+ 
+ 
- Second step: For lines with issues, move to the left side where a plus sign appears. Click the plus, leave a comment, and then click "Start a review". At this point, all review comments are saved temporarily and are not visible to others.
- 
+ 
- Third step: After all the comments are added, click "Review changes", choose your feedback — "Approve" if it’s ready to merge, "Request changes" if there are bugs that need correction, or "Comment" if you're unsure. Finally, submit your review comments which will then be visible to the person who submitted the PR.
- 
+ 
#### Merge a PR
diff --git a/src/Development/stage/HowtoContributeCode.md b/src/Development/stage/HowtoContributeCode.md
index bc4241e4e..3cb156ba5 100644
--- a/src/Development/stage/HowtoContributeCode.md
+++ b/src/Development/stage/HowtoContributeCode.md
@@ -127,11 +127,11 @@ Checklist
How to review a pr
1. Click Files changed
-
+
-2. Add review comments. First, move your mouse to the left. And then there will be a plus sign, click the plus sign. Second, write comments. Third, click Start a Review. At this step, all review comments will be temporarily saved, which others can not see.
+2. Add review comments. First, move your mouse to the left. And then there will be a plus sign, click the plus sign. Second, write comments. Third, click Start a Review. At this step, all review comments will be temporarily saved, which others can not see.
3. Submit review. After all the comments are added, click Review Changes and select your opinion. Select "Approve" for those that can be combined. Select "Request Changes" or "Comment" for those that need to be modified. Select "Comment" for those that are not sure. Finally, submit a review and only the person submitting the PR can see the review.
-
+
### Merging a PR
diff --git a/src/UserGuide/Master/Table/Basic-Concept/Sample-Data.md b/src/UserGuide/Master/Table/Basic-Concept/Sample-Data.md
index 843b61d55..4b6f45201 100644
--- a/src/UserGuide/Master/Table/Basic-Concept/Sample-Data.md
+++ b/src/UserGuide/Master/Table/Basic-Concept/Sample-Data.md
@@ -28,11 +28,11 @@ This chapter mainly introduces a simple temporal data application scenario and t
Table 1 and Table 2 both have the following table structure:
-
+
## 2 Import Statement
-The following is the SQL statement used to construct the above table structure and data. You can click here（[sample_data.sql](https://alioss.timecho.com/upload/sample_data.sql)）to download all the SQL statements and execute them in CLI to import the data into your IoTDB.
+The following is the SQL statement used to construct the above table structure and data. You can click here（[sample_data.sql](/img/sample_data.sql)）to download all the SQL statements and execute them in CLI to import the data into your IoTDB.
```SQL
-- Create a table with table names that are close to business semantics. Here, we use table1 instead
diff --git a/src/UserGuide/Master/Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md b/src/UserGuide/Master/Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md
index 6fe8203b5..2b7445540 100644
--- a/src/UserGuide/Master/Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md
+++ b/src/UserGuide/Master/Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md
@@ -23,7 +23,7 @@
This section describes how to manually deploy an instance that includes 3 ConfigNodes and 3 DataNodes, commonly known as a 3C3D cluster.
+
+ 
+
- 
+ 
+ 
- 
- 
+ 
+ 
+ 
+
## Built-in MQTT Service
@@ -56,7 +56,7 @@ or
```
or json array of the above two.
-
+
## MQTT Configurations
The IoTDB MQTT service load configurations from `${IOTDB_HOME}/${IOTDB_CONF}/iotdb-system.properties` by default.
diff --git a/src/UserGuide/Master/Tree/API/Programming-ODBC.md b/src/UserGuide/Master/Tree/API/Programming-ODBC.md
index 8e0d74852..7d2b9bb20 100644
--- a/src/UserGuide/Master/Tree/API/Programming-ODBC.md
+++ b/src/UserGuide/Master/Tree/API/Programming-ODBC.md
@@ -39,7 +39,7 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
1. **Download Zappy-Sys ODBC-JDBC Bridge**:
Enter the https://zappysys.com/products/odbc-powerpack/odbc-jdbc-bridge-driver/ website, and click "download".
- 
+ 
2. **Prepare IoTDB**: Set up IoTDB cluster, and write a row of data arbitrarily.
```sql
@@ -49,11 +49,11 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
3. **Deploy and Test the Bridge**:
1. Open ODBC Data Sources(32/64 bit), depending on the bits of Windows. One possible position is `C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools`.
- 
+ 
2. Click on "add" and select ZappySys JDBC Bridge.
- 
+ 
3. Fill in the following settings:
@@ -65,15 +65,15 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
| User name | IoTDB's user name | root |
| User password | IoTDB's password | root |
- 
+ 
4. Click on "Test Connection" button, and a "Test Connection: SUCCESSFUL" should appear.
- 
+ 
5. Click the "Preview" button above, and replace the original query text with `select * from root.**`, then click "Preview Data", and the query result should correctly.
- 
+ 
4. **Operate IoTDB's data with ODBC**: After correct deployment, you can use Microsoft's ODBC library to operate IoTDB's data. Here's an example written in C#:
```C#
diff --git a/src/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md b/src/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md
index 703b47c68..a1084df2a 100644
--- a/src/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md
+++ b/src/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md
@@ -30,7 +30,7 @@ OPC UA is a technical specification used in the automation field for communicati
- **Client/Server Mode**：In this mode, IoTDB's stream processing engine establishes a connection with the OPC UA Server via an OPC UA Sink. The OPC UA Server maintains data within its Address Space, from which IoTDB can request and retrieve data. Additionally, other OPC UA Clients can access the data on the server.
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+
IoTDB's cluster includes the following common concepts:
diff --git a/src/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md b/src/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md
index af669a6d6..42344aa47 100644
--- a/src/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md
@@ -36,7 +36,7 @@
The following figure shows a common IoTDB 3C3D (3 ConfigNodes, 3 DataNodes) cluster deployment pattern:
-
+
IoTDB's cluster includes the following common concepts:
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 e1aeb3564..27573b2b6 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
@@ -25,7 +25,7 @@ A wind power IoT scenario is taken as an example to illustrate how to create a c
According to the enterprise organization structure and equipment entity hierarchy, it is expressed as an attribute hierarchy structure, as shown below. The hierarchical from top to bottom is: power group layer - power plant layer - entity layer - measurement layer. ROOT is the root node, and each node of measurement layer is a leaf node. In the process of using IoTDB, the attributes on the path from ROOT node is directly connected to each leaf node with ".", thus forming the name of a timeseries in IoTDB. For example, The left-most path in Figure 2.1 can generate a timeseries named `root.ln.wf01.wt01.status`.
-
+
In the following chapters of data definition language, data operation language and Java Native Interface, various operations related to aligned timeseries will be introduced one by one.
diff --git a/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md b/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md
index 20aaef327..6ff81da37 100644
--- a/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/Master/Tree/Basic-Concept/Navigating_Time_Series_Data.md
@@ -24,25 +24,25 @@
In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
-
+
Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
-
+
The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
## Key Concepts of Time Series Data
The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
-
+
### Data Point
- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
-
+
### Measurement Points
diff --git a/src/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md b/src/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md
index 58c01a886..89b9bee35 100644
--- a/src/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md
+++ b/src/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md
@@ -352,9 +352,9 @@ All devices will definitely have a TTL, meaning it cannot be null. INF represent
IoTDB supports the device template function, enabling different entities of the same type to share metadata, reduce the memory usage of metadata, and simplify the management of numerous entities and measurements.
-
+
-
+
### Create Device Template
@@ -793,7 +793,7 @@ It costs 0.004s
Then the Metadata Tree will be as below:
-
+
+
##### Precautions for Use
@@ -1096,7 +1096,7 @@ A given interval threshold to create a new group of data when the difference bet
The figure below is a grouping diagram under `GROUP BY SESSION`.
-
+
##### Precautions for Use
diff --git a/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md
index 610ec0b72..83baab566 100644
--- a/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md
+++ b/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md
@@ -31,8 +31,8 @@
### Deployment mode
- 
+ 
+ 
+ 
- There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction.
- It is recommended to create a Python interpreter venv virtual environment in the `iotdb-enterprise-ainode-
-
+
+
+
- There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction.
- It is recommended to create a Python interpreter venv virtual environment in the `iotdb-enterprise-ainode-
+
+
+
+
-
+
+
-
-
+
+
+
- 
+ 
+ 
-
+
+
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
- 
+ 
+ 
- 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+
-
+
### Design in dashboard
Add diagrams in dashboard and customize your query. See http://docs.grafana.org/guides/getting_started/
-
+
## config grafana
diff --git a/src/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md b/src/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md
index dc8370464..0597fb124 100644
--- a/src/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md
@@ -119,9 +119,9 @@ Grafana displays data in a web page dashboard. Please open your browser and visi
Click the `Settings` icon on the left, select the `Data Source` option, and then click `Add data source`.
-
+
-
+
Select the `Apache IoTDB` data source.
@@ -132,22 +132,22 @@ Select the `Apache IoTDB` data source.
Click `Save & Test`, and `Data source is working` will appear.
-
+
### Create a new Panel
Click the `Dashboards` icon on the left, and select `Manage` option.
-
+
Click the `New Dashboard` icon on the top right, and select `Add an empty panel` option.
-
+
Grafana plugin supports SQL: Full Customized mode and SQL: Drop-down List mode, and the default mode is SQL: Full Customized mode.
-
+
#### SQL: Full Customized input method
@@ -155,7 +155,7 @@ Enter content in the SELECT, FROM , WHERE and CONTROL input box, where the WHERE
If a query involves multiple expressions, we can click `+` on the right side of the SELECT input box to add expressions in the SELECT clause, or click `+` on the right side of the FROM input box to add a path prefix:
-
+
SELECT input box: contents can be the time series suffix, function, udf, arithmetic expression, or nested expressions. You can also use the as clause to rename the result.
@@ -193,7 +193,7 @@ Tip: Statements like `select * from root.xx.**` are not recommended because thos
Select a time series in the TIME-SERIES selection box, select a function in the FUNCTION option, and enter the contents in the SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL input boxes, where TIME-SERIES is a required item and the rest are non required items.
-
+
### Support for variables and template functions
@@ -201,34 +201,34 @@ Both SQL: Full Customized and SQL: Drop-down List input methods support the vari
After creating a new Panel, click the Settings button in the upper right corner:
-
+
Select `Variables`, click `Add variable`:
-
+
Example 1：Enter `Name`, `Label`, and `Query`, and then click the `Update` button:
-
+
Apply Variables, enter the variable in the `grafana panel` and click the `save` button:
-
+
Example 2: Nested use of variables:
-
+
-
+
-
+
Example 3: using function variables
-
+
-
+
The Name in the above figure is the variable name and the variable name we will use in the panel in the future. Label is the display name of the variable. If it is empty, the variable of Name will be displayed. Otherwise, the name of the Label will be displayed.
There are Query, Custom, Text box, Constant, DataSource, Interval, Ad hoc filters, etc. in the Type drop-down, all of which can be used in IoTDB's Grafana Plugin
@@ -252,14 +252,14 @@ This plugin supports Grafana alert function.
1. In the Grafana panel, click the `alerting` button, as shown in the following figure:
-
+
2. Click `Create alert rule from this panel`, as shown in the figure below:
-
+
3. Set query and alarm conditions in step 1. Conditions represent query conditions, and multiple combined query conditions can be configured. As shown below:
-
+
The query condition in the figure: `min() OF A IS BELOW 0`, means that the condition will be triggered when the minimum value in the A tab is 0, click this function to change it to another function.
@@ -269,27 +269,27 @@ For example, we have 3 conditions in the following order: Condition: B (Evaluate
4. After selecting indicators and alarm rules, click the `Preview` button to preview the data as shown in the figure below:
-
+
5. In step 2, specify the alert evaluation interval, and for `Evaluate every`, specify the evaluation frequency. Must be a multiple of 10 seconds. For example, 1m, 30s.
For `Evaluate for`, specify the duration before the alert fires. As shown below:
-
+
6. In step 3, add the storage location, rule group, and other metadata associated with the rule. Where `Rule name` specifies the name of the rule. Rule names must be unique.
-
+
7. In step 4, add a custom label. Add a custom label by selecting an existing key-value pair from the drop-down list, or add a new label by entering a new key or value. As shown below:
-
+
8. Click `Save` to save the rule or click `Save and Exit` to save the rule and return to the alerts page.
9. Commonly used alarm states include `Normal`, `Pending`, `Firing` and other states, as shown in the figure below:
-
-
+
+
10. We can also configure `Contact points` for alarms to receive alarm notifications. For more detailed operations, please refer to the official document (https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/).
diff --git a/src/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index 5d9402db8..41332ee21 100644
--- a/src/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -36,7 +36,7 @@
The specific relationship and content between the two modules and ignition are shown in the following figure.
- 
+ 
## Installation Requirements
@@ -59,23 +59,23 @@ The Ignition-IoTDB Connector provides the ability to store the data collected by
Step 1: Enter the `Configuration` - `System` - `Modules` module and click on the `Install or Upgrade a Module` button at the bottom
-
+
Step 2: Select the obtained `modl`, select the file and upload it, click `Install`, and trust the relevant certificate.
-
+
Step 3: After installation is completed, you can see the following content
-
+
Step 4: Enter the `Configuration` - `Tags` - `History` module and click on `Create new Historical Tag Provider` below
-
+
Step 5: Select `IoTDB` and fill in the configuration information
-
+
The configuration content is as follows:
@@ -163,21 +163,21 @@ The configuration content is as follows:
- After configuring the `Provider`, you can use the `IoTDB Tag Historian` in the `Designer`, just like using other `Providers`. Right click on the corresponding `Tag` and select `Edit Tag (s) `, then select the History category in the Tag Editor
- 
+ 
- Set `History Disabled` to `true`, select `Storage Provider` as the `Provider` created in the previous step, configure other parameters as needed, click `OK`, and then save the project. At this point, the data will be continuously stored in the 'IoTDB' instance according to the set content.
- 
+ 
#### Read Data
- You can also directly select the tags stored in IoTDB under the Data tab of the Report
- 
+ 
- You can also directly browse relevant data in Components
- 
+ 
#### Script module: This function can interact with IoTDB
@@ -243,15 +243,15 @@ The configuration content is as follows:
Step 1: Enter the `Configuration` - `Databases` -`Drivers` module and create the `Translator`
-
+
Step 2: Enter the `Configuration` - `Databases` - `Drivers` module, create a `JDBC Driver` , select the `Translator` configured in the previous step, and upload the downloaded `IoTDB JDBC`. Set the Classname to `org. apache. iotdb. jdbc.IoTDBDriver`
-
+
Step 3: Enter the `Configuration` - `Databases` - `Connections` module, create a new `Connections` , select the`IoTDB Driver` created in the previous step for `JDBC Driver`, configure the relevant information, and save it to use
-
+
### Instructions
@@ -265,11 +265,11 @@ Select the previously created `Connection` from the `Data Source` in the `Transa
Do not select other options, set the fields, and after `enabled` , the data will be installed and stored in the corresponding IoTDB
-
+
#### Query
- Select `Data Source` in the `Database Query Browser` and select the previously created `Connection` to write an SQL statement to query the data in IoTDB
-
+
diff --git a/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md b/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md
index 0fb5b73d9..fca46c19d 100644
--- a/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md
+++ b/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 Manually Create Directories
@@ -400,7 +400,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. Scaling
@@ -408,7 +408,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
Add a new PV; scaling is only possible with available PVs.
-
+
**Note: DataNode cannot join the cluster after restart**
@@ -425,7 +425,7 @@ Modify the values.yaml file in iotdb-cluster-k8s/helm to change the number of Co
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 Scale DataNode
diff --git a/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md b/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md
index c59ddcce6..f16d4727b 100644
--- a/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md
+++ b/src/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 Manually Create Directories
@@ -400,7 +400,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. Scaling
@@ -408,7 +408,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
Add a new PV; scaling is only possible with available PVs.
-
+
**Note: DataNode cannot join the cluster after restart**
@@ -425,7 +425,7 @@ Modify the values.yaml file in iotdb-cluster-k8s/helm to change the number of Co
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 Scale DataNode
diff --git a/src/UserGuide/Master/Tree/Ecosystem-Integration/Spark-TsFile.md b/src/UserGuide/Master/Tree/Ecosystem-Integration/Spark-TsFile.md
index ed00538c7..151d81e14 100644
--- a/src/UserGuide/Master/Tree/Ecosystem-Integration/Spark-TsFile.md
+++ b/src/UserGuide/Master/Tree/Ecosystem-Integration/Spark-TsFile.md
@@ -94,7 +94,7 @@ The way to display TsFile is dependent on the schema. Take the following TsFile
The existing data in the TsFile are:
-
+
The corresponding SparkSQL table is:
@@ -239,7 +239,7 @@ The way to display TsFile is related to TsFile Schema. Take the following TsFile
The existing data in the file are:
-
+
A set of time-series data
diff --git a/src/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md b/src/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md
index 072d3c378..4689f4d7e 100644
--- a/src/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md
+++ b/src/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard is an open-source IoT platform that enables rapid development, management, and expansion of IoT projects. For more detailed information, please refer to [ThingsBoard Official Website](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/).
- 
+ 
1. Introduction to ThingsBoard-IoTDB
@@ -34,7 +34,7 @@
### Relationship Diagram
- 
+ 
## Installation Requirements
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ## Thingsboard data is written to the da
1. Set up devices and connect datasource: Add a new device under "Entities" - "Devices" in Thingsboard and send data to the specified devices through gateway.
- 
+ 
2. Set rule chain: Set alarm rules for "SD-032F pump" in the rule chain library and set the rule chain as the root chain
- 
+ 
+ 
- 
+ 
+ 
+ 
+ 
TimechoDB Performance Monitoring
- 
- 
+ 
+ 
+ 
SQL Query
- 
- 
+ 
+ 
+ 
+ 
+
| Function Name | Allowed Input Series Data Types | Required Attributes | Output Series Data Type | Description |
| ------------- | ------------------------------- | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2382,7 +2382,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `windowSize`: The number of points in a window. Int data type. **Required**.
+ `slidingStep`: Slide a window by the number of points. Int data type. Optional. If not set, default to the same as `windowSize`.
-
+
**(2) Attributes for the time window:**
@@ -2391,7 +2391,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `displayWindowBegin`: The starting position of the window (included). Long data type. Optional. If not set, default to Long.MIN_VALUE, meaning using the time of the first data point of the input time series as the starting position of the window.
+ `displayWindowEnd`: End time limit (excluded, essentially playing the same role as `WHERE time < displayWindowEnd`). Long data type. Optional. If not set, default to Long.MAX_VALUE, meaning there is no additional end time limit other than the end of the input time series itself.
-
+
#### Examples
diff --git a/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md b/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md
index 2a078041c..ff94c24e2 100644
--- a/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md
+++ b/src/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md
@@ -107,13 +107,13 @@ IoTDB> SHOW DEVICES
For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md).
-
+
-
+
### Create Device Template
diff --git a/src/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md b/src/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md
index c10a6e6a1..479f95527 100644
--- a/src/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md
+++ b/src/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB implements tailored partitioning algorithms for time series data. Building
### Partitioning Strategy
IoTDB maps each sensor in the production environment to a time series. The time series are then partitioned using the series partitioning algorithm to manage their schema, and combined with the time partitioning algorithm to manage their data. The following figure illustrates how IoTDB partitions time series data.
-
+
#### Partitioning Algorithm
Because numerous devices and sensors are commonly deployed in production environments, IoTDB employs a series partitioning algorithm to ensure the size of partition information is manageable. Since the generated time series associated with timestamps, IoTDB uses a time partioning algorithm to clearly distinguish between hot and cold partitions.
@@ -56,7 +56,7 @@ Combining a series partition with a time partition creates a data partition. Sin
### Partition Allocation
IoTDB uses RegionGroups to enable elastic storage of time series, with the number of RegionGroups in the cluster determined by the total resources available across all DataNodes. Since the number of RegionGroups is dynamic, IoTDB can easily scale out. Both the SchemaRegionGroup and DataRegionGroup follow the same partition allocation algorithm, which evenly splits all series partitions. The following figure demonstrates the partition allocation process, where the dynamic RegionGroups match the variously expending time series and cluster.
-
+
#### RegionGroup Expansion
The number of RegionGroups is given by
@@ -76,7 +76,7 @@ To enhance the cluster's availability and performance, IoTDB employs sophisticat
### Storage Load Balance
The number of Regions held by a DataNode reflects its storage load. If the difference in the number of Regions across DataNodes is relatively large, the DataNode with more Regions is likely to become a storage bottleneck. Although a straightforward Round Robin placement algorithm can achieve storage balance by ensuring that each DataNode hosts an equal number of Regions, it compromises the cluster's fault tolerance, as illustrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Place RegionGroup $r_1$'s 2 Regions on DataNodes $n_1$ and $n_2$.
@@ -91,7 +91,7 @@ To address this issue, IoTDB employs a Region placement algorithm that not only
### Computing Load Balance
The number of leader Regions held by a DataNode reflects its Computing load. If the difference in the number of leaders across DataNodes is relatively large, the DataNode with more leaders is likely to become a Computing bottleneck. If the leader selection process is conducted using a transparent Greedy algorithm, the result may be an unbalanced leader distribution when the Regions are fault-tolerantly placed, as demonstrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Select RegionGroup $r_5$'s Region on DataNode $n_5$ as the leader.
diff --git a/src/UserGuide/Master/Tree/Tools-System/Benchmark.md b/src/UserGuide/Master/Tree/Tools-System/Benchmark.md
index 86ff88bca..3ffd64c78 100644
--- a/src/UserGuide/Master/Tree/Tools-System/Benchmark.md
+++ b/src/UserGuide/Master/Tree/Tools-System/Benchmark.md
@@ -25,14 +25,14 @@ IoT-benchmark is a time-series database benchmarking tool based on Java and big
Figure 1-1 below includes the test benchmark process and other extended functions. These processes can be unified by IoT-benchmark. IoT Benchmark supports a variety of workloads, including **pure write, pure query, write query mixed**, etc., supports **software and hardware system monitoring, test metric measurement** and other monitoring functions, and also realizes **initializing the database automatically, test data analysis and system parameter optimization** functions.
-
+
Figure 1-1
Referring to the YCSB test tool's design idea of separating the three components of workload generation, performance metric measurement and database interface, the modular design of IoT-benchmark is shown in Figure 1-2. Different from the YCSB-based test tool system, IoT-benchmark adds a system monitoring module to support the persistence of test data and system monitoring data. In addition, some special load testing functions especially designed for time series data scenarios have been added, such as supporting batch writing and multiple out-of-sequence data writing modes for IoT scenarios.
-
+
Figure 1-2
@@ -71,7 +71,7 @@ Table 1-1 Comparison of big data test benchmarks
The directory structure of the test package is shown in Figure 1-3 below. The test configuration file is conf/config.properties, and the test startup scripts are benchmark\.sh (Linux & MacOS) and benchmark.bat (Windows). The detailed usage of the files is shown in Table 1-2.
-
+
Figure 1-3 List of files and folders
@@ -101,7 +101,7 @@ Table 1-2 Usage list of files and folders
All the log files of the test are stored in the logs folder, and the test results are stored in the data/csvOutput folder after the test is completed. For example, after the test, we get the following result matrix:
-
+
- Result Matrix
- OkOperation: successful operations
@@ -256,24 +256,24 @@ Table 2-3 Configuration parameter information
First, start the tested time series databases Apache-IoTDB and KairosDB on 172.21.4.3 and 172.21.4.4 respectively, and then start server resource monitoring through the ser-benchamrk\.sh script on 172.21.4.2, 172.21.4.3 and 172.21.4.4 (Figure 2-1). Then modify the conf/config.properties files in the iotdb-0.13-0.0.1 and kairosdb-0.0.1 folders in 172.21.4.2 according to Table 2-3 to meet the test requirements. Use benchmark\.sh to start the writing test of Apache-IoTDB and KairosDB successively.
-
+
Figure 2-1 Server monitoring tasks
For example, if we first start the test on KairosDB, IoT-benchmark will create a CONFIG data table in the MySQL database to store the configuration information of this test (Figure 2-2), and there will be a log output of the current test progress during the test execution (Figure 2-3) . When the test is completed, the test result will be output (Figure 2-3), and the result will be written into the FINAL_RESULT data table (Figure 2-4).
-
+
Figure 2-2 Test configuration information table
-
-
-
-
+
+
+
+
Figure 2-3 Test progress and results
-
+
@@ -283,7 +283,7 @@ Afterwards, we will start the test on Apache-IoTDB. The same IoT-benchmark will
According to the test result information, we know that under the same configuration the write delay times of Apache-IoTDB and KairosDB are 55.98ms and 1324.45ms respectively; the write throughputs are 5,125,600.86 points/second and 224,819.01 points/second respectively; the tests were executed respectively 585.30 seconds and 11777.99 seconds. And KairosDB has a write failure. After investigation, it is found that the data disk usage has reached 100%, and there is no disk space to continue receiving data. However, Apache-IoTDB has no write failure, and the disk space occupied after all data is written is only 4.7G (as shown in Figure 2-5); Apache-IoTDB is better than KairosDB in terms of write throughput and disk occupation. Of course, there will be other tests in the follow-up to observe and compare from various aspects, such as query performance, file compression ratio, data security, etc.
-
+
Figure 2-5 Disk usage
@@ -291,8 +291,8 @@ Figure 2-5 Disk usage
So what is the resource usage of each server during the test? What is the specific performance of each write operation? At this time, we can visualize the data in the server monitoring table and test process recording table by installing and using Tableau. The use of Tableau will not be introduced in this article. After connecting to the data table for test data persistence, the specific results are as follows (taking Apache-IoTDB as an example):
-
-
+
+
Figure 2-6 Visualization of testing process in Tableau
@@ -318,7 +318,7 @@ Table 2-4 Configuration parameter information
Results:
-
+
Figure 2-7 Query test results
diff --git a/src/UserGuide/Master/Tree/Tools-System/CLI.md b/src/UserGuide/Master/Tree/Tools-System/CLI.md
index 34803c3e6..47a038952 100644
--- a/src/UserGuide/Master/Tree/Tools-System/CLI.md
+++ b/src/UserGuide/Master/Tree/Tools-System/CLI.md
@@ -154,46 +154,46 @@ Shell >./kc.sh start-dev
```
2、use url(https://ip:port) login keycloack, the first login needs to create a user
-
+
3、Click administration console
-
+
4、In the master menu on the left, click Create realm and enter Realm name to create a new realm
-
+
-
+
5、Click the menu clients on the left to create clients
-
+
6、Click user on the left menu to create user
-
+
7、Click the newly created user ID, click the credentials navigation, enter the password and close the temporary option. The configuration of keycloud is completed
-
+
8、To create a role, click Roles on the left menu and then click the Create Role button to add a role
-
+
9、 Enter `iotdb_admin` in the Role Name and click the save button. Tip: `iotdb_admin` here cannot be any other name, otherwise even after successful login, you will not have permission to use iotdb's query, insert, create database, add users, roles and other functions
-
+
10、Click on the User menu on the left and then click on the user in the user list to add the `iotdb_admin` role we just created for that user
-
+
11、 Select Role Mappings, select the `iotdb_admin` role in Assign Role
-
+
-
+
Tip: If the user role is adjusted, you need to regenerate the token and log in to iotdb again to take effect
@@ -209,7 +209,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
Among them, you need to replace {my access token} (note, including {}) with your token, that is, the value corresponding to access_token. The password is empty and needs to be confirmed again.
-
+
How to get the token is dependent on your OpenID Connect setup and not covered here.
diff --git a/src/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md b/src/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md
index e6a945011..75229ae09 100644
--- a/src/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md
+++ b/src/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md
@@ -154,7 +154,7 @@ IoTDB currently supports importing data in CSV, SQL, and TsFile (IoTDB's underly
The TsFile Active Listening & Loading Feature can actively monitor TsFile file changes in the specified target path (configured by the user) and automatically synchronize TsFile files from the target path to the specified reception path (configured by the user). Through this feature, IoTDB can automatically detect and load these files without the need for any additional manual loading operations. This automated process not only simplifies the user's operational steps but also reduces potential errors that may occur during the operation, effectively reducing the complexity for users during the usage process.
-
+
### 4.1 Configuration Parameters
diff --git a/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md b/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md
index b1de82e6c..9aa65e5dd 100644
--- a/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md
+++ b/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md
@@ -71,7 +71,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -112,9 +112,9 @@ The following documents may help you have a good journey with Prometheus and Gra
`Apache IoTDB Dashboard` is available as a supplement to IoTDB Enterprise Edition, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel. You can access the Dashboard's Json file by contacting Commerce.
-
+
-
+
@@ -129,7 +129,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -140,7 +140,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -154,7 +154,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -164,7 +164,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -175,8 +175,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md b/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md
index c13db8cc3..5e0964932 100644
--- a/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md
+++ b/src/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md
@@ -71,7 +71,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -111,9 +111,9 @@ The following documents may help you have a good journey with Prometheus and Gra
We introduce the Apache IoTDB Dashboard, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel.
-
+
-
+
You can access the Dashboard's Json file in the enterprise edition.
@@ -129,7 +129,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -140,7 +140,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -154,7 +154,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -164,7 +164,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -175,8 +175,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md b/src/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md
index b2d113db3..8b124a643 100644
--- a/src/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md
+++ b/src/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md
@@ -24,10 +24,10 @@ IoTDB Visualization Console is an extension component developed for industrial s
Main feature showcase
* Home
-
+
* Measurement Point List
-
+
* Data Query
-
+
* Trend
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/Master/Tree/User-Manual/AINode_apache.md b/src/UserGuide/Master/Tree/User-Manual/AINode_apache.md
index f0c6647ec..3045fa625 100644
--- a/src/UserGuide/Master/Tree/User-Manual/AINode_apache.md
+++ b/src/UserGuide/Master/Tree/User-Manual/AINode_apache.md
@@ -25,7 +25,7 @@ AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB,
The system architecture is shown below:
::: center
-
+
:::
The responsibilities of the three nodes are as follows:
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection).
::: center
-
+
::::
## Installation and Deployment
@@ -304,13 +304,13 @@ After completing the registration of the model, the inference function can be us
- **sql**: sql query statement, the result of the query is used as input to the model for model inference. The dimensions of the rows and columns in the result of the query need to match the size specified in the specific model config. (It is not recommended to use the `SELECT *` clause for the sql here because in IoTDB, `*` does not sort the columns, so the order of the columns is undefined, you can use `SELECT s0,s1` to ensure that the columns order matches the expectations of the model input)
- **window_function**: Window functions that can be used in the inference process, there are currently three types of window functions provided to assist in model inference:
- **head(window_size)**: Get the top window_size points in the data for model inference, this window can be used for data cropping.
- 
+ 
- **tail(window_size)**: get the last window_size point in the data for model inference, this window can be used for data cropping.
- 
+ 
- **count(window_size, sliding_step)**: sliding window based on the number of points, the data in each window will be reasoned through the model respectively, as shown in the example below, window_size for 2 window function will be divided into three windows of the input dataset, and each window will perform reasoning operations to generate results respectively. The window can be used for continuous inference
- 
+ 
**Explanation 1**: window can be used to solve the problem of cropping rows when the results of the sql query and the input row requirements of the model do not match. Note that when the number of columns does not match or the number of rows is directly less than the model requirement, the inference cannot proceed and an error message will be returned.
@@ -459,7 +459,7 @@ When using AINode related functions, the authentication of IoTDB itself can be u
In some industrial scenarios, there is a need to predict power loads, which can be used to optimise power supply, conserve energy and resources, support planning and expansion, and enhance power system reliability.
-The data for the test set of ETTh1 that we use is [ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv).
+The data for the test set of ETTh1 that we use is [ETTh1](/img/ETTh1.csv).
It contains power data collected at 1h intervals, and each data consists of load and oil temperature as High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature.
@@ -522,7 +522,7 @@ We compare the results of the prediction of the oil temperature with the real re
The data before 10/24 00:00 represents the past data input to the model, the blue line after 10/24 00:00 is the oil temperature forecast result given by the model, and the red line is the actual oil temperature data from the dataset (used for comparison).
-
+
As can be seen, we have used the relationship between the six load information and the corresponding time oil temperatures for the past 96 hours (4 days) to model the possible changes in this data for the oil temperature for the next 48 hours (2 days) based on the inter-relationships between the sequences learned previously, and it can be seen that the predicted curves maintain a high degree of consistency in trend with the actual results after visualisation.
@@ -532,7 +532,7 @@ Power monitoring of current, voltage and power data is required in substations f
We have used the current, voltage and power data in a substation to form a dataset in a real scenario. The dataset consists of data such as A-phase voltage, B-phase voltage, and C-phase voltage collected every 5 - 6s for a time span of nearly four months in the substation.
-The test set data content is [data](https://alioss.timecho.com/docs/img/data.csv).
+The test set data content is [data](/img/data.csv).
On this dataset, the model inference function of IoTDB-ML can predict the C-phase voltage in the future period through the previous values and corresponding timestamps of A-phase voltage, B-phase voltage and C-phase voltage, empowering the monitoring management of the substation.
@@ -588,7 +588,7 @@ Comparing the predicted results of the C-phase voltage with the real results, we
The data before 02/14 20:48 represents the past data input to the model, the blue line after 02/14 20:48 is the predicted result of phase C voltage given by the model, while the red line is the actual phase C voltage data from the dataset (used for comparison).
-
+
It can be seen that we used the voltage data from the past 10 minutes and, based on the previously learned inter-sequence relationships, modeled the possible changes in the phase C voltage data for the next 5 minutes. The visualized forecast curve shows a certain degree of synchronicity with the actual results in terms of trend.
@@ -596,7 +596,7 @@ It can be seen that we used the voltage data from the past 10 minutes and, based
In the civil aviation and transport industry, there exists a need for anomaly detection of the number of passengers travelling on an aircraft. The results of anomaly detection can be used to guide the adjustment of flight scheduling to make the organisation more efficient.
-Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](https://alioss.timecho.com/docs/img/airline.csv).
+Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](/img/airline.csv).
On this dataset, the model inference function of IoTDB-ML can empower the transport industry by capturing the changing patterns of the sequence in order to detect anomalies at the sequence time points.
#### Step 1: Data Import
@@ -649,6 +649,6 @@ Total line number = 144
We plot the results detected as anomalies to get the following image. Where the blue curve is the original time series and the time points specially marked with red dots are the time points that the algorithm detects as anomalies.
-
+
It can be seen that the Stray model has modelled the input sequence changes and successfully detected the time points where anomalies occur.
\ No newline at end of file
diff --git a/src/UserGuide/Master/Tree/User-Manual/AINode_timecho.md b/src/UserGuide/Master/Tree/User-Manual/AINode_timecho.md
index 215e370e4..b797c8dfb 100644
--- a/src/UserGuide/Master/Tree/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/Master/Tree/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB,
The system architecture is shown below:
::: center
-
+
:::
The responsibilities of the three nodes are as follows:
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection).
