_MSthesis.pdf) - -**Name:** SAMPLE - -**Input Series:** Only support a single input series. The type is arbitrary. - -**Parameters:** - -+ `method`: The method of sampling, which is 'reservoir', 'isometric' or 'triangle'. By default, reservoir sampling is used. -+ `k`: The number of sampling, which is a positive integer. By default, it's 1. - -**Output Series:** Output a single series. The type is the same as the input. The length of the output series is `k`. Each data point in the output series comes from the input series. - -**Note:** If `k` is greater than the length of input series, all data points in the input series will be output. - -#### Examples - -##### Reservoir Sampling - -When `method` is 'reservoir' or the default, reservoir sampling is used. -Due to the randomness of this method, the output series shown below is only a possible result. - - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1.0| -|2020-01-01T00:00:02.000+08:00| 2.0| -|2020-01-01T00:00:03.000+08:00| 3.0| -|2020-01-01T00:00:04.000+08:00| 4.0| -|2020-01-01T00:00:05.000+08:00| 5.0| -|2020-01-01T00:00:06.000+08:00| 6.0| -|2020-01-01T00:00:07.000+08:00| 7.0| -|2020-01-01T00:00:08.000+08:00| 8.0| -|2020-01-01T00:00:09.000+08:00| 9.0| -|2020-01-01T00:00:10.000+08:00| 10.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select sample(s1,'method'='reservoir','k'='5') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|sample(root.test.d1.s1, "method"="reservoir", "k"="5")| -+-----------------------------+------------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 2.0| -|2020-01-01T00:00:03.000+08:00| 3.0| -|2020-01-01T00:00:05.000+08:00| 5.0| -|2020-01-01T00:00:08.000+08:00| 8.0| -|2020-01-01T00:00:10.000+08:00| 10.0| -+-----------------------------+------------------------------------------------------+ -``` - -##### Isometric Sampling - -When `method` is 'isometric', isometric sampling is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select sample(s1,'method'='isometric','k'='5') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|sample(root.test.d1.s1, "method"="isometric", "k"="5")| -+-----------------------------+------------------------------------------------------+ -|2020-01-01T00:00:01.000+08:00| 1.0| -|2020-01-01T00:00:03.000+08:00| 3.0| -|2020-01-01T00:00:05.000+08:00| 5.0| -|2020-01-01T00:00:07.000+08:00| 7.0| -|2020-01-01T00:00:09.000+08:00| 9.0| -+-----------------------------+------------------------------------------------------+ -``` - -### Segment - -#### Registration statement - -```sql -create function segment as 'org.apache.iotdb.library.dprofile.UDTFSegment' -``` - -#### Usage - -This function is used to segment a time series into subsequences according to linear trend, and returns linear fitted values of first values in each subsequence or every data point. - -**Name:** SEGMENT - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `output` :"all" to output all fitted points; "first" to output first fitted points in each subsequence. - -+ `error`: error allowed at linear regression. It is defined as mean absolute error of a subsequence. - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note:** This function treat input series as equal-interval sampled. All data are loaded, so downsample input series first if there are too many data points. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 5.0| -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 1.0| -|1970-01-01T08:00:00.300+08:00| 2.0| -|1970-01-01T08:00:00.400+08:00| 3.0| -|1970-01-01T08:00:00.500+08:00| 4.0| -|1970-01-01T08:00:00.600+08:00| 5.0| -|1970-01-01T08:00:00.700+08:00| 6.0| -|1970-01-01T08:00:00.800+08:00| 7.0| -|1970-01-01T08:00:00.900+08:00| 8.0| -|1970-01-01T08:00:01.000+08:00| 9.0| -|1970-01-01T08:00:01.100+08:00| 9.1| -|1970-01-01T08:00:01.200+08:00| 9.2| -|1970-01-01T08:00:01.300+08:00| 9.3| -|1970-01-01T08:00:01.400+08:00| 9.4| -|1970-01-01T08:00:01.500+08:00| 9.5| -|1970-01-01T08:00:01.600+08:00| 9.6| -|1970-01-01T08:00:01.700+08:00| 9.7| -|1970-01-01T08:00:01.800+08:00| 9.8| -|1970-01-01T08:00:01.900+08:00| 9.9| -|1970-01-01T08:00:02.000+08:00| 10.0| -|1970-01-01T08:00:02.100+08:00| 8.0| -|1970-01-01T08:00:02.200+08:00| 6.0| -|1970-01-01T08:00:02.300+08:00| 4.0| -|1970-01-01T08:00:02.400+08:00| 2.0| -|1970-01-01T08:00:02.500+08:00| 0.0| -|1970-01-01T08:00:02.600+08:00| -2.0| -|1970-01-01T08:00:02.700+08:00| -4.0| -|1970-01-01T08:00:02.800+08:00| -6.0| -|1970-01-01T08:00:02.900+08:00| -8.0| -|1970-01-01T08:00:03.000+08:00| -10.0| -|1970-01-01T08:00:03.100+08:00| 10.0| -|1970-01-01T08:00:03.200+08:00| 10.0| -|1970-01-01T08:00:03.300+08:00| 10.0| -|1970-01-01T08:00:03.400+08:00| 10.0| -|1970-01-01T08:00:03.500+08:00| 10.0| -|1970-01-01T08:00:03.600+08:00| 10.0| -|1970-01-01T08:00:03.700+08:00| 10.0| -|1970-01-01T08:00:03.800+08:00| 10.0| -|1970-01-01T08:00:03.900+08:00| 10.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select segment(s1, "error"="0.1") from root.test -``` - -Output series: - -``` -+-----------------------------+------------------------------------+ -| Time|segment(root.test.s1, "error"="0.1")| -+-----------------------------+------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 5.0| -|1970-01-01T08:00:00.200+08:00| 1.0| -|1970-01-01T08:00:01.000+08:00| 9.0| -|1970-01-01T08:00:02.000+08:00| 10.0| -|1970-01-01T08:00:03.000+08:00| -10.0| -|1970-01-01T08:00:03.200+08:00| 10.0| -+-----------------------------+------------------------------------+ -``` - -### Skew - -#### Registration statement - -```sql -create function skew as 'org.apache.iotdb.library.dprofile.UDAFSkew' -``` - -#### Usage - -This function is used to calculate the population skewness. - -**Name:** SKEW - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the population skewness. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| -|2020-01-01T00:00:01.000+08:00| 2.0| -|2020-01-01T00:00:02.000+08:00| 3.0| -|2020-01-01T00:00:03.000+08:00| 4.0| -|2020-01-01T00:00:04.000+08:00| 5.0| -|2020-01-01T00:00:05.000+08:00| 6.0| -|2020-01-01T00:00:06.000+08:00| 7.0| -|2020-01-01T00:00:07.000+08:00| 8.0| -|2020-01-01T00:00:08.000+08:00| 9.0| -|2020-01-01T00:00:09.000+08:00| 10.0| -|2020-01-01T00:00:10.000+08:00| 10.0| -|2020-01-01T00:00:11.000+08:00| 10.0| -|2020-01-01T00:00:12.000+08:00| 10.0| -|2020-01-01T00:00:13.000+08:00| 10.0| -|2020-01-01T00:00:14.000+08:00| 10.0| -|2020-01-01T00:00:15.000+08:00| 10.0| -|2020-01-01T00:00:16.000+08:00| 10.0| -|2020-01-01T00:00:17.000+08:00| 10.0| -|2020-01-01T00:00:18.000+08:00| 10.0| -|2020-01-01T00:00:19.000+08:00| 10.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select skew(s1) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time| skew(root.test.d1.s1)| -+-----------------------------+-----------------------+ -|1970-01-01T08:00:00.000+08:00| -0.9998427402292644| -+-----------------------------+-----------------------+ -``` - -### Spline - -#### Registration statement - -```sql -create function spline as 'org.apache.iotdb.library.dprofile.UDTFSpline' -``` - -#### Usage - -This function is used to calculate cubic spline interpolation of input series. - -**Name:** SPLINE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `points`: Number of resampling points. - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note**: Output series retains the first and last timestamps of input series. Interpolation points are selected at equal intervals. The function tries to calculate only when there are no less than 4 points in input series. - -#### Examples - -##### Assigning number of interpolation points - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.2| -|1970-01-01T08:00:00.500+08:00| 1.7| -|1970-01-01T08:00:00.700+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 2.1| -|1970-01-01T08:00:01.100+08:00| 2.0| -|1970-01-01T08:00:01.200+08:00| 1.8| -|1970-01-01T08:00:01.300+08:00| 1.2| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 1.6| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select spline(s1, "points"="151") from root.test -``` - -Output series: - -``` -+-----------------------------+------------------------------------+ -| Time|spline(root.test.s1, "points"="151")| -+-----------------------------+------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.010+08:00| 0.04870000251134237| -|1970-01-01T08:00:00.020+08:00| 0.09680000495910646| -|1970-01-01T08:00:00.030+08:00| 0.14430000734329226| -|1970-01-01T08:00:00.040+08:00| 0.19120000966389972| -|1970-01-01T08:00:00.050+08:00| 0.23750001192092896| -|1970-01-01T08:00:00.060+08:00| 0.2832000141143799| -|1970-01-01T08:00:00.070+08:00| 0.32830001624425253| -|1970-01-01T08:00:00.080+08:00| 0.3728000183105469| -|1970-01-01T08:00:00.090+08:00| 0.416700020313263| -|1970-01-01T08:00:00.100+08:00| 0.4600000222524008| -|1970-01-01T08:00:00.110+08:00| 0.5027000241279602| -|1970-01-01T08:00:00.120+08:00| 0.5448000259399414| -|1970-01-01T08:00:00.130+08:00| 0.5863000276883443| -|1970-01-01T08:00:00.140+08:00| 0.627200029373169| -|1970-01-01T08:00:00.150+08:00| 0.6675000309944153| -|1970-01-01T08:00:00.160+08:00| 0.7072000325520833| -|1970-01-01T08:00:00.170+08:00| 0.7463000340461731| -|1970-01-01T08:00:00.180+08:00| 0.7848000354766846| -|1970-01-01T08:00:00.190+08:00| 0.8227000368436178| -|1970-01-01T08:00:00.200+08:00| 0.8600000381469728| -|1970-01-01T08:00:00.210+08:00| 0.8967000393867494| -|1970-01-01T08:00:00.220+08:00| 0.9328000405629477| -|1970-01-01T08:00:00.230+08:00| 0.9683000416755676| -|1970-01-01T08:00:00.240+08:00| 1.0032000427246095| -|1970-01-01T08:00:00.250+08:00| 1.037500043710073| -|1970-01-01T08:00:00.260+08:00| 1.071200044631958| -|1970-01-01T08:00:00.270+08:00| 1.1043000454902647| -|1970-01-01T08:00:00.280+08:00| 1.1368000462849934| -|1970-01-01T08:00:00.290+08:00| 1.1687000470161437| -|1970-01-01T08:00:00.300+08:00| 1.2000000476837158| -|1970-01-01T08:00:00.310+08:00| 1.2307000483103594| -|1970-01-01T08:00:00.320+08:00| 1.2608000489139557| -|1970-01-01T08:00:00.330+08:00| 1.2903000494873524| -|1970-01-01T08:00:00.340+08:00| 1.3192000500233967| -|1970-01-01T08:00:00.350+08:00| 1.3475000505149364| -|1970-01-01T08:00:00.360+08:00| 1.3752000509548186| -|1970-01-01T08:00:00.370+08:00| 1.402300051335891| -|1970-01-01T08:00:00.380+08:00| 1.4288000516510009| -|1970-01-01T08:00:00.390+08:00| 1.4547000518929958| -|1970-01-01T08:00:00.400+08:00| 1.480000052054723| -|1970-01-01T08:00:00.410+08:00| 1.5047000521290301| -|1970-01-01T08:00:00.420+08:00| 1.5288000521087646| -|1970-01-01T08:00:00.430+08:00| 1.5523000519867738| -|1970-01-01T08:00:00.440+08:00| 1.575200051755905| -|1970-01-01T08:00:00.450+08:00| 1.597500051409006| -|1970-01-01T08:00:00.460+08:00| 1.619200050938924| -|1970-01-01T08:00:00.470+08:00| 1.6403000503385066| -|1970-01-01T08:00:00.480+08:00| 1.660800049600601| -|1970-01-01T08:00:00.490+08:00| 1.680700048718055| -|1970-01-01T08:00:00.500+08:00| 1.7000000476837158| -|1970-01-01T08:00:00.510+08:00| 1.7188475466453037| -|1970-01-01T08:00:00.520+08:00| 1.7373800457262996| -|1970-01-01T08:00:00.530+08:00| 1.7555825448831923| -|1970-01-01T08:00:00.540+08:00| 1.7734400440724702| -|1970-01-01T08:00:00.550+08:00| 1.790937543250622| -|1970-01-01T08:00:00.560+08:00| 1.8080600423741364| -|1970-01-01T08:00:00.570+08:00| 1.8247925413995016| -|1970-01-01T08:00:00.580+08:00| 1.8411200402832066| -|1970-01-01T08:00:00.590+08:00| 1.8570275389817397| -|1970-01-01T08:00:00.600+08:00| 1.8725000374515897| -|1970-01-01T08:00:00.610+08:00| 1.8875225356492449| -|1970-01-01T08:00:00.620+08:00| 1.902080033531194| -|1970-01-01T08:00:00.630+08:00| 1.9161575310539258| -|1970-01-01T08:00:00.640+08:00| 1.9297400281739288| -|1970-01-01T08:00:00.650+08:00| 1.9428125248476913| -|1970-01-01T08:00:00.660+08:00| 1.9553600210317021| -|1970-01-01T08:00:00.670+08:00| 1.96736751668245| -|1970-01-01T08:00:00.680+08:00| 1.9788200117564232| -|1970-01-01T08:00:00.690+08:00| 1.9897025062101101| -|1970-01-01T08:00:00.700+08:00| 2.0| -|1970-01-01T08:00:00.710+08:00| 2.0097024933913334| -|1970-01-01T08:00:00.720+08:00| 2.0188199867081615| -|1970-01-01T08:00:00.730+08:00| 2.027367479995188| -|1970-01-01T08:00:00.740+08:00| 2.0353599732971155| -|1970-01-01T08:00:00.750+08:00| 2.0428124666586482| -|1970-01-01T08:00:00.760+08:00| 2.049739960124489| -|1970-01-01T08:00:00.770+08:00| 2.056157453739342| -|1970-01-01T08:00:00.780+08:00| 2.06207994754791| -|1970-01-01T08:00:00.790+08:00| 2.067522441594897| -|1970-01-01T08:00:00.800+08:00| 2.072499935925006| -|1970-01-01T08:00:00.810+08:00| 2.07702743058294| -|1970-01-01T08:00:00.820+08:00| 2.081119925613404| -|1970-01-01T08:00:00.830+08:00| 2.0847924210611| -|1970-01-01T08:00:00.840+08:00| 2.0880599169707317| -|1970-01-01T08:00:00.850+08:00| 2.0909374133870027| -|1970-01-01T08:00:00.860+08:00| 2.0934399103546166| -|1970-01-01T08:00:00.870+08:00| 2.0955824079182768| -|1970-01-01T08:00:00.880+08:00| 2.0973799061226863| -|1970-01-01T08:00:00.890+08:00| 2.098847405012549| -|1970-01-01T08:00:00.900+08:00| 2.0999999046325684| -|1970-01-01T08:00:00.910+08:00| 2.1005574051201332| -|1970-01-01T08:00:00.920+08:00| 2.1002599065303778| -|1970-01-01T08:00:00.930+08:00| 2.0991524087846245| -|1970-01-01T08:00:00.940+08:00| 2.0972799118041947| -|1970-01-01T08:00:00.950+08:00| 2.0946874155104105| -|1970-01-01T08:00:00.960+08:00| 2.0914199198245944| -|1970-01-01T08:00:00.970+08:00| 2.0875224246680673| -|1970-01-01T08:00:00.980+08:00| 2.083039929962151| -|1970-01-01T08:00:00.990+08:00| 2.0780174356281687| -|1970-01-01T08:00:01.000+08:00| 2.0724999415874406| -|1970-01-01T08:00:01.010+08:00| 2.06653244776129| -|1970-01-01T08:00:01.020+08:00| 2.060159954071038| -|1970-01-01T08:00:01.030+08:00| 2.053427460438006| -|1970-01-01T08:00:01.040+08:00| 2.046379966783517| -|1970-01-01T08:00:01.050+08:00| 2.0390624730288924| -|1970-01-01T08:00:01.060+08:00| 2.031519979095454| -|1970-01-01T08:00:01.070+08:00| 2.0237974849045237| -|1970-01-01T08:00:01.080+08:00| 2.015939990377423| -|1970-01-01T08:00:01.090+08:00| 2.0079924954354746| -|1970-01-01T08:00:01.100+08:00| 2.0| -|1970-01-01T08:00:01.110+08:00| 1.9907018211101906| -|1970-01-01T08:00:01.120+08:00| 1.9788509124245144| -|1970-01-01T08:00:01.130+08:00| 1.9645127287932083| -|1970-01-01T08:00:01.140+08:00| 1.9477527250665083| -|1970-01-01T08:00:01.150+08:00| 1.9286363560946513| -|1970-01-01T08:00:01.160+08:00| 1.9072290767278735| -|1970-01-01T08:00:01.170+08:00| 1.8835963418164114| -|1970-01-01T08:00:01.180+08:00| 1.8578036062105014| -|1970-01-01T08:00:01.190+08:00| 1.8299163247603802| -|1970-01-01T08:00:01.200+08:00| 1.7999999523162842| -|1970-01-01T08:00:01.210+08:00| 1.7623635841923329| -|1970-01-01T08:00:01.220+08:00| 1.7129696477516976| -|1970-01-01T08:00:01.230+08:00| 1.6543635959181928| -|1970-01-01T08:00:01.240+08:00| 1.5890908816156328| -|1970-01-01T08:00:01.250+08:00| 1.5196969577678319| -|1970-01-01T08:00:01.260+08:00| 1.4487272772986044| -|1970-01-01T08:00:01.270+08:00| 1.3787272931317647| -|1970-01-01T08:00:01.280+08:00| 1.3122424581911272| -|1970-01-01T08:00:01.290+08:00| 1.251818225400506| -|1970-01-01T08:00:01.300+08:00| 1.2000000476837158| -|1970-01-01T08:00:01.310+08:00| 1.1548000470995912| -|1970-01-01T08:00:01.320+08:00| 1.1130667107899999| -|1970-01-01T08:00:01.330+08:00| 1.0756000393033045| -|1970-01-01T08:00:01.340+08:00| 1.043200033187868| -|1970-01-01T08:00:01.350+08:00| 1.016666692992053| -|1970-01-01T08:00:01.360+08:00| 0.9968000192642223| -|1970-01-01T08:00:01.370+08:00| 0.9844000125527389| -|1970-01-01T08:00:01.380+08:00| 0.9802666734059655| -|1970-01-01T08:00:01.390+08:00| 0.9852000023722649| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.410+08:00| 1.023999999165535| -|1970-01-01T08:00:01.420+08:00| 1.0559999990463256| -|1970-01-01T08:00:01.430+08:00| 1.0959999996423722| -|1970-01-01T08:00:01.440+08:00| 1.1440000009536744| -|1970-01-01T08:00:01.450+08:00| 1.2000000029802322| -|1970-01-01T08:00:01.460+08:00| 1.264000005722046| -|1970-01-01T08:00:01.470+08:00| 1.3360000091791153| -|1970-01-01T08:00:01.480+08:00| 1.4160000133514405| -|1970-01-01T08:00:01.490+08:00| 1.5040000182390214| -|1970-01-01T08:00:01.500+08:00| 1.600000023841858| -+-----------------------------+------------------------------------+ -``` - -### Spread - -#### Registration statement - -```sql -create function spread as 'org.apache.iotdb.library.dprofile.UDAFSpread' -``` - -#### Usage - -This function is used to calculate the spread of time series, that is, the maximum value minus the minimum value. - -**Name:** SPREAD - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is the same as the input. There is only one data point in the series, whose timestamp is 0 and value is the spread. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select spread(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time|spread(root.test.d1.s1)| -+-----------------------------+-----------------------+ -|1970-01-01T08:00:00.000+08:00| 26.0| -+-----------------------------+-----------------------+ -``` - - - -### ZScore - -#### Registration statement - -```sql -create function zscore as 'org.apache.iotdb.library.dprofile.UDTFZScore' -``` - -#### Usage - -This function is used to standardize the input series with z-score. - -**Name:** ZSCORE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `compute`: When set to "batch", anomaly test is conducted after importing all data points; when set to "stream", it is required to provide mean and standard deviation. The default method is "batch". -+ `avg`: Mean value when method is set to "stream". -+ `sd`: Standard deviation when method is set to "stream". - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select zscore(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|zscore(root.test.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.100+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.200+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.300+08:00| 0.20672455764868078| -|1970-01-01T08:00:00.400+08:00| -0.6201736729460423| -|1970-01-01T08:00:00.500+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.600+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.700+08:00| -1.033622788243404| -|1970-01-01T08:00:00.800+08:00| 0.6201736729460423| -|1970-01-01T08:00:00.900+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.000+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.100+08:00| 0.20672455764868078| -|1970-01-01T08:00:01.200+08:00| -0.6201736729460423| -|1970-01-01T08:00:01.300+08:00| -0.6201736729460423| -|1970-01-01T08:00:01.400+08:00| 0.20672455764868078| -|1970-01-01T08:00:01.500+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.600+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.700+08:00| 3.9277665953249348| -|1970-01-01T08:00:01.800+08:00| 0.6201736729460423| -|1970-01-01T08:00:01.900+08:00| -1.033622788243404| -|1970-01-01T08:00:02.000+08:00|-0.20672455764868078| -+-----------------------------+--------------------+ -``` - - -## Anomaly Detection - -### IQR - -#### Registration statement - -```sql -create function iqr as 'org.apache.iotdb.library.anomaly.UDTFIQR' -``` - -#### Usage - -This function is used to detect anomalies based on IQR. Points distributing beyond 1.5 times IQR are selected. - -**Name:** IQR - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `method`: When set to "batch", anomaly test is conducted after importing all data points; when set to "stream", it is required to provide upper and lower quantiles. The default method is "batch". -+ `q1`: The lower quantile when method is set to "stream". -+ `q3`: The upper quantile when method is set to "stream". - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note:** $IQR=Q_3-Q_1$ - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select iqr(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+-----------------+ -| Time|iqr(root.test.s1)| -+-----------------------------+-----------------+ -|1970-01-01T08:00:01.700+08:00| 10.0| -+-----------------------------+-----------------+ -``` - -### KSigma - -#### Registration statement - -```sql -create function ksigma as 'org.apache.iotdb.library.anomaly.UDTFKSigma' -``` - -#### Usage - -This function is used to detect anomalies based on the Dynamic K-Sigma Algorithm. -Within a sliding window, the input value with a deviation of more than k times the standard deviation from the average will be output as anomaly. - -**Name:** KSIGMA - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `k`: How many times to multiply on standard deviation to define anomaly, the default value is 3. -+ `window`: The window size of Dynamic K-Sigma Algorithm, the default value is 10000. - -**Output Series:** Output a single series. The type is same as input series. - -**Note:** Only when is larger than 0, the anomaly detection will be performed. Otherwise, nothing will be output. - -#### Examples - -##### Assigning k - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 0.0| -|2020-01-01T00:00:03.000+08:00| 50.0| -|2020-01-01T00:00:04.000+08:00| 100.0| -|2020-01-01T00:00:06.000+08:00| 150.0| -|2020-01-01T00:00:08.000+08:00| 200.0| -|2020-01-01T00:00:10.000+08:00| 200.0| -|2020-01-01T00:00:14.000+08:00| 200.0| -|2020-01-01T00:00:15.000+08:00| 200.0| -|2020-01-01T00:00:16.000+08:00| 200.0| -|2020-01-01T00:00:18.000+08:00| 200.0| -|2020-01-01T00:00:20.000+08:00| 150.0| -|2020-01-01T00:00:22.000+08:00| 100.0| -|2020-01-01T00:00:26.000+08:00| 50.0| -|2020-01-01T00:00:28.000+08:00| 0.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select ksigma(s1,"k"="1.0") from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -|Time |ksigma(root.test.d1.s1,"k"="3.0")| -+-----------------------------+---------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.0| -|2020-01-01T00:00:03.000+08:00| 50.0| -|2020-01-01T00:00:26.000+08:00| 50.0| -|2020-01-01T00:00:28.000+08:00| 0.0| -+-----------------------------+---------------------------------+ -``` - -### LOF - -#### Registration statement - -```sql -create function LOF as 'org.apache.iotdb.library.anomaly.UDTFLOF' -``` - -#### Usage - -This function is used to detect density anomaly of time series. According to k-th distance calculation parameter and local outlier factor (lof) threshold, the function judges if a set of input values is an density anomaly, and a bool mark of anomaly values will be output. - -**Name:** LOF - -**Input Series:** Multiple input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `method`:assign a detection method. The default value is "default", when input data has multiple dimensions. The alternative is "series", when a input series will be transformed to high dimension. -+ `k`:use the k-th distance to calculate lof. Default value is 3. -+ `window`: size of window to split origin data points. Default value is 10000. -+ `windowsize`:dimension that will be transformed into when method is "series". The default value is 5. - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note:** Incomplete rows will be ignored. They are neither calculated nor marked as anomaly. - -#### Examples - -##### Using default parameters - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| 1.0| -|1970-01-01T08:00:00.300+08:00| 1.0| 1.0| -|1970-01-01T08:00:00.400+08:00| 1.0| 0.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -1.0| -|1970-01-01T08:00:00.600+08:00| -1.0| -1.0| -|1970-01-01T08:00:00.700+08:00| -1.0| 0.0| -|1970-01-01T08:00:00.800+08:00| 2.0| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| null| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select lof(s1,s2) from root.test.d1 where time<1000 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|lof(root.test.d1.s1, root.test.d1.s2)| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.100+08:00| 3.8274824267668244| -|1970-01-01T08:00:00.200+08:00| 3.0117631741126156| -|1970-01-01T08:00:00.300+08:00| 2.838155437762879| -|1970-01-01T08:00:00.400+08:00| 3.0117631741126156| -|1970-01-01T08:00:00.500+08:00| 2.73518261244453| -|1970-01-01T08:00:00.600+08:00| 2.371440975708148| -|1970-01-01T08:00:00.700+08:00| 2.73518261244453| -|1970-01-01T08:00:00.800+08:00| 1.7561416374270742| -+-----------------------------+-------------------------------------+ -``` - -##### Diagnosing 1d timeseries - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.100+08:00| 1.0| -|1970-01-01T08:00:00.200+08:00| 2.0| -|1970-01-01T08:00:00.300+08:00| 3.0| -|1970-01-01T08:00:00.400+08:00| 4.0| -|1970-01-01T08:00:00.500+08:00| 5.0| -|1970-01-01T08:00:00.600+08:00| 6.0| -|1970-01-01T08:00:00.700+08:00| 7.0| -|1970-01-01T08:00:00.800+08:00| 8.0| -|1970-01-01T08:00:00.900+08:00| 9.0| -|1970-01-01T08:00:01.000+08:00| 10.0| -|1970-01-01T08:00:01.100+08:00| 11.0| -|1970-01-01T08:00:01.200+08:00| 12.0| -|1970-01-01T08:00:01.300+08:00| 13.0| -|1970-01-01T08:00:01.400+08:00| 14.0| -|1970-01-01T08:00:01.500+08:00| 15.0| -|1970-01-01T08:00:01.600+08:00| 16.0| -|1970-01-01T08:00:01.700+08:00| 17.0| -|1970-01-01T08:00:01.800+08:00| 18.0| -|1970-01-01T08:00:01.900+08:00| 19.0| -|1970-01-01T08:00:02.000+08:00| 20.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select lof(s1, "method"="series") from root.test.d1 where time<1000 -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|lof(root.test.d1.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.100+08:00| 3.77777777777778| -|1970-01-01T08:00:00.200+08:00| 4.32727272727273| -|1970-01-01T08:00:00.300+08:00| 4.85714285714286| -|1970-01-01T08:00:00.400+08:00| 5.40909090909091| -|1970-01-01T08:00:00.500+08:00| 5.94999999999999| -|1970-01-01T08:00:00.600+08:00| 6.43243243243243| -|1970-01-01T08:00:00.700+08:00| 6.79999999999999| -|1970-01-01T08:00:00.800+08:00| 7.0| -|1970-01-01T08:00:00.900+08:00| 7.0| -|1970-01-01T08:00:01.000+08:00| 6.79999999999999| -|1970-01-01T08:00:01.100+08:00| 6.43243243243243| -|1970-01-01T08:00:01.200+08:00| 5.94999999999999| -|1970-01-01T08:00:01.300+08:00| 5.40909090909091| -|1970-01-01T08:00:01.400+08:00| 4.85714285714286| -|1970-01-01T08:00:01.500+08:00| 4.32727272727273| -|1970-01-01T08:00:01.600+08:00| 3.77777777777778| -+-----------------------------+--------------------+ -``` - -### MissDetect - -#### Registration statement - -```sql -create function missdetect as 'org.apache.iotdb.library.anomaly.UDTFMissDetect' -``` - -#### Usage - -This function is used to detect missing anomalies. -In some datasets, missing values are filled by linear interpolation. -Thus, there are several long perfect linear segments. -By discovering these perfect linear segments, -missing anomalies are detected. - -**Name:** MISSDETECT - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -`error`: The minimum length of the detected missing anomalies, which is an integer greater than or equal to 10. By default, it is 10. - -**Output Series:** Output a single series. The type is BOOLEAN. Each data point which is miss anomaly will be labeled as true. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s2| -+-----------------------------+---------------+ -|2021-07-01T12:00:00.000+08:00| 0.0| -|2021-07-01T12:00:01.000+08:00| 1.0| -|2021-07-01T12:00:02.000+08:00| 0.0| -|2021-07-01T12:00:03.000+08:00| 1.0| -|2021-07-01T12:00:04.000+08:00| 0.0| -|2021-07-01T12:00:05.000+08:00| 0.0| -|2021-07-01T12:00:06.000+08:00| 0.0| -|2021-07-01T12:00:07.000+08:00| 0.0| -|2021-07-01T12:00:08.000+08:00| 0.0| -|2021-07-01T12:00:09.000+08:00| 0.0| -|2021-07-01T12:00:10.000+08:00| 0.0| -|2021-07-01T12:00:11.000+08:00| 0.0| -|2021-07-01T12:00:12.000+08:00| 0.0| -|2021-07-01T12:00:13.000+08:00| 0.0| -|2021-07-01T12:00:14.000+08:00| 0.0| -|2021-07-01T12:00:15.000+08:00| 0.0| -|2021-07-01T12:00:16.000+08:00| 1.0| -|2021-07-01T12:00:17.000+08:00| 0.0| -|2021-07-01T12:00:18.000+08:00| 1.0| -|2021-07-01T12:00:19.000+08:00| 0.0| -|2021-07-01T12:00:20.000+08:00| 1.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select missdetect(s2,'minlen'='10') from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------+ -| Time|missdetect(root.test.d2.s2, "minlen"="10")| -+-----------------------------+------------------------------------------+ -|2021-07-01T12:00:00.000+08:00| false| -|2021-07-01T12:00:01.000+08:00| false| -|2021-07-01T12:00:02.000+08:00| false| -|2021-07-01T12:00:03.000+08:00| false| -|2021-07-01T12:00:04.000+08:00| true| -|2021-07-01T12:00:05.000+08:00| true| -|2021-07-01T12:00:06.000+08:00| true| -|2021-07-01T12:00:07.000+08:00| true| -|2021-07-01T12:00:08.000+08:00| true| -|2021-07-01T12:00:09.000+08:00| true| -|2021-07-01T12:00:10.000+08:00| true| -|2021-07-01T12:00:11.000+08:00| true| -|2021-07-01T12:00:12.000+08:00| true| -|2021-07-01T12:00:13.000+08:00| true| -|2021-07-01T12:00:14.000+08:00| true| -|2021-07-01T12:00:15.000+08:00| true| -|2021-07-01T12:00:16.000+08:00| false| -|2021-07-01T12:00:17.000+08:00| false| -|2021-07-01T12:00:18.000+08:00| false| -|2021-07-01T12:00:19.000+08:00| false| -|2021-07-01T12:00:20.000+08:00| false| -+-----------------------------+------------------------------------------+ -``` - -### Range - -#### Registration statement - -```sql -create function range as 'org.apache.iotdb.library.anomaly.UDTFRange' -``` - -#### Usage - -This function is used to detect range anomaly of time series. According to upper bound and lower bound parameters, the function judges if a input value is beyond range, aka range anomaly, and a new time series of anomaly will be output. - -**Name:** RANGE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `lower_bound`:lower bound of range anomaly detection. -+ `upper_bound`:upper bound of range anomaly detection. - -**Output Series:** Output a single series. The type is the same as the input. - -**Note:** Only when `upper_bound` is larger than `lower_bound`, the anomaly detection will be performed. Otherwise, nothing will be output. - - - -#### Examples - -##### Assigning Lower and Upper Bound - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select range(s1,"lower_bound"="101.0","upper_bound"="125.0") from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------------------+ -|Time |range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")| -+-----------------------------+------------------------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -+-----------------------------+------------------------------------------------------------------+ -``` - -### TwoSidedFilter - -#### Registration statement - -```sql -create function twosidedfilter as 'org.apache.iotdb.library.anomaly.UDTFTwoSidedFilter' -``` - -#### Usage - -The function is used to filter anomalies of a numeric time series based on two-sided window detection. - -**Name:** TWOSIDEDFILTER - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE - -**Output Series:** Output a single series. The type is the same as the input. It is the input without anomalies. - -**Parameter:** - -- `len`: The size of the window, which is a positive integer. By default, it's 5. When `len`=3, the algorithm detects forward window and backward window with length 3 and calculates the outlierness of the current point. - -- `threshold`: The threshold of outlierness, which is a floating number in (0,1). By default, it's 0.3. The strict standard of detecting anomalies is in proportion to the threshold. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s0| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 2002.0| -|1970-01-01T08:00:01.000+08:00| 1946.0| -|1970-01-01T08:00:02.000+08:00| 1958.0| -|1970-01-01T08:00:03.000+08:00| 2012.0| -|1970-01-01T08:00:04.000+08:00| 2051.0| -|1970-01-01T08:00:05.000+08:00| 1898.0| -|1970-01-01T08:00:06.000+08:00| 2014.0| -|1970-01-01T08:00:07.000+08:00| 2052.0| -|1970-01-01T08:00:08.000+08:00| 1935.0| -|1970-01-01T08:00:09.000+08:00| 1901.0| -|1970-01-01T08:00:10.000+08:00| 1972.0| -|1970-01-01T08:00:11.000+08:00| 1969.0| -|1970-01-01T08:00:12.000+08:00| 1984.0| -|1970-01-01T08:00:13.000+08:00| 2018.0| -|1970-01-01T08:00:37.000+08:00| 1484.0| -|1970-01-01T08:00:38.000+08:00| 1055.0| -|1970-01-01T08:00:39.000+08:00| 1050.0| -|1970-01-01T08:01:05.000+08:00| 1023.0| -|1970-01-01T08:01:06.000+08:00| 1056.0| -|1970-01-01T08:01:07.000+08:00| 978.0| -|1970-01-01T08:01:08.000+08:00| 1050.0| -|1970-01-01T08:01:09.000+08:00| 1123.0| -|1970-01-01T08:01:10.000+08:00| 1150.0| -|1970-01-01T08:01:11.000+08:00| 1034.0| -|1970-01-01T08:01:12.000+08:00| 950.0| -|1970-01-01T08:01:13.000+08:00| 1059.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select TwoSidedFilter(s0, 'len'='5', 'threshold'='0.3') from root.test -``` - -Output series: - -``` -+-----------------------------+------------+ -| Time|root.test.s0| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 2002.0| -|1970-01-01T08:00:01.000+08:00| 1946.0| -|1970-01-01T08:00:02.000+08:00| 1958.0| -|1970-01-01T08:00:03.000+08:00| 2012.0| -|1970-01-01T08:00:04.000+08:00| 2051.0| -|1970-01-01T08:00:05.000+08:00| 1898.0| -|1970-01-01T08:00:06.000+08:00| 2014.0| -|1970-01-01T08:00:07.000+08:00| 2052.0| -|1970-01-01T08:00:08.000+08:00| 1935.0| -|1970-01-01T08:00:09.000+08:00| 1901.0| -|1970-01-01T08:00:10.000+08:00| 1972.0| -|1970-01-01T08:00:11.000+08:00| 1969.0| -|1970-01-01T08:00:12.000+08:00| 1984.0| -|1970-01-01T08:00:13.000+08:00| 2018.0| -|1970-01-01T08:01:05.000+08:00| 1023.0| -|1970-01-01T08:01:06.000+08:00| 1056.0| -|1970-01-01T08:01:07.000+08:00| 978.0| -|1970-01-01T08:01:08.000+08:00| 1050.0| -|1970-01-01T08:01:09.000+08:00| 1123.0| -|1970-01-01T08:01:10.000+08:00| 1150.0| -|1970-01-01T08:01:11.000+08:00| 1034.0| -|1970-01-01T08:01:12.000+08:00| 950.0| -|1970-01-01T08:01:13.000+08:00| 1059.0| -+-----------------------------+------------+ -``` - -### Outlier - -#### Registration statement - -```sql -create function outlier as 'org.apache.iotdb.library.anomaly.UDTFOutlier' -``` - -#### Usage - -This function is used to detect distance-based outliers. For each point in the current window, if the number of its neighbors within the distance of neighbor distance threshold is less than the neighbor count threshold, the point in detected as an outlier. - -**Name:** OUTLIER - -**Input Series：** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `r`：the neighbor distance threshold. -+ `k`：the neighbor count threshold. -+ `w`：the window size. -+ `s`：the slide size. - -**Output Series:** Output a single series. The type is the same as the input. - -#### Examples - -##### Assigning Parameters of Queries - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|2020-01-04T23:59:55.000+08:00| 56.0| -|2020-01-04T23:59:56.000+08:00| 55.1| -|2020-01-04T23:59:57.000+08:00| 54.2| -|2020-01-04T23:59:58.000+08:00| 56.3| -|2020-01-04T23:59:59.000+08:00| 59.0| -|2020-01-05T00:00:00.000+08:00| 60.0| -|2020-01-05T00:00:01.000+08:00| 60.5| -|2020-01-05T00:00:02.000+08:00| 64.5| -|2020-01-05T00:00:03.000+08:00| 69.0| -|2020-01-05T00:00:04.000+08:00| 64.2| -|2020-01-05T00:00:05.000+08:00| 62.3| -|2020-01-05T00:00:06.000+08:00| 58.0| -|2020-01-05T00:00:07.000+08:00| 58.9| -|2020-01-05T00:00:08.000+08:00| 52.0| -|2020-01-05T00:00:09.000+08:00| 62.3| -|2020-01-05T00:00:10.000+08:00| 61.0| -|2020-01-05T00:00:11.000+08:00| 64.2| -|2020-01-05T00:00:12.000+08:00| 61.8| -|2020-01-05T00:00:13.000+08:00| 64.0| -|2020-01-05T00:00:14.000+08:00| 63.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------+ -| Time|outlier(root.test.s1,"r"="5.0","k"="4","w"="10","s"="5")| -+-----------------------------+--------------------------------------------------------+ -|2020-01-05T00:00:03.000+08:00| 69.0| -+-----------------------------+--------------------------------------------------------+ -|2020-01-05T00:00:08.000+08:00| 52.0| -+-----------------------------+--------------------------------------------------------+ -``` - - -### MasterTrain - -#### Usage - -This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points. - -**Name:** MasterTrain - -**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `p`: The order of the model. -+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule. - -**Output Series:** Output a single series. The type is the same as the input. - -**Installation** -- Install IoTDB from branch `research/master-detector`. -- Run `mvn spotless:apply`. -- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`. -- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`. -- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+------------+--------------+--------------+ -| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| -+-----------------------------+------------+------------+--------------+--------------+ -|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| -|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| -|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| -|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| -|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| -|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| -|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014| -|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| -|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| -|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| -|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| -|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| -|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| -|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| -|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| -|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| -|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| -|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| -|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| -|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| -|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| -|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| -|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| -|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| -+-----------------------------+------------+------------+--------------+--------------+ -``` - -SQL for query: - -```sql -select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------------------------------------------------------------------+ -| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")| -+-----------------------------+---------------------------------------------------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 0.13656607660463288| -|1970-01-01T08:00:00.002+08:00| 0.8291884323013894| -|1970-01-01T08:00:00.003+08:00| 0.05012816073171693| -|1970-01-01T08:00:00.004+08:00| -0.5495287787485761| -|1970-01-01T08:00:00.005+08:00| 0.03740486307345578| -|1970-01-01T08:00:00.006+08:00| 1.0500132150475212| -|1970-01-01T08:00:00.007+08:00| 0.04583944643116993| -|1970-01-01T08:00:00.008+08:00| -0.07863708480736269| -+-----------------------------+---------------------------------------------------------------------------------------------+ -``` - -### MasterDetect - -#### Usage - -This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain. - -**Name:** MasterDetect - -**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `p`: The order of the model. -+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data. -+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule. -+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule. -+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection. -+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column. - -**Output Series:** Output a single series. The type is the same as the input. - -**Installation** -- Install IoTDB from branch `research/master-detector`. -- Run `mvn spotless:apply`. -- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`. -- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`. -- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+------------+--------------+--------------+--------------------+ -| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model| -+-----------------------------+------------+------------+--------------+--------------+--------------------+ -|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288| -|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894| -|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693| -|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761| -|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578| -|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212| -|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993| -|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269| -|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null| -|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null| -|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null| -|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null| -|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null| -|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null| -|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null| -|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null| -|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null| -|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null| -|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null| -|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null| -|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null| -|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null| -|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null| -|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null| -|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null| -+-----------------------------+------------+------------+--------------+--------------+--------------------+ -``` - -##### Repairing - -SQL for query: - -```sql -select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------------------------------------+ -| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')| -+-----------------------------+--------------------------------------------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 116.327274| -|1970-01-01T08:00:00.002+08:00| 116.327305| -|1970-01-01T08:00:00.003+08:00| 116.3273291| -|1970-01-01T08:00:00.004+08:00| 116.327342| -|1970-01-01T08:00:00.005+08:00| 116.3273744| -|1970-01-01T08:00:00.006+08:00| 116.3274117| -|1970-01-01T08:00:00.007+08:00| 116.3274396| -|1970-01-01T08:00:00.008+08:00| 116.3274668| -|1970-01-01T08:00:00.009+08:00| 116.3275026| -|1970-01-01T08:00:00.010+08:00| 116.3274967| -|1970-01-01T08:00:00.011+08:00| 116.3274929| -|1970-01-01T08:00:00.012+08:00| 116.3274745| -|1970-01-01T08:00:00.013+08:00| 116.3275095| -|1970-01-01T08:00:00.014+08:00| 116.3274787| -|1970-01-01T08:00:00.015+08:00| 116.3274693| -|1970-01-01T08:00:00.016+08:00| 116.3274941| -|1970-01-01T08:00:00.017+08:00| 116.3275401| -|1970-01-01T08:00:00.018+08:00| 116.3275713| -|1970-01-01T08:00:00.019+08:00| 116.3276003| -|1970-01-01T08:00:00.020+08:00| 116.3276308| -|1970-01-01T08:00:00.021+08:00| 116.3276338| -|1970-01-01T08:00:00.022+08:00| 116.3276684| -|1970-01-01T08:00:00.023+08:00| 116.3277016| -|1970-01-01T08:00:00.024+08:00| 116.3277284| -|1970-01-01T08:00:00.025+08:00| 116.3277562| -+-----------------------------+--------------------------------------------------------------------------------------+ -``` - -##### Anomaly Detection - -SQL for query: - -```sql -select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------------------------------------------------------------+ -| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')| -+-----------------------------+---------------------------------------------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| false| -|1970-01-01T08:00:00.002+08:00| false| -|1970-01-01T08:00:00.003+08:00| false| -|1970-01-01T08:00:00.004+08:00| false| -|1970-01-01T08:00:00.005+08:00| true| -|1970-01-01T08:00:00.006+08:00| true| -|1970-01-01T08:00:00.007+08:00| false| -|1970-01-01T08:00:00.008+08:00| false| -|1970-01-01T08:00:00.009+08:00| false| -|1970-01-01T08:00:00.010+08:00| false| -|1970-01-01T08:00:00.011+08:00| false| -|1970-01-01T08:00:00.012+08:00| false| -|1970-01-01T08:00:00.013+08:00| false| -|1970-01-01T08:00:00.014+08:00| true| -|1970-01-01T08:00:00.015+08:00| false| -|1970-01-01T08:00:00.016+08:00| false| -|1970-01-01T08:00:00.017+08:00| false| -|1970-01-01T08:00:00.018+08:00| false| -|1970-01-01T08:00:00.019+08:00| false| -|1970-01-01T08:00:00.020+08:00| false| -|1970-01-01T08:00:00.021+08:00| false| -|1970-01-01T08:00:00.022+08:00| false| -|1970-01-01T08:00:00.023+08:00| false| -|1970-01-01T08:00:00.024+08:00| false| -|1970-01-01T08:00:00.025+08:00| false| -+-----------------------------+---------------------------------------------------------------------------------------+ -``` - - - -## Frequency Domain Analysis - -### Conv - -#### Registration statement - -```sql -create function conv as 'org.apache.iotdb.library.frequency.UDTFConv' -``` - -#### Usage - -This function is used to calculate the convolution, i.e. polynomial multiplication. - -**Name:** CONV - -**Input:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output:** Output a single series. The type is DOUBLE. It is the result of convolution whose timestamps starting from 0 only indicate the order. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 1.0| 7.0| -|1970-01-01T08:00:00.001+08:00| 0.0| 2.0| -|1970-01-01T08:00:00.002+08:00| 1.0| null| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select conv(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------+ -| Time|conv(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+--------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 7.0| -|1970-01-01T08:00:00.001+08:00| 2.0| -|1970-01-01T08:00:00.002+08:00| 7.0| -|1970-01-01T08:00:00.003+08:00| 2.0| -+-----------------------------+--------------------------------------+ -``` - -### Deconv - -#### Registration statement - -```sql -create function deconv as 'org.apache.iotdb.library.frequency.UDTFDeconv' -``` - -#### Usage - -This function is used to calculate the deconvolution, i.e. polynomial division. - -**Name:** DECONV - -**Input:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `result`: The result of deconvolution, which is 'quotient' or 'remainder'. By default, the quotient will be output. - -**Output:** Output a single series. The type is DOUBLE. It is the result of deconvolving the second series from the first series (dividing the first series by the second series) whose timestamps starting from 0 only indicate the order. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - - -##### Calculate the quotient - -When `result` is 'quotient' or the default, this function calculates the quotient of the deconvolution. - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s3|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 8.0| 7.0| -|1970-01-01T08:00:00.001+08:00| 2.0| 2.0| -|1970-01-01T08:00:00.002+08:00| 7.0| null| -|1970-01-01T08:00:00.003+08:00| 2.0| null| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select deconv(s3,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------+ -| Time|deconv(root.test.d2.s3, root.test.d2.s2)| -+-----------------------------+----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 1.0| -|1970-01-01T08:00:00.001+08:00| 0.0| -|1970-01-01T08:00:00.002+08:00| 1.0| -+-----------------------------+----------------------------------------+ -``` - -##### Calculate the remainder - -When `result` is 'remainder', this function calculates the remainder of the deconvolution. - -Input series is the same as above, the SQL for query is shown below: - - -```sql -select deconv(s3,s2,'result'='remainder') from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------------+ -| Time|deconv(root.test.d2.s3, root.test.d2.s2, "result"="remainder")| -+-----------------------------+--------------------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 1.0| -|1970-01-01T08:00:00.001+08:00| 0.0| -|1970-01-01T08:00:00.002+08:00| 0.0| -|1970-01-01T08:00:00.003+08:00| 0.0| -+-----------------------------+--------------------------------------------------------------+ -``` - -### DWT - -#### Registration statement - -```sql -create function dwt as 'org.apache.iotdb.library.frequency.UDTFDWT' -``` - -#### Usage - -This function is used to calculate 1d discrete wavelet transform of a numerical series. - -**Name:** DWT - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The type of wavelet. May select 'Haar', 'DB4', 'DB6', 'DB8', where DB means Daubechies. User may offer coefficients of wavelet transform and ignore this parameter. Case ignored. -+ `coef`: Coefficients of wavelet transform. When providing this parameter, use comma ',' to split them, and leave no spaces or other punctuations. -+ `layer`: Times to transform. The number of output vectors equals $layer+1$. Default is 1. - -**Output:** Output a single series. The type is DOUBLE. The length is the same as the input. - -**Note:** The length of input series must be an integer number power of 2. - -#### Examples - - -##### Haar wavelet transform - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.100+08:00| 0.2| -|1970-01-01T08:00:00.200+08:00| 1.5| -|1970-01-01T08:00:00.300+08:00| 1.2| -|1970-01-01T08:00:00.400+08:00| 0.6| -|1970-01-01T08:00:00.500+08:00| 1.7| -|1970-01-01T08:00:00.600+08:00| 0.8| -|1970-01-01T08:00:00.700+08:00| 2.0| -|1970-01-01T08:00:00.800+08:00| 2.5| -|1970-01-01T08:00:00.900+08:00| 2.1| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 2.0| -|1970-01-01T08:00:01.200+08:00| 1.8| -|1970-01-01T08:00:01.300+08:00| 1.2| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 1.6| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select dwt(s1,"method"="haar") from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|dwt(root.test.d1.s1, "method"="haar")| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.14142135834465192| -|1970-01-01T08:00:00.100+08:00| 1.909188342921157| -|1970-01-01T08:00:00.200+08:00| 1.6263456473052773| -|1970-01-01T08:00:00.300+08:00| 1.9798989957517026| -|1970-01-01T08:00:00.400+08:00| 3.252691126023161| -|1970-01-01T08:00:00.500+08:00| 1.414213562373095| -|1970-01-01T08:00:00.600+08:00| 2.1213203435596424| -|1970-01-01T08:00:00.700+08:00| 1.8384776479437628| -|1970-01-01T08:00:00.800+08:00| -0.14142135834465192| -|1970-01-01T08:00:00.900+08:00| 0.21213200063848547| -|1970-01-01T08:00:01.000+08:00| -0.7778174761639416| -|1970-01-01T08:00:01.100+08:00| -0.8485281289944873| -|1970-01-01T08:00:01.200+08:00| 0.2828427799095765| -|1970-01-01T08:00:01.300+08:00| -1.414213562373095| -|1970-01-01T08:00:01.400+08:00| 0.42426400127697095| -|1970-01-01T08:00:01.500+08:00| -0.42426408557066786| -+-----------------------------+-------------------------------------+ -``` - -### FFT - -#### Registration statement - -```sql -create function fft as 'org.apache.iotdb.library.frequency.UDTFFFT' -``` - -#### Usage - -This function is used to calculate the fast Fourier transform (FFT) of a numerical series. - -**Name:** FFT - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The type of FFT, which is 'uniform' (by default) or 'nonuniform'. If the value is 'uniform', the timestamps will be ignored and all data points will be regarded as equidistant. Thus, the equidistant fast Fourier transform algorithm will be applied. If the value is 'nonuniform' (TODO), the non-equidistant fast Fourier transform algorithm will be applied based on timestamps. -+ `result`: The result of FFT, which is 'real', 'imag', 'abs' or 'angle', corresponding to the real part, imaginary part, magnitude and phase angle. By default, the magnitude will be output. -+ `compress`: The parameter of compression, which is within (0,1]. It is the reserved energy ratio of lossy compression. By default, there is no compression. - - -**Output:** Output a single series. The type is DOUBLE. The length is the same as the input. The timestamps starting from 0 only indicate the order. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - - -##### Uniform FFT - -With the default `type`, uniform FFT is applied. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 2.902113| -|1970-01-01T08:00:01.000+08:00| 1.1755705| -|1970-01-01T08:00:02.000+08:00| -2.1755705| -|1970-01-01T08:00:03.000+08:00| -1.9021131| -|1970-01-01T08:00:04.000+08:00| 1.0| -|1970-01-01T08:00:05.000+08:00| 1.9021131| -|1970-01-01T08:00:06.000+08:00| 0.1755705| -|1970-01-01T08:00:07.000+08:00| -1.1755705| -|1970-01-01T08:00:08.000+08:00| -0.902113| -|1970-01-01T08:00:09.000+08:00| 0.0| -|1970-01-01T08:00:10.000+08:00| 0.902113| -|1970-01-01T08:00:11.000+08:00| 1.1755705| -|1970-01-01T08:00:12.000+08:00| -0.1755705| -|1970-01-01T08:00:13.000+08:00| -1.9021131| -|1970-01-01T08:00:14.000+08:00| -1.0| -|1970-01-01T08:00:15.000+08:00| 1.9021131| -|1970-01-01T08:00:16.000+08:00| 2.1755705| -|1970-01-01T08:00:17.000+08:00| -1.1755705| -|1970-01-01T08:00:18.000+08:00| -2.902113| -|1970-01-01T08:00:19.000+08:00| 0.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select fft(s1) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------+ -| Time| fft(root.test.d1.s1)| -+-----------------------------+----------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.001+08:00| 1.2727111142703152E-8| -|1970-01-01T08:00:00.002+08:00| 2.385520799101839E-7| -|1970-01-01T08:00:00.003+08:00| 8.723291723972645E-8| -|1970-01-01T08:00:00.004+08:00| 19.999999960195904| -|1970-01-01T08:00:00.005+08:00| 9.999999850988388| -|1970-01-01T08:00:00.006+08:00| 3.2260694930700566E-7| -|1970-01-01T08:00:00.007+08:00| 8.723291605373329E-8| -|1970-01-01T08:00:00.008+08:00| 1.108657103979944E-7| -|1970-01-01T08:00:00.009+08:00| 1.2727110997246171E-8| -|1970-01-01T08:00:00.010+08:00|1.9852334701272664E-23| -|1970-01-01T08:00:00.011+08:00| 1.2727111194499847E-8| -|1970-01-01T08:00:00.012+08:00| 1.108657103979944E-7| -|1970-01-01T08:00:00.013+08:00| 8.723291785769131E-8| -|1970-01-01T08:00:00.014+08:00| 3.226069493070057E-7| -|1970-01-01T08:00:00.015+08:00| 9.999999850988388| -|1970-01-01T08:00:00.016+08:00| 19.999999960195904| -|1970-01-01T08:00:00.017+08:00| 8.723291747109068E-8| -|1970-01-01T08:00:00.018+08:00| 2.3855207991018386E-7| -|1970-01-01T08:00:00.019+08:00| 1.2727112069910878E-8| -+-----------------------------+----------------------+ -``` - -Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, there are peaks in $k=4$ and $k=5$ of the output. - -##### Uniform FFT with Compression - -Input series is the same as above, the SQL for query is shown below: - -```sql -select fft(s1, 'result'='real', 'compress'='0.99'), fft(s1, 'result'='imag','compress'='0.99') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------+----------------------+ -| Time| fft(root.test.d1.s1,| fft(root.test.d1.s1,| -| | "result"="real",| "result"="imag",| -| | "compress"="0.99")| "compress"="0.99")| -+-----------------------------+----------------------+----------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| 0.0| -|1970-01-01T08:00:00.001+08:00| -3.932894010461041E-9| 1.2104201863039066E-8| -|1970-01-01T08:00:00.002+08:00|-1.4021739447490164E-7| 1.9299268669082926E-7| -|1970-01-01T08:00:00.003+08:00| -7.057291240286645E-8| 5.127422242345858E-8| -|1970-01-01T08:00:00.004+08:00| 19.021130288047125| -6.180339875198807| -|1970-01-01T08:00:00.005+08:00| 9.999999850988388| 3.501852745067114E-16| -|1970-01-01T08:00:00.019+08:00| -3.932894898639461E-9|-1.2104202549376264E-8| -+-----------------------------+----------------------+----------------------+ -``` - -Note: Based on the conjugation of the Fourier transform result, only the first half of the compression result is reserved. -According to the given parameter, data points are reserved from low frequency to high frequency until the reserved energy ratio exceeds it. -The last data point is reserved to indicate the length of the series. - -### HighPass - -#### Registration statement - -```sql -create function highpass as 'org.apache.iotdb.library.frequency.UDTFHighPass' -``` - -#### Usage - -This function performs low-pass filtering on the input series and extracts components above the cutoff frequency. -The timestamps of input will be ignored and all data points will be regarded as equidistant. - -**Name:** HIGHPASS - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `wpass`: The normalized cutoff frequency which values (0,1). This parameter cannot be lacked. - -**Output:** Output a single series. The type is DOUBLE. It is the input after filtering. The length and timestamps of output are the same as the input. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 2.902113| -|1970-01-01T08:00:01.000+08:00| 1.1755705| -|1970-01-01T08:00:02.000+08:00| -2.1755705| -|1970-01-01T08:00:03.000+08:00| -1.9021131| -|1970-01-01T08:00:04.000+08:00| 1.0| -|1970-01-01T08:00:05.000+08:00| 1.9021131| -|1970-01-01T08:00:06.000+08:00| 0.1755705| -|1970-01-01T08:00:07.000+08:00| -1.1755705| -|1970-01-01T08:00:08.000+08:00| -0.902113| -|1970-01-01T08:00:09.000+08:00| 0.0| -|1970-01-01T08:00:10.000+08:00| 0.902113| -|1970-01-01T08:00:11.000+08:00| 1.1755705| -|1970-01-01T08:00:12.000+08:00| -0.1755705| -|1970-01-01T08:00:13.000+08:00| -1.9021131| -|1970-01-01T08:00:14.000+08:00| -1.0| -|1970-01-01T08:00:15.000+08:00| 1.9021131| -|1970-01-01T08:00:16.000+08:00| 2.1755705| -|1970-01-01T08:00:17.000+08:00| -1.1755705| -|1970-01-01T08:00:18.000+08:00| -2.902113| -|1970-01-01T08:00:19.000+08:00| 0.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select highpass(s1,'wpass'='0.45') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-----------------------------------------+ -| Time|highpass(root.test.d1.s1, "wpass"="0.45")| -+-----------------------------+-----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.9999999534830373| -|1970-01-01T08:00:01.000+08:00| 1.7462829277628608E-8| -|1970-01-01T08:00:02.000+08:00| -0.9999999593178128| -|1970-01-01T08:00:03.000+08:00| -4.1115269056426626E-8| -|1970-01-01T08:00:04.000+08:00| 0.9999999925494194| -|1970-01-01T08:00:05.000+08:00| 3.328126513330016E-8| -|1970-01-01T08:00:06.000+08:00| -1.0000000183304454| -|1970-01-01T08:00:07.000+08:00| 6.260191433311374E-10| -|1970-01-01T08:00:08.000+08:00| 1.0000000018134796| -|1970-01-01T08:00:09.000+08:00| -3.097210911744423E-17| -|1970-01-01T08:00:10.000+08:00| -1.0000000018134794| -|1970-01-01T08:00:11.000+08:00| -6.260191627862097E-10| -|1970-01-01T08:00:12.000+08:00| 1.0000000183304454| -|1970-01-01T08:00:13.000+08:00| -3.328126501424346E-8| -|1970-01-01T08:00:14.000+08:00| -0.9999999925494196| -|1970-01-01T08:00:15.000+08:00| 4.111526915498874E-8| -|1970-01-01T08:00:16.000+08:00| 0.9999999593178128| -|1970-01-01T08:00:17.000+08:00| -1.7462829341296528E-8| -|1970-01-01T08:00:18.000+08:00| -0.9999999534830369| -|1970-01-01T08:00:19.000+08:00| -1.035237222742873E-16| -+-----------------------------+-----------------------------------------+ -``` - -Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, the output is $y=sin(2\pi t/4)$ after high-pass filtering. - -### IFFT - -#### Registration statement - -```sql -create function ifft as 'org.apache.iotdb.library.frequency.UDTFIFFT' -``` - -#### Usage - -This function treats the two input series as the real and imaginary part of a complex series, performs an inverse fast Fourier transform (IFFT), and outputs the real part of the result. -For the input format, please refer to the output format of `FFT` function. -Moreover, the compressed output of `FFT` function is also supported. - -**Name:** IFFT - -**Input:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `start`: The start time of the output series with the format 'yyyy-MM-dd HH:mm:ss'. By default, it is '1970-01-01 08:00:00'. -+ `interval`: The interval of the output series, which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, it is 1s. - -**Output:** Output a single series. The type is DOUBLE. It is strictly equispaced. The values are the results of IFFT. - -**Note:** If a row contains null points or `NaN`, it will be ignored. - -#### Examples - - -Input series: - -``` -+-----------------------------+----------------------+----------------------+ -| Time| root.test.d1.re| root.test.d1.im| -+-----------------------------+----------------------+----------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| 0.0| -|1970-01-01T08:00:00.001+08:00| -3.932894010461041E-9| 1.2104201863039066E-8| -|1970-01-01T08:00:00.002+08:00|-1.4021739447490164E-7| 1.9299268669082926E-7| -|1970-01-01T08:00:00.003+08:00| -7.057291240286645E-8| 5.127422242345858E-8| -|1970-01-01T08:00:00.004+08:00| 19.021130288047125| -6.180339875198807| -|1970-01-01T08:00:00.005+08:00| 9.999999850988388| 3.501852745067114E-16| -|1970-01-01T08:00:00.019+08:00| -3.932894898639461E-9|-1.2104202549376264E-8| -+-----------------------------+----------------------+----------------------+ -``` - - -SQL for query: - -```sql -select ifft(re, im, 'interval'='1m', 'start'='2021-01-01 00:00:00') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------------+ -| Time|ifft(root.test.d1.re, root.test.d1.im, "interval"="1m",| -| | "start"="2021-01-01 00:00:00")| -+-----------------------------+-------------------------------------------------------+ -|2021-01-01T00:00:00.000+08:00| 2.902112992431231| -|2021-01-01T00:01:00.000+08:00| 1.1755704705132448| -|2021-01-01T00:02:00.000+08:00| -2.175570513757101| -|2021-01-01T00:03:00.000+08:00| -1.9021130389094498| -|2021-01-01T00:04:00.000+08:00| 0.9999999925494194| -|2021-01-01T00:05:00.000+08:00| 1.902113046743454| -|2021-01-01T00:06:00.000+08:00| 0.17557053610884188| -|2021-01-01T00:07:00.000+08:00| -1.1755704886020932| -|2021-01-01T00:08:00.000+08:00| -0.9021130371347148| -|2021-01-01T00:09:00.000+08:00| 3.552713678800501E-16| -|2021-01-01T00:10:00.000+08:00| 0.9021130371347154| -|2021-01-01T00:11:00.000+08:00| 1.1755704886020932| -|2021-01-01T00:12:00.000+08:00| -0.17557053610884144| -|2021-01-01T00:13:00.000+08:00| -1.902113046743454| -|2021-01-01T00:14:00.000+08:00| -0.9999999925494196| -|2021-01-01T00:15:00.000+08:00| 1.9021130389094498| -|2021-01-01T00:16:00.000+08:00| 2.1755705137571004| -|2021-01-01T00:17:00.000+08:00| -1.1755704705132448| -|2021-01-01T00:18:00.000+08:00| -2.902112992431231| -|2021-01-01T00:19:00.000+08:00| -3.552713678800501E-16| -+-----------------------------+-------------------------------------------------------+ -``` - -### LowPass - -#### Registration statement - -```sql -create function lowpass as 'org.apache.iotdb.library.frequency.UDTFLowPass' -``` - -#### Usage - -This function performs low-pass filtering on the input series and extracts components below the cutoff frequency. -The timestamps of input will be ignored and all data points will be regarded as equidistant. - -**Name:** LOWPASS - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `wpass`: The normalized cutoff frequency which values (0,1). This parameter cannot be lacked. - -**Output:** Output a single series. The type is DOUBLE. It is the input after filtering. The length and timestamps of output are the same as the input. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 2.902113| -|1970-01-01T08:00:01.000+08:00| 1.1755705| -|1970-01-01T08:00:02.000+08:00| -2.1755705| -|1970-01-01T08:00:03.000+08:00| -1.9021131| -|1970-01-01T08:00:04.000+08:00| 1.0| -|1970-01-01T08:00:05.000+08:00| 1.9021131| -|1970-01-01T08:00:06.000+08:00| 0.1755705| -|1970-01-01T08:00:07.000+08:00| -1.1755705| -|1970-01-01T08:00:08.000+08:00| -0.902113| -|1970-01-01T08:00:09.000+08:00| 0.0| -|1970-01-01T08:00:10.000+08:00| 0.902113| -|1970-01-01T08:00:11.000+08:00| 1.1755705| -|1970-01-01T08:00:12.000+08:00| -0.1755705| -|1970-01-01T08:00:13.000+08:00| -1.9021131| -|1970-01-01T08:00:14.000+08:00| -1.0| -|1970-01-01T08:00:15.000+08:00| 1.9021131| -|1970-01-01T08:00:16.000+08:00| 2.1755705| -|1970-01-01T08:00:17.000+08:00| -1.1755705| -|1970-01-01T08:00:18.000+08:00| -2.902113| -|1970-01-01T08:00:19.000+08:00| 0.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select lowpass(s1,'wpass'='0.45') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------+ -| Time|lowpass(root.test.d1.s1, "wpass"="0.45")| -+-----------------------------+----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 1.9021130073323922| -|1970-01-01T08:00:01.000+08:00| 1.1755704705132448| -|1970-01-01T08:00:02.000+08:00| -1.1755705286582614| -|1970-01-01T08:00:03.000+08:00| -1.9021130389094498| -|1970-01-01T08:00:04.000+08:00| 7.450580419288145E-9| -|1970-01-01T08:00:05.000+08:00| 1.902113046743454| -|1970-01-01T08:00:06.000+08:00| 1.1755705212076808| -|1970-01-01T08:00:07.000+08:00| -1.1755704886020932| -|1970-01-01T08:00:08.000+08:00| -1.9021130222335536| -|1970-01-01T08:00:09.000+08:00| 3.552713678800501E-16| -|1970-01-01T08:00:10.000+08:00| 1.9021130222335536| -|1970-01-01T08:00:11.000+08:00| 1.1755704886020932| -|1970-01-01T08:00:12.000+08:00| -1.1755705212076801| -|1970-01-01T08:00:13.000+08:00| -1.902113046743454| -|1970-01-01T08:00:14.000+08:00| -7.45058112983088E-9| -|1970-01-01T08:00:15.000+08:00| 1.9021130389094498| -|1970-01-01T08:00:16.000+08:00| 1.1755705286582616| -|1970-01-01T08:00:17.000+08:00| -1.1755704705132448| -|1970-01-01T08:00:18.000+08:00| -1.9021130073323924| -|1970-01-01T08:00:19.000+08:00| -2.664535259100376E-16| -+-----------------------------+----------------------------------------+ -``` - -Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, the output is $y=2sin(2\pi t/5)$ after low-pass filtering. - - - -## Data Matching - -### Cov - -#### Registration statement - -```sql -create function cov as 'org.apache.iotdb.library.dmatch.UDAFCov' -``` - -#### Usage - -This function is used to calculate the population covariance. - -**Name:** COV - -**Input Series:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the population covariance. - -**Note:** - -+ If a row contains missing points, null points or `NaN`, it will be ignored; -+ If all rows are ignored, `NaN` will be output. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| 101.0| -|2020-01-01T00:00:03.000+08:00| 101.0| null| -|2020-01-01T00:00:04.000+08:00| 102.0| 101.0| -|2020-01-01T00:00:06.000+08:00| 104.0| 102.0| -|2020-01-01T00:00:08.000+08:00| 126.0| 102.0| -|2020-01-01T00:00:10.000+08:00| 108.0| 103.0| -|2020-01-01T00:00:12.000+08:00| null| 103.0| -|2020-01-01T00:00:14.000+08:00| 112.0| 104.0| -|2020-01-01T00:00:15.000+08:00| 113.0| null| -|2020-01-01T00:00:16.000+08:00| 114.0| 104.0| -|2020-01-01T00:00:18.000+08:00| 116.0| 105.0| -|2020-01-01T00:00:20.000+08:00| 118.0| 105.0| -|2020-01-01T00:00:22.000+08:00| 100.0| 106.0| -|2020-01-01T00:00:26.000+08:00| 124.0| 108.0| -|2020-01-01T00:00:28.000+08:00| 126.0| 108.0| -|2020-01-01T00:00:30.000+08:00| NaN| 108.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select cov(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|cov(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 12.291666666666666| -+-----------------------------+-------------------------------------+ -``` - -### DTW - -#### Registration statement - -```sql -create function dtw as 'org.apache.iotdb.library.dmatch.UDAFDtw' -``` - -#### Usage - -This function is used to calculate the DTW distance between two input series. - -**Name:** DTW - -**Input Series:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the DTW distance. - -**Note:** - -+ If a row contains missing points, null points or `NaN`, it will be ignored; -+ If all rows are ignored, `0` will be output. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.001+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.002+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.003+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.004+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.005+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.006+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.007+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.008+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.009+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.010+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.011+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.012+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.013+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.014+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.015+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.016+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.017+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.018+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.019+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.020+08:00| 1.0| 2.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select dtw(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|dtw(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 20.0| -+-----------------------------+-------------------------------------+ -``` - -### Pearson - -#### Registration statement - -```sql -create function pearson as 'org.apache.iotdb.library.dmatch.UDAFPearson' -``` - -#### Usage - -This function is used to calculate the Pearson Correlation Coefficient. - -**Name:** PEARSON - -**Input Series:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the Pearson Correlation Coefficient. - -**Note:** - -+ If a row contains missing points, null points or `NaN`, it will be ignored; -+ If all rows are ignored, `NaN` will be output. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| 101.0| -|2020-01-01T00:00:03.000+08:00| 101.0| null| -|2020-01-01T00:00:04.000+08:00| 102.0| 101.0| -|2020-01-01T00:00:06.000+08:00| 104.0| 102.0| -|2020-01-01T00:00:08.000+08:00| 126.0| 102.0| -|2020-01-01T00:00:10.000+08:00| 108.0| 103.0| -|2020-01-01T00:00:12.000+08:00| null| 103.0| -|2020-01-01T00:00:14.000+08:00| 112.0| 104.0| -|2020-01-01T00:00:15.000+08:00| 113.0| null| -|2020-01-01T00:00:16.000+08:00| 114.0| 104.0| -|2020-01-01T00:00:18.000+08:00| 116.0| 105.0| -|2020-01-01T00:00:20.000+08:00| 118.0| 105.0| -|2020-01-01T00:00:22.000+08:00| 100.0| 106.0| -|2020-01-01T00:00:26.000+08:00| 124.0| 108.0| -|2020-01-01T00:00:28.000+08:00| 126.0| 108.0| -|2020-01-01T00:00:30.000+08:00| NaN| 108.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select pearson(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-----------------------------------------+ -| Time|pearson(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+-----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.5630881927754872| -+-----------------------------+-----------------------------------------+ -``` - -### PtnSym - -#### Registration statement - -```sql -create function ptnsym as 'org.apache.iotdb.library.dmatch.UDTFPtnSym' -``` - -#### Usage - -This function is used to find all symmetric subseries in the input whose degree of symmetry is less than the threshold. -The degree of symmetry is calculated by DTW. -The smaller the degree, the more symmetrical the series is. - -**Name:** PATTERNSYMMETRIC - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE - -**Parameter:** - -+ `window`: The length of the symmetric subseries. It's a positive integer and the default value is 10. -+ `threshold`: The threshold of the degree of symmetry. It's non-negative. Only the subseries whose degree of symmetry is below it will be output. By default, all subseries will be output. - - -**Output Series:** Output a single series. The type is DOUBLE. Each data point in the output series corresponds to a symmetric subseries. The output timestamp is the starting timestamp of the subseries and the output value is the degree of symmetry. - -#### Example - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s4| -+-----------------------------+---------------+ -|2021-01-01T12:00:00.000+08:00| 1.0| -|2021-01-01T12:00:01.000+08:00| 2.0| -|2021-01-01T12:00:02.000+08:00| 3.0| -|2021-01-01T12:00:03.000+08:00| 2.0| -|2021-01-01T12:00:04.000+08:00| 1.0| -|2021-01-01T12:00:05.000+08:00| 1.0| -|2021-01-01T12:00:06.000+08:00| 1.0| -|2021-01-01T12:00:07.000+08:00| 1.0| -|2021-01-01T12:00:08.000+08:00| 2.0| -|2021-01-01T12:00:09.000+08:00| 3.0| -|2021-01-01T12:00:10.000+08:00| 2.0| -|2021-01-01T12:00:11.000+08:00| 1.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select ptnsym(s4, 'window'='5', 'threshold'='0') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|ptnsym(root.test.d1.s4, "window"="5", "threshold"="0")| -+-----------------------------+------------------------------------------------------+ -|2021-01-01T12:00:00.000+08:00| 0.0| -|2021-01-01T12:00:07.000+08:00| 0.0| -+-----------------------------+------------------------------------------------------+ -``` - -### XCorr - -#### Registration statement - -```sql -create function xcorr as 'org.apache.iotdb.library.dmatch.UDTFXCorr' -``` - -#### Usage - -This function is used to calculate the cross correlation function of given two time series. -For discrete time series, cross correlation is given by -$$CR(n) = \frac{1}{N} \sum_{m=1}^N S_1[m]S_2[m+n]$$ -which represent the similarities between two series with different index shifts. - -**Name:** XCORR - -**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series with DOUBLE as datatype. -There are $2N-1$ data points in the series, the center of which represents the cross correlation -calculated with pre-aligned series(that is $CR(0)$ in the formula above), -and the previous(or post) values represent those with shifting the latter series forward(or backward otherwise) -until the two series are no longer overlapped(not included). -In short, the values of output series are given by(index starts from 1) -$$OS[i] = CR(-N+i) = \frac{1}{N} \sum_{m=1}^{i} S_1[m]S_2[N-i+m],\ if\ i <= N$$ -$$OS[i] = CR(i-N) = \frac{1}{N} \sum_{m=1}^{2N-i} S_1[i-N+m]S_2[m],\ if\ i > N$$ - -**Note:** - -+ `null` and `NaN` values in the input series will be ignored and treated as 0. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:01.000+08:00| null| 6| -|2020-01-01T00:00:02.000+08:00| 2| 7| -|2020-01-01T00:00:03.000+08:00| 3| NaN| -|2020-01-01T00:00:04.000+08:00| 4| 9| -|2020-01-01T00:00:05.000+08:00| 5| 10| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 -``` - -Output series: - -``` -+-----------------------------+---------------------------------------+ -| Time|xcorr(root.test.d1.s1, root.test.d1.s2)| -+-----------------------------+---------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 0.0| -|1970-01-01T08:00:00.002+08:00| 4.0| -|1970-01-01T08:00:00.003+08:00| 9.6| -|1970-01-01T08:00:00.004+08:00| 13.4| -|1970-01-01T08:00:00.005+08:00| 20.0| -|1970-01-01T08:00:00.006+08:00| 15.6| -|1970-01-01T08:00:00.007+08:00| 9.2| -|1970-01-01T08:00:00.008+08:00| 11.8| -|1970-01-01T08:00:00.009+08:00| 6.0| -+-----------------------------+---------------------------------------+ -``` - - - -## Data Repairing - -### TimestampRepair - -#### Registration statement - -```sql -create function timestamprepair as 'org.apache.iotdb.library.drepair.UDTFTimestampRepair' -``` - -#### Usage - -This function is used for timestamp repair. -According to the given standard time interval, -the method of minimizing the repair cost is adopted. -By fine-tuning the timestamps, -the original data with unstable timestamp interval is repaired to strictly equispaced data. -If no standard time interval is given, -this function will use the **median**, **mode** or **cluster** of the time interval to estimate the standard time interval. - -**Name:** TIMESTAMPREPAIR - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `interval`: The standard time interval whose unit is millisecond. It is a positive integer. By default, it will be estimated according to the given method. -+ `method`: The method to estimate the standard time interval, which is 'median', 'mode' or 'cluster'. This parameter is only valid when `interval` is not given. By default, median will be used. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -#### Examples - -##### Manually Specify the Standard Time Interval - -When `interval` is given, this function repairs according to the given standard time interval. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2021-07-01T12:00:00.000+08:00| 1.0| -|2021-07-01T12:00:10.000+08:00| 2.0| -|2021-07-01T12:00:19.000+08:00| 3.0| -|2021-07-01T12:00:30.000+08:00| 4.0| -|2021-07-01T12:00:40.000+08:00| 5.0| -|2021-07-01T12:00:50.000+08:00| 6.0| -|2021-07-01T12:01:01.000+08:00| 7.0| -|2021-07-01T12:01:11.000+08:00| 8.0| -|2021-07-01T12:01:21.000+08:00| 9.0| -|2021-07-01T12:01:31.000+08:00| 10.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select timestamprepair(s1,'interval'='10000') from root.test.d2 -``` - -Output series: - - -``` -+-----------------------------+----------------------------------------------------+ -| Time|timestamprepair(root.test.d2.s1, "interval"="10000")| -+-----------------------------+----------------------------------------------------+ -|2021-07-01T12:00:00.000+08:00| 1.0| -|2021-07-01T12:00:10.000+08:00| 2.0| -|2021-07-01T12:00:20.000+08:00| 3.0| -|2021-07-01T12:00:30.000+08:00| 4.0| -|2021-07-01T12:00:40.000+08:00| 5.0| -|2021-07-01T12:00:50.000+08:00| 6.0| -|2021-07-01T12:01:00.000+08:00| 7.0| -|2021-07-01T12:01:10.000+08:00| 8.0| -|2021-07-01T12:01:20.000+08:00| 9.0| -|2021-07-01T12:01:30.000+08:00| 10.0| -+-----------------------------+----------------------------------------------------+ -``` - -##### Automatically Estimate the Standard Time Interval - -When `interval` is default, this function estimates the standard time interval. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select timestamprepair(s1) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------+ -| Time|timestamprepair(root.test.d2.s1)| -+-----------------------------+--------------------------------+ -|2021-07-01T12:00:00.000+08:00| 1.0| -|2021-07-01T12:00:10.000+08:00| 2.0| -|2021-07-01T12:00:20.000+08:00| 3.0| -|2021-07-01T12:00:30.000+08:00| 4.0| -|2021-07-01T12:00:40.000+08:00| 5.0| -|2021-07-01T12:00:50.000+08:00| 6.0| -|2021-07-01T12:01:00.000+08:00| 7.0| -|2021-07-01T12:01:10.000+08:00| 8.0| -|2021-07-01T12:01:20.000+08:00| 9.0| -|2021-07-01T12:01:30.000+08:00| 10.0| -+-----------------------------+--------------------------------+ -``` - -### ValueFill - -#### Registration statement - -```sql -create function valuefill as 'org.apache.iotdb.library.drepair.UDTFValueFill' -``` - -#### Usage - -This function is used to impute time series. Several methods are supported. - -**Name**: ValueFill -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: {"mean", "previous", "linear", "likelihood", "AR", "MA", "SCREEN"}, default "linear". - Method to use for imputation in series. "mean": use global mean value to fill holes; "previous": propagate last valid observation forward to next valid. "linear": simplest interpolation method; "likelihood":Maximum likelihood estimation based on the normal distribution of speed; "AR": auto regression; "MA": moving average; "SCREEN": speed constraint. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -**Note:** AR method use AR(1) model. Input value should be auto-correlated, or the function would output a single point (0, 0.0). - -#### Examples - -##### Fill with linear - -When `method` is "linear" or the default, Screen method is used to impute. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| NaN| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| NaN| -|2020-01-01T00:00:22.000+08:00| NaN| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select valuefill(s1) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time|valuefill(root.test.d2)| -+-----------------------------+-----------------------+ -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 108.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.7| -|2020-01-01T00:00:22.000+08:00| 121.3| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+-----------------------+ -``` - -##### Previous Fill - -When `method` is "previous", previous method is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select valuefill(s1,"method"="previous") from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------+ -| Time|valuefill(root.test.d2,"method"="previous")| -+-----------------------------+-------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 110.5| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 116.0| -|2020-01-01T00:00:22.000+08:00| 116.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+-------------------------------------------+ -``` - -### ValueRepair - -#### Registration statement - -```sql -create function valuerepair as 'org.apache.iotdb.library.drepair.UDTFValueRepair' -``` - -#### Usage - -This function is used to repair the value of the time series. -Currently, two methods are supported: -**Screen** is a method based on speed threshold, which makes all speeds meet the threshold requirements under the premise of minimum changes; -**LsGreedy** is a method based on speed change likelihood, which models speed changes as Gaussian distribution, and uses a greedy algorithm to maximize the likelihood. - - -**Name:** VALUEREPAIR - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The method used to repair, which is 'Screen' or 'LsGreedy'. By default, Screen is used. -+ `minSpeed`: This parameter is only valid with Screen. It is the speed threshold. Speeds below it will be regarded as outliers. By default, it is the median minus 3 times of median absolute deviation. -+ `maxSpeed`: This parameter is only valid with Screen. It is the speed threshold. Speeds above it will be regarded as outliers. By default, it is the median plus 3 times of median absolute deviation. -+ `center`: This parameter is only valid with LsGreedy. It is the center of the Gaussian distribution of speed changes. By default, it is 0. -+ `sigma`: This parameter is only valid with LsGreedy. It is the standard deviation of the Gaussian distribution of speed changes. By default, it is the median absolute deviation. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -**Note:** `NaN` will be filled with linear interpolation before repairing. - -#### Examples - -##### Repair with Screen - -When `method` is 'Screen' or the default, Screen method is used. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 100.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select valuerepair(s1) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+----------------------------+ -| Time|valuerepair(root.test.d2.s1)| -+-----------------------------+----------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 106.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+----------------------------+ -``` - -##### Repair with LsGreedy - -When `method` is 'LsGreedy', LsGreedy method is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select valuerepair(s1,'method'='LsGreedy') from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------+ -| Time|valuerepair(root.test.d2.s1, "method"="LsGreedy")| -+-----------------------------+-------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 106.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+-------------------------------------------------+ -``` - -### MasterRepair - -#### Usage - -This function is used to clean time series with master data. - -**Name**: MasterRepair -**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences. -+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows. -+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data. -+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+------------+------------+------------+------------+------------+ -| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3| -+-----------------------------+------------+------------+------------+------------+------------+------------+ -|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195| -|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193| -|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192| -|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194| -|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195| -|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193| -|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194| -|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190| -+-----------------------------+------------+------------+------------+------------+------------+------------+ -``` - -SQL for query: - -```sql -select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test -``` - -Output series: - - -``` -+-----------------------------+-------------------------------------------------------------------------------------------+ -| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)| -+-----------------------------+-------------------------------------------------------------------------------------------+ -|2021-07-01T12:00:01.000+08:00| 1704| -|2021-07-01T12:00:02.000+08:00| 1702| -|2021-07-01T12:00:03.000+08:00| 1702| -|2021-07-01T12:00:04.000+08:00| 1701| -|2021-07-01T12:00:07.000+08:00| 1703| -|2021-07-01T12:00:08.000+08:00| 1704| -|2021-07-01T12:01:09.000+08:00| 1705| -|2021-07-01T12:01:10.000+08:00| 1706| -+-----------------------------+-------------------------------------------------------------------------------------------+ -``` - -### SeasonalRepair - -#### Usage -This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median. - -**Name:** SEASONALREPAIR - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used. -+ `period`: It is the period of the time series. -+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9. -+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -**Note:** `NaN` will be filled with linear interpolation before repairing. - -#### Examples - -##### Repair with Classical - -When `method` is 'Classical' or default value, classical decomposition method is used. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:04.000+08:00| 120.0| -|2020-01-01T00:00:06.000+08:00| 80.0| -|2020-01-01T00:00:08.000+08:00| 100.5| -|2020-01-01T00:00:10.000+08:00| 119.5| -|2020-01-01T00:00:12.000+08:00| 101.0| -|2020-01-01T00:00:14.000+08:00| 99.5| -|2020-01-01T00:00:16.000+08:00| 119.0| -|2020-01-01T00:00:18.000+08:00| 80.5| -|2020-01-01T00:00:20.000+08:00| 99.0| -|2020-01-01T00:00:22.000+08:00| 121.0| -|2020-01-01T00:00:24.000+08:00| 79.5| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------+ -| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)| -+-----------------------------+--------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:04.000+08:00| 120.0| -|2020-01-01T00:00:06.000+08:00| 80.0| -|2020-01-01T00:00:08.000+08:00| 100.5| -|2020-01-01T00:00:10.000+08:00| 119.5| -|2020-01-01T00:00:12.000+08:00| 87.0| -|2020-01-01T00:00:14.000+08:00| 99.5| -|2020-01-01T00:00:16.000+08:00| 119.0| -|2020-01-01T00:00:18.000+08:00| 80.5| -|2020-01-01T00:00:20.000+08:00| 99.0| -|2020-01-01T00:00:22.000+08:00| 121.0| -|2020-01-01T00:00:24.000+08:00| 79.5| -+-----------------------------+--------------------------------------------------+ -``` - -##### Repair with Improved -When `method` is 'Improved', improved decomposition method is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------------------+ -| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)| -+-----------------------------+-------------------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:04.000+08:00| 120.0| -|2020-01-01T00:00:06.000+08:00| 80.0| -|2020-01-01T00:00:08.000+08:00| 100.5| -|2020-01-01T00:00:10.000+08:00| 119.5| -|2020-01-01T00:00:12.000+08:00| 81.5| -|2020-01-01T00:00:14.000+08:00| 99.5| -|2020-01-01T00:00:16.000+08:00| 119.0| -|2020-01-01T00:00:18.000+08:00| 80.5| -|2020-01-01T00:00:20.000+08:00| 99.0| -|2020-01-01T00:00:22.000+08:00| 121.0| -|2020-01-01T00:00:24.000+08:00| 79.5| -+-----------------------------+-------------------------------------------------------------+ -``` - - - -## Series Discovery - -### ConsecutiveSequences - -#### Registration statement - -```sql -create function consecutivesequences as 'org.apache.iotdb.library.series.UDTFConsecutiveSequences' -``` - -#### Usage - -This function is used to find locally longest consecutive subsequences in strictly equispaced multidimensional data. - -Strictly equispaced data is the data whose time intervals are strictly equal. Missing data, including missing rows and missing values, is allowed in it, while data redundancy and timestamp drift is not allowed. - -Consecutive subsequence is the subsequence that is strictly equispaced with the standard time interval without any missing data. If a consecutive subsequence is not a proper subsequence of any consecutive subsequence, it is locally longest. - -**Name:** CONSECUTIVESEQUENCES - -**Input Series:** Support multiple input series. The type is arbitrary but the data is strictly equispaced. - -**Parameters:** - -+ `gap`: The standard time interval which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, it will be estimated by the mode of time intervals. - -**Output Series:** Output a single series. The type is INT32. Each data point in the output series corresponds to a locally longest consecutive subsequence. The output timestamp is the starting timestamp of the subsequence and the output value is the number of data points in the subsequence. - -**Note:** For input series that is not strictly equispaced, there is no guarantee on the output. - -#### Examples - -##### Manually Specify the Standard Time Interval - -It's able to manually specify the standard time interval by the parameter `gap`. It's notable that false parameter leads to false output. - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:05:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:10:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:20:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:25:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:30:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:35:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:40:00.000+08:00| 1.0| null| -|2020-01-01T00:45:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:50:00.000+08:00| 1.0| 1.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select consecutivesequences(s1,s2,'gap'='5m') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------------------+ -| Time|consecutivesequences(root.test.d1.s1, root.test.d1.s2, "gap"="5m")| -+-----------------------------+------------------------------------------------------------------+ -|2020-01-01T00:00:00.000+08:00| 3| -|2020-01-01T00:20:00.000+08:00| 4| -|2020-01-01T00:45:00.000+08:00| 2| -+-----------------------------+------------------------------------------------------------------+ -``` - - -##### Automatically Estimate the Standard Time Interval - -When `gap` is default, this function estimates the standard time interval by the mode of time intervals and gets the same results. Therefore, this usage is more recommended. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select consecutivesequences(s1,s2) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|consecutivesequences(root.test.d1.s1, root.test.d1.s2)| -+-----------------------------+------------------------------------------------------+ -|2020-01-01T00:00:00.000+08:00| 3| -|2020-01-01T00:20:00.000+08:00| 4| -|2020-01-01T00:45:00.000+08:00| 2| -+-----------------------------+------------------------------------------------------+ -``` - -### ConsecutiveWindows - -#### Registration statement - -```sql -create function consecutivewindows as 'org.apache.iotdb.library.series.UDTFConsecutiveWindows' -``` - -#### Usage - -This function is used to find consecutive windows of specified length in strictly equispaced multidimensional data. - -Strictly equispaced data is the data whose time intervals are strictly equal. Missing data, including missing rows and missing values, is allowed in it, while data redundancy and timestamp drift is not allowed. - -Consecutive window is the subsequence that is strictly equispaced with the standard time interval without any missing data. - -**Name:** CONSECUTIVEWINDOWS - -**Input Series:** Support multiple input series. The type is arbitrary but the data is strictly equispaced. - -**Parameters:** - -+ `gap`: The standard time interval which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, it will be estimated by the mode of time intervals. -+ `length`: The length of the window which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. This parameter cannot be lacked. - -**Output Series:** Output a single series. The type is INT32. Each data point in the output series corresponds to a consecutive window. The output timestamp is the starting timestamp of the window and the output value is the number of data points in the window. - -**Note:** For input series that is not strictly equispaced, there is no guarantee on the output. - -#### Examples - - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:05:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:10:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:20:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:25:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:30:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:35:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:40:00.000+08:00| 1.0| null| -|2020-01-01T00:45:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:50:00.000+08:00| 1.0| 1.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------------------+ -| Time|consecutivewindows(root.test.d1.s1, root.test.d1.s2, "length"="10m")| -+-----------------------------+--------------------------------------------------------------------+ -|2020-01-01T00:00:00.000+08:00| 3| -|2020-01-01T00:20:00.000+08:00| 3| -|2020-01-01T00:25:00.000+08:00| 3| -+-----------------------------+--------------------------------------------------------------------+ -``` - - - -## Machine Learning - -### AR - -#### Registration statement - -```sql -create function ar as 'org.apache.iotdb.library.dlearn.UDTFAR' -``` - -#### Usage - -This function is used to learn the coefficients of the autoregressive models for a time series. - -**Name:** AR - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -- `p`: The order of the autoregressive model. Its default value is 1. - -**Output Series:** Output a single series. The type is DOUBLE. The first line corresponds to the first order coefficient, and so on. - -**Note:** - -- Parameter `p` should be a positive integer. -- Most points in the series should be sampled at a constant time interval. -- Linear interpolation is applied for the missing points in the series. - -#### Examples - -##### Assigning Model Order - -Input Series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d0.s0| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| -4.0| -|2020-01-01T00:00:02.000+08:00| -3.0| -|2020-01-01T00:00:03.000+08:00| -2.0| -|2020-01-01T00:00:04.000+08:00| -1.0| -|2020-01-01T00:00:05.000+08:00| 0.0| -|2020-01-01T00:00:06.000+08:00| 1.0| -|2020-01-01T00:00:07.000+08:00| 2.0| -|2020-01-01T00:00:08.000+08:00| 3.0| -|2020-01-01T00:00:09.000+08:00| 4.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select ar(s0,"p"="2") from root.test.d0 -``` - -Output Series: - -``` -+-----------------------------+---------------------------+ -| Time|ar(root.test.d0.s0,"p"="2")| -+-----------------------------+---------------------------+ -|1970-01-01T08:00:00.001+08:00| 0.9429| -|1970-01-01T08:00:00.002+08:00| -0.2571| -+-----------------------------+---------------------------+ -``` - -### Representation - -#### Usage - -This function is used to represent a time series. - -**Name:** Representation - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -- `tb`: The number of timestamp blocks. Its default value is 10. -- `vb`: The number of value blocks. Its default value is 10. - -**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order. - -**Note:** - -- Parameters `tb` and `vb` should be positive integers. - -#### Examples - -##### Assigning Window Size and Dimension - -Input Series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d0.s0| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| -4.0| -|2020-01-01T00:00:02.000+08:00| -3.0| -|2020-01-01T00:00:03.000+08:00| -2.0| -|2020-01-01T00:00:04.000+08:00| -1.0| -|2020-01-01T00:00:05.000+08:00| 0.0| -|2020-01-01T00:00:06.000+08:00| 1.0| -|2020-01-01T00:00:07.000+08:00| 2.0| -|2020-01-01T00:00:08.000+08:00| 3.0| -|2020-01-01T00:00:09.000+08:00| 4.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select representation(s0,"tb"="3","vb"="2") from root.test.d0 -``` - -Output Series: - -``` -+-----------------------------+-------------------------------------------------+ -| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")| -+-----------------------------+-------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 1| -|1970-01-01T08:00:00.002+08:00| 1| -|1970-01-01T08:00:00.003+08:00| 0| -|1970-01-01T08:00:00.004+08:00| 0| -|1970-01-01T08:00:00.005+08:00| 1| -|1970-01-01T08:00:00.006+08:00| 1| -+-----------------------------+-------------------------------------------------+ -``` - -### RM - -#### Usage - -This function is used to calculate the matching score of two time series according to the representation. - -**Name:** RM - -**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -- `tb`: The number of timestamp blocks. Its default value is 10. -- `vb`: The number of value blocks. Its default value is 10. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score. - -**Note:** - -- Parameters `tb` and `vb` should be positive integers. - -#### Examples - -##### Assigning Window Size and Dimension - -Input Series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d0.s0|root.test.d0.s1 -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:01.000+08:00| -4.0| -4.0| -|2020-01-01T00:00:02.000+08:00| -3.0| -3.0| -|2020-01-01T00:00:03.000+08:00| -3.0| -3.0| -|2020-01-01T00:00:04.000+08:00| -1.0| -1.0| -|2020-01-01T00:00:05.000+08:00| 0.0| 0.0| -|2020-01-01T00:00:06.000+08:00| 1.0| 1.0| -|2020-01-01T00:00:07.000+08:00| 2.0| 2.0| -|2020-01-01T00:00:08.000+08:00| 3.0| 3.0| -|2020-01-01T00:00:09.000+08:00| 4.0| 4.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0 -``` - -Output Series: - -``` -+-----------------------------+-----------------------------------------------------+ -| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")| -+-----------------------------+-----------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 1.00| -+-----------------------------+-----------------------------------------------------+ -``` - diff --git a/src/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md b/src/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md index 438cd2431..1b6fd667f 100644 --- a/src/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md +++ b/src/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md @@ -21,11 +21,11 @@ # Operator and Expression -This chapter describes the operators and functions supported by IoTDB. IoTDB provides a wealth of built-in operators and functions to meet your computing needs, and supports extensions through the [User-Defined Function](../Reference/UDF-Libraries.md). +This chapter describes the operators and functions supported by IoTDB. IoTDB provides a wealth of built-in operators and functions to meet your computing needs, and supports extensions through the [User-Defined Function](../SQL-Manual/UDF-Libraries.md). A list of all available functions, both built-in and custom, can be displayed with `SHOW FUNCTIONS` command. -See the documentation [Select-Expression](../Reference/Function-and-Expression.md#selector-functions) for the behavior of operators and functions in SQL. +See the documentation [Select-Expression](../SQL-Manual/Function-and-Expression.md#selector-functions) for the behavior of operators and functions in SQL. ## OPERATORS @@ -41,7 +41,7 @@ See the documentation [Select-Expression](../Reference/Function-and-Expression.m | `+` | addition | | `-` | subtraction | -For details and examples, see the document [Arithmetic Operators and Functions](../Reference/Function-and-Expression.md#arithmetic-functions). +For details and examples, see the document [Arithmetic Operators and Functions](../SQL-Manual/Function-and-Expression.md#arithmetic-functions). ### Comparison Operators @@ -64,7 +64,7 @@ For details and examples, see the document [Arithmetic Operators and Functions]( | `IN` / `CONTAINS` | is a value in the specified list | | `NOT IN` / `NOT CONTAINS` | is not a value in the specified list | -For details and examples, see the document [Comparison Operators and Functions](../Reference/Function-and-Expression.md#comparison-operators-and-functions). +For details and examples, see the document [Comparison Operators and Functions](../SQL-Manual/Function-and-Expression.md#comparison-operators-and-functions). ### Logical Operators @@ -74,7 +74,7 @@ For details and examples, see the document [Comparison Operators and Functions]( | `AND` / `&` / `&&` | logical AND | | `OR`/ | / || | logical OR | -For details and examples, see the document [Logical Operators](../Reference/Function-and-Expression.md#logical-operators). +For details and examples, see the document [Logical Operators](../SQL-Manual/Function-and-Expression.md#logical-operators). ### Operator Precedence @@ -123,7 +123,7 @@ The built-in functions can be used in IoTDB without registration, and the functi | MAX_BY | MAX_BY(x, y) returns the value of x corresponding to the maximum value of the input y. MAX_BY(time, x) returns the timestamp when x is at its maximum value. | The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE. | / | Consistent with the data type of the first input x. | | MIN_BY | MIN_BY(x, y) returns the value of x corresponding to the minimum value of the input y. MIN_BY(time, x) returns the timestamp when x is at its minimum value. | The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE. | / | Consistent with the data type of the first input x. | -For details and examples, see the document [Aggregate Functions](../Reference/Function-and-Expression.md#aggregate-functions). +For details and examples, see the document [Aggregate Functions](../SQL-Manual/Function-and-Expression.md#aggregate-functions). ### Arithmetic Functions @@ -150,7 +150,7 @@ For details and examples, see the document [Aggregate Functions](../Reference/Fu | LOG10 | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | / | Math#log10(double) | | SQRT | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | / | Math#sqrt(double) | -For details and examples, see the document [Arithmetic Operators and Functions](../Reference/Function-and-Expression.md#arithmetic-operators-and-functions). +For details and examples, see the document [Arithmetic Operators and Functions](../SQL-Manual/Function-and-Expression.md#arithmetic-operators-and-functions). ### Comparison Functions @@ -159,7 +159,7 @@ For details and examples, see the document [Arithmetic Operators and Functions]( | ON_OFF | INT32 / INT64 / FLOAT / DOUBLE | `threshold`: a double type variate | BOOLEAN | Return `ts_value >= threshold`. | | IN_RANGR | INT32 / INT64 / FLOAT / DOUBLE | `lower`: DOUBLE type `upper`: DOUBLE type | BOOLEAN | Return `ts_value >= lower && value <= upper`. | -For details and examples, see the document [Comparison Operators and Functions](../Reference/Function-and-Expression.md#comparison-operators-and-functions). +For details and examples, see the document [Comparison Operators and Functions](../SQL-Manual/Function-and-Expression.md#comparison-operators-and-functions). ### String Processing Functions @@ -179,7 +179,7 @@ For details and examples, see the document [Comparison Operators and Functions]( | TRIM | TEXT STRING | / | TEXT | Get the string whose value is same to input series, with all leading and trailing space removed. | | STRCMP | TEXT STRING | / | TEXT | Get the compare result of two input series. Returns `0` if series value are the same, a `negative integer` if value of series1 is smaller than series2,
a `positive integer` if value of series1 is more than series2. | -For details and examples, see the document [String Processing](../Reference/Function-and-Expression.md#string-processing). +For details and examples, see the document [String Processing](../SQL-Manual/Function-and-Expression.md#string-processing). ### Data Type Conversion Function @@ -187,7 +187,7 @@ For details and examples, see the document [String Processing](../Reference/Func | ------------- | ------------------------------------------------------------ | ----------------------- | ------------------------------------------------------------ | | CAST | `type`: Output data type, INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT | determined by `type` | Convert the data to the type specified by the `type` parameter. | -For details and examples, see the document [Data Type Conversion Function](../Reference/Function-and-Expression.md#data-type-conversion-function). +For details and examples, see the document [Data Type Conversion Function](../SQL-Manual/Function-and-Expression.md#data-type-conversion-function). ### Constant Timeseries Generating Functions @@ -197,7 +197,7 @@ For details and examples, see the document [Data Type Conversion Function](../Re | PI | None | DOUBLE | Data point value: a `double` value of `π`, the ratio of the circumference of a circle to its diameter, which is equals to `Math.PI` in the *Java Standard Library*. | | E | None | DOUBLE | Data point value: a `double` value of `e`, the base of the natural logarithms, which is equals to `Math.E` in the *Java Standard Library*. | -For details and examples, see the document [Constant Timeseries Generating Functions](../Reference/Function-and-Expression.md#constant-timeseries-generating-functions). +For details and examples, see the document [Constant Timeseries Generating Functions](../SQL-Manual/Function-and-Expression.md#constant-timeseries-generating-functions). ### Selector Functions @@ -206,7 +206,7 @@ For details and examples, see the document [Constant Timeseries Generating Funct | TOP_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000 | Same type as the input series | Returns `k` data points with the largest values in a time series. | | BOTTOM_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000 | Same type as the input series | Returns `k` data points with the smallest values in a time series. | -For details and examples, see the document [Selector Functions](../Reference/Function-and-Expression.md#selector-functions). +For details and examples, see the document [Selector Functions](../SQL-Manual/Function-and-Expression.md#selector-functions). ### Continuous Interval Functions @@ -217,7 +217,7 @@ For details and examples, see the document [Selector Functions](../Reference/Fun | ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE` | Long | Return intervals' start times and the number of data points in the interval in which the value is always 0(false). Data points number `n` satisfy `n >= min && n <= max` | | NON_ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE` | Long | Return intervals' start times and the number of data points in the interval in which the value is always not 0(false). Data points number `n` satisfy `n >= min && n <= max` | -For details and examples, see the document [Continuous Interval Functions](../Reference/Function-and-Expression.md#continuous-interval-functions). +For details and examples, see the document [Continuous Interval Functions](../SQL-Manual/Function-and-Expression.md#continuous-interval-functions). ### Variation Trend Calculation Functions @@ -230,7 +230,7 @@ For details and examples, see the document [Continuous Interval Functions](../Re | NON_NEGATIVE_DERIVATIVE | INT32 / INT64 / FLOAT / DOUBLE | / | DOUBLE | Calculates the absolute value of the rate of change of a data point compared to the previous data point, the result is equals to NON_NEGATIVE_DIFFERENCE / TIME_DIFFERENCE. There is no corresponding output for the first data point. | | DIFF | INT32 / INT64 / FLOAT / DOUBLE | `ignoreNull`：optional，default is true. If is true, the previous data point is ignored when it is null and continues to find the first non-null value forwardly. If the value is false, previous data point is not ignored when it is null, the result is also null because null is used for subtraction | DOUBLE | Calculates the difference between the value of a data point and the value of the previous data point. There is no corresponding output for the first data point, so output is null | -For details and examples, see the document [Variation Trend Calculation Functions](../Reference/Function-and-Expression.md#variation-trend-calculation-functions). +For details and examples, see the document [Variation Trend Calculation Functions](../SQL-Manual/Function-and-Expression.md#variation-trend-calculation-functions). ### Sample Functions @@ -242,7 +242,7 @@ For details and examples, see the document [Variation Trend Calculation Function | EQUAL_SIZE_BUCKET_OUTLIER_SAMPLE | INT32 / INT64 / FLOAT / DOUBLE | The value range of `proportion` is `(0, 1]`, the default is `0.1`
The value of `type` is `avg` or `stendis` or `cos` or `prenextdis`, the default is `avg`
The value of `number` should be greater than 0, the default is `3` | INT32 / INT64 / FLOAT / DOUBLE | Returns outlier samples in equal buckets that match the sampling ratio and the number of samples in the bucket | | M4 | INT32 / INT64 / FLOAT / DOUBLE | Different attributes used by the size window and the time window. The size window uses attributes `windowSize` and `slidingStep`. The time window uses attributes `timeInterval`, `slidingStep`, `displayWindowBegin`, and `displayWindowEnd`. More details see below. | INT32 / INT64 / FLOAT / DOUBLE | Returns the `first, last, bottom, top` points in each sliding window. M4 sorts and deduplicates the aggregated points within the window before outputting them. | -For details and examples, see the document [Sample Functions](../Reference/Function-and-Expression.md#sample-functions). +For details and examples, see the document [Sample Functions](../SQL-Manual/Function-and-Expression.md#sample-functions). ### Change Points Function @@ -250,7 +250,7 @@ For details and examples, see the document [Sample Functions](../Reference/Funct | ------------- | ------------------------------- | ------------------- | ----------------------------- | ----------------------------------------------------------- | | CHANGE_POINTS | INT32 / INT64 / FLOAT / DOUBLE | / | Same type as the input series | Remove consecutive identical values from an input sequence. | -For details and examples, see the document [Time-Series](../Reference/Function-and-Expression.md#time-series-processing). +For details and examples, see the document [Time-Series](../SQL-Manual/Function-and-Expression.md#time-series-processing). ## LAMBDA EXPRESSION @@ -259,7 +259,7 @@ For details and examples, see the document [Time-Series](../Reference/Function-a | ------------- | ----------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------- | ------------------------------------------------------------ | | JEXL | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | `expr` is a lambda expression that supports standard one or multi arguments in the form `x -> {...}` or `(x, y, z) -> {...}`, e.g. `x -> {x * 2}`, `(x, y, z) -> {x + y * z}` | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | Returns the input time series transformed by a lambda expression | -For details and examples, see the document [Lambda](../Reference/Function-and-Expression.md#lambda-expression). +For details and examples, see the document [Lambda](../SQL-Manual/Function-and-Expression.md#lambda-expression). ## CONDITIONAL EXPRESSION @@ -267,7 +267,7 @@ For details and examples, see the document [Lambda](../Reference/Function-and-Ex | --------------- | -------------------- | | `CASE` | similar to "if else" | -For details and examples, see the document [Conditional Expressions](../Reference/Function-and-Expression.md#conditional-expressions). +For details and examples, see the document [Conditional Expressions](../SQL-Manual/Function-and-Expression.md#conditional-expressions). ## SELECT EXPRESSION @@ -322,7 +322,7 @@ Aggregate functions are many-to-one functions. They perform aggregate calculatio > select a, count(a) from root.sg group by ([10,100),10ms) > ``` -For the aggregation functions supported by IoTDB, see the document [Aggregate Functions](../Reference/Function-and-Expression.md#aggregate-functions). +For the aggregation functions supported by IoTDB, see the document [Aggregate Functions](../SQL-Manual/Function-and-Expression.md#aggregate-functions). #### Time Series Generation Function @@ -337,7 +337,7 @@ See this documentation for a list of built-in functions supported in IoTDB. ##### User-Defined Time Series Generation Functions -IoTDB supports function extension through User Defined Function (click for [User-Defined Function](./Database-Programming.md#udtfuser-defined-timeseries-generating-function)) capability. +IoTDB supports function extension through User Defined Function (click for [User-Defined Function](../User-Manual/Database-Programming.md#udtfuser-defined-timeseries-generating-function)) capability. ### Nested Expressions diff --git a/src/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md b/src/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md index 9f0967438..2a078041c 100644 --- a/src/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md +++ b/src/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md @@ -23,7 +23,7 @@ ## DATABASE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Create Database @@ -105,7 +105,7 @@ IoTDB> SHOW DEVICES ## DEVICE TEMPLATE -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ![img](https://alioss.timecho.com/docs/img/%E6%A8%A1%E6%9D%BF.png) @@ -184,7 +184,7 @@ IoTDB> alter device template t1 add (speed FLOAT encoding=RLE, FLOAT TEXT encodi ## TIMESERIES MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Create Timeseries @@ -364,7 +364,7 @@ The above operations are supported for timeseries tag, attribute updates, etc. ## NODE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Show Child Paths @@ -409,7 +409,7 @@ IoTDB> count devices root.ln.** ### Insert Data -For more details, see document [Write-Delete-Data](../User-Manual/Write-Delete-Data.md). +For more details, see document [Write-Delete-Data](../Basic-Concept/Write-Delete-Data.md). #### Use of INSERT Statements @@ -471,7 +471,7 @@ For more details, see document [Data Import](../Tools-System/Data-Import-Tool.md ## DELETE DATA -For more details, see document [Write-Delete-Data](../User-Manual/Write-Delete-Data.md). +For more details, see document [Write-Delete-Data](../Basic-Concept/Write-Delete-Data.md). ### Delete Single Timeseries @@ -508,7 +508,7 @@ IoTDB > DELETE PARTITION root.ln 0,1,2 ## QUERY DATA -For more details, see document [Query-Data](../User-Manual/Query-Data.md). +For more details, see document [Query-Data](../Basic-Concept/Query-Data.md). ```sql SELECT [LAST] selectExpr [, selectExpr] ... @@ -1090,11 +1090,11 @@ select change_points(s1), change_points(s2), change_points(s3), change_points(s4 ## DATA QUALITY FUNCTION LIBRARY -For more details, see document [Operator-and-Expression](./UDF-Libraries_timecho.md). +For more details, see document [Operator-and-Expression](../SQL-Manual/UDF-Libraries.md). ### Data Quality -For details and examples, see the document [Data-Quality](./UDF-Libraries_timecho.md#data-quality). +For details and examples, see the document [Data-Quality](../SQL-Manual/UDF-Libraries.md#data-quality). ```sql # Completeness @@ -1119,7 +1119,7 @@ select Accuracy(t1,t2,t3,m1,m2,m3) from root.test ### Data Profiling -For details and examples, see the document [Data-Profiling](./UDF-Libraries_timecho.md#data-profiling). +For details and examples, see the document [Data-Profiling](../SQL-Manual/UDF-Libraries.md#data-profiling). ```sql # ACF @@ -1199,7 +1199,7 @@ select zscore(s1) from root.test ### Anomaly Detection -For details and examples, see the document [Anomaly-Detection](./UDF-Libraries_timecho.md#anomaly-detection). +For details and examples, see the document [Anomaly-Detection](../SQL-Manual/UDF-Libraries.md#anomaly-detection). ```sql # IQR @@ -1234,7 +1234,7 @@ select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3 ### Frequency Domain -For details and examples, see the document [Frequency-Domain](./UDF-Libraries_timecho.md#frequency-domain-analysis). +For details and examples, see the document [Frequency-Domain](../SQL-Manual/UDF-Libraries.md#frequency-domain-analysis). ```sql # Conv @@ -1266,7 +1266,7 @@ select envelope(s1) from root.test.d1 ### Data Matching -For details and examples, see the document [Data-Matching](./UDF-Libraries_timecho.md#data-matching). +For details and examples, see the document [Data-Matching](../SQL-Manual/UDF-Libraries.md#data-matching). ```sql # Cov @@ -1287,7 +1287,7 @@ select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 ### Data Repairing -For details and examples, see the document [Data-Repairing](./UDF-Libraries_timecho.md#data-repairing). +For details and examples, see the document [Data-Repairing](../SQL-Manual/UDF-Libraries.md#data-repairing). ```sql # TimestampRepair @@ -1312,7 +1312,7 @@ select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 ### Series Discovery -For details and examples, see the document [Series-Discovery](./UDF-Libraries_timecho.md#series-discovery). +For details and examples, see the document [Series-Discovery](../SQL-Manual/UDF-Libraries.md#series-discovery). ```sql # ConsecutiveSequences @@ -1325,7 +1325,7 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 ### Machine Learning -For details and examples, see the document [Machine-Learning](./UDF-Libraries_timecho.md#machine-learning). +For details and examples, see the document [Machine-Learning](../SQL-Manual/UDF-Libraries.md#machine-learning). ```sql # AR @@ -1340,7 +1340,7 @@ select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0 ## LAMBDA EXPRESSION -For details and examples, see the document [Lambda](./UDF-Libraries_timecho.md#lambda-expression). +For details and examples, see the document [Lambda](../SQL-Manual/UDF-Libraries.md#lambda-expression). ```sql select jexl(temperature, 'expr'='x -> {x + x}') as jexl1, jexl(temperature, 'expr'='x -> {x * 3}') as jexl2, jexl(temperature, 'expr'='x -> {x * x}') as jexl3, jexl(temperature, 'expr'='x -> {multiply(x, 100)}') as jexl4, jexl(temperature, st, 'expr'='(x, y) -> {x + y}') as jexl5, jexl(temperature, st, str, 'expr'='(x, y, z) -> {x + y + z}') as jexl6 from root.ln.wf01.wt01;``` @@ -1348,7 +1348,7 @@ select jexl(temperature, 'expr'='x -> {x + x}') as jexl1, jexl(temperature, 'exp ## CONDITIONAL EXPRESSION -For details and examples, see the document [Conditional Expressions](./UDF-Libraries_timecho.md#conditional-expressions). +For details and examples, see the document [Conditional Expressions](../SQL-Manual/UDF-Libraries.md#conditional-expressions). ```sql select T, P, case @@ -1548,7 +1548,7 @@ CQs can't be altered once they're created. To change a CQ, you must `DROP` and r ## USER-DEFINED FUNCTION (UDF) -For more details, see document [Operator-and-Expression](./UDF-Libraries_timecho.md). +For more details, see document [Operator-and-Expression](../SQL-Manual/UDF-Libraries.md). ### UDF Registration diff --git a/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md b/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md new file mode 100644 index 000000000..2867a78eb --- /dev/null +++ b/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md @@ -0,0 +1,23 @@ +--- +redirectTo: UDF-Libraries_apache.html +--- + \ No newline at end of file diff --git a/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_apache.md index fcef49528..c2a0dcd54 100644 --- a/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_apache.md +++ b/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_apache.md @@ -606,14 +606,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_timecho.md index d96a60b14..d4ee30c76 100644 --- a/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_timecho.md +++ b/src/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries_timecho.md @@ -606,14 +606,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md b/src/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md index 4a9476b04..7bde227f8 100644 --- a/src/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md +++ b/src/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md @@ -255,7 +255,7 @@ Detailed introduction of pre-installed plugins is as follows (for detailed param -For importing custom plugins, please refer to the [Stream Processing](./Streaming_timecho.md#custom-stream-processing-plugin-management) section. +For importing custom plugins, please refer to the [Stream Processing](./Streaming_apache.md#custom-stream-processing-plugin-management) section. ## Use examples diff --git a/src/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md b/src/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md index 7f1a5f796..e1147c6f4 100644 --- a/src/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md +++ b/src/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md @@ -308,7 +308,7 @@ AS SELECT temperature FROM root.db.* ``` -This is modelled on the query writeback (`SELECT INTO`) convention for naming rules, which uses variable placeholders to specify naming rules. See also: [QUERY WRITEBACK (SELECT INTO)](../User-Manual/Query-Data.md#into-clause-query-write-back) +This is modelled on the query writeback (`SELECT INTO`) convention for naming rules, which uses variable placeholders to specify naming rules. See also: [QUERY WRITEBACK (SELECT INTO)](../Basic-Concept/Query-Data.md#into-clause-query-write-back) Here `root.db.*.temperature` specifies what time series will be included in the view; and `${2}` specifies from which node in the time series the name is extracted to name the sequence view. diff --git a/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md b/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md index 72325f08e..42413bcad 100644 --- a/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md +++ b/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md @@ -190,7 +190,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 When users use UDF, they will be involved in the `USE_UDF` permission, and only users with this permission are allowed to perform UDF registration, uninstallation, and query operations. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ## 4. UDF Libraries diff --git a/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md b/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md index 2b91554ba..63c195ce8 100644 --- a/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md +++ b/src/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md @@ -190,7 +190,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 When users use UDF, they will be involved in the `USE_UDF` permission, and only users with this permission are allowed to perform UDF registration, uninstallation, and query operations. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ## 4. UDF Libraries diff --git a/src/UserGuide/latest/API/Programming-Java-Native-API.md b/src/UserGuide/latest/API/Programming-Java-Native-API.md index 6061d473c..baefdf68e 100644 --- a/src/UserGuide/latest/API/Programming-Java-Native-API.md +++ b/src/UserGuide/latest/API/Programming-Java-Native-API.md @@ -39,7 +39,7 @@ ## Syntax Convention -- **IoTDB-SQL interface:** The input SQL parameter needs to conform to the [syntax conventions](../User-Manual/Syntax-Rule.md#Literal-Values) and be escaped for JAVA strings. For example, you need to add a backslash before the double-quotes. (That is: after JAVA escaping, it is consistent with the SQL statement executed on the command line.) +- **IoTDB-SQL interface:** The input SQL parameter needs to conform to the [syntax conventions](../Reference/Syntax-Rule.md#Literal-Values) and be escaped for JAVA strings. For example, you need to add a backslash before the double-quotes. (That is: after JAVA escaping, it is consistent with the SQL statement executed on the command line.) - **Other interfaces:** - The node names in path or path prefix as parameter: The node names which should be escaped by backticks (`) in the SQL statement, escaping is required here. - Identifiers (such as template names) as parameters: The identifiers which should be escaped by backticks (`) in the SQL statement, and escaping is not required here. diff --git a/src/UserGuide/latest/Background-knowledge/Data-Type.md b/src/UserGuide/latest/Background-knowledge/Data-Type.md index e442c7b53..03fcf7a6e 100644 --- a/src/UserGuide/latest/Background-knowledge/Data-Type.md +++ b/src/UserGuide/latest/Background-knowledge/Data-Type.md @@ -37,7 +37,7 @@ The difference between STRING and TEXT types is that STRING type has more statis ### Float Precision -The time series of **FLOAT** and **DOUBLE** type can specify (MAX_POINT_NUMBER, see [this page](../SQL-Manual/SQL-Manual.md) for more information on how to specify), which is the number of digits after the decimal point of the floating point number, if the encoding method is [RLE](Encoding-and-Compression.md) or [TS_2DIFF](Encoding-and-Compression.md). If MAX_POINT_NUMBER is not specified, the system will use [float_precision](../Reference/DataNode-Config-Manual.md) in the configuration file `iotdb-system.properties`. +The time series of **FLOAT** and **DOUBLE** type can specify (MAX_POINT_NUMBER, see [this page](../SQL-Manual/SQL-Manual.md) for more information on how to specify), which is the number of digits after the decimal point of the floating point number, if the encoding method is [RLE](../Technical-Insider/Encoding-and-Compression.md) or [TS_2DIFF](../Technical-Insider/Encoding-and-Compression.md). If MAX_POINT_NUMBER is not specified, the system will use [float_precision](../Reference/DataNode-Config-Manual.md) in the configuration file `iotdb-system.properties`. ```sql CREATE TIMESERIES root.vehicle.d0.s0 WITH DATATYPE=FLOAT, ENCODING=RLE, 'MAX_POINT_NUMBER'='2'; diff --git a/src/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md b/src/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md index ff08286e6..7a10118a9 100644 --- a/src/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md @@ -110,7 +110,7 @@ In order to make it easier and faster to express multiple timeseries paths, IoTD ### Timestamp -The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps. For detailed description, please go to [Data Type doc](./Data-Type.md). +The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps. For detailed description, please go to [Data Type doc](../Background-knowledge/Data-Type.md). ### Data point @@ -148,6 +148,6 @@ In the following chapters of data definition language, data operation language a ## Schema Template -In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../User-Manual/Operate-Metadata_timecho.md#Device-Template). +In the actual scenario, many entities collect the same measurements, that is, they have the same measurements name and type. A **schema template** can be declared to define the collectable measurements set. Schema template helps save memory by implementing schema sharing. For detailed description, please refer to [Schema Template doc](../Basic-Concept/Operate-Metadata.md#Device-Template). In the following chapters of, data definition language, data operation language and Java Native Interface, various operations related to schema template will be introduced one by one. diff --git a/src/UserGuide/latest/Basic-Concept/Operate-Metadata.md b/src/UserGuide/latest/Basic-Concept/Operate-Metadata.md new file mode 100644 index 000000000..4eb80c594 --- /dev/null +++ b/src/UserGuide/latest/Basic-Concept/Operate-Metadata.md @@ -0,0 +1,23 @@ +--- +redirectTo: Operate-Metadata_apache.html +--- + diff --git a/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md b/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md index 58c01a886..45becb7bd 100644 --- a/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md +++ b/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md @@ -612,7 +612,7 @@ IoTDB > create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODI error: encoding TS_2DIFF does not support BOOLEAN ``` -Please refer to [Encoding](../Basic-Concept/Encoding-and-Compression.md) for correspondence between data type and encoding. +Please refer to [Encoding](../Technical-Insider/Encoding-and-Compression.md) for correspondence between data type and encoding. ### Create Aligned Timeseries diff --git a/src/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md b/src/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md index 8d57facb1..8306823fc 100644 --- a/src/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md +++ b/src/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md @@ -613,7 +613,7 @@ IoTDB > create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODI error: encoding TS_2DIFF does not support BOOLEAN ``` -Please refer to [Encoding](../Basic-Concept/Encoding-and-Compression.md) for correspondence between data type and encoding. +Please refer to [Encoding](../Technical-Insider/Encoding-and-Compression.md) for correspondence between data type and encoding. ### Create Aligned Timeseries diff --git a/src/UserGuide/latest/Basic-Concept/Query-Data.md b/src/UserGuide/latest/Basic-Concept/Query-Data.md index 62fc3c9f9..4f38b287e 100644 --- a/src/UserGuide/latest/Basic-Concept/Query-Data.md +++ b/src/UserGuide/latest/Basic-Concept/Query-Data.md @@ -440,7 +440,7 @@ The supported operators are as follows: ### Time Filter -Use time filters to filter data for a specific time range. For supported formats of timestamps, please refer to [Timestamp](../Basic-Concept/Data-Type.md) . +Use time filters to filter data for a specific time range. For supported formats of timestamps, please refer to [Timestamp](../Background-knowledge/Data-Type.md) . An example is as follows: @@ -2934,7 +2934,7 @@ This statement specifies that `root.sg_copy.d1` is an unaligned device and `root #### Other points to note - For general aggregation queries, the timestamp is meaningless, and the convention is to use 0 to store. -- When the target time-series exists, the data type of the source column and the target time-series must be compatible. About data type compatibility, see the document [Data Type](../Basic-Concept/Data-Type.md#Data Type Compatibility). +- When the target time-series exists, the data type of the source column and the target time-series must be compatible. About data type compatibility, see the document [Data Type](../Background-knowledge/Data-Type.md#Data Type Compatibility). - When the target time series does not exist, the system automatically creates it (including the database). - When the queried time series does not exist, or the queried sequence does not have data, the target time series will not be created automatically. @@ -3002,7 +3002,7 @@ The user must have the following permissions to execute a query write-back state * All `WRITE_SCHEMA` permissions for the source series in the `select` clause. * All `WRITE_DATA` permissions for the target series in the `into` clause. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ### Configurable Properties diff --git a/src/UserGuide/latest/Basic-Concept/Write-Delete-Data.md b/src/UserGuide/latest/Basic-Concept/Write-Delete-Data.md index 9c694a616..b445bf3ef 100644 --- a/src/UserGuide/latest/Basic-Concept/Write-Delete-Data.md +++ b/src/UserGuide/latest/Basic-Concept/Write-Delete-Data.md @@ -31,7 +31,7 @@ Writing a repeat timestamp covers the original timestamp data, which can be rega ### Use of INSERT Statements -The [INSERT SQL statement](../SQL-Manual/SQL-Manual.md#insert-data) statement is used to insert data into one or more specified timeseries created. For each point of data inserted, it consists of a [timestamp](../Basic-Concept/Data-Model-and-Terminology.md) and a sensor acquisition value (see [Data Type](../Basic-Concept/Data-Type.md)). +The [INSERT SQL statement](../SQL-Manual/SQL-Manual.md#insert-data) statement is used to insert data into one or more specified timeseries created. For each point of data inserted, it consists of a [timestamp](../Basic-Concept/Data-Model-and-Terminology.md) and a sensor acquisition value (see [Data Type](../Background-knowledge/Data-Type.md)). **Schema-less writing**: When metadata is not defined, data can be directly written through an insert statement, and the required metadata will be automatically recognized and registered in the database, achieving automatic modeling. diff --git a/src/UserGuide/latest/Deployment-and-Maintenance/IoTDB-Package.md b/src/UserGuide/latest/Deployment-and-Maintenance/IoTDB-Package.md new file mode 100644 index 000000000..6057ef6a2 --- /dev/null +++ b/src/UserGuide/latest/Deployment-and-Maintenance/IoTDB-Package.md @@ -0,0 +1,23 @@ +--- +redirectTo: IoTDB-Package_apache.html +--- + \ No newline at end of file diff --git a/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md b/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md index 072d3c378..c0fdf50e8 100644 --- a/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md +++ b/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md @@ -41,7 +41,7 @@ | **Preparation Content** | **Version Requirements** | | :---------------------------------------- | :----------------------------------------------------------- | | JDK | JDK17 or above. Please refer to the downloads on [Oracle Official Website](https://www.oracle.com/java/technologies/downloads/) | -| IoTDB |IoTDB v1.3.0 or above. Please refer to the [Deployment guidance](../Deployment-and-Maintenance/IoTDB-Package_timecho.md) | +| IoTDB |IoTDB v1.3.0 or above. Please refer to the [Deployment guidance](../Deployment-and-Maintenance/IoTDB-Package.md) | | ThingsBoard