::: center
-
+
::::
## Installation and Deployment
@@ -304,13 +304,13 @@ After completing the registration of the model, the inference function can be us
- **sql**: sql query statement, the result of the query is used as input to the model for model inference. The dimensions of the rows and columns in the result of the query need to match the size specified in the specific model config. (It is not recommended to use the `SELECT *` clause for the sql here because in IoTDB, `*` does not sort the columns, so the order of the columns is undefined, you can use `SELECT s0,s1` to ensure that the columns order matches the expectations of the model input)
- **window_function**: Window functions that can be used in the inference process, there are currently three types of window functions provided to assist in model inference:
- **head(window_size)**: Get the top window_size points in the data for model inference, this window can be used for data cropping.
- 
+ 
- **tail(window_size)**: get the last window_size point in the data for model inference, this window can be used for data cropping.
- 
+ 
- **count(window_size, sliding_step)**: sliding window based on the number of points, the data in each window will be reasoned through the model respectively, as shown in the example below, window_size for 2 window function will be divided into three windows of the input dataset, and each window will perform reasoning operations to generate results respectively. The window can be used for continuous inference
- 
+ 
**Explanation 1**: window can be used to solve the problem of cropping rows when the results of the sql query and the input row requirements of the model do not match. Note that when the number of columns does not match or the number of rows is directly less than the model requirement, the inference cannot proceed and an error message will be returned.
@@ -459,7 +459,7 @@ When using AINode related functions, the authentication of IoTDB itself can be u
In some industrial scenarios, there is a need to predict power loads, which can be used to optimise power supply, conserve energy and resources, support planning and expansion, and enhance power system reliability.
-The data for the test set of ETTh1 that we use is [ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv).
+The data for the test set of ETTh1 that we use is [ETTh1](/img/ETTh1.csv).
It contains power data collected at 1h intervals, and each data consists of load and oil temperature as High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature.
@@ -522,7 +522,7 @@ We compare the results of the prediction of the oil temperature with the real re
The data before 10/24 00:00 represents the past data input to the model, the blue line after 10/24 00:00 is the oil temperature forecast result given by the model, and the red line is the actual oil temperature data from the dataset (used for comparison).
-
+
As can be seen, we have used the relationship between the six load information and the corresponding time oil temperatures for the past 96 hours (4 days) to model the possible changes in this data for the oil temperature for the next 48 hours (2 days) based on the inter-relationships between the sequences learned previously, and it can be seen that the predicted curves maintain a high degree of consistency in trend with the actual results after visualisation.
@@ -532,7 +532,7 @@ Power monitoring of current, voltage and power data is required in substations f
We have used the current, voltage and power data in a substation to form a dataset in a real scenario. The dataset consists of data such as A-phase voltage, B-phase voltage, and C-phase voltage collected every 5 - 6s for a time span of nearly four months in the substation.
-The test set data content is [data](https://alioss.timecho.com/docs/img/data.csv).
+The test set data content is [data](/img/data.csv).
On this dataset, the model inference function of IoTDB-ML can predict the C-phase voltage in the future period through the previous values and corresponding timestamps of A-phase voltage, B-phase voltage and C-phase voltage, empowering the monitoring management of the substation.
@@ -588,7 +588,7 @@ Comparing the predicted results of the C-phase voltage with the real results, we
The data before 02/14 20:48 represents the past data input to the model, the blue line after 02/14 20:48 is the predicted result of phase C voltage given by the model, while the red line is the actual phase C voltage data from the dataset (used for comparison).
-
+
It can be seen that we used the voltage data from the past 10 minutes and, based on the previously learned inter-sequence relationships, modeled the possible changes in the phase C voltage data for the next 5 minutes. The visualized forecast curve shows a certain degree of synchronicity with the actual results in terms of trend.
@@ -596,7 +596,7 @@ It can be seen that we used the voltage data from the past 10 minutes and, based
In the civil aviation and transport industry, there exists a need for anomaly detection of the number of passengers travelling on an aircraft. The results of anomaly detection can be used to guide the adjustment of flight scheduling to make the organisation more efficient.
-Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](https://alioss.timecho.com/docs/img/airline.csv).
+Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](/img/airline.csv).
On this dataset, the model inference function of IoTDB-ML can empower the transport industry by capturing the changing patterns of the sequence in order to detect anomalies at the sequence time points.
#### Step 1: Data Import
@@ -649,6 +649,6 @@ Total line number = 144
We plot the results detected as anomalies to get the following image. Where the blue curve is the original time series and the time points specially marked with red dots are the time points that the algorithm detects as anomalies.
-
+
It can be seen that the Stray model has modelled the input sequence changes and successfully detected the time points where anomalies occur.
\ No newline at end of file
diff --git a/src/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md b/src/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md
index 741135b0d..dcca2ff41 100644
--- a/src/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md
+++ b/src/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md
@@ -37,7 +37,7 @@ The audit log function traces all user operations in the database. The informati
Client Sources：
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
#### Audit logging can be turned off for some user connections
diff --git a/src/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md b/src/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md
index 4a9476b04..98ec4214c 100644
--- a/src/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md
+++ b/src/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -93,7 +93,7 @@ The schema and auth synchronization functions have the following limitations:
Data synchronization tasks have three states: RUNNING, STOPPED, and DROPPED. The task state transitions are shown in the following diagram:
-
+
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
@@ -263,7 +263,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -278,7 +278,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -302,7 +302,7 @@ with SINK (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -354,7 +354,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
diff --git a/src/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md b/src/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md
index c7e26b795..1cec246a3 100644
--- a/src/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md
+++ b/src/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -95,7 +95,7 @@ The schema and auth synchronization functions have the following limitations:
Data synchronization tasks have three states: RUNNING, STOPPED, and DROPPED. The task state transitions are shown in the following diagram:
-
+
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
@@ -271,7 +271,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -286,7 +286,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -310,7 +310,7 @@ with SINK (
This example is used to demonstrate the scenario where two IoTDB act as active-active pairs, with the data link shown in the figure below:
-
+
In this example, to avoid infinite data loops, the `forwarding-pipe-requests` parameter on A and B needs to be set to `false`, indicating that data transmitted from another pipe is not forwarded, and to keep the data consistent on both sides, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata.
@@ -348,7 +348,7 @@ with sink (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -400,7 +400,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
@@ -433,7 +433,7 @@ with sink (
This example is used to demonstrate the scenario where data from one IoTDB is synchronized to another IoTDB through a unidirectional gateway, as shown in the figure below:
-
+
In this example, the iotdb-air-gap-sink plugin in the sink task needs to be used . After configuring the gateway, execute the following statement on A IoTDB. Fill in the node-urls with the URL of the data service port of the DataNode node on the target IoTDB configured by the gateway, as detailed below:
diff --git a/src/UserGuide/Master/Tree/User-Manual/Data-subscription.md b/src/UserGuide/Master/Tree/User-Manual/Data-subscription.md
index 3eefc3f8f..5cba93a71 100644
--- a/src/UserGuide/Master/Tree/User-Manual/Data-subscription.md
+++ b/src/UserGuide/Master/Tree/User-Manual/Data-subscription.md
@@ -15,7 +15,7 @@ Using the IoTDB Subscription Client to consume data has significant advantages i
The IoTDB Subscription Client encompasses three core concepts: Topic, Consumer, and Consumer Group. The specific relationships are illustrated in the diagram below:
+ 
+ 
+
`SlidingTimeWindowAccessStrategy`: `SlidingTimeWindowAccessStrategy` has many constructors, you can pass 3 types of parameters to them:
@@ -149,7 +149,7 @@ The sliding step parameter is also optional. If the parameter is not provided, t
The relationship between the three types of parameters can be seen in the figure below. Please see the Javadoc for more details.
-
+
`SlidingSizeWindowAccessStrategy`: `SlidingSizeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -170,7 +170,7 @@ The sliding step parameter is optional. If the parameter is not provided, the sl
Window opening diagram: **Time intervals less than or equal to the given minimum time interval `sessionGap` are assigned in one group.**
-
+
`SessionTimeWindowAccessStrategy`: `SessionTimeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -181,7 +181,7 @@ Window opening diagram: **Time intervals less than or equal to the given minimum
Window opening diagram: **For numerical data, if the state difference is less than or equal to the given threshold `delta`, it will be assigned in one group.**
-
+
`StateWindowAccessStrategy` has four constructors.
diff --git a/src/UserGuide/Master/Tree/User-Manual/White-List_timecho.md b/src/UserGuide/Master/Tree/User-Manual/White-List_timecho.md
index 75fe11866..17785f8da 100644
--- a/src/UserGuide/Master/Tree/User-Manual/White-List_timecho.md
+++ b/src/UserGuide/Master/Tree/User-Manual/White-List_timecho.md
@@ -66,5 +66,5 @@ Decide which IP addresses can connect to IoTDB
5. whether to enable the configuration of the white list function, can be hot loaded.
6. Use the Java native interface to modify the whitelist, must be the root user to modify, reject non-root user to modify; modify the content must be legal, otherwise it will throw a StatementExecutionException.
-
+
diff --git a/src/UserGuide/V0.13.x/API/Programming-MQTT.md b/src/UserGuide/V0.13.x/API/Programming-MQTT.md
index e0762d65b..4858d68e5 100644
--- a/src/UserGuide/V0.13.x/API/Programming-MQTT.md
+++ b/src/UserGuide/V0.13.x/API/Programming-MQTT.md
@@ -27,7 +27,7 @@ It is useful for connections with remote locations where a small code footprint
IoTDB supports the MQTT v3.1(an OASIS Standard) protocol.
IoTDB server includes a built-in MQTT service that allows remote devices send messages into IoTDB server directly.
-
+
### Built-in MQTT Service
@@ -55,7 +55,7 @@ or
}
```
-
+
### MQTT Configurations
The IoTDB MQTT service load configurations from `${IOTDB_HOME}/${IOTDB_CONF}/iotdb-engine.properties` by default.
diff --git a/src/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md b/src/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md
index 3b0119539..16a0020cd 100644
--- a/src/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md
+++ b/src/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@ A wind power IoT scenario is taken as an example to illustrate how to creat a co
According to the enterprise organization structure and equipment entity hierarchy, it is expressed as an attribute hierarchy structure, as shown below. The hierarchical from top to bottom is: power group layer - power plant layer - entity layer - measurement layer. ROOT is the root node, and each node of measurement layer is a leaf node. In the process of using IoTDB, the attributes on the path from ROOT node is directly connected to each leaf node with ".", thus forming the name of a timeseries in IoTDB. For example, The left-most path in Figure 2.1 can generate a timeseries named `root.ln.wf01.wt01.status`.
-
+
In the following chapters of data definition language, data operation language and Java Native Interface, various operations related to aligned timeseries will be introduced one by one.
diff --git a/src/UserGuide/V0.13.x/Data-Concept/Schema-Template.md b/src/UserGuide/V0.13.x/Data-Concept/Schema-Template.md
index a669ea3a9..d489cbeaa 100644
--- a/src/UserGuide/V0.13.x/Data-Concept/Schema-Template.md
+++ b/src/UserGuide/V0.13.x/Data-Concept/Schema-Template.md
@@ -29,7 +29,7 @@ In order to enable different entities of the same type to share metadata, reduce
The following picture illustrates the data model of petrol vehicle scenario. The velocity, fuel amount, acceleration, and angular velocity of each petrol vehicle spread over cities will be collected. Obviously, the measurements of single petrol vehicle are the same as those of another.
-
+
## Concepts
@@ -47,7 +47,7 @@ In the following chapters of data definition language, data operation language a
After applying schema template, the following picture illustrates the new data model of petrol vehicle scenario. All petrol vehicles share the schemas defined in template. There are no redundancy storage of measurement schemas.
-
+
### Lifetime of Schema Template
@@ -62,7 +62,7 @@ The term about lifetime of schema template may help you utilize it in a better w
It should be complemented that the distinction between SET and ACTIVATE is meant to serve an ubiquitous scenario where massive nodes with a common ancestor may need to apply the template. Under this circumstance, it is more feasible to SET the template on the common ancestor rather than all those descendant. For those who needs to apply the template, ACTIVATE is a more appropriate arrangement.
-
+
## Usage
diff --git a/src/UserGuide/V0.13.x/Data-Concept/Time-Partition.md b/src/UserGuide/V0.13.x/Data-Concept/Time-Partition.md
index 2796d98f1..337dc51c3 100644
--- a/src/UserGuide/V0.13.x/Data-Concept/Time-Partition.md
+++ b/src/UserGuide/V0.13.x/Data-Concept/Time-Partition.md
@@ -49,7 +49,7 @@ Notice: Currently, it's not recommend to open this function. If open, please cal
Enable time partition and set partition_interval to 86400 (one day), then the data distribution is shown as the following figure:
-
+
* Insert one datapoint with timestamp 0, calculate 0/86400 = 0, then this datapoint will be stored in TsFile under folder 0
diff --git a/src/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md b/src/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md
index f7a716675..c22210571 100644
--- a/src/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md
+++ b/src/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md
@@ -45,11 +45,11 @@ DBeaver is a SQL client software application and a database administration tool.
2. Start DBeaver
3. Open Driver Manager
- 
+ 
4. Create a new driver type for IoTDB
- 
+ 
5. Download [Sources](https://iotdb.apache.org/Download/)，unzip it and compile jdbc driver by the following command
@@ -58,15 +58,15 @@ DBeaver is a SQL client software application and a database administration tool.
```
6. Find and add a lib named `apache-iotdb-jdbc-{version}-jar-with-dependencies.jar`, which should be under `jdbc/target/`, then select `Find Class`.
- 
+ 
8. Edit the driver Settings
- 
+ 
9. Open New DataBase Connection and select iotdb
- 
+ 
10. Edit JDBC Connection Settings
@@ -75,12 +75,12 @@ DBeaver is a SQL client software application and a database administration tool.
Username: root
Password: root
```
- 
+ 
11. Test Connection
- 
+ 
12. Enjoy IoTDB with DBeaver
- 
+ 
diff --git a/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md b/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md
index 6e35dd6ec..72d26ca40 100644
--- a/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md
+++ b/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md
@@ -128,15 +128,15 @@ Username and password are both "admin" by default.
Select `Data Sources` and then `Add data source`, select `SimpleJson` in `Type` and `URL` is http://localhost:8888.
After that, make sure IoTDB has been started, click "Save & Test", and "Data Source is working" will be shown to indicate successful configuration.
-
+
-
+
#### Design in dashboard
Add diagrams in dashboard and customize your query. See http://docs.grafana.org/guides/getting_started/
-
+
### config grafana
diff --git a/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md b/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md
index 1936518fc..2aa740519 100644
--- a/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md
@@ -67,7 +67,7 @@ yarn build
If compiling successful, you can see the `dist` directory , which contains the compiled Grafana-Plugin:
-
+
##### Compile Method 2
@@ -81,7 +81,7 @@ Execute following command in the IoTDB root directory:
If compiling successful, you can see that the `distribution/target` directory contains the compiled Grafana-Plugin:
-
+
#### Install Grafana-Plugin
@@ -156,9 +156,9 @@ Grafana displays data in a web page dashboard. Please open your browser and visi
Click the `Settings` icon on the left, select the `Data Source` option, and then click `Add data source`.
-
+
-
+
Select the `Apache IoTDB` data source.
@@ -169,24 +169,24 @@ Select the `Apache IoTDB` data source.
Click `Save & Test`, and `Success` will appear.
-
+
#### Create a new Panel
Click the `Dashboards` icon on the left, and select `Manage` option.
-
+
Click the `New Dashboard` icon on the top right, and select `Add an empty panel` option.
-
+
Enter content in the SELECT, FROM , WHERE and CONTROL input box, where the WHERE and CONTROL input boxes are optional.
If a query involves multiple expressions, we can click `+` on the right side of the SELECT input box to add expressions in the SELECT clause, or click `+` on the right side of the FROM input box to add a path prefix:
-
+
SELECT input box: contents can be the time series suffix, function, udf, arithmetic expression, or nested expressions. You can also use the as clause to rename the result.
@@ -222,19 +222,19 @@ This plugin supports Grafana's variables and templates (https://grafana.com/docs
After creating a new Panel, click the Settings button in the upper right corner:
-
+
Select `Variables`, click `Add variable`:
-
+
Enter `Name`, `Label`, and `Query`, and then click the `Update` button:
-
+
Apply Variables, enter the variable in the `grafana panel` and click the `save` button:
-
+
### More Details about Grafana
diff --git a/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-IoTDB.md b/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-IoTDB.md
index 86ab673d1..c878559c4 100644
--- a/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-IoTDB.md
+++ b/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-IoTDB.md
@@ -93,7 +93,7 @@ Take the following TsFile structure as an example: There are three Measurements
The existing data in the TsFile is as follows:
-
+
The wide(default) table form is as follows:
diff --git a/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-TsFile.md b/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-TsFile.md
index ee054fc6d..f5232314f 100644
--- a/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-TsFile.md
+++ b/src/UserGuide/V0.13.x/Ecosystem-Integration/Spark-TsFile.md
@@ -94,7 +94,7 @@ The way to display TsFile is dependent on the schema. Take the following TsFile
The existing data in the TsFile are:
-
+
The corresponding SparkSQL table is:
@@ -239,7 +239,7 @@ The way to display TsFile is related to TsFile Schema. Take the following TsFile
The existing data in the file are:
-
+
A set of time-series data
diff --git a/src/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md b/src/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md
index db3980d48..33d9f18fa 100644
--- a/src/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md
+++ b/src/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md
@@ -29,7 +29,7 @@ Currently, TsFiles(including both TsFile and related data files) are supported t
When you config to store TSFile on HDFS, your data files will be in distributed storage. The system architecture is as below:
-
+
#### Config and usage
diff --git a/src/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md b/src/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md
index 8ba79cdad..c28fe5a56 100644
--- a/src/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md
+++ b/src/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md
@@ -25,7 +25,7 @@
Zeppelin is a web-based notebook that enables interactive data analytics. You can connect to data sources and perform interactive operations with SQL, Scala, etc. The operations can be saved as documents, just like Jupyter. Zeppelin has already supported many data sources, including Spark, ElasticSearch, Cassandra, and InfluxDB. Now, we have enabled Zeppelin to operate IoTDB via SQL.
-
+
@@ -124,7 +124,7 @@ In the interpreter page:
Now you are ready to use your interpreter.
-
+
We provide some simple SQL to show the use of Zeppelin-IoTDB interpreter:
@@ -158,7 +158,7 @@ We provide some simple SQL to show the use of Zeppelin-IoTDB interpreter:
The screenshot is as follows:
-
+
You can also design more fantasy documents referring to [[1]](https://zeppelin.apache.org/docs/0.9.0/usage/display_system/basic.html) and others.
@@ -170,7 +170,7 @@ The above demo notebook can be found at `$IoTDB_HOME/zeppelin-interpreter/Zeppe
You can configure the connection parameters in http://127.0.0.1:8080/#/interpreter :
-
+
The parameters you can configure are as follows:
diff --git a/src/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md b/src/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md
index a18ac805c..e6757a8c4 100644
--- a/src/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md
+++ b/src/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md
@@ -25,7 +25,7 @@ Besides IoTDB engine, we also developed several components to provide better IoT
IoTDB suite can provide a series of functions in the real situation such as data collection, data writing, data storage, data query, data visualization and data analysis. Figure 1.1 shows the overall application architecture brought by all the components of the IoTDB suite.
-
+
As shown in Figure 1.1, users can use JDBC to import timeseries data collected by sensor on the device to local/remote IoTDB. These timeseries data may be system state data (such as server load and CPU memory, etc.), message queue data, timeseries data from applications, or other timeseries data in the database. Users can also write the data directly to the TsFile (local or on HDFS).
diff --git a/src/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md b/src/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md
index bd44de235..c3e0e72ec 100644
--- a/src/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md
+++ b/src/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md
@@ -31,11 +31,11 @@ In order to improve the quality of the printing, it is necessary for the company
At this point, the data can be stored using TsFile component, TsFileSync tool, and Hadoop/Spark integration component in the IoTDB suite.That is, each time a new chip is printed, a data is written on the SPI device using the SDK, which ultimately forms a TsFile. Through the TsFileSync tool, the generated TsFile will be synchronized to the data center according to certain rules (such as daily) and analyzed by data analysts tools.
-
+
In this scenario, only TsFile and TsFileSync are required to be deployed on a PC, and a Hadoop/Spark cluster is required. Figure below shows the architecture at this time.
-
+
* Scenario 2
@@ -43,11 +43,11 @@ A company has several wind turbines which are installed hundreds of sensors on e
In order to ensure the normal operation of the turbines and timely monitoring and analysis of the turbines, the company needs to collect these sensor data, perform partial calculation and analysis in the turbines working environment, and upload the original data collected to the data center.
-
+
In this situation, IoTDB, TsFileSync tools, and Hadoop/Spark integration components in the IoTDB suite can be used. A PC needs to be deployed with IoTDB and TsFileSync tools installed to support reading and writing data, local computing and analysis, and uploading data to the data center. In addition, Hadoop/Spark clusters need to be deployed for data storage and analysis on the data center side. Figure below shows the architecture at this time.
-
+
* Scenario 3
@@ -57,11 +57,11 @@ A variety of sensors are installed on each robotic device to monitor the robot's
In order to ensure that the data of the robot can be monitored and analyzed in time, the company needs to collect the information of these robot sensors, send them to the server that can connect to the external network, and then upload the original data information to the data center for complex calculation and analysis.
-
+
At this point, IoTDB, IoTDB-Client tools, TsFileSync tools, and Hadoop/Spark integration components in the IoTDB suite can be used. IoTDB-Client tool is installed on the robot and each of them is connected to the LAN of the factory. When sensors generate real-time data, the data will be uploaded to the server in the factory. The IoTDB server and TsFileSync is installed on the server connected to the external network. Once triggered, the data on the server will be upload to the data center. In addition, Hadoop/Spark clusters need to be deployed for data storage and analysis on the data center side. The Figure below shows the architecture at this time.
-
+
* Scenario 4
@@ -73,4 +73,4 @@ At this point, IoTDB, IoTDB-Client, and Hadoop/Spark integration components in t
In addition, Hadoop/Spark clusters need to be deployed for data storage and analysis on the data center side. As shown in Figure below.
-
+
diff --git a/src/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md b/src/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md
index 14678d6a1..171bb222f 100644
--- a/src/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md
+++ b/src/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md
@@ -48,12 +48,12 @@ Step 4: Use jvisualvm
1. Make sure jdk 8 is installed. For versions later than jdk 8, you need to [download visualvm](https://visualvm.github.io/download.html)
2. Open jvisualvm
3. Right-click at the left navigation area -> Add JMX connection
-
+
4. Fill in information and log in as below. Remember to check "Do not require SSL connection".
An example is:
Connection：192.168.130.15:31999
Username：iotdb
Password：passw!d
-
+
diff --git a/src/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md b/src/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md
index 3b6f8592a..0ce3c0512 100644
--- a/src/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md
+++ b/src/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md
@@ -35,14 +35,14 @@ Here we use JConsole to connect with JMX.
Start the JConsole, establish a new JMX connection with the IoTDB Server (you can select the local process or input the IP and PORT for remote connection, the default operation port of the IoTDB JMX service is 31999). Fig 4.1 shows the connection GUI of JConsole.
-
+
After connected, click `MBean` and find `ch.qos.logback.classic.default.ch.qos.logback.classic.jmx.JMXConfigurator`(As shown in fig 4.2).
-
+
In the JMXConfigurator Window, there are 6 operations provided, as shown in fig 4.3. You can use these interfaces to perform operation.
-
+
#### Interface Instruction
diff --git a/src/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md b/src/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md
index 0e4ad7d0a..4debc73d3 100644
--- a/src/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md
+++ b/src/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md
@@ -319,7 +319,7 @@ As above descriptions，IoTDB provides metrics data in standard Prometheus forma
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -357,7 +357,7 @@ The following documents may help you have a good journey with Prometheus and Gra
### 1.4.3. Apache IoTDB Dashboard
We provide the Apache IoTDB Dashboard, and the rendering shown in Grafana is as follows:
-
+
How to get Apache IoTDB Dashboard:
@@ -375,7 +375,7 @@ By using JConsole tool and connecting with JMX you are provided with some system
This section describes how to use the JConsole ```Mbean```tab of jconsole to monitor some system configurations of IoTDB, the statistics of writing, and so on. After connecting to JMX, you can find the "MBean" of "org.apache.iotdb.service", as shown in the figure below.
-
+
## 4.2. Configuration parameter
@@ -403,4 +403,4 @@ To improve query performance, IOTDB caches ChunkMetaData and TsFileMetaData. Use
1. Connect to jconsole with port 31999 and select 'MBean' in the menu item above.
2. Expand the sidebar and select 'org.apache.iotdb.db.service'. You will get the results shown in the following figure:
-
+
diff --git a/src/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md b/src/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md
index dd6ead97a..18a423146 100644
--- a/src/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md
+++ b/src/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md
@@ -51,7 +51,7 @@ Illustrated as the following figure:
* When default_storage_group_level=2, root.turbine1.d1, root.turbine1.d2, root.turbine2.d1 and root.turbine2.d2 will be set as storage group.
-
+
## Auto create time series metadata(specify data type in the frontend)
diff --git a/src/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md b/src/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md
index cd7e0f2e6..5b4c96973 100644
--- a/src/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md
+++ b/src/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md
@@ -181,7 +181,7 @@ It costs 0.004s
Then the Metadata Tree will be as below:
-
+
diff --git a/src/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md b/src/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md
index 70b04fd1e..5539d5dd9 100644
--- a/src/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md
+++ b/src/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md
@@ -190,7 +190,7 @@ The sliding step parameter is also optional. If the parameter is not provided, t
The relationship between the three types of parameters can be seen in the figure below. Please see the Javadoc for more details.
-
+
+
| Function Name | Allowed Input Series Data Types | Attributes | Output Series Data Type | Series Data Type Description |
| ------------- | ------------------------------- | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -791,7 +791,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `windowSize`: The number of points in a window. Int data type. **Required**.
+ `slidingStep`: Slide a window by the number of points. Int data type. Optional. If not set, default to the same as `windowSize`.
-
+
**(2) Attributes for the time window:**
@@ -800,7 +800,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `displayWindowBegin`: The starting position of the window (included). Long data type. Optional. If not set, default to Long.MIN_VALUE, meaning using the time of the first data point of the input time series as the starting position of the window.
+ `displayWindowEnd`: End time limit (excluded, essentially playing the same role as `WHERE time < displayWindowEnd`). Long data type. Optional. If not set, default to Long.MAX_VALUE, meaning there is no additional end time limit other than the end of the input time series itself.
-
+
#### Examples
diff --git a/src/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md b/src/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md
index 4c75a3d0e..aad3c1400 100644
--- a/src/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md
+++ b/src/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md
@@ -127,48 +127,48 @@ Shell >cd bin
Shell >./standalone.sh
```
2、use url(https://ip:port/auth) login keycloack, the first login needs to create a user
-
+
3、Click administration console
-
+
4、In the master menu on the left, click Add realm and enter name to create a new realm
-
+
-
+
5、Click the menu clients on the left to create clients
-
+
6、Click user on the left menu to create user
-
+
7、Click the newly created user ID, click the credentials navigation, enter the password and close the temporary option. The configuration of keycloud is completed
-
+
8、To create a role, click Roles on the left menu and then click the Add Role button to add a role
-
+
9、 Enter `iotdb_admin` in the Role Name and click the save button. Tip: `iotdb_admin` here cannot be any other name, otherwise even after successful login, you will not have permission to use iotdb's query, insert, create storage group, add users, roles and other functions
-
+
10、Click the User menu on the left and click the Edit button in the user list to add the `iotdb_admin` role we just created for this user
-
+
11、 Select Role Mappings, select the `iotdb_admin` role in Available Role and click the Add selected button to add the role
-
+
12. If the `iotdb_admin` role is in Assigned Roles and the `Success Role mappings updated` prompt appears, it proves that the role was added successfully
-
+
Tip: If the user role is adjusted, you need to regenerate the token and log in to iotdb again to take effect
diff --git a/src/UserGuide/V0.13.x/Reference/TSDB-Comparison.md b/src/UserGuide/V0.13.x/Reference/TSDB-Comparison.md
index 0b9e272bb..76f9db116 100644
--- a/src/UserGuide/V0.13.x/Reference/TSDB-Comparison.md
+++ b/src/UserGuide/V0.13.x/Reference/TSDB-Comparison.md
@@ -23,7 +23,7 @@
## Overview
-
+
@@ -323,7 +323,7 @@ IoTDB uses batch insertion API and the batch size is distributed from 0 to 6000
The write throughput (points/second) is:
-
+
+
## Built-in MQTT Service
@@ -56,7 +56,7 @@ or
```
or json array of the above two.
-
+
## MQTT Configurations
The IoTDB MQTT service load configurations from `${IOTDB_HOME}/${IOTDB_CONF}/iotdb-common.properties` by default.
diff --git a/src/UserGuide/V1.2.x/API/Programming-Python-Native-API.md b/src/UserGuide/V1.2.x/API/Programming-Python-Native-API.md
index 86b453426..c1ae19364 100644
--- a/src/UserGuide/V1.2.x/API/Programming-Python-Native-API.md
+++ b/src/UserGuide/V1.2.x/API/Programming-Python-Native-API.md
@@ -568,7 +568,7 @@ The mapping relationship between them is：
The following figure shows the relationship between the two more intuitively:
-
+
### Data type mapping
| data type in IoTDB | data type in SQLAlchemy |
diff --git a/src/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md b/src/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md
index 838f379a2..31c668eab 100644
--- a/src/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md
+++ b/src/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md
@@ -23,13 +23,13 @@
IoTDB manages metadata and data based on data partitions (DataRegion), dividing the data from both the sequence and time dimensions.
-
+
## Partition Slots
A sequence partition slot combined with a time partition slot can generate a data partition (when the sequence slot has corresponding data under that time slot).
-
+
### Sequence Partition Slots
@@ -45,13 +45,13 @@ Time partition slots slice the time-series data from the time dimension (typical
The metadata partition management of a single database, following a specific load balancing strategy, assigns all sequence slots to the corresponding SchemaRegionGroup, further horizontally scaling within the cluster.
-
+
## Data Partitioning
It follows a certain load balancing strategy, dividing time and sequence partition slots, and allocating them to the relevant DataRegionGroup, further allowing horizontal scaling in the cluster.
-
+
## Load Balancing
diff --git a/src/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md b/src/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md
index 2382f6b32..524e7e7e0 100644
--- a/src/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md
+++ b/src/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@ A wind power IoT scenario is taken as an example to illustrate how to create a c
According to the enterprise organization structure and equipment entity hierarchy, it is expressed as an attribute hierarchy structure, as shown below. The hierarchical from top to bottom is: power group layer - power plant layer - entity layer - measurement layer. ROOT is the root node, and each node of measurement layer is a leaf node. In the process of using IoTDB, the attributes on the path from ROOT node is directly connected to each leaf node with ".", thus forming the name of a timeseries in IoTDB. For example, The left-most path in Figure 2.1 can generate a timeseries named `root.ln.wf01.wt01.status`.
-
+
In the following chapters of data definition language, data operation language and Java Native Interface, various operations related to aligned timeseries will be introduced one by one.
diff --git a/src/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md b/src/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md
index 48a9ba2f1..db748485c 100644
--- a/src/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md
+++ b/src/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md
@@ -83,8 +83,8 @@ scrape_configs:
4. Confirm the startup is successful. Enter http://localhost:9090 in the browser to enter Prometheus, click to enter the Target interface under Status (Figure 1 below), when you see State are Up, it means the configuration is successful and connected (Figure 2 below), click the link on the left side to jump to the webpage monitoring.
-
-
+
+
@@ -108,51 +108,51 @@ cd grafana-*
3. Enter http://localhost:3000 in your browser to access Grafana, the default initial username and password are both admin.
4. First we configure the Data Source in Configuration to be Prometheus.
-
+
5. When configuring the Data Source, pay attention to the URL where Prometheus is located, and click Save & Test after configuration, the Data source is working prompt appears, then the configuration is successful.
-
+
### Step2：Use the official Grafana dashboard provided by IoTDB
1. Enter Grafana，click Browse of Dashboards
-
+
2. Click the Import button on the right
-
+
3. Select a way to import Dashboard
a. Upload the Json file of the downloaded Dashboard locally
b. Enter the URL or ID of the Dashboard obtained from the Grafana website
c. Paste the contents of the Dashboard's Json file
-
+
4. Select Prometheus in the Dashboard as the Data Source you just configured and click Import
-
+
5. Then enter Dashboard，select job to be ConfigNode，then following monitoring dashboard will be seen:
-
+
6. Similarly, we can import the Apache DataNode Dashboard, select job as DataNode,then following monitoring dashboard will be seen:
-
+
### Step3：Creating a new Dashboard for data visualisation
1. First create the Dashboard, then create the Panel.
-
+
2. After that, you can visualize the monitoring-related data in the panel according to your needs (all relevant monitoring metrics can be filtered by selecting confignode/datanode in the job first).
-
+
3. Once the visualisation of the monitoring metrics selected for attention is complete, we get a panel like this:
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md b/src/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md
index 3ff801ffb..2e7c5c871 100644
--- a/src/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md
+++ b/src/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md
@@ -45,21 +45,21 @@ DBeaver is a SQL client software application and a database administration tool.
2. Start DBeaver
3. Open Driver Manager
- 
+ 
4. Create a new driver type for IoTDB
- 
+ 
5. Download `iotdb-jdbc` ， from [source1](https://maven.proxy.ustclug.org/maven2/org/apache/iotdb/iotdb-jdbc/1.2.1/iotdb-jdbc-1.2.1-jar-with-dependencies.jar) or [source2](https://repo1.maven.org/maven2/org/apache/iotdb/iotdb-jdbc/1.2.1/iotdb-jdbc-1.2.1-jar-with-dependencies.jar).
6. Add the downloaded jar file， then select `Find Class`.
- 
+ 
7. Edit the driver Settings
- 
+ 
```
Driver Name: IoTDB
@@ -71,7 +71,7 @@ DBeaver is a SQL client software application and a database administration tool.
8. Open New DataBase Connection and select iotdb
- 
+ 
9. Edit JDBC Connection Settings
@@ -80,12 +80,12 @@ DBeaver is a SQL client software application and a database administration tool.
Username: root
Password: root
```
- 
+ 
10. Test Connection
- 
+ 
11. Enjoy IoTDB with DBeaver
- 
+ 
diff --git a/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md b/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md
index ceb6ec9f6..92fb176fa 100644
--- a/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md
+++ b/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md
@@ -127,15 +127,15 @@ Username and password are both "admin" by default.