(IoTDB adapted version) | Please contact Timecho staff to obtain the installation package. Detailed installation steps are provided below. | ## Installation Steps diff --git a/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md index e783f74b3..94a1a30d0 100644 --- a/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -74,4 +74,4 @@ Timecho provides a more diverse range of product features, stronger performance - Timecho Official website：https://www.timecho.com/ -- TimechoDB installation, deployment and usage documentation：[QuickStart](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB installation, deployment and usage documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/UserGuide/latest/Reference/Syntax-Rule.md b/src/UserGuide/latest/Reference/Syntax-Rule.md index 3143014f8..40d858e28 100644 --- a/src/UserGuide/latest/Reference/Syntax-Rule.md +++ b/src/UserGuide/latest/Reference/Syntax-Rule.md @@ -146,7 +146,7 @@ An integer may be used in floating-point context; it is interpreted as the equiv ### Timestamp Literals -The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps in IoTDB. For information about timestamp support in IoTDB, see [Data Type Doc](../Basic-Concept/Data-Type.md). +The timestamp is the time point at which data is produced. It includes absolute timestamps and relative timestamps in IoTDB. For information about timestamp support in IoTDB, see [Data Type Doc](../Background-knowledge/Data-Type.md). Specially, `NOW()` represents a constant timestamp that indicates the system time at which the statement began to execute. diff --git a/src/UserGuide/latest/Reference/UDF-Libraries_apache.md b/src/UserGuide/latest/Reference/UDF-Libraries_apache.md deleted file mode 100644 index ab63a071c..000000000 --- a/src/UserGuide/latest/Reference/UDF-Libraries_apache.md +++ /dev/null @@ -1,5244 +0,0 @@ - - -# UDF Libraries - -# UDF Libraries - -Based on the ability of user-defined functions, IoTDB provides a series of functions for temporal data processing, including data quality, data profiling, anomaly detection, frequency domain analysis, data matching, data repairing, sequence discovery, machine learning, etc., which can meet the needs of industrial fields for temporal data processing. - -> Note: The functions in the current UDF library only support millisecond level timestamp accuracy. - -## Installation steps - -1. Please obtain the compressed file of the UDF library JAR package that is compatible with the IoTDB version. - - | UDF installation package | Supported IoTDB versions | Download link | - | --------------- | ----------------- | ------------------------------------------------------------ | - | apache-UDF-1.3.3.zip | V1.3.3 and above |Please contact Timecho for assistance | - | apache-UDF-1.3.2.zip | V1.0.0～V1.3.2 | Please contact Timecho for assistance| - -2. Place the library-udf.jar file in the compressed file obtained in the directory `/ext/udf ` of all nodes in the IoTDB cluster -3. In the SQL operation interface of IoTDB's SQL command line terminal (CLI), execute the corresponding function registration statement as follows. -4. Batch registration: Two registration methods: registration script or SQL full statement -- Register Script - - Copy the registration script (register-UDF.sh or register-UDF.bat) from the compressed package to the `tools` directory of IoTDB as needed, and modify the parameters in the script (default is host=127.0.0.1, rpcPort=6667, user=root, pass=root); - - Start IoTDB service, run registration script to batch register UDF - -- All SQL statements - - Open the SQl file in the compressed package, copy all SQL statements, and in the SQL operation interface of IoTDB's SQL command line terminal (CLI), execute all SQl statements to batch register UDFs - -## Data Quality - -### Completeness - -#### Registration statement - -```sql -create function completeness as 'org.apache.iotdb.library.dquality.UDTFCompleteness' -``` - -#### Usage - -This function is used to calculate the completeness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the completeness of each window will be output. - -**Name:** COMPLETENESS - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. -+ `downtime`: Whether the downtime exception is considered in the calculation of completeness. It is 'true' or 'false' (default). When considering the downtime exception, long-term missing data will be considered as downtime exception without any influence on completeness. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select completeness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+-----------------------------+ -| Time|completeness(root.test.d1.s1)| -+-----------------------------+-----------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.875| -+-----------------------------+-----------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select completeness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------+ -| Time|completeness(root.test.d1.s1, "window"="15")| -+-----------------------------+--------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.875| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+--------------------------------------------+ -``` - -### Consistency - -#### Registration statement - -```sql -create function consistency as 'org.apache.iotdb.library.dquality.UDTFConsistency' -``` - -#### Usage - -This function is used to calculate the consistency of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the consistency of each window will be output. - -**Name:** CONSISTENCY - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select consistency(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+----------------------------+ -| Time|consistency(root.test.d1.s1)| -+-----------------------------+----------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -+-----------------------------+----------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select consistency(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------+ -| Time|consistency(root.test.d1.s1, "window"="15")| -+-----------------------------+-------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+-------------------------------------------+ -``` - -### Timeliness - -#### Registration statement - -```sql -create function timeliness as 'org.apache.iotdb.library.dquality.UDTFTimeliness' -``` - -#### Usage - -This function is used to calculate the timeliness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the timeliness of each window will be output. - -**Name:** TIMELINESS - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select timeliness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+---------------------------+ -| Time|timeliness(root.test.d1.s1)| -+-----------------------------+---------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -+-----------------------------+---------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select timeliness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------+ -| Time|timeliness(root.test.d1.s1, "window"="15")| -+-----------------------------+------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.9333333333333333| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+------------------------------------------+ -``` - -### Validity - -#### Registration statement - -```sql -create function validity as 'org.apache.iotdb.library.dquality.UDTFValidity' -``` - -#### Usage - -This function is used to calculate the Validity of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the Validity of each window will be output. - -**Name:** VALIDITY - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `window`: The size of each window. It is a positive integer or a positive number with an unit. The former is the number of data points in each window. The number of data points in the last window may be less than it. The latter is the time of the window. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, all input data belongs to the same window. - -**Output Series:** Output a single series. The type is DOUBLE. The range of each value is [0,1]. - -**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output. - -#### Examples - -##### Default Parameters - -With default parameters, this function will regard all input data as the same window. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select Validity(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|validity(root.test.d1.s1)| -+-----------------------------+-------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.8833333333333333| -+-----------------------------+-------------------------+ -``` - -##### Specific Window Size - -When the window size is given, this function will divide the input data as multiple windows. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -|2020-01-01T00:00:32.000+08:00| 130.0| -|2020-01-01T00:00:34.000+08:00| 132.0| -|2020-01-01T00:00:36.000+08:00| 134.0| -|2020-01-01T00:00:38.000+08:00| 136.0| -|2020-01-01T00:00:40.000+08:00| 138.0| -|2020-01-01T00:00:42.000+08:00| 140.0| -|2020-01-01T00:00:44.000+08:00| 142.0| -|2020-01-01T00:00:46.000+08:00| 144.0| -|2020-01-01T00:00:48.000+08:00| 146.0| -|2020-01-01T00:00:50.000+08:00| 148.0| -|2020-01-01T00:00:52.000+08:00| 150.0| -|2020-01-01T00:00:54.000+08:00| 152.0| -|2020-01-01T00:00:56.000+08:00| 154.0| -|2020-01-01T00:00:58.000+08:00| 156.0| -|2020-01-01T00:01:00.000+08:00| 158.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select Validity(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------+ -| Time|validity(root.test.d1.s1, "window"="15")| -+-----------------------------+----------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.8833333333333333| -|2020-01-01T00:00:32.000+08:00| 1.0| -+-----------------------------+----------------------------------------+ -``` - - - - - -## Data Profiling - -### ACF - -#### Registration statement - -```sql -create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' -``` - -#### Usage - -This function is used to calculate the auto-correlation factor of the input time series, -which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. - -**Name:** ACF - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. - -**Note:** - -+ `null` and `NaN` values in the input series will be ignored and treated as 0. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| null| -|2020-01-01T00:00:03.000+08:00| 3| -|2020-01-01T00:00:04.000+08:00| NaN| -|2020-01-01T00:00:05.000+08:00| 5| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select acf(s1) from root.test.d1 where time <= 2020-01-01 00:00:05 -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|acf(root.test.d1.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.001+08:00| 1.0| -|1970-01-01T08:00:00.002+08:00| 0.0| -|1970-01-01T08:00:00.003+08:00| 3.6| -|1970-01-01T08:00:00.004+08:00| 0.0| -|1970-01-01T08:00:00.005+08:00| 7.0| -|1970-01-01T08:00:00.006+08:00| 0.0| -|1970-01-01T08:00:00.007+08:00| 3.6| -|1970-01-01T08:00:00.008+08:00| 0.0| -|1970-01-01T08:00:00.009+08:00| 1.0| -+-----------------------------+--------------------+ -``` - -### Distinct - -#### Registration statement - -```sql -create function distinct as 'org.apache.iotdb.library.dprofile.UDTFDistinct' -``` - -#### Usage - -This function returns all unique values in time series. - -**Name:** DISTINCT - -**Input Series:** Only support a single input series. The type is arbitrary. - -**Output Series:** Output a single series. The type is the same as the input. - -**Note:** - -+ The timestamp of the output series is meaningless. The output order is arbitrary. -+ Missing points and null points in the input series will be ignored, but `NaN` will not. -+ Case Sensitive. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s2| -+-----------------------------+---------------+ -|2020-01-01T08:00:00.001+08:00| Hello| -|2020-01-01T08:00:00.002+08:00| hello| -|2020-01-01T08:00:00.003+08:00| Hello| -|2020-01-01T08:00:00.004+08:00| World| -|2020-01-01T08:00:00.005+08:00| World| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select distinct(s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|distinct(root.test.d2.s2)| -+-----------------------------+-------------------------+ -|1970-01-01T08:00:00.001+08:00| Hello| -|1970-01-01T08:00:00.002+08:00| hello| -|1970-01-01T08:00:00.003+08:00| World| -+-----------------------------+-------------------------+ -``` - -### Histogram - -#### Registration statement - -```sql -create function histogram as 'org.apache.iotdb.library.dprofile.UDTFHistogram' -``` - -#### Usage - -This function is used to calculate the distribution histogram of a single column of numerical data. - -**Name:** HISTOGRAM - -**Input Series:** Only supports a single input sequence, the type is INT32 / INT64 / FLOAT / DOUBLE - -**Parameters:** - -+ `min`: The lower limit of the requested data range, the default value is -Double.MAX_VALUE. -+ `max`: The upper limit of the requested data range, the default value is Double.MAX_VALUE, and the value of start must be less than or equal to end. -+ `count`: The number of buckets of the histogram, the default value is 1. It must be a positive integer. - -**Output Series:** The value of the bucket of the histogram, where the lower bound represented by the i-th bucket (index starts from 1) is $min+ (i-1)\cdot\frac{max-min}{count}$ and the upper bound is $min + i \cdot \frac{max-min}{count}$. - -**Note:** - -+ If the value is lower than `min`, it will be put into the 1st bucket. If the value is larger than `max`, it will be put into the last bucket. -+ Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| -|2020-01-01T00:00:01.000+08:00| 2.0| -|2020-01-01T00:00:02.000+08:00| 3.0| -|2020-01-01T00:00:03.000+08:00| 4.0| -|2020-01-01T00:00:04.000+08:00| 5.0| -|2020-01-01T00:00:05.000+08:00| 6.0| -|2020-01-01T00:00:06.000+08:00| 7.0| -|2020-01-01T00:00:07.000+08:00| 8.0| -|2020-01-01T00:00:08.000+08:00| 9.0| -|2020-01-01T00:00:09.000+08:00| 10.0| -|2020-01-01T00:00:10.000+08:00| 11.0| -|2020-01-01T00:00:11.000+08:00| 12.0| -|2020-01-01T00:00:12.000+08:00| 13.0| -|2020-01-01T00:00:13.000+08:00| 14.0| -|2020-01-01T00:00:14.000+08:00| 15.0| -|2020-01-01T00:00:15.000+08:00| 16.0| -|2020-01-01T00:00:16.000+08:00| 17.0| -|2020-01-01T00:00:17.000+08:00| 18.0| -|2020-01-01T00:00:18.000+08:00| 19.0| -|2020-01-01T00:00:19.000+08:00| 20.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select histogram(s1,"min"="1","max"="20","count"="10") from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+---------------------------------------------------------------+ -| Time|histogram(root.test.d1.s1, "min"="1", "max"="20", "count"="10")| -+-----------------------------+---------------------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 2| -|1970-01-01T08:00:00.001+08:00| 2| -|1970-01-01T08:00:00.002+08:00| 2| -|1970-01-01T08:00:00.003+08:00| 2| -|1970-01-01T08:00:00.004+08:00| 2| -|1970-01-01T08:00:00.005+08:00| 2| -|1970-01-01T08:00:00.006+08:00| 2| -|1970-01-01T08:00:00.007+08:00| 2| -|1970-01-01T08:00:00.008+08:00| 2| -|1970-01-01T08:00:00.009+08:00| 2| -+-----------------------------+---------------------------------------------------------------+ -``` - -### Integral - -#### Registration statement - -```sql -create function integral as 'org.apache.iotdb.library.dprofile.UDAFIntegral' -``` - -#### Usage - -This function is used to calculate the integration of time series, -which equals to the area under the curve with time as X-axis and values as Y-axis. - -**Name:** INTEGRAL - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `unit`: The unit of time used when computing the integral. - The value should be chosen from "1S", "1s", "1m", "1H", "1d"(case-sensitive), - and each represents taking one millisecond / second / minute / hour / day as 1.0 while calculating the area and integral. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the integration. - -**Note:** - -+ The integral value equals to the sum of the areas of right-angled trapezoids consisting of each two adjacent points and the time-axis. - Choosing different `unit` implies different scaling of time axis, thus making it apparent to convert the value among those results with constant coefficient. - -+ `NaN` values in the input series will be ignored. The curve or trapezoids will skip these points and use the next valid point. - -#### Examples - -##### Default Parameters - -With default parameters, this function will take one second as 1.0. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| 2| -|2020-01-01T00:00:03.000+08:00| 5| -|2020-01-01T00:00:04.000+08:00| 6| -|2020-01-01T00:00:05.000+08:00| 7| -|2020-01-01T00:00:08.000+08:00| 8| -|2020-01-01T00:00:09.000+08:00| NaN| -|2020-01-01T00:00:10.000+08:00| 10| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select integral(s1) from root.test.d1 where time <= 2020-01-01 00:00:10 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|integral(root.test.d1.s1)| -+-----------------------------+-------------------------+ -|1970-01-01T08:00:00.000+08:00| 57.5| -+-----------------------------+-------------------------+ -``` - -Calculation expression: -$$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 57.5$$ - -##### Specific time unit - -With time unit specified as "1m", this function will take one minute as 1.0. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select integral(s1, "unit"="1m") from root.test.d1 where time <= 2020-01-01 00:00:10 -``` - -Output series: - -``` -+-----------------------------+-------------------------+ -| Time|integral(root.test.d1.s1)| -+-----------------------------+-------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.958| -+-----------------------------+-------------------------+ -``` - -Calculation expression: -$$\frac{1}{2\times 60}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] = 0.958$$ - -### IntegralAvg - -#### Registration statement - -```sql -create function integralavg as 'org.apache.iotdb.library.dprofile.UDAFIntegralAvg' -``` - -#### Usage - -This function is used to calculate the function average of time series. -The output equals to the area divided by the time interval using the same time `unit`. -For more information of the area under the curve, please refer to `Integral` function. - -**Name:** INTEGRALAVG - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the time-weighted average. - -**Note:** - -+ The time-weighted value equals to the integral value with any `unit` divided by the time interval of input series. - The result is irrelevant to the time unit used in integral, and it's consistent with the timestamp precision of IoTDB by default. - -+ `NaN` values in the input series will be ignored. The curve or trapezoids will skip these points and use the next valid point. - -+ If the input series is empty, the output value will be 0.0, but if there is only one data point, the value will equal to the input value. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| 2| -|2020-01-01T00:00:03.000+08:00| 5| -|2020-01-01T00:00:04.000+08:00| 6| -|2020-01-01T00:00:05.000+08:00| 7| -|2020-01-01T00:00:08.000+08:00| 8| -|2020-01-01T00:00:09.000+08:00| NaN| -|2020-01-01T00:00:10.000+08:00| 10| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select integralavg(s1) from root.test.d1 where time <= 2020-01-01 00:00:10 -``` - -Output series: - -``` -+-----------------------------+----------------------------+ -| Time|integralavg(root.test.d1.s1)| -+-----------------------------+----------------------------+ -|1970-01-01T08:00:00.000+08:00| 5.75| -+-----------------------------+----------------------------+ -``` - -Calculation expression: -$$\frac{1}{2}[(1+2) \times 1 + (2+5) \times 1 + (5+6) \times 1 + (6+7) \times 1 + (7+8) \times 3 + (8+10) \times 2] / 10 = 5.75$$ - -### Mad - -#### Registration statement - -```sql -create function mad as 'org.apache.iotdb.library.dprofile.UDAFMad' -``` - -#### Usage - -The function is used to compute the exact or approximate median absolute deviation (MAD) of a numeric time series. MAD is the median of the deviation of each element from the elements' median. - -Take a dataset $\{1,3,3,5,5,6,7,8,9\}$ as an instance. Its median is 5 and the deviation of each element from the median is $\{0,0,1,2,2,2,3,4,4\}$, whose median is 2. Therefore, the MAD of the original dataset is 2. - -**Name:** MAD - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `error`: The relative error of the approximate MAD. It should be within [0,1) and the default value is 0. Taking `error`=0.01 as an instance, suppose the exact MAD is $a$ and the approximate MAD is $b$, we have $0.99a \le b \le 1.01a$. With `error`=0, the output is the exact MAD. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the MAD. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -##### Exact Query - -With the default `error`(`error`=0), the function queries the exact MAD. - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -............ -Total line number = 20 -``` - -SQL for query: - -```sql -select mad(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -| Time|median(root.test.s1, "error"="0")| -+-----------------------------+---------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -+-----------------------------+---------------------------------+ -``` - -##### Approximate Query - -By setting `error` within (0,1), the function queries the approximate MAD. - -SQL for query: - -```sql -select mad(s1, "error"="0.01") from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -| Time|mad(root.test.s1, "error"="0.01")| -+-----------------------------+---------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.9900000000000001| -+-----------------------------+---------------------------------+ -``` - -### Median - -#### Registration statement - -```sql -create function median as 'org.apache.iotdb.library.dprofile.UDAFMedian' -``` - -#### Usage - -The function is used to compute the exact or approximate median of a numeric time series. Median is the value separating the higher half from the lower half of a data sample. - -**Name:** MEDIAN - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `error`: The rank error of the approximate median. It should be within [0,1) and the default value is 0. For instance, a median with `error`=0.01 is the value of the element with rank percentage 0.49~0.51. With `error`=0, the output is the exact median. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the median. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -Total line number = 20 -``` - -SQL for query: - -```sql -select median(s1, "error"="0.01") from root.test -``` - -Output series: - -``` -+-----------------------------+------------------------------------+ -| Time|median(root.test.s1, "error"="0.01")| -+-----------------------------+------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -+-----------------------------+------------------------------------+ -``` - -### MinMax - -#### Registration statement - -```sql -create function minmax as 'org.apache.iotdb.library.dprofile.UDTFMinMax' -``` - -#### Usage - -This function is used to standardize the input series with min-max. Minimum value is transformed to 0; maximum value is transformed to 1. - -**Name:** MINMAX - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `compute`: When set to "batch", anomaly test is conducted after importing all data points; when set to "stream", it is required to provide minimum and maximum values. The default method is "batch". -+ `min`: The maximum value when method is set to "stream". -+ `max`: The minimum value when method is set to "stream". - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select minmax(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|minmax(root.test.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.100+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.200+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.300+08:00| 0.25| -|1970-01-01T08:00:00.400+08:00| 0.08333333333333333| -|1970-01-01T08:00:00.500+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.600+08:00| 0.16666666666666666| -|1970-01-01T08:00:00.700+08:00| 0.0| -|1970-01-01T08:00:00.800+08:00| 0.3333333333333333| -|1970-01-01T08:00:00.900+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.000+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.100+08:00| 0.25| -|1970-01-01T08:00:01.200+08:00| 0.08333333333333333| -|1970-01-01T08:00:01.300+08:00| 0.08333333333333333| -|1970-01-01T08:00:01.400+08:00| 0.25| -|1970-01-01T08:00:01.500+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.600+08:00| 0.16666666666666666| -|1970-01-01T08:00:01.700+08:00| 1.0| -|1970-01-01T08:00:01.800+08:00| 0.3333333333333333| -|1970-01-01T08:00:01.900+08:00| 0.0| -|1970-01-01T08:00:02.000+08:00| 0.16666666666666666| -+-----------------------------+--------------------+ -``` - - -### MvAvg - -#### Registration statement - -```sql -create function mvavg as 'org.apache.iotdb.library.dprofile.UDTFMvAvg' -``` - -#### Usage - -This function is used to calculate moving average of input series. - -**Name:** MVAVG - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `window`: Length of the moving window. Default value is 10. - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select mvavg(s1, "window"="3") from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -| Time|mvavg(root.test.s1, "window"="3")| -+-----------------------------+---------------------------------+ -|1970-01-01T08:00:00.300+08:00| 0.3333333333333333| -|1970-01-01T08:00:00.400+08:00| 0.0| -|1970-01-01T08:00:00.500+08:00| -0.3333333333333333| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -0.6666666666666666| -|1970-01-01T08:00:00.800+08:00| 0.0| -|1970-01-01T08:00:00.900+08:00| 0.6666666666666666| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 0.3333333333333333| -|1970-01-01T08:00:01.200+08:00| 0.0| -|1970-01-01T08:00:01.300+08:00| -0.6666666666666666| -|1970-01-01T08:00:01.400+08:00| 0.0| -|1970-01-01T08:00:01.500+08:00| 0.3333333333333333| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 3.3333333333333335| -|1970-01-01T08:00:01.800+08:00| 4.0| -|1970-01-01T08:00:01.900+08:00| 0.0| -|1970-01-01T08:00:02.000+08:00| -0.6666666666666666| -+-----------------------------+---------------------------------+ -``` - -### PACF - -#### Registration statement - -```sql -create function pacf as 'org.apache.iotdb.library.dprofile.UDTFPACF' -``` - -#### Usage - -This function is used to calculate partial autocorrelation of input series by solving Yule-Walker equation. For some cases, the equation may not be solved, and NaN will be output. - -**Name:** PACF - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `lag`: Maximum lag of pacf to calculate. The default value is $\min(10\log_{10}n,n-1)$, where $n$ is the number of data points. - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Assigning maximum lag - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1| -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 3| -|2020-01-01T00:00:04.000+08:00| NaN| -|2020-01-01T00:00:05.000+08:00| 5| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select pacf(s1, "lag"="5") from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------------------+ -| Time|pacf(root.test.d1.s1, "lag"="5")| -+-----------------------------+--------------------------------+ -|2020-01-01T00:00:01.000+08:00| 1.0| -|2020-01-01T00:00:02.000+08:00| -0.5744680851063829| -|2020-01-01T00:00:03.000+08:00| 0.3172297297297296| -|2020-01-01T00:00:04.000+08:00| -0.2977686586304181| -|2020-01-01T00:00:05.000+08:00| -2.0609033521065867| -+-----------------------------+--------------------------------+ -``` - -### Percentile - -#### Registration statement - -```sql -create function percentile as 'org.apache.iotdb.library.dprofile.UDAFPercentile' -``` - -#### Usage - -The function is used to compute the exact or approximate percentile of a numeric time series. A percentile is value of element in the certain rank of the sorted series. - -**Name:** PERCENTILE - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `rank`: The rank percentage of the percentile. It should be (0,1] and the default value is 0.5. For instance, a percentile with `rank`=0.5 is the median. -+ `error`: The rank error of the approximate percentile. It should be within [0,1) and the default value is 0. For instance, a 0.5-percentile with `error`=0.01 is the value of the element with rank percentage 0.49~0.51. With `error`=0, the output is the exact percentile. - -**Output Series:** Output a single series. The type is the same as input series. If `error`=0, there is only one data point in the series, whose timestamp is the same has which the first percentile value has, and value is the percentile, otherwise the timestamp of the only data point is 0. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+-------------+ -| Time|root.test2.s1| -+-----------------------------+-------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+-------------+ -Total line number = 20 -``` - -SQL for query: - -```sql -select percentile(s0, "rank"="0.2", "error"="0.01") from root.test -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------------+ -| Time|percentile(root.test2.s1, "rank"="0.2", "error"="0.01")| -+-----------------------------+-------------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| -1.0| -+-----------------------------+-------------------------------------------------------+ -``` - -### Quantile - -#### Registration statement - -```sql -create function quantile as 'org.apache.iotdb.library.dprofile.UDAFQuantile' -``` - -#### Usage - -The function is used to compute the approximate quantile of a numeric time series. A quantile is value of element in the certain rank of the sorted series. - -**Name:** QUANTILE - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -+ `rank`: The rank of the quantile. It should be (0,1] and the default value is 0.5. For instance, a quantile with `rank`=0.5 is the median. -+ `K`: The size of KLL sketch maintained in the query. It should be within [100,+inf) and the default value is 800. For instance, the 0.5-quantile computed by a KLL sketch with K=800 items is a value with rank quantile 0.49~0.51 with a confidence of at least 99%. The result will be more accurate as K increases. - -**Output Series:** Output a single series. The type is the same as input series. The timestamp of the only data point is 0. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+-------------+ -| Time|root.test1.s1| -+-----------------------------+-------------+ -|2021-03-17T10:32:17.054+08:00| 7| -|2021-03-17T10:32:18.054+08:00| 15| -|2021-03-17T10:32:19.054+08:00| 36| -|2021-03-17T10:32:20.054+08:00| 39| -|2021-03-17T10:32:21.054+08:00| 40| -|2021-03-17T10:32:22.054+08:00| 41| -|2021-03-17T10:32:23.054+08:00| 20| -|2021-03-17T10:32:24.054+08:00| 18| -+-----------------------------+-------------+ -............ -Total line number = 8 -``` - -SQL for query: - -```sql -select quantile(s1, "rank"="0.2", "K"="800") from root.test1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------+ -| Time|quantile(root.test1.s1, "rank"="0.2", "K"="800")| -+-----------------------------+------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 7.000000000000001| -+-----------------------------+------------------------------------------------+ -``` - -### Period - -#### Registration statement - -```sql -create function period as 'org.apache.iotdb.library.dprofile.UDAFPeriod' -``` - -#### Usage - -The function is used to compute the period of a numeric time series. - -**Name:** PERIOD - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is INT32. There is only one data point in the series, whose timestamp is 0 and value is the period. - -#### Examples - -Input series: - - -``` -+-----------------------------+---------------+ -| Time|root.test.d3.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.001+08:00| 1.0| -|1970-01-01T08:00:00.002+08:00| 2.0| -|1970-01-01T08:00:00.003+08:00| 3.0| -|1970-01-01T08:00:00.004+08:00| 1.0| -|1970-01-01T08:00:00.005+08:00| 2.0| -|1970-01-01T08:00:00.006+08:00| 3.0| -|1970-01-01T08:00:00.007+08:00| 1.0| -|1970-01-01T08:00:00.008+08:00| 2.0| -|1970-01-01T08:00:00.009+08:00| 3.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select period(s1) from root.test.d3 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time|period(root.test.d3.s1)| -+-----------------------------+-----------------------+ -|1970-01-01T08:00:00.000+08:00| 3| -+-----------------------------+-----------------------+ -``` - -### QLB - -#### Registration statement - -```sql -create function qlb as 'org.apache.iotdb.library.dprofile.UDTFQLB' -``` - -#### Usage - -This function is used to calculate Ljung-Box statistics $Q_{LB}$ for time series, and convert it to p value. - -**Name:** QLB - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters**: - -`lag`: max lag to calculate. Legal input shall be integer from 1 to n-2, where n is the sample number. Default value is n-2. - -**Output Series:** Output a single series. The type is DOUBLE. The output series is p value, and timestamp means lag. - -**Note:** If you want to calculate Ljung-Box statistics $Q_{LB}$ instead of p value, you may use ACF function. - -#### Examples - -##### Using Default Parameter - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T00:00:00.100+08:00| 1.22| -|1970-01-01T00:00:00.200+08:00| -2.78| -|1970-01-01T00:00:00.300+08:00| 1.53| -|1970-01-01T00:00:00.400+08:00| 0.70| -|1970-01-01T00:00:00.500+08:00| 0.75| -|1970-01-01T00:00:00.600+08:00| -0.72| -|1970-01-01T00:00:00.700+08:00| -0.22| -|1970-01-01T00:00:00.800+08:00| 0.28| -|1970-01-01T00:00:00.900+08:00| 0.57| -|1970-01-01T00:00:01.000+08:00| -0.22| -|1970-01-01T00:00:01.100+08:00| -0.72| -|1970-01-01T00:00:01.200+08:00| 1.34| -|1970-01-01T00:00:01.300+08:00| -0.25| -|1970-01-01T00:00:01.400+08:00| 0.17| -|1970-01-01T00:00:01.500+08:00| 2.51| -|1970-01-01T00:00:01.600+08:00| 1.42| -|1970-01-01T00:00:01.700+08:00| -1.34| -|1970-01-01T00:00:01.800+08:00| -0.01| -|1970-01-01T00:00:01.900+08:00| -0.49| -|1970-01-01T00:00:02.000+08:00| 1.63| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select QLB(s1) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|QLB(root.test.d1.s1)| -+-----------------------------+--------------------+ -|1970-01-01T00:00:00.001+08:00| 0.2168702295315677| -|1970-01-01T00:00:00.002+08:00| 0.3068948509261751| -|1970-01-01T00:00:00.003+08:00| 0.4217859150918444| -|1970-01-01T00:00:00.004+08:00| 0.5114539874276656| -|1970-01-01T00:00:00.005+08:00| 0.6560619525616759| -|1970-01-01T00:00:00.006+08:00| 0.7722398654053280| -|1970-01-01T00:00:00.007+08:00| 0.8532491661465290| -|1970-01-01T00:00:00.008+08:00| 0.9028575017542528| -|1970-01-01T00:00:00.009+08:00| 0.9434989988192729| -|1970-01-01T00:00:00.010+08:00| 0.8950280161464689| -|1970-01-01T00:00:00.011+08:00| 0.7701048398839656| -|1970-01-01T00:00:00.012+08:00| 0.7845536060001281| -|1970-01-01T00:00:00.013+08:00| 0.5943030981705825| -|1970-01-01T00:00:00.014+08:00| 0.4618413512531093| -|1970-01-01T00:00:00.015+08:00| 0.2645948244673964| -|1970-01-01T00:00:00.016+08:00| 0.3167530476666645| -|1970-01-01T00:00:00.017+08:00| 0.2330010780351453| -|1970-01-01T00:00:00.018+08:00| 0.0666611237622325| -+-----------------------------+--------------------+ -``` - -### Resample - -#### Registration statement - -```sql -create function re_sample as 'org.apache.iotdb.library.dprofile.UDTFResample' -``` - -#### Usage - -This function is used to resample the input series according to a given frequency, -including up-sampling and down-sampling. -Currently, the supported up-sampling methods are -NaN (filling with `NaN`), -FFill (filling with previous value), -BFill (filling with next value) and -Linear (filling with linear interpolation). -Down-sampling relies on group aggregation, -which supports Max, Min, First, Last, Mean and Median. - -**Name:** RESAMPLE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - - -+ `every`: The frequency of resampling, which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. This parameter cannot be lacked. -+ `interp`: The interpolation method of up-sampling, which is 'NaN', 'FFill', 'BFill' or 'Linear'. By default, NaN is used. -+ `aggr`: The aggregation method of down-sampling, which is 'Max', 'Min', 'First', 'Last', 'Mean' or 'Median'. By default, Mean is used. -+ `start`: The start time (inclusive) of resampling with the format 'yyyy-MM-dd HH:mm:ss'. By default, it is the timestamp of the first valid data point. -+ `end`: The end time (exclusive) of resampling with the format 'yyyy-MM-dd HH:mm:ss'. By default, it is the timestamp of the last valid data point. - -**Output Series:** Output a single series. The type is DOUBLE. It is strictly equispaced with the frequency `every`. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -##### Up-sampling - -When the frequency of resampling is higher than the original frequency, up-sampling starts. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2021-03-06T16:00:00.000+08:00| 3.09| -|2021-03-06T16:15:00.000+08:00| 3.53| -|2021-03-06T16:30:00.000+08:00| 3.5| -|2021-03-06T16:45:00.000+08:00| 3.51| -|2021-03-06T17:00:00.000+08:00| 3.41| -+-----------------------------+---------------+ -``` - - -SQL for query: - -```sql -select resample(s1,'every'='5m','interp'='linear') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------------------------+ -| Time|resample(root.test.d1.s1, "every"="5m", "interp"="linear")| -+-----------------------------+----------------------------------------------------------+ -|2021-03-06T16:00:00.000+08:00| 3.0899999141693115| -|2021-03-06T16:05:00.000+08:00| 3.2366665999094644| -|2021-03-06T16:10:00.000+08:00| 3.3833332856496177| -|2021-03-06T16:15:00.000+08:00| 3.5299999713897705| -|2021-03-06T16:20:00.000+08:00| 3.5199999809265137| -|2021-03-06T16:25:00.000+08:00| 3.509999990463257| -|2021-03-06T16:30:00.000+08:00| 3.5| -|2021-03-06T16:35:00.000+08:00| 3.503333330154419| -|2021-03-06T16:40:00.000+08:00| 3.506666660308838| -|2021-03-06T16:45:00.000+08:00| 3.509999990463257| -|2021-03-06T16:50:00.000+08:00| 3.4766666889190674| -|2021-03-06T16:55:00.000+08:00| 3.443333387374878| -|2021-03-06T17:00:00.000+08:00| 3.4100000858306885| -+-----------------------------+----------------------------------------------------------+ -``` - -##### Down-sampling - -When the frequency of resampling is lower than the original frequency, down-sampling starts. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select resample(s1,'every'='30m','aggr'='first') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------+ -| Time|resample(root.test.d1.s1, "every"="30m", "aggr"="first")| -+-----------------------------+--------------------------------------------------------+ -|2021-03-06T16:00:00.000+08:00| 3.0899999141693115| -|2021-03-06T16:30:00.000+08:00| 3.5| -|2021-03-06T17:00:00.000+08:00| 3.4100000858306885| -+-----------------------------+--------------------------------------------------------+ -``` - - - -##### Specify the time period - -The time period of resampling can be specified with `start` and `end`. -The period outside the actual time range will be interpolated. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select resample(s1,'every'='30m','start'='2021-03-06 15:00:00') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-----------------------------------------------------------------------+ -| Time|resample(root.test.d1.s1, "every"="30m", "start"="2021-03-06 15:00:00")| -+-----------------------------+-----------------------------------------------------------------------+ -|2021-03-06T15:00:00.000+08:00| NaN| -|2021-03-06T15:30:00.000+08:00| NaN| -|2021-03-06T16:00:00.000+08:00| 3.309999942779541| -|2021-03-06T16:30:00.000+08:00| 3.5049999952316284| -|2021-03-06T17:00:00.000+08:00| 3.4100000858306885| -+-----------------------------+-----------------------------------------------------------------------+ -``` - -### Sample - -#### Registration statement - -```sql -create function sample as 'org.apache.iotdb.library.dprofile.UDTFSample' -``` - -#### Usage - -This function is used to sample the input series, -that is, select a specified number of data points from the input series and output them. -Currently, three sampling methods are supported: -**Reservoir sampling** randomly selects data points. -All of the points have the same probability of being sampled. -**Isometric sampling** selects data points at equal index intervals. -**Triangle sampling** assigns data points to the buckets based on the number of sampling. -Then it calculates the area of the triangle based on these points inside the bucket and selects the point with the largest area of the triangle. -For more detail, please read [paper](http://skemman.is/stream/get/1946/15343/37285/3/SS_MSthesis.pdf) - -**Name:** SAMPLE - -**Input Series:** Only support a single input series. The type is arbitrary. - -**Parameters:** - -+ `method`: The method of sampling, which is 'reservoir', 'isometric' or 'triangle'. By default, reservoir sampling is used. -+ `k`: The number of sampling, which is a positive integer. By default, it's 1. - -**Output Series:** Output a single series. The type is the same as the input. The length of the output series is `k`. Each data point in the output series comes from the input series. - -**Note:** If `k` is greater than the length of input series, all data points in the input series will be output. - -#### Examples - -##### Reservoir Sampling - -When `method` is 'reservoir' or the default, reservoir sampling is used. -Due to the randomness of this method, the output series shown below is only a possible result. - - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| 1.0| -|2020-01-01T00:00:02.000+08:00| 2.0| -|2020-01-01T00:00:03.000+08:00| 3.0| -|2020-01-01T00:00:04.000+08:00| 4.0| -|2020-01-01T00:00:05.000+08:00| 5.0| -|2020-01-01T00:00:06.000+08:00| 6.0| -|2020-01-01T00:00:07.000+08:00| 7.0| -|2020-01-01T00:00:08.000+08:00| 8.0| -|2020-01-01T00:00:09.000+08:00| 9.0| -|2020-01-01T00:00:10.000+08:00| 10.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select sample(s1,'method'='reservoir','k'='5') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|sample(root.test.d1.s1, "method"="reservoir", "k"="5")| -+-----------------------------+------------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 2.0| -|2020-01-01T00:00:03.000+08:00| 3.0| -|2020-01-01T00:00:05.000+08:00| 5.0| -|2020-01-01T00:00:08.000+08:00| 8.0| -|2020-01-01T00:00:10.000+08:00| 10.0| -+-----------------------------+------------------------------------------------------+ -``` - -##### Isometric Sampling - -When `method` is 'isometric', isometric sampling is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select sample(s1,'method'='isometric','k'='5') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|sample(root.test.d1.s1, "method"="isometric", "k"="5")| -+-----------------------------+------------------------------------------------------+ -|2020-01-01T00:00:01.000+08:00| 1.0| -|2020-01-01T00:00:03.000+08:00| 3.0| -|2020-01-01T00:00:05.000+08:00| 5.0| -|2020-01-01T00:00:07.000+08:00| 7.0| -|2020-01-01T00:00:09.000+08:00| 9.0| -+-----------------------------+------------------------------------------------------+ -``` - -### Segment - -#### Registration statement - -```sql -create function segment as 'org.apache.iotdb.library.dprofile.UDTFSegment' -``` - -#### Usage - -This function is used to segment a time series into subsequences according to linear trend, and returns linear fitted values of first values in each subsequence or every data point. - -**Name:** SEGMENT - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `output` :"all" to output all fitted points; "first" to output first fitted points in each subsequence. - -+ `error`: error allowed at linear regression. It is defined as mean absolute error of a subsequence. - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note:** This function treat input series as equal-interval sampled. All data are loaded, so downsample input series first if there are too many data points. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 5.0| -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 1.0| -|1970-01-01T08:00:00.300+08:00| 2.0| -|1970-01-01T08:00:00.400+08:00| 3.0| -|1970-01-01T08:00:00.500+08:00| 4.0| -|1970-01-01T08:00:00.600+08:00| 5.0| -|1970-01-01T08:00:00.700+08:00| 6.0| -|1970-01-01T08:00:00.800+08:00| 7.0| -|1970-01-01T08:00:00.900+08:00| 8.0| -|1970-01-01T08:00:01.000+08:00| 9.0| -|1970-01-01T08:00:01.100+08:00| 9.1| -|1970-01-01T08:00:01.200+08:00| 9.2| -|1970-01-01T08:00:01.300+08:00| 9.3| -|1970-01-01T08:00:01.400+08:00| 9.4| -|1970-01-01T08:00:01.500+08:00| 9.5| -|1970-01-01T08:00:01.600+08:00| 9.6| -|1970-01-01T08:00:01.700+08:00| 9.7| -|1970-01-01T08:00:01.800+08:00| 9.8| -|1970-01-01T08:00:01.900+08:00| 9.9| -|1970-01-01T08:00:02.000+08:00| 10.0| -|1970-01-01T08:00:02.100+08:00| 8.0| -|1970-01-01T08:00:02.200+08:00| 6.0| -|1970-01-01T08:00:02.300+08:00| 4.0| -|1970-01-01T08:00:02.400+08:00| 2.0| -|1970-01-01T08:00:02.500+08:00| 0.0| -|1970-01-01T08:00:02.600+08:00| -2.0| -|1970-01-01T08:00:02.700+08:00| -4.0| -|1970-01-01T08:00:02.800+08:00| -6.0| -|1970-01-01T08:00:02.900+08:00| -8.0| -|1970-01-01T08:00:03.000+08:00| -10.0| -|1970-01-01T08:00:03.100+08:00| 10.0| -|1970-01-01T08:00:03.200+08:00| 10.0| -|1970-01-01T08:00:03.300+08:00| 10.0| -|1970-01-01T08:00:03.400+08:00| 10.0| -|1970-01-01T08:00:03.500+08:00| 10.0| -|1970-01-01T08:00:03.600+08:00| 10.0| -|1970-01-01T08:00:03.700+08:00| 10.0| -|1970-01-01T08:00:03.800+08:00| 10.0| -|1970-01-01T08:00:03.900+08:00| 10.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select segment(s1, "error"="0.1") from root.test -``` - -Output series: - -``` -+-----------------------------+------------------------------------+ -| Time|segment(root.test.s1, "error"="0.1")| -+-----------------------------+------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 5.0| -|1970-01-01T08:00:00.200+08:00| 1.0| -|1970-01-01T08:00:01.000+08:00| 9.0| -|1970-01-01T08:00:02.000+08:00| 10.0| -|1970-01-01T08:00:03.000+08:00| -10.0| -|1970-01-01T08:00:03.200+08:00| 10.0| -+-----------------------------+------------------------------------+ -``` - -### Skew - -#### Registration statement - -```sql -create function skew as 'org.apache.iotdb.library.dprofile.UDAFSkew' -``` - -#### Usage - -This function is used to calculate the population skewness. - -**Name:** SKEW - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the population skewness. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| -|2020-01-01T00:00:01.000+08:00| 2.0| -|2020-01-01T00:00:02.000+08:00| 3.0| -|2020-01-01T00:00:03.000+08:00| 4.0| -|2020-01-01T00:00:04.000+08:00| 5.0| -|2020-01-01T00:00:05.000+08:00| 6.0| -|2020-01-01T00:00:06.000+08:00| 7.0| -|2020-01-01T00:00:07.000+08:00| 8.0| -|2020-01-01T00:00:08.000+08:00| 9.0| -|2020-01-01T00:00:09.000+08:00| 10.0| -|2020-01-01T00:00:10.000+08:00| 10.0| -|2020-01-01T00:00:11.000+08:00| 10.0| -|2020-01-01T00:00:12.000+08:00| 10.0| -|2020-01-01T00:00:13.000+08:00| 10.0| -|2020-01-01T00:00:14.000+08:00| 10.0| -|2020-01-01T00:00:15.000+08:00| 10.0| -|2020-01-01T00:00:16.000+08:00| 10.0| -|2020-01-01T00:00:17.000+08:00| 10.0| -|2020-01-01T00:00:18.000+08:00| 10.0| -|2020-01-01T00:00:19.000+08:00| 10.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select skew(s1) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time| skew(root.test.d1.s1)| -+-----------------------------+-----------------------+ -|1970-01-01T08:00:00.000+08:00| -0.9998427402292644| -+-----------------------------+-----------------------+ -``` - -### Spline - -#### Registration statement - -```sql -create function spline as 'org.apache.iotdb.library.dprofile.UDTFSpline' -``` - -#### Usage - -This function is used to calculate cubic spline interpolation of input series. - -**Name:** SPLINE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `points`: Number of resampling points. - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note**: Output series retains the first and last timestamps of input series. Interpolation points are selected at equal intervals. The function tries to calculate only when there are no less than 4 points in input series. - -#### Examples - -##### Assigning number of interpolation points - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.2| -|1970-01-01T08:00:00.500+08:00| 1.7| -|1970-01-01T08:00:00.700+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 2.1| -|1970-01-01T08:00:01.100+08:00| 2.0| -|1970-01-01T08:00:01.200+08:00| 1.8| -|1970-01-01T08:00:01.300+08:00| 1.2| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 1.6| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select spline(s1, "points"="151") from root.test -``` - -Output series: - -``` -+-----------------------------+------------------------------------+ -| Time|spline(root.test.s1, "points"="151")| -+-----------------------------+------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.010+08:00| 0.04870000251134237| -|1970-01-01T08:00:00.020+08:00| 0.09680000495910646| -|1970-01-01T08:00:00.030+08:00| 0.14430000734329226| -|1970-01-01T08:00:00.040+08:00| 0.19120000966389972| -|1970-01-01T08:00:00.050+08:00| 0.23750001192092896| -|1970-01-01T08:00:00.060+08:00| 0.2832000141143799| -|1970-01-01T08:00:00.070+08:00| 0.32830001624425253| -|1970-01-01T08:00:00.080+08:00| 0.3728000183105469| -|1970-01-01T08:00:00.090+08:00| 0.416700020313263| -|1970-01-01T08:00:00.100+08:00| 0.4600000222524008| -|1970-01-01T08:00:00.110+08:00| 0.5027000241279602| -|1970-01-01T08:00:00.120+08:00| 0.5448000259399414| -|1970-01-01T08:00:00.130+08:00| 0.5863000276883443| -|1970-01-01T08:00:00.140+08:00| 0.627200029373169| -|1970-01-01T08:00:00.150+08:00| 0.6675000309944153| -|1970-01-01T08:00:00.160+08:00| 0.7072000325520833| -|1970-01-01T08:00:00.170+08:00| 0.7463000340461731| -|1970-01-01T08:00:00.180+08:00| 0.7848000354766846| -|1970-01-01T08:00:00.190+08:00| 0.8227000368436178| -|1970-01-01T08:00:00.200+08:00| 0.8600000381469728| -|1970-01-01T08:00:00.210+08:00| 0.8967000393867494| -|1970-01-01T08:00:00.220+08:00| 0.9328000405629477| -|1970-01-01T08:00:00.230+08:00| 0.9683000416755676| -|1970-01-01T08:00:00.240+08:00| 1.0032000427246095| -|1970-01-01T08:00:00.250+08:00| 1.037500043710073| -|1970-01-01T08:00:00.260+08:00| 1.071200044631958| -|1970-01-01T08:00:00.270+08:00| 1.1043000454902647| -|1970-01-01T08:00:00.280+08:00| 1.1368000462849934| -|1970-01-01T08:00:00.290+08:00| 1.1687000470161437| -|1970-01-01T08:00:00.300+08:00| 1.2000000476837158| -|1970-01-01T08:00:00.310+08:00| 1.2307000483103594| -|1970-01-01T08:00:00.320+08:00| 1.2608000489139557| -|1970-01-01T08:00:00.330+08:00| 1.2903000494873524| -|1970-01-01T08:00:00.340+08:00| 1.3192000500233967| -|1970-01-01T08:00:00.350+08:00| 1.3475000505149364| -|1970-01-01T08:00:00.360+08:00| 1.3752000509548186| -|1970-01-01T08:00:00.370+08:00| 1.402300051335891| -|1970-01-01T08:00:00.380+08:00| 1.4288000516510009| -|1970-01-01T08:00:00.390+08:00| 1.4547000518929958| -|1970-01-01T08:00:00.400+08:00| 1.480000052054723| -|1970-01-01T08:00:00.410+08:00| 1.5047000521290301| -|1970-01-01T08:00:00.420+08:00| 1.5288000521087646| -|1970-01-01T08:00:00.430+08:00| 1.5523000519867738| -|1970-01-01T08:00:00.440+08:00| 1.575200051755905| -|1970-01-01T08:00:00.450+08:00| 1.597500051409006| -|1970-01-01T08:00:00.460+08:00| 1.619200050938924| -|1970-01-01T08:00:00.470+08:00| 1.6403000503385066| -|1970-01-01T08:00:00.480+08:00| 1.660800049600601| -|1970-01-01T08:00:00.490+08:00| 1.680700048718055| -|1970-01-01T08:00:00.500+08:00| 1.7000000476837158| -|1970-01-01T08:00:00.510+08:00| 1.7188475466453037| -|1970-01-01T08:00:00.520+08:00| 1.7373800457262996| -|1970-01-01T08:00:00.530+08:00| 1.7555825448831923| -|1970-01-01T08:00:00.540+08:00| 1.7734400440724702| -|1970-01-01T08:00:00.550+08:00| 1.790937543250622| -|1970-01-01T08:00:00.560+08:00| 1.8080600423741364| -|1970-01-01T08:00:00.570+08:00| 1.8247925413995016| -|1970-01-01T08:00:00.580+08:00| 1.8411200402832066| -|1970-01-01T08:00:00.590+08:00| 1.8570275389817397| -|1970-01-01T08:00:00.600+08:00| 1.8725000374515897| -|1970-01-01T08:00:00.610+08:00| 1.8875225356492449| -|1970-01-01T08:00:00.620+08:00| 1.902080033531194| -|1970-01-01T08:00:00.630+08:00| 1.9161575310539258| -|1970-01-01T08:00:00.640+08:00| 1.9297400281739288| -|1970-01-01T08:00:00.650+08:00| 1.9428125248476913| -|1970-01-01T08:00:00.660+08:00| 1.9553600210317021| -|1970-01-01T08:00:00.670+08:00| 1.96736751668245| -|1970-01-01T08:00:00.680+08:00| 1.9788200117564232| -|1970-01-01T08:00:00.690+08:00| 1.9897025062101101| -|1970-01-01T08:00:00.700+08:00| 2.0| -|1970-01-01T08:00:00.710+08:00| 2.0097024933913334| -|1970-01-01T08:00:00.720+08:00| 2.0188199867081615| -|1970-01-01T08:00:00.730+08:00| 2.027367479995188| -|1970-01-01T08:00:00.740+08:00| 2.0353599732971155| -|1970-01-01T08:00:00.750+08:00| 2.0428124666586482| -|1970-01-01T08:00:00.760+08:00| 2.049739960124489| -|1970-01-01T08:00:00.770+08:00| 2.056157453739342| -|1970-01-01T08:00:00.780+08:00| 2.06207994754791| -|1970-01-01T08:00:00.790+08:00| 2.067522441594897| -|1970-01-01T08:00:00.800+08:00| 2.072499935925006| -|1970-01-01T08:00:00.810+08:00| 2.07702743058294| -|1970-01-01T08:00:00.820+08:00| 2.081119925613404| -|1970-01-01T08:00:00.830+08:00| 2.0847924210611| -|1970-01-01T08:00:00.840+08:00| 2.0880599169707317| -|1970-01-01T08:00:00.850+08:00| 2.0909374133870027| -|1970-01-01T08:00:00.860+08:00| 2.0934399103546166| -|1970-01-01T08:00:00.870+08:00| 2.0955824079182768| -|1970-01-01T08:00:00.880+08:00| 2.0973799061226863| -|1970-01-01T08:00:00.890+08:00| 2.098847405012549| -|1970-01-01T08:00:00.900+08:00| 2.0999999046325684| -|1970-01-01T08:00:00.910+08:00| 2.1005574051201332| -|1970-01-01T08:00:00.920+08:00| 2.1002599065303778| -|1970-01-01T08:00:00.930+08:00| 2.0991524087846245| -|1970-01-01T08:00:00.940+08:00| 2.0972799118041947| -|1970-01-01T08:00:00.950+08:00| 2.0946874155104105| -|1970-01-01T08:00:00.960+08:00| 2.0914199198245944| -|1970-01-01T08:00:00.970+08:00| 2.0875224246680673| -|1970-01-01T08:00:00.980+08:00| 2.083039929962151| -|1970-01-01T08:00:00.990+08:00| 2.0780174356281687| -|1970-01-01T08:00:01.000+08:00| 2.0724999415874406| -|1970-01-01T08:00:01.010+08:00| 2.06653244776129| -|1970-01-01T08:00:01.020+08:00| 2.060159954071038| -|1970-01-01T08:00:01.030+08:00| 2.053427460438006| -|1970-01-01T08:00:01.040+08:00| 2.046379966783517| -|1970-01-01T08:00:01.050+08:00| 2.0390624730288924| -|1970-01-01T08:00:01.060+08:00| 2.031519979095454| -|1970-01-01T08:00:01.070+08:00| 2.0237974849045237| -|1970-01-01T08:00:01.080+08:00| 2.015939990377423| -|1970-01-01T08:00:01.090+08:00| 2.0079924954354746| -|1970-01-01T08:00:01.100+08:00| 2.0| -|1970-01-01T08:00:01.110+08:00| 1.9907018211101906| -|1970-01-01T08:00:01.120+08:00| 1.9788509124245144| -|1970-01-01T08:00:01.130+08:00| 1.9645127287932083| -|1970-01-01T08:00:01.140+08:00| 1.9477527250665083| -|1970-01-01T08:00:01.150+08:00| 1.9286363560946513| -|1970-01-01T08:00:01.160+08:00| 1.9072290767278735| -|1970-01-01T08:00:01.170+08:00| 1.8835963418164114| -|1970-01-01T08:00:01.180+08:00| 1.8578036062105014| -|1970-01-01T08:00:01.190+08:00| 1.8299163247603802| -|1970-01-01T08:00:01.200+08:00| 1.7999999523162842| -|1970-01-01T08:00:01.210+08:00| 1.7623635841923329| -|1970-01-01T08:00:01.220+08:00| 1.7129696477516976| -|1970-01-01T08:00:01.230+08:00| 1.6543635959181928| -|1970-01-01T08:00:01.240+08:00| 1.5890908816156328| -|1970-01-01T08:00:01.250+08:00| 1.5196969577678319| -|1970-01-01T08:00:01.260+08:00| 1.4487272772986044| -|1970-01-01T08:00:01.270+08:00| 1.3787272931317647| -|1970-01-01T08:00:01.280+08:00| 1.3122424581911272| -|1970-01-01T08:00:01.290+08:00| 1.251818225400506| -|1970-01-01T08:00:01.300+08:00| 1.2000000476837158| -|1970-01-01T08:00:01.310+08:00| 1.1548000470995912| -|1970-01-01T08:00:01.320+08:00| 1.1130667107899999| -|1970-01-01T08:00:01.330+08:00| 1.0756000393033045| -|1970-01-01T08:00:01.340+08:00| 1.043200033187868| -|1970-01-01T08:00:01.350+08:00| 1.016666692992053| -|1970-01-01T08:00:01.360+08:00| 0.9968000192642223| -|1970-01-01T08:00:01.370+08:00| 0.9844000125527389| -|1970-01-01T08:00:01.380+08:00| 0.9802666734059655| -|1970-01-01T08:00:01.390+08:00| 0.9852000023722649| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.410+08:00| 1.023999999165535| -|1970-01-01T08:00:01.420+08:00| 1.0559999990463256| -|1970-01-01T08:00:01.430+08:00| 1.0959999996423722| -|1970-01-01T08:00:01.440+08:00| 1.1440000009536744| -|1970-01-01T08:00:01.450+08:00| 1.2000000029802322| -|1970-01-01T08:00:01.460+08:00| 1.264000005722046| -|1970-01-01T08:00:01.470+08:00| 1.3360000091791153| -|1970-01-01T08:00:01.480+08:00| 1.4160000133514405| -|1970-01-01T08:00:01.490+08:00| 1.5040000182390214| -|1970-01-01T08:00:01.500+08:00| 1.600000023841858| -+-----------------------------+------------------------------------+ -``` - -### Spread - -#### Registration statement - -```sql -create function spread as 'org.apache.iotdb.library.dprofile.UDAFSpread' -``` - -#### Usage - -This function is used to calculate the spread of time series, that is, the maximum value minus the minimum value. - -**Name:** SPREAD - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is the same as the input. There is only one data point in the series, whose timestamp is 0 and value is the spread. - -**Note:** Missing points, null points and `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select spread(s1) from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time|spread(root.test.d1.s1)| -+-----------------------------+-----------------------+ -|1970-01-01T08:00:00.000+08:00| 26.0| -+-----------------------------+-----------------------+ -``` - - - -### ZScore - -#### Registration statement - -```sql -create function zscore as 'org.apache.iotdb.library.dprofile.UDTFZScore' -``` - -#### Usage - -This function is used to standardize the input series with z-score. - -**Name:** ZSCORE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `compute`: When set to "batch", anomaly test is conducted after importing all data points; when set to "stream", it is required to provide mean and standard deviation. The default method is "batch". -+ `avg`: Mean value when method is set to "stream". -+ `sd`: Standard deviation when method is set to "stream". - -**Output Series:** Output a single series. The type is DOUBLE. - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select zscore(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|zscore(root.test.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.100+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.200+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.300+08:00| 0.20672455764868078| -|1970-01-01T08:00:00.400+08:00| -0.6201736729460423| -|1970-01-01T08:00:00.500+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.600+08:00|-0.20672455764868078| -|1970-01-01T08:00:00.700+08:00| -1.033622788243404| -|1970-01-01T08:00:00.800+08:00| 0.6201736729460423| -|1970-01-01T08:00:00.900+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.000+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.100+08:00| 0.20672455764868078| -|1970-01-01T08:00:01.200+08:00| -0.6201736729460423| -|1970-01-01T08:00:01.300+08:00| -0.6201736729460423| -|1970-01-01T08:00:01.400+08:00| 0.20672455764868078| -|1970-01-01T08:00:01.500+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.600+08:00|-0.20672455764868078| -|1970-01-01T08:00:01.700+08:00| 3.9277665953249348| -|1970-01-01T08:00:01.800+08:00| 0.6201736729460423| -|1970-01-01T08:00:01.900+08:00| -1.033622788243404| -|1970-01-01T08:00:02.000+08:00|-0.20672455764868078| -+-----------------------------+--------------------+ -``` - - -## Anomaly Detection - -### IQR - -#### Registration statement - -```sql -create function iqr as 'org.apache.iotdb.library.anomaly.UDTFIQR' -``` - -#### Usage - -This function is used to detect anomalies based on IQR. Points distributing beyond 1.5 times IQR are selected. - -**Name:** IQR - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `method`: When set to "batch", anomaly test is conducted after importing all data points; when set to "stream", it is required to provide upper and lower quantiles. The default method is "batch". -+ `q1`: The lower quantile when method is set to "stream". -+ `q3`: The upper quantile when method is set to "stream". - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note:** $IQR=Q_3-Q_1$ - -#### Examples - -##### Batch computing - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| -|1970-01-01T08:00:00.300+08:00| 1.0| -|1970-01-01T08:00:00.400+08:00| -1.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -|1970-01-01T08:00:00.600+08:00| 0.0| -|1970-01-01T08:00:00.700+08:00| -2.0| -|1970-01-01T08:00:00.800+08:00| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 1.0| -|1970-01-01T08:00:01.200+08:00| -1.0| -|1970-01-01T08:00:01.300+08:00| -1.0| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 0.0| -|1970-01-01T08:00:01.600+08:00| 0.0| -|1970-01-01T08:00:01.700+08:00| 10.0| -|1970-01-01T08:00:01.800+08:00| 2.0| -|1970-01-01T08:00:01.900+08:00| -2.0| -|1970-01-01T08:00:02.000+08:00| 0.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select iqr(s1) from root.test -``` - -Output series: - -``` -+-----------------------------+-----------------+ -| Time|iqr(root.test.s1)| -+-----------------------------+-----------------+ -|1970-01-01T08:00:01.700+08:00| 10.0| -+-----------------------------+-----------------+ -``` - -### KSigma - -#### Registration statement - -```sql -create function ksigma as 'org.apache.iotdb.library.anomaly.UDTFKSigma' -``` - -#### Usage - -This function is used to detect anomalies based on the Dynamic K-Sigma Algorithm. -Within a sliding window, the input value with a deviation of more than k times the standard deviation from the average will be output as anomaly. - -**Name:** KSIGMA - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `k`: How many times to multiply on standard deviation to define anomaly, the default value is 3. -+ `window`: The window size of Dynamic K-Sigma Algorithm, the default value is 10000. - -**Output Series:** Output a single series. The type is same as input series. - -**Note:** Only when is larger than 0, the anomaly detection will be performed. Otherwise, nothing will be output. - -#### Examples - -##### Assigning k - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 0.0| -|2020-01-01T00:00:03.000+08:00| 50.0| -|2020-01-01T00:00:04.000+08:00| 100.0| -|2020-01-01T00:00:06.000+08:00| 150.0| -|2020-01-01T00:00:08.000+08:00| 200.0| -|2020-01-01T00:00:10.000+08:00| 200.0| -|2020-01-01T00:00:14.000+08:00| 200.0| -|2020-01-01T00:00:15.000+08:00| 200.0| -|2020-01-01T00:00:16.000+08:00| 200.0| -|2020-01-01T00:00:18.000+08:00| 200.0| -|2020-01-01T00:00:20.000+08:00| 150.0| -|2020-01-01T00:00:22.000+08:00| 100.0| -|2020-01-01T00:00:26.000+08:00| 50.0| -|2020-01-01T00:00:28.000+08:00| 0.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select ksigma(s1,"k"="1.0") from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+---------------------------------+ -|Time |ksigma(root.test.d1.s1,"k"="3.0")| -+-----------------------------+---------------------------------+ -|2020-01-01T00:00:02.000+08:00| 0.0| -|2020-01-01T00:00:03.000+08:00| 50.0| -|2020-01-01T00:00:26.000+08:00| 50.0| -|2020-01-01T00:00:28.000+08:00| 0.0| -+-----------------------------+---------------------------------+ -``` - -### LOF - -#### Registration statement - -```sql -create function LOF as 'org.apache.iotdb.library.anomaly.UDTFLOF' -``` - -#### Usage - -This function is used to detect density anomaly of time series. According to k-th distance calculation parameter and local outlier factor (lof) threshold, the function judges if a set of input values is an density anomaly, and a bool mark of anomaly values will be output. - -**Name:** LOF - -**Input Series:** Multiple input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `method`:assign a detection method. The default value is "default", when input data has multiple dimensions. The alternative is "series", when a input series will be transformed to high dimension. -+ `k`:use the k-th distance to calculate lof. Default value is 3. -+ `window`: size of window to split origin data points. Default value is 10000. -+ `windowsize`:dimension that will be transformed into when method is "series". The default value is 5. - -**Output Series:** Output a single series. The type is DOUBLE. - -**Note:** Incomplete rows will be ignored. They are neither calculated nor marked as anomaly. - -#### Examples - -##### Using default parameters - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.100+08:00| 0.0| 0.0| -|1970-01-01T08:00:00.200+08:00| 0.0| 1.0| -|1970-01-01T08:00:00.300+08:00| 1.0| 1.0| -|1970-01-01T08:00:00.400+08:00| 1.0| 0.0| -|1970-01-01T08:00:00.500+08:00| 0.0| -1.0| -|1970-01-01T08:00:00.600+08:00| -1.0| -1.0| -|1970-01-01T08:00:00.700+08:00| -1.0| 0.0| -|1970-01-01T08:00:00.800+08:00| 2.0| 2.0| -|1970-01-01T08:00:00.900+08:00| 0.0| null| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select lof(s1,s2) from root.test.d1 where time<1000 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|lof(root.test.d1.s1, root.test.d1.s2)| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.100+08:00| 3.8274824267668244| -|1970-01-01T08:00:00.200+08:00| 3.0117631741126156| -|1970-01-01T08:00:00.300+08:00| 2.838155437762879| -|1970-01-01T08:00:00.400+08:00| 3.0117631741126156| -|1970-01-01T08:00:00.500+08:00| 2.73518261244453| -|1970-01-01T08:00:00.600+08:00| 2.371440975708148| -|1970-01-01T08:00:00.700+08:00| 2.73518261244453| -|1970-01-01T08:00:00.800+08:00| 1.7561416374270742| -+-----------------------------+-------------------------------------+ -``` - -##### Diagnosing 1d timeseries - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.100+08:00| 1.0| -|1970-01-01T08:00:00.200+08:00| 2.0| -|1970-01-01T08:00:00.300+08:00| 3.0| -|1970-01-01T08:00:00.400+08:00| 4.0| -|1970-01-01T08:00:00.500+08:00| 5.0| -|1970-01-01T08:00:00.600+08:00| 6.0| -|1970-01-01T08:00:00.700+08:00| 7.0| -|1970-01-01T08:00:00.800+08:00| 8.0| -|1970-01-01T08:00:00.900+08:00| 9.0| -|1970-01-01T08:00:01.000+08:00| 10.0| -|1970-01-01T08:00:01.100+08:00| 11.0| -|1970-01-01T08:00:01.200+08:00| 12.0| -|1970-01-01T08:00:01.300+08:00| 13.0| -|1970-01-01T08:00:01.400+08:00| 14.0| -|1970-01-01T08:00:01.500+08:00| 15.0| -|1970-01-01T08:00:01.600+08:00| 16.0| -|1970-01-01T08:00:01.700+08:00| 17.0| -|1970-01-01T08:00:01.800+08:00| 18.0| -|1970-01-01T08:00:01.900+08:00| 19.0| -|1970-01-01T08:00:02.000+08:00| 20.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select lof(s1, "method"="series") from root.test.d1 where time<1000 -``` - -Output series: - -``` -+-----------------------------+--------------------+ -| Time|lof(root.test.d1.s1)| -+-----------------------------+--------------------+ -|1970-01-01T08:00:00.100+08:00| 3.77777777777778| -|1970-01-01T08:00:00.200+08:00| 4.32727272727273| -|1970-01-01T08:00:00.300+08:00| 4.85714285714286| -|1970-01-01T08:00:00.400+08:00| 5.40909090909091| -|1970-01-01T08:00:00.500+08:00| 5.94999999999999| -|1970-01-01T08:00:00.600+08:00| 6.43243243243243| -|1970-01-01T08:00:00.700+08:00| 6.79999999999999| -|1970-01-01T08:00:00.800+08:00| 7.0| -|1970-01-01T08:00:00.900+08:00| 7.0| -|1970-01-01T08:00:01.000+08:00| 6.79999999999999| -|1970-01-01T08:00:01.100+08:00| 6.43243243243243| -|1970-01-01T08:00:01.200+08:00| 5.94999999999999| -|1970-01-01T08:00:01.300+08:00| 5.40909090909091| -|1970-01-01T08:00:01.400+08:00| 4.85714285714286| -|1970-01-01T08:00:01.500+08:00| 4.32727272727273| -|1970-01-01T08:00:01.600+08:00| 3.77777777777778| -+-----------------------------+--------------------+ -``` - -### MissDetect - -#### Registration statement - -```sql -create function missdetect as 'org.apache.iotdb.library.anomaly.UDTFMissDetect' -``` - -#### Usage - -This function is used to detect missing anomalies. -In some datasets, missing values are filled by linear interpolation. -Thus, there are several long perfect linear segments. -By discovering these perfect linear segments, -missing anomalies are detected. - -**Name:** MISSDETECT - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameter:** - -`error`: The minimum length of the detected missing anomalies, which is an integer greater than or equal to 10. By default, it is 10. - -**Output Series:** Output a single series. The type is BOOLEAN. Each data point which is miss anomaly will be labeled as true. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s2| -+-----------------------------+---------------+ -|2021-07-01T12:00:00.000+08:00| 0.0| -|2021-07-01T12:00:01.000+08:00| 1.0| -|2021-07-01T12:00:02.000+08:00| 0.0| -|2021-07-01T12:00:03.000+08:00| 1.0| -|2021-07-01T12:00:04.000+08:00| 0.0| -|2021-07-01T12:00:05.000+08:00| 0.0| -|2021-07-01T12:00:06.000+08:00| 0.0| -|2021-07-01T12:00:07.000+08:00| 0.0| -|2021-07-01T12:00:08.000+08:00| 0.0| -|2021-07-01T12:00:09.000+08:00| 0.0| -|2021-07-01T12:00:10.000+08:00| 0.0| -|2021-07-01T12:00:11.000+08:00| 0.0| -|2021-07-01T12:00:12.000+08:00| 0.0| -|2021-07-01T12:00:13.000+08:00| 0.0| -|2021-07-01T12:00:14.000+08:00| 0.0| -|2021-07-01T12:00:15.000+08:00| 0.0| -|2021-07-01T12:00:16.000+08:00| 1.0| -|2021-07-01T12:00:17.000+08:00| 0.0| -|2021-07-01T12:00:18.000+08:00| 1.0| -|2021-07-01T12:00:19.000+08:00| 0.0| -|2021-07-01T12:00:20.000+08:00| 1.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select missdetect(s2,'minlen'='10') from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------+ -| Time|missdetect(root.test.d2.s2, "minlen"="10")| -+-----------------------------+------------------------------------------+ -|2021-07-01T12:00:00.000+08:00| false| -|2021-07-01T12:00:01.000+08:00| false| -|2021-07-01T12:00:02.000+08:00| false| -|2021-07-01T12:00:03.000+08:00| false| -|2021-07-01T12:00:04.000+08:00| true| -|2021-07-01T12:00:05.000+08:00| true| -|2021-07-01T12:00:06.000+08:00| true| -|2021-07-01T12:00:07.000+08:00| true| -|2021-07-01T12:00:08.000+08:00| true| -|2021-07-01T12:00:09.000+08:00| true| -|2021-07-01T12:00:10.000+08:00| true| -|2021-07-01T12:00:11.000+08:00| true| -|2021-07-01T12:00:12.000+08:00| true| -|2021-07-01T12:00:13.000+08:00| true| -|2021-07-01T12:00:14.000+08:00| true| -|2021-07-01T12:00:15.000+08:00| true| -|2021-07-01T12:00:16.000+08:00| false| -|2021-07-01T12:00:17.000+08:00| false| -|2021-07-01T12:00:18.000+08:00| false| -|2021-07-01T12:00:19.000+08:00| false| -|2021-07-01T12:00:20.000+08:00| false| -+-----------------------------+------------------------------------------+ -``` - -### Range - -#### Registration statement - -```sql -create function range as 'org.apache.iotdb.library.anomaly.UDTFRange' -``` - -#### Usage - -This function is used to detect range anomaly of time series. According to upper bound and lower bound parameters, the function judges if a input value is beyond range, aka range anomaly, and a new time series of anomaly will be output. - -**Name:** RANGE - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `lower_bound`:lower bound of range anomaly detection. -+ `upper_bound`:upper bound of range anomaly detection. - -**Output Series:** Output a single series. The type is the same as the input. - -**Note:** Only when `upper_bound` is larger than `lower_bound`, the anomaly detection will be performed. Otherwise, nothing will be output. - - - -#### Examples - -##### Assigning Lower and Upper Bound - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select range(s1,"lower_bound"="101.0","upper_bound"="125.0") from root.test.d1 where time <= 2020-01-01 00:00:30 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------------------+ -|Time |range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")| -+-----------------------------+------------------------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -+-----------------------------+------------------------------------------------------------------+ -``` - -### TwoSidedFilter - -#### Registration statement - -```sql -create function twosidedfilter as 'org.apache.iotdb.library.anomaly.UDTFTwoSidedFilter' -``` - -#### Usage - -The function is used to filter anomalies of a numeric time series based on two-sided window detection. - -**Name:** TWOSIDEDFILTER - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE - -**Output Series:** Output a single series. The type is the same as the input. It is the input without anomalies. - -**Parameter:** - -- `len`: The size of the window, which is a positive integer. By default, it's 5. When `len`=3, the algorithm detects forward window and backward window with length 3 and calculates the outlierness of the current point. - -- `threshold`: The threshold of outlierness, which is a floating number in (0,1). By default, it's 0.3. The strict standard of detecting anomalies is in proportion to the threshold. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s0| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 2002.0| -|1970-01-01T08:00:01.000+08:00| 1946.0| -|1970-01-01T08:00:02.000+08:00| 1958.0| -|1970-01-01T08:00:03.000+08:00| 2012.0| -|1970-01-01T08:00:04.000+08:00| 2051.0| -|1970-01-01T08:00:05.000+08:00| 1898.0| -|1970-01-01T08:00:06.000+08:00| 2014.0| -|1970-01-01T08:00:07.000+08:00| 2052.0| -|1970-01-01T08:00:08.000+08:00| 1935.0| -|1970-01-01T08:00:09.000+08:00| 1901.0| -|1970-01-01T08:00:10.000+08:00| 1972.0| -|1970-01-01T08:00:11.000+08:00| 1969.0| -|1970-01-01T08:00:12.000+08:00| 1984.0| -|1970-01-01T08:00:13.000+08:00| 2018.0| -|1970-01-01T08:00:37.000+08:00| 1484.0| -|1970-01-01T08:00:38.000+08:00| 1055.0| -|1970-01-01T08:00:39.000+08:00| 1050.0| -|1970-01-01T08:01:05.000+08:00| 1023.0| -|1970-01-01T08:01:06.000+08:00| 1056.0| -|1970-01-01T08:01:07.000+08:00| 978.0| -|1970-01-01T08:01:08.000+08:00| 1050.0| -|1970-01-01T08:01:09.000+08:00| 1123.0| -|1970-01-01T08:01:10.000+08:00| 1150.0| -|1970-01-01T08:01:11.000+08:00| 1034.0| -|1970-01-01T08:01:12.000+08:00| 950.0| -|1970-01-01T08:01:13.000+08:00| 1059.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select TwoSidedFilter(s0, 'len'='5', 'threshold'='0.3') from root.test -``` - -Output series: - -``` -+-----------------------------+------------+ -| Time|root.test.s0| -+-----------------------------+------------+ -|1970-01-01T08:00:00.000+08:00| 2002.0| -|1970-01-01T08:00:01.000+08:00| 1946.0| -|1970-01-01T08:00:02.000+08:00| 1958.0| -|1970-01-01T08:00:03.000+08:00| 2012.0| -|1970-01-01T08:00:04.000+08:00| 2051.0| -|1970-01-01T08:00:05.000+08:00| 1898.0| -|1970-01-01T08:00:06.000+08:00| 2014.0| -|1970-01-01T08:00:07.000+08:00| 2052.0| -|1970-01-01T08:00:08.000+08:00| 1935.0| -|1970-01-01T08:00:09.000+08:00| 1901.0| -|1970-01-01T08:00:10.000+08:00| 1972.0| -|1970-01-01T08:00:11.000+08:00| 1969.0| -|1970-01-01T08:00:12.000+08:00| 1984.0| -|1970-01-01T08:00:13.000+08:00| 2018.0| -|1970-01-01T08:01:05.000+08:00| 1023.0| -|1970-01-01T08:01:06.000+08:00| 1056.0| -|1970-01-01T08:01:07.000+08:00| 978.0| -|1970-01-01T08:01:08.000+08:00| 1050.0| -|1970-01-01T08:01:09.000+08:00| 1123.0| -|1970-01-01T08:01:10.000+08:00| 1150.0| -|1970-01-01T08:01:11.000+08:00| 1034.0| -|1970-01-01T08:01:12.000+08:00| 950.0| -|1970-01-01T08:01:13.000+08:00| 1059.0| -+-----------------------------+------------+ -``` - -### Outlier - -#### Registration statement - -```sql -create function outlier as 'org.apache.iotdb.library.anomaly.UDTFOutlier' -``` - -#### Usage - -This function is used to detect distance-based outliers. For each point in the current window, if the number of its neighbors within the distance of neighbor distance threshold is less than the neighbor count threshold, the point in detected as an outlier. - -**Name:** OUTLIER - -**Input Series：** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -+ `r`：the neighbor distance threshold. -+ `k`：the neighbor count threshold. -+ `w`：the window size. -+ `s`：the slide size. - -**Output Series:** Output a single series. The type is the same as the input. - -#### Examples - -##### Assigning Parameters of Queries - -Input series: - -``` -+-----------------------------+------------+ -| Time|root.test.s1| -+-----------------------------+------------+ -|2020-01-04T23:59:55.000+08:00| 56.0| -|2020-01-04T23:59:56.000+08:00| 55.1| -|2020-01-04T23:59:57.000+08:00| 54.2| -|2020-01-04T23:59:58.000+08:00| 56.3| -|2020-01-04T23:59:59.000+08:00| 59.0| -|2020-01-05T00:00:00.000+08:00| 60.0| -|2020-01-05T00:00:01.000+08:00| 60.5| -|2020-01-05T00:00:02.000+08:00| 64.5| -|2020-01-05T00:00:03.000+08:00| 69.0| -|2020-01-05T00:00:04.000+08:00| 64.2| -|2020-01-05T00:00:05.000+08:00| 62.3| -|2020-01-05T00:00:06.000+08:00| 58.0| -|2020-01-05T00:00:07.000+08:00| 58.9| -|2020-01-05T00:00:08.000+08:00| 52.0| -|2020-01-05T00:00:09.000+08:00| 62.3| -|2020-01-05T00:00:10.000+08:00| 61.0| -|2020-01-05T00:00:11.000+08:00| 64.2| -|2020-01-05T00:00:12.000+08:00| 61.8| -|2020-01-05T00:00:13.000+08:00| 64.0| -|2020-01-05T00:00:14.000+08:00| 63.0| -+-----------------------------+------------+ -``` - -SQL for query: - -```sql -select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------+ -| Time|outlier(root.test.s1,"r"="5.0","k"="4","w"="10","s"="5")| -+-----------------------------+--------------------------------------------------------+ -|2020-01-05T00:00:03.000+08:00| 69.0| -+-----------------------------+--------------------------------------------------------+ -|2020-01-05T00:00:08.000+08:00| 52.0| -+-----------------------------+--------------------------------------------------------+ -``` - - -### MasterTrain - -#### Usage - -This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points. - -**Name:** MasterTrain - -**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `p`: The order of the model. -+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule. - -**Output Series:** Output a single series. The type is the same as the input. - -**Installation** -- Install IoTDB from branch `research/master-detector`. -- Run `mvn spotless:apply`. -- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`. -- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`. -- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+------------+--------------+--------------+ -| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| -+-----------------------------+------------+------------+--------------+--------------+ -|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| -|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| -|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| -|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| -|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| -|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| -|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014| -|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| -|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| -|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| -|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| -|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| -|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| -|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| -|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| -|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| -|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| -|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| -|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| -|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| -|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| -|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| -|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| -|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| -+-----------------------------+------------+------------+--------------+--------------+ -``` - -SQL for query: - -```sql -select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------------------------------------------------------------------+ -| Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")| -+-----------------------------+---------------------------------------------------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 0.13656607660463288| -|1970-01-01T08:00:00.002+08:00| 0.8291884323013894| -|1970-01-01T08:00:00.003+08:00| 0.05012816073171693| -|1970-01-01T08:00:00.004+08:00| -0.5495287787485761| -|1970-01-01T08:00:00.005+08:00| 0.03740486307345578| -|1970-01-01T08:00:00.006+08:00| 1.0500132150475212| -|1970-01-01T08:00:00.007+08:00| 0.04583944643116993| -|1970-01-01T08:00:00.008+08:00| -0.07863708480736269| -+-----------------------------+---------------------------------------------------------------------------------------------+ -``` - -### MasterDetect - -#### Usage - -This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain. - -**Name:** MasterDetect - -**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `p`: The order of the model. -+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data. -+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule. -+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule. -+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection. -+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column. - -**Output Series:** Output a single series. The type is the same as the input. - -**Installation** -- Install IoTDB from branch `research/master-detector`. -- Run `mvn spotless:apply`. -- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`. -- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`. -- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+------------+--------------+--------------+--------------------+ -| Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo| root.test.model| -+-----------------------------+------------+------------+--------------+--------------+--------------------+ -|1970-01-01T08:00:00.001+08:00| 39.99982556| 116.327274| 116.3271939| 39.99984748| 0.13656607660463288| -|1970-01-01T08:00:00.002+08:00| 39.99983865| 116.327305| 116.3272269| 39.99984748| 0.8291884323013894| -|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291| 116.3272634| 39.99984769| 0.05012816073171693| -|1970-01-01T08:00:00.004+08:00| 39.99982556| 116.327342| 116.3273015| 39.9998483| -0.5495287787485761| -|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744| 116.327339| 39.99984892| 0.03740486307345578| -|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117| 116.3273759| 39.99984892| 1.0500132150475212| -|1970-01-01T08:00:00.007+08:00| 39.9998259| 116.3274396| 116.3274163| 39.99984953| 0.04583944643116993| -|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668| 116.3274525| 39.99985014|-0.07863708480736269| -|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026| 116.3274915| 39.99985076| null| -|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967| 116.3275235| 39.99985137| null| -|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929| 116.3275611| 39.99985199| null| -|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745| 116.3275974| 39.9998526| null| -|1970-01-01T08:00:00.013+08:00| 39.9998259| 116.3275095| 116.3276338| 39.99985384| null| -|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787| 116.3276695| 39.99985446| null| -|1970-01-01T08:00:00.015+08:00| 39.9998343| 116.3274693| 116.3277045| 39.99985569| null| -|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941| 116.3277389| 39.99985631| null| -|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401| 116.3277747| 39.99985693| null| -|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713| 116.3278041| 39.99985756| null| -|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003| 116.3278379| 39.99985818| null| -|1970-01-01T08:00:00.020+08:00| 39.9998355| 116.3276308| 116.3278723| 39.9998588| null| -|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107| 116.3279026| 39.99985942| null| -|1970-01-01T08:00:00.022+08:00| 39.9998404| 116.3276684| null| null| null| -|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016| null| null| null| -|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284| null| null| null| -|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562| null| null| null| -+-----------------------------+------------+------------+--------------+--------------+--------------------+ -``` - -##### Repairing - -SQL for query: - -```sql -select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------------------------------------+ -| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')| -+-----------------------------+--------------------------------------------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 116.327274| -|1970-01-01T08:00:00.002+08:00| 116.327305| -|1970-01-01T08:00:00.003+08:00| 116.3273291| -|1970-01-01T08:00:00.004+08:00| 116.327342| -|1970-01-01T08:00:00.005+08:00| 116.3273744| -|1970-01-01T08:00:00.006+08:00| 116.3274117| -|1970-01-01T08:00:00.007+08:00| 116.3274396| -|1970-01-01T08:00:00.008+08:00| 116.3274668| -|1970-01-01T08:00:00.009+08:00| 116.3275026| -|1970-01-01T08:00:00.010+08:00| 116.3274967| -|1970-01-01T08:00:00.011+08:00| 116.3274929| -|1970-01-01T08:00:00.012+08:00| 116.3274745| -|1970-01-01T08:00:00.013+08:00| 116.3275095| -|1970-01-01T08:00:00.014+08:00| 116.3274787| -|1970-01-01T08:00:00.015+08:00| 116.3274693| -|1970-01-01T08:00:00.016+08:00| 116.3274941| -|1970-01-01T08:00:00.017+08:00| 116.3275401| -|1970-01-01T08:00:00.018+08:00| 116.3275713| -|1970-01-01T08:00:00.019+08:00| 116.3276003| -|1970-01-01T08:00:00.020+08:00| 116.3276308| -|1970-01-01T08:00:00.021+08:00| 116.3276338| -|1970-01-01T08:00:00.022+08:00| 116.3276684| -|1970-01-01T08:00:00.023+08:00| 116.3277016| -|1970-01-01T08:00:00.024+08:00| 116.3277284| -|1970-01-01T08:00:00.025+08:00| 116.3277562| -+-----------------------------+--------------------------------------------------------------------------------------+ -``` - -##### Anomaly Detection - -SQL for query: - -```sql -select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test -``` - -Output series: - -``` -+-----------------------------+---------------------------------------------------------------------------------------+ -| Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')| -+-----------------------------+---------------------------------------------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| false| -|1970-01-01T08:00:00.002+08:00| false| -|1970-01-01T08:00:00.003+08:00| false| -|1970-01-01T08:00:00.004+08:00| false| -|1970-01-01T08:00:00.005+08:00| true| -|1970-01-01T08:00:00.006+08:00| true| -|1970-01-01T08:00:00.007+08:00| false| -|1970-01-01T08:00:00.008+08:00| false| -|1970-01-01T08:00:00.009+08:00| false| -|1970-01-01T08:00:00.010+08:00| false| -|1970-01-01T08:00:00.011+08:00| false| -|1970-01-01T08:00:00.012+08:00| false| -|1970-01-01T08:00:00.013+08:00| false| -|1970-01-01T08:00:00.014+08:00| true| -|1970-01-01T08:00:00.015+08:00| false| -|1970-01-01T08:00:00.016+08:00| false| -|1970-01-01T08:00:00.017+08:00| false| -|1970-01-01T08:00:00.018+08:00| false| -|1970-01-01T08:00:00.019+08:00| false| -|1970-01-01T08:00:00.020+08:00| false| -|1970-01-01T08:00:00.021+08:00| false| -|1970-01-01T08:00:00.022+08:00| false| -|1970-01-01T08:00:00.023+08:00| false| -|1970-01-01T08:00:00.024+08:00| false| -|1970-01-01T08:00:00.025+08:00| false| -+-----------------------------+---------------------------------------------------------------------------------------+ -``` - - - -## Frequency Domain Analysis - -### Conv - -#### Registration statement - -```sql -create function conv as 'org.apache.iotdb.library.frequency.UDTFConv' -``` - -#### Usage - -This function is used to calculate the convolution, i.e. polynomial multiplication. - -**Name:** CONV - -**Input:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output:** Output a single series. The type is DOUBLE. It is the result of convolution whose timestamps starting from 0 only indicate the order. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 1.0| 7.0| -|1970-01-01T08:00:00.001+08:00| 0.0| 2.0| -|1970-01-01T08:00:00.002+08:00| 1.0| null| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select conv(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------+ -| Time|conv(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+--------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 7.0| -|1970-01-01T08:00:00.001+08:00| 2.0| -|1970-01-01T08:00:00.002+08:00| 7.0| -|1970-01-01T08:00:00.003+08:00| 2.0| -+-----------------------------+--------------------------------------+ -``` - -### Deconv - -#### Registration statement - -```sql -create function deconv as 'org.apache.iotdb.library.frequency.UDTFDeconv' -``` - -#### Usage - -This function is used to calculate the deconvolution, i.e. polynomial division. - -**Name:** DECONV - -**Input:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `result`: The result of deconvolution, which is 'quotient' or 'remainder'. By default, the quotient will be output. - -**Output:** Output a single series. The type is DOUBLE. It is the result of deconvolving the second series from the first series (dividing the first series by the second series) whose timestamps starting from 0 only indicate the order. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - - -##### Calculate the quotient - -When `result` is 'quotient' or the default, this function calculates the quotient of the deconvolution. - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s3|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 8.0| 7.0| -|1970-01-01T08:00:00.001+08:00| 2.0| 2.0| -|1970-01-01T08:00:00.002+08:00| 7.0| null| -|1970-01-01T08:00:00.003+08:00| 2.0| null| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select deconv(s3,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------+ -| Time|deconv(root.test.d2.s3, root.test.d2.s2)| -+-----------------------------+----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 1.0| -|1970-01-01T08:00:00.001+08:00| 0.0| -|1970-01-01T08:00:00.002+08:00| 1.0| -+-----------------------------+----------------------------------------+ -``` - -##### Calculate the remainder - -When `result` is 'remainder', this function calculates the remainder of the deconvolution. - -Input series is the same as above, the SQL for query is shown below: - - -```sql -select deconv(s3,s2,'result'='remainder') from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------------+ -| Time|deconv(root.test.d2.s3, root.test.d2.s2, "result"="remainder")| -+-----------------------------+--------------------------------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 1.0| -|1970-01-01T08:00:00.001+08:00| 0.0| -|1970-01-01T08:00:00.002+08:00| 0.0| -|1970-01-01T08:00:00.003+08:00| 0.0| -+-----------------------------+--------------------------------------------------------------+ -``` - -### DWT - -#### Registration statement - -```sql -create function dwt as 'org.apache.iotdb.library.frequency.UDTFDWT' -``` - -#### Usage - -This function is used to calculate 1d discrete wavelet transform of a numerical series. - -**Name:** DWT - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The type of wavelet. May select 'Haar', 'DB4', 'DB6', 'DB8', where DB means Daubechies. User may offer coefficients of wavelet transform and ignore this parameter. Case ignored. -+ `coef`: Coefficients of wavelet transform. When providing this parameter, use comma ',' to split them, and leave no spaces or other punctuations. -+ `layer`: Times to transform. The number of output vectors equals $layer+1$. Default is 1. - -**Output:** Output a single series. The type is DOUBLE. The length is the same as the input. - -**Note:** The length of input series must be an integer number power of 2. - -#### Examples - - -##### Haar wavelet transform - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.100+08:00| 0.2| -|1970-01-01T08:00:00.200+08:00| 1.5| -|1970-01-01T08:00:00.300+08:00| 1.2| -|1970-01-01T08:00:00.400+08:00| 0.6| -|1970-01-01T08:00:00.500+08:00| 1.7| -|1970-01-01T08:00:00.600+08:00| 0.8| -|1970-01-01T08:00:00.700+08:00| 2.0| -|1970-01-01T08:00:00.800+08:00| 2.5| -|1970-01-01T08:00:00.900+08:00| 2.1| -|1970-01-01T08:00:01.000+08:00| 0.0| -|1970-01-01T08:00:01.100+08:00| 2.0| -|1970-01-01T08:00:01.200+08:00| 1.8| -|1970-01-01T08:00:01.300+08:00| 1.2| -|1970-01-01T08:00:01.400+08:00| 1.0| -|1970-01-01T08:00:01.500+08:00| 1.6| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select dwt(s1,"method"="haar") from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|dwt(root.test.d1.s1, "method"="haar")| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.14142135834465192| -|1970-01-01T08:00:00.100+08:00| 1.909188342921157| -|1970-01-01T08:00:00.200+08:00| 1.6263456473052773| -|1970-01-01T08:00:00.300+08:00| 1.9798989957517026| -|1970-01-01T08:00:00.400+08:00| 3.252691126023161| -|1970-01-01T08:00:00.500+08:00| 1.414213562373095| -|1970-01-01T08:00:00.600+08:00| 2.1213203435596424| -|1970-01-01T08:00:00.700+08:00| 1.8384776479437628| -|1970-01-01T08:00:00.800+08:00| -0.14142135834465192| -|1970-01-01T08:00:00.900+08:00| 0.21213200063848547| -|1970-01-01T08:00:01.000+08:00| -0.7778174761639416| -|1970-01-01T08:00:01.100+08:00| -0.8485281289944873| -|1970-01-01T08:00:01.200+08:00| 0.2828427799095765| -|1970-01-01T08:00:01.300+08:00| -1.414213562373095| -|1970-01-01T08:00:01.400+08:00| 0.42426400127697095| -|1970-01-01T08:00:01.500+08:00| -0.42426408557066786| -+-----------------------------+-------------------------------------+ -``` - -### FFT - -#### Registration statement - -```sql -create function fft as 'org.apache.iotdb.library.frequency.UDTFFFT' -``` - -#### Usage - -This function is used to calculate the fast Fourier transform (FFT) of a numerical series. - -**Name:** FFT - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The type of FFT, which is 'uniform' (by default) or 'nonuniform'. If the value is 'uniform', the timestamps will be ignored and all data points will be regarded as equidistant. Thus, the equidistant fast Fourier transform algorithm will be applied. If the value is 'nonuniform' (TODO), the non-equidistant fast Fourier transform algorithm will be applied based on timestamps. -+ `result`: The result of FFT, which is 'real', 'imag', 'abs' or 'angle', corresponding to the real part, imaginary part, magnitude and phase angle. By default, the magnitude will be output. -+ `compress`: The parameter of compression, which is within (0,1]. It is the reserved energy ratio of lossy compression. By default, there is no compression. - - -**Output:** Output a single series. The type is DOUBLE. The length is the same as the input. The timestamps starting from 0 only indicate the order. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - - -##### Uniform FFT - -With the default `type`, uniform FFT is applied. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 2.902113| -|1970-01-01T08:00:01.000+08:00| 1.1755705| -|1970-01-01T08:00:02.000+08:00| -2.1755705| -|1970-01-01T08:00:03.000+08:00| -1.9021131| -|1970-01-01T08:00:04.000+08:00| 1.0| -|1970-01-01T08:00:05.000+08:00| 1.9021131| -|1970-01-01T08:00:06.000+08:00| 0.1755705| -|1970-01-01T08:00:07.000+08:00| -1.1755705| -|1970-01-01T08:00:08.000+08:00| -0.902113| -|1970-01-01T08:00:09.000+08:00| 0.0| -|1970-01-01T08:00:10.000+08:00| 0.902113| -|1970-01-01T08:00:11.000+08:00| 1.1755705| -|1970-01-01T08:00:12.000+08:00| -0.1755705| -|1970-01-01T08:00:13.000+08:00| -1.9021131| -|1970-01-01T08:00:14.000+08:00| -1.0| -|1970-01-01T08:00:15.000+08:00| 1.9021131| -|1970-01-01T08:00:16.000+08:00| 2.1755705| -|1970-01-01T08:00:17.000+08:00| -1.1755705| -|1970-01-01T08:00:18.000+08:00| -2.902113| -|1970-01-01T08:00:19.000+08:00| 0.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select fft(s1) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------+ -| Time| fft(root.test.d1.s1)| -+-----------------------------+----------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| -|1970-01-01T08:00:00.001+08:00| 1.2727111142703152E-8| -|1970-01-01T08:00:00.002+08:00| 2.385520799101839E-7| -|1970-01-01T08:00:00.003+08:00| 8.723291723972645E-8| -|1970-01-01T08:00:00.004+08:00| 19.999999960195904| -|1970-01-01T08:00:00.005+08:00| 9.999999850988388| -|1970-01-01T08:00:00.006+08:00| 3.2260694930700566E-7| -|1970-01-01T08:00:00.007+08:00| 8.723291605373329E-8| -|1970-01-01T08:00:00.008+08:00| 1.108657103979944E-7| -|1970-01-01T08:00:00.009+08:00| 1.2727110997246171E-8| -|1970-01-01T08:00:00.010+08:00|1.9852334701272664E-23| -|1970-01-01T08:00:00.011+08:00| 1.2727111194499847E-8| -|1970-01-01T08:00:00.012+08:00| 1.108657103979944E-7| -|1970-01-01T08:00:00.013+08:00| 8.723291785769131E-8| -|1970-01-01T08:00:00.014+08:00| 3.226069493070057E-7| -|1970-01-01T08:00:00.015+08:00| 9.999999850988388| -|1970-01-01T08:00:00.016+08:00| 19.999999960195904| -|1970-01-01T08:00:00.017+08:00| 8.723291747109068E-8| -|1970-01-01T08:00:00.018+08:00| 2.3855207991018386E-7| -|1970-01-01T08:00:00.019+08:00| 1.2727112069910878E-8| -+-----------------------------+----------------------+ -``` - -Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, there are peaks in $k=4$ and $k=5$ of the output. - -##### Uniform FFT with Compression - -Input series is the same as above, the SQL for query is shown below: - -```sql -select fft(s1, 'result'='real', 'compress'='0.99'), fft(s1, 'result'='imag','compress'='0.99') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------+----------------------+ -| Time| fft(root.test.d1.s1,| fft(root.test.d1.s1,| -| | "result"="real",| "result"="imag",| -| | "compress"="0.99")| "compress"="0.99")| -+-----------------------------+----------------------+----------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| 0.0| -|1970-01-01T08:00:00.001+08:00| -3.932894010461041E-9| 1.2104201863039066E-8| -|1970-01-01T08:00:00.002+08:00|-1.4021739447490164E-7| 1.9299268669082926E-7| -|1970-01-01T08:00:00.003+08:00| -7.057291240286645E-8| 5.127422242345858E-8| -|1970-01-01T08:00:00.004+08:00| 19.021130288047125| -6.180339875198807| -|1970-01-01T08:00:00.005+08:00| 9.999999850988388| 3.501852745067114E-16| -|1970-01-01T08:00:00.019+08:00| -3.932894898639461E-9|-1.2104202549376264E-8| -+-----------------------------+----------------------+----------------------+ -``` - -Note: Based on the conjugation of the Fourier transform result, only the first half of the compression result is reserved. -According to the given parameter, data points are reserved from low frequency to high frequency until the reserved energy ratio exceeds it. -The last data point is reserved to indicate the length of the series. - -### HighPass - -#### Registration statement - -```sql -create function highpass as 'org.apache.iotdb.library.frequency.UDTFHighPass' -``` - -#### Usage - -This function performs low-pass filtering on the input series and extracts components above the cutoff frequency. -The timestamps of input will be ignored and all data points will be regarded as equidistant. - -**Name:** HIGHPASS - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `wpass`: The normalized cutoff frequency which values (0,1). This parameter cannot be lacked. - -**Output:** Output a single series. The type is DOUBLE. It is the input after filtering. The length and timestamps of output are the same as the input. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 2.902113| -|1970-01-01T08:00:01.000+08:00| 1.1755705| -|1970-01-01T08:00:02.000+08:00| -2.1755705| -|1970-01-01T08:00:03.000+08:00| -1.9021131| -|1970-01-01T08:00:04.000+08:00| 1.0| -|1970-01-01T08:00:05.000+08:00| 1.9021131| -|1970-01-01T08:00:06.000+08:00| 0.1755705| -|1970-01-01T08:00:07.000+08:00| -1.1755705| -|1970-01-01T08:00:08.000+08:00| -0.902113| -|1970-01-01T08:00:09.000+08:00| 0.0| -|1970-01-01T08:00:10.000+08:00| 0.902113| -|1970-01-01T08:00:11.000+08:00| 1.1755705| -|1970-01-01T08:00:12.000+08:00| -0.1755705| -|1970-01-01T08:00:13.000+08:00| -1.9021131| -|1970-01-01T08:00:14.000+08:00| -1.0| -|1970-01-01T08:00:15.000+08:00| 1.9021131| -|1970-01-01T08:00:16.000+08:00| 2.1755705| -|1970-01-01T08:00:17.000+08:00| -1.1755705| -|1970-01-01T08:00:18.000+08:00| -2.902113| -|1970-01-01T08:00:19.000+08:00| 0.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select highpass(s1,'wpass'='0.45') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-----------------------------------------+ -| Time|highpass(root.test.d1.s1, "wpass"="0.45")| -+-----------------------------+-----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.9999999534830373| -|1970-01-01T08:00:01.000+08:00| 1.7462829277628608E-8| -|1970-01-01T08:00:02.000+08:00| -0.9999999593178128| -|1970-01-01T08:00:03.000+08:00| -4.1115269056426626E-8| -|1970-01-01T08:00:04.000+08:00| 0.9999999925494194| -|1970-01-01T08:00:05.000+08:00| 3.328126513330016E-8| -|1970-01-01T08:00:06.000+08:00| -1.0000000183304454| -|1970-01-01T08:00:07.000+08:00| 6.260191433311374E-10| -|1970-01-01T08:00:08.000+08:00| 1.0000000018134796| -|1970-01-01T08:00:09.000+08:00| -3.097210911744423E-17| -|1970-01-01T08:00:10.000+08:00| -1.0000000018134794| -|1970-01-01T08:00:11.000+08:00| -6.260191627862097E-10| -|1970-01-01T08:00:12.000+08:00| 1.0000000183304454| -|1970-01-01T08:00:13.000+08:00| -3.328126501424346E-8| -|1970-01-01T08:00:14.000+08:00| -0.9999999925494196| -|1970-01-01T08:00:15.000+08:00| 4.111526915498874E-8| -|1970-01-01T08:00:16.000+08:00| 0.9999999593178128| -|1970-01-01T08:00:17.000+08:00| -1.7462829341296528E-8| -|1970-01-01T08:00:18.000+08:00| -0.9999999534830369| -|1970-01-01T08:00:19.000+08:00| -1.035237222742873E-16| -+-----------------------------+-----------------------------------------+ -``` - -Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, the output is $y=sin(2\pi t/4)$ after high-pass filtering. - -### IFFT - -#### Registration statement - -```sql -create function ifft as 'org.apache.iotdb.library.frequency.UDTFIFFT' -``` - -#### Usage - -This function treats the two input series as the real and imaginary part of a complex series, performs an inverse fast Fourier transform (IFFT), and outputs the real part of the result. -For the input format, please refer to the output format of `FFT` function. -Moreover, the compressed output of `FFT` function is also supported. - -**Name:** IFFT - -**Input:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `start`: The start time of the output series with the format 'yyyy-MM-dd HH:mm:ss'. By default, it is '1970-01-01 08:00:00'. -+ `interval`: The interval of the output series, which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, it is 1s. - -**Output:** Output a single series. The type is DOUBLE. It is strictly equispaced. The values are the results of IFFT. - -**Note:** If a row contains null points or `NaN`, it will be ignored. - -#### Examples - - -Input series: - -``` -+-----------------------------+----------------------+----------------------+ -| Time| root.test.d1.re| root.test.d1.im| -+-----------------------------+----------------------+----------------------+ -|1970-01-01T08:00:00.000+08:00| 0.0| 0.0| -|1970-01-01T08:00:00.001+08:00| -3.932894010461041E-9| 1.2104201863039066E-8| -|1970-01-01T08:00:00.002+08:00|-1.4021739447490164E-7| 1.9299268669082926E-7| -|1970-01-01T08:00:00.003+08:00| -7.057291240286645E-8| 5.127422242345858E-8| -|1970-01-01T08:00:00.004+08:00| 19.021130288047125| -6.180339875198807| -|1970-01-01T08:00:00.005+08:00| 9.999999850988388| 3.501852745067114E-16| -|1970-01-01T08:00:00.019+08:00| -3.932894898639461E-9|-1.2104202549376264E-8| -+-----------------------------+----------------------+----------------------+ -``` - - -SQL for query: - -```sql -select ifft(re, im, 'interval'='1m', 'start'='2021-01-01 00:00:00') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------------+ -| Time|ifft(root.test.d1.re, root.test.d1.im, "interval"="1m",| -| | "start"="2021-01-01 00:00:00")| -+-----------------------------+-------------------------------------------------------+ -|2021-01-01T00:00:00.000+08:00| 2.902112992431231| -|2021-01-01T00:01:00.000+08:00| 1.1755704705132448| -|2021-01-01T00:02:00.000+08:00| -2.175570513757101| -|2021-01-01T00:03:00.000+08:00| -1.9021130389094498| -|2021-01-01T00:04:00.000+08:00| 0.9999999925494194| -|2021-01-01T00:05:00.000+08:00| 1.902113046743454| -|2021-01-01T00:06:00.000+08:00| 0.17557053610884188| -|2021-01-01T00:07:00.000+08:00| -1.1755704886020932| -|2021-01-01T00:08:00.000+08:00| -0.9021130371347148| -|2021-01-01T00:09:00.000+08:00| 3.552713678800501E-16| -|2021-01-01T00:10:00.000+08:00| 0.9021130371347154| -|2021-01-01T00:11:00.000+08:00| 1.1755704886020932| -|2021-01-01T00:12:00.000+08:00| -0.17557053610884144| -|2021-01-01T00:13:00.000+08:00| -1.902113046743454| -|2021-01-01T00:14:00.000+08:00| -0.9999999925494196| -|2021-01-01T00:15:00.000+08:00| 1.9021130389094498| -|2021-01-01T00:16:00.000+08:00| 2.1755705137571004| -|2021-01-01T00:17:00.000+08:00| -1.1755704705132448| -|2021-01-01T00:18:00.000+08:00| -2.902112992431231| -|2021-01-01T00:19:00.000+08:00| -3.552713678800501E-16| -+-----------------------------+-------------------------------------------------------+ -``` - -### LowPass - -#### Registration statement - -```sql -create function lowpass as 'org.apache.iotdb.library.frequency.UDTFLowPass' -``` - -#### Usage - -This function performs low-pass filtering on the input series and extracts components below the cutoff frequency. -The timestamps of input will be ignored and all data points will be regarded as equidistant. - -**Name:** LOWPASS - -**Input:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `wpass`: The normalized cutoff frequency which values (0,1). This parameter cannot be lacked. - -**Output:** Output a single series. The type is DOUBLE. It is the input after filtering. The length and timestamps of output are the same as the input. - -**Note:** `NaN` in the input series will be ignored. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s1| -+-----------------------------+---------------+ -|1970-01-01T08:00:00.000+08:00| 2.902113| -|1970-01-01T08:00:01.000+08:00| 1.1755705| -|1970-01-01T08:00:02.000+08:00| -2.1755705| -|1970-01-01T08:00:03.000+08:00| -1.9021131| -|1970-01-01T08:00:04.000+08:00| 1.0| -|1970-01-01T08:00:05.000+08:00| 1.9021131| -|1970-01-01T08:00:06.000+08:00| 0.1755705| -|1970-01-01T08:00:07.000+08:00| -1.1755705| -|1970-01-01T08:00:08.000+08:00| -0.902113| -|1970-01-01T08:00:09.000+08:00| 0.0| -|1970-01-01T08:00:10.000+08:00| 0.902113| -|1970-01-01T08:00:11.000+08:00| 1.1755705| -|1970-01-01T08:00:12.000+08:00| -0.1755705| -|1970-01-01T08:00:13.000+08:00| -1.9021131| -|1970-01-01T08:00:14.000+08:00| -1.0| -|1970-01-01T08:00:15.000+08:00| 1.9021131| -|1970-01-01T08:00:16.000+08:00| 2.1755705| -|1970-01-01T08:00:17.000+08:00| -1.1755705| -|1970-01-01T08:00:18.000+08:00| -2.902113| -|1970-01-01T08:00:19.000+08:00| 0.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select lowpass(s1,'wpass'='0.45') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+----------------------------------------+ -| Time|lowpass(root.test.d1.s1, "wpass"="0.45")| -+-----------------------------+----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 1.9021130073323922| -|1970-01-01T08:00:01.000+08:00| 1.1755704705132448| -|1970-01-01T08:00:02.000+08:00| -1.1755705286582614| -|1970-01-01T08:00:03.000+08:00| -1.9021130389094498| -|1970-01-01T08:00:04.000+08:00| 7.450580419288145E-9| -|1970-01-01T08:00:05.000+08:00| 1.902113046743454| -|1970-01-01T08:00:06.000+08:00| 1.1755705212076808| -|1970-01-01T08:00:07.000+08:00| -1.1755704886020932| -|1970-01-01T08:00:08.000+08:00| -1.9021130222335536| -|1970-01-01T08:00:09.000+08:00| 3.552713678800501E-16| -|1970-01-01T08:00:10.000+08:00| 1.9021130222335536| -|1970-01-01T08:00:11.000+08:00| 1.1755704886020932| -|1970-01-01T08:00:12.000+08:00| -1.1755705212076801| -|1970-01-01T08:00:13.000+08:00| -1.902113046743454| -|1970-01-01T08:00:14.000+08:00| -7.45058112983088E-9| -|1970-01-01T08:00:15.000+08:00| 1.9021130389094498| -|1970-01-01T08:00:16.000+08:00| 1.1755705286582616| -|1970-01-01T08:00:17.000+08:00| -1.1755704705132448| -|1970-01-01T08:00:18.000+08:00| -1.9021130073323924| -|1970-01-01T08:00:19.000+08:00| -2.664535259100376E-16| -+-----------------------------+----------------------------------------+ -``` - -Note: The input is $y=sin(2\pi t/4)+2sin(2\pi t/5)$ with a length of 20. Thus, the output is $y=2sin(2\pi t/5)$ after low-pass filtering. - - - -## Data Matching - -### Cov - -#### Registration statement - -```sql -create function cov as 'org.apache.iotdb.library.dmatch.UDAFCov' -``` - -#### Usage - -This function is used to calculate the population covariance. - -**Name:** COV - -**Input Series:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the population covariance. - -**Note:** - -+ If a row contains missing points, null points or `NaN`, it will be ignored; -+ If all rows are ignored, `NaN` will be output. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| 101.0| -|2020-01-01T00:00:03.000+08:00| 101.0| null| -|2020-01-01T00:00:04.000+08:00| 102.0| 101.0| -|2020-01-01T00:00:06.000+08:00| 104.0| 102.0| -|2020-01-01T00:00:08.000+08:00| 126.0| 102.0| -|2020-01-01T00:00:10.000+08:00| 108.0| 103.0| -|2020-01-01T00:00:12.000+08:00| null| 103.0| -|2020-01-01T00:00:14.000+08:00| 112.0| 104.0| -|2020-01-01T00:00:15.000+08:00| 113.0| null| -|2020-01-01T00:00:16.000+08:00| 114.0| 104.0| -|2020-01-01T00:00:18.000+08:00| 116.0| 105.0| -|2020-01-01T00:00:20.000+08:00| 118.0| 105.0| -|2020-01-01T00:00:22.000+08:00| 100.0| 106.0| -|2020-01-01T00:00:26.000+08:00| 124.0| 108.0| -|2020-01-01T00:00:28.000+08:00| 126.0| 108.0| -|2020-01-01T00:00:30.000+08:00| NaN| 108.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select cov(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|cov(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 12.291666666666666| -+-----------------------------+-------------------------------------+ -``` - -### DTW - -#### Registration statement - -```sql -create function dtw as 'org.apache.iotdb.library.dmatch.UDAFDtw' -``` - -#### Usage - -This function is used to calculate the DTW distance between two input series. - -**Name:** DTW - -**Input Series:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the DTW distance. - -**Note:** - -+ If a row contains missing points, null points or `NaN`, it will be ignored; -+ If all rows are ignored, `0` will be output. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|1970-01-01T08:00:00.001+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.002+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.003+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.004+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.005+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.006+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.007+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.008+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.009+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.010+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.011+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.012+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.013+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.014+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.015+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.016+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.017+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.018+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.019+08:00| 1.0| 2.0| -|1970-01-01T08:00:00.020+08:00| 1.0| 2.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select dtw(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------+ -| Time|dtw(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+-------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 20.0| -+-----------------------------+-------------------------------------+ -``` - -### Pearson - -#### Registration statement - -```sql -create function pearson as 'org.apache.iotdb.library.dmatch.UDAFPearson' -``` - -#### Usage - -This function is used to calculate the Pearson Correlation Coefficient. - -**Name:** PEARSON - -**Input Series:** Only support two input series. The types are both INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the Pearson Correlation Coefficient. - -**Note:** - -+ If a row contains missing points, null points or `NaN`, it will be ignored; -+ If all rows are ignored, `NaN` will be output. - - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d2.s1|root.test.d2.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| 101.0| -|2020-01-01T00:00:03.000+08:00| 101.0| null| -|2020-01-01T00:00:04.000+08:00| 102.0| 101.0| -|2020-01-01T00:00:06.000+08:00| 104.0| 102.0| -|2020-01-01T00:00:08.000+08:00| 126.0| 102.0| -|2020-01-01T00:00:10.000+08:00| 108.0| 103.0| -|2020-01-01T00:00:12.000+08:00| null| 103.0| -|2020-01-01T00:00:14.000+08:00| 112.0| 104.0| -|2020-01-01T00:00:15.000+08:00| 113.0| null| -|2020-01-01T00:00:16.000+08:00| 114.0| 104.0| -|2020-01-01T00:00:18.000+08:00| 116.0| 105.0| -|2020-01-01T00:00:20.000+08:00| 118.0| 105.0| -|2020-01-01T00:00:22.000+08:00| 100.0| 106.0| -|2020-01-01T00:00:26.000+08:00| 124.0| 108.0| -|2020-01-01T00:00:28.000+08:00| 126.0| 108.0| -|2020-01-01T00:00:30.000+08:00| NaN| 108.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select pearson(s1,s2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-----------------------------------------+ -| Time|pearson(root.test.d2.s1, root.test.d2.s2)| -+-----------------------------+-----------------------------------------+ -|1970-01-01T08:00:00.000+08:00| 0.5630881927754872| -+-----------------------------+-----------------------------------------+ -``` - -### PtnSym - -#### Registration statement - -```sql -create function ptnsym as 'org.apache.iotdb.library.dmatch.UDTFPtnSym' -``` - -#### Usage - -This function is used to find all symmetric subseries in the input whose degree of symmetry is less than the threshold. -The degree of symmetry is calculated by DTW. -The smaller the degree, the more symmetrical the series is. - -**Name:** PATTERNSYMMETRIC - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE - -**Parameter:** - -+ `window`: The length of the symmetric subseries. It's a positive integer and the default value is 10. -+ `threshold`: The threshold of the degree of symmetry. It's non-negative. Only the subseries whose degree of symmetry is below it will be output. By default, all subseries will be output. - - -**Output Series:** Output a single series. The type is DOUBLE. Each data point in the output series corresponds to a symmetric subseries. The output timestamp is the starting timestamp of the subseries and the output value is the degree of symmetry. - -#### Example - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d1.s4| -+-----------------------------+---------------+ -|2021-01-01T12:00:00.000+08:00| 1.0| -|2021-01-01T12:00:01.000+08:00| 2.0| -|2021-01-01T12:00:02.000+08:00| 3.0| -|2021-01-01T12:00:03.000+08:00| 2.0| -|2021-01-01T12:00:04.000+08:00| 1.0| -|2021-01-01T12:00:05.000+08:00| 1.0| -|2021-01-01T12:00:06.000+08:00| 1.0| -|2021-01-01T12:00:07.000+08:00| 1.0| -|2021-01-01T12:00:08.000+08:00| 2.0| -|2021-01-01T12:00:09.000+08:00| 3.0| -|2021-01-01T12:00:10.000+08:00| 2.0| -|2021-01-01T12:00:11.000+08:00| 1.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select ptnsym(s4, 'window'='5', 'threshold'='0') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|ptnsym(root.test.d1.s4, "window"="5", "threshold"="0")| -+-----------------------------+------------------------------------------------------+ -|2021-01-01T12:00:00.000+08:00| 0.0| -|2021-01-01T12:00:07.000+08:00| 0.0| -+-----------------------------+------------------------------------------------------+ -``` - -### XCorr - -#### Registration statement - -```sql -create function xcorr as 'org.apache.iotdb.library.dmatch.UDTFXCorr' -``` - -#### Usage - -This function is used to calculate the cross correlation function of given two time series. -For discrete time series, cross correlation is given by -$$CR(n) = \frac{1}{N} \sum_{m=1}^N S_1[m]S_2[m+n]$$ -which represent the similarities between two series with different index shifts. - -**Name:** XCORR - -**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Output Series:** Output a single series with DOUBLE as datatype. -There are $2N-1$ data points in the series, the center of which represents the cross correlation -calculated with pre-aligned series(that is $CR(0)$ in the formula above), -and the previous(or post) values represent those with shifting the latter series forward(or backward otherwise) -until the two series are no longer overlapped(not included). -In short, the values of output series are given by(index starts from 1) -$$OS[i] = CR(-N+i) = \frac{1}{N} \sum_{m=1}^{i} S_1[m]S_2[N-i+m],\ if\ i <= N$$ -$$OS[i] = CR(i-N) = \frac{1}{N} \sum_{m=1}^{2N-i} S_1[i-N+m]S_2[m],\ if\ i > N$$ - -**Note:** - -+ `null` and `NaN` values in the input series will be ignored and treated as 0. - -#### Examples - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:01.000+08:00| null| 6| -|2020-01-01T00:00:02.000+08:00| 2| 7| -|2020-01-01T00:00:03.000+08:00| 3| NaN| -|2020-01-01T00:00:04.000+08:00| 4| 9| -|2020-01-01T00:00:05.000+08:00| 5| 10| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 -``` - -Output series: - -``` -+-----------------------------+---------------------------------------+ -| Time|xcorr(root.test.d1.s1, root.test.d1.s2)| -+-----------------------------+---------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 0.0| -|1970-01-01T08:00:00.002+08:00| 4.0| -|1970-01-01T08:00:00.003+08:00| 9.6| -|1970-01-01T08:00:00.004+08:00| 13.4| -|1970-01-01T08:00:00.005+08:00| 20.0| -|1970-01-01T08:00:00.006+08:00| 15.6| -|1970-01-01T08:00:00.007+08:00| 9.2| -|1970-01-01T08:00:00.008+08:00| 11.8| -|1970-01-01T08:00:00.009+08:00| 6.0| -+-----------------------------+---------------------------------------+ -``` - - - -## Data Repairing - -### TimestampRepair - -#### Registration statement - -```sql -create function timestamprepair as 'org.apache.iotdb.library.drepair.UDTFTimestampRepair' -``` - -#### Usage - -This function is used for timestamp repair. -According to the given standard time interval, -the method of minimizing the repair cost is adopted. -By fine-tuning the timestamps, -the original data with unstable timestamp interval is repaired to strictly equispaced data. -If no standard time interval is given, -this function will use the **median**, **mode** or **cluster** of the time interval to estimate the standard time interval. - -**Name:** TIMESTAMPREPAIR - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `interval`: The standard time interval whose unit is millisecond. It is a positive integer. By default, it will be estimated according to the given method. -+ `method`: The method to estimate the standard time interval, which is 'median', 'mode' or 'cluster'. This parameter is only valid when `interval` is not given. By default, median will be used. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -#### Examples - -##### Manually Specify the Standard Time Interval - -When `interval` is given, this function repairs according to the given standard time interval. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2021-07-01T12:00:00.000+08:00| 1.0| -|2021-07-01T12:00:10.000+08:00| 2.0| -|2021-07-01T12:00:19.000+08:00| 3.0| -|2021-07-01T12:00:30.000+08:00| 4.0| -|2021-07-01T12:00:40.000+08:00| 5.0| -|2021-07-01T12:00:50.000+08:00| 6.0| -|2021-07-01T12:01:01.000+08:00| 7.0| -|2021-07-01T12:01:11.000+08:00| 8.0| -|2021-07-01T12:01:21.000+08:00| 9.0| -|2021-07-01T12:01:31.000+08:00| 10.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select timestamprepair(s1,'interval'='10000') from root.test.d2 -``` - -Output series: - - -``` -+-----------------------------+----------------------------------------------------+ -| Time|timestamprepair(root.test.d2.s1, "interval"="10000")| -+-----------------------------+----------------------------------------------------+ -|2021-07-01T12:00:00.000+08:00| 1.0| -|2021-07-01T12:00:10.000+08:00| 2.0| -|2021-07-01T12:00:20.000+08:00| 3.0| -|2021-07-01T12:00:30.000+08:00| 4.0| -|2021-07-01T12:00:40.000+08:00| 5.0| -|2021-07-01T12:00:50.000+08:00| 6.0| -|2021-07-01T12:01:00.000+08:00| 7.0| -|2021-07-01T12:01:10.000+08:00| 8.0| -|2021-07-01T12:01:20.000+08:00| 9.0| -|2021-07-01T12:01:30.000+08:00| 10.0| -+-----------------------------+----------------------------------------------------+ -``` - -##### Automatically Estimate the Standard Time Interval - -When `interval` is default, this function estimates the standard time interval. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select timestamprepair(s1) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------+ -| Time|timestamprepair(root.test.d2.s1)| -+-----------------------------+--------------------------------+ -|2021-07-01T12:00:00.000+08:00| 1.0| -|2021-07-01T12:00:10.000+08:00| 2.0| -|2021-07-01T12:00:20.000+08:00| 3.0| -|2021-07-01T12:00:30.000+08:00| 4.0| -|2021-07-01T12:00:40.000+08:00| 5.0| -|2021-07-01T12:00:50.000+08:00| 6.0| -|2021-07-01T12:01:00.000+08:00| 7.0| -|2021-07-01T12:01:10.000+08:00| 8.0| -|2021-07-01T12:01:20.000+08:00| 9.0| -|2021-07-01T12:01:30.000+08:00| 10.0| -+-----------------------------+--------------------------------+ -``` - -### ValueFill - -#### Registration statement - -```sql -create function valuefill as 'org.apache.iotdb.library.drepair.UDTFValueFill' -``` - -#### Usage - -This function is used to impute time series. Several methods are supported. - -**Name**: ValueFill -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: {"mean", "previous", "linear", "likelihood", "AR", "MA", "SCREEN"}, default "linear". - Method to use for imputation in series. "mean": use global mean value to fill holes; "previous": propagate last valid observation forward to next valid. "linear": simplest interpolation method; "likelihood":Maximum likelihood estimation based on the normal distribution of speed; "AR": auto regression; "MA": moving average; "SCREEN": speed constraint. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -**Note:** AR method use AR(1) model. Input value should be auto-correlated, or the function would output a single point (0, 0.0). - -#### Examples - -##### Fill with linear - -When `method` is "linear" or the default, Screen method is used to impute. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| NaN| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| NaN| -|2020-01-01T00:00:22.000+08:00| NaN| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select valuefill(s1) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-----------------------+ -| Time|valuefill(root.test.d2)| -+-----------------------------+-----------------------+ -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 108.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.7| -|2020-01-01T00:00:22.000+08:00| 121.3| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+-----------------------+ -``` - -##### Previous Fill - -When `method` is "previous", previous method is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select valuefill(s1,"method"="previous") from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------+ -| Time|valuefill(root.test.d2,"method"="previous")| -+-----------------------------+-------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| NaN| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 110.5| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 116.0| -|2020-01-01T00:00:22.000+08:00| 116.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+-------------------------------------------+ -``` - -### ValueRepair - -#### Registration statement - -```sql -create function valuerepair as 'org.apache.iotdb.library.drepair.UDTFValueRepair' -``` - -#### Usage - -This function is used to repair the value of the time series. -Currently, two methods are supported: -**Screen** is a method based on speed threshold, which makes all speeds meet the threshold requirements under the premise of minimum changes; -**LsGreedy** is a method based on speed change likelihood, which models speed changes as Gaussian distribution, and uses a greedy algorithm to maximize the likelihood. - - -**Name:** VALUEREPAIR - -**Input Series:** Only support a single input series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The method used to repair, which is 'Screen' or 'LsGreedy'. By default, Screen is used. -+ `minSpeed`: This parameter is only valid with Screen. It is the speed threshold. Speeds below it will be regarded as outliers. By default, it is the median minus 3 times of median absolute deviation. -+ `maxSpeed`: This parameter is only valid with Screen. It is the speed threshold. Speeds above it will be regarded as outliers. By default, it is the median plus 3 times of median absolute deviation. -+ `center`: This parameter is only valid with LsGreedy. It is the center of the Gaussian distribution of speed changes. By default, it is 0. -+ `sigma`: This parameter is only valid with LsGreedy. It is the standard deviation of the Gaussian distribution of speed changes. By default, it is the median absolute deviation. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -**Note:** `NaN` will be filled with linear interpolation before repairing. - -#### Examples - -##### Repair with Screen - -When `method` is 'Screen' or the default, Screen method is used. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 126.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 100.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| NaN| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select valuerepair(s1) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+----------------------------+ -| Time|valuerepair(root.test.d2.s1)| -+-----------------------------+----------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 106.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+----------------------------+ -``` - -##### Repair with LsGreedy - -When `method` is 'LsGreedy', LsGreedy method is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select valuerepair(s1,'method'='LsGreedy') from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------+ -| Time|valuerepair(root.test.d2.s1, "method"="LsGreedy")| -+-----------------------------+-------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:03.000+08:00| 101.0| -|2020-01-01T00:00:04.000+08:00| 102.0| -|2020-01-01T00:00:06.000+08:00| 104.0| -|2020-01-01T00:00:08.000+08:00| 106.0| -|2020-01-01T00:00:10.000+08:00| 108.0| -|2020-01-01T00:00:14.000+08:00| 112.0| -|2020-01-01T00:00:15.000+08:00| 113.0| -|2020-01-01T00:00:16.000+08:00| 114.0| -|2020-01-01T00:00:18.000+08:00| 116.0| -|2020-01-01T00:00:20.000+08:00| 118.0| -|2020-01-01T00:00:22.000+08:00| 120.0| -|2020-01-01T00:00:26.000+08:00| 124.0| -|2020-01-01T00:00:28.000+08:00| 126.0| -|2020-01-01T00:00:30.000+08:00| 128.0| -+-----------------------------+-------------------------------------------------+ -``` - -### MasterRepair - -#### Usage - -This function is used to clean time series with master data. - -**Name**: MasterRepair -**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences. -+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows. -+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data. -+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -#### Examples - -Input series: - -``` -+-----------------------------+------------+------------+------------+------------+------------+------------+ -| Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3| -+-----------------------------+------------+------------+------------+------------+------------+------------+ -|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195| 1704| 1154.55| 0.195| -|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193| 1702| 1152.30| 0.193| -|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192| 1702| 1148.65| 0.192| -|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194| 1701| 1145.20| 0.194| -|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195| 1703| 1150.55| 0.195| -|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193| 1704| 1151.55| 0.193| -|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194| 1705| 1153.55| 0.194| -|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190| 1706| 1152.30| 0.190| -+-----------------------------+------------+------------+------------+------------+------------+------------+ -``` - -SQL for query: - -```sql -select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test -``` - -Output series: - - -``` -+-----------------------------+-------------------------------------------------------------------------------------------+ -| Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)| -+-----------------------------+-------------------------------------------------------------------------------------------+ -|2021-07-01T12:00:01.000+08:00| 1704| -|2021-07-01T12:00:02.000+08:00| 1702| -|2021-07-01T12:00:03.000+08:00| 1702| -|2021-07-01T12:00:04.000+08:00| 1701| -|2021-07-01T12:00:07.000+08:00| 1703| -|2021-07-01T12:00:08.000+08:00| 1704| -|2021-07-01T12:01:09.000+08:00| 1705| -|2021-07-01T12:01:10.000+08:00| 1706| -+-----------------------------+-------------------------------------------------------------------------------------------+ -``` - -### SeasonalRepair - -#### Usage -This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average; **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median. - -**Name:** SEASONALREPAIR - -**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used. -+ `period`: It is the period of the time series. -+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9. -+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10. - -**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing. - -**Note:** `NaN` will be filled with linear interpolation before repairing. - -#### Examples - -##### Repair with Classical - -When `method` is 'Classical' or default value, classical decomposition method is used. - -Input series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d2.s1| -+-----------------------------+---------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:04.000+08:00| 120.0| -|2020-01-01T00:00:06.000+08:00| 80.0| -|2020-01-01T00:00:08.000+08:00| 100.5| -|2020-01-01T00:00:10.000+08:00| 119.5| -|2020-01-01T00:00:12.000+08:00| 101.0| -|2020-01-01T00:00:14.000+08:00| 99.5| -|2020-01-01T00:00:16.000+08:00| 119.0| -|2020-01-01T00:00:18.000+08:00| 80.5| -|2020-01-01T00:00:20.000+08:00| 99.0| -|2020-01-01T00:00:22.000+08:00| 121.0| -|2020-01-01T00:00:24.000+08:00| 79.5| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------+ -| Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)| -+-----------------------------+--------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:04.000+08:00| 120.0| -|2020-01-01T00:00:06.000+08:00| 80.0| -|2020-01-01T00:00:08.000+08:00| 100.5| -|2020-01-01T00:00:10.000+08:00| 119.5| -|2020-01-01T00:00:12.000+08:00| 87.0| -|2020-01-01T00:00:14.000+08:00| 99.5| -|2020-01-01T00:00:16.000+08:00| 119.0| -|2020-01-01T00:00:18.000+08:00| 80.5| -|2020-01-01T00:00:20.000+08:00| 99.0| -|2020-01-01T00:00:22.000+08:00| 121.0| -|2020-01-01T00:00:24.000+08:00| 79.5| -+-----------------------------+--------------------------------------------------+ -``` - -##### Repair with Improved -When `method` is 'Improved', improved decomposition method is used. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 -``` - -Output series: - -``` -+-----------------------------+-------------------------------------------------------------+ -| Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)| -+-----------------------------+-------------------------------------------------------------+ -|2020-01-01T00:00:02.000+08:00| 100.0| -|2020-01-01T00:00:04.000+08:00| 120.0| -|2020-01-01T00:00:06.000+08:00| 80.0| -|2020-01-01T00:00:08.000+08:00| 100.5| -|2020-01-01T00:00:10.000+08:00| 119.5| -|2020-01-01T00:00:12.000+08:00| 81.5| -|2020-01-01T00:00:14.000+08:00| 99.5| -|2020-01-01T00:00:16.000+08:00| 119.0| -|2020-01-01T00:00:18.000+08:00| 80.5| -|2020-01-01T00:00:20.000+08:00| 99.0| -|2020-01-01T00:00:22.000+08:00| 121.0| -|2020-01-01T00:00:24.000+08:00| 79.5| -+-----------------------------+-------------------------------------------------------------+ -``` - - - -## Series Discovery - -### ConsecutiveSequences - -#### Registration statement - -```sql -create function consecutivesequences as 'org.apache.iotdb.library.series.UDTFConsecutiveSequences' -``` - -#### Usage - -This function is used to find locally longest consecutive subsequences in strictly equispaced multidimensional data. - -Strictly equispaced data is the data whose time intervals are strictly equal. Missing data, including missing rows and missing values, is allowed in it, while data redundancy and timestamp drift is not allowed. - -Consecutive subsequence is the subsequence that is strictly equispaced with the standard time interval without any missing data. If a consecutive subsequence is not a proper subsequence of any consecutive subsequence, it is locally longest. - -**Name:** CONSECUTIVESEQUENCES - -**Input Series:** Support multiple input series. The type is arbitrary but the data is strictly equispaced. - -**Parameters:** - -+ `gap`: The standard time interval which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, it will be estimated by the mode of time intervals. - -**Output Series:** Output a single series. The type is INT32. Each data point in the output series corresponds to a locally longest consecutive subsequence. The output timestamp is the starting timestamp of the subsequence and the output value is the number of data points in the subsequence. - -**Note:** For input series that is not strictly equispaced, there is no guarantee on the output. - -#### Examples - -##### Manually Specify the Standard Time Interval - -It's able to manually specify the standard time interval by the parameter `gap`. It's notable that false parameter leads to false output. - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:05:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:10:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:20:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:25:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:30:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:35:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:40:00.000+08:00| 1.0| null| -|2020-01-01T00:45:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:50:00.000+08:00| 1.0| 1.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select consecutivesequences(s1,s2,'gap'='5m') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------------------+ -| Time|consecutivesequences(root.test.d1.s1, root.test.d1.s2, "gap"="5m")| -+-----------------------------+------------------------------------------------------------------+ -|2020-01-01T00:00:00.000+08:00| 3| -|2020-01-01T00:20:00.000+08:00| 4| -|2020-01-01T00:45:00.000+08:00| 2| -+-----------------------------+------------------------------------------------------------------+ -``` - - -##### Automatically Estimate the Standard Time Interval - -When `gap` is default, this function estimates the standard time interval by the mode of time intervals and gets the same results. Therefore, this usage is more recommended. - -Input series is the same as above, the SQL for query is shown below: - -```sql -select consecutivesequences(s1,s2) from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+------------------------------------------------------+ -| Time|consecutivesequences(root.test.d1.s1, root.test.d1.s2)| -+-----------------------------+------------------------------------------------------+ -|2020-01-01T00:00:00.000+08:00| 3| -|2020-01-01T00:20:00.000+08:00| 4| -|2020-01-01T00:45:00.000+08:00| 2| -+-----------------------------+------------------------------------------------------+ -``` - -### ConsecutiveWindows - -#### Registration statement - -```sql -create function consecutivewindows as 'org.apache.iotdb.library.series.UDTFConsecutiveWindows' -``` - -#### Usage - -This function is used to find consecutive windows of specified length in strictly equispaced multidimensional data. - -Strictly equispaced data is the data whose time intervals are strictly equal. Missing data, including missing rows and missing values, is allowed in it, while data redundancy and timestamp drift is not allowed. - -Consecutive window is the subsequence that is strictly equispaced with the standard time interval without any missing data. - -**Name:** CONSECUTIVEWINDOWS - -**Input Series:** Support multiple input series. The type is arbitrary but the data is strictly equispaced. - -**Parameters:** - -+ `gap`: The standard time interval which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. By default, it will be estimated by the mode of time intervals. -+ `length`: The length of the window which is a positive number with an unit. The unit is 'ms' for millisecond, 's' for second, 'm' for minute, 'h' for hour and 'd' for day. This parameter cannot be lacked. - -**Output Series:** Output a single series. The type is INT32. Each data point in the output series corresponds to a consecutive window. The output timestamp is the starting timestamp of the window and the output value is the number of data points in the window. - -**Note:** For input series that is not strictly equispaced, there is no guarantee on the output. - -#### Examples - - -Input series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d1.s1|root.test.d1.s2| -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:05:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:10:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:20:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:25:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:30:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:35:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:40:00.000+08:00| 1.0| null| -|2020-01-01T00:45:00.000+08:00| 1.0| 1.0| -|2020-01-01T00:50:00.000+08:00| 1.0| 1.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 -``` - -Output series: - -``` -+-----------------------------+--------------------------------------------------------------------+ -| Time|consecutivewindows(root.test.d1.s1, root.test.d1.s2, "length"="10m")| -+-----------------------------+--------------------------------------------------------------------+ -|2020-01-01T00:00:00.000+08:00| 3| -|2020-01-01T00:20:00.000+08:00| 3| -|2020-01-01T00:25:00.000+08:00| 3| -+-----------------------------+--------------------------------------------------------------------+ -``` - - - -## Machine Learning - -### AR - -#### Registration statement - -```sql -create function ar as 'org.apache.iotdb.library.dlearn.UDTFAR' -``` - -#### Usage - -This function is used to learn the coefficients of the autoregressive models for a time series. - -**Name:** AR - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -- `p`: The order of the autoregressive model. Its default value is 1. - -**Output Series:** Output a single series. The type is DOUBLE. The first line corresponds to the first order coefficient, and so on. - -**Note:** - -- Parameter `p` should be a positive integer. -- Most points in the series should be sampled at a constant time interval. -- Linear interpolation is applied for the missing points in the series. - -#### Examples - -##### Assigning Model Order - -Input Series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d0.s0| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| -4.0| -|2020-01-01T00:00:02.000+08:00| -3.0| -|2020-01-01T00:00:03.000+08:00| -2.0| -|2020-01-01T00:00:04.000+08:00| -1.0| -|2020-01-01T00:00:05.000+08:00| 0.0| -|2020-01-01T00:00:06.000+08:00| 1.0| -|2020-01-01T00:00:07.000+08:00| 2.0| -|2020-01-01T00:00:08.000+08:00| 3.0| -|2020-01-01T00:00:09.000+08:00| 4.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select ar(s0,"p"="2") from root.test.d0 -``` - -Output Series: - -``` -+-----------------------------+---------------------------+ -| Time|ar(root.test.d0.s0,"p"="2")| -+-----------------------------+---------------------------+ -|1970-01-01T08:00:00.001+08:00| 0.9429| -|1970-01-01T08:00:00.002+08:00| -0.2571| -+-----------------------------+---------------------------+ -``` - -### Representation - -#### Usage - -This function is used to represent a time series. - -**Name:** Representation - -**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -- `tb`: The number of timestamp blocks. Its default value is 10. -- `vb`: The number of value blocks. Its default value is 10. - -**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order. - -**Note:** - -- Parameters `tb` and `vb` should be positive integers. - -#### Examples - -##### Assigning Window Size and Dimension - -Input Series: - -``` -+-----------------------------+---------------+ -| Time|root.test.d0.s0| -+-----------------------------+---------------+ -|2020-01-01T00:00:01.000+08:00| -4.0| -|2020-01-01T00:00:02.000+08:00| -3.0| -|2020-01-01T00:00:03.000+08:00| -2.0| -|2020-01-01T00:00:04.000+08:00| -1.0| -|2020-01-01T00:00:05.000+08:00| 0.0| -|2020-01-01T00:00:06.000+08:00| 1.0| -|2020-01-01T00:00:07.000+08:00| 2.0| -|2020-01-01T00:00:08.000+08:00| 3.0| -|2020-01-01T00:00:09.000+08:00| 4.0| -+-----------------------------+---------------+ -``` - -SQL for query: - -```sql -select representation(s0,"tb"="3","vb"="2") from root.test.d0 -``` - -Output Series: - -``` -+-----------------------------+-------------------------------------------------+ -| Time|representation(root.test.d0.s0,"tb"="3","vb"="2")| -+-----------------------------+-------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 1| -|1970-01-01T08:00:00.002+08:00| 1| -|1970-01-01T08:00:00.003+08:00| 0| -|1970-01-01T08:00:00.004+08:00| 0| -|1970-01-01T08:00:00.005+08:00| 1| -|1970-01-01T08:00:00.006+08:00| 1| -+-----------------------------+-------------------------------------------------+ -``` - -### RM - -#### Usage - -This function is used to calculate the matching score of two time series according to the representation. - -**Name:** RM - -**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. - -**Parameters:** - -- `tb`: The number of timestamp blocks. Its default value is 10. -- `vb`: The number of value blocks. Its default value is 10. - -**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score. - -**Note:** - -- Parameters `tb` and `vb` should be positive integers. - -#### Examples - -##### Assigning Window Size and Dimension - -Input Series: - -``` -+-----------------------------+---------------+---------------+ -| Time|root.test.d0.s0|root.test.d0.s1 -+-----------------------------+---------------+---------------+ -|2020-01-01T00:00:01.000+08:00| -4.0| -4.0| -|2020-01-01T00:00:02.000+08:00| -3.0| -3.0| -|2020-01-01T00:00:03.000+08:00| -3.0| -3.0| -|2020-01-01T00:00:04.000+08:00| -1.0| -1.0| -|2020-01-01T00:00:05.000+08:00| 0.0| 0.0| -|2020-01-01T00:00:06.000+08:00| 1.0| 1.0| -|2020-01-01T00:00:07.000+08:00| 2.0| 2.0| -|2020-01-01T00:00:08.000+08:00| 3.0| 3.0| -|2020-01-01T00:00:09.000+08:00| 4.0| 4.0| -+-----------------------------+---------------+---------------+ -``` - -SQL for query: - -```sql -select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0 -``` - -Output Series: - -``` -+-----------------------------+-----------------------------------------------------+ -| Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")| -+-----------------------------+-----------------------------------------------------+ -|1970-01-01T08:00:00.001+08:00| 1.00| -+-----------------------------+-----------------------------------------------------+ -``` - diff --git a/src/UserGuide/latest/SQL-Manual/Operator-and-Expression.md b/src/UserGuide/latest/SQL-Manual/Operator-and-Expression.md index ff2507a84..c6fec7f61 100644 --- a/src/UserGuide/latest/SQL-Manual/Operator-and-Expression.md +++ b/src/UserGuide/latest/SQL-Manual/Operator-and-Expression.md @@ -21,11 +21,11 @@ # Operator and Expression -This chapter describes the operators and functions supported by IoTDB. IoTDB provides a wealth of built-in operators and functions to meet your computing needs, and supports extensions through the [User-Defined Function](../Reference/UDF-Libraries.md). +This chapter describes the operators and functions supported by IoTDB. IoTDB provides a wealth of built-in operators and functions to meet your computing needs, and supports extensions through the [User-Defined Function](../SQL-Manual/UDF-Libraries.md). A list of all available functions, both built-in and custom, can be displayed with `SHOW FUNCTIONS` command. -See the documentation [Select-Expression](../Reference/Function-and-Expression.md#selector-functions) for the behavior of operators and functions in SQL. +See the documentation [Select-Expression](./Function-and-Expression.md#selector-functions) for the behavior of operators and functions in SQL. ## OPERATORS @@ -41,7 +41,7 @@ See the documentation [Select-Expression](../Reference/Function-and-Expression.m | `+` | addition | | `-` | subtraction | -For details and examples, see the document [Arithmetic Operators and Functions](../Reference/Function-and-Expression.md#arithmetic-functions). +For details and examples, see the document [Arithmetic Operators and Functions](./Function-and-Expression.md#arithmetic-functions). ### Comparison Operators @@ -64,7 +64,7 @@ For details and examples, see the document [Arithmetic Operators and Functions]( | `IN` / `CONTAINS` | is a value in the specified list | | `NOT IN` / `NOT CONTAINS` | is not a value in the specified list | -For details and examples, see the document [Comparison Operators and Functions](../Reference/Function-and-Expression.md#comparison-operators-and-functions). +For details and examples, see the document [Comparison Operators and Functions](./Function-and-Expression.md#comparison-operators-and-functions). ### Logical Operators @@ -74,7 +74,7 @@ For details and examples, see the document [Comparison Operators and Functions]( | `AND` / `&` / `&&` | logical AND | | `OR`/ | / || | logical OR | -For details and examples, see the document [Logical Operators](../Reference/Function-and-Expression.md#logical-operators). +For details and examples, see the document [Logical Operators](./Function-and-Expression.md#logical-operators). ### Operator Precedence @@ -123,7 +123,7 @@ The built-in functions can be used in IoTDB without registration, and the functi | MAX_BY | MAX_BY(x, y) returns the value of x corresponding to the maximum value of the input y. MAX_BY(time, x) returns the timestamp when x is at its maximum value. | The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE. | / | Consistent with the data type of the first input x. | | MIN_BY | MIN_BY(x, y) returns the value of x corresponding to the minimum value of the input y. MIN_BY(time, x) returns the timestamp when x is at its minimum value. | The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE. | / | Consistent with the data type of the first input x. | -For details and examples, see the document [Aggregate Functions](../Reference/Function-and-Expression.md#aggregate-functions). +For details and examples, see the document [Aggregate Functions](./Function-and-Expression.md#aggregate-functions). ### Arithmetic Functions @@ -150,7 +150,7 @@ For details and examples, see the document [Aggregate Functions](../Reference/Fu | LOG10 | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | / | Math#log10(double) | | SQRT | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | / | Math#sqrt(double) | -For details and examples, see the document [Arithmetic Operators and Functions](../Reference/Function-and-Expression.md#arithmetic-operators-and-functions). +For details and examples, see the document [Arithmetic Operators and Functions](./Function-and-Expression.md#arithmetic-operators-and-functions). ### Comparison Functions @@ -159,7 +159,7 @@ For details and examples, see the document [Arithmetic Operators and Functions]( | ON_OFF | INT32 / INT64 / FLOAT / DOUBLE | `threshold`: a double type variate | BOOLEAN | Return `ts_value >= threshold`. | | IN_RANGR | INT32 / INT64 / FLOAT / DOUBLE | `lower`: DOUBLE type `upper`: DOUBLE type | BOOLEAN | Return `ts_value >= lower && value <= upper`. | -For details and examples, see the document [Comparison Operators and Functions](../Reference/Function-and-Expression.md#comparison-operators-and-functions). +For details and examples, see the document [Comparison Operators and Functions](./Function-and-Expression.md#comparison-operators-and-functions). ### String Processing Functions @@ -179,7 +179,7 @@ For details and examples, see the document [Comparison Operators and Functions]( | TRIM | TEXT STRING | / | TEXT | Get the string whose value is same to input series, with all leading and trailing space removed. | | STRCMP | TEXT STRING | / | TEXT | Get the compare result of two input series. Returns `0` if series value are the same, a `negative integer` if value of series1 is smaller than series2,
a `positive integer` if value of series1 is more than series2. | -For details and examples, see the document [String Processing](../Reference/Function-and-Expression.md#string-processing). +For details and examples, see the document [String Processing](./Function-and-Expression.md#string-processing). ### Data Type Conversion Function @@ -187,7 +187,7 @@ For details and examples, see the document [String Processing](../Reference/Func | ------------- | ------------------------------------------------------------ | ----------------------- | ------------------------------------------------------------ | | CAST | `type`: Output data type, INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT | determined by `type` | Convert the data to the type specified by the `type` parameter. | -For details and examples, see the document [Data Type Conversion Function](../Reference/Function-and-Expression.md#data-type-conversion-function). +For details and examples, see the document [Data Type Conversion Function](./Function-and-Expression.md#data-type-conversion-function). ### Constant Timeseries Generating Functions @@ -197,7 +197,7 @@ For details and examples, see the document [Data Type Conversion Function](../Re | PI | None | DOUBLE | Data point value: a `double` value of `π`, the ratio of the circumference of a circle to its diameter, which is equals to `Math.PI` in the *Java Standard Library*. | | E | None | DOUBLE | Data point value: a `double` value of `e`, the base of the natural logarithms, which is equals to `Math.E` in the *Java Standard Library*. | -For details and examples, see the document [Constant Timeseries Generating Functions](../Reference/Function-and-Expression.md#constant-timeseries-generating-functions). +For details and examples, see the document [Constant Timeseries Generating Functions](./Function-and-Expression.md#constant-timeseries-generating-functions). ### Selector Functions @@ -206,7 +206,7 @@ For details and examples, see the document [Constant Timeseries Generating Funct | TOP_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000 | Same type as the input series | Returns `k` data points with the largest values in a time series. | | BOTTOM_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000 | Same type as the input series | Returns `k` data points with the smallest values in a time series. | -For details and examples, see the document [Selector Functions](../Reference/Function-and-Expression.md#selector-functions). +For details and examples, see the document [Selector Functions](./Function-and-Expression.md#selector-functions). ### Continuous Interval Functions @@ -217,7 +217,7 @@ For details and examples, see the document [Selector Functions](../Reference/Fun | ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE` | Long | Return intervals' start times and the number of data points in the interval in which the value is always 0(false). Data points number `n` satisfy `n >= min && n <= max` | | NON_ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE` | Long | Return intervals' start times and the number of data points in the interval in which the value is always not 0(false). Data points number `n` satisfy `n >= min && n <= max` | -For details and examples, see the document [Continuous Interval Functions](../Reference/Function-and-Expression.md#continuous-interval-functions). +For details and examples, see the document [Continuous Interval Functions](./Function-and-Expression.md#continuous-interval-functions). ### Variation Trend Calculation Functions @@ -230,7 +230,7 @@ For details and examples, see the document [Continuous Interval Functions](../Re | NON_NEGATIVE_DERIVATIVE | INT32 / INT64 / FLOAT / DOUBLE | / | DOUBLE | Calculates the absolute value of the rate of change of a data point compared to the previous data point, the result is equals to NON_NEGATIVE_DIFFERENCE / TIME_DIFFERENCE. There is no corresponding output for the first data point. | | DIFF | INT32 / INT64 / FLOAT / DOUBLE | `ignoreNull`：optional，default is true. If is true, the previous data point is ignored when it is null and continues to find the first non-null value forwardly. If the value is false, previous data point is not ignored when it is null, the result is also null because null is used for subtraction | DOUBLE | Calculates the difference between the value of a data point and the value of the previous data point. There is no corresponding output for the first data point, so output is null | -For details and examples, see the document [Variation Trend Calculation Functions](../Reference/Function-and-Expression.md#variation-trend-calculation-functions). +For details and examples, see the document [Variation Trend Calculation Functions](./Function-and-Expression.md#variation-trend-calculation-functions). ### Sample Functions @@ -250,7 +250,7 @@ For details and examples, see the document [Sample Functions](../SQL-Manual/Func | ------------- | ------------------------------- | ------------------- | ----------------------------- | ----------------------------------------------------------- | | CHANGE_POINTS | INT32 / INT64 / FLOAT / DOUBLE | / | Same type as the input series | Remove consecutive identical values from an input sequence. | -For details and examples, see the document [Time-Series](../Reference/Function-and-Expression.md#time-series-processing). +For details and examples, see the document [Time-Series](./Function-and-Expression.md#time-series-processing). ## LAMBDA EXPRESSION @@ -259,7 +259,7 @@ For details and examples, see the document [Time-Series](../Reference/Function-a | ------------- | ----------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------- | ------------------------------------------------------------ | | JEXL | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | `expr` is a lambda expression that supports standard one or multi arguments in the form `x -> {...}` or `(x, y, z) -> {...}`, e.g. `x -> {x * 2}`, `(x, y, z) -> {x + y * z}` | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | Returns the input time series transformed by a lambda expression | -For details and examples, see the document [Lambda](../Reference/Function-and-Expression.md#lambda-expression). +For details and examples, see the document [Lambda](./Function-and-Expression.md#lambda-expression). ## CONDITIONAL EXPRESSION @@ -267,7 +267,7 @@ For details and examples, see the document [Lambda](../Reference/Function-and-Ex | --------------- | -------------------- | | `CASE` | similar to "if else" | -For details and examples, see the document [Conditional Expressions](../Reference/Function-and-Expression.md#conditional-expressions). +For details and examples, see the document [Conditional Expressions](./Function-and-Expression.md#conditional-expressions). ## SELECT EXPRESSION @@ -322,7 +322,7 @@ Aggregate functions are many-to-one functions. They perform aggregate calculatio > select a, count(a) from root.sg group by ([10,100),10ms) > ``` -For the aggregation functions supported by IoTDB, see the document [Aggregate Functions](../Reference/Function-and-Expression.md#aggregate-functions). +For the aggregation functions supported by IoTDB, see the document [Aggregate Functions](./Function-and-Expression.md#aggregate-functions). #### Time Series Generation Function @@ -337,7 +337,7 @@ See this documentation for a list of built-in functions supported in IoTDB. ##### User-Defined Time Series Generation Functions -IoTDB supports function extension through User Defined Function (click for [User-Defined Function](./Database-Programming.md#udtfuser-defined-timeseries-generating-function)) capability. +IoTDB supports function extension through User Defined Function (click for [User-Defined Function](../User-Manual/Database-Programming.md#udtfuser-defined-timeseries-generating-function)) capability. ### Nested Expressions diff --git a/src/UserGuide/latest/SQL-Manual/SQL-Manual.md b/src/UserGuide/latest/SQL-Manual/SQL-Manual.md index 1905bbb7d..4ac977278 100644 --- a/src/UserGuide/latest/SQL-Manual/SQL-Manual.md +++ b/src/UserGuide/latest/SQL-Manual/SQL-Manual.md @@ -23,7 +23,7 @@ ## DATABASE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Create Database @@ -105,7 +105,7 @@ IoTDB> SHOW DEVICES ## DEVICE TEMPLATE -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ![img](https://alioss.timecho.com/docs/img/%E6%A8%A1%E6%9D%BF.png) @@ -184,7 +184,7 @@ IoTDB> alter device template t1 add (speed FLOAT encoding=RLE, FLOAT TEXT encodi ## TIMESERIES MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Create Timeseries @@ -364,7 +364,7 @@ The above operations are supported for timeseries tag, attribute updates, etc. ## NODE MANAGEMENT -For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata_timecho.md). +For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md). ### Show Child Paths @@ -409,7 +409,7 @@ IoTDB> count devices root.ln.** ### Insert Data -For more details, see document [Write-Delete-Data](../User-Manual/Write-Delete-Data.md). +For more details, see document [Write-Delete-Data](../Basic-Concept/Write-Delete-Data.md). #### Use of INSERT Statements @@ -471,7 +471,7 @@ For more details, see document [Data Import](../Tools-System/Data-Import-Tool.md ## DELETE DATA -For more details, see document [Write-Delete-Data](../User-Manual/Write-Delete-Data.md). +For more details, see document [Write-Delete-Data](../Basic-Concept/Write-Delete-Data.md). ### Delete Single Timeseries @@ -508,7 +508,7 @@ IoTDB > DELETE PARTITION root.ln 0,1,2 ## QUERY DATA -For more details, see document [Query-Data](../User-Manual/Query-Data.md). +For more details, see document [Query-Data](../Basic-Concept/Query-Data.md). ```sql SELECT [LAST] selectExpr [, selectExpr] ... @@ -1090,11 +1090,11 @@ select change_points(s1), change_points(s2), change_points(s3), change_points(s4 ## DATA QUALITY FUNCTION LIBRARY -For more details, see document [Operator-and-Expression](./UDF-Libraries_timecho.md). +For more details, see document [Operator-and-Expression](./UDF-Libraries.md). ### Data Quality -For details and examples, see the document [Data-Quality](./UDF-Libraries_timecho.md#data-quality). +For details and examples, see the document [Data-Quality](./UDF-Libraries.md#data-quality). ```sql # Completeness @@ -1119,7 +1119,7 @@ select Accuracy(t1,t2,t3,m1,m2,m3) from root.test ### Data Profiling -For details and examples, see the document [Data-Profiling](./UDF-Libraries_timecho.md#data-profiling). +For details and examples, see the document [Data-Profiling](./UDF-Libraries.md#data-profiling). ```sql # ACF @@ -1199,7 +1199,7 @@ select zscore(s1) from root.test ### Anomaly Detection -For details and examples, see the document [Anomaly-Detection](./UDF-Libraries_timecho.md#anomaly-detection). +For details and examples, see the document [Anomaly-Detection](./UDF-Libraries.md#anomaly-detection). ```sql # IQR @@ -1234,7 +1234,7 @@ select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3 ### Frequency Domain -For details and examples, see the document [Frequency-Domain](./UDF-Libraries_timecho.md#frequency-domain-analysis). +For details and examples, see the document [Frequency-Domain](./UDF-Libraries.md#frequency-domain-analysis). ```sql # Conv @@ -1266,7 +1266,7 @@ select envelope(s1) from root.test.d1 ### Data Matching -For details and examples, see the document [Data-Matching](./UDF-Libraries_timecho.md#data-matching). +For details and examples, see the document [Data-Matching](./UDF-Libraries.md#data-matching). ```sql # Cov @@ -1287,7 +1287,7 @@ select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 ### Data Repairing -For details and examples, see the document [Data-Repairing](./UDF-Libraries_timecho.md#data-repairing). +For details and examples, see the document [Data-Repairing](./UDF-Libraries.md#data-repairing). ```sql # TimestampRepair @@ -1312,7 +1312,7 @@ select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 ### Series Discovery -For details and examples, see the document [Series-Discovery](./UDF-Libraries_timecho.md#series-discovery). +For details and examples, see the document [Series-Discovery](./UDF-Libraries.md#series-discovery). ```sql # ConsecutiveSequences @@ -1325,7 +1325,7 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 ### Machine Learning -For details and examples, see the document [Machine-Learning](./UDF-Libraries_timecho.md#machine-learning). +For details and examples, see the document [Machine-Learning](./UDF-Libraries.md#machine-learning). ```sql # AR @@ -1340,7 +1340,7 @@ select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0 ## LAMBDA EXPRESSION -For details and examples, see the document [Lambda](./UDF-Libraries_timecho.md#lambda-expression). +For details and examples, see the document [Lambda](./UDF-Libraries.md#lambda-expression). ```sql select jexl(temperature, 'expr'='x -> {x + x}') as jexl1, jexl(temperature, 'expr'='x -> {x * 3}') as jexl2, jexl(temperature, 'expr'='x -> {x * x}') as jexl3, jexl(temperature, 'expr'='x -> {multiply(x, 100)}') as jexl4, jexl(temperature, st, 'expr'='(x, y) -> {x + y}') as jexl5, jexl(temperature, st, str, 'expr'='(x, y, z) -> {x + y + z}') as jexl6 from root.ln.wf01.wt01;``` @@ -1348,7 +1348,7 @@ select jexl(temperature, 'expr'='x -> {x + x}') as jexl1, jexl(temperature, 'exp ## CONDITIONAL EXPRESSION -For details and examples, see the document [Conditional Expressions](./UDF-Libraries_timecho.md#conditional-expressions). +For details and examples, see the document [Conditional Expressions](./UDF-Libraries.md#conditional-expressions). ```sql select T, P, case @@ -1548,7 +1548,7 @@ CQs can't be altered once they're created. To change a CQ, you must `DROP` and r ## USER-DEFINED FUNCTION (UDF) -For more details, see document [Operator-and-Expression](./UDF-Libraries_timecho.md). +For more details, see document [Operator-and-Expression](./UDF-Libraries.md). ### UDF Registration diff --git a/src/UserGuide/latest/SQL-Manual/UDF-Libraries.md b/src/UserGuide/latest/SQL-Manual/UDF-Libraries.md new file mode 100644 index 000000000..2867a78eb --- /dev/null +++ b/src/UserGuide/latest/SQL-Manual/UDF-Libraries.md @@ -0,0 +1,23 @@ +--- +redirectTo: UDF-Libraries_apache.html +--- + \ No newline at end of file diff --git a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md index a4f786005..8bab853b8 100644 --- a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md +++ b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md @@ -607,14 +607,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md index d96a60b14..d4ee30c76 100644 --- a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md +++ b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md @@ -606,14 +606,14 @@ create function acf as 'org.apache.iotdb.library.dprofile.UDTFACF' This function is used to calculate the auto-correlation factor of the input time series, which equals to cross correlation between the same series. -For more information, please refer to [XCorr](./UDF-Libraries.md#xcorr) function. +For more information, please refer to [XCorr](#XCorr) function. **Name:** ACF **Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE. **Output Series:** Output a single series. The type is DOUBLE. -There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](./UDF-Libraries.md#XCorr) function. +There are $2N-1$ data points in the series, and the values are interpreted in details in [XCorr](#XCorr) function. **Note:** diff --git a/src/UserGuide/latest/User-Manual/Data-Sync_apache.md b/src/UserGuide/latest/User-Manual/Data-Sync_apache.md index 4a9476b04..7bde227f8 100644 --- a/src/UserGuide/latest/User-Manual/Data-Sync_apache.md +++ b/src/UserGuide/latest/User-Manual/Data-Sync_apache.md @@ -255,7 +255,7 @@ Detailed introduction of pre-installed plugins is as follows (for detailed param -For importing custom plugins, please refer to the [Stream Processing](./Streaming_timecho.md#custom-stream-processing-plugin-management) section. +For importing custom plugins, please refer to the [Stream Processing](./Streaming_apache.md#custom-stream-processing-plugin-management) section. ## Use examples diff --git a/src/UserGuide/latest/User-Manual/IoTDB-View_timecho.md b/src/UserGuide/latest/User-Manual/IoTDB-View_timecho.md index 6b1f3e855..8e3605849 100644 --- a/src/UserGuide/latest/User-Manual/IoTDB-View_timecho.md +++ b/src/UserGuide/latest/User-Manual/IoTDB-View_timecho.md @@ -308,7 +308,7 @@ AS SELECT temperature FROM root.db.* ``` -This is modelled on the query writeback (`SELECT INTO`) convention for naming rules, which uses variable placeholders to specify naming rules. See also: [QUERY WRITEBACK (SELECT INTO)](../User-Manual/Query-Data.md#into-clause-query-write-back) +This is modelled on the query writeback (`SELECT INTO`) convention for naming rules, which uses variable placeholders to specify naming rules. See also: [QUERY WRITEBACK (SELECT INTO)](../Basic-Concept/Query-Data.md#into-clause-query-write-back) Here `root.db.*.temperature` specifies what time series will be included in the view; and `${2}` specifies from which node in the time series the name is extracted to name the sequence view. diff --git a/src/UserGuide/latest/User-Manual/User-defined-function_apache.md b/src/UserGuide/latest/User-Manual/User-defined-function_apache.md index 72325f08e..42413bcad 100644 --- a/src/UserGuide/latest/User-Manual/User-defined-function_apache.md +++ b/src/UserGuide/latest/User-Manual/User-defined-function_apache.md @@ -190,7 +190,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 When users use UDF, they will be involved in the `USE_UDF` permission, and only users with this permission are allowed to perform UDF registration, uninstallation, and query operations. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ## 4. UDF Libraries diff --git a/src/UserGuide/latest/User-Manual/User-defined-function_timecho.md b/src/UserGuide/latest/User-Manual/User-defined-function_timecho.md index 2b91554ba..63c195ce8 100644 --- a/src/UserGuide/latest/User-Manual/User-defined-function_timecho.md +++ b/src/UserGuide/latest/User-Manual/User-defined-function_timecho.md @@ -190,7 +190,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 When users use UDF, they will be involved in the `USE_UDF` permission, and only users with this permission are allowed to perform UDF registration, uninstallation, and query operations. -For more user permissions related content, please refer to [Account Management Statements](./Authority-Management.md). +For more user permissions related content, please refer to [Account Management Statements](../User-Manual/Authority-Management.md). ## 4. UDF Libraries diff --git a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md index 2fc384714..8f2892a12 100644 --- a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -73,4 +73,4 @@ Apache IoTDB 具备以下优势和特性： - 天谋科技官网：https://www.timecho.com/ -- TimechoDB 安装部署与使用文档：[快速上手](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB 安装部署与使用文档：[快速上手](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md b/src/zh/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md index 3a9358e4e..79c1c21b8 100644 --- a/src/zh/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/zh/UserGuide/Master/Tree/Basic-Concept/Data-Model-and-Terminology.md @@ -27,7 +27,7 @@ -在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../User-Manual/Operate-Metadata_timecho.md#元数据模板管理)。 +在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../Basic-Concept/Operate-Metadata.md#元数据模板管理)。 IoTDB 模型结构涉及的基本概念在下文将做详细叙述。 @@ -64,7 +64,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例 ### 时间戳 (Timestamp) -时间戳是一个数据到来的时间点，其中包括绝对时间戳和相对时间戳，详细描述参见 [数据类型文档](./Data-Type.md)。 +时间戳是一个数据到来的时间点，其中包括绝对时间戳和相对时间戳，详细描述参见 [数据类型文档](../Background-knowledge/Data-Type.md)。 ### 数据点（Data Point） diff --git a/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata.md b/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata.md new file mode 100644 index 000000000..e0ddf712e --- /dev/null +++ b/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata.md @@ -0,0 +1,23 @@ +--- +redirectTo: Operate-Metadata_apache.html +--- + \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Tree/Basic-Concept/Query-Data.md b/src/zh/UserGuide/Master/Tree/Basic-Concept/Query-Data.md index 9988c1ee3..204667946 100644 --- a/src/zh/UserGuide/Master/Tree/Basic-Concept/Query-Data.md +++ b/src/zh/UserGuide/Master/Tree/Basic-Concept/Query-Data.md @@ -3027,7 +3027,7 @@ It costs 0.375s * 所有 `SELECT` 子句中源序列的 `WRITE_SCHEMA` 权限。 * 所有 `INTO` 子句中目标序列 `WRITE_DATA` 权限。 -更多用户权限相关的内容，请参考[权限管理语句](./Authority-Management.md)。 +更多用户权限相关的内容，请参考[权限管理语句](../User-Manual/Authority-Management.md)。 ### 相关配置参数 diff --git a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md index 2fc384714..8f2892a12 100644 --- a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -73,4 +73,4 @@ Apache IoTDB 具备以下优势和特性： - 天谋科技官网：https://www.timecho.com/ -- TimechoDB 安装部署与使用文档：[快速上手](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB 安装部署与使用文档：[快速上手](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md b/src/zh/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md index 5304a35ac..df99144bf 100644 --- a/src/zh/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md +++ b/src/zh/UserGuide/Master/Tree/SQL-Manual/Operator-and-Expression.md @@ -33,7 +33,7 @@ |`+` |加| |`-` |减| -详细说明及示例见文档 [算数运算符和函数](../Reference/Function-and-Expression.md#算数运算符)。 +详细说明及示例见文档 [算数运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符)。 ### 比较运算符 |运算符 |含义| @@ -55,7 +55,7 @@ |`IN` / `CONTAINS` |是指定列表中的值| |`NOT IN` / `NOT CONTAINS` |不是指定列表中的值| -详细说明及示例见文档 [比较运算符和函数](../Reference/Function-and-Expression.md#比较运算符和函数)。 +详细说明及示例见文档 [比较运算符和函数](../SQL-Manual/Function-and-Expression.md#比较运算符和函数)。 ### 逻辑运算符 |运算符 |含义| @@ -64,7 +64,7 @@ |`AND` / `&` / `&&` |逻辑与| |`OR`/ | / || |逻辑或| -详细说明及示例见文档 [逻辑运算符](../Reference/Function-and-Expression.md#逻辑运算符)。 +详细说明及示例见文档 [逻辑运算符](../SQL-Manual/Function-and-Expression.md#逻辑运算符)。 ### 运算符优先级 @@ -110,7 +110,7 @@ OR, |, || | MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | 第一个输入 x 可以是任意类型，第二个输入 y 只能是 INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE | 与第一个输入 x 的数据类型一致 | | MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | 第一个输入 x 可以是任意类型，第二个输入 y 只能是 INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE | 与第一个输入 x 的数据类型一致 | -详细说明及示例见文档 [聚合函数](../Reference/Function-and-Expression.md#聚合函数)。 +详细说明及示例见文档 [聚合函数](../SQL-Manual/Function-and-Expression.md#聚合函数)。 ### 数学函数 @@ -138,7 +138,7 @@ OR, |, || | SQRT | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | | Math#sqrt(double) | -详细说明及示例见文档 [数学函数](../Reference/Function-and-Expression.md#数学函数)。 +详细说明及示例见文档 [数学函数](../SQL-Manual/Function-and-Expression.md#数学函数)。 ### 比较函数 @@ -147,7 +147,7 @@ OR, |, || | ON_OFF | INT32 / INT64 / FLOAT / DOUBLE | `threshold`:DOUBLE | BOOLEAN | 返回`ts_value >= threshold`的bool值 | | IN_RANGE | INT32 / INT64 / FLOAT / DOUBLE | `lower`:DOUBLE
`upper`:DOUBLE | BOOLEAN | 返回`ts_value >= lower && ts_value <= upper`的bool值 | | -详细说明及示例见文档 [比较运算符和函数](../Reference/Function-and-Expression.md#比较运算符和函数)。 +详细说明及示例见文档 [比较运算符和函数](../SQL-Manual/Function-and-Expression.md#比较运算符和函数)。 ### 字符串函数 @@ -167,7 +167,7 @@ OR, |, || | TRIM | TEXT STRING | 无 | TEXT | 移除字符串前后的空格 | | STRCMP | TEXT STRING | 无 | TEXT | 用于比较两个输入序列，如果值相同返回 `0` , 序列1的值小于序列2的值返回一个`负数`，序列1的值大于序列2的值返回一个`正数` | -详细说明及示例见文档 [字符串处理函数](../Reference/Function-and-Expression.md#字符串处理)。 +详细说明及示例见文档 [字符串处理函数](../SQL-Manual/Function-and-Expression.md#字符串处理)。 ### 数据类型转换函数 @@ -175,7 +175,7 @@ OR, |, || | ------ | ------------------------------------------------------------ | ------------------------ | ---------------------------------- | | CAST | `type`:输出的数据点的类型，只能是 INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT | 由输入属性参数`type`决定 | 将数据转换为`type`参数指定的类型。 | -详细说明及示例见文档 [数据类型转换](../Reference/Function-and-Expression.md#数据类型转换)。 +详细说明及示例见文档 [数据类型转换](../SQL-Manual/Function-and-Expression.md#数据类型转换)。 ### 常序列生成函数 @@ -185,7 +185,7 @@ OR, |, || | PI | 无 | DOUBLE | 常序列的值：`π` 的 `double` 值，圆的周长与其直径的比值，即圆周率，等于 *Java标准库* 中的`Math.PI`。 | | E | 无 | DOUBLE | 常序列的值：`e` 的 `double` 值，自然对数的底，它等于 *Java 标准库* 中的 `Math.E`。 | -详细说明及示例见文档 [常序列生成函数](../Reference/Function-and-Expression.md#常序列生成函数)。 +详细说明及示例见文档 [常序列生成函数](../SQL-Manual/Function-and-Expression.md#常序列生成函数)。 ### 选择函数 @@ -194,7 +194,7 @@ OR, |, || | TOP_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: 最多选择的数据点数，必须大于 0 小于等于 1000 | 与输入序列的实际类型一致 | 返回某时间序列中值最大的`k`个数据点。若多于`k`个数据点的值并列最大，则返回时间戳最小的数据点。 | | BOTTOM_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: 最多选择的数据点数，必须大于 0 小于等于 1000 | 与输入序列的实际类型一致 | 返回某时间序列中值最小的`k`个数据点。若多于`k`个数据点的值并列最小，则返回时间戳最小的数据点。 | -详细说明及示例见文档 [选择函数](../Reference/Function-and-Expression.md#选择函数)。 +详细说明及示例见文档 [选择函数](../SQL-Manual/Function-and-Expression.md#选择函数)。 ### 区间查询函数 @@ -205,7 +205,7 @@ OR, |, || | ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:可选，默认值1
`max`:可选，默认值`Long.MAX_VALUE` | Long | 返回时间序列连续为0(false)的开始时间与其后数据点的个数，数据点个数n满足`n >= min && n <= max` | | | NON_ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:可选，默认值1