Select `Data Sources` and then `Add data source`, select `SimpleJson` in `Type` and `URL` is http://localhost:8888.
After that, make sure IoTDB has been started, click "Save & Test", and "Data Source is working" will be shown to indicate successful configuration.
-
+
-
+
### Design in dashboard
Add diagrams in dashboard and customize your query. See http://docs.grafana.org/guides/getting_started/
-
+
## config grafana
diff --git a/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md b/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md
index 8ae17332a..911d19503 100644
--- a/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md
@@ -119,9 +119,9 @@ Grafana displays data in a web page dashboard. Please open your browser and visi
Click the `Settings` icon on the left, select the `Data Source` option, and then click `Add data source`.
-
+
-
+
Select the `Apache IoTDB` data source.
@@ -132,22 +132,22 @@ Select the `Apache IoTDB` data source.
Click `Save & Test`, and `Data source is working` will appear.
-
+
### Create a new Panel
Click the `Dashboards` icon on the left, and select `Manage` option.
-
+
Click the `New Dashboard` icon on the top right, and select `Add an empty panel` option.
-
+
Grafana plugin supports SQL: Full Customized mode and SQL: Drop-down List mode, and the default mode is SQL: Full Customized mode.
-
+
#### SQL: Full Customized input method
@@ -155,7 +155,7 @@ Enter content in the SELECT, FROM , WHERE and CONTROL input box, where the WHERE
If a query involves multiple expressions, we can click `+` on the right side of the SELECT input box to add expressions in the SELECT clause, or click `+` on the right side of the FROM input box to add a path prefix:
-
+
SELECT input box: contents can be the time series suffix, function, udf, arithmetic expression, or nested expressions. You can also use the as clause to rename the result.
@@ -193,7 +193,7 @@ Tip: Statements like `select * from root.xx.**` are not recommended because thos
Select a time series in the TIME-SERIES selection box, select a function in the FUNCTION option, and enter the contents in the SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL input boxes, where TIME-SERIES is a required item and the rest are non required items.
-
+
### Support for variables and template functions
@@ -201,34 +201,34 @@ Both SQL: Full Customized and SQL: Drop-down List input methods support the vari
After creating a new Panel, click the Settings button in the upper right corner:
-
+
Select `Variables`, click `Add variable`:
-
+
Example 1：Enter `Name`, `Label`, and `Query`, and then click the `Update` button:
-
+
Apply Variables, enter the variable in the `grafana panel` and click the `save` button:
-
+
Example 2: Nested use of variables:
-
+
-
+
-
+
Example 3: using function variables
-
+
-
+
The Name in the above figure is the variable name and the variable name we will use in the panel in the future. Label is the display name of the variable. If it is empty, the variable of Name will be displayed. Otherwise, the name of the Label will be displayed.
There are Query, Custom, Text box, Constant, DataSource, Interval, Ad hoc filters, etc. in the Type drop-down, all of which can be used in IoTDB's Grafana Plugin
@@ -252,14 +252,14 @@ This plugin supports Grafana alert function.
1. In the Grafana panel, click the `alerting` button, as shown in the following figure:
-
+
2. Click `Create alert rule from this panel`, as shown in the figure below:
-
+
3. Set query and alarm conditions in step 1. Conditions represent query conditions, and multiple combined query conditions can be configured. As shown below:
-
+
The query condition in the figure: `min() OF A IS BELOW 0`, means that the condition will be triggered when the minimum value in the A tab is 0, click this function to change it to another function.
@@ -269,27 +269,27 @@ For example, we have 3 conditions in the following order: Condition: B (Evaluate
4. After selecting indicators and alarm rules, click the `Preview` button to preview the data as shown in the figure below:
-
+
5. In step 2, specify the alert evaluation interval, and for `Evaluate every`, specify the evaluation frequency. Must be a multiple of 10 seconds. For example, 1m, 30s.
For `Evaluate for`, specify the duration before the alert fires. As shown below:
-
+
6. In step 3, add the storage location, rule group, and other metadata associated with the rule. Where `Rule name` specifies the name of the rule. Rule names must be unique.
-
+
7. In step 4, add a custom label. Add a custom label by selecting an existing key-value pair from the drop-down list, or add a new label by entering a new key or value. As shown below:
-
+
8. Click `Save` to save the rule or click `Save and Exit` to save the rule and return to the alerts page.
9. Commonly used alarm states include `Normal`, `Pending`, `Firing` and other states, as shown in the figure below:
-
-
+
+
10. We can also configure `Contact points` for alarms to receive alarm notifications. For more detailed operations, please refer to the official document (https://grafana.com/docs/grafana/latest/alerting/alerting-rules/manage-contact-points/)
diff --git a/src/UserGuide/V1.2.x/Ecosystem-Integration/Spark-TsFile.md b/src/UserGuide/V1.2.x/Ecosystem-Integration/Spark-TsFile.md
index ed00538c7..151d81e14 100644
--- a/src/UserGuide/V1.2.x/Ecosystem-Integration/Spark-TsFile.md
+++ b/src/UserGuide/V1.2.x/Ecosystem-Integration/Spark-TsFile.md
@@ -94,7 +94,7 @@ The way to display TsFile is dependent on the schema. Take the following TsFile
The existing data in the TsFile are:
-
+
The corresponding SparkSQL table is:
@@ -239,7 +239,7 @@ The way to display TsFile is related to TsFile Schema. Take the following TsFile
The existing data in the file are:
-
+
A set of time-series data
diff --git a/src/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md b/src/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md
index 0353d5375..69150777d 100644
--- a/src/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md
+++ b/src/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md
@@ -25,7 +25,7 @@
Zeppelin is a web-based notebook that enables interactive data analytics. You can connect to data sources and perform interactive operations with SQL, Scala, etc. The operations can be saved as documents, just like Jupyter. Zeppelin has already supported many data sources, including Spark, ElasticSearch, Cassandra, and InfluxDB. Now, we have enabled Zeppelin to operate IoTDB via SQL.
-
+
@@ -123,7 +123,7 @@ In the interpreter page:
Now you are ready to use your interpreter.
-
+
We provide some simple SQL to show the use of Zeppelin-IoTDB interpreter:
@@ -157,7 +157,7 @@ We provide some simple SQL to show the use of Zeppelin-IoTDB interpreter:
The screenshot is as follows:
-
+
You can also design more fantasy documents referring to [[1]](https://zeppelin.apache.org/docs/0.9.0/usage/display_system/basic.html) and others.
@@ -169,7 +169,7 @@ The above demo notebook can be found at `$IoTDB_HOME/zeppelin-interpreter/Zeppe
You can configure the connection parameters in http://127.0.0.1:8080/#/interpreter :
-
+
The parameters you can configure are as follows:
diff --git a/src/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md b/src/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md
index e135da540..ec9aa25ae 100644
--- a/src/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md
+++ b/src/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md
@@ -25,7 +25,7 @@ Besides IoTDB engine, we also developed several components to provide better IoT
IoTDB suite can provide a series of functions in the real situation such as data collection, data writing, data storage, data query, data visualization and data analysis. Figure 1.1 shows the overall application architecture brought by all the components of the IoTDB suite.
-
+
As shown in Figure 1.1, users can use JDBC to import timeseries data collected by sensor on the device to local/remote IoTDB. These timeseries data may be system state data (such as server load and CPU memory, etc.), message queue data, timeseries data from applications, or other timeseries data in the database. Users can also write the data directly to the TsFile (local or on HDFS).
diff --git a/src/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md b/src/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md
index 60bbc0960..b295af566 100644
--- a/src/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md
+++ b/src/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md
@@ -36,7 +36,7 @@ In the original architecture, the HBase cluster was used as the storage database
The data management architecture of the car company using IoTDB as the time-series data storage engine is shown in the figure below.
-
+
The vehicle data is encoded based on TCP and industrial protocols and sent to the edge gateway, and the gateway sends the data to the message queue Kafka cluster, decoupling the two ends of production and consumption. Kafka sends data to Flink for real-time processing, and the processed data is written into IoTDB. Both historical data and latest data are queried in IoTDB, and finally the data flows into the visualization platform through API for application.
@@ -59,7 +59,7 @@ After selecting IoTDB as the storage database of the intelligent operation and m
The figure below shows the architecture design of the intelligent operation and maintenance platform of the steel plant.
-
+
## Application 3: Smart Factory
@@ -73,7 +73,7 @@ A cigarette factory hopes to upgrade from a "traditional factory" to a "high-end
The figure below shows the factory's IoT system architecture. IoTDB runs through the three-level IoT platform of the company, factory, and workshop to realize unified joint debugging and joint control of equipment. The data at the workshop level is collected, processed and stored in real time through the IoTDB at the edge layer, and a series of analysis tasks are realized. The preprocessed data is sent to the IoTDB at the platform layer for data governance at the business level, such as device management, connection management, and service support. Eventually, the data will be integrated into the IoTDB at the group level for comprehensive analysis and decision-making across the organization.
-
+
## Application 4: Condition monitoring
@@ -90,5 +90,5 @@ After using IoTDB as the storage and analysis engine, combined with meteorologic
The figure below shows the data management architecture of the power plant in the heating scene.
-
+
diff --git a/src/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md b/src/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md
index e2980a9ea..548dcc8c6 100644
--- a/src/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md
+++ b/src/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md
@@ -110,7 +110,7 @@ IoTDB> SHOW TTL ON StorageGroupNames
For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata.md).
-
+
@@ -120,7 +120,7 @@ Create device root.sg.d1
-
+
### Create Schema Template
diff --git a/src/UserGuide/V1.2.x/Tools-System/Benchmark.md b/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
index 0f22a52ee..ba743c724 100644
--- a/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
+++ b/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
@@ -25,14 +25,14 @@ IoT-benchmark is a time-series database benchmarking tool based on Java and big
Figure 1-1 below includes the test benchmark process and other extended functions. These processes can be unified by IoT-benchmark. IoT Benchmark supports a variety of workloads, including **pure write, pure query, write query mixed**, etc., supports **software and hardware system monitoring, test metric measurement** and other monitoring functions, and also realizes **initializing the database automatically, test data analysis and system parameter optimization** functions.
-
+
Figure 1-1
Referring to the YCSB test tool's design idea of separating the three components of workload generation, performance metric measurement and database interface, the modular design of IoT-benchmark is shown in Figure 1-2. Different from the YCSB-based test tool system, IoT-benchmark adds a system monitoring module to support the persistence of test data and system monitoring data. In addition, some special load testing functions especially designed for time series data scenarios have been added, such as supporting batch writing and multiple out-of-sequence data writing modes for IoT scenarios.
-
+
@@ -72,7 +72,7 @@ Table 1-1 Comparison of big data test benchmarks
The directory structure of the test package is shown in Figure 1-3 below. The test configuration file is conf/config.properties, and the test startup scripts are benchmark\.sh (Linux & MacOS) and benchmark.bat (Windows). The detailed usage of the files is shown in Table 1-2.
-
+
Figure 1-3 List of files and folders
@@ -100,7 +100,7 @@ Table 1-2 Usage list of files and folders
All the log files of the test are stored in the logs folder, and the test results are stored in the data/csvOutput folder after the test is completed. For example, after the test, we get the following result matrix:
-
+
- Result Matrix
- OkOperation: successful operations
@@ -254,24 +254,24 @@ Table 2-3 Configuration parameter information
First, start the tested time series databases Apache-IoTDB and KairosDB on 172.21.4.3 and 172.21.4.4 respectively, and then start server resource monitoring through the ser-benchamrk\.sh script on 172.21.4.2, 172.21.4.3 and 172.21.4.4 (Figure 2-1). Then modify the conf/config.properties files in the iotdb-0.13-0.0.1 and kairosdb-0.0.1 folders in 172.21.4.2 according to Table 2-3 to meet the test requirements. Use benchmark\.sh to start the writing test of Apache-IoTDB and KairosDB successively.
-
+
Figure 2-1 Server monitoring tasks
For example, if we first start the test on KairosDB, IoT-benchmark will create a CONFIG data table in the MySQL database to store the configuration information of this test (Figure 2-2), and there will be a log output of the current test progress during the test execution (Figure 2-3) . When the test is completed, the test result will be output (Figure 2-3), and the result will be written into the FINAL_RESULT data table (Figure 2-4).
-
+
Figure 2-2 Test configuration information table
-
-
-
-
+
+
+
+
Figure 2-3 Test progress and results
-
+
Figure 2-4 Test result table
@@ -279,14 +279,14 @@ Afterwards, we will start the test on Apache-IoTDB. The same IoT-benchmark will
According to the test result information, we know that under the same configuration the write delay times of Apache-IoTDB and KairosDB are 55.98ms and 1324.45ms respectively; the write throughputs are 5,125,600.86 points/second and 224,819.01 points/second respectively; the tests were executed respectively 585.30 seconds and 11777.99 seconds. And KairosDB has a write failure. After investigation, it is found that the data disk usage has reached 100%, and there is no disk space to continue receiving data. However, Apache-IoTDB has no write failure, and the disk space occupied after all data is written is only 4.7G (as shown in Figure 2-5); Apache-IoTDB is better than KairosDB in terms of write throughput and disk occupation. Of course, there will be other tests in the follow-up to observe and compare from various aspects, such as query performance, file compression ratio, data security, etc.
-
+
Figure 2-5 Disk usage
So what is the resource usage of each server during the test? What is the specific performance of each write operation? At this time, we can visualize the data in the server monitoring table and test process recording table by installing and using Tableau. The use of Tableau will not be introduced in this article. After connecting to the data table for test data persistence, the specific results are as follows (taking Apache-IoTDB as an example):
-
-
+
+
Figure 2-6 Visualization of testing process in Tableau
@@ -311,7 +311,7 @@ Table 2-4 Configuration parameter information
Results:
-
+
Figure 2-7 Query test results
diff --git a/src/UserGuide/V1.2.x/Tools-System/CLI.md b/src/UserGuide/V1.2.x/Tools-System/CLI.md
index ff3f4a12f..d5034ea71 100644
--- a/src/UserGuide/V1.2.x/Tools-System/CLI.md
+++ b/src/UserGuide/V1.2.x/Tools-System/CLI.md
@@ -154,46 +154,46 @@ Shell >./kc.sh start-dev
```
2、use url(https://ip:port) login keycloack, the first login needs to create a user
-
+
3、Click administration console
-
+
4、In the master menu on the left, click Create realm and enter Realm name to create a new realm
-
+
-
+
5、Click the menu clients on the left to create clients
-
+
6、Click user on the left menu to create user
-
+
7、Click the newly created user ID, click the credentials navigation, enter the password and close the temporary option. The configuration of keycloud is completed
-
+
8、To create a role, click Roles on the left menu and then click the Create Role button to add a role
-
+
9、 Enter `iotdb_admin` in the Role Name and click the save button. Tip: `iotdb_admin` here cannot be any other name, otherwise even after successful login, you will not have permission to use iotdb's query, insert, create database, add users, roles and other functions
-
+
10、Click on the User menu on the left and then click on the user in the user list to add the `iotdb_admin` role we just created for that user
-
+
11、 Select Role Mappings, select the `iotdb_admin` role in Assign Role
-
+
-
+
Tip: If the user role is adjusted, you need to regenerate the token and log in to iotdb again to take effect
@@ -209,7 +209,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
Among them, you need to replace {my access token} (note, including {}) with your token, that is, the value corresponding to access_token. The password is empty and needs to be confirmed again.
-
+
How to get the token is dependent on your OpenID Connect setup and not covered here.
diff --git a/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md b/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md
index b6eb2650a..feef5f5c2 100644
--- a/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md
+++ b/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md
@@ -69,7 +69,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -109,9 +109,9 @@ The following documents may help you have a good journey with Prometheus and Gra
`Apache IoTDB Dashboard` is available as a supplement to IoTDB Enterprise Edition, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel. You can access the Dashboard's Json file by contacting Commerce.
-
+
-
+
### Cluster Overview
@@ -125,7 +125,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -136,7 +136,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -150,7 +150,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -160,7 +160,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -171,8 +171,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md b/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md
index 983914770..55faee221 100644
--- a/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md
+++ b/src/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md
@@ -69,7 +69,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -109,9 +109,9 @@ The following documents may help you have a good journey with Prometheus and Gra
We introduce the Apache IoTDB Dashboard, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel.
-
+
-
+
You can access the Dashboard's Json file in the enterprise edition.
@@ -127,7 +127,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -138,7 +138,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -152,7 +152,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -162,7 +162,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -173,8 +173,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md b/src/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md
index c4fe8a14c..06cd1f743 100644
--- a/src/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md
+++ b/src/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md
@@ -21,10 +21,10 @@ IoTDB Visualization Console is an extension component developed for industrial s
Main feature showcase
* Home
-
+
* Measurement Point List
-
+
* Data Query
-
+
* Trend
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/V1.2.x/User-Manual/Data-Sync.md b/src/UserGuide/V1.2.x/User-Manual/Data-Sync.md
index 1d8857bbf..380459c39 100644
--- a/src/UserGuide/V1.2.x/User-Manual/Data-Sync.md
+++ b/src/UserGuide/V1.2.x/User-Manual/Data-Sync.md
@@ -32,7 +32,7 @@
**The model of a Pipe task is as follows:**
-
+
It describes a data sync task, which essentially describes the attributes of the Pipe Extractor, Pipe Processor, and Pipe Connector plugins. Users can declaratively configure the specific attributes of the three subtasks through SQL statements. By combining different attributes, flexible data ETL (Extract, Transform, Load) capabilities can be achieved.
@@ -244,7 +244,7 @@ The task running status can transition through several states during the lifecyc
The following diagram illustrates the different states and their transitions:
-
+
## System Pre-built Data Sync Plugin
diff --git a/src/UserGuide/V1.2.x/User-Manual/Data-Sync_timecho.md b/src/UserGuide/V1.2.x/User-Manual/Data-Sync_timecho.md
index 3032a65fa..f41f03a92 100644
--- a/src/UserGuide/V1.2.x/User-Manual/Data-Sync_timecho.md
+++ b/src/UserGuide/V1.2.x/User-Manual/Data-Sync_timecho.md
@@ -32,7 +32,7 @@
**The model of a Pipe task is as follows:**
-
+
It describes a data sync task, which essentially describes the attributes of the Pipe Extractor, Pipe Processor, and Pipe Connector plugins. Users can declaratively configure the specific attributes of the three subtasks through SQL statements. By combining different attributes, flexible data ETL (Extract, Transform, Load) capabilities can be achieved.
@@ -245,7 +245,7 @@ The task running status can transition through several states during the lifecyc
The following diagram illustrates the different states and their transitions:
-
+
## System Pre-built Data Sync Plugin
### View pre-built plugin
diff --git a/src/UserGuide/V1.2.x/User-Manual/Database-Programming.md b/src/UserGuide/V1.2.x/User-Manual/Database-Programming.md
index 1c02b2899..9e9e7f53e 100644
--- a/src/UserGuide/V1.2.x/User-Manual/Database-Programming.md
+++ b/src/UserGuide/V1.2.x/User-Manual/Database-Programming.md
@@ -528,19 +528,19 @@ END
##### `
+
`SlidingTimeWindowAccessStrategy`: `SlidingTimeWindowAccessStrategy` has many constructors, you can pass 3 types of parameters to them:
@@ -1198,12 +1198,12 @@ The sliding step parameter is also optional. If the parameter is not provided, t
The relationship between the three types of parameters can be seen in the figure below. Please see the Javadoc for more details.
-
+
`SlidingSizeWindowAccessStrategy`: `SlidingSizeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -1213,7 +1213,7 @@ The `SlidingSizeWindowAccessStrategy` is shown schematically below.
The sliding step parameter is optional. If the parameter is not provided, the sliding step will be set to the same as the window size.
The `SessionTimeWindowAccessStrategy` is shown schematically below. **Time intervals less than or equal to the given minimum time interval `sessionGap` are assigned in one group**
-
+
`SessionTimeWindowAccessStrategy`: `SessionTimeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -1222,7 +1222,7 @@ The `SessionTimeWindowAccessStrategy` is shown schematically below. **Time inter
The `StateWindowAccessStrategy` is shown schematically below. **For numerical data, if the state difference is less than or equal to the given threshold `delta`, it will be assigned in one group. **
-
+
`StateWindowAccessStrategy` has four constructors.
diff --git a/src/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md b/src/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md
index 4651b03fd..05efafbfe 100644
--- a/src/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md
+++ b/src/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md
@@ -751,7 +751,7 @@ It costs 0.004s
Then the Metadata Tree will be as below:
-
+
+
##### Precautions for Use
@@ -1096,7 +1096,7 @@ A given interval threshold to create a new group of data when the difference bet
The figure below is a grouping diagram under `GROUP BY SESSION`.
-
+
##### Precautions for Use
diff --git a/src/UserGuide/V1.2.x/User-Manual/Streaming.md b/src/UserGuide/V1.2.x/User-Manual/Streaming.md
index 721d1538d..ff21b4e71 100644
--- a/src/UserGuide/V1.2.x/User-Manual/Streaming.md
+++ b/src/UserGuide/V1.2.x/User-Manual/Streaming.md
@@ -35,7 +35,7 @@ Pipe Extractor is used to extract data, Pipe Processor is used to process data,
**The model for a Pipe task is as follows:**
-
+
A data stream processing task essentially describes the attributes of the Pipe Extractor, Pipe Processor, and Pipe Connector plugins.
Users can configure the specific attributes of these three subtasks declaratively using SQL statements. By combining different attributes, flexible data ETL (Extract, Transform, Load) capabilities can be achieved.
@@ -716,7 +716,7 @@ A stream processing task status can transition through several states during the
The following diagram illustrates the different states and their transitions:
-
+
## Authority Management
diff --git a/src/UserGuide/V1.2.x/User-Manual/Streaming_timecho.md b/src/UserGuide/V1.2.x/User-Manual/Streaming_timecho.md
index cc25db81d..99b6d243d 100644
--- a/src/UserGuide/V1.2.x/User-Manual/Streaming_timecho.md
+++ b/src/UserGuide/V1.2.x/User-Manual/Streaming_timecho.md
@@ -35,7 +35,7 @@ Pipe Extractor is used to extract data, Pipe Processor is used to process data,
**The model of the Pipe task is as follows:**
-
+
Describing a data flow processing task essentially describes the properties of Pipe Extractor, Pipe Processor and Pipe Connector plugins.
Users can declaratively configure the specific attributes of the three subtasks through SQL statements, and achieve flexible data ETL capabilities by combining different attributes.
@@ -760,7 +760,7 @@ A stream processing pipe will pass through various states during its managed lif
The following diagram shows all states and state transitions:
-
+
## authority management
diff --git a/src/UserGuide/V1.3.0-2/API/Programming-MQTT.md b/src/UserGuide/V1.3.0-2/API/Programming-MQTT.md
index 9dbe31485..9f4a3e86a 100644
--- a/src/UserGuide/V1.3.0-2/API/Programming-MQTT.md
+++ b/src/UserGuide/V1.3.0-2/API/Programming-MQTT.md
@@ -27,7 +27,7 @@ It is useful for connections with remote locations where a small code footprint
IoTDB supports the MQTT v3.1(an OASIS Standard) protocol.
IoTDB server includes a built-in MQTT service that allows remote devices send messages into IoTDB server directly.
-
+
## Built-in MQTT Service
@@ -56,7 +56,7 @@ or
```
or json array of the above two.
-
+
## MQTT Configurations
The IoTDB MQTT service load configurations from `${IOTDB_HOME}/${IOTDB_CONF}/iotdb-common.properties` by default.
diff --git a/src/UserGuide/V1.3.0-2/API/Programming-ODBC.md b/src/UserGuide/V1.3.0-2/API/Programming-ODBC.md
index 12af3d818..443f9ef1a 100644
--- a/src/UserGuide/V1.3.0-2/API/Programming-ODBC.md
+++ b/src/UserGuide/V1.3.0-2/API/Programming-ODBC.md
@@ -39,7 +39,7 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
1. **Download Zappy-Sys ODBC-JDBC Bridge**:
Enter the https://zappysys.com/products/odbc-powerpack/odbc-jdbc-bridge-driver/ website, and click "download".
- 
+ 
2. **Prepare IoTDB**: Set up IoTDB cluster, and write a row of data arbitrarily.
```sql
@@ -49,11 +49,11 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
3. **Deploy and Test the Bridge**:
1. Open ODBC Data Sources(32/64 bit), depending on the bits of Windows. One possible position is `C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools`.
- 
+ 
2. Click on "add" and select ZappySys JDBC Bridge.
- 
+ 
3. Fill in the following settings:
@@ -65,15 +65,15 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
| User name | IoTDB's user name | root |
| User password | IoTDB's password | root |
- 
+ 
4. Click on "Test Connection" button, and a "Test Connection: SUCCESSFUL" should appear.
- 
+ 
5. Click the "Preview" button above, and replace the original query text with `select * from root.**`, then click "Preview Data", and the query result should correctly.
- 
+ 
4. **Operate IoTDB's data with ODBC**: After correct deployment, you can use Microsoft's ODBC library to operate IoTDB's data. Here's an example written in C#:
```C#
diff --git a/src/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md b/src/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md
index ee54b010c..fa3f939b8 100644
--- a/src/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md
+++ b/src/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md
@@ -568,7 +568,7 @@ The mapping relationship between them is：
The following figure shows the relationship between the two more intuitively:
-
+
### Data type mapping
| data type in IoTDB | data type in SQLAlchemy |
diff --git a/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md b/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md
index 42e47ffeb..4c86aa2ea 100644
--- a/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md
+++ b/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md
@@ -21,7 +21,7 @@
# Cluster-related Concepts
The figure below illustrates a typical IoTDB 3C3D1A cluster deployment mode, comprising 3 ConfigNodes, 3 DataNodes, and 1 AINode:
-
+
This deployment involves several key concepts that users commonly encounter when working with IoTDB clusters, including:
- **Nodes** (ConfigNode, DataNode, AINode);
diff --git a/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md b/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md
index c10a6e6a1..479f95527 100644
--- a/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md
+++ b/src/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB implements tailored partitioning algorithms for time series data. Building
### Partitioning Strategy
IoTDB maps each sensor in the production environment to a time series. The time series are then partitioned using the series partitioning algorithm to manage their schema, and combined with the time partitioning algorithm to manage their data. The following figure illustrates how IoTDB partitions time series data.
-
+
#### Partitioning Algorithm
Because numerous devices and sensors are commonly deployed in production environments, IoTDB employs a series partitioning algorithm to ensure the size of partition information is manageable. Since the generated time series associated with timestamps, IoTDB uses a time partioning algorithm to clearly distinguish between hot and cold partitions.
@@ -56,7 +56,7 @@ Combining a series partition with a time partition creates a data partition. Sin
### Partition Allocation
IoTDB uses RegionGroups to enable elastic storage of time series, with the number of RegionGroups in the cluster determined by the total resources available across all DataNodes. Since the number of RegionGroups is dynamic, IoTDB can easily scale out. Both the SchemaRegionGroup and DataRegionGroup follow the same partition allocation algorithm, which evenly splits all series partitions. The following figure demonstrates the partition allocation process, where the dynamic RegionGroups match the variously expending time series and cluster.
-
+
#### RegionGroup Expansion
The number of RegionGroups is given by
@@ -76,7 +76,7 @@ To enhance the cluster's availability and performance, IoTDB employs sophisticat
### Storage Load Balance
The number of Regions held by a DataNode reflects its storage load. If the difference in the number of Regions across DataNodes is relatively large, the DataNode with more Regions is likely to become a storage bottleneck. Although a straightforward Round Robin placement algorithm can achieve storage balance by ensuring that each DataNode hosts an equal number of Regions, it compromises the cluster's fault tolerance, as illustrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Place RegionGroup $r_1$'s 2 Regions on DataNodes $n_1$ and $n_2$.
@@ -91,7 +91,7 @@ To address this issue, IoTDB employs a Region placement algorithm that not only
### Computing Load Balance
The number of leader Regions held by a DataNode reflects its Computing load. If the difference in the number of leaders across DataNodes is relatively large, the DataNode with more leaders is likely to become a Computing bottleneck. If the leader selection process is conducted using a transparent Greedy algorithm, the result may be an unbalanced leader distribution when the Regions are fault-tolerantly placed, as demonstrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Select RegionGroup $r_5$'s Region on DataNode $n_5$ as the leader.
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 9571fdf11..4b63a268f 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
@@ -25,7 +25,7 @@ A wind power IoT scenario is taken as an example to illustrate how to create a c
According to the enterprise organization structure and equipment entity hierarchy, it is expressed as an attribute hierarchy structure, as shown below. The hierarchical from top to bottom is: power group layer - power plant layer - entity layer - measurement layer. ROOT is the root node, and each node of measurement layer is a leaf node. In the process of using IoTDB, the attributes on the path from ROOT node is directly connected to each leaf node with ".", thus forming the name of a timeseries in IoTDB. For example, The left-most path in Figure 2.1 can generate a timeseries named `root.ln.wf01.wt01.status`.
-
+
In the following chapters of data definition language, data operation language and Java Native Interface, various operations related to aligned timeseries will be introduced one by one.
diff --git a/src/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md b/src/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md
index 61dc07b52..ca47e475b 100644
--- a/src/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md
@@ -24,11 +24,11 @@
In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
-
+
Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
-
+
The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
@@ -36,14 +36,14 @@ The massive time series data generated by sensors is the foundation of digital t
The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
-
+
### Data Point
- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
-
+
### Measurement Points
diff --git a/src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md
index ae57e0fb7..a1e95efaf 100644
--- a/src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md
+++ b/src/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md
@@ -31,8 +31,8 @@
### Deployment mode
-
+
+
+
- There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction.
- It is recommended to create a Python interpreter venv virtual environment in the `iotdb-enterprise-ainode-
+
+
+
+
-
+
+
-
-
+
+
+
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
### Design in dashboard
Add diagrams in dashboard and customize your query. See http://docs.grafana.org/guides/getting_started/
-
+
## config grafana
diff --git a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md
index 712c400b6..fd0db53db 100644
--- a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md
@@ -119,9 +119,9 @@ Grafana displays data in a web page dashboard. Please open your browser and visi
Click the `Settings` icon on the left, select the `Data Source` option, and then click `Add data source`.
-
+
-
+
Select the `Apache IoTDB` data source.
@@ -132,22 +132,22 @@ Select the `Apache IoTDB` data source.
Click `Save & Test`, and `Data source is working` will appear.
-
+
### Create a new Panel
Click the `Dashboards` icon on the left, and select `Manage` option.
-
+
Click the `New Dashboard` icon on the top right, and select `Add an empty panel` option.
-
+
Grafana plugin supports SQL: Full Customized mode and SQL: Drop-down List mode, and the default mode is SQL: Full Customized mode.
-
+
#### SQL: Full Customized input method
@@ -155,7 +155,7 @@ Enter content in the SELECT, FROM , WHERE and CONTROL input box, where the WHERE
If a query involves multiple expressions, we can click `+` on the right side of the SELECT input box to add expressions in the SELECT clause, or click `+` on the right side of the FROM input box to add a path prefix:
-
+
SELECT input box: contents can be the time series suffix, function, udf, arithmetic expression, or nested expressions. You can also use the as clause to rename the result.
@@ -193,7 +193,7 @@ Tip: Statements like `select * from root.xx.**` are not recommended because thos
Select a time series in the TIME-SERIES selection box, select a function in the FUNCTION option, and enter the contents in the SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL input boxes, where TIME-SERIES is a required item and the rest are non required items.
-
+
### Support for variables and template functions
@@ -201,34 +201,34 @@ Both SQL: Full Customized and SQL: Drop-down List input methods support the vari
After creating a new Panel, click the Settings button in the upper right corner:
-
+
Select `Variables`, click `Add variable`:
-
+
Example 1：Enter `Name`, `Label`, and `Query`, and then click the `Update` button:
-
+
Apply Variables, enter the variable in the `grafana panel` and click the `save` button:
-
+
Example 2: Nested use of variables:
-
+
-
+
-
+
Example 3: using function variables
-
+
-
+
The Name in the above figure is the variable name and the variable name we will use in the panel in the future. Label is the display name of the variable. If it is empty, the variable of Name will be displayed. Otherwise, the name of the Label will be displayed.
There are Query, Custom, Text box, Constant, DataSource, Interval, Ad hoc filters, etc. in the Type drop-down, all of which can be used in IoTDB's Grafana Plugin
@@ -252,14 +252,14 @@ This plugin supports Grafana alert function.
1. In the Grafana panel, click the `alerting` button, as shown in the following figure:
-
+
2. Click `Create alert rule from this panel`, as shown in the figure below:
-
+
3. Set query and alarm conditions in step 1. Conditions represent query conditions, and multiple combined query conditions can be configured. As shown below:
-
+
The query condition in the figure: `min() OF A IS BELOW 0`, means that the condition will be triggered when the minimum value in the A tab is 0, click this function to change it to another function.
@@ -269,27 +269,27 @@ For example, we have 3 conditions in the following order: Condition: B (Evaluate
4. After selecting indicators and alarm rules, click the `Preview` button to preview the data as shown in the figure below:
-
+
5. In step 2, specify the alert evaluation interval, and for `Evaluate every`, specify the evaluation frequency. Must be a multiple of 10 seconds. For example, 1m, 30s.
For `Evaluate for`, specify the duration before the alert fires. As shown below:
-
+
6. In step 3, add the storage location, rule group, and other metadata associated with the rule. Where `Rule name` specifies the name of the rule. Rule names must be unique.
-
+
7. In step 4, add a custom label. Add a custom label by selecting an existing key-value pair from the drop-down list, or add a new label by entering a new key or value. As shown below:
-
+
8. Click `Save` to save the rule or click `Save and Exit` to save the rule and return to the alerts page.
9. Commonly used alarm states include `Normal`, `Pending`, `Firing` and other states, as shown in the figure below:
-
-
+
+
10. We can also configure `Contact points` for alarms to receive alarm notifications. For more detailed operations, please refer to the official document (https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/).
diff --git a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index 5d9402db8..41332ee21 100644
--- a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -36,7 +36,7 @@
The specific relationship and content between the two modules and ignition are shown in the following figure.
- 
+ 
## Installation Requirements
@@ -59,23 +59,23 @@ The Ignition-IoTDB Connector provides the ability to store the data collected by
Step 1: Enter the `Configuration` - `System` - `Modules` module and click on the `Install or Upgrade a Module` button at the bottom
-
+
Step 2: Select the obtained `modl`, select the file and upload it, click `Install`, and trust the relevant certificate.
-
+
Step 3: After installation is completed, you can see the following content
-
+
Step 4: Enter the `Configuration` - `Tags` - `History` module and click on `Create new Historical Tag Provider` below
-
+
Step 5: Select `IoTDB` and fill in the configuration information
-
+
The configuration content is as follows:
@@ -163,21 +163,21 @@ The configuration content is as follows:
- After configuring the `Provider`, you can use the `IoTDB Tag Historian` in the `Designer`, just like using other `Providers`. Right click on the corresponding `Tag` and select `Edit Tag (s) `, then select the History category in the Tag Editor
- 
+ 
- Set `History Disabled` to `true`, select `Storage Provider` as the `Provider` created in the previous step, configure other parameters as needed, click `OK`, and then save the project. At this point, the data will be continuously stored in the 'IoTDB' instance according to the set content.
- 
+ 
#### Read Data
- You can also directly select the tags stored in IoTDB under the Data tab of the Report
- 
+ 
- You can also directly browse relevant data in Components
- 
+ 
#### Script module: This function can interact with IoTDB
@@ -243,15 +243,15 @@ The configuration content is as follows:
Step 1: Enter the `Configuration` - `Databases` -`Drivers` module and create the `Translator`
-
+
Step 2: Enter the `Configuration` - `Databases` - `Drivers` module, create a `JDBC Driver` , select the `Translator` configured in the previous step, and upload the downloaded `IoTDB JDBC`. Set the Classname to `org. apache. iotdb. jdbc.IoTDBDriver`
-
+
Step 3: Enter the `Configuration` - `Databases` - `Connections` module, create a new `Connections` , select the`IoTDB Driver` created in the previous step for `JDBC Driver`, configure the relevant information, and save it to use
-
+
### Instructions
@@ -265,11 +265,11 @@ Select the previously created `Connection` from the `Data Source` in the `Transa
Do not select other options, set the fields, and after `enabled` , the data will be installed and stored in the corresponding IoTDB
-
+
#### Query
- Select `Data Source` in the `Database Query Browser` and select the previously created `Connection` to write an SQL statement to query the data in IoTDB
-
+
diff --git a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Spark-TsFile.md b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Spark-TsFile.md
index ed00538c7..151d81e14 100644
--- a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Spark-TsFile.md
+++ b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Spark-TsFile.md
@@ -94,7 +94,7 @@ The way to display TsFile is dependent on the schema. Take the following TsFile
The existing data in the TsFile are:
-
+
The corresponding SparkSQL table is:
@@ -239,7 +239,7 @@ The way to display TsFile is related to TsFile Schema. Take the following TsFile
The existing data in the file are:
-
+
A set of time-series data
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 46b37ee77..e746af93c 100644
--- a/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md
+++ b/src/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard is an open-source IoT platform that enables rapid development, management, and expansion of IoT projects. For more detailed information, please refer to [ThingsBoard Official Website](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/).
- 
+ 
2. Introduction to ThingsBoard-IoTDB
@@ -34,7 +34,7 @@
### Relationship Diagram
- 
+ 
## Installation Requirements
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ## Thingsboard data is written to the da
1. Set up devices and connect datasource: Add a new device under "Entities" - "Devices" in Thingsboard and send data to the specified devices through gateway.
- 
+ 
2. Set rule chain: Set alarm rules for "SD-032F pump" in the rule chain library and set the rule chain as the root chain.
-
+
+
-
+
+
+
As shown in Figure 1.1, users can use JDBC to import timeseries data collected by sensor on the device to local/remote IoTDB. These timeseries data may be system state data (such as server load and CPU memory, etc.), message queue data, timeseries data from applications, or other timeseries data in the database. Users can also write the data directly to the TsFile (local or on HDFS).
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 2e6b05e48..1a1f23ec4 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
@@ -35,7 +35,7 @@ Apache IoTDB is a low-cost, high-performance native temporal database for the In
IoTDB products consist of several components that help users efficiently manage and analyze the massive amount of time-series data generated by the IoT.
+
+
TimechoDB Performance Monitoring
-
-
+
+
+
SQL Query
-
-
+
+
+
+
+
| Function Name | Allowed Input Series Data Types | Required Attributes | Output Series Data Type | Description |
| ------------- | ------------------------------- | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2382,7 +2382,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `windowSize`: The number of points in a window. Int data type. **Required**.
+ `slidingStep`: Slide a window by the number of points. Int data type. Optional. If not set, default to the same as `windowSize`.
-
+
**(2) Attributes for the time window:**
@@ -2391,7 +2391,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `displayWindowBegin`: The starting position of the window (included). Long data type. Optional. If not set, default to Long.MIN_VALUE, meaning using the time of the first data point of the input time series as the starting position of the window.
+ `displayWindowEnd`: End time limit (excluded, essentially playing the same role as `WHERE time < displayWindowEnd`). Long data type. Optional. If not set, default to Long.MAX_VALUE, meaning there is no additional end time limit other than the end of the input time series itself.
-
+
#### Examples
diff --git a/src/UserGuide/V1.3.0-2/Reference/UDF-development.md b/src/UserGuide/V1.3.0-2/Reference/UDF-development.md
index 057aabbe1..0a3efb6bb 100644
--- a/src/UserGuide/V1.3.0-2/Reference/UDF-development.md
+++ b/src/UserGuide/V1.3.0-2/Reference/UDF-development.md
@@ -134,7 +134,7 @@ The following are the strategies you can set:
Window opening diagram:
-
+
`SlidingTimeWindowAccessStrategy`: `SlidingTimeWindowAccessStrategy` has many constructors, you can pass 3 types of parameters to them:
@@ -149,7 +149,7 @@ The sliding step parameter is also optional. If the parameter is not provided, t
The relationship between the three types of parameters can be seen in the figure below. Please see the Javadoc for more details.
-
+
`SlidingSizeWindowAccessStrategy`: `SlidingSizeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -170,7 +170,7 @@ The sliding step parameter is optional. If the parameter is not provided, the sl
Window opening diagram: **Time intervals less than or equal to the given minimum time interval `sessionGap` are assigned in one group.**
-
+
`SessionTimeWindowAccessStrategy`: `SessionTimeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -181,7 +181,7 @@ Window opening diagram: **Time intervals less than or equal to the given minimum
Window opening diagram: **For numerical data, if the state difference is less than or equal to the given threshold `delta`, it will be assigned in one group.**
-
+
`StateWindowAccessStrategy` has four constructors.
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 bd49782cb..08adb19bf 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
@@ -106,9 +106,9 @@ IoTDB> SHOW TTL ON StorageGroupNames
For more details, see document [Operate-Metadata](../User-Manual/Operate-Metadata.md).
-
+
-
+
### Create Device Template
diff --git a/src/UserGuide/V1.3.0-2/Tools-System/Benchmark.md b/src/UserGuide/V1.3.0-2/Tools-System/Benchmark.md
index c529997ca..f63405b7c 100644
--- a/src/UserGuide/V1.3.0-2/Tools-System/Benchmark.md
+++ b/src/UserGuide/V1.3.0-2/Tools-System/Benchmark.md
@@ -25,14 +25,14 @@ IoT-benchmark is a time-series database benchmarking tool based on Java and big
Figure 1-1 below includes the test benchmark process and other extended functions. These processes can be unified by IoT-benchmark. IoT Benchmark supports a variety of workloads, including **pure write, pure query, write query mixed**, etc., supports **software and hardware system monitoring, test metric measurement** and other monitoring functions, and also realizes **initializing the database automatically, test data analysis and system parameter optimization** functions.
-
+
Figure 1-1
Referring to the YCSB test tool's design idea of separating the three components of workload generation, performance metric measurement and database interface, the modular design of IoT-benchmark is shown in Figure 1-2. Different from the YCSB-based test tool system, IoT-benchmark adds a system monitoring module to support the persistence of test data and system monitoring data. In addition, some special load testing functions especially designed for time series data scenarios have been added, such as supporting batch writing and multiple out-of-sequence data writing modes for IoT scenarios.
-
+
Figure 1-2
@@ -71,7 +71,7 @@ Table 1-1 Comparison of big data test benchmarks
The directory structure of the test package is shown in Figure 1-3 below. The test configuration file is conf/config.properties, and the test startup scripts are benchmark\.sh (Linux & MacOS) and benchmark.bat (Windows). The detailed usage of the files is shown in Table 1-2.
-
+
Figure 1-3 List of files and folders
@@ -99,7 +99,7 @@ Table 1-2 Usage list of files and folders
All the log files of the test are stored in the logs folder, and the test results are stored in the data/csvOutput folder after the test is completed. For example, after the test, we get the following result matrix:
-
+
- Result Matrix
- OkOperation: successful operations
@@ -253,24 +253,24 @@ Table 2-3 Configuration parameter information
First, start the tested time series databases Apache-IoTDB and KairosDB on 172.21.4.3 and 172.21.4.4 respectively, and then start server resource monitoring through the ser-benchamrk\.sh script on 172.21.4.2, 172.21.4.3 and 172.21.4.4 (Figure 2-1). Then modify the conf/config.properties files in the iotdb-0.13-0.0.1 and kairosdb-0.0.1 folders in 172.21.4.2 according to Table 2-3 to meet the test requirements. Use benchmark\.sh to start the writing test of Apache-IoTDB and KairosDB successively.
-
+
Figure 2-1 Server monitoring tasks
For example, if we first start the test on KairosDB, IoT-benchmark will create a CONFIG data table in the MySQL database to store the configuration information of this test (Figure 2-2), and there will be a log output of the current test progress during the test execution (Figure 2-3) . When the test is completed, the test result will be output (Figure 2-3), and the result will be written into the FINAL_RESULT data table (Figure 2-4).
-
+
Figure 2-2 Test configuration information table
-
-
-
-
+
+
+
+
Figure 2-3 Test progress and results
-
+
Figure 2-4 Test result table
@@ -278,14 +278,14 @@ Afterwards, we will start the test on Apache-IoTDB. The same IoT-benchmark will
According to the test result information, we know that under the same configuration the write delay times of Apache-IoTDB and KairosDB are 55.98ms and 1324.45ms respectively; the write throughputs are 5,125,600.86 points/second and 224,819.01 points/second respectively; the tests were executed respectively 585.30 seconds and 11777.99 seconds. And KairosDB has a write failure. After investigation, it is found that the data disk usage has reached 100%, and there is no disk space to continue receiving data. However, Apache-IoTDB has no write failure, and the disk space occupied after all data is written is only 4.7G (as shown in Figure 2-5); Apache-IoTDB is better than KairosDB in terms of write throughput and disk occupation. Of course, there will be other tests in the follow-up to observe and compare from various aspects, such as query performance, file compression ratio, data security, etc.
-
+
Figure 2-5 Disk usage
So what is the resource usage of each server during the test? What is the specific performance of each write operation? At this time, we can visualize the data in the server monitoring table and test process recording table by installing and using Tableau. The use of Tableau will not be introduced in this article. After connecting to the data table for test data persistence, the specific results are as follows (taking Apache-IoTDB as an example):
-
-
+
+
Figure 2-6 Visualization of testing process in Tableau
@@ -310,7 +310,7 @@ Table 2-4 Configuration parameter information
Results:
-
+
Figure 2-7 Query test results
diff --git a/src/UserGuide/V1.3.0-2/Tools-System/CLI.md b/src/UserGuide/V1.3.0-2/Tools-System/CLI.md
index ff3f4a12f..d5034ea71 100644
--- a/src/UserGuide/V1.3.0-2/Tools-System/CLI.md
+++ b/src/UserGuide/V1.3.0-2/Tools-System/CLI.md
@@ -154,46 +154,46 @@ Shell >./kc.sh start-dev
```
2、use url(https://ip:port) login keycloack, the first login needs to create a user
-
+
3、Click administration console
-
+
4、In the master menu on the left, click Create realm and enter Realm name to create a new realm
-
+
-
+
5、Click the menu clients on the left to create clients
-
+
6、Click user on the left menu to create user
-
+
7、Click the newly created user ID, click the credentials navigation, enter the password and close the temporary option. The configuration of keycloud is completed
-
+
8、To create a role, click Roles on the left menu and then click the Create Role button to add a role
-
+
9、 Enter `iotdb_admin` in the Role Name and click the save button. Tip: `iotdb_admin` here cannot be any other name, otherwise even after successful login, you will not have permission to use iotdb's query, insert, create database, add users, roles and other functions
-
+
10、Click on the User menu on the left and then click on the user in the user list to add the `iotdb_admin` role we just created for that user
-
+
11、 Select Role Mappings, select the `iotdb_admin` role in Assign Role
-
+
-
+
Tip: If the user role is adjusted, you need to regenerate the token and log in to iotdb again to take effect
@@ -209,7 +209,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
Among them, you need to replace {my access token} (note, including {}) with your token, that is, the value corresponding to access_token. The password is empty and needs to be confirmed again.
-
+
How to get the token is dependent on your OpenID Connect setup and not covered here.
diff --git a/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md b/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md
index 7325f4dc3..ec3b8048d 100644
--- a/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md
+++ b/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md
@@ -69,7 +69,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -110,9 +110,9 @@ The following documents may help you have a good journey with Prometheus and Gra
`Apache IoTDB Dashboard` is available as a supplement to IoTDB Enterprise Edition, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel. You can access the Dashboard's Json file by contacting Commerce.
-
+
-
+
@@ -127,7 +127,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -138,7 +138,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -152,7 +152,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -162,7 +162,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -173,8 +173,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md b/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md
index 7f621359e..d15b5d0e6 100644
--- a/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md
+++ b/src/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md
@@ -69,7 +69,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -109,9 +109,9 @@ The following documents may help you have a good journey with Prometheus and Gra
We introduce the Apache IoTDB Dashboard, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel.
-
+
-
+
You can access the Dashboard's Json file in the enterprise edition.
@@ -127,7 +127,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -138,7 +138,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -152,7 +152,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -162,7 +162,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -173,8 +173,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md b/src/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md
index 94ad3674c..18955836a 100644
--- a/src/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md
+++ b/src/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md
@@ -21,10 +21,10 @@ IoTDB Visualization Console is an extension component developed for industrial s
Main feature showcase
* Home
-
+
* Measurement Point List
-
+
* Data Query
-
+
* Trend
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md b/src/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md
index feb3b2bdc..ba4f69d38 100644
--- a/src/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB,
The system architecture is shown below:
::: center
-
+
:::
The responsibilities of the three nodes are as follows:
@@ -42,7 +42,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time.
@@ -59,7 +59,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection).
::: center
-
+
::::
## Installation and Deployment
@@ -305,13 +305,13 @@ After completing the registration of the model, the inference function can be us
- **sql**: sql query statement, the result of the query is used as input to the model for model inference. The dimensions of the rows and columns in the result of the query need to match the size specified in the specific model config. (It is not recommended to use the `SELECT *` clause for the sql here because in IoTDB, `*` does not sort the columns, so the order of the columns is undefined, you can use `SELECT s0,s1` to ensure that the columns order matches the expectations of the model input)
- **window_function**: Window functions that can be used in the inference process, there are currently three types of window functions provided to assist in model inference:
- **head(window_size)**: Get the top window_size points in the data for model inference, this window can be used for data cropping.
- 
+ 
- **tail(window_size)**: get the last window_size point in the data for model inference, this window can be used for data cropping.
- 
+ 
- **count(window_size, sliding_step)**: sliding window based on the number of points, the data in each window will be reasoned through the model respectively, as shown in the example below, window_size for 2 window function will be divided into three windows of the input dataset, and each window will perform reasoning operations to generate results respectively. The window can be used for continuous inference
- 
+ 
**Explanation 1**: window can be used to solve the problem of cropping rows when the results of the sql query and the input row requirements of the model do not match. Note that when the number of columns does not match or the number of rows is directly less than the model requirement, the inference cannot proceed and an error message will be returned.
@@ -460,7 +460,7 @@ When using AINode related functions, the authentication of IoTDB itself can be u
In some industrial scenarios, there is a need to predict power loads, which can be used to optimise power supply, conserve energy and resources, support planning and expansion, and enhance power system reliability.
-The data for the test set of ETTh1 that we use is [ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv).
+The data for the test set of ETTh1 that we use is [ETTh1](/img/ETTh1.csv).
It contains power data collected at 1h intervals, and each data consists of load and oil temperature as High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature.
@@ -523,7 +523,7 @@ We compare the results of the prediction of the oil temperature with the real re
The data before 10/24 00:00 represents the past data input to the model, the blue line after 10/24 00:00 is the oil temperature forecast result given by the model, and the red line is the actual oil temperature data from the dataset (used for comparison).
-
+
As can be seen, we have used the relationship between the six load information and the corresponding time oil temperatures for the past 96 hours (4 days) to model the possible changes in this data for the oil temperature for the next 48 hours (2 days) based on the inter-relationships between the sequences learned previously, and it can be seen that the predicted curves maintain a high degree of consistency in trend with the actual results after visualisation.
@@ -533,7 +533,7 @@ Power monitoring of current, voltage and power data is required in substations f
We have used the current, voltage and power data in a substation to form a dataset in a real scenario. The dataset consists of data such as A-phase voltage, B-phase voltage, and C-phase voltage collected every 5 - 6s for a time span of nearly four months in the substation.
-The test set data content is [data](https://alioss.timecho.com/docs/img/data.csv).
+The test set data content is [data](/img/data.csv).
On this dataset, the model inference function of IoTDB-ML can predict the C-phase voltage in the future period through the previous values and corresponding timestamps of A-phase voltage, B-phase voltage and C-phase voltage, empowering the monitoring management of the substation.
@@ -589,7 +589,7 @@ Comparing the predicted results of the C-phase voltage with the real results, we
The data before 02/14 20:48 represents the past data input to the model, the blue line after 02/14 20:48 is the predicted result of phase C voltage given by the model, while the red line is the actual phase C voltage data from the dataset (used for comparison).
-
+
It can be seen that we used the voltage data from the past 10 minutes and, based on the previously learned inter-sequence relationships, modeled the possible changes in the phase C voltage data for the next 5 minutes. The visualized forecast curve shows a certain degree of synchronicity with the actual results in terms of trend.
@@ -597,7 +597,7 @@ It can be seen that we used the voltage data from the past 10 minutes and, based
In the civil aviation and transport industry, there exists a need for anomaly detection of the number of passengers travelling on an aircraft. The results of anomaly detection can be used to guide the adjustment of flight scheduling to make the organisation more efficient.
-Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](https://alioss.timecho.com/docs/img/airline.csv).
+Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](/img/airline.csv).
On this dataset, the model inference function of IoTDB-ML can empower the transport industry by capturing the changing patterns of the sequence in order to detect anomalies at the sequence time points.
#### Step 1: Data Import
@@ -650,6 +650,6 @@ Total line number = 144
We plot the results detected as anomalies to get the following image. Where the blue curve is the original time series and the time points specially marked with red dots are the time points that the algorithm detects as anomalies.
-
+
It can be seen that the Stray model has modelled the input sequence changes and successfully detected the time points where anomalies occur.
\ No newline at end of file
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 883009c42..691dfd4e3 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
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -97,13 +97,13 @@ V1.3.0 and earlier versions:
After creation, it will not start immediately and needs to execute the `START PIPE` statement to start the task.
-
+
V1.3.1 and later versions:
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
-
+
Provide the following SQL statements for state management of synchronization tasks.
@@ -271,7 +271,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -286,7 +286,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -310,7 +310,7 @@ with SINK (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -362,7 +362,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
diff --git a/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md b/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md
index d0afbbe1d..47671ea71 100644
--- a/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md
+++ b/src/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -100,13 +100,13 @@ V1.3.0 and earlier versions:
After creation, it will not start immediately and needs to execute the `START PIPE` statement to start the task.
-
+
V1.3.1 and later versions:
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
-
+
Provide the following SQL statements for state management of synchronization tasks.
@@ -274,7 +274,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -289,7 +289,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -313,7 +313,7 @@ with SINK (
This example is used to demonstrate the scenario where two IoTDB act as active-active pairs, with the data link shown in the figure below:
-
+
In this example, to avoid infinite data loops, the `forwarding-pipe-requests` parameter on A and B needs to be set to `false`, indicating that data transmitted from another pipe is not forwarded, and to keep the data consistent on both sides, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata.
@@ -351,7 +351,7 @@ with sink (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -403,7 +403,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
@@ -436,7 +436,7 @@ with sink (
This example is used to demonstrate the scenario where data from one IoTDB is synchronized to another IoTDB through a unidirectional gateway, as shown in the figure below:
-
+
In this example, the iotdb-air-gap-sink plugin in the sink task needs to be used (currently supports some gateway models, for specific models, please contact Timecho staff for confirmation). After configuring the gateway, execute the following statement on A IoTDB. Fill in the node-urls with the URL of the data service port of the DataNode node on the target IoTDB configured by the gateway, as detailed below:
diff --git a/src/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md b/src/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md
index a0dd3d788..9791a13ee 100644
--- a/src/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md
+++ b/src/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md
@@ -528,19 +528,19 @@ END
##### `
+
+
##### Precautions for Use
@@ -1096,7 +1096,7 @@ A given interval threshold to create a new group of data when the difference bet
The figure below is a grouping diagram under `GROUP BY SESSION`.
-
+
##### Precautions for Use
diff --git a/src/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md b/src/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md
index 1c0d5b0bd..29967ecde 100644
--- a/src/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md
+++ b/src/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md
@@ -68,7 +68,7 @@ Decide which IP addresses can connect to IoTDB
5. whether to enable the configuration of the white list function, can be hot loaded.
6. Use the Java native interface to modify the whitelist, must be the root user to modify, reject non-root user to modify; modify the content must be legal, otherwise it will throw a StatementExecutionException.
-
+
## Audit log
@@ -88,7 +88,7 @@ The audit log function traces all user operations in the database. The informati
Client Sources：
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
##### Audit logging can be turned off for some user connections
diff --git a/src/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md b/src/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md
index 6f7d3b196..be8d1c08b 100644
--- a/src/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md
+++ b/src/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md
@@ -35,7 +35,7 @@ Pipe Source is used to extract data, Pipe Processor is used to process data, Pip
**The model for a Pipe task is as follows:**
-
+
A data stream processing task essentially describes the attributes of the Pipe Source, Pipe Processor, and Pipe Sink plugins.
@@ -711,7 +711,7 @@ A stream processing task status can transition through several states during the
The following diagram illustrates the different states and their transitions:
-
+
## Authority Management
diff --git a/src/UserGuide/V1.3.0-2/User-Manual/Streaming_timecho.md b/src/UserGuide/V1.3.0-2/User-Manual/Streaming_timecho.md
index 026bb18b9..e3c6a0b12 100644
--- a/src/UserGuide/V1.3.0-2/User-Manual/Streaming_timecho.md
+++ b/src/UserGuide/V1.3.0-2/User-Manual/Streaming_timecho.md
@@ -35,7 +35,7 @@ Pipe Source is used to extract data, Pipe Processor is used to process data, Pip
**The model of the Pipe task is as follows:**
-
+
Describing a data flow processing task essentially describes the properties of Pipe Source, Pipe Processor and Pipe Sink plugins.
Users can declaratively configure the specific attributes of the three subtasks through SQL statements, and achieve flexible data ETL capabilities by combining different attributes.
@@ -753,7 +753,7 @@ A stream processing pipe will pass through various states during its managed lif
The following diagram shows all states and state transitions:
-
+
## authority management
diff --git a/src/UserGuide/V1.3.3/API/Programming-MQTT.md b/src/UserGuide/V1.3.3/API/Programming-MQTT.md
index d33270105..1015986d6 100644
--- a/src/UserGuide/V1.3.3/API/Programming-MQTT.md
+++ b/src/UserGuide/V1.3.3/API/Programming-MQTT.md
@@ -25,7 +25,7 @@ It is useful for connections with remote locations where a small code footprint
IoTDB supports the MQTT v3.1(an OASIS Standard) protocol.
IoTDB server includes a built-in MQTT service that allows remote devices send messages into IoTDB server directly.
-
+
## Built-in MQTT Service
@@ -60,7 +60,7 @@ or
or json array of the above two.
-
+
## MQTT Configurations
diff --git a/src/UserGuide/V1.3.3/API/Programming-ODBC.md b/src/UserGuide/V1.3.3/API/Programming-ODBC.md
index 51ac098ba..7f763d5a9 100644
--- a/src/UserGuide/V1.3.3/API/Programming-ODBC.md
+++ b/src/UserGuide/V1.3.3/API/Programming-ODBC.md
@@ -44,7 +44,7 @@ _Note: Here we only provide one kind of ODBC-JDBC bridge as the instance. Reader
1. **Download Zappy-Sys ODBC-JDBC Bridge**:
Enter the
+
:::
@@ -44,7 +44,7 @@ OPC UA is a technical specification used in the automation field for communicati
::: center
-
+
:::
@@ -149,13 +149,13 @@ start pipe p1;
::: center
-
+
:::
::: center
-
+
:::
@@ -163,13 +163,13 @@ start pipe p1;
::: center
-
+
:::
::: center
-
+
:::
@@ -177,7 +177,7 @@ start pipe p1;
::: center
-
+
:::
@@ -218,7 +218,7 @@ The steps are as follows:
::: center
-
+
:::
@@ -226,7 +226,7 @@ The steps are as follows:
::: center
-
+
:::
@@ -234,7 +234,7 @@ The steps are as follows:
::: center
-
+
:::
@@ -242,7 +242,7 @@ The steps are as follows:
::: center
-
+
:::
@@ -250,7 +250,7 @@ The steps are as follows:
::: center
-
+
:::
@@ -258,7 +258,7 @@ The steps are as follows:
::: center
-
+
:::
@@ -268,7 +268,7 @@ The steps are as follows:
::: center
-
+
:::
diff --git a/src/UserGuide/V1.3.3/API/Programming-Python-Native-API.md b/src/UserGuide/V1.3.3/API/Programming-Python-Native-API.md
index 370522c1c..cb8916d0f 100644
--- a/src/UserGuide/V1.3.3/API/Programming-Python-Native-API.md
+++ b/src/UserGuide/V1.3.3/API/Programming-Python-Native-API.md
@@ -598,7 +598,7 @@ The mapping relationship between them is：
The following figure shows the relationship between the two more intuitively:
-
+
### Data type mapping
diff --git a/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_apache.md b/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_apache.md
index 1edc7c292..674a74e69 100644
--- a/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_apache.md
+++ b/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_apache.md
@@ -36,7 +36,7 @@
The following figure shows a common IoTDB 3C3D (3 ConfigNodes, 3 DataNodes) cluster deployment pattern:
-
+
IoTDB's cluster includes the following common concepts:
diff --git a/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md b/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md
index af669a6d6..42344aa47 100644
--- a/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md
@@ -36,7 +36,7 @@
The following figure shows a common IoTDB 3C3D (3 ConfigNodes, 3 DataNodes) cluster deployment pattern:
-
+
IoTDB's cluster includes the following common concepts:
diff --git a/src/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md b/src/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md
index 7a10118a9..614be1bf2 100644
--- a/src/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md
+++ b/src/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@ A wind power IoT scenario is taken as an example to illustrate how to create a c
According to the enterprise organization structure and equipment entity hierarchy, it is expressed as an attribute hierarchy structure, as shown below. The hierarchical from top to bottom is: power group layer - power plant layer - entity layer - measurement layer. ROOT is the root node, and each node of measurement layer is a leaf node. In the process of using IoTDB, the attributes on the path from ROOT node is directly connected to each leaf node with ".", thus forming the name of a timeseries in IoTDB. For example, The left-most path in Figure 2.1 can generate a timeseries named `root.ln.wf01.wt01.status`.
-
+
In the following chapters of data definition language, data operation language and Java Native Interface, various operations related to aligned timeseries will be introduced one by one.
diff --git a/src/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md b/src/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md
index 20aaef327..6ff81da37 100644
--- a/src/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md
@@ -24,25 +24,25 @@
In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
-
+
Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
-
+
The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
## Key Concepts of Time Series Data
The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
-
+
### Data Point
- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
-
+
### Measurement Points
diff --git a/src/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md b/src/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md
index 45becb7bd..00bd8e68a 100644
--- a/src/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md
+++ b/src/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md
@@ -352,9 +352,9 @@ All devices will definitely have a TTL, meaning it cannot be null. INF represent
IoTDB supports the device template function, enabling different entities of the same type to share metadata, reduce the memory usage of metadata, and simplify the management of numerous entities and measurements.
-
+
-
+
### Create Device Template
@@ -793,7 +793,7 @@ It costs 0.004s
Then the Metadata Tree will be as below:
-
+
+
##### Precautions for Use
@@ -1096,7 +1096,7 @@ A given interval threshold to create a new group of data when the difference bet
The figure below is a grouping diagram under `GROUP BY SESSION`.
-
+
##### Precautions for Use
diff --git a/src/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md
index 610ec0b72..83baab566 100644
--- a/src/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md
+++ b/src/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md
@@ -31,8 +31,8 @@
### Deployment mode
-
+
+
+
- There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction.
- It is recommended to create a Python interpreter venv virtual environment in the `iotdb-enterprise-ainode-
-
+
+
+
- There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction.
- It is recommended to create a Python interpreter venv virtual environment in the `iotdb-enterprise-ainode-
+
+
+
+
-
+
+
-
-
+
+
+
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
### Design in dashboard
Add diagrams in dashboard and customize your query. See http://docs.grafana.org/guides/getting_started/
-
+
## config grafana
diff --git a/src/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md b/src/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md
index dc8370464..0597fb124 100644
--- a/src/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md
@@ -119,9 +119,9 @@ Grafana displays data in a web page dashboard. Please open your browser and visi
Click the `Settings` icon on the left, select the `Data Source` option, and then click `Add data source`.
-
+
-
+
Select the `Apache IoTDB` data source.
@@ -132,22 +132,22 @@ Select the `Apache IoTDB` data source.
Click `Save & Test`, and `Data source is working` will appear.
-
+
### Create a new Panel
Click the `Dashboards` icon on the left, and select `Manage` option.
-
+
Click the `New Dashboard` icon on the top right, and select `Add an empty panel` option.
-
+
Grafana plugin supports SQL: Full Customized mode and SQL: Drop-down List mode, and the default mode is SQL: Full Customized mode.
-
+
#### SQL: Full Customized input method
@@ -155,7 +155,7 @@ Enter content in the SELECT, FROM , WHERE and CONTROL input box, where the WHERE
If a query involves multiple expressions, we can click `+` on the right side of the SELECT input box to add expressions in the SELECT clause, or click `+` on the right side of the FROM input box to add a path prefix:
-
+
SELECT input box: contents can be the time series suffix, function, udf, arithmetic expression, or nested expressions. You can also use the as clause to rename the result.
@@ -193,7 +193,7 @@ Tip: Statements like `select * from root.xx.**` are not recommended because thos
Select a time series in the TIME-SERIES selection box, select a function in the FUNCTION option, and enter the contents in the SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL input boxes, where TIME-SERIES is a required item and the rest are non required items.
-
+
### Support for variables and template functions
@@ -201,34 +201,34 @@ Both SQL: Full Customized and SQL: Drop-down List input methods support the vari
After creating a new Panel, click the Settings button in the upper right corner:
-
+
Select `Variables`, click `Add variable`:
-
+
Example 1：Enter `Name`, `Label`, and `Query`, and then click the `Update` button:
-
+
Apply Variables, enter the variable in the `grafana panel` and click the `save` button:
-
+
Example 2: Nested use of variables:
-
+
-
+
-
+
Example 3: using function variables
-
+
-
+
The Name in the above figure is the variable name and the variable name we will use in the panel in the future. Label is the display name of the variable. If it is empty, the variable of Name will be displayed. Otherwise, the name of the Label will be displayed.
There are Query, Custom, Text box, Constant, DataSource, Interval, Ad hoc filters, etc. in the Type drop-down, all of which can be used in IoTDB's Grafana Plugin
@@ -252,14 +252,14 @@ This plugin supports Grafana alert function.
1. In the Grafana panel, click the `alerting` button, as shown in the following figure:
-
+
2. Click `Create alert rule from this panel`, as shown in the figure below:
-
+
3. Set query and alarm conditions in step 1. Conditions represent query conditions, and multiple combined query conditions can be configured. As shown below:
-
+
The query condition in the figure: `min() OF A IS BELOW 0`, means that the condition will be triggered when the minimum value in the A tab is 0, click this function to change it to another function.
@@ -269,27 +269,27 @@ For example, we have 3 conditions in the following order: Condition: B (Evaluate
4. After selecting indicators and alarm rules, click the `Preview` button to preview the data as shown in the figure below:
-
+
5. In step 2, specify the alert evaluation interval, and for `Evaluate every`, specify the evaluation frequency. Must be a multiple of 10 seconds. For example, 1m, 30s.
For `Evaluate for`, specify the duration before the alert fires. As shown below:
-
+
6. In step 3, add the storage location, rule group, and other metadata associated with the rule. Where `Rule name` specifies the name of the rule. Rule names must be unique.
-
+
7. In step 4, add a custom label. Add a custom label by selecting an existing key-value pair from the drop-down list, or add a new label by entering a new key or value. As shown below:
-
+
8. Click `Save` to save the rule or click `Save and Exit` to save the rule and return to the alerts page.
9. Commonly used alarm states include `Normal`, `Pending`, `Firing` and other states, as shown in the figure below:
-
-
+
+
10. We can also configure `Contact points` for alarms to receive alarm notifications. For more detailed operations, please refer to the official document (https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/).
diff --git a/src/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index 5d9402db8..41332ee21 100644
--- a/src/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -36,7 +36,7 @@
The specific relationship and content between the two modules and ignition are shown in the following figure.
- 
+ 
## Installation Requirements
@@ -59,23 +59,23 @@ The Ignition-IoTDB Connector provides the ability to store the data collected by
Step 1: Enter the `Configuration` - `System` - `Modules` module and click on the `Install or Upgrade a Module` button at the bottom
-
+
Step 2: Select the obtained `modl`, select the file and upload it, click `Install`, and trust the relevant certificate.
-
+
Step 3: After installation is completed, you can see the following content
-
+
Step 4: Enter the `Configuration` - `Tags` - `History` module and click on `Create new Historical Tag Provider` below
-
+
Step 5: Select `IoTDB` and fill in the configuration information
-
+
The configuration content is as follows:
@@ -163,21 +163,21 @@ The configuration content is as follows:
- After configuring the `Provider`, you can use the `IoTDB Tag Historian` in the `Designer`, just like using other `Providers`. Right click on the corresponding `Tag` and select `Edit Tag (s) `, then select the History category in the Tag Editor
- 
+ 
- Set `History Disabled` to `true`, select `Storage Provider` as the `Provider` created in the previous step, configure other parameters as needed, click `OK`, and then save the project. At this point, the data will be continuously stored in the 'IoTDB' instance according to the set content.