`max`:可选，默认值`Long.MAX_VALUE` | Long | 返回时间序列连续不为0(false)的开始时间与其后数据点的个数，数据点个数n满足`n >= min && n <= max` | | -详细说明及示例见文档 [区间查询函数](../Reference/Function-and-Expression.md#区间查询函数)。 +详细说明及示例见文档 [区间查询函数](../SQL-Manual/Function-and-Expression.md#区间查询函数)。 ### 趋势计算函数 @@ -222,7 +222,7 @@ OR, |, || |------|--------------------------------|------------------------------------------------------------------------------------------------------------------------|--------|------------------------------------------------| | DIFF | INT32 / INT64 / FLOAT / DOUBLE | `ignoreNull`：可选，默认为true；为true时，前一个数据点值为null时，忽略该数据点继续向前找到第一个出现的不为null的值；为false时，如果前一个数据点为null，则不忽略，使用null进行相减，结果也为null | DOUBLE | 统计序列中某数据点的值与前一数据点的值的差。第一个数据点没有对应的结果输出，输出值为null | -详细说明及示例见文档 [趋势计算函数](../Reference/Function-and-Expression.md#趋势计算函数)。 +详细说明及示例见文档 [趋势计算函数](../SQL-Manual/Function-and-Expression.md#趋势计算函数)。 ### 采样函数 @@ -241,7 +241,7 @@ OR, |, || | ------------- | ------------------------------ | ---- | ------------------------ | -------------------------- | | CHANGE_POINTS | INT32 / INT64 / FLOAT / DOUBLE | / | 与输入序列的实际类型一致 | 去除输入序列中的连续相同值 | -详细说明及示例见文档 [时间序列处理](../Reference/Function-and-Expression.md#时间序列处理)。 +详细说明及示例见文档 [时间序列处理](../SQL-Manual/Function-and-Expression.md#时间序列处理)。 ## Lambda 表达式 @@ -250,7 +250,7 @@ OR, |, || | ------ | ----------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------- | ---------------------------------------------- | | JEXL | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | `expr`是一个支持标准的一元或多元参数的lambda表达式，符合`x -> {...}`或`(x, y, z) -> {...}`的格式，例如`x -> {x * 2}`, `(x, y, z) -> {x + y * z}` | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | 返回将输入的时间序列通过lambda表达式变换的序列 | -详细说明及示例见文档 [Lambda 表达式](../Reference/Function-and-Expression.md#Lambda表达式) +详细说明及示例见文档 [Lambda 表达式](../SQL-Manual/Function-and-Expression.md#Lambda表达式) ## 条件表达式 @@ -258,7 +258,7 @@ OR, |, || |---------------------------|-----------| | `CASE` | 类似if else | -详细说明及示例见文档 [条件表达式](../Reference/Function-and-Expression.md#条件表达式) +详细说明及示例见文档 [条件表达式](../SQL-Manual/Function-and-Expression.md#条件表达式) ## SELECT 表达式 @@ -296,7 +296,7 @@ select s1 as temperature, s2 as speed from root.ln.wf01.wt01; #### 运算符 -IoTDB 中支持的运算符列表见文档 [运算符和函数](../Reference/Function-and-Expression.md#算数运算符和函数)。 +IoTDB 中支持的运算符列表见文档 [运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符和函数)。 #### 函数 @@ -314,7 +314,7 @@ select sin(s1), count(s1) from root.sg.d1; select s1, count(s1) from root.sg.d1 group by ([10,100),10ms); ``` -IoTDB 支持的聚合函数见文档 [聚合函数](../Reference/Function-and-Expression.md#聚合函数)。 +IoTDB 支持的聚合函数见文档 [聚合函数](../SQL-Manual/Function-and-Expression.md#聚合函数)。 ##### 时间序列生成函数 @@ -324,11 +324,11 @@ IoTDB 支持的聚合函数见文档 [聚合函数](../Reference/Function-and-Ex ###### 内置时间序列生成函数 -IoTDB 中支持的内置函数列表见文档 [运算符和函数](../Reference/Function-and-Expression.md#算数运算符)。 +IoTDB 中支持的内置函数列表见文档 [运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符)。 ###### 自定义时间序列生成函数 -IoTDB 支持通过用户自定义函数（点击查看： [用户自定义函数](../Reference/UDF-Libraries.md) ）能力进行函数功能扩展。 +IoTDB 支持通过用户自定义函数（点击查看： [用户自定义函数](../SQL-Manual/UDF-Libraries.md) ）能力进行函数功能扩展。 #### 嵌套表达式举例 diff --git a/src/zh/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md index c3591e036..038c85164 100644 --- a/src/zh/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md +++ b/src/zh/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md @@ -1214,11 +1214,11 @@ select change_points(s1), change_points(s2), change_points(s3), change_points(s4 ## 数据质量函数库 -更多见文档[UDF-Libraries](./UDF-Libraries_timecho.md) +更多见文档[UDF-Libraries](../SQL-Manual/UDF-Libraries.md) ### 数据质量 -更多见文档[Data-Quality](./UDF-Libraries_timecho.md#数据质量) +更多见文档[Data-Quality](../SQL-Manual/UDF-Libraries.md#数据质量) ```sql # Completeness @@ -1243,7 +1243,7 @@ select Accuracy(t1,t2,t3,m1,m2,m3) from root.test ### 数据画像 -更多见文档[Data-Profiling](./UDF-Libraries_timecho.md#数据画像) +更多见文档[Data-Profiling](../SQL-Manual/UDF-Libraries.md#数据画像) ```sql # ACF @@ -1323,7 +1323,7 @@ select zscore(s1) from root.test ### 异常检测 -更多见文档[Anomaly-Detection](./UDF-Libraries_timecho.md#异常检测) +更多见文档[Anomaly-Detection](../SQL-Manual/UDF-Libraries.md#异常检测) ```sql # IQR @@ -1358,7 +1358,7 @@ select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3 ### 频域分析 -更多见文档[Frequency-Domain](./UDF-Libraries_timecho.md#频域分析) +更多见文档[Frequency-Domain](../SQL-Manual/UDF-Libraries.md#频域分析) ```sql # Conv @@ -1390,7 +1390,7 @@ select envelope(s1) from root.test.d1 ### 数据匹配 -更多见文档[Data-Matching](./UDF-Libraries_timecho.md#数据匹配) +更多见文档[Data-Matching](../SQL-Manual/UDF-Libraries.md#数据匹配) ```sql # Cov @@ -1411,7 +1411,7 @@ select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 ### 数据修复 -更多见文档[Data-Repairing](./UDF-Libraries_timecho.md#数据修复) +更多见文档[Data-Repairing](../SQL-Manual/UDF-Libraries.md#数据修复) ```sql # TimestampRepair @@ -1436,7 +1436,7 @@ select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 ### 序列发现 -更多见文档[Series-Discovery](./UDF-Libraries_timecho.md#序列发现) +更多见文档[Series-Discovery](../SQL-Manual/UDF-Libraries.md#序列发现) ```sql # ConsecutiveSequences @@ -1449,7 +1449,7 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 ### 机器学习 -更多见文档[Machine-Learning](./UDF-Libraries_timecho.md#机器学习) +更多见文档[Machine-Learning](../SQL-Manual/UDF-Libraries.md#机器学习) ```sql # AR diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md b/src/zh/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md index 17817fde5..96f8de72c 100644 --- a/src/zh/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md +++ b/src/zh/UserGuide/Master/Tree/User-Manual/IoTDB-View_timecho.md @@ -308,7 +308,7 @@ AS SELECT temperature FROM root.db.* ``` -这里仿照了查询写回（`SELECT INTO`）对命名规则的约定，使用变量占位符来指定命名规则。可以参考：[查询写回（SELECT INTO）](../User-Manual/Query-Data.md#查询写回（INTO-子句）) +这里仿照了查询写回（`SELECT INTO`）对命名规则的约定，使用变量占位符来指定命名规则。可以参考：[查询写回（SELECT INTO）](../Basic-Concept/Query-Data.md#查询写回（INTO-子句）) 这里`root.db.*.temperature`指定了有哪些时间序列会被包含在视图中；`${2}`则指定了从时间序列中的哪个节点提取出名字来命名序列视图。 diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md b/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md index 237031ae4..b375a8911 100644 --- a/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md +++ b/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_apache.md @@ -188,7 +188,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 用户在使用 UDF 时会涉及到 `USE_UDF` 权限，具备该权限的用户才被允许执行 UDF 注册、卸载和查询操作。 -更多用户权限相关的内容，请参考 [权限管理语句](./Authority-Management.md##权限管理)。 +更多用户权限相关的内容，请参考 [权限管理语句](../User-Manual/Authority-Management.md##权限管理)。 ## 4. UDF 函数库 diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md b/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md index 05c253517..0cc6c55a3 100644 --- a/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md +++ b/src/zh/UserGuide/Master/Tree/User-Manual/User-defined-function_timecho.md @@ -188,7 +188,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 用户在使用 UDF 时会涉及到 `USE_UDF` 权限，具备该权限的用户才被允许执行 UDF 注册、卸载和查询操作。 -更多用户权限相关的内容，请参考 [权限管理语句](./Authority-Management.md##权限管理)。 +更多用户权限相关的内容，请参考 [权限管理语句](../User-Manual/Authority-Management.md##权限管理)。 ## 4. UDF 函数库 diff --git a/src/zh/UserGuide/V1.2.x/QuickStart/QuickStart.md b/src/zh/UserGuide/V1.2.x/QuickStart/QuickStart.md index 22d619ba6..76864286e 100644 --- a/src/zh/UserGuide/V1.2.x/QuickStart/QuickStart.md +++ b/src/zh/UserGuide/V1.2.x/QuickStart/QuickStart.md @@ -263,7 +263,7 @@ ALTER USER SET PASSWORD ; Example: IoTDB > ALTER USER root SET PASSWORD 'newpwd'; ``` -权限管理的具体内容可以参考：[权限管理](../User-Manual/Security-Management_timecho.md#权限管理) +权限管理的具体内容可以参考：[权限管理](../User-Manual/Authority-Management.md#权限管理) ## 基础配置 diff --git a/src/zh/UserGuide/V1.2.x/User-Manual/Database-Programming.md b/src/zh/UserGuide/V1.2.x/User-Manual/Database-Programming.md index 35410d6c9..9ce2bf8ab 100644 --- a/src/zh/UserGuide/V1.2.x/User-Manual/Database-Programming.md +++ b/src/zh/UserGuide/V1.2.x/User-Manual/Database-Programming.md @@ -1503,7 +1503,7 @@ SHOW FUNCTIONS * `DROP_FUNCTION`：具备该权限的用户才被允许执行 UDF 卸载操作 * `READ_TIMESERIES`：具备该权限的用户才被允许使用 UDF 进行查询 -更多用户权限相关的内容，请参考 [权限管理语句](./Security-Management_timecho.md##权限管理)。 +更多用户权限相关的内容，请参考 [权限管理语句](./Authority-Management.md##权限管理)。 ### 配置项 diff --git a/src/zh/UserGuide/V1.2.x/User-Manual/Query-Data.md b/src/zh/UserGuide/V1.2.x/User-Manual/Query-Data.md index fd78c80e6..0ee29bef2 100644 --- a/src/zh/UserGuide/V1.2.x/User-Manual/Query-Data.md +++ b/src/zh/UserGuide/V1.2.x/User-Manual/Query-Data.md @@ -2904,7 +2904,7 @@ It costs 0.375s * 所有 `SELECT` 子句中源序列的 `READ_TIMESERIES` 权限。 * 所有 `INTO` 子句中目标序列 `INSERT_TIMESERIES` 权限。 -更多用户权限相关的内容，请参考[权限管理语句](../User-Manual/Security-Management_timecho.md#权限管理)。 +更多用户权限相关的内容，请参考[权限管理语句](../User-Manual/Authority-Management.md#权限管理)。 ### 相关配置参数 diff --git a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md index a108d52e4..5aca749d7 100644 --- a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Data-Model-and-Terminology.md @@ -27,7 +27,7 @@ -在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../User-Manual/Operate-Metadata_timecho.md#元数据模板管理)。 +在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../User-Manual/Operate-Metadata.md#元数据模板管理)。 IoTDB 模型结构涉及的基本概念在下文将做详细叙述。 diff --git a/src/zh/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md index 4ae9c662c..1d4633245 100644 --- a/src/zh/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/V1.3.0-2/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -73,4 +73,4 @@ Apache IoTDB 具备以下优势和特性： - 天谋科技官网：https://www.timecho.com/ -- TimechoDB 安装部署与使用文档：[快速上手](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB 安装部署与使用文档：[快速上手](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/zh/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md b/src/zh/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md new file mode 100644 index 000000000..2867a78eb --- /dev/null +++ b/src/zh/UserGuide/V1.3.0-2/Reference/UDF-Libraries.md @@ -0,0 +1,23 @@ +--- +redirectTo: UDF-Libraries_apache.html +--- + \ No newline at end of file diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md index e91c2e18c..ee116a082 100644 --- a/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md +++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_apache.md @@ -257,7 +257,7 @@ IoTDB> SHOW PIPEPLUGINS -导入自定义插件可参考[流处理框架](./Streaming_timecho.md#自定义流处理插件管理)章节。 +导入自定义插件可参考[流处理框架](./Streaming_apache.md#自定义流处理插件管理)章节。 ## 使用示例 diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md new file mode 100644 index 000000000..e0ddf712e --- /dev/null +++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata.md @@ -0,0 +1,23 @@ +--- +redirectTo: Operate-Metadata_apache.html +--- + \ No newline at end of file diff --git a/src/zh/UserGuide/V2.0.1/Table/Background-knowledge/Data-Type.md b/src/zh/UserGuide/V2.0.1/Table/Background-knowledge/Data-Type.md index 3584aabb1..cf11bab02 100644 --- a/src/zh/UserGuide/V2.0.1/Table/Background-knowledge/Data-Type.md +++ b/src/zh/UserGuide/V2.0.1/Table/Background-knowledge/Data-Type.md @@ -47,7 +47,7 @@ IoTDB 支持以下十种数据类型： CREATE TIMESERIES root.vehicle.d0.s0 WITH DATATYPE=FLOAT, ENCODING=RLE, 'MAX_POINT_NUMBER'='2'; ``` -若不指定，系统会按照配置文件 `iotdb-system.properties` 中的 [float_precision](../Reference/Common-Config-Manual.md) 项配置（默认为 2 位）。 +若不指定，系统会按照配置文件 `iotdb-system.properties` 中的 [float_precision](../Reference/System-Config-Manual.md) 项配置（默认为 2 位）。 ### 数据类型兼容性 diff --git a/src/zh/UserGuide/V2.0.1/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/V2.0.1/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md index 2fc384714..c72c982af 100644 --- a/src/zh/UserGuide/V2.0.1/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/V2.0.1/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -27,7 +27,7 @@ Apache IoTDB 是一款低成本、高性能的物联网原生时序数据库。 - 开源安装包下载：https://iotdb.apache.org/zh/Download/ -- 安装部署与使用文档：[快速上手](../QuickStart/QuickStart_apache.md) +- 安装部署与使用文档：[快速上手](../QuickStart/QuickStart.md) ## 产品体系 @@ -73,4 +73,4 @@ Apache IoTDB 具备以下优势和特性： - 天谋科技官网：https://www.timecho.com/ -- TimechoDB 安装部署与使用文档：[快速上手](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB 安装部署与使用文档：[快速上手](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/zh/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md b/src/zh/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md index 7c952a91b..81c7cac7d 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md +++ b/src/zh/UserGuide/V2.0.1/Tree/API/Programming-Java-Native-API.md @@ -43,7 +43,7 @@ ## 语法说明 - - 对于 IoTDB-SQL 接口：传入的 SQL 参数需要符合 [语法规范](../User-Manual/Syntax-Rule.md#字面值常量) ，并且针对 JAVA 字符串进行反转义，如双引号前需要加反斜杠。（即：经 JAVA 转义之后与命令行执行的 SQL 语句一致。） + - 对于 IoTDB-SQL 接口：传入的 SQL 参数需要符合 [语法规范](../Reference/Syntax-Rule.md#字面值常量) ，并且针对 JAVA 字符串进行反转义，如双引号前需要加反斜杠。（即：经 JAVA 转义之后与命令行执行的 SQL 语句一致。） - 对于其他接口： - 经参数传入的路径或路径前缀中的节点： 在 SQL 语句中需要使用反引号（`）进行转义的，此处均需要进行转义。 - 经参数传入的标识符（如模板名）：在 SQL 语句中需要使用反引号（`）进行转义的，均可以不用进行转义。 diff --git a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md index 3a9358e4e..79c1c21b8 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Data-Model-and-Terminology.md @@ -27,7 +27,7 @@ -在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../User-Manual/Operate-Metadata_timecho.md#元数据模板管理)。 +在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../Basic-Concept/Operate-Metadata.md#元数据模板管理)。 IoTDB 模型结构涉及的基本概念在下文将做详细叙述。 @@ -64,7 +64,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例 ### 时间戳 (Timestamp) -时间戳是一个数据到来的时间点，其中包括绝对时间戳和相对时间戳，详细描述参见 [数据类型文档](./Data-Type.md)。 +时间戳是一个数据到来的时间点，其中包括绝对时间戳和相对时间戳，详细描述参见 [数据类型文档](../Background-knowledge/Data-Type.md)。 ### 数据点（Data Point） diff --git a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md new file mode 100644 index 000000000..e0ddf712e --- /dev/null +++ b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata.md @@ -0,0 +1,23 @@ +--- +redirectTo: Operate-Metadata_apache.html +--- + \ No newline at end of file diff --git a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md index e67ae0750..44e8df63a 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md +++ b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_apache.md @@ -597,7 +597,7 @@ IoTDB> create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODIN error: encoding TS_2DIFF does not support BOOLEAN ``` -详细的数据类型与编码方式的对应列表请参见 [编码方式](../Basic-Concept/Encoding-and-Compression.md)。 +详细的数据类型与编码方式的对应列表请参见 [编码方式](../Technical-Insider/Encoding-and-Compression.md)。 ### 创建对齐时间序列 diff --git a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md index 01cf39e7e..5ba32f82a 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md +++ b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Operate-Metadata_timecho.md @@ -597,7 +597,7 @@ IoTDB> create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODIN error: encoding TS_2DIFF does not support BOOLEAN ``` -详细的数据类型与编码方式的对应列表请参见 [编码方式](../Basic-Concept/Encoding-and-Compression.md)。 +详细的数据类型与编码方式的对应列表请参见 [编码方式](../Technical-Insider/Encoding-and-Compression.md)。 ### 创建对齐时间序列 diff --git a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md index 9988c1ee3..060940e48 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md +++ b/src/zh/UserGuide/V2.0.1/Tree/Basic-Concept/Query-Data.md @@ -691,7 +691,7 @@ It costs 0.002s ### 时间过滤条件 -使用时间过滤条件可以筛选特定时间范围的数据。对于时间戳支持的格式，请参考 [时间戳类型](../Basic-Concept/Data-Type.md) 。 +使用时间过滤条件可以筛选特定时间范围的数据。对于时间戳支持的格式，请参考 [时间戳类型](../Background-knowledge/Data-Type.md) 。 示例如下： @@ -2961,7 +2961,7 @@ select s1, s2 into root.sg_copy.d1(t1, t2), aligned root.sg_copy.d2(t1, t2) from #### 其他要注意的点 - 对于一般的聚合查询，时间戳是无意义的，约定使用 0 来存储。 -- 当目标序列存在时，需要保证源序列和目标时间序列的数据类型兼容。关于数据类型的兼容性，查看文档 [数据类型](../Basic-Concept/Data-Type.md#数据类型兼容性)。 +- 当目标序列存在时，需要保证源序列和目标时间序列的数据类型兼容。关于数据类型的兼容性，查看文档 [数据类型](../Background-knowledge/Data-Type.md#数据类型兼容性)。 - 当目标序列不存在时，系统将自动创建目标序列（包括 database）。 - 当查询的序列不存在或查询的序列不存在数据，则不会自动创建目标序列。 @@ -3027,7 +3027,7 @@ It costs 0.375s * 所有 `SELECT` 子句中源序列的 `WRITE_SCHEMA` 权限。 * 所有 `INTO` 子句中目标序列 `WRITE_DATA` 权限。 -更多用户权限相关的内容，请参考[权限管理语句](./Authority-Management.md)。 +更多用户权限相关的内容，请参考[权限管理语句](../User-Manual/Authority-Management.md)。 ### 相关配置参数 diff --git a/src/zh/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md index 2fc384714..8f2892a12 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/V2.0.1/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -73,4 +73,4 @@ Apache IoTDB 具备以下优势和特性： - 天谋科技官网：https://www.timecho.com/ -- TimechoDB 安装部署与使用文档：[快速上手](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB 安装部署与使用文档：[快速上手](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/zh/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md b/src/zh/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md index 6faaa277c..fe90486a1 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md +++ b/src/zh/UserGuide/V2.0.1/Tree/Reference/Syntax-Rule.md @@ -138,7 +138,7 @@ ### 时间戳常量 -时间戳是一个数据到来的时间点，在 IoTDB 中分为绝对时间戳和相对时间戳。详细信息可参考 [数据类型文档](../Basic-Concept/Data-Type.md)。 +时间戳是一个数据到来的时间点，在 IoTDB 中分为绝对时间戳和相对时间戳。详细信息可参考 [数据类型文档](../Background-knowledge/Data-Type.md)。 特别地，`NOW()`表示语句开始执行时的服务端系统时间戳。 diff --git a/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md b/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md index 6afc120a6..1d02a15bb 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md +++ b/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/Operator-and-Expression.md @@ -33,7 +33,7 @@ |`+` |加| |`-` |减| -详细说明及示例见文档 [算数运算符和函数](../Reference/Function-and-Expression.md#算数运算符)。 +详细说明及示例见文档 [算数运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符)。 ### 比较运算符 |运算符 |含义| @@ -55,7 +55,7 @@ |`IN` / `CONTAINS` |是指定列表中的值| |`NOT IN` / `NOT CONTAINS` |不是指定列表中的值| -详细说明及示例见文档 [比较运算符和函数](../Reference/Function-and-Expression.md#比较运算符和函数)。 +详细说明及示例见文档 [比较运算符和函数](../SQL-Manual/Function-and-Expression.md#比较运算符和函数)。 ### 逻辑运算符 |运算符 |含义| @@ -64,7 +64,7 @@ |`AND` / `&` / `&&` |逻辑与| |`OR`/ | / || |逻辑或| -详细说明及示例见文档 [逻辑运算符](../Reference/Function-and-Expression.md#逻辑运算符)。 +详细说明及示例见文档 [逻辑运算符](../SQL-Manual/Function-and-Expression.md#逻辑运算符)。 ### 运算符优先级 @@ -110,7 +110,7 @@ OR, |, || | MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | 第一个输入 x 可以是任意类型，第二个输入 y 只能是 INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE | 与第一个输入 x 的数据类型一致 | | MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | 第一个输入 x 可以是任意类型，第二个输入 y 只能是 INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE | 与第一个输入 x 的数据类型一致 | -详细说明及示例见文档 [聚合函数](../Reference/Function-and-Expression.md#聚合函数)。 +详细说明及示例见文档 [聚合函数](../SQL-Manual/Function-and-Expression.md#聚合函数)。 ### 数学函数 @@ -138,7 +138,7 @@ OR, |, || | SQRT | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | | Math#sqrt(double) | -详细说明及示例见文档 [数学函数](../Reference/Function-and-Expression.md#数学函数)。 +详细说明及示例见文档 [数学函数](../SQL-Manual/Function-and-Expression.md#数学函数)。 ### 比较函数 @@ -147,7 +147,7 @@ OR, |, || | ON_OFF | INT32 / INT64 / FLOAT / DOUBLE | `threshold`:DOUBLE | BOOLEAN | 返回`ts_value >= threshold`的bool值 | | IN_RANGE | INT32 / INT64 / FLOAT / DOUBLE | `lower`:DOUBLE
`upper`:DOUBLE | BOOLEAN | 返回`ts_value >= lower && ts_value <= upper`的bool值 | | -详细说明及示例见文档 [比较运算符和函数](../Reference/Function-and-Expression.md#比较运算符和函数)。 +详细说明及示例见文档 [比较运算符和函数](../SQL-Manual/Function-and-Expression.md#比较运算符和函数)。 ### 字符串函数 @@ -167,7 +167,7 @@ OR, |, || | TRIM | TEXT STRING | 无 | TEXT | 移除字符串前后的空格 | | STRCMP | TEXT STRING | 无 | TEXT | 用于比较两个输入序列，如果值相同返回 `0` , 序列1的值小于序列2的值返回一个`负数`，序列1的值大于序列2的值返回一个`正数` | -详细说明及示例见文档 [字符串处理函数](../Reference/Function-and-Expression.md#字符串处理)。 +详细说明及示例见文档 [字符串处理函数](../SQL-Manual/Function-and-Expression.md#字符串处理)。 ### 数据类型转换函数 @@ -175,7 +175,7 @@ OR, |, || | ------ | ------------------------------------------------------------ | ------------------------ | ---------------------------------- | | CAST | `type`:输出的数据点的类型，只能是 INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT | 由输入属性参数`type`决定 | 将数据转换为`type`参数指定的类型。 | -详细说明及示例见文档 [数据类型转换](../Reference/Function-and-Expression.md#数据类型转换)。 +详细说明及示例见文档 [数据类型转换](../SQL-Manual/Function-and-Expression.md#数据类型转换)。 ### 常序列生成函数 @@ -185,7 +185,7 @@ OR, |, || | PI | 无 | DOUBLE | 常序列的值：`π` 的 `double` 值，圆的周长与其直径的比值，即圆周率，等于 *Java标准库* 中的`Math.PI`。 | | E | 无 | DOUBLE | 常序列的值：`e` 的 `double` 值，自然对数的底，它等于 *Java 标准库* 中的 `Math.E`。 | -详细说明及示例见文档 [常序列生成函数](../Reference/Function-and-Expression.md#常序列生成函数)。 +详细说明及示例见文档 [常序列生成函数](../SQL-Manual/Function-and-Expression.md#常序列生成函数)。 ### 选择函数 @@ -194,7 +194,7 @@ OR, |, || | TOP_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: 最多选择的数据点数，必须大于 0 小于等于 1000 | 与输入序列的实际类型一致 | 返回某时间序列中值最大的`k`个数据点。若多于`k`个数据点的值并列最大，则返回时间戳最小的数据点。 | | BOTTOM_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: 最多选择的数据点数，必须大于 0 小于等于 1000 | 与输入序列的实际类型一致 | 返回某时间序列中值最小的`k`个数据点。若多于`k`个数据点的值并列最小，则返回时间戳最小的数据点。 | -详细说明及示例见文档 [选择函数](../Reference/Function-and-Expression.md#选择函数)。 +详细说明及示例见文档 [选择函数](../SQL-Manual/Function-and-Expression.md#选择函数)。 ### 区间查询函数 @@ -205,7 +205,7 @@ OR, |, || | ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:可选，默认值1
`max`:可选，默认值`Long.MAX_VALUE` | Long | 返回时间序列连续为0(false)的开始时间与其后数据点的个数，数据点个数n满足`n >= min && n <= max` | | | NON_ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:可选，默认值1
`max`:可选，默认值`Long.MAX_VALUE` | Long | 返回时间序列连续不为0(false)的开始时间与其后数据点的个数，数据点个数n满足`n >= min && n <= max` | | -详细说明及示例见文档 [区间查询函数](../Reference/Function-and-Expression.md#区间查询函数)。 +详细说明及示例见文档 [区间查询函数](../SQL-Manual/Function-and-Expression.md#区间查询函数)。 ### 趋势计算函数 @@ -222,7 +222,7 @@ OR, |, || |------|--------------------------------|------------------------------------------------------------------------------------------------------------------------|--------|------------------------------------------------| | DIFF | INT32 / INT64 / FLOAT / DOUBLE | `ignoreNull`：可选，默认为true；为true时，前一个数据点值为null时，忽略该数据点继续向前找到第一个出现的不为null的值；为false时，如果前一个数据点为null，则不忽略，使用null进行相减，结果也为null | DOUBLE | 统计序列中某数据点的值与前一数据点的值的差。第一个数据点没有对应的结果输出，输出值为null | -详细说明及示例见文档 [趋势计算函数](../Reference/Function-and-Expression.md#趋势计算函数)。 +详细说明及示例见文档 [趋势计算函数](../SQL-Manual/Function-and-Expression.md#趋势计算函数)。 ### 采样函数 @@ -234,14 +234,14 @@ OR, |, || | EQUAL_SIZE_BUCKET_OUTLIER_SAMPLE | INT32 / INT64 / FLOAT / DOUBLE | `proportion`取值范围为`(0, 1]`，默认为`0.1`
`type`取值为`avg`或`stendis`或`cos`或`prenextdis`，默认为`avg`
`number`取值应大于0，默认`3`| INT32 / INT64 / FLOAT / DOUBLE | 返回符合采样比例和桶内采样个数的等分桶离群值采样 | | M4 | INT32 / INT64 / FLOAT / DOUBLE | 包含固定点数的窗口和滑动时间窗口使用不同的属性参数。包含固定点数的窗口使用属性`windowSize`和`slidingStep`。滑动时间窗口使用属性`timeInterval`、`slidingStep`、`displayWindowBegin`和`displayWindowEnd`。更多细节见下文。 | INT32 / INT64 / FLOAT / DOUBLE | 返回每个窗口内的第一个点（`first`）、最后一个点（`last`）、最小值点（`bottom`）、最大值点（`top`）。在一个窗口内的聚合点输出之前，M4会将它们按照时间戳递增排序并且去重。 | -详细说明及示例见文档 [采样函数](../Reference/Function-and-Expression.md#采样函数)。 +详细说明及示例见文档 [采样函数](../SQL-Manual/Function-and-Expression.md#采样函数)。 ### 时间序列处理函数 | 函数名 | 输入序列类型 | 参数 | 输出序列类型 | 功能描述 | | ------------- | ------------------------------ | ---- | ------------------------ | -------------------------- | | CHANGE_POINTS | INT32 / INT64 / FLOAT / DOUBLE | / | 与输入序列的实际类型一致 | 去除输入序列中的连续相同值 | -详细说明及示例见文档 [时间序列处理](../Reference/Function-and-Expression.md#时间序列处理)。 +详细说明及示例见文档 [时间序列处理](../SQL-Manual/Function-and-Expression.md#时间序列处理)。 ## Lambda 表达式 @@ -250,7 +250,7 @@ OR, |, || | ------ | ----------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------- | ---------------------------------------------- | | JEXL | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | `expr`是一个支持标准的一元或多元参数的lambda表达式，符合`x -> {...}`或`(x, y, z) -> {...}`的格式，例如`x -> {x * 2}`, `(x, y, z) -> {x + y * z}` | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | 返回将输入的时间序列通过lambda表达式变换的序列 | -详细说明及示例见文档 [Lambda 表达式](../Reference/Function-and-Expression.md#Lambda表达式) +详细说明及示例见文档 [Lambda 表达式](../SQL-Manual/Function-and-Expression.md#Lambda表达式) ## 条件表达式 @@ -258,7 +258,7 @@ OR, |, || |---------------------------|-----------| | `CASE` | 类似if else | -详细说明及示例见文档 [条件表达式](../Reference/Function-and-Expression.md#条件表达式) +详细说明及示例见文档 [条件表达式](../SQL-Manual/Function-and-Expression.md#条件表达式) ## SELECT 表达式 @@ -296,7 +296,7 @@ select s1 as temperature, s2 as speed from root.ln.wf01.wt01; #### 运算符 -IoTDB 中支持的运算符列表见文档 [运算符和函数](../Reference/Function-and-Expression.md#算数运算符和函数)。 +IoTDB 中支持的运算符列表见文档 [运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符和函数)。 #### 函数 @@ -314,7 +314,7 @@ select sin(s1), count(s1) from root.sg.d1; select s1, count(s1) from root.sg.d1 group by ([10,100),10ms); ``` -IoTDB 支持的聚合函数见文档 [聚合函数](../Reference/Function-and-Expression.md#聚合函数)。 +IoTDB 支持的聚合函数见文档 [聚合函数](../SQL-Manual/Function-and-Expression.md#聚合函数)。 ##### 时间序列生成函数 @@ -324,11 +324,11 @@ IoTDB 支持的聚合函数见文档 [聚合函数](../Reference/Function-and-Ex ###### 内置时间序列生成函数 -IoTDB 中支持的内置函数列表见文档 [运算符和函数](../Reference/Function-and-Expression.md#算数运算符)。 +IoTDB 中支持的内置函数列表见文档 [运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符)。 ###### 自定义时间序列生成函数 -IoTDB 支持通过用户自定义函数（点击查看： [用户自定义函数](../Reference/UDF-Libraries.md) ）能力进行函数功能扩展。 +IoTDB 支持通过用户自定义函数（点击查看： [用户自定义函数](../SQL-Manual/UDF-Libraries.md) ）能力进行函数功能扩展。 #### 嵌套表达式举例 diff --git a/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md index c3591e036..038c85164 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md +++ b/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/SQL-Manual.md @@ -1214,11 +1214,11 @@ select change_points(s1), change_points(s2), change_points(s3), change_points(s4 ## 数据质量函数库 -更多见文档[UDF-Libraries](./UDF-Libraries_timecho.md) +更多见文档[UDF-Libraries](../SQL-Manual/UDF-Libraries.md) ### 数据质量 -更多见文档[Data-Quality](./UDF-Libraries_timecho.md#数据质量) +更多见文档[Data-Quality](../SQL-Manual/UDF-Libraries.md#数据质量) ```sql # Completeness @@ -1243,7 +1243,7 @@ select Accuracy(t1,t2,t3,m1,m2,m3) from root.test ### 数据画像 -更多见文档[Data-Profiling](./UDF-Libraries_timecho.md#数据画像) +更多见文档[Data-Profiling](../SQL-Manual/UDF-Libraries.md#数据画像) ```sql # ACF @@ -1323,7 +1323,7 @@ select zscore(s1) from root.test ### 异常检测 -更多见文档[Anomaly-Detection](./UDF-Libraries_timecho.md#异常检测) +更多见文档[Anomaly-Detection](../SQL-Manual/UDF-Libraries.md#异常检测) ```sql # IQR @@ -1358,7 +1358,7 @@ select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3 ### 频域分析 -更多见文档[Frequency-Domain](./UDF-Libraries_timecho.md#频域分析) +更多见文档[Frequency-Domain](../SQL-Manual/UDF-Libraries.md#频域分析) ```sql # Conv @@ -1390,7 +1390,7 @@ select envelope(s1) from root.test.d1 ### 数据匹配 -更多见文档[Data-Matching](./UDF-Libraries_timecho.md#数据匹配) +更多见文档[Data-Matching](../SQL-Manual/UDF-Libraries.md#数据匹配) ```sql # Cov @@ -1411,7 +1411,7 @@ select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 ### 数据修复 -更多见文档[Data-Repairing](./UDF-Libraries_timecho.md#数据修复) +更多见文档[Data-Repairing](../SQL-Manual/UDF-Libraries.md#数据修复) ```sql # TimestampRepair @@ -1436,7 +1436,7 @@ select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 ### 序列发现 -更多见文档[Series-Discovery](./UDF-Libraries_timecho.md#序列发现) +更多见文档[Series-Discovery](../SQL-Manual/UDF-Libraries.md#序列发现) ```sql # ConsecutiveSequences @@ -1449,7 +1449,7 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 ### 机器学习 -更多见文档[Machine-Learning](./UDF-Libraries_timecho.md#机器学习) +更多见文档[Machine-Learning](../SQL-Manual/UDF-Libraries.md#机器学习) ```sql # AR diff --git a/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md b/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md new file mode 100644 index 000000000..2867a78eb --- /dev/null +++ b/src/zh/UserGuide/V2.0.1/Tree/SQL-Manual/UDF-Libraries.md @@ -0,0 +1,23 @@ +--- +redirectTo: UDF-Libraries_apache.html +--- + \ No newline at end of file diff --git a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md index 0ce0c4ee8..3c84f50a2 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md +++ b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/Data-Sync_apache.md @@ -255,7 +255,7 @@ IoTDB> SHOW PIPEPLUGINS -导入自定义插件可参考[流处理框架](./Streaming_timecho.md#自定义流处理插件管理)章节。 +导入自定义插件可参考[流处理框架](./Streaming_apache.md#自定义流处理插件管理)章节。 ## 使用示例 diff --git a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md index 17817fde5..96f8de72c 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md +++ b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/IoTDB-View_timecho.md @@ -308,7 +308,7 @@ AS SELECT temperature FROM root.db.* ``` -这里仿照了查询写回（`SELECT INTO`）对命名规则的约定，使用变量占位符来指定命名规则。可以参考：[查询写回（SELECT INTO）](../User-Manual/Query-Data.md#查询写回（INTO-子句）) +这里仿照了查询写回（`SELECT INTO`）对命名规则的约定，使用变量占位符来指定命名规则。可以参考：[查询写回（SELECT INTO）](../Basic-Concept/Query-Data.md#查询写回（INTO-子句）) 这里`root.db.*.temperature`指定了有哪些时间序列会被包含在视图中；`${2}`则指定了从时间序列中的哪个节点提取出名字来命名序列视图。 diff --git a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md index 237031ae4..b375a8911 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md +++ b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_apache.md @@ -188,7 +188,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 用户在使用 UDF 时会涉及到 `USE_UDF` 权限，具备该权限的用户才被允许执行 UDF 注册、卸载和查询操作。 -更多用户权限相关的内容，请参考 [权限管理语句](./Authority-Management.md##权限管理)。 +更多用户权限相关的内容，请参考 [权限管理语句](../User-Manual/Authority-Management.md##权限管理)。 ## 4. UDF 函数库 diff --git a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md index 05c253517..0cc6c55a3 100644 --- a/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md +++ b/src/zh/UserGuide/V2.0.1/Tree/User-Manual/User-defined-function_timecho.md @@ -188,7 +188,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 用户在使用 UDF 时会涉及到 `USE_UDF` 权限，具备该权限的用户才被允许执行 UDF 注册、卸载和查询操作。 -更多用户权限相关的内容，请参考 [权限管理语句](./Authority-Management.md##权限管理)。 +更多用户权限相关的内容，请参考 [权限管理语句](../User-Manual/Authority-Management.md##权限管理)。 ## 4. UDF 函数库 diff --git a/src/zh/UserGuide/latest/API/Programming-Java-Native-API.md b/src/zh/UserGuide/latest/API/Programming-Java-Native-API.md index 8c68005d8..6b6f99aa2 100644 --- a/src/zh/UserGuide/latest/API/Programming-Java-Native-API.md +++ b/src/zh/UserGuide/latest/API/Programming-Java-Native-API.md @@ -42,7 +42,7 @@ ## 语法说明 - - 对于 IoTDB-SQL 接口：传入的 SQL 参数需要符合 [语法规范](../User-Manual/Syntax-Rule.md#字面值常量) ，并且针对 JAVA 字符串进行反转义，如双引号前需要加反斜杠。（即：经 JAVA 转义之后与命令行执行的 SQL 语句一致。） + - 对于 IoTDB-SQL 接口：传入的 SQL 参数需要符合 [语法规范](../Reference/Syntax-Rule.md#字面值常量) ，并且针对 JAVA 字符串进行反转义，如双引号前需要加反斜杠。（即：经 JAVA 转义之后与命令行执行的 SQL 语句一致。） - 对于其他接口： - 经参数传入的路径或路径前缀中的节点： 在 SQL 语句中需要使用反引号（`）进行转义的，此处均需要进行转义。 - 经参数传入的标识符（如模板名）：在 SQL 语句中需要使用反引号（`）进行转义的，均可以不用进行转义。 diff --git a/src/zh/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md b/src/zh/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md index 3a9358e4e..79c1c21b8 100644 --- a/src/zh/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md +++ b/src/zh/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md @@ -27,7 +27,7 @@ -在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../User-Manual/Operate-Metadata_timecho.md#元数据模板管理)。 +在上图所描述的实际场景中，有许多实体所采集的物理量相同，即具有相同的工况名称和类型，因此，可以声明一个**元数据模板**来定义可采集的物理量集合。在实践中，元数据模板的使用可帮助减少元数据的资源占用，详细内容参见 [元数据模板](../Basic-Concept/Operate-Metadata.md#元数据模板管理)。 IoTDB 模型结构涉及的基本概念在下文将做详细叙述。 @@ -64,7 +64,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例 ### 时间戳 (Timestamp) -时间戳是一个数据到来的时间点，其中包括绝对时间戳和相对时间戳，详细描述参见 [数据类型文档](./Data-Type.md)。 +时间戳是一个数据到来的时间点，其中包括绝对时间戳和相对时间戳，详细描述参见 [数据类型文档](../Background-knowledge/Data-Type.md)。 ### 数据点（Data Point） diff --git a/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata.md b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata.md new file mode 100644 index 000000000..e0ddf712e --- /dev/null +++ b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata.md @@ -0,0 +1,23 @@ +--- +redirectTo: Operate-Metadata_apache.html +--- + \ No newline at end of file diff --git a/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md index e67ae0750..44e8df63a 100644 --- a/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md +++ b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md @@ -597,7 +597,7 @@ IoTDB> create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODIN error: encoding TS_2DIFF does not support BOOLEAN ``` -详细的数据类型与编码方式的对应列表请参见 [编码方式](../Basic-Concept/Encoding-and-Compression.md)。 +详细的数据类型与编码方式的对应列表请参见 [编码方式](../Technical-Insider/Encoding-and-Compression.md)。 ### 创建对齐时间序列 diff --git a/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md index 21d98799a..3c1ed63c8 100644 --- a/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md +++ b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md @@ -596,7 +596,7 @@ IoTDB> create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODIN error: encoding TS_2DIFF does not support BOOLEAN ``` -详细的数据类型与编码方式的对应列表请参见 [编码方式](../Basic-Concept/Encoding-and-Compression.md)。 +详细的数据类型与编码方式的对应列表请参见 [编码方式](../Technical-Insider/Encoding-and-Compression.md)。 ### 创建对齐时间序列 diff --git a/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md b/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md index 18b39edaa..060940e48 100644 --- a/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md +++ b/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md @@ -368,7 +368,7 @@ select s1 as temperature, s2 as speed from root.ln.wf01.wt01; ### 运算符 -IoTDB 中支持的运算符列表见文档 [运算符和函数](../User-Manual/Operator-and-Expression.md)。 +IoTDB 中支持的运算符列表见文档 [运算符和函数](../SQL-Manual/Operator-and-Expression.md)。 ### 函数 @@ -386,7 +386,7 @@ select sin(s1), count(s1) from root.sg.d1; select s1, count(s1) from root.sg.d1 group by ([10,100),10ms); ``` -IoTDB 支持的聚合函数见文档 [聚合函数](../User-Manual/Operator-and-Expression.md#内置函数)。 +IoTDB 支持的聚合函数见文档 [聚合函数](../SQL-Manual/Operator-and-Expression.md#内置函数)。 #### 时间序列生成函数 @@ -396,7 +396,7 @@ IoTDB 支持的聚合函数见文档 [聚合函数](../User-Manual/Operator-and- ##### 内置时间序列生成函数 -IoTDB 中支持的内置函数列表见文档 [运算符和函数](../User-Manual/Operator-and-Expression.md)。 +IoTDB 中支持的内置函数列表见文档 [运算符和函数](../SQL-Manual/Operator-and-Expression.md)。 ##### 自定义时间序列生成函数 @@ -691,7 +691,7 @@ It costs 0.002s ### 时间过滤条件 -使用时间过滤条件可以筛选特定时间范围的数据。对于时间戳支持的格式，请参考 [时间戳类型](../Basic-Concept/Data-Type.md) 。 +使用时间过滤条件可以筛选特定时间范围的数据。对于时间戳支持的格式，请参考 [时间戳类型](../Background-knowledge/Data-Type.md) 。 示例如下： @@ -2961,7 +2961,7 @@ select s1, s2 into root.sg_copy.d1(t1, t2), aligned root.sg_copy.d2(t1, t2) from #### 其他要注意的点 - 对于一般的聚合查询，时间戳是无意义的，约定使用 0 来存储。 -- 当目标序列存在时，需要保证源序列和目标时间序列的数据类型兼容。关于数据类型的兼容性，查看文档 [数据类型](../Basic-Concept/Data-Type.md#数据类型兼容性)。 +- 当目标序列存在时，需要保证源序列和目标时间序列的数据类型兼容。关于数据类型的兼容性，查看文档 [数据类型](../Background-knowledge/Data-Type.md#数据类型兼容性)。 - 当目标序列不存在时，系统将自动创建目标序列（包括 database）。 - 当查询的序列不存在或查询的序列不存在数据，则不会自动创建目标序列。 @@ -3027,7 +3027,7 @@ It costs 0.375s * 所有 `SELECT` 子句中源序列的 `WRITE_SCHEMA` 权限。 * 所有 `INTO` 子句中目标序列 `WRITE_DATA` 权限。 -更多用户权限相关的内容，请参考[权限管理语句](./Authority-Management.md)。 +更多用户权限相关的内容，请参考[权限管理语句](../User-Manual/Authority-Management.md)。 ### 相关配置参数 diff --git a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md index 2fc384714..8f2892a12 100644 --- a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md +++ b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md @@ -73,4 +73,4 @@ Apache IoTDB 具备以下优势和特性： - 天谋科技官网：https://www.timecho.com/ -- TimechoDB 安装部署与使用文档：[快速上手](../QuickStart/QuickStart_timecho.md) \ No newline at end of file +- TimechoDB 安装部署与使用文档：[快速上手](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html) \ No newline at end of file diff --git a/src/zh/UserGuide/latest/Reference/Syntax-Rule.md b/src/zh/UserGuide/latest/Reference/Syntax-Rule.md index dbf6d7e24..6a80b7287 100644 --- a/src/zh/UserGuide/latest/Reference/Syntax-Rule.md +++ b/src/zh/UserGuide/latest/Reference/Syntax-Rule.md @@ -138,7 +138,7 @@ ### 时间戳常量 -时间戳是一个数据到来的时间点，在 IoTDB 中分为绝对时间戳和相对时间戳。详细信息可参考 [数据类型文档](../Basic-Concept/Data-Type.md)。 +时间戳是一个数据到来的时间点，在 IoTDB 中分为绝对时间戳和相对时间戳。详细信息可参考 [数据类型文档](../Background-knowledge/Data-Type.md)。 特别地，`NOW()`表示语句开始执行时的服务端系统时间戳。 diff --git a/src/zh/UserGuide/latest/SQL-Manual/Operator-and-Expression.md b/src/zh/UserGuide/latest/SQL-Manual/Operator-and-Expression.md index 5304a35ac..df99144bf 100644 --- a/src/zh/UserGuide/latest/SQL-Manual/Operator-and-Expression.md +++ b/src/zh/UserGuide/latest/SQL-Manual/Operator-and-Expression.md @@ -33,7 +33,7 @@ |`+` |加| |`-` |减| -详细说明及示例见文档 [算数运算符和函数](../Reference/Function-and-Expression.md#算数运算符)。 +详细说明及示例见文档 [算数运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符)。 ### 比较运算符 |运算符 |含义| @@ -55,7 +55,7 @@ |`IN` / `CONTAINS` |是指定列表中的值| |`NOT IN` / `NOT CONTAINS` |不是指定列表中的值| -详细说明及示例见文档 [比较运算符和函数](../Reference/Function-and-Expression.md#比较运算符和函数)。 +详细说明及示例见文档 [比较运算符和函数](../SQL-Manual/Function-and-Expression.md#比较运算符和函数)。 ### 逻辑运算符 |运算符 |含义| @@ -64,7 +64,7 @@ |`AND` / `&` / `&&` |逻辑与| |`OR`/ | / || |逻辑或| -详细说明及示例见文档 [逻辑运算符](../Reference/Function-and-Expression.md#逻辑运算符)。 +详细说明及示例见文档 [逻辑运算符](../SQL-Manual/Function-and-Expression.md#逻辑运算符)。 ### 运算符优先级 @@ -110,7 +110,7 @@ OR, |, || | MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | 第一个输入 x 可以是任意类型，第二个输入 y 只能是 INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE | 与第一个输入 x 的数据类型一致 | | MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | 第一个输入 x 可以是任意类型，第二个输入 y 只能是 INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE | 与第一个输入 x 的数据类型一致 | -详细说明及示例见文档 [聚合函数](../Reference/Function-and-Expression.md#聚合函数)。 +详细说明及示例见文档 [聚合函数](../SQL-Manual/Function-and-Expression.md#聚合函数)。 ### 数学函数 @@ -138,7 +138,7 @@ OR, |, || | SQRT | INT32 / INT64 / FLOAT / DOUBLE | DOUBLE | | Math#sqrt(double) | -详细说明及示例见文档 [数学函数](../Reference/Function-and-Expression.md#数学函数)。 +详细说明及示例见文档 [数学函数](../SQL-Manual/Function-and-Expression.md#数学函数)。 ### 比较函数 @@ -147,7 +147,7 @@ OR, |, || | ON_OFF | INT32 / INT64 / FLOAT / DOUBLE | `threshold`:DOUBLE | BOOLEAN | 返回`ts_value >= threshold`的bool值 | | IN_RANGE | INT32 / INT64 / FLOAT / DOUBLE | `lower`:DOUBLE
`upper`:DOUBLE | BOOLEAN | 返回`ts_value >= lower && ts_value <= upper`的bool值 | | -详细说明及示例见文档 [比较运算符和函数](../Reference/Function-and-Expression.md#比较运算符和函数)。 +详细说明及示例见文档 [比较运算符和函数](../SQL-Manual/Function-and-Expression.md#比较运算符和函数)。 ### 字符串函数 @@ -167,7 +167,7 @@ OR, |, || | TRIM | TEXT STRING | 无 | TEXT | 移除字符串前后的空格 | | STRCMP | TEXT STRING | 无 | TEXT | 用于比较两个输入序列，如果值相同返回 `0` , 序列1的值小于序列2的值返回一个`负数`，序列1的值大于序列2的值返回一个`正数` | -详细说明及示例见文档 [字符串处理函数](../Reference/Function-and-Expression.md#字符串处理)。 +详细说明及示例见文档 [字符串处理函数](../SQL-Manual/Function-and-Expression.md#字符串处理)。 ### 数据类型转换函数 @@ -175,7 +175,7 @@ OR, |, || | ------ | ------------------------------------------------------------ | ------------------------ | ---------------------------------- | | CAST | `type`:输出的数据点的类型，只能是 INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT | 由输入属性参数`type`决定 | 将数据转换为`type`参数指定的类型。 | -详细说明及示例见文档 [数据类型转换](../Reference/Function-and-Expression.md#数据类型转换)。 +详细说明及示例见文档 [数据类型转换](../SQL-Manual/Function-and-Expression.md#数据类型转换)。 ### 常序列生成函数 @@ -185,7 +185,7 @@ OR, |, || | PI | 无 | DOUBLE | 常序列的值：`π` 的 `double` 值，圆的周长与其直径的比值，即圆周率，等于 *Java标准库* 中的`Math.PI`。 | | E | 无 | DOUBLE | 常序列的值：`e` 的 `double` 值，自然对数的底，它等于 *Java 标准库* 中的 `Math.E`。 | -详细说明及示例见文档 [常序列生成函数](../Reference/Function-and-Expression.md#常序列生成函数)。 +详细说明及示例见文档 [常序列生成函数](../SQL-Manual/Function-and-Expression.md#常序列生成函数)。 ### 选择函数 @@ -194,7 +194,7 @@ OR, |, || | TOP_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: 最多选择的数据点数，必须大于 0 小于等于 1000 | 与输入序列的实际类型一致 | 返回某时间序列中值最大的`k`个数据点。若多于`k`个数据点的值并列最大，则返回时间戳最小的数据点。 | | BOTTOM_K | INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP | `k`: 最多选择的数据点数，必须大于 0 小于等于 1000 | 与输入序列的实际类型一致 | 返回某时间序列中值最小的`k`个数据点。若多于`k`个数据点的值并列最小，则返回时间戳最小的数据点。 | -详细说明及示例见文档 [选择函数](../Reference/Function-and-Expression.md#选择函数)。 +详细说明及示例见文档 [选择函数](../SQL-Manual/Function-and-Expression.md#选择函数)。 ### 区间查询函数 @@ -205,7 +205,7 @@ OR, |, || | ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:可选，默认值1
`max`:可选，默认值`Long.MAX_VALUE` | Long | 返回时间序列连续为0(false)的开始时间与其后数据点的个数，数据点个数n满足`n >= min && n <= max` | | | NON_ZERO_COUNT | INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN | `min`:可选，默认值1
`max`:可选，默认值`Long.MAX_VALUE` | Long | 返回时间序列连续不为0(false)的开始时间与其后数据点的个数，数据点个数n满足`n >= min && n <= max` | | -详细说明及示例见文档 [区间查询函数](../Reference/Function-and-Expression.md#区间查询函数)。 +详细说明及示例见文档 [区间查询函数](../SQL-Manual/Function-and-Expression.md#区间查询函数)。 ### 趋势计算函数 @@ -222,7 +222,7 @@ OR, |, || |------|--------------------------------|------------------------------------------------------------------------------------------------------------------------|--------|------------------------------------------------| | DIFF | INT32 / INT64 / FLOAT / DOUBLE | `ignoreNull`：可选，默认为true；为true时，前一个数据点值为null时，忽略该数据点继续向前找到第一个出现的不为null的值；为false时，如果前一个数据点为null，则不忽略，使用null进行相减，结果也为null | DOUBLE | 统计序列中某数据点的值与前一数据点的值的差。第一个数据点没有对应的结果输出，输出值为null | -详细说明及示例见文档 [趋势计算函数](../Reference/Function-and-Expression.md#趋势计算函数)。 +详细说明及示例见文档 [趋势计算函数](../SQL-Manual/Function-and-Expression.md#趋势计算函数)。 ### 采样函数 @@ -241,7 +241,7 @@ OR, |, || | ------------- | ------------------------------ | ---- | ------------------------ | -------------------------- | | CHANGE_POINTS | INT32 / INT64 / FLOAT / DOUBLE | / | 与输入序列的实际类型一致 | 去除输入序列中的连续相同值 | -详细说明及示例见文档 [时间序列处理](../Reference/Function-and-Expression.md#时间序列处理)。 +详细说明及示例见文档 [时间序列处理](../SQL-Manual/Function-and-Expression.md#时间序列处理)。 ## Lambda 表达式 @@ -250,7 +250,7 @@ OR, |, || | ------ | ----------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------- | ---------------------------------------------- | | JEXL | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | `expr`是一个支持标准的一元或多元参数的lambda表达式，符合`x -> {...}`或`(x, y, z) -> {...}`的格式，例如`x -> {x * 2}`, `(x, y, z) -> {x + y * z}` | INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN | 返回将输入的时间序列通过lambda表达式变换的序列 | -详细说明及示例见文档 [Lambda 表达式](../Reference/Function-and-Expression.md#Lambda表达式) +详细说明及示例见文档 [Lambda 表达式](../SQL-Manual/Function-and-Expression.md#Lambda表达式) ## 条件表达式 @@ -258,7 +258,7 @@ OR, |, || |---------------------------|-----------| | `CASE` | 类似if else | -详细说明及示例见文档 [条件表达式](../Reference/Function-and-Expression.md#条件表达式) +详细说明及示例见文档 [条件表达式](../SQL-Manual/Function-and-Expression.md#条件表达式) ## SELECT 表达式 @@ -296,7 +296,7 @@ select s1 as temperature, s2 as speed from root.ln.wf01.wt01; #### 运算符 -IoTDB 中支持的运算符列表见文档 [运算符和函数](../Reference/Function-and-Expression.md#算数运算符和函数)。 +IoTDB 中支持的运算符列表见文档 [运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符和函数)。 #### 函数 @@ -314,7 +314,7 @@ select sin(s1), count(s1) from root.sg.d1; select s1, count(s1) from root.sg.d1 group by ([10,100),10ms); ``` -IoTDB 支持的聚合函数见文档 [聚合函数](../Reference/Function-and-Expression.md#聚合函数)。 +IoTDB 支持的聚合函数见文档 [聚合函数](../SQL-Manual/Function-and-Expression.md#聚合函数)。 ##### 时间序列生成函数 @@ -324,11 +324,11 @@ IoTDB 支持的聚合函数见文档 [聚合函数](../Reference/Function-and-Ex ###### 内置时间序列生成函数 -IoTDB 中支持的内置函数列表见文档 [运算符和函数](../Reference/Function-and-Expression.md#算数运算符)。 +IoTDB 中支持的内置函数列表见文档 [运算符和函数](../SQL-Manual/Function-and-Expression.md#算数运算符)。 ###### 自定义时间序列生成函数 -IoTDB 支持通过用户自定义函数（点击查看： [用户自定义函数](../Reference/UDF-Libraries.md) ）能力进行函数功能扩展。 +IoTDB 支持通过用户自定义函数（点击查看： [用户自定义函数](../SQL-Manual/UDF-Libraries.md) ）能力进行函数功能扩展。 #### 嵌套表达式举例 diff --git a/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md index c3591e036..038c85164 100644 --- a/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md +++ b/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md @@ -1214,11 +1214,11 @@ select change_points(s1), change_points(s2), change_points(s3), change_points(s4 ## 数据质量函数库 -更多见文档[UDF-Libraries](./UDF-Libraries_timecho.md) +更多见文档[UDF-Libraries](../SQL-Manual/UDF-Libraries.md) ### 数据质量 -更多见文档[Data-Quality](./UDF-Libraries_timecho.md#数据质量) +更多见文档[Data-Quality](../SQL-Manual/UDF-Libraries.md#数据质量) ```sql # Completeness @@ -1243,7 +1243,7 @@ select Accuracy(t1,t2,t3,m1,m2,m3) from root.test ### 数据画像 -更多见文档[Data-Profiling](./UDF-Libraries_timecho.md#数据画像) +更多见文档[Data-Profiling](../SQL-Manual/UDF-Libraries.md#数据画像) ```sql # ACF @@ -1323,7 +1323,7 @@ select zscore(s1) from root.test ### 异常检测 -更多见文档[Anomaly-Detection](./UDF-Libraries_timecho.md#异常检测) +更多见文档[Anomaly-Detection](../SQL-Manual/UDF-Libraries.md#异常检测) ```sql # IQR @@ -1358,7 +1358,7 @@ select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3 ### 频域分析 -更多见文档[Frequency-Domain](./UDF-Libraries_timecho.md#频域分析) +更多见文档[Frequency-Domain](../SQL-Manual/UDF-Libraries.md#频域分析) ```sql # Conv @@ -1390,7 +1390,7 @@ select envelope(s1) from root.test.d1 ### 数据匹配 -更多见文档[Data-Matching](./UDF-Libraries_timecho.md#数据匹配) +更多见文档[Data-Matching](../SQL-Manual/UDF-Libraries.md#数据匹配) ```sql # Cov @@ -1411,7 +1411,7 @@ select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05 ### 数据修复 -更多见文档[Data-Repairing](./UDF-Libraries_timecho.md#数据修复) +更多见文档[Data-Repairing](../SQL-Manual/UDF-Libraries.md#数据修复) ```sql # TimestampRepair @@ -1436,7 +1436,7 @@ select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2 ### 序列发现 -更多见文档[Series-Discovery](./UDF-Libraries_timecho.md#序列发现) +更多见文档[Series-Discovery](../SQL-Manual/UDF-Libraries.md#序列发现) ```sql # ConsecutiveSequences @@ -1449,7 +1449,7 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1 ### 机器学习 -更多见文档[Machine-Learning](./UDF-Libraries_timecho.md#机器学习) +更多见文档[Machine-Learning](../SQL-Manual/UDF-Libraries.md#机器学习) ```sql # AR diff --git a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries.md b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries.md new file mode 100644 index 000000000..2867a78eb --- /dev/null +++ b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries.md @@ -0,0 +1,23 @@ +--- +redirectTo: UDF-Libraries_apache.html +--- + \ No newline at end of file diff --git a/src/zh/UserGuide/latest/User-Manual/Data-Sync_apache.md b/src/zh/UserGuide/latest/User-Manual/Data-Sync_apache.md index 0ce0c4ee8..3c84f50a2 100644 --- a/src/zh/UserGuide/latest/User-Manual/Data-Sync_apache.md +++ b/src/zh/UserGuide/latest/User-Manual/Data-Sync_apache.md @@ -255,7 +255,7 @@ IoTDB> SHOW PIPEPLUGINS -导入自定义插件可参考[流处理框架](./Streaming_timecho.md#自定义流处理插件管理)章节。 +导入自定义插件可参考[流处理框架](./Streaming_apache.md#自定义流处理插件管理)章节。 ## 使用示例 diff --git a/src/zh/UserGuide/latest/User-Manual/IoTDB-View_timecho.md b/src/zh/UserGuide/latest/User-Manual/IoTDB-View_timecho.md index 17817fde5..96f8de72c 100644 --- a/src/zh/UserGuide/latest/User-Manual/IoTDB-View_timecho.md +++ b/src/zh/UserGuide/latest/User-Manual/IoTDB-View_timecho.md @@ -308,7 +308,7 @@ AS SELECT temperature FROM root.db.* ``` -这里仿照了查询写回（`SELECT INTO`）对命名规则的约定，使用变量占位符来指定命名规则。可以参考：[查询写回（SELECT INTO）](../User-Manual/Query-Data.md#查询写回（INTO-子句）) +这里仿照了查询写回（`SELECT INTO`）对命名规则的约定，使用变量占位符来指定命名规则。可以参考：[查询写回（SELECT INTO）](../Basic-Concept/Query-Data.md#查询写回（INTO-子句）) 这里`root.db.*.temperature`指定了有哪些时间序列会被包含在视图中；`${2}`则指定了从时间序列中的哪个节点提取出名字来命名序列视图。 diff --git a/src/zh/UserGuide/latest/User-Manual/User-defined-function_apache.md b/src/zh/UserGuide/latest/User-Manual/User-defined-function_apache.md index 237031ae4..b375a8911 100644 --- a/src/zh/UserGuide/latest/User-Manual/User-defined-function_apache.md +++ b/src/zh/UserGuide/latest/User-Manual/User-defined-function_apache.md @@ -188,7 +188,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 用户在使用 UDF 时会涉及到 `USE_UDF` 权限，具备该权限的用户才被允许执行 UDF 注册、卸载和查询操作。 -更多用户权限相关的内容，请参考 [权限管理语句](./Authority-Management.md##权限管理)。 +更多用户权限相关的内容，请参考 [权限管理语句](../User-Manual/Authority-Management.md##权限管理)。 ## 4. UDF 函数库 diff --git a/src/zh/UserGuide/latest/User-Manual/User-defined-function_timecho.md b/src/zh/UserGuide/latest/User-Manual/User-defined-function_timecho.md index 05c253517..0cc6c55a3 100644 --- a/src/zh/UserGuide/latest/User-Manual/User-defined-function_timecho.md +++ b/src/zh/UserGuide/latest/User-Manual/User-defined-function_timecho.md @@ -188,7 +188,7 @@ udf_reader_transformer_collector_memory_proportion=1:1:1 用户在使用 UDF 时会涉及到 `USE_UDF` 权限，具备该权限的用户才被允许执行 UDF 注册、卸载和查询操作。 -更多用户权限相关的内容，请参考 [权限管理语句](./Authority-Management.md##权限管理)。 +更多用户权限相关的内容，请参考 [权限管理语句](../User-Manual/Authority-Management.md##权限管理)。 ## 4. UDF 函数库