- 
+ 
#### Read Data
- You can also directly select the tags stored in IoTDB under the Data tab of the Report
- 
+ 
- You can also directly browse relevant data in Components
- 
+ 
#### Script module: This function can interact with IoTDB
@@ -243,15 +243,15 @@ The configuration content is as follows:
Step 1: Enter the `Configuration` - `Databases` -`Drivers` module and create the `Translator`
-
+
Step 2: Enter the `Configuration` - `Databases` - `Drivers` module, create a `JDBC Driver` , select the `Translator` configured in the previous step, and upload the downloaded `IoTDB JDBC`. Set the Classname to `org. apache. iotdb. jdbc.IoTDBDriver`
-
+
Step 3: Enter the `Configuration` - `Databases` - `Connections` module, create a new `Connections` , select the`IoTDB Driver` created in the previous step for `JDBC Driver`, configure the relevant information, and save it to use
-
+
### Instructions
@@ -265,11 +265,11 @@ Select the previously created `Connection` from the `Data Source` in the `Transa
Do not select other options, set the fields, and after `enabled` , the data will be installed and stored in the corresponding IoTDB
-
+
#### Query
- Select `Data Source` in the `Database Query Browser` and select the previously created `Connection` to write an SQL statement to query the data in IoTDB
-
+
diff --git a/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md b/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md
index 0fb5b73d9..fca46c19d 100644
--- a/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md
+++ b/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 Manually Create Directories
@@ -400,7 +400,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. Scaling
@@ -408,7 +408,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
Add a new PV; scaling is only possible with available PVs.
-
+
**Note: DataNode cannot join the cluster after restart**
@@ -425,7 +425,7 @@ Modify the values.yaml file in iotdb-cluster-k8s/helm to change the number of Co
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 Scale DataNode
diff --git a/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md b/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md
index c59ddcce6..f16d4727b 100644
--- a/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md
+++ b/src/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 Manually Create Directories
@@ -400,7 +400,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. Scaling
@@ -408,7 +408,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
Add a new PV; scaling is only possible with available PVs.
-
+
**Note: DataNode cannot join the cluster after restart**
@@ -425,7 +425,7 @@ Modify the values.yaml file in iotdb-cluster-k8s/helm to change the number of Co
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 Scale DataNode
diff --git a/src/UserGuide/V1.3.3/Ecosystem-Integration/Spark-TsFile.md b/src/UserGuide/V1.3.3/Ecosystem-Integration/Spark-TsFile.md
index ed00538c7..151d81e14 100644
--- a/src/UserGuide/V1.3.3/Ecosystem-Integration/Spark-TsFile.md
+++ b/src/UserGuide/V1.3.3/Ecosystem-Integration/Spark-TsFile.md
@@ -94,7 +94,7 @@ The way to display TsFile is dependent on the schema. Take the following TsFile
The existing data in the TsFile are:
-
+
The corresponding SparkSQL table is:
@@ -239,7 +239,7 @@ The way to display TsFile is related to TsFile Schema. Take the following TsFile
The existing data in the file are:
-
+
A set of time-series data
diff --git a/src/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md b/src/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md
index 1c84d9bef..2c0d82514 100644
--- a/src/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md
+++ b/src/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md
@@ -38,7 +38,7 @@ Telegraf has the following characteristics:
The Telegraf IoTDB plugin can output and store monitoring information saved in Telegraf to IoTDB. The output plugin uses IoTDB sessions for connection and data writing.
-
+
## 2、Installation Requirements
@@ -66,7 +66,7 @@ Find 'INPUT PLUGINS' in the' telegraf. conf 'configuration file to configure the
| name_prefix | Specify the prefix attached to the measurement name | |
| name_suffix | Specify the suffix attached to the measurement name | |
-
+
### 4.2 Set Output Source
@@ -81,7 +81,7 @@ Find "outputs. iotdb" in the "telegraf. conf" configuration file to configure th
| timestamp_precision | Timestamp accuracy | timestamp_precision = "millisecond" | timestamp_precision = "Same timestamp accuracy as IoTDB" | You can check the 'timestamp-precision' field in 'iotdb system. properties' |
| sanitize_tag | Database version | none | sanitize_tag = "0.13/1.0/1.1/1.2/1.3" | |
-
+
### 4.3 Start Telegraf Service
diff --git a/src/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md b/src/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md
index c0fdf50e8..bbc9c675f 100644
--- a/src/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md
+++ b/src/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard is an open-source IoT platform that enables rapid development, management, and expansion of IoT projects. For more detailed information, please refer to [ThingsBoard Official Website](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/).
- 
+ 
1. Introduction to ThingsBoard-IoTDB
@@ -34,7 +34,7 @@
### Relationship Diagram
- 
+ 
## Installation Requirements
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ## Thingsboard data is written to the da
1. Set up devices and connect datasource: Add a new device under "Entities" - "Devices" in Thingsboard and send data to the specified devices through gateway.
- 
+ 
2. Set rule chain: Set alarm rules for "SD-032F pump" in the rule chain library and set the rule chain as the root chain
-
+
+
-
+
+
+
+
TimechoDB Performance Monitoring
-
-
+
+
+
SQL Query
-
-
+
+
+
+
+
| Function Name | Allowed Input Series Data Types | Required Attributes | Output Series Data Type | Description |
| ------------- | ------------------------------- | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2382,7 +2382,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `windowSize`: The number of points in a window. Int data type. **Required**.
+ `slidingStep`: Slide a window by the number of points. Int data type. Optional. If not set, default to the same as `windowSize`.
-
+
**(2) Attributes for the time window:**
@@ -2391,7 +2391,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `displayWindowBegin`: The starting position of the window (included). Long data type. Optional. If not set, default to Long.MIN_VALUE, meaning using the time of the first data point of the input time series as the starting position of the window.
+ `displayWindowEnd`: End time limit (excluded, essentially playing the same role as `WHERE time < displayWindowEnd`). Long data type. Optional. If not set, default to Long.MAX_VALUE, meaning there is no additional end time limit other than the end of the input time series itself.
-
+
#### Examples
diff --git a/src/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md b/src/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md
index 4ac977278..4befc2772 100644
--- a/src/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md
+++ b/src/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md
@@ -107,13 +107,13 @@ IoTDB> SHOW DEVICES
For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md).
-
+
-
+
### Create Device Template
diff --git a/src/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md b/src/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md
index c10a6e6a1..479f95527 100644
--- a/src/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md
+++ b/src/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB implements tailored partitioning algorithms for time series data. Building
### Partitioning Strategy
IoTDB maps each sensor in the production environment to a time series. The time series are then partitioned using the series partitioning algorithm to manage their schema, and combined with the time partitioning algorithm to manage their data. The following figure illustrates how IoTDB partitions time series data.
-
+
#### Partitioning Algorithm
Because numerous devices and sensors are commonly deployed in production environments, IoTDB employs a series partitioning algorithm to ensure the size of partition information is manageable. Since the generated time series associated with timestamps, IoTDB uses a time partioning algorithm to clearly distinguish between hot and cold partitions.
@@ -56,7 +56,7 @@ Combining a series partition with a time partition creates a data partition. Sin
### Partition Allocation
IoTDB uses RegionGroups to enable elastic storage of time series, with the number of RegionGroups in the cluster determined by the total resources available across all DataNodes. Since the number of RegionGroups is dynamic, IoTDB can easily scale out. Both the SchemaRegionGroup and DataRegionGroup follow the same partition allocation algorithm, which evenly splits all series partitions. The following figure demonstrates the partition allocation process, where the dynamic RegionGroups match the variously expending time series and cluster.
-
+
#### RegionGroup Expansion
The number of RegionGroups is given by
@@ -76,7 +76,7 @@ To enhance the cluster's availability and performance, IoTDB employs sophisticat
### Storage Load Balance
The number of Regions held by a DataNode reflects its storage load. If the difference in the number of Regions across DataNodes is relatively large, the DataNode with more Regions is likely to become a storage bottleneck. Although a straightforward Round Robin placement algorithm can achieve storage balance by ensuring that each DataNode hosts an equal number of Regions, it compromises the cluster's fault tolerance, as illustrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Place RegionGroup $r_1$'s 2 Regions on DataNodes $n_1$ and $n_2$.
@@ -91,7 +91,7 @@ To address this issue, IoTDB employs a Region placement algorithm that not only
### Computing Load Balance
The number of leader Regions held by a DataNode reflects its Computing load. If the difference in the number of leaders across DataNodes is relatively large, the DataNode with more leaders is likely to become a Computing bottleneck. If the leader selection process is conducted using a transparent Greedy algorithm, the result may be an unbalanced leader distribution when the Regions are fault-tolerantly placed, as demonstrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Select RegionGroup $r_5$'s Region on DataNode $n_5$ as the leader.
diff --git a/src/UserGuide/V1.3.3/Tools-System/Benchmark.md b/src/UserGuide/V1.3.3/Tools-System/Benchmark.md
index 86ff88bca..3ffd64c78 100644
--- a/src/UserGuide/V1.3.3/Tools-System/Benchmark.md
+++ b/src/UserGuide/V1.3.3/Tools-System/Benchmark.md
@@ -25,14 +25,14 @@ IoT-benchmark is a time-series database benchmarking tool based on Java and big
Figure 1-1 below includes the test benchmark process and other extended functions. These processes can be unified by IoT-benchmark. IoT Benchmark supports a variety of workloads, including **pure write, pure query, write query mixed**, etc., supports **software and hardware system monitoring, test metric measurement** and other monitoring functions, and also realizes **initializing the database automatically, test data analysis and system parameter optimization** functions.
-
+
Figure 1-1
Referring to the YCSB test tool's design idea of separating the three components of workload generation, performance metric measurement and database interface, the modular design of IoT-benchmark is shown in Figure 1-2. Different from the YCSB-based test tool system, IoT-benchmark adds a system monitoring module to support the persistence of test data and system monitoring data. In addition, some special load testing functions especially designed for time series data scenarios have been added, such as supporting batch writing and multiple out-of-sequence data writing modes for IoT scenarios.
-
+
Figure 1-2
@@ -71,7 +71,7 @@ Table 1-1 Comparison of big data test benchmarks
The directory structure of the test package is shown in Figure 1-3 below. The test configuration file is conf/config.properties, and the test startup scripts are benchmark\.sh (Linux & MacOS) and benchmark.bat (Windows). The detailed usage of the files is shown in Table 1-2.
-
+
Figure 1-3 List of files and folders
@@ -101,7 +101,7 @@ Table 1-2 Usage list of files and folders
All the log files of the test are stored in the logs folder, and the test results are stored in the data/csvOutput folder after the test is completed. For example, after the test, we get the following result matrix:
-
+
- Result Matrix
- OkOperation: successful operations
@@ -256,24 +256,24 @@ Table 2-3 Configuration parameter information
First, start the tested time series databases Apache-IoTDB and KairosDB on 172.21.4.3 and 172.21.4.4 respectively, and then start server resource monitoring through the ser-benchamrk\.sh script on 172.21.4.2, 172.21.4.3 and 172.21.4.4 (Figure 2-1). Then modify the conf/config.properties files in the iotdb-0.13-0.0.1 and kairosdb-0.0.1 folders in 172.21.4.2 according to Table 2-3 to meet the test requirements. Use benchmark\.sh to start the writing test of Apache-IoTDB and KairosDB successively.
-
+
Figure 2-1 Server monitoring tasks
For example, if we first start the test on KairosDB, IoT-benchmark will create a CONFIG data table in the MySQL database to store the configuration information of this test (Figure 2-2), and there will be a log output of the current test progress during the test execution (Figure 2-3) . When the test is completed, the test result will be output (Figure 2-3), and the result will be written into the FINAL_RESULT data table (Figure 2-4).
-
+
Figure 2-2 Test configuration information table
-
-
-
-
+
+
+
+
Figure 2-3 Test progress and results
-
+
@@ -283,7 +283,7 @@ Afterwards, we will start the test on Apache-IoTDB. The same IoT-benchmark will
According to the test result information, we know that under the same configuration the write delay times of Apache-IoTDB and KairosDB are 55.98ms and 1324.45ms respectively; the write throughputs are 5,125,600.86 points/second and 224,819.01 points/second respectively; the tests were executed respectively 585.30 seconds and 11777.99 seconds. And KairosDB has a write failure. After investigation, it is found that the data disk usage has reached 100%, and there is no disk space to continue receiving data. However, Apache-IoTDB has no write failure, and the disk space occupied after all data is written is only 4.7G (as shown in Figure 2-5); Apache-IoTDB is better than KairosDB in terms of write throughput and disk occupation. Of course, there will be other tests in the follow-up to observe and compare from various aspects, such as query performance, file compression ratio, data security, etc.
-
+
Figure 2-5 Disk usage
@@ -291,8 +291,8 @@ Figure 2-5 Disk usage
So what is the resource usage of each server during the test? What is the specific performance of each write operation? At this time, we can visualize the data in the server monitoring table and test process recording table by installing and using Tableau. The use of Tableau will not be introduced in this article. After connecting to the data table for test data persistence, the specific results are as follows (taking Apache-IoTDB as an example):
-
-
+
+
Figure 2-6 Visualization of testing process in Tableau
@@ -318,7 +318,7 @@ Table 2-4 Configuration parameter information
Results:
-
+
Figure 2-7 Query test results
diff --git a/src/UserGuide/V1.3.3/Tools-System/CLI.md b/src/UserGuide/V1.3.3/Tools-System/CLI.md
index 34803c3e6..47a038952 100644
--- a/src/UserGuide/V1.3.3/Tools-System/CLI.md
+++ b/src/UserGuide/V1.3.3/Tools-System/CLI.md
@@ -154,46 +154,46 @@ Shell >./kc.sh start-dev
```
2、use url(https://ip:port) login keycloack, the first login needs to create a user
-
+
3、Click administration console
-
+
4、In the master menu on the left, click Create realm and enter Realm name to create a new realm
-
+
-
+
5、Click the menu clients on the left to create clients
-
+
6、Click user on the left menu to create user
-
+
7、Click the newly created user ID, click the credentials navigation, enter the password and close the temporary option. The configuration of keycloud is completed
-
+
8、To create a role, click Roles on the left menu and then click the Create Role button to add a role
-
+
9、 Enter `iotdb_admin` in the Role Name and click the save button. Tip: `iotdb_admin` here cannot be any other name, otherwise even after successful login, you will not have permission to use iotdb's query, insert, create database, add users, roles and other functions
-
+
10、Click on the User menu on the left and then click on the user in the user list to add the `iotdb_admin` role we just created for that user
-
+
11、 Select Role Mappings, select the `iotdb_admin` role in Assign Role
-
+
-
+
Tip: If the user role is adjusted, you need to regenerate the token and log in to iotdb again to take effect
@@ -209,7 +209,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
Among them, you need to replace {my access token} (note, including {}) with your token, that is, the value corresponding to access_token. The password is empty and needs to be confirmed again.
-
+
How to get the token is dependent on your OpenID Connect setup and not covered here.
diff --git a/src/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md b/src/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md
index e6a945011..75229ae09 100644
--- a/src/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md
+++ b/src/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md
@@ -154,7 +154,7 @@ IoTDB currently supports importing data in CSV, SQL, and TsFile (IoTDB's underly
The TsFile Active Listening & Loading Feature can actively monitor TsFile file changes in the specified target path (configured by the user) and automatically synchronize TsFile files from the target path to the specified reception path (configured by the user). Through this feature, IoTDB can automatically detect and load these files without the need for any additional manual loading operations. This automated process not only simplifies the user's operational steps but also reduces potential errors that may occur during the operation, effectively reducing the complexity for users during the usage process.
-
+
### 4.1 Configuration Parameters
diff --git a/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md b/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md
index b1de82e6c..9aa65e5dd 100644
--- a/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md
+++ b/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md
@@ -71,7 +71,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -112,9 +112,9 @@ The following documents may help you have a good journey with Prometheus and Gra
`Apache IoTDB Dashboard` is available as a supplement to IoTDB Enterprise Edition, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel. You can access the Dashboard's Json file by contacting Commerce.
-
+
-
+
@@ -129,7 +129,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -140,7 +140,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -154,7 +154,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -164,7 +164,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -175,8 +175,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md b/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md
index c13db8cc3..5e0964932 100644
--- a/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md
+++ b/src/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md
@@ -71,7 +71,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -111,9 +111,9 @@ The following documents may help you have a good journey with Prometheus and Gra
We introduce the Apache IoTDB Dashboard, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel.
-
+
-
+
You can access the Dashboard's Json file in the enterprise edition.
@@ -129,7 +129,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -140,7 +140,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -154,7 +154,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -164,7 +164,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -175,8 +175,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md b/src/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md
index b2d113db3..8b124a643 100644
--- a/src/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md
+++ b/src/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md
@@ -24,10 +24,10 @@ IoTDB Visualization Console is an extension component developed for industrial s
Main feature showcase
* Home
-
+
* Measurement Point List
-
+
* Data Query
-
+
* Trend
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/V1.3.3/User-Manual/AINode_apache.md b/src/UserGuide/V1.3.3/User-Manual/AINode_apache.md
index f0c6647ec..3045fa625 100644
--- a/src/UserGuide/V1.3.3/User-Manual/AINode_apache.md
+++ b/src/UserGuide/V1.3.3/User-Manual/AINode_apache.md
@@ -25,7 +25,7 @@ AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB,
The system architecture is shown below:
::: center
-
+
:::
The responsibilities of the three nodes are as follows:
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection).
::: center
-
+
::::
## Installation and Deployment
@@ -304,13 +304,13 @@ After completing the registration of the model, the inference function can be us
- **sql**: sql query statement, the result of the query is used as input to the model for model inference. The dimensions of the rows and columns in the result of the query need to match the size specified in the specific model config. (It is not recommended to use the `SELECT *` clause for the sql here because in IoTDB, `*` does not sort the columns, so the order of the columns is undefined, you can use `SELECT s0,s1` to ensure that the columns order matches the expectations of the model input)
- **window_function**: Window functions that can be used in the inference process, there are currently three types of window functions provided to assist in model inference:
- **head(window_size)**: Get the top window_size points in the data for model inference, this window can be used for data cropping.
- 
+ 
- **tail(window_size)**: get the last window_size point in the data for model inference, this window can be used for data cropping.
- 
+ 
- **count(window_size, sliding_step)**: sliding window based on the number of points, the data in each window will be reasoned through the model respectively, as shown in the example below, window_size for 2 window function will be divided into three windows of the input dataset, and each window will perform reasoning operations to generate results respectively. The window can be used for continuous inference
- 
+ 
**Explanation 1**: window can be used to solve the problem of cropping rows when the results of the sql query and the input row requirements of the model do not match. Note that when the number of columns does not match or the number of rows is directly less than the model requirement, the inference cannot proceed and an error message will be returned.
@@ -459,7 +459,7 @@ When using AINode related functions, the authentication of IoTDB itself can be u
In some industrial scenarios, there is a need to predict power loads, which can be used to optimise power supply, conserve energy and resources, support planning and expansion, and enhance power system reliability.
-The data for the test set of ETTh1 that we use is [ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv).
+The data for the test set of ETTh1 that we use is [ETTh1](/img/ETTh1.csv).
It contains power data collected at 1h intervals, and each data consists of load and oil temperature as High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature.
@@ -522,7 +522,7 @@ We compare the results of the prediction of the oil temperature with the real re
The data before 10/24 00:00 represents the past data input to the model, the blue line after 10/24 00:00 is the oil temperature forecast result given by the model, and the red line is the actual oil temperature data from the dataset (used for comparison).
-
+
As can be seen, we have used the relationship between the six load information and the corresponding time oil temperatures for the past 96 hours (4 days) to model the possible changes in this data for the oil temperature for the next 48 hours (2 days) based on the inter-relationships between the sequences learned previously, and it can be seen that the predicted curves maintain a high degree of consistency in trend with the actual results after visualisation.
@@ -532,7 +532,7 @@ Power monitoring of current, voltage and power data is required in substations f
We have used the current, voltage and power data in a substation to form a dataset in a real scenario. The dataset consists of data such as A-phase voltage, B-phase voltage, and C-phase voltage collected every 5 - 6s for a time span of nearly four months in the substation.
-The test set data content is [data](https://alioss.timecho.com/docs/img/data.csv).
+The test set data content is [data](/img/data.csv).
On this dataset, the model inference function of IoTDB-ML can predict the C-phase voltage in the future period through the previous values and corresponding timestamps of A-phase voltage, B-phase voltage and C-phase voltage, empowering the monitoring management of the substation.
@@ -588,7 +588,7 @@ Comparing the predicted results of the C-phase voltage with the real results, we
The data before 02/14 20:48 represents the past data input to the model, the blue line after 02/14 20:48 is the predicted result of phase C voltage given by the model, while the red line is the actual phase C voltage data from the dataset (used for comparison).
-
+
It can be seen that we used the voltage data from the past 10 minutes and, based on the previously learned inter-sequence relationships, modeled the possible changes in the phase C voltage data for the next 5 minutes. The visualized forecast curve shows a certain degree of synchronicity with the actual results in terms of trend.
@@ -596,7 +596,7 @@ It can be seen that we used the voltage data from the past 10 minutes and, based
In the civil aviation and transport industry, there exists a need for anomaly detection of the number of passengers travelling on an aircraft. The results of anomaly detection can be used to guide the adjustment of flight scheduling to make the organisation more efficient.
-Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](https://alioss.timecho.com/docs/img/airline.csv).
+Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](/img/airline.csv).
On this dataset, the model inference function of IoTDB-ML can empower the transport industry by capturing the changing patterns of the sequence in order to detect anomalies at the sequence time points.
#### Step 1: Data Import
@@ -649,6 +649,6 @@ Total line number = 144
We plot the results detected as anomalies to get the following image. Where the blue curve is the original time series and the time points specially marked with red dots are the time points that the algorithm detects as anomalies.
-
+
It can be seen that the Stray model has modelled the input sequence changes and successfully detected the time points where anomalies occur.
\ No newline at end of file
diff --git a/src/UserGuide/V1.3.3/User-Manual/AINode_timecho.md b/src/UserGuide/V1.3.3/User-Manual/AINode_timecho.md
index 215e370e4..b797c8dfb 100644
--- a/src/UserGuide/V1.3.3/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/V1.3.3/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB,
The system architecture is shown below:
::: center
-
+
:::
The responsibilities of the three nodes are as follows:
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection).
::: center
-
+
::::
## Installation and Deployment
@@ -304,13 +304,13 @@ After completing the registration of the model, the inference function can be us
- **sql**: sql query statement, the result of the query is used as input to the model for model inference. The dimensions of the rows and columns in the result of the query need to match the size specified in the specific model config. (It is not recommended to use the `SELECT *` clause for the sql here because in IoTDB, `*` does not sort the columns, so the order of the columns is undefined, you can use `SELECT s0,s1` to ensure that the columns order matches the expectations of the model input)
- **window_function**: Window functions that can be used in the inference process, there are currently three types of window functions provided to assist in model inference:
- **head(window_size)**: Get the top window_size points in the data for model inference, this window can be used for data cropping.
- 
+ 
- **tail(window_size)**: get the last window_size point in the data for model inference, this window can be used for data cropping.
- 
+ 
- **count(window_size, sliding_step)**: sliding window based on the number of points, the data in each window will be reasoned through the model respectively, as shown in the example below, window_size for 2 window function will be divided into three windows of the input dataset, and each window will perform reasoning operations to generate results respectively. The window can be used for continuous inference
- 
+ 
**Explanation 1**: window can be used to solve the problem of cropping rows when the results of the sql query and the input row requirements of the model do not match. Note that when the number of columns does not match or the number of rows is directly less than the model requirement, the inference cannot proceed and an error message will be returned.
@@ -459,7 +459,7 @@ When using AINode related functions, the authentication of IoTDB itself can be u
In some industrial scenarios, there is a need to predict power loads, which can be used to optimise power supply, conserve energy and resources, support planning and expansion, and enhance power system reliability.
-The data for the test set of ETTh1 that we use is [ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv).
+The data for the test set of ETTh1 that we use is [ETTh1](/img/ETTh1.csv).
It contains power data collected at 1h intervals, and each data consists of load and oil temperature as High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature.
@@ -522,7 +522,7 @@ We compare the results of the prediction of the oil temperature with the real re
The data before 10/24 00:00 represents the past data input to the model, the blue line after 10/24 00:00 is the oil temperature forecast result given by the model, and the red line is the actual oil temperature data from the dataset (used for comparison).
-
+
As can be seen, we have used the relationship between the six load information and the corresponding time oil temperatures for the past 96 hours (4 days) to model the possible changes in this data for the oil temperature for the next 48 hours (2 days) based on the inter-relationships between the sequences learned previously, and it can be seen that the predicted curves maintain a high degree of consistency in trend with the actual results after visualisation.
@@ -532,7 +532,7 @@ Power monitoring of current, voltage and power data is required in substations f
We have used the current, voltage and power data in a substation to form a dataset in a real scenario. The dataset consists of data such as A-phase voltage, B-phase voltage, and C-phase voltage collected every 5 - 6s for a time span of nearly four months in the substation.
-The test set data content is [data](https://alioss.timecho.com/docs/img/data.csv).
+The test set data content is [data](/img/data.csv).
On this dataset, the model inference function of IoTDB-ML can predict the C-phase voltage in the future period through the previous values and corresponding timestamps of A-phase voltage, B-phase voltage and C-phase voltage, empowering the monitoring management of the substation.
@@ -588,7 +588,7 @@ Comparing the predicted results of the C-phase voltage with the real results, we
The data before 02/14 20:48 represents the past data input to the model, the blue line after 02/14 20:48 is the predicted result of phase C voltage given by the model, while the red line is the actual phase C voltage data from the dataset (used for comparison).
-
+
It can be seen that we used the voltage data from the past 10 minutes and, based on the previously learned inter-sequence relationships, modeled the possible changes in the phase C voltage data for the next 5 minutes. The visualized forecast curve shows a certain degree of synchronicity with the actual results in terms of trend.
@@ -596,7 +596,7 @@ It can be seen that we used the voltage data from the past 10 minutes and, based
In the civil aviation and transport industry, there exists a need for anomaly detection of the number of passengers travelling on an aircraft. The results of anomaly detection can be used to guide the adjustment of flight scheduling to make the organisation more efficient.
-Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](https://alioss.timecho.com/docs/img/airline.csv).
+Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](/img/airline.csv).
On this dataset, the model inference function of IoTDB-ML can empower the transport industry by capturing the changing patterns of the sequence in order to detect anomalies at the sequence time points.
#### Step 1: Data Import
@@ -649,6 +649,6 @@ Total line number = 144
We plot the results detected as anomalies to get the following image. Where the blue curve is the original time series and the time points specially marked with red dots are the time points that the algorithm detects as anomalies.
-
+
It can be seen that the Stray model has modelled the input sequence changes and successfully detected the time points where anomalies occur.
\ No newline at end of file
diff --git a/src/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md b/src/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md
index 741135b0d..dcca2ff41 100644
--- a/src/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md
+++ b/src/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md
@@ -37,7 +37,7 @@ The audit log function traces all user operations in the database. The informati
Client Sources：
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
#### Audit logging can be turned off for some user connections
diff --git a/src/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md b/src/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md
index 7bde227f8..a2ffa995e 100644
--- a/src/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md
+++ b/src/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -93,7 +93,7 @@ The schema and auth synchronization functions have the following limitations:
Data synchronization tasks have three states: RUNNING, STOPPED, and DROPPED. The task state transitions are shown in the following diagram:
-
+
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
@@ -263,7 +263,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -278,7 +278,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -302,7 +302,7 @@ with SINK (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -354,7 +354,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
diff --git a/src/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md b/src/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md
index 4669d357f..819ab8e51 100644
--- a/src/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md
+++ b/src/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -95,7 +95,7 @@ The schema and auth synchronization functions have the following limitations:
Data synchronization tasks have three states: RUNNING, STOPPED, and DROPPED. The task state transitions are shown in the following diagram:
-
+
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
@@ -271,7 +271,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -286,7 +286,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -310,7 +310,7 @@ with SINK (
This example is used to demonstrate the scenario where two IoTDB act as active-active pairs, with the data link shown in the figure below:
-
+
In this example, to avoid infinite data loops, the `forwarding-pipe-requests` parameter on A and B needs to be set to `false`, indicating that data transmitted from another pipe is not forwarded, and to keep the data consistent on both sides, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata.
@@ -348,7 +348,7 @@ with sink (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -400,7 +400,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
@@ -433,7 +433,7 @@ with sink (
This example is used to demonstrate the scenario where data from one IoTDB is synchronized to another IoTDB through a unidirectional gateway, as shown in the figure below:
-
+
In this example, the iotdb-air-gap-sink plugin in the sink task needs to be used . After configuring the gateway, execute the following statement on A IoTDB. Fill in the node-urls with the URL of the data service port of the DataNode node on the target IoTDB configured by the gateway, as detailed below:
diff --git a/src/UserGuide/V1.3.3/User-Manual/Data-subscription.md b/src/UserGuide/V1.3.3/User-Manual/Data-subscription.md
index 3eefc3f8f..5cba93a71 100644
--- a/src/UserGuide/V1.3.3/User-Manual/Data-subscription.md
+++ b/src/UserGuide/V1.3.3/User-Manual/Data-subscription.md
@@ -15,7 +15,7 @@ Using the IoTDB Subscription Client to consume data has significant advantages i
The IoTDB Subscription Client encompasses three core concepts: Topic, Consumer, and Consumer Group. The specific relationships are illustrated in the diagram below:
+
+
+
`SlidingTimeWindowAccessStrategy`: `SlidingTimeWindowAccessStrategy` has many constructors, you can pass 3 types of parameters to them:
@@ -149,7 +149,7 @@ The sliding step parameter is also optional. If the parameter is not provided, t
The relationship between the three types of parameters can be seen in the figure below. Please see the Javadoc for more details.
-
+
`SlidingSizeWindowAccessStrategy`: `SlidingSizeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -170,7 +170,7 @@ The sliding step parameter is optional. If the parameter is not provided, the sl
Window opening diagram: **Time intervals less than or equal to the given minimum time interval `sessionGap` are assigned in one group.**
-
+
`SessionTimeWindowAccessStrategy`: `SessionTimeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -181,7 +181,7 @@ Window opening diagram: **Time intervals less than or equal to the given minimum
Window opening diagram: **For numerical data, if the state difference is less than or equal to the given threshold `delta`, it will be assigned in one group.**
-
+
`StateWindowAccessStrategy` has four constructors.
diff --git a/src/UserGuide/V1.3.3/User-Manual/White-List_timecho.md b/src/UserGuide/V1.3.3/User-Manual/White-List_timecho.md
index 75fe11866..17785f8da 100644
--- a/src/UserGuide/V1.3.3/User-Manual/White-List_timecho.md
+++ b/src/UserGuide/V1.3.3/User-Manual/White-List_timecho.md
@@ -66,5 +66,5 @@ Decide which IP addresses can connect to IoTDB
5. whether to enable the configuration of the white list function, can be hot loaded.
6. Use the Java native interface to modify the whitelist, must be the root user to modify, reject non-root user to modify; modify the content must be legal, otherwise it will throw a StatementExecutionException.
-
+
diff --git a/src/UserGuide/latest-Table/Basic-Concept/Sample-Data.md b/src/UserGuide/latest-Table/Basic-Concept/Sample-Data.md
index 843b61d55..4b6f45201 100644
--- a/src/UserGuide/latest-Table/Basic-Concept/Sample-Data.md
+++ b/src/UserGuide/latest-Table/Basic-Concept/Sample-Data.md
@@ -28,11 +28,11 @@ This chapter mainly introduces a simple temporal data application scenario and t
Table 1 and Table 2 both have the following table structure:
-
+
## 2 Import Statement
-The following is the SQL statement used to construct the above table structure and data. You can click here（[sample_data.sql](https://alioss.timecho.com/upload/sample_data.sql)）to download all the SQL statements and execute them in CLI to import the data into your IoTDB.
+The following is the SQL statement used to construct the above table structure and data. You can click here（[sample_data.sql](/img/sample_data.sql)）to download all the SQL statements and execute them in CLI to import the data into your IoTDB.
```SQL
-- Create a table with table names that are close to business semantics. Here, we use table1 instead
diff --git a/src/UserGuide/latest-Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md b/src/UserGuide/latest-Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md
index 2b4e70e0e..a58cff767 100644
--- a/src/UserGuide/latest-Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md
+++ b/src/UserGuide/latest-Table/Deployment-and-Maintenance/Cluster-Deployment_timecho.md
@@ -23,7 +23,7 @@
This section describes how to manually deploy an instance that includes 3 ConfigNodes and 3 DataNodes, commonly known as a 3C3D cluster.
+
+
+
-
+
+
-
-
+
+
+
+
## Built-in MQTT Service
@@ -56,7 +56,7 @@ or
```
or json array of the above two.
-
+
## MQTT Configurations
The IoTDB MQTT service load configurations from `${IOTDB_HOME}/${IOTDB_CONF}/iotdb-system.properties` by default.
diff --git a/src/UserGuide/latest/API/Programming-ODBC.md b/src/UserGuide/latest/API/Programming-ODBC.md
index 8e0d74852..7d2b9bb20 100644
--- a/src/UserGuide/latest/API/Programming-ODBC.md
+++ b/src/UserGuide/latest/API/Programming-ODBC.md
@@ -39,7 +39,7 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
1. **Download Zappy-Sys ODBC-JDBC Bridge**:
Enter the https://zappysys.com/products/odbc-powerpack/odbc-jdbc-bridge-driver/ website, and click "download".
- 
+ 
2. **Prepare IoTDB**: Set up IoTDB cluster, and write a row of data arbitrarily.
```sql
@@ -49,11 +49,11 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
3. **Deploy and Test the Bridge**:
1. Open ODBC Data Sources(32/64 bit), depending on the bits of Windows. One possible position is `C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools`.
- 
+ 
2. Click on "add" and select ZappySys JDBC Bridge.
- 
+ 
3. Fill in the following settings:
@@ -65,15 +65,15 @@ Then, you can see the output `iotdb-jdbc-1.3.2-SNAPSHOT-jar-with-dependencies.ja
| User name | IoTDB's user name | root |
| User password | IoTDB's password | root |
- 
+ 
4. Click on "Test Connection" button, and a "Test Connection: SUCCESSFUL" should appear.
- 
+ 
5. Click the "Preview" button above, and replace the original query text with `select * from root.**`, then click "Preview Data", and the query result should correctly.
- 
+ 
4. **Operate IoTDB's data with ODBC**: After correct deployment, you can use Microsoft's ODBC library to operate IoTDB's data. Here's an example written in C#:
```C#
diff --git a/src/UserGuide/latest/API/Programming-OPC-UA_timecho.md b/src/UserGuide/latest/API/Programming-OPC-UA_timecho.md
index 703b47c68..a1084df2a 100644
--- a/src/UserGuide/latest/API/Programming-OPC-UA_timecho.md
+++ b/src/UserGuide/latest/API/Programming-OPC-UA_timecho.md
@@ -30,7 +30,7 @@ OPC UA is a technical specification used in the automation field for communicati
- **Client/Server Mode**：In this mode, IoTDB's stream processing engine establishes a connection with the OPC UA Server via an OPC UA Sink. The OPC UA Server maintains data within its Address Space, from which IoTDB can request and retrieve data. Additionally, other OPC UA Clients can access the data on the server.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
IoTDB's cluster includes the following common concepts:
diff --git a/src/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md b/src/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md
index af669a6d6..42344aa47 100644
--- a/src/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md
@@ -36,7 +36,7 @@
The following figure shows a common IoTDB 3C3D (3 ConfigNodes, 3 DataNodes) cluster deployment pattern:
-
+
IoTDB's cluster includes the following common concepts:
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 e1aeb3564..27573b2b6 100644
--- a/src/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md
+++ b/src/UserGuide/latest/Basic-Concept/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@ A wind power IoT scenario is taken as an example to illustrate how to create a c
According to the enterprise organization structure and equipment entity hierarchy, it is expressed as an attribute hierarchy structure, as shown below. The hierarchical from top to bottom is: power group layer - power plant layer - entity layer - measurement layer. ROOT is the root node, and each node of measurement layer is a leaf node. In the process of using IoTDB, the attributes on the path from ROOT node is directly connected to each leaf node with ".", thus forming the name of a timeseries in IoTDB. For example, The left-most path in Figure 2.1 can generate a timeseries named `root.ln.wf01.wt01.status`.
-
+
In the following chapters of data definition language, data operation language and Java Native Interface, various operations related to aligned timeseries will be introduced one by one.
diff --git a/src/UserGuide/latest/Basic-Concept/Navigating_Time_Series_Data.md b/src/UserGuide/latest/Basic-Concept/Navigating_Time_Series_Data.md
index 20aaef327..6ff81da37 100644
--- a/src/UserGuide/latest/Basic-Concept/Navigating_Time_Series_Data.md
+++ b/src/UserGuide/latest/Basic-Concept/Navigating_Time_Series_Data.md
@@ -24,25 +24,25 @@
In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
-
+
Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
-
+
The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
## Key Concepts of Time Series Data
The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
-
+
### Data Point
- Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
- Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
-
+
### Measurement Points
diff --git a/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md b/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md
index 45becb7bd..00bd8e68a 100644
--- a/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md
+++ b/src/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md
@@ -352,9 +352,9 @@ All devices will definitely have a TTL, meaning it cannot be null. INF represent
IoTDB supports the device template function, enabling different entities of the same type to share metadata, reduce the memory usage of metadata, and simplify the management of numerous entities and measurements.
-
+
-
+
### Create Device Template
@@ -793,7 +793,7 @@ It costs 0.004s
Then the Metadata Tree will be as below:
-
+
+
##### Precautions for Use
@@ -1096,7 +1096,7 @@ A given interval threshold to create a new group of data when the difference bet
The figure below is a grouping diagram under `GROUP BY SESSION`.
-
+
##### Precautions for Use
diff --git a/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md
index 610ec0b72..83baab566 100644
--- a/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md
+++ b/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md
@@ -31,8 +31,8 @@
### Deployment mode
-
+
+
+
- There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction.
- It is recommended to create a Python interpreter venv virtual environment in the `iotdb-enterprise-ainode-
-
+
+
+
- There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction.
- It is recommended to create a Python interpreter venv virtual environment in the `iotdb-enterprise-ainode-
+
+
+
+
-
+
+
-
-
+
+
+
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
### Design in dashboard
Add diagrams in dashboard and customize your query. See http://docs.grafana.org/guides/getting_started/
-
+
## config grafana
diff --git a/src/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md b/src/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md
index dc8370464..0597fb124 100644
--- a/src/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md
@@ -119,9 +119,9 @@ Grafana displays data in a web page dashboard. Please open your browser and visi
Click the `Settings` icon on the left, select the `Data Source` option, and then click `Add data source`.
-
+
-
+
Select the `Apache IoTDB` data source.
@@ -132,22 +132,22 @@ Select the `Apache IoTDB` data source.
Click `Save & Test`, and `Data source is working` will appear.
-
+
### Create a new Panel
Click the `Dashboards` icon on the left, and select `Manage` option.
-
+
Click the `New Dashboard` icon on the top right, and select `Add an empty panel` option.
-
+
Grafana plugin supports SQL: Full Customized mode and SQL: Drop-down List mode, and the default mode is SQL: Full Customized mode.
-
+
#### SQL: Full Customized input method
@@ -155,7 +155,7 @@ Enter content in the SELECT, FROM , WHERE and CONTROL input box, where the WHERE
If a query involves multiple expressions, we can click `+` on the right side of the SELECT input box to add expressions in the SELECT clause, or click `+` on the right side of the FROM input box to add a path prefix:
-
+
SELECT input box: contents can be the time series suffix, function, udf, arithmetic expression, or nested expressions. You can also use the as clause to rename the result.
@@ -193,7 +193,7 @@ Tip: Statements like `select * from root.xx.**` are not recommended because thos
Select a time series in the TIME-SERIES selection box, select a function in the FUNCTION option, and enter the contents in the SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL input boxes, where TIME-SERIES is a required item and the rest are non required items.
-
+
### Support for variables and template functions
@@ -201,34 +201,34 @@ Both SQL: Full Customized and SQL: Drop-down List input methods support the vari
After creating a new Panel, click the Settings button in the upper right corner:
-
+
Select `Variables`, click `Add variable`:
-
+
Example 1：Enter `Name`, `Label`, and `Query`, and then click the `Update` button:
-
+
Apply Variables, enter the variable in the `grafana panel` and click the `save` button:
-
+
Example 2: Nested use of variables:
-
+
-
+
-
+
Example 3: using function variables
-
+
-
+
The Name in the above figure is the variable name and the variable name we will use in the panel in the future. Label is the display name of the variable. If it is empty, the variable of Name will be displayed. Otherwise, the name of the Label will be displayed.
There are Query, Custom, Text box, Constant, DataSource, Interval, Ad hoc filters, etc. in the Type drop-down, all of which can be used in IoTDB's Grafana Plugin
@@ -252,14 +252,14 @@ This plugin supports Grafana alert function.
1. In the Grafana panel, click the `alerting` button, as shown in the following figure:
-
+
2. Click `Create alert rule from this panel`, as shown in the figure below:
-
+
3. Set query and alarm conditions in step 1. Conditions represent query conditions, and multiple combined query conditions can be configured. As shown below:
-
+
The query condition in the figure: `min() OF A IS BELOW 0`, means that the condition will be triggered when the minimum value in the A tab is 0, click this function to change it to another function.
@@ -269,27 +269,27 @@ For example, we have 3 conditions in the following order: Condition: B (Evaluate
4. After selecting indicators and alarm rules, click the `Preview` button to preview the data as shown in the figure below:
-
+
5. In step 2, specify the alert evaluation interval, and for `Evaluate every`, specify the evaluation frequency. Must be a multiple of 10 seconds. For example, 1m, 30s.
For `Evaluate for`, specify the duration before the alert fires. As shown below:
-
+
6. In step 3, add the storage location, rule group, and other metadata associated with the rule. Where `Rule name` specifies the name of the rule. Rule names must be unique.
-
+
7. In step 4, add a custom label. Add a custom label by selecting an existing key-value pair from the drop-down list, or add a new label by entering a new key or value. As shown below:
-
+
8. Click `Save` to save the rule or click `Save and Exit` to save the rule and return to the alerts page.
9. Commonly used alarm states include `Normal`, `Pending`, `Firing` and other states, as shown in the figure below:
-
-
+
+
10. We can also configure `Contact points` for alarms to receive alarm notifications. For more detailed operations, please refer to the official document (https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/).
diff --git a/src/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index 5d9402db8..41332ee21 100644
--- a/src/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -36,7 +36,7 @@
The specific relationship and content between the two modules and ignition are shown in the following figure.
- 
+ 
## Installation Requirements
@@ -59,23 +59,23 @@ The Ignition-IoTDB Connector provides the ability to store the data collected by
Step 1: Enter the `Configuration` - `System` - `Modules` module and click on the `Install or Upgrade a Module` button at the bottom
-
+
Step 2: Select the obtained `modl`, select the file and upload it, click `Install`, and trust the relevant certificate.
-
+
Step 3: After installation is completed, you can see the following content
-
+
Step 4: Enter the `Configuration` - `Tags` - `History` module and click on `Create new Historical Tag Provider` below
-
+
Step 5: Select `IoTDB` and fill in the configuration information
-
+
The configuration content is as follows:
@@ -163,21 +163,21 @@ The configuration content is as follows:
- After configuring the `Provider`, you can use the `IoTDB Tag Historian` in the `Designer`, just like using other `Providers`. Right click on the corresponding `Tag` and select `Edit Tag (s) `, then select the History category in the Tag Editor
- 
+ 
- Set `History Disabled` to `true`, select `Storage Provider` as the `Provider` created in the previous step, configure other parameters as needed, click `OK`, and then save the project. At this point, the data will be continuously stored in the 'IoTDB' instance according to the set content.
- 
+ 
#### Read Data
- You can also directly select the tags stored in IoTDB under the Data tab of the Report
- 
+ 
- You can also directly browse relevant data in Components
- 
+ 
#### Script module: This function can interact with IoTDB
@@ -243,15 +243,15 @@ The configuration content is as follows:
Step 1: Enter the `Configuration` - `Databases` -`Drivers` module and create the `Translator`
-
+
Step 2: Enter the `Configuration` - `Databases` - `Drivers` module, create a `JDBC Driver` , select the `Translator` configured in the previous step, and upload the downloaded `IoTDB JDBC`. Set the Classname to `org. apache. iotdb. jdbc.IoTDBDriver`
-
+
Step 3: Enter the `Configuration` - `Databases` - `Connections` module, create a new `Connections` , select the`IoTDB Driver` created in the previous step for `JDBC Driver`, configure the relevant information, and save it to use
-
+
### Instructions
@@ -265,11 +265,11 @@ Select the previously created `Connection` from the `Data Source` in the `Transa
Do not select other options, set the fields, and after `enabled` , the data will be installed and stored in the corresponding IoTDB
-
+
#### Query
- Select `Data Source` in the `Database Query Browser` and select the previously created `Connection` to write an SQL statement to query the data in IoTDB
-
+
diff --git a/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md b/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md
index 0fb5b73d9..fca46c19d 100644
--- a/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md
+++ b/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 Manually Create Directories
@@ -400,7 +400,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. Scaling
@@ -408,7 +408,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
Add a new PV; scaling is only possible with available PVs.
-
+
**Note: DataNode cannot join the cluster after restart**
@@ -425,7 +425,7 @@ Modify the values.yaml file in iotdb-cluster-k8s/helm to change the number of Co
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 Scale DataNode
diff --git a/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md b/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md
index c59ddcce6..f16d4727b 100644
--- a/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md
+++ b/src/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 Manually Create Directories
@@ -400,7 +400,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. Scaling
@@ -408,7 +408,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
Add a new PV; scaling is only possible with available PVs.
-
+
**Note: DataNode cannot join the cluster after restart**
@@ -425,7 +425,7 @@ Modify the values.yaml file in iotdb-cluster-k8s/helm to change the number of Co
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 Scale DataNode
diff --git a/src/UserGuide/latest/Ecosystem-Integration/Spark-TsFile.md b/src/UserGuide/latest/Ecosystem-Integration/Spark-TsFile.md
index ed00538c7..151d81e14 100644
--- a/src/UserGuide/latest/Ecosystem-Integration/Spark-TsFile.md
+++ b/src/UserGuide/latest/Ecosystem-Integration/Spark-TsFile.md
@@ -94,7 +94,7 @@ The way to display TsFile is dependent on the schema. Take the following TsFile
The existing data in the TsFile are:
-
+
The corresponding SparkSQL table is:
@@ -239,7 +239,7 @@ The way to display TsFile is related to TsFile Schema. Take the following TsFile
The existing data in the file are:
-
+
A set of time-series data
diff --git a/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md b/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md
index c0fdf50e8..bbc9c675f 100644
--- a/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md
+++ b/src/UserGuide/latest/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard is an open-source IoT platform that enables rapid development, management, and expansion of IoT projects. For more detailed information, please refer to [ThingsBoard Official Website](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/).
- 
+ 
1. Introduction to ThingsBoard-IoTDB
@@ -34,7 +34,7 @@
### Relationship Diagram
- 
+ 
## Installation Requirements
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ## Thingsboard data is written to the da
1. Set up devices and connect datasource: Add a new device under "Entities" - "Devices" in Thingsboard and send data to the specified devices through gateway.
- 
+ 
2. Set rule chain: Set alarm rules for "SD-032F pump" in the rule chain library and set the rule chain as the root chain
-
+
+
-
+
+
+
+
TimechoDB Performance Monitoring
-
-
+
+
+
SQL Query
-
-
+
+
+
+
+
| Function Name | Allowed Input Series Data Types | Required Attributes | Output Series Data Type | Description |
| ------------- | ------------------------------- | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2382,7 +2382,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `windowSize`: The number of points in a window. Int data type. **Required**.
+ `slidingStep`: Slide a window by the number of points. Int data type. Optional. If not set, default to the same as `windowSize`.
-
+
**(2) Attributes for the time window:**
@@ -2391,7 +2391,7 @@ M4 is used to sample the `first, last, bottom, top` points for each sliding wind
+ `displayWindowBegin`: The starting position of the window (included). Long data type. Optional. If not set, default to Long.MIN_VALUE, meaning using the time of the first data point of the input time series as the starting position of the window.
+ `displayWindowEnd`: End time limit (excluded, essentially playing the same role as `WHERE time < displayWindowEnd`). Long data type. Optional. If not set, default to Long.MAX_VALUE, meaning there is no additional end time limit other than the end of the input time series itself.
-
+
#### Examples
diff --git a/src/UserGuide/latest/SQL-Manual/SQL-Manual.md b/src/UserGuide/latest/SQL-Manual/SQL-Manual.md
index 2a078041c..ff94c24e2 100644
--- a/src/UserGuide/latest/SQL-Manual/SQL-Manual.md
+++ b/src/UserGuide/latest/SQL-Manual/SQL-Manual.md
@@ -107,13 +107,13 @@ IoTDB> SHOW DEVICES
For more details, see document [Operate-Metadata](../Basic-Concept/Operate-Metadata.md).
-
+
-
+
### Create Device Template
diff --git a/src/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md b/src/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md
index c10a6e6a1..479f95527 100644
--- a/src/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md
+++ b/src/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB implements tailored partitioning algorithms for time series data. Building
### Partitioning Strategy
IoTDB maps each sensor in the production environment to a time series. The time series are then partitioned using the series partitioning algorithm to manage their schema, and combined with the time partitioning algorithm to manage their data. The following figure illustrates how IoTDB partitions time series data.
-
+
#### Partitioning Algorithm
Because numerous devices and sensors are commonly deployed in production environments, IoTDB employs a series partitioning algorithm to ensure the size of partition information is manageable. Since the generated time series associated with timestamps, IoTDB uses a time partioning algorithm to clearly distinguish between hot and cold partitions.
@@ -56,7 +56,7 @@ Combining a series partition with a time partition creates a data partition. Sin
### Partition Allocation
IoTDB uses RegionGroups to enable elastic storage of time series, with the number of RegionGroups in the cluster determined by the total resources available across all DataNodes. Since the number of RegionGroups is dynamic, IoTDB can easily scale out. Both the SchemaRegionGroup and DataRegionGroup follow the same partition allocation algorithm, which evenly splits all series partitions. The following figure demonstrates the partition allocation process, where the dynamic RegionGroups match the variously expending time series and cluster.
-
+
#### RegionGroup Expansion
The number of RegionGroups is given by
@@ -76,7 +76,7 @@ To enhance the cluster's availability and performance, IoTDB employs sophisticat
### Storage Load Balance
The number of Regions held by a DataNode reflects its storage load. If the difference in the number of Regions across DataNodes is relatively large, the DataNode with more Regions is likely to become a storage bottleneck. Although a straightforward Round Robin placement algorithm can achieve storage balance by ensuring that each DataNode hosts an equal number of Regions, it compromises the cluster's fault tolerance, as illustrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Place RegionGroup $r_1$'s 2 Regions on DataNodes $n_1$ and $n_2$.
@@ -91,7 +91,7 @@ To address this issue, IoTDB employs a Region placement algorithm that not only
### Computing Load Balance
The number of leader Regions held by a DataNode reflects its Computing load. If the difference in the number of leaders across DataNodes is relatively large, the DataNode with more leaders is likely to become a Computing bottleneck. If the leader selection process is conducted using a transparent Greedy algorithm, the result may be an unbalanced leader distribution when the Regions are fault-tolerantly placed, as demonstrated below:
-
+
+ Assume the cluster has 4 DataNodes, 4 RegionGroups and a replication factor of 2.
+ Select RegionGroup $r_5$'s Region on DataNode $n_5$ as the leader.
diff --git a/src/UserGuide/latest/Tools-System/Benchmark.md b/src/UserGuide/latest/Tools-System/Benchmark.md
index 86ff88bca..3ffd64c78 100644
--- a/src/UserGuide/latest/Tools-System/Benchmark.md
+++ b/src/UserGuide/latest/Tools-System/Benchmark.md
@@ -25,14 +25,14 @@ IoT-benchmark is a time-series database benchmarking tool based on Java and big
Figure 1-1 below includes the test benchmark process and other extended functions. These processes can be unified by IoT-benchmark. IoT Benchmark supports a variety of workloads, including **pure write, pure query, write query mixed**, etc., supports **software and hardware system monitoring, test metric measurement** and other monitoring functions, and also realizes **initializing the database automatically, test data analysis and system parameter optimization** functions.
-
+
Figure 1-1
Referring to the YCSB test tool's design idea of separating the three components of workload generation, performance metric measurement and database interface, the modular design of IoT-benchmark is shown in Figure 1-2. Different from the YCSB-based test tool system, IoT-benchmark adds a system monitoring module to support the persistence of test data and system monitoring data. In addition, some special load testing functions especially designed for time series data scenarios have been added, such as supporting batch writing and multiple out-of-sequence data writing modes for IoT scenarios.
-
+
Figure 1-2
@@ -71,7 +71,7 @@ Table 1-1 Comparison of big data test benchmarks
The directory structure of the test package is shown in Figure 1-3 below. The test configuration file is conf/config.properties, and the test startup scripts are benchmark\.sh (Linux & MacOS) and benchmark.bat (Windows). The detailed usage of the files is shown in Table 1-2.
-
+
Figure 1-3 List of files and folders
@@ -101,7 +101,7 @@ Table 1-2 Usage list of files and folders
All the log files of the test are stored in the logs folder, and the test results are stored in the data/csvOutput folder after the test is completed. For example, after the test, we get the following result matrix:
-
+
- Result Matrix
- OkOperation: successful operations
@@ -256,24 +256,24 @@ Table 2-3 Configuration parameter information
First, start the tested time series databases Apache-IoTDB and KairosDB on 172.21.4.3 and 172.21.4.4 respectively, and then start server resource monitoring through the ser-benchamrk\.sh script on 172.21.4.2, 172.21.4.3 and 172.21.4.4 (Figure 2-1). Then modify the conf/config.properties files in the iotdb-0.13-0.0.1 and kairosdb-0.0.1 folders in 172.21.4.2 according to Table 2-3 to meet the test requirements. Use benchmark\.sh to start the writing test of Apache-IoTDB and KairosDB successively.
-
+
Figure 2-1 Server monitoring tasks
For example, if we first start the test on KairosDB, IoT-benchmark will create a CONFIG data table in the MySQL database to store the configuration information of this test (Figure 2-2), and there will be a log output of the current test progress during the test execution (Figure 2-3) . When the test is completed, the test result will be output (Figure 2-3), and the result will be written into the FINAL_RESULT data table (Figure 2-4).
-
+
Figure 2-2 Test configuration information table
-
-
-
-
+
+
+
+
Figure 2-3 Test progress and results
-
+
@@ -283,7 +283,7 @@ Afterwards, we will start the test on Apache-IoTDB. The same IoT-benchmark will
According to the test result information, we know that under the same configuration the write delay times of Apache-IoTDB and KairosDB are 55.98ms and 1324.45ms respectively; the write throughputs are 5,125,600.86 points/second and 224,819.01 points/second respectively; the tests were executed respectively 585.30 seconds and 11777.99 seconds. And KairosDB has a write failure. After investigation, it is found that the data disk usage has reached 100%, and there is no disk space to continue receiving data. However, Apache-IoTDB has no write failure, and the disk space occupied after all data is written is only 4.7G (as shown in Figure 2-5); Apache-IoTDB is better than KairosDB in terms of write throughput and disk occupation. Of course, there will be other tests in the follow-up to observe and compare from various aspects, such as query performance, file compression ratio, data security, etc.
-
+
Figure 2-5 Disk usage
@@ -291,8 +291,8 @@ Figure 2-5 Disk usage
So what is the resource usage of each server during the test? What is the specific performance of each write operation? At this time, we can visualize the data in the server monitoring table and test process recording table by installing and using Tableau. The use of Tableau will not be introduced in this article. After connecting to the data table for test data persistence, the specific results are as follows (taking Apache-IoTDB as an example):
-
-
+
+
Figure 2-6 Visualization of testing process in Tableau
@@ -318,7 +318,7 @@ Table 2-4 Configuration parameter information
Results:
-
+
Figure 2-7 Query test results
diff --git a/src/UserGuide/latest/Tools-System/CLI.md b/src/UserGuide/latest/Tools-System/CLI.md
index 34803c3e6..47a038952 100644
--- a/src/UserGuide/latest/Tools-System/CLI.md
+++ b/src/UserGuide/latest/Tools-System/CLI.md
@@ -154,46 +154,46 @@ Shell >./kc.sh start-dev
```
2、use url(https://ip:port) login keycloack, the first login needs to create a user
-
+
3、Click administration console
-
+
4、In the master menu on the left, click Create realm and enter Realm name to create a new realm
-
+
-
+
5、Click the menu clients on the left to create clients
-
+
6、Click user on the left menu to create user
-
+
7、Click the newly created user ID, click the credentials navigation, enter the password and close the temporary option. The configuration of keycloud is completed
-
+
8、To create a role, click Roles on the left menu and then click the Create Role button to add a role
-
+
9、 Enter `iotdb_admin` in the Role Name and click the save button. Tip: `iotdb_admin` here cannot be any other name, otherwise even after successful login, you will not have permission to use iotdb's query, insert, create database, add users, roles and other functions
-
+
10、Click on the User menu on the left and then click on the user in the user list to add the `iotdb_admin` role we just created for that user
-
+
11、 Select Role Mappings, select the `iotdb_admin` role in Assign Role
-
+
-
+
Tip: If the user role is adjusted, you need to regenerate the token and log in to iotdb again to take effect
@@ -209,7 +209,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
Among them, you need to replace {my access token} (note, including {}) with your token, that is, the value corresponding to access_token. The password is empty and needs to be confirmed again.
-
+
How to get the token is dependent on your OpenID Connect setup and not covered here.
diff --git a/src/UserGuide/latest/Tools-System/Data-Import-Tool.md b/src/UserGuide/latest/Tools-System/Data-Import-Tool.md
index e6a945011..75229ae09 100644
--- a/src/UserGuide/latest/Tools-System/Data-Import-Tool.md
+++ b/src/UserGuide/latest/Tools-System/Data-Import-Tool.md
@@ -154,7 +154,7 @@ IoTDB currently supports importing data in CSV, SQL, and TsFile (IoTDB's underly
The TsFile Active Listening & Loading Feature can actively monitor TsFile file changes in the specified target path (configured by the user) and automatically synchronize TsFile files from the target path to the specified reception path (configured by the user). Through this feature, IoTDB can automatically detect and load these files without the need for any additional manual loading operations. This automated process not only simplifies the user's operational steps but also reduces potential errors that may occur during the operation, effectively reducing the complexity for users during the usage process.
-
+
### 4.1 Configuration Parameters
diff --git a/src/UserGuide/latest/Tools-System/Monitor-Tool_apache.md b/src/UserGuide/latest/Tools-System/Monitor-Tool_apache.md
index b1de82e6c..9aa65e5dd 100644
--- a/src/UserGuide/latest/Tools-System/Monitor-Tool_apache.md
+++ b/src/UserGuide/latest/Tools-System/Monitor-Tool_apache.md
@@ -71,7 +71,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -112,9 +112,9 @@ The following documents may help you have a good journey with Prometheus and Gra
`Apache IoTDB Dashboard` is available as a supplement to IoTDB Enterprise Edition, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel. You can access the Dashboard's Json file by contacting Commerce.
-
+
-
+
@@ -129,7 +129,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -140,7 +140,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -154,7 +154,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -164,7 +164,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -175,8 +175,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md b/src/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md
index c13db8cc3..5e0964932 100644
--- a/src/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md
+++ b/src/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md
@@ -71,7 +71,7 @@ can be used to collect and store monitoring indicators, and Grafana can be used
The following picture describes the relationships among IoTDB, Prometheus and Grafana
-
+
1. Along with running, IoTDB will collect its metrics continuously.
2. Prometheus scrapes metrics from IoTDB at a constant interval (can be configured).
@@ -111,9 +111,9 @@ The following documents may help you have a good journey with Prometheus and Gra
We introduce the Apache IoTDB Dashboard, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel.
-
+
-
+
You can access the Dashboard's Json file in the enterprise edition.
@@ -129,7 +129,7 @@ Including but not limited to:
- Current CPU, memory, and disk usage across all nodes in the cluster.
- Information on individual nodes.
-
+
### Data Writing
@@ -140,7 +140,7 @@ Including but not limited to:
- Number and size of WAL files.
- Node WAL flush SyncBuffer latency.
-
+
### Data Querying
@@ -154,7 +154,7 @@ Including but not limited to:
- Node filtering duration based on Chunk metadata.
- Average time to construct a Chunk Reader.
-
+
### Storage Engine
@@ -164,7 +164,7 @@ Including but not limited to:
- The count and size of TsFiles at various stages.
- Number and duration of various tasks.
-
+
### System Monitoring
@@ -175,8 +175,8 @@ Including but not limited to:
- JVM GC time percentage, GC occurrences by type, GC volume, and heap memory usage across generations.
- Network transmission rate, packet sending rate
-
+
-
+
-
+
diff --git a/src/UserGuide/latest/Tools-System/Workbench_timecho.md b/src/UserGuide/latest/Tools-System/Workbench_timecho.md
index b2d113db3..8b124a643 100644
--- a/src/UserGuide/latest/Tools-System/Workbench_timecho.md
+++ b/src/UserGuide/latest/Tools-System/Workbench_timecho.md
@@ -24,10 +24,10 @@ IoTDB Visualization Console is an extension component developed for industrial s
Main feature showcase
* Home
-
+
* Measurement Point List
-
+
* Data Query
-
+
* Trend
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/UserGuide/latest/User-Manual/AINode_apache.md b/src/UserGuide/latest/User-Manual/AINode_apache.md
index f0c6647ec..3045fa625 100644
--- a/src/UserGuide/latest/User-Manual/AINode_apache.md
+++ b/src/UserGuide/latest/User-Manual/AINode_apache.md
@@ -25,7 +25,7 @@ AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB,
The system architecture is shown below:
::: center
-
+
:::
The responsibilities of the three nodes are as follows:
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection).
::: center
-
+
::::
## Installation and Deployment
@@ -304,13 +304,13 @@ After completing the registration of the model, the inference function can be us
- **sql**: sql query statement, the result of the query is used as input to the model for model inference. The dimensions of the rows and columns in the result of the query need to match the size specified in the specific model config. (It is not recommended to use the `SELECT *` clause for the sql here because in IoTDB, `*` does not sort the columns, so the order of the columns is undefined, you can use `SELECT s0,s1` to ensure that the columns order matches the expectations of the model input)
- **window_function**: Window functions that can be used in the inference process, there are currently three types of window functions provided to assist in model inference:
- **head(window_size)**: Get the top window_size points in the data for model inference, this window can be used for data cropping.
- 
+ 
- **tail(window_size)**: get the last window_size point in the data for model inference, this window can be used for data cropping.
- 
+ 
- **count(window_size, sliding_step)**: sliding window based on the number of points, the data in each window will be reasoned through the model respectively, as shown in the example below, window_size for 2 window function will be divided into three windows of the input dataset, and each window will perform reasoning operations to generate results respectively. The window can be used for continuous inference
- 
+ 
**Explanation 1**: window can be used to solve the problem of cropping rows when the results of the sql query and the input row requirements of the model do not match. Note that when the number of columns does not match or the number of rows is directly less than the model requirement, the inference cannot proceed and an error message will be returned.
@@ -459,7 +459,7 @@ When using AINode related functions, the authentication of IoTDB itself can be u
In some industrial scenarios, there is a need to predict power loads, which can be used to optimise power supply, conserve energy and resources, support planning and expansion, and enhance power system reliability.
-The data for the test set of ETTh1 that we use is [ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv).
+The data for the test set of ETTh1 that we use is [ETTh1](/img/ETTh1.csv).
It contains power data collected at 1h intervals, and each data consists of load and oil temperature as High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature.
@@ -522,7 +522,7 @@ We compare the results of the prediction of the oil temperature with the real re
The data before 10/24 00:00 represents the past data input to the model, the blue line after 10/24 00:00 is the oil temperature forecast result given by the model, and the red line is the actual oil temperature data from the dataset (used for comparison).
-
+
As can be seen, we have used the relationship between the six load information and the corresponding time oil temperatures for the past 96 hours (4 days) to model the possible changes in this data for the oil temperature for the next 48 hours (2 days) based on the inter-relationships between the sequences learned previously, and it can be seen that the predicted curves maintain a high degree of consistency in trend with the actual results after visualisation.
@@ -532,7 +532,7 @@ Power monitoring of current, voltage and power data is required in substations f
We have used the current, voltage and power data in a substation to form a dataset in a real scenario. The dataset consists of data such as A-phase voltage, B-phase voltage, and C-phase voltage collected every 5 - 6s for a time span of nearly four months in the substation.
-The test set data content is [data](https://alioss.timecho.com/docs/img/data.csv).
+The test set data content is [data](/img/data.csv).
On this dataset, the model inference function of IoTDB-ML can predict the C-phase voltage in the future period through the previous values and corresponding timestamps of A-phase voltage, B-phase voltage and C-phase voltage, empowering the monitoring management of the substation.
@@ -588,7 +588,7 @@ Comparing the predicted results of the C-phase voltage with the real results, we
The data before 02/14 20:48 represents the past data input to the model, the blue line after 02/14 20:48 is the predicted result of phase C voltage given by the model, while the red line is the actual phase C voltage data from the dataset (used for comparison).
-
+
It can be seen that we used the voltage data from the past 10 minutes and, based on the previously learned inter-sequence relationships, modeled the possible changes in the phase C voltage data for the next 5 minutes. The visualized forecast curve shows a certain degree of synchronicity with the actual results in terms of trend.
@@ -596,7 +596,7 @@ It can be seen that we used the voltage data from the past 10 minutes and, based
In the civil aviation and transport industry, there exists a need for anomaly detection of the number of passengers travelling on an aircraft. The results of anomaly detection can be used to guide the adjustment of flight scheduling to make the organisation more efficient.
-Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](https://alioss.timecho.com/docs/img/airline.csv).
+Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](/img/airline.csv).
On this dataset, the model inference function of IoTDB-ML can empower the transport industry by capturing the changing patterns of the sequence in order to detect anomalies at the sequence time points.
#### Step 1: Data Import
@@ -649,6 +649,6 @@ Total line number = 144
We plot the results detected as anomalies to get the following image. Where the blue curve is the original time series and the time points specially marked with red dots are the time points that the algorithm detects as anomalies.
-
+
It can be seen that the Stray model has modelled the input sequence changes and successfully detected the time points where anomalies occur.
\ No newline at end of file
diff --git a/src/UserGuide/latest/User-Manual/AINode_timecho.md b/src/UserGuide/latest/User-Manual/AINode_timecho.md
index 215e370e4..b797c8dfb 100644
--- a/src/UserGuide/latest/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/latest/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB,
The system architecture is shown below:
::: center
-
+
:::
The responsibilities of the three nodes are as follows:
@@ -41,7 +41,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
-
+
- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as:
- **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time.
@@ -58,7 +58,7 @@ Compared with building a machine learning service alone, it has the following ad
- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection).
::: center
-
+
::::
## Installation and Deployment
@@ -304,13 +304,13 @@ After completing the registration of the model, the inference function can be us
- **sql**: sql query statement, the result of the query is used as input to the model for model inference. The dimensions of the rows and columns in the result of the query need to match the size specified in the specific model config. (It is not recommended to use the `SELECT *` clause for the sql here because in IoTDB, `*` does not sort the columns, so the order of the columns is undefined, you can use `SELECT s0,s1` to ensure that the columns order matches the expectations of the model input)
- **window_function**: Window functions that can be used in the inference process, there are currently three types of window functions provided to assist in model inference:
- **head(window_size)**: Get the top window_size points in the data for model inference, this window can be used for data cropping.
- 
+ 
- **tail(window_size)**: get the last window_size point in the data for model inference, this window can be used for data cropping.
- 
+ 
- **count(window_size, sliding_step)**: sliding window based on the number of points, the data in each window will be reasoned through the model respectively, as shown in the example below, window_size for 2 window function will be divided into three windows of the input dataset, and each window will perform reasoning operations to generate results respectively. The window can be used for continuous inference
- 
+ 
**Explanation 1**: window can be used to solve the problem of cropping rows when the results of the sql query and the input row requirements of the model do not match. Note that when the number of columns does not match or the number of rows is directly less than the model requirement, the inference cannot proceed and an error message will be returned.
@@ -459,7 +459,7 @@ When using AINode related functions, the authentication of IoTDB itself can be u
In some industrial scenarios, there is a need to predict power loads, which can be used to optimise power supply, conserve energy and resources, support planning and expansion, and enhance power system reliability.
-The data for the test set of ETTh1 that we use is [ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv).
+The data for the test set of ETTh1 that we use is [ETTh1](/img/ETTh1.csv).
It contains power data collected at 1h intervals, and each data consists of load and oil temperature as High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature.
@@ -522,7 +522,7 @@ We compare the results of the prediction of the oil temperature with the real re
The data before 10/24 00:00 represents the past data input to the model, the blue line after 10/24 00:00 is the oil temperature forecast result given by the model, and the red line is the actual oil temperature data from the dataset (used for comparison).
-
+
As can be seen, we have used the relationship between the six load information and the corresponding time oil temperatures for the past 96 hours (4 days) to model the possible changes in this data for the oil temperature for the next 48 hours (2 days) based on the inter-relationships between the sequences learned previously, and it can be seen that the predicted curves maintain a high degree of consistency in trend with the actual results after visualisation.
@@ -532,7 +532,7 @@ Power monitoring of current, voltage and power data is required in substations f
We have used the current, voltage and power data in a substation to form a dataset in a real scenario. The dataset consists of data such as A-phase voltage, B-phase voltage, and C-phase voltage collected every 5 - 6s for a time span of nearly four months in the substation.
-The test set data content is [data](https://alioss.timecho.com/docs/img/data.csv).
+The test set data content is [data](/img/data.csv).
On this dataset, the model inference function of IoTDB-ML can predict the C-phase voltage in the future period through the previous values and corresponding timestamps of A-phase voltage, B-phase voltage and C-phase voltage, empowering the monitoring management of the substation.
@@ -588,7 +588,7 @@ Comparing the predicted results of the C-phase voltage with the real results, we
The data before 02/14 20:48 represents the past data input to the model, the blue line after 02/14 20:48 is the predicted result of phase C voltage given by the model, while the red line is the actual phase C voltage data from the dataset (used for comparison).
-
+
It can be seen that we used the voltage data from the past 10 minutes and, based on the previously learned inter-sequence relationships, modeled the possible changes in the phase C voltage data for the next 5 minutes. The visualized forecast curve shows a certain degree of synchronicity with the actual results in terms of trend.
@@ -596,7 +596,7 @@ It can be seen that we used the voltage data from the past 10 minutes and, based
In the civil aviation and transport industry, there exists a need for anomaly detection of the number of passengers travelling on an aircraft. The results of anomaly detection can be used to guide the adjustment of flight scheduling to make the organisation more efficient.
-Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](https://alioss.timecho.com/docs/img/airline.csv).
+Airline Passengers is a time-series dataset that records the number of international air passengers between 1949 and 1960, sampled at one-month intervals. The dataset contains a total of one time series. The dataset is [airline](/img/airline.csv).
On this dataset, the model inference function of IoTDB-ML can empower the transport industry by capturing the changing patterns of the sequence in order to detect anomalies at the sequence time points.
#### Step 1: Data Import
@@ -649,6 +649,6 @@ Total line number = 144
We plot the results detected as anomalies to get the following image. Where the blue curve is the original time series and the time points specially marked with red dots are the time points that the algorithm detects as anomalies.
-
+
It can be seen that the Stray model has modelled the input sequence changes and successfully detected the time points where anomalies occur.
\ No newline at end of file
diff --git a/src/UserGuide/latest/User-Manual/Audit-Log_timecho.md b/src/UserGuide/latest/User-Manual/Audit-Log_timecho.md
index 741135b0d..dcca2ff41 100644
--- a/src/UserGuide/latest/User-Manual/Audit-Log_timecho.md
+++ b/src/UserGuide/latest/User-Manual/Audit-Log_timecho.md
@@ -37,7 +37,7 @@ The audit log function traces all user operations in the database. The informati
Client Sources：
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
#### Audit logging can be turned off for some user connections
diff --git a/src/UserGuide/latest/User-Manual/Data-Sync_apache.md b/src/UserGuide/latest/User-Manual/Data-Sync_apache.md
index 7bde227f8..a2ffa995e 100644
--- a/src/UserGuide/latest/User-Manual/Data-Sync_apache.md
+++ b/src/UserGuide/latest/User-Manual/Data-Sync_apache.md
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -93,7 +93,7 @@ The schema and auth synchronization functions have the following limitations:
Data synchronization tasks have three states: RUNNING, STOPPED, and DROPPED. The task state transitions are shown in the following diagram:
-
+
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
@@ -263,7 +263,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -278,7 +278,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -302,7 +302,7 @@ with SINK (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -354,7 +354,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
diff --git a/src/UserGuide/latest/User-Manual/Data-Sync_timecho.md b/src/UserGuide/latest/User-Manual/Data-Sync_timecho.md
index 425bf0118..5b7075004 100644
--- a/src/UserGuide/latest/User-Manual/Data-Sync_timecho.md
+++ b/src/UserGuide/latest/User-Manual/Data-Sync_timecho.md
@@ -29,7 +29,7 @@ Data synchronization is a typical requirement in industrial Internet of Things (
A data synchronization task consists of three stages:
-
+
- Source Stage：This part is used to extract data from the source IoTDB, defined in the source section of the SQL statement.
- Process Stage：This part is used to process the data extracted from the source IoTDB, defined in the processor section of the SQL statement.
@@ -95,7 +95,7 @@ The schema and auth synchronization functions have the following limitations:
Data synchronization tasks have three states: RUNNING, STOPPED, and DROPPED. The task state transitions are shown in the following diagram:
-
+
After creation, the task will start directly, and when the task stops abnormally, the system will automatically attempt to restart the task.
@@ -271,7 +271,7 @@ For importing custom plugins, please refer to the [Stream Processing](./Streamin
This example is used to demonstrate the synchronisation of all data from one IoTDB to another IoTDB with the data link as shown below:
-
+
In this example, we can create a synchronization task named A2B to synchronize the full data from A IoTDB to B IoTDB. The iotdb-thrift-sink plugin (built-in plugin) for the sink is required. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls, as shown in the following example statement:
@@ -286,7 +286,7 @@ with sink (
This example is used to demonstrate the synchronisation of data from a certain historical time range (8:00pm 23 August 2023 to 8:00pm 23 October 2023) to another IoTDB, the data link is shown below:
-
+
In this example, we can create a synchronization task named A2B. First, we need to define the range of data to be transferred in the source. Since the data being transferred is historical data (historical data refers to data that existed before the creation of the synchronization task), we need to configure the start-time and end-time of the data and the transfer mode mode. The URL of the data service port of the DataNode node on the target IoTDB needs to be configured through node-urls.
@@ -310,7 +310,7 @@ with SINK (
This example is used to demonstrate the scenario where two IoTDB act as active-active pairs, with the data link shown in the figure below:
-
+
In this example, to avoid infinite data loops, the `forwarding-pipe-requests` parameter on A and B needs to be set to `false`, indicating that data transmitted from another pipe is not forwarded, and to keep the data consistent on both sides, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata.
@@ -348,7 +348,7 @@ with sink (
This example is used to demonstrate the scenario where data from multiple IoTDB is transferred to the cloud, with data from clusters B, C, and D all synchronized to cluster A, as shown in the figure below:
-
+
In this example, to synchronize the data from clusters B, C, and D to A, the pipe between BA, CA, and DA needs to configure the `path` to limit the range, and to keep the edge and cloud data consistent, the pipe needs to be configured with `inclusion=all` to synchronize full data and metadata. The detailed statement is as follows:
@@ -400,7 +400,7 @@ with sink (
This example is used to demonstrate the scenario where data is transferred in a cascading manner between multiple IoTDB, with data from cluster A synchronized to cluster B, and then to cluster C, as shown in the figure below:
-
+
In this example, to synchronize the data from cluster A to C, the `forwarding-pipe-requests` needs to be set to `true` between BC. The detailed statement is as follows:
@@ -433,7 +433,7 @@ with sink (
This example is used to demonstrate the scenario where data from one IoTDB is synchronized to another IoTDB through a unidirectional gateway, as shown in the figure below:
-
+
In this example, the iotdb-air-gap-sink plugin in the sink task needs to be used . After configuring the gateway, execute the following statement on A IoTDB. Fill in the node-urls with the URL of the data service port of the DataNode node on the target IoTDB configured by the gateway, as detailed below:
diff --git a/src/UserGuide/latest/User-Manual/Data-subscription.md b/src/UserGuide/latest/User-Manual/Data-subscription.md
index 250115e36..3d219df6b 100644
--- a/src/UserGuide/latest/User-Manual/Data-subscription.md
+++ b/src/UserGuide/latest/User-Manual/Data-subscription.md
@@ -15,7 +15,7 @@ Using the IoTDB Subscription Client to consume data has significant advantages i
The IoTDB Subscription Client encompasses three core concepts: Topic, Consumer, and Consumer Group. The specific relationships are illustrated in the diagram below:
+
+
+
`SlidingTimeWindowAccessStrategy`: `SlidingTimeWindowAccessStrategy` has many constructors, you can pass 3 types of parameters to them:
@@ -149,7 +149,7 @@ The sliding step parameter is also optional. If the parameter is not provided, t
The relationship between the three types of parameters can be seen in the figure below. Please see the Javadoc for more details.
-
+
`SlidingSizeWindowAccessStrategy`: `SlidingSizeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -170,7 +170,7 @@ The sliding step parameter is optional. If the parameter is not provided, the sl
Window opening diagram: **Time intervals less than or equal to the given minimum time interval `sessionGap` are assigned in one group.**
-
+
`SessionTimeWindowAccessStrategy`: `SessionTimeWindowAccessStrategy` has many constructors, you can pass 2 types of parameters to them:
@@ -181,7 +181,7 @@ Window opening diagram: **Time intervals less than or equal to the given minimum
Window opening diagram: **For numerical data, if the state difference is less than or equal to the given threshold `delta`, it will be assigned in one group.**
-
+
`StateWindowAccessStrategy` has four constructors.
diff --git a/src/UserGuide/latest/User-Manual/White-List_timecho.md b/src/UserGuide/latest/User-Manual/White-List_timecho.md
index 75fe11866..17785f8da 100644
--- a/src/UserGuide/latest/User-Manual/White-List_timecho.md
+++ b/src/UserGuide/latest/User-Manual/White-List_timecho.md
@@ -66,5 +66,5 @@ Decide which IP addresses can connect to IoTDB
5. whether to enable the configuration of the white list function, can be hot loaded.
6. Use the Java native interface to modify the whitelist, must be the root user to modify, reject non-root user to modify; modify the content must be legal, otherwise it will throw a StatementExecutionException.
-
+
diff --git a/src/zh/Community/Development-Guide.md b/src/zh/Community/Development-Guide.md
index 3df18b9c0..9cf9dceb4 100644
--- a/src/zh/Community/Development-Guide.md
+++ b/src/zh/Community/Development-Guide.md
@@ -234,7 +234,7 @@
#### 认领 issue
- 在 JIRA 上认领 issue：分配给自己。建议添加一句评论：I'm doing this。避免与其他贡献者重复开发。
- 
+ 
注：如果发现自己无法认领 issue，则是因为自己的账户权限不够。遇到此情况，请向
+
- 第二步：对于有问题的行，移动到左侧，会出现加号，点击加号，然后评论，点击 Start a review，此时，所有的 Review 意见都会暂存起来，别人看不到。
-
+
- 第三步： 所有评论加完后，需要点击 Review changes，选择你的意见，已经可以合并的选择 Approve，有 Bug 需要改的选择 Request changes 或者 Comment，不确定的选择 Comment。最后 Submit review 提交审阅意见，提 PR 的人才能看见此意见。
-
+
#### 合并 PR
diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_apache.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_apache.md
index b60d645de..0ec59a31f 100644
--- a/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_apache.md
+++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_apache.md
@@ -52,7 +52,7 @@ IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
-
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_timecho.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_timecho.md
index 21dadde38..ad0e513f4 100644
--- a/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Cluster-Concept_timecho.md
@@ -52,7 +52,7 @@ IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
-
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology.md b/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology.md
index bf396459a..65fc48659 100644
--- a/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology.md
+++ b/src/zh/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology.md
@@ -38,7 +38,7 @@
示例：下图是一个风电场的建模管理，通过多个层级【集团】-【风电场】-【风机】-【物理量】可以唯一确定一个实体测点。
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
|
+| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
|
| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 |
| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 |
\ No newline at end of file
diff --git a/src/zh/UserGuide/Master/Table/Basic-Concept/Write-Updata-Data.md b/src/zh/UserGuide/Master/Table/Basic-Concept/Write-Updata-Data.md
index a546a9fa8..2b82430ae 100644
--- a/src/zh/UserGuide/Master/Table/Basic-Concept/Write-Updata-Data.md
+++ b/src/zh/UserGuide/Master/Table/Basic-Concept/Write-Updata-Data.md
@@ -43,7 +43,7 @@ INSERT INTO 
+ 
+ 
+
+ 
+
-
+
+
-
-
+
+
+
+ 
+
## 产品特性
diff --git a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md
index 03ede2159..8bbd61f78 100644
--- a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md
+++ b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md
@@ -32,7 +32,7 @@ TimechoDB 是一款低成本、高性能的物联网原生时序数据库，是
天谋产品体系由若干个组件构成，覆盖由【数据采集】到【数据管理】到【数据分析&应用】的全时序数据生命周期，做到“采-存-用”一体化时序数据解决方案，帮助用户高效地管理和分析物联网产生的海量时序数据。
+ 
+
## 产品特性
@@ -230,9 +230,9 @@ TimechoDB 将为用户提供更简单、易用的工具体系，通过集群监
IoTDB 性能监控
-
-
+
+
+
SQL 查询
-
-
+
+
+
+
+
#### 1.1.1 分区算法
由于生产环境中通常部署大量设备和传感器，IoTDB 使用序列分区算法以确保分区信息的大小可控。由于生成的时间序列与时间戳相关联，IoTDB 使用时间分区算法来清晰区分冷热分区。
@@ -57,7 +57,7 @@ $$\left\lfloor\frac{\text{Timestamp}-\text{StartTimestamp}}{\text{TimePartitionI
### 1.2 分区分配
IoTDB 使用分片来实现时间序列的弹性存储，集群中分片的数量由所有数据节点的总资源决定。由于分片的数量是动态的，IoTDB 可以轻松扩展。元数据分片和数据分片都遵循相同的分区分配算法，即均匀划分所有序列分区。下图展示了分区分配过程，其中动态扩展的分片匹配不断扩展的时间序列和集群。
-
+
#### 1.2.1 分片扩容
分片的数量由下式给出
@@ -77,7 +77,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 2.1 存储均衡
数据节点持有的副本数量反映了它的存储负载。如果数据节点之间的副本数量差异较大，拥有更多副本的数据节点可能成为存储瓶颈。尽管简单的轮询（Round Robin）放置算法可以通过确保每个数据节点持有等量副本来实现存储均衡，但它会降低集群的容错能力，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 将分片 $r_1$ 的 2 个副本放置在数据节点 $n_1$ 和 $n_2$ 上。
@@ -92,7 +92,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 2.2 计算均衡
数据节点持有的主副本数量反映了它的计算负载。如果数据节点之间持有主副本数量差异较大，拥有更多主副本的数据节点可能成为计算瓶颈。如果主副本选择过程使用直观的贪心算法，当副本以容错算法放置时，可能会导致主副本分布不均，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 选择分片 $r_5$ 在数据节点 $n_5$ 上的副本作为主副本。
diff --git a/src/zh/UserGuide/Master/Table/Tools-System/CLI.md b/src/zh/UserGuide/Master/Table/Tools-System/CLI.md
index fa72ab417..36a5d5f0b 100644
--- a/src/zh/UserGuide/Master/Table/Tools-System/CLI.md
+++ b/src/zh/UserGuide/Master/Table/Tools-System/CLI.md
@@ -67,7 +67,7 @@ Shell> sbin\start-cli.bat -h 127.0.0.1 -p 6667 -u root -pw root -sql_dialect tab
启动后出现如图提示即为启动成功。
-
+
## 2 在 CLI 中执行语句
@@ -80,7 +80,7 @@ Shell> sbin\start-cli.bat -h 127.0.0.1 -p 6667 -u root -pw root -sql_dialect tab
create database test
```
-
+
- 查看数据库
@@ -89,7 +89,7 @@ create database test
show databases
```
-
+
## 3 CLI 退出
diff --git a/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_apache.md b/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_apache.md
index cbad5d698..bc4dbdf81 100644
--- a/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_apache.md
+++ b/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_apache.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -46,7 +46,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -210,7 +210,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -226,7 +226,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode.streaming。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -250,7 +250,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`source.mode.double-living` 均设置为 `true`，表示不转发从另一 pipe 传输而来的数据。
@@ -285,7 +285,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置database-name 和 table-name 限制范围，详细语句如下：
@@ -335,7 +335,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `source.mode.double-living` 配置为`true`，详细语句如下：
diff --git a/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_timecho.md b/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_timecho.md
index 2acd17dd1..bd7f6661f 100644
--- a/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_timecho.md
+++ b/src/zh/UserGuide/Master/Table/User-Manual/Data-Sync_timecho.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -46,7 +46,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -215,7 +215,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -231,7 +231,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode.streaming。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -255,7 +255,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`source.mode.double-living` 均设置为 `true`，表示不转发从另一 pipe 传输而来的数据。
@@ -290,7 +290,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置database-name 和 table-name 限制范围，详细语句如下：
@@ -340,7 +340,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `source.mode.double-living` 配置为`true`，详细语句如下：
@@ -371,7 +371,7 @@ with sink (
本例子用来演示将一个 IoTDB 的数据，经过单向网闸，同步至另一个 IoTDB 的场景，数据链路如下图所示：
-
+
在这个例子中，需要使用 sink 任务中的 iotdb-air-gap-sink 插件，配置网闸后，在 A IoTDB 上执行下列语句，其中 node-urls 填写网闸配置的目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，详细语句如下：
diff --git a/src/zh/UserGuide/Master/Tree/API/Programming-MQTT.md b/src/zh/UserGuide/Master/Tree/API/Programming-MQTT.md
index bb8dd1a03..4a113cd13 100644
--- a/src/zh/UserGuide/Master/Tree/API/Programming-MQTT.md
+++ b/src/zh/UserGuide/Master/Tree/API/Programming-MQTT.md
@@ -30,7 +30,7 @@
IoTDB 支持 MQTT v3.1（OASIS 标准）协议。
IoTDB 服务器包括内置的 MQTT 服务，该服务允许远程设备将消息直接发送到 IoTDB 服务器。
-
+
## 内置 MQTT 服务
内置的 MQTT 服务提供了通过 MQTT 直接连接到 IoTDB 的能力。 它侦听来自 MQTT 客户端的发布消息，然后立即将数据写入存储。
@@ -57,7 +57,7 @@ MQTT 主题与 IoTDB 时间序列相对应。
```
或者以上两者的JSON数组形式。
-
+
## MQTT 配置
默认情况下，IoTDB MQTT 服务从`${IOTDB_HOME}/${IOTDB_CONF}/iotdb-system.properties`加载配置。
diff --git a/src/zh/UserGuide/Master/Tree/API/Programming-ODBC.md b/src/zh/UserGuide/Master/Tree/API/Programming-ODBC.md
index df78dd589..08a7fc6b8 100644
--- a/src/zh/UserGuide/Master/Tree/API/Programming-ODBC.md
+++ b/src/zh/UserGuide/Master/Tree/API/Programming-ODBC.md
@@ -39,7 +39,7 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
1. **下载 Zappy-Sys ODBC-JDBC 桥插件**：
进入 https://zappysys.com/products/odbc-powerpack/odbc-jdbc-bridge-driver/ 网站，点击下载按钮并直接安装。
- 
+ 
2. **准备 IoTDB**：打开 IoTDB 集群，并任意写入一条数据。
```sql
@@ -49,11 +49,11 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
3. **部署及调试插件**：
1. 打开 ODBC 数据源 32/64 位，取决于 Windows 的位数，一个示例的位置是 `C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools`。
- 
+ 
2. 点击添加，选择 ZappySys JDBC Bridge。
- 
+ 
3. 填写如下配置：
@@ -65,15 +65,15 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
| User name | IoTDB 的用户名 | root |
| User password | IoTDB 的密码 | root |
- 
+ 
4. 点击 Test Connection 按钮，应该显示连接成功。
- 
+ 
5. 点击上方的 Preview， 将查询文本换为 `select * from root.**`，点击 Preview Data，应该正确显示查询结果。
- 
+ 
4. **使用 ODBC 操作数据**：正确部署后，就可以使用 Windows 的 ODBC 库，对 IoTDB 的数据进行操作。 这里给出 C# 语言的代码示例：
```C#
diff --git a/src/zh/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md b/src/zh/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md
index ea23ccfbc..47e31ea4e 100644
--- a/src/zh/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/API/Programming-OPC-UA_timecho.md
@@ -30,7 +30,7 @@ OPC UA 是一种在自动化领域用于不同设备和系统之间进行通信
- **Client/Server 模式**：在这种模式下，IoTDB 的流处理引擎通过 OPC UA Sink 与 OPC UA 服务器（Server）建立连接。OPC UA 服务器在其地址空间(Address Space) 中维护数据，IoTDB可以请求并获取这些数据。同时，其他OPC UA客户端（Client）也能访问服务器上的数据。
+ 
+ 
+ 
+
+
+ 
+
+
+
+
+
+
+
+
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md
index 21dadde38..ad0e513f4 100644
--- a/src/zh/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Cluster-Concept_timecho.md
@@ -52,7 +52,7 @@ IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
-
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology.md
index 79c1c21b8..de2256cf7 100644
--- a/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology.md
+++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@
根据企业组织结构和设备实体层次结构，我们将其物联网数据模型表示为如下图所示的属性层级组织结构，即电力集团层-风电场层-实体层-物理量层。其中 ROOT 为根节点，物理量层的每一个节点为叶子节点。IoTDB 采用树形结构定义数据模式，以从 ROOT 节点到叶子节点的路径来命名一个时间序列，层次间以“.”连接。例如，下图最左侧路径对应的时间序列名称为`ROOT.ln.wf01.wt01.status`。
-
+
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)。
@@ -94,7 +94,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例
插入数据时，对齐的时间序列中某列的某些行允许有空值。
-
+
在后续数据定义语言、数据操作语言和 Java 原生接口章节，将对涉及到对齐时间序列的各种操作进行逐一介绍。
diff --git a/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md
index 96e9fdf9a..35f789271 100644
--- a/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md
+++ b/src/zh/UserGuide/Master/Tree/Background-knowledge/Navigating_Time_Series_Data.md
@@ -24,13 +24,13 @@
万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。
-
+
通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。
-
+
传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。
@@ -38,14 +38,14 @@
时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。
-
+
### 数据点
- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。
- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
-
+
### 测点
diff --git a/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md b/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md
index e67ae0750..594389b1b 100644
--- a/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md
+++ b/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_apache.md
@@ -346,9 +346,9 @@ It costs 0.058s
IoTDB 支持设备模板功能，实现同类型不同实体的物理量元数据共享，减少元数据内存占用，同时简化同类型实体的管理。
-
+
-
+
### 创建设备模板
@@ -775,7 +775,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
diff --git a/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_timecho.md b/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_timecho.md
index 01cf39e7e..a20a65bb4 100644
--- a/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/Basic-Concept/Operate-Metadata_timecho.md
@@ -346,9 +346,9 @@ It costs 0.058s
IoTDB 支持设备模板功能，实现同类型不同实体的物理量元数据共享，减少元数据内存占用，同时简化同类型实体的管理。
-
+
-
+
### 创建设备模板
@@ -775,7 +775,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
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 204667946..3ba87782e 100644
--- a/src/zh/UserGuide/Master/Tree/Basic-Concept/Query-Data.md
+++ b/src/zh/UserGuide/Master/Tree/Basic-Concept/Query-Data.md
@@ -866,7 +866,7 @@ IoTDB支持通过`GROUP BY`子句对序列进行分段或者分组聚合。
下图中指出了这三个参数的含义：
-
+
接下来，我们给出几个典型例子：
@@ -1115,7 +1115,7 @@ group by variation(controlExpression[,delta][,ignoreNull=true/false])
下图为差值分段的一个分段方式示意图，与组中第一个数据的控制列值的差值在delta内的控制列对应的点属于相同的分组。
-
+
##### 使用注意事项
1. `controlExpression`的结果应该为唯一值，如果使用通配符拼接后出现多列，则报错。
@@ -1288,7 +1288,7 @@ group by session(timeInterval)
下图为`group by session`下的一个分组示意图
-
+
##### 使用注意事项
1. 对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select `__endTime`的方式来使得结果输出分组的结束时间。
diff --git a/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md
index 4c4710469..143b1eda8 100644
--- a/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md
+++ b/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md
@@ -31,8 +31,8 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，
### 部署模式
- 
+ 
+ 
+ 
- 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用
- 建议在 `iotdb-enterprise-ainode-
-
+
+
+
- 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用
- 建议在 `iotdb-enterprise-ainode-
+
+
+
+
-
+
+
-
-
+
+
+
- 
+ 
+ 
-
+
+
-
+ 
+
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
- 
+ 
+ 
- 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+
在编辑数据源的时候，`Type`一栏选择`Simplejson`，`URL`一栏填写 http://\
+
### 操作 Grafana
进入 Grafana 可视化页面后，可以选择添加时间序列，如下图。您也可以按照 Grafana 官方文档进行相应的操作，详情可参看 Grafana 官方文档：http://docs.grafana.org/guides/getting_started/。
-
+
## 配置 grafana
diff --git a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md
index 7e3853018..f7f3297f7 100644
--- a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Grafana-Plugin.md
@@ -115,9 +115,9 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
点击左侧的 `设置` 图标，选择 `Data Source` 选项，然后再点击 `Add data source`。
-
+
-
+
选择 `Apache IoTDB` 数据源，`URL` 一栏填写 `http://
+
@@ -133,15 +133,15 @@ Ip 为您的 IoTDB 服务器所在的宿主机 IP，port 为 REST 服务的运
点击左侧的 `Dashboards` 图标，选择 `Manage`，如下图所示：
-
+
点击右上方的 `New Dashboard` 图标，选择 `Add an empty panel`，如下图所示：
-
+
Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，默认是SQL: Full Customized方式。
-
+
#### SQL: Full Customized 输入方式
@@ -149,7 +149,7 @@ Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，
如果一个查询涉及多个表达式，我们可以点击 SELECT 输入框右侧的 `+` 来添加 SELECT 子句中的表达式，也可以点击 FROM 输入框右侧的 `+` 来添加路径前缀，如下图所示：
-
+
SELECT 输入框中的内容可以是时间序列的后缀，可以是函数或自定义函数，可以是算数表达式，也可以是它们的嵌套表达式。您还可以使用 as 子句来重命名需要显示的结果序列名字。
@@ -182,7 +182,7 @@ CONTROL 输入框为非必须填写项目，填写内容应当是控制查询类
#### SQL: Drop-down List 输入方式
在 TIME-SERIES 选择框中选择一条时间序列、FUNCTION 选择一个函数、SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL 输入框中输入内容，其中 TIME-SERIESL 为必填项其余为非必填项。
-
+
### 变量与模板功能的支持
@@ -190,32 +190,32 @@ SQL: Full Customized和SQL: Drop-down List两种输入方式都支持 Grafana
创建一个新的 Panel 后，点击右上角的设置按钮，如下图所示：
-
+
选择 `Variables`，点击 `Add variable` ，如下图所示：
-
+
示例一：输入 `Name`，`Label`，选择Type的`Query`、在Query 中输入show child paths xx ， 点击 `Update` 按钮，如下图所示：
-
+
应用 Variables，在 `grafana panel` 中输入变量点击 `save` 按钮，如下图所示
-
+
示例二：变量嵌套使用，如下图所示
-
+
-
+
-
+
示例三：函数变量使用，如下图所示
-
+
-
+
上图中Name 是变量名称也是将来我们在panel中使用的变量名称，Label是变量的展示名称如果为空就显示Name的变量反之则显示Label的名称，
Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、Ad hoc filters等这些都可以在IoTDB的Grafana Plugin 中使用
@@ -244,15 +244,15 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
1. 在Grafana panel中，点击alerting按钮，如下图所示：
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+
2. 点击`Create alert rule from this panel`，如下图所示：
-
+
3. 在第1步中设置查询和警报条件，Conditions 表示查询条件，可以配置多个组合查询条件。如下图所示：
-
+
图中的查询条件：min() OF A IS BELOW 0，表示将A选项卡中的最小值在0一下就会触发条件，单击该函数可将其更改为另一个函数。
@@ -260,23 +260,23 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
例如，我们按以下顺序有 3 个条件： 条件：B（计算为：TRUE）或条件：C（计算为：FALSE）和条件：D（计算为：TRUE） 所以结果将计算为（（对或错）和对）=对。
4. 选择完指标及告警规则后点击`Preview`按钮，进行数据预览如下图所示：
-
+
5. 在第 2 步中，指定警报评估间隔，对于`Evaluate every`，指定评估频率。必须是 10 秒的倍数。例如，1m，30s。
对于`Evaluate for`，指定在警报触发之前的持续时间。如下图所示：
-
+
6. 在第 3 步中，添加存储位置、规则组以及与规则关联的其他元数据。 其中`Rule name`指定规则的名称。规则名称必须是唯一的。
-
+
7. 在第 4 步中，添加自定义标签。 从下拉列表中选择现有键值对添加自定义标签，或通过输入新键或值来添加新标签。如下图所示：
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+
8. 单击保存以保存规则或单击保存并退出以保存规则并返回到警报页面。
9. 告警状态常用的有`Normal`、`Pending`、`Firing`等状态，如下图所示：
-
-
+
+
10. 我们也可以为告警配置`Contact points`用来接收告警通知，更加详细操作可以参考官方文档(https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/)。
diff --git a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index efbcbaaf9..42f5ed013 100644
--- a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -35,7 +35,7 @@ Ignition 是一个基于WEB的监控和数据采集工具（SCADA）- 一个开
两个模块与Ignition的具体关系与内容如下图所示。
- 
+ 
## 安装要求
@@ -58,23 +58,23 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
步骤一：进入 `Config` - `System`- `Modules` 模块，点击最下方的`Install or Upgrade a Module...`
-
+
步骤二：选择获取到的 `modl`，选择文件并上传，点击 `Install`，信任相关证书。
-
+
步骤三：安装完成后可以看到如下内容
-
+
步骤四：进入 `Config` - `Tags`- `History` 模块，点击下方的`Create new Historical Tag Provider...`
-
+
步骤五：选择 `IoTDB`并填写配置信息
-
+
配置内容如下：
@@ -161,21 +161,21 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
- 配置好 `Provider` 后就可以在 `Designer` 中使用 `IoTDB Tag Historian` 了，就跟使用其他的 `Provider` 一样，右键点击对应 `Tag` 选择 `Edit tag(s)`，在 Tag Editor 中选择 History 分类
- 
+ 
- 设置 `History Enabled` 为 `true`，并选择 `Storage Provider` 为上一步创建的 `Provider`，按需要配置其它参数，并点击 `OK`，然后保存项目。此时数据将会按照设置的内容持续的存入 `IoTDB` 实例中。
- 
+ 
#### 读取数据
- 也可以在 Report 的 Data 标签下面直接选择存入 IoTDB 的 Tags
- 
+ 
- 在 Components 中也可以直接浏览相关数据
- 
+ 
#### 脚本模块：该功能能够与 IoTDB 进行交互
@@ -242,15 +242,15 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
步骤一：进入 `Config` - `Databases` -`Drivers` 模块，创建 `Translator`
-
+
步骤二：进入 `Config` - `Databases` -`Drivers` 模块，创建 `JDBC Driver`，选择上一步配置的 `Translator`并上传下载的 `IoTDB-JDBC`，Classname 配置为 `org.apache.iotdb.jdbc.IoTDBDriver`
-
+
步骤三：进入 `Config` - `Databases` -`Connections` 模块，创建新的 `Connections`，`JDBC Driver` 选择上一步创建的 `IoTDB Driver`，配置相关信息后保存即可使用
-
+
### 使用说明
@@ -264,11 +264,11 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
不选择其他项，设置好字段，并 `Enabled` 后 数据会安装设置存入对应的 IoTDB
-
+
#### 数据查询
- 在 `Database Query Browser` 中选择`Data Source`选择之前创建的 `Connection`，即可编写 SQL 语句查询 IoTDB 中的数据
-
+
diff --git a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md
index 566007d21..a21e6df28 100644
--- a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md
+++ b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_apache.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 手动创建文件夹
@@ -398,7 +398,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. 扩容
@@ -406,7 +406,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
新增pv，必须有可用的pv才可以扩容。
-
+
**注意：DataNode重启后无法加入集群**
@@ -424,7 +424,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 扩容datanode
diff --git a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md
index 74947c84c..2a6847ff9 100644
--- a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Kubernetes_timecho.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 手动创建文件夹
@@ -398,7 +398,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. 扩容
@@ -406,7 +406,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
新增pv，必须有可用的pv才可以扩容。
-
+
**注意：DataNode重启后无法加入集群**
@@ -424,7 +424,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 扩容datanode
diff --git a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Telegraf.md b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Telegraf.md
index 080b70483..a4b194800 100644
--- a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Telegraf.md
+++ b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Telegraf.md
@@ -38,7 +38,7 @@ Telegraf 有以下这些特点：
Telegraf-IoTDB 插件可以将保存在 Telegraf 中的监控信息输出存储到 IoTDB，输出插件使用了 IoTDB session 进行连接和数据写入。
-
+
## 2、安装要求
@@ -66,7 +66,7 @@ Telegraf支持多种操作系统，包括Linux、Windows、macOS，Telegraf 的
| name_prefix | 指定附加到测量名称的前缀 | |
| name_suffix | 指定附加到测量名称的后缀 | |
-
+
### 4.2 设置输出源
@@ -81,7 +81,7 @@ Telegraf支持多种操作系统，包括Linux、Windows、macOS，Telegraf 的
| timestamp_precision | 时间戳精度 | timestamp_precision = "millisecond" | timestamp_precision = "与 IoTDB 相同的时间戳精度" | 您可以通过查看"iotdb-system.properties"中"timestamp_precision"字段即可 |
| sanitize_tag | 数据库版本 | 无 | sanitize_tag = "0.13/1.0/1.1/1.2/1.3" | |
-
+
### 4.3 启动Telegraf服务
diff --git a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md
index 4761428cd..38728b6bb 100644
--- a/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md
+++ b/src/zh/UserGuide/Master/Tree/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard 是一个开源物联网平台，可实现物联网项目的快速开发、管理和扩展。更多介绍详情请参考[ ThingsBoard 官网](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/)。
- 
+ 
2. ThingsBoard-IoTDB 简介
@@ -34,7 +34,7 @@
### 关系示意图
-
+
## 安装要求
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ##thingsboard数据写入iotdb所存储
1. 创建设备并接入数据：在 Thingsboard 的实体-设备中创建设备并通过工业网关将数据发送到 ThingsBoard 指定设备中
-
+
2. 设置规则链：在规则链库中对于“SD-032F 泵”设置告警规则并将该规则链设置为根链
- 
+ 
+ 
- 
+ 
+ 
+
+
## 产品特性
diff --git a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_timecho.md b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_timecho.md
index 03ede2159..8bbd61f78 100644
--- a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_timecho.md
@@ -32,7 +32,7 @@ TimechoDB 是一款低成本、高性能的物联网原生时序数据库，是
天谋产品体系由若干个组件构成，覆盖由【数据采集】到【数据管理】到【数据分析&应用】的全时序数据生命周期，做到“采-存-用”一体化时序数据解决方案，帮助用户高效地管理和分析物联网产生的海量时序数据。
+
+
## 产品特性
@@ -230,9 +230,9 @@ TimechoDB 将为用户提供更简单、易用的工具体系，通过集群监
IoTDB 性能监控
-
-
+
+
+
SQL 查询
-
-
+
+
+
+
+
| 函数名 | 可接收的输入序列类型 | 属性参数 | 输出序列类型 | 功能类型 |
| ------ | ------------------------------ | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2574,7 +2574,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `windowSize`: 一个窗口内的点数。Int数据类型。必需的属性参数。
+ `slidingStep`: 按照设定的点数来滑动窗口。Int数据类型。可选的属性参数；如果没有设置，默认取值和`windowSize`一样。
-
+
**(2) 滑动时间窗口（SlidingTimeWindowAccessStrategy）使用的属性参数:**
@@ -2583,7 +2583,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `displayWindowBegin`: 窗口滑动的起始时间戳位置（包含在内）。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MIN_VALUE，意为使用输入的时间序列的第一个点的时间戳作为窗口滑动的起始时间戳位置。
+ `displayWindowEnd`: 结束时间限制（不包含在内；本质上和`WHERE time < displayWindowEnd`起的效果是一样的)。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MAX_VALUE，意为除了输入的时间序列自身数据读取完毕之外没有增加额外的结束时间过滤条件限制。
-
+
#### 示例
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 038c85164..279c3d1c9 100644
--- a/src/zh/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md
+++ b/src/zh/UserGuide/Master/Tree/SQL-Manual/SQL-Manual.md
@@ -254,8 +254,8 @@ IoTDB> count devices where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:
### 设备模板管理
-
-
+
+
diff --git a/src/zh/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md b/src/zh/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md
index 3d188f07e..119e003a0 100644
--- a/src/zh/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md
+++ b/src/zh/UserGuide/Master/Tree/Technical-Insider/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB 为时间序列数据实现了量身定制的分区算法。在此基础
### 分区策略
IoTDB 将生产环境中的每个传感器映射为一个时间序列。然后，使用序列分区算法对时间序列进行分区以管理其元数据，再结合时间分区算法来管理其数据。下图展示了 IoTDB 如何对时序数据进行分区。
-
+
#### 分区算法
由于生产环境中通常部署大量设备和传感器，IoTDB 使用序列分区算法以确保分区信息的大小可控。由于生成的时间序列与时间戳相关联，IoTDB 使用时间分区算法来清晰区分冷热分区。
@@ -56,7 +56,7 @@ $$\left\lfloor\frac{\text{Timestamp}-\text{StartTimestamp}}{\text{TimePartitionI
### 分区分配
IoTDB 使用分片来实现时间序列的弹性存储，集群中分片的数量由所有数据节点的总资源决定。由于分片的数量是动态的，IoTDB 可以轻松扩展。元数据分片和数据分片都遵循相同的分区分配算法，即均匀划分所有序列分区。下图展示了分区分配过程，其中动态扩展的分片匹配不断扩展的时间序列和集群。
-
+
#### 分片扩容
分片的数量由下式给出
@@ -76,7 +76,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 存储均衡
数据节点持有的副本数量反映了它的存储负载。如果数据节点之间的副本数量差异较大，拥有更多副本的数据节点可能成为存储瓶颈。尽管简单的轮询（Round Robin）放置算法可以通过确保每个数据节点持有等量副本来实现存储均衡，但它会降低集群的容错能力，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 将分片 $r_1$ 的 2 个副本放置在数据节点 $n_1$ 和 $n_2$ 上。
@@ -91,7 +91,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 计算均衡
数据节点持有的主副本数量反映了它的计算负载。如果数据节点之间持有主副本数量差异较大，拥有更多主副本的数据节点可能成为计算瓶颈。如果主副本选择过程使用直观的贪心算法，当副本以容错算法放置时，可能会导致主副本分布不均，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 选择分片 $r_5$ 在数据节点 $n_5$ 上的副本作为主副本。
diff --git a/src/zh/UserGuide/Master/Tree/Tools-System/Benchmark.md b/src/zh/UserGuide/Master/Tree/Tools-System/Benchmark.md
index 9c89db2a2..bf607f481 100644
--- a/src/zh/UserGuide/Master/Tree/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/Master/Tree/Tools-System/Benchmark.md
@@ -27,13 +27,13 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
下图1-1囊括了测试基准流程及其他扩展功能。这些流程可以由IoT-benchmark 统一来完成。IoT Benchmark 支持多种工作负载，包括**纯写入、纯查询、写入查询混合**等，支持**软硬件系统监控、测试指标度量**等监控功能，还实现了**初始化数据库自动化、测试数据分析及系统参数优化**等功能。
-
+
图1-1
借鉴 YCSB 测试工具将工作负载生成、性能指标测量和数据库接口三个组件分离的设计思想，IoT-benchmark 的模块化设计如图1-2所示。与基于 YCSB 的测试工具系统不同的是，IoT-benchmark 增加了系统监控模块，支持测试数据和系统指标监控数据的持久化。此外也增加了一些特别针对时序数据场景的特殊负载测试功能，如支持物联网场景的批量写入和多种乱序数据写入模式。
-
+
图1-2
@@ -77,7 +77,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试包的目录结构如下图1-3所示。其中测试配置文件为conf/config.properties，测试启动脚本为benchmark\.sh (Linux & MacOS) 和 benchmark.bat (Windows)，详细文件用途见表1-2所示。
-
+
图1-3文件和文件夹列表
@@ -105,7 +105,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试的所有日志文件被存放于 logs 文件夹下，测试的结果在测试完成后被存放到 data/csvOutput 文件夹下，例如测试后我们得到了如下的结果矩阵：
-
+
- Result Matrix
- OkOperation：成功的对应操作次数
@@ -267,24 +267,24 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
首先在172.21.4.3和172.21.4.4上分别启动被测时间序列数据库Apache-IoTDB和KairosDB，之后在172.21.4.2、172.21.4.3和172.21.4.4上通过ser-benchamrk.sh脚本启动服务器资源监控（图2-1）。然后按照表2-3在172.21.4.2分别修改iotdb-0.13-0.0.1和kairosdb-0.0.1文件夹内的conf/config.properties文件满足测试需求。先后使用benchmark.sh启动对Apache-IoTDB和KairosDB的写入测试。
-
+
图2-1服务器监控任务
例如我们首先启动对KairosDB的测试，IoT-benchmark会在MySQL数据库中创建CONFIG数据表存放本次测试配置信息（图2-2），测试执行中会有日志输出当前测试进度（图2-3）。测试完成时会输出本次测试结果（图2-3），同时将结果写入FINAL_RESULT数据表中（图2-4）。
-
+
图2-2测试配置信息表
-
-
-
-
+
+
+
+
图2-3测试进度和结果
-
+
图2-4测试结果表
@@ -292,14 +292,14 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
依照测试结果信息我们知道同样的配置写入Apache-IoTDB和KairosDB写入延时时间分别为：55.98ms和1324.45ms；写入吞吐分别为：5,125,600.86点/秒和224,819.01点/秒；测试分别执行了585.30秒和11777.99秒。并且KairosDB有写入失败出现，排查后发现是数据磁盘使用率已达到100%，无磁盘空间继续接收数据。而Apache-IoTDB无写入失败现象，全部数据写入完毕后占用磁盘空间仅为4.7G（如图2-5所示）；从写入吞吐和磁盘占用情况上看Apache-IoTDB均优于KairosDB。当然后续还有其他测试来从多方面观察和对比，比如查询性能、文件压缩比、数据安全性等。
-
+
图2-5磁盘使用情况
那么测试过程中各个服务器资源使用情况如何呢？每个写操作具体的表现如何呢？这个时候我们就可以通过安装和使用Tableau来可视化服务器监控表和测试过程记录表内的数据了。Tableau的使用本文不展开介绍，通过它连接测试数据持久化的数据表后具体结果下如图（以Apache-IoTDB为例）：
-
-
+
+
图2-6Tableau可视化测试过程
@@ -326,7 +326,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
执行结果：
-
+
图2-7查询测试结果
diff --git a/src/zh/UserGuide/Master/Tree/Tools-System/CLI.md b/src/zh/UserGuide/Master/Tree/Tools-System/CLI.md
index 80728257b..aaff80934 100644
--- a/src/zh/UserGuide/Master/Tree/Tools-System/CLI.md
+++ b/src/zh/UserGuide/Master/Tree/Tools-System/CLI.md
@@ -132,47 +132,47 @@ Shell > ./kc.sh start-dev
```
2、使用 https://ip:port 登陆 keycloack, 首次登陆需要创建用户
-
+
3、点击 Administration Console 进入管理端
-
+
4、在左侧的 Master 菜单点击 Create Realm, 输入 Realm Name 创建一个新的 Realm
-
+
-
+
5、点击左侧菜单 Clients，创建 client
-
+
6、点击左侧菜单 User，创建 user
-
+
7、点击新创建的用户 id，点击 Credentials 导航输入密码和关闭 Temporary 选项，至此 keyclork 配置完成
-
+
8、创建角色，点击左侧菜单的 Roles然后点击Create Role 按钮添加角色
-
+
9、在Role Name 中输入`iotdb_admin`，点击save 按钮。提示：这里的`iotdb_admin`不能为其他名称否则即使登陆成功后也将无权限使用iotdb的查询、插入、创建 database、添加用户、角色等功能
-
+
10、点击左侧的User 菜单然后点击用户列表中的用户为该用户添加我们刚创建的`iotdb_admin`角色
-
+
11、选择Role Mappings ，在Assign role选择`iotdb_admin`增加角色
-
+
-
+
提示：如果用户角色有调整需要重新生成token并且重新登陆iotdb才会生效
@@ -188,7 +188,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
其中，需要将{my-access-token} （注意，包括{}）替换成你的 token，即 access_token 对应的值。密码为空需要再次确认。
-
+
如何获取 token 取决于你的 OIDC 设置。 最简单的一种情况是使用`password-grant`。例如，假设你在用 keycloack 作为你的 OIDC 服务，
并且你在 keycloack 中有一个被定义成 public 的`iotdb`客户的 realm，那么你可以使用如下`curl`命令获得 token。
diff --git a/src/zh/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md b/src/zh/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md
index ed67d75ae..4c14acdf9 100644
--- a/src/zh/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md
+++ b/src/zh/UserGuide/Master/Tree/Tools-System/Data-Import-Tool.md
@@ -145,7 +145,7 @@ IoTDB 目前支持将 CSV、SQL、及TsFile（IoTDB底层开放式时间序列
TsFile 主动监听&加载功能能够主动监听指定目标路径（用户配置）下TsFile的文件变化，并将目标路径下的TsFile文件自动同步至指定接收路径（用户配置）。通过此功能，IoTDB 能自动检测并加载这些文件，无需手动执行任何额外的加载操作。这种自动化流程不仅简化了用户的操作步骤，还减少了操作过程中可能出现的错误，有效降低了用户在使用过程中的复杂性。
-
+
### 4.1 配置参数
diff --git a/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md b/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md
index ebb3b4f6c..3ef0730da 100644
--- a/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md
+++ b/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_apache.md
@@ -67,7 +67,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -107,9 +107,9 @@ static_configs:
`Apache IoTDB Dashboard`是 IoTDB 企业版的配套产品，支持统一集中式运维管理，可通过一个监控面板监控多个集群。你可以联系商务获取到 Dashboard 的 Json文件。
-
+
-
+
### 集群概览
@@ -122,7 +122,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -131,7 +131,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -144,7 +144,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -153,7 +153,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -163,8 +163,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md b/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md
index 8b6396ded..d3cc9dceb 100644
--- a/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/Tools-System/Monitor-Tool_timecho.md
@@ -67,7 +67,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -106,9 +106,9 @@ static_configs:
我们提供了Apache IoTDB Dashboard，支持统一集中式运维管理，可通过一个监控面板监控多个集群。
-
+
-
+
你可以在企业版中获取到 Dashboard 的 Json文件。
@@ -122,7 +122,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -131,7 +131,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -144,7 +144,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -153,7 +153,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -163,8 +163,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md b/src/zh/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md
index 2df219d05..2eb3f47b4 100644
--- a/src/zh/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/Tools-System/Workbench_timecho.md
@@ -25,10 +25,10 @@ IoTDB的可视化控制台包含以下功能模块：
主要功能展示：
* 首页
-
+
* 测点列表
-
+
* 数据查询
-
+
* 趋势
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/AINode_apache.md b/src/zh/UserGuide/Master/Tree/User-Manual/AINode_apache.md
index 2da3c824d..331365d1a 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/AINode_apache.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/AINode_apache.md
@@ -25,7 +25,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
系统架构如下图所示：
::: center
-
+
:::
三种节点的职责如下：
@@ -41,7 +41,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
-
+
- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如：
- **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。
@@ -57,7 +57,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
::: center
-
+
:::
## 安装部署
@@ -301,13 +301,13 @@ window_function:
- **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期)
- **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理：
- **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪
- 
+ 
- **tail(window_size)**：获取数据中最后的window_size个点用于模型推，该窗口可用于数据裁剪
- 
+ 
- **count(window_size, sliding_step)**：基于点数的滑动窗口，每个窗口的数据会分别通过模型进行推理，如下图示例所示，window_size为2的窗口函数将输入数据集分为三个窗口，每个窗口分别进行推理运算生成结果。该窗口可用于连续推理
- 
+ 
**说明1: window可以用来解决sql查询结果和模型的输入行数要求不一致时的问题，对行进行裁剪。需要注意的是，当列数不匹配或是行数直接少于模型需求时，推理无法进行，会返回错误信息。**
@@ -456,7 +456,7 @@ Total line number = 4
在部分工业场景下，会存在预测电力负载的需求，预测结果可用于优化电力供应、节约能源和资源、支持规划和扩展以及增强电力系统的可靠性。
-我们所使用的 ETTh1 的测试集的数据为[ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv)。
+我们所使用的 ETTh1 的测试集的数据为[ETTh1](/img/ETTh1.csv)。
包含间隔1h采集一次的电力数据，每条数据由负载和油温构成，分别为：High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature。
@@ -519,7 +519,7 @@ Total line number = 48
图中10/24 00:00之前的数据为输入模型的过去数据，10/24 00:00后的蓝色线条为模型给出的油温预测结果，而红色为数据集中实际的油温数据（用于进行对比）。
-
+
可以看到，我们使用了过去96个小时（4天）的六个负载信息和对应时间油温的关系，基于之前学习到的序列间相互关系对未来48个小时（2天）的油温这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了较高程度的一致性。
@@ -529,7 +529,7 @@ Total line number = 48
我们利用某变电站中的电流、电压和功率等数据构成了真实场景下的数据集。该数据集包括变电站近四个月时间跨度，每5 - 6s 采集一次的 A相电压、B相电压、C相电压等数据。
-测试集数据内容为[data](https://alioss.timecho.com/docs/img/data.csv)。
+测试集数据内容为[data](/img/data.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过以往A相电压，B相电压和C相电压的数值和对应时间戳，预测未来一段时间内的C相电压，赋能变电站的监视管理。
@@ -584,7 +584,7 @@ Total line number = 48
图中 02/14 20:48 之前的数据为输入模型的过去数据， 02/14 20:48 后的蓝色线条为模型给出的C相电压预测结果，而红色为数据集中实际的C相电压数据（用于进行对比）。
-
+
可以看到，我们使用了过去10分钟的电压的数据，基于之前学习到的序列间相互关系对未来5分钟的C相电压这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了一定的同步性。
@@ -592,7 +592,7 @@ Total line number = 48
在民航交通运输业，存在着对乘机旅客数量进行异常检测的需求。异常检测的结果可用于指导调整航班的调度，以使得企业获得更大效益。
-Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](https://alioss.timecho.com/docs/img/airline.csv)。
+Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](/img/airline.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过捕捉序列的变化规律以对序列时间点进行异常检测，赋能交通运输业。
#### 步骤一：数据导入
@@ -645,6 +645,6 @@ Total line number = 144
我们将检测为异常的结果进行绘制，可以得到以下图像。其中蓝色曲线为原时间序列，用红色点特殊标注的时间点为算法检测为异常的时间点。
-
+
可以看到，Stray模型对输入序列变化进行了建模，成功检测出出现异常的时间点。
\ No newline at end of file
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/AINode_timecho.md b/src/zh/UserGuide/Master/Tree/User-Manual/AINode_timecho.md
index 291d2a1e1..32dfb729d 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
系统架构如下图所示：
::: center
-
+
:::
三种节点的职责如下：
@@ -41,7 +41,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
-
+
- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如：
- **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。
@@ -57,7 +57,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
::: center
-
+
:::
## 安装部署
@@ -301,13 +301,13 @@ window_function:
- **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期)
- **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理：
- **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪
- 
+ 
- **tail(window_size)**：获取数据中最后的window_size个点用于模型推，该窗口可用于数据裁剪
- 
+ 
- **count(window_size, sliding_step)**：基于点数的滑动窗口，每个窗口的数据会分别通过模型进行推理，如下图示例所示，window_size为2的窗口函数将输入数据集分为三个窗口，每个窗口分别进行推理运算生成结果。该窗口可用于连续推理
- 
+ 
**说明1: window可以用来解决sql查询结果和模型的输入行数要求不一致时的问题，对行进行裁剪。需要注意的是，当列数不匹配或是行数直接少于模型需求时，推理无法进行，会返回错误信息。**
@@ -456,7 +456,7 @@ Total line number = 4
在部分工业场景下，会存在预测电力负载的需求，预测结果可用于优化电力供应、节约能源和资源、支持规划和扩展以及增强电力系统的可靠性。
-我们所使用的 ETTh1 的测试集的数据为[ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv)。
+我们所使用的 ETTh1 的测试集的数据为[ETTh1](/img/ETTh1.csv)。
包含间隔1h采集一次的电力数据，每条数据由负载和油温构成，分别为：High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature。
@@ -519,7 +519,7 @@ Total line number = 48
图中10/24 00:00之前的数据为输入模型的过去数据，10/24 00:00后的蓝色线条为模型给出的油温预测结果，而红色为数据集中实际的油温数据（用于进行对比）。
-
+
可以看到，我们使用了过去96个小时（4天）的六个负载信息和对应时间油温的关系，基于之前学习到的序列间相互关系对未来48个小时（2天）的油温这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了较高程度的一致性。
@@ -529,7 +529,7 @@ Total line number = 48
我们利用某变电站中的电流、电压和功率等数据构成了真实场景下的数据集。该数据集包括变电站近四个月时间跨度，每5 - 6s 采集一次的 A相电压、B相电压、C相电压等数据。
-测试集数据内容为[data](https://alioss.timecho.com/docs/img/data.csv)。
+测试集数据内容为[data](/img/data.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过以往A相电压，B相电压和C相电压的数值和对应时间戳，预测未来一段时间内的C相电压，赋能变电站的监视管理。
@@ -584,7 +584,7 @@ Total line number = 48
图中 02/14 20:48 之前的数据为输入模型的过去数据， 02/14 20:48 后的蓝色线条为模型给出的C相电压预测结果，而红色为数据集中实际的C相电压数据（用于进行对比）。
-
+
可以看到，我们使用了过去10分钟的电压的数据，基于之前学习到的序列间相互关系对未来5分钟的C相电压这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了一定的同步性。
@@ -592,7 +592,7 @@ Total line number = 48
在民航交通运输业，存在着对乘机旅客数量进行异常检测的需求。异常检测的结果可用于指导调整航班的调度，以使得企业获得更大效益。
-Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](https://alioss.timecho.com/docs/img/airline.csv)。
+Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](/img/airline.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过捕捉序列的变化规律以对序列时间点进行异常检测，赋能交通运输业。
#### 步骤一：数据导入
@@ -645,6 +645,6 @@ Total line number = 144
我们将检测为异常的结果进行绘制，可以得到以下图像。其中蓝色曲线为原时间序列，用红色点特殊标注的时间点为算法检测为异常的时间点。
-
+
可以看到，Stray模型对输入序列变化进行了建模，成功检测出出现异常的时间点。
\ No newline at end of file
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md b/src/zh/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md
index 0165a99d8..1912bc5e6 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/Audit-Log_timecho.md
@@ -45,7 +45,7 @@
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
#### 可关闭部分用户连接的审计日志
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md b/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md
index 0ce0c4ee8..079184a76 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_apache.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -92,7 +92,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -263,7 +263,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -279,7 +279,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -303,7 +303,7 @@ with SINK (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -353,7 +353,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md b/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md
index 7d6b2d91c..7d9b059a5 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/Data-Sync_timecho.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -94,7 +94,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -271,7 +271,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -287,7 +287,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -311,7 +311,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`forwarding-pipe-requests` 均设置为 `false`，表示不转发从另一 pipe 传输而来的数据，以及要保持两侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据。
@@ -348,7 +348,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -398,7 +398,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
@@ -429,7 +429,7 @@ with sink (
本例子用来演示将一个 IoTDB 的数据，经过单向网闸，同步至另一个 IoTDB 的场景，数据链路如下图所示：
-
+
在这个例子中，需要使用 sink 任务中的 iotdb-air-gap-sink 插件，配置网闸后，在 A IoTDB 上执行下列语句，其中 node-urls 填写网闸配置的目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，详细语句如下：
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/Data-subscription.md b/src/zh/UserGuide/Master/Tree/User-Manual/Data-subscription.md
index 6b6be6063..cb46617a5 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/Data-subscription.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/Data-subscription.md
@@ -14,7 +14,7 @@ IoTDB 数据订阅模块（又称 IoTDB 订阅客户端）是IoTDB V1.3.3 版本
IoTDB 订阅客户端包含 3 个核心概念：Topic、Consumer、Consumer Group，具体关系如下图
+ 
+ 
+
-
+
### 示例
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/UDF-development.md b/src/zh/UserGuide/Master/Tree/User-Manual/UDF-development.md
index d2ecb5dcd..24e0366ad 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/UDF-development.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/UDF-development.md
@@ -127,7 +127,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 3 类参数：
@@ -142,7 +142,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
3 类参数的关系可见下图。策略的构造方法详见 Javadoc。
-
+
> 注意，最后的一些时间窗口的实际时间间隔可能小于规定的时间间隔参数。另外，可能存在某些时间窗口内数据行数量为 0 的情况，这种情况框架也会为该窗口调用一次`transform`方法。
@@ -150,7 +150,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingSizeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 个参数：
@@ -163,7 +163,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**时间间隔小于等于给定的最小时间间隔 sessionGap 则分为一组。**
-
+
`SessionTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 类参数：
@@ -175,7 +175,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**对于数值型数据，状态差值小于等于给定的阈值 delta 则分为一组。**
-
+
`StateWindowAccessStrategy`有四种构造方法：
diff --git a/src/zh/UserGuide/Master/Tree/User-Manual/White-List_timecho.md b/src/zh/UserGuide/Master/Tree/User-Manual/White-List_timecho.md
index 4b43a03fc..33245504e 100644
--- a/src/zh/UserGuide/Master/Tree/User-Manual/White-List_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/User-Manual/White-List_timecho.md
@@ -66,5 +66,5 @@ white.list:
5. 是否开启白名单功能的配置，可以热加载。
6. 使用Java 原生接口修改白名单，必须是root用户才能修改，拒绝非root用户修改；修改内容必须合法，否则会抛出StatementExecutionException异常。
-
+
diff --git a/src/zh/UserGuide/V0.13.x/API/InfluxDB-Protocol.md b/src/zh/UserGuide/V0.13.x/API/InfluxDB-Protocol.md
index 020e4bf83..18aab0ba5 100644
--- a/src/zh/UserGuide/V0.13.x/API/InfluxDB-Protocol.md
+++ b/src/zh/UserGuide/V0.13.x/API/InfluxDB-Protocol.md
@@ -39,9 +39,9 @@ InfluxDB influxDB = IoTDBInfluxDBFactory.connect(openurl, username, password);
该适配器以 IoTDB Java Session 接口为底层基础，实现了 InfluxDB 的 Java 接口 `interface InfluxDB`，对用户提供了所有 InfluxDB 的接口方法，最终用户可以无感知地使用 InfluxDB 协议向 IoTDB 发起写入和读取请求。
-
+
-
+
#### 2.2 元数据格式转换
@@ -55,7 +55,7 @@ InfluxDB 的元数据是 tag-field 模型，IoTDB 的元数据是树形模型。
3. tags : 各种有索引的属性。
4. fields : 各种记录值（没有索引的属性）。
-
+
##### 2.2.2 IoTDB 元数据
@@ -63,7 +63,7 @@ InfluxDB 的元数据是 tag-field 模型，IoTDB 的元数据是树形模型。
2. path(time series ID)：存储路径。
3. measurement： 物理量。
-
+
##### 2.2.3 两者映射关系
@@ -76,7 +76,7 @@ InfluxDB 元数据向 IoTDB 元数据的转换关系可以由下面的公示表
`root.{database}.{measurement}.{tag value 1}.{tag value 2}...{tag value N-1}.{tag value N}.{field key}`
-
+
如上图所示，可以看出：
diff --git a/src/zh/UserGuide/V0.13.x/API/Programming-MQTT.md b/src/zh/UserGuide/V0.13.x/API/Programming-MQTT.md
index 3a6fa5ac9..8db145d57 100644
--- a/src/zh/UserGuide/V0.13.x/API/Programming-MQTT.md
+++ b/src/zh/UserGuide/V0.13.x/API/Programming-MQTT.md
@@ -30,7 +30,7 @@
IoTDB 支持 MQTT v3.1（OASIS 标准）协议。
IoTDB 服务器包括内置的 MQTT 服务，该服务允许远程设备将消息直接发送到 IoTDB 服务器。
-
+
### 内置 MQTT 服务
内置的 MQTT 服务提供了通过 MQTT 直接连接到 IoTDB 的能力。 它侦听来自 MQTT 客户端的发布消息，然后立即将数据写入存储。
@@ -56,7 +56,7 @@ MQTT 主题与 IoTDB 时间序列相对应。
}
```
-
+
### MQTT 配置
默认情况下，IoTDB MQTT 服务从`${IOTDB_HOME}/${IOTDB_CONF}/iotdbengine.properties`加载配置。
diff --git a/src/zh/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md b/src/zh/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md
index a408549b3..9b0a1f362 100644
--- a/src/zh/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md
+++ b/src/zh/UserGuide/V0.13.x/Data-Concept/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@
根据企业组织结构和设备实体层次结构，我们将其物联网数据模型表示为如下图所示的属性层级组织结构，即电力集团层-风电场层-实体层-物理量层。其中 ROOT 为根节点，物理量层的每一个节点为叶子节点。IoTDB 采用树形结构定义数据模式，以从 ROOT 节点到叶子节点的路径来命名一个时间序列，层次间以“.”连接。例如，下图最左侧路径对应的时间序列名称为`ROOT.ln.wf01.wt01.status`。
-
+
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 [元数据模板文档](./Schema-Template.md)。
@@ -143,6 +143,6 @@ wildcard
插入数据时，对齐的时间序列中某列的某些行允许有空值。
-
+
在后续数据定义语言、数据操作语言和 Java 原生接口章节，将对涉及到对齐时间序列的各种操作进行逐一介绍。
diff --git a/src/zh/UserGuide/V0.13.x/Data-Concept/Schema-Template.md b/src/zh/UserGuide/V0.13.x/Data-Concept/Schema-Template.md
index 01ae24b5b..62b897c84 100644
--- a/src/zh/UserGuide/V0.13.x/Data-Concept/Schema-Template.md
+++ b/src/zh/UserGuide/V0.13.x/Data-Concept/Schema-Template.md
@@ -29,7 +29,7 @@
下图展示了一个燃油车场景的数据模型，各地区的多台燃油车的速度、油量、加速度、角速度四个物理量将会被采集，显然这些燃油车实体具备相同的物理量。
-
+
### 概念定义
@@ -45,7 +45,7 @@
使用元数据模板后，问题背景中示例的燃油车数据模型将会转变至下图所示的形式。所有的物理量元数据仅在模板中保存一份，所有的实体共享模板中的元数据。
-
+
### 生命周期
@@ -60,7 +60,7 @@
最后需要补充的是，**对挂载模板与激活模板进行区分，是为了服务一种常见的场景**：在 Apache IoTDB 元数据模型 MTree 中，经常需要在数量众多的节点上“应用”元数据模板，而这些节点一般拥有共同的祖先节点。因此，可以在其共同祖先节点**挂载**模板，而不必对其大量的孩子节点进行挂载操作。对于需要“应用”模板的节点，则应该使用**激活模板**的操作。
-
+
### 使用
diff --git a/src/zh/UserGuide/V0.13.x/Data-Concept/Time-Partition.md b/src/zh/UserGuide/V0.13.x/Data-Concept/Time-Partition.md
index 9fb64db1b..dc7eaeb6d 100644
--- a/src/zh/UserGuide/V0.13.x/Data-Concept/Time-Partition.md
+++ b/src/zh/UserGuide/V0.13.x/Data-Concept/Time-Partition.md
@@ -49,7 +49,7 @@
开启时间分区功能，并设置 partition_interval 为 86400（一天），则数据的分布情况如下图所示：
-
+
* 插入一条时间戳为 0 的数据，计算 0 / 86400 = 0，则该数据会被存储到 0 号文件夹下的TsFile中
diff --git a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md
index 5a5a721cd..65cd7799a 100644
--- a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md
+++ b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/DBeaver.md
@@ -46,10 +46,10 @@ DBeaver 是一个 SQL 客户端和数据库管理工具。DBeaver 可以使用 I
3. 打开 Driver Manager
- 
+ 
4. 为 IoTDB 新建一个驱动类型
- 
+ 
5. 下载[源代码](https://iotdb.apache.org/zh/Download/)，解压并运行下面的命令编译 jdbc 驱动
@@ -58,15 +58,15 @@ DBeaver 是一个 SQL 客户端和数据库管理工具。DBeaver 可以使用 I
```
7. 在`jdbc/target/`下找到并添加名为`apache-iotdb-jdbc-{version}-jar-with-dependencies.jar`的库，点击 `Find Class`。
- 
+ 
8. 编辑驱动设置
- 
+ 
9. 新建 DataBase Connection， 选择 iotdb
- 
+ 
10. 编辑 JDBC 连接设置
@@ -75,12 +75,12 @@ DBeaver 是一个 SQL 客户端和数据库管理工具。DBeaver 可以使用 I
Username: root
Password: root
```
- 
+ 
11. 测试连接
- 
+ 
12. 可以开始通过 DBeaver 使用 IoTDB
- 
+ 
diff --git a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md
index 953f3bfa5..c18f9d6d8 100644
--- a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md
+++ b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Connector.md
@@ -131,16 +131,16 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
#### 添加 IoTDB 数据源
点击左上角的“Grafana”图标，选择`Data Source`选项，然后再点击`Add data source`。
-
+
在编辑数据源的时候，`Type`一栏选择`Simplejson`，`URL`一栏填写 http://\
+
#### 操作 Grafana
进入 Grafana 可视化页面后，可以选择添加时间序列，如下图。您也可以按照 Grafana 官方文档进行相应的操作，详情可参看 Grafana 官方文档：http://docs.grafana.org/guides/getting_started/。
-
+
### 配置 grafana
diff --git a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md
index 86c045602..51dffabc8 100644
--- a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Grafana-Plugin.md
@@ -76,7 +76,7 @@ yarn build
如果编译成功，我们将看到生成的目标文件夹 `dist`，它包含了编译好的 Grafana 前端插件：
-
+
##### 方案二
@@ -90,7 +90,7 @@ yarn build
如果编译成功，我们将看到 `distribution/target` 路径下包含了编译好的 Grafana 前端插件：
-
+
@@ -168,9 +168,9 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
点击左侧的 `设置` 图标，选择 `Data Source` 选项，然后再点击 `Add data source`。
-
+
-
+
选择 `Apache IoTDB` 数据源，`URL` 一栏填写 `http://
+
@@ -186,17 +186,17 @@ Ip 为您的 IoTDB 服务器所在的宿主机 IP，port 为 REST 服务的运
点击左侧的 `Dashboards` 图标，选择 `Manage`，如下图所示：
-
+
点击右上方的 `New Dashboard` 图标，选择 `Add an empty panel`，如下图所示：
-
+
在 SELECT 输入框、FROM 输入框、WHERE输入框、CONTROL输入框中输入内容，其中 WHERE 和 CONTROL 输入框为非必填。
如果一个查询涉及多个表达式，我们可以点击 SELECT 输入框右侧的 `+` 来添加 SELECT 子句中的表达式，也可以点击 FROM 输入框右侧的 `+` 来添加路径前缀，如下图所示：
-
+
SELECT 输入框中的内容可以是时间序列的后缀，可以是函数或自定义函数，可以是算数表达式，也可以是它们的嵌套表达式。您还可以使用 as 子句来重命名需要显示的结果序列名字。
@@ -229,19 +229,19 @@ CONTROL 输入框为非必须填写项目，填写内容应当是控制查询类
创建一个新的 Panel 后，点击右上角的设置按钮，如下图所示：
-
+
选择 `Variables`，点击 `Add variable` ，如下图所示：
-
+
输入 `Name`，`Label`，和 `Query`， 点击 `Update` 按钮，如下图所示：
-
+
应用 Variables，在 `grafana panel` 中输入变量点击 `save` 按钮，如下图所示
-
+
diff --git a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md
index 866143bfa..8e730fed8 100644
--- a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md
+++ b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Writing-Data-on-HDFS.md
@@ -29,7 +29,7 @@
当你配置使用 HDFS 存储 TSFile 之后，你的数据文件将会被分布式存储。系统架构如下：
-
+
#### Config and usage
diff --git a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md
index 55e0876eb..fd2aea371 100644
--- a/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md
+++ b/src/zh/UserGuide/V0.13.x/Ecosystem-Integration/Zeppelin-IoTDB.md
@@ -25,7 +25,7 @@
Apache Zeppelin 是一个基于网页的交互式数据分析系统。用户可以通过 Zeppelin 连接数据源并使用 SQL、Scala 等进行交互式操作。操作可以保存为文档（类似于 Jupyter）。Zeppelin 支持多种数据源，包括 Spark、ElasticSearch、Cassandra 和 InfluxDB 等等。现在，IoTDB 已经支持使用 Zeppelin 进行操作。样例如下：
-
+
### Zeppelin-IoTDB 解释器
@@ -116,7 +116,7 @@ or
现在可以开始使用 Zeppelin 操作 IoTDB 了。
-
+
我们提供了一些简单的 SQL 来展示 Zeppelin-IoTDB 解释器的使用：
@@ -149,7 +149,7 @@ WHERE time >= 1
样例如下：
-
+
用户也可以参考 [[1]](https://zeppelin.apache.org/docs/0.9.0/usage/display_system/basic.html) 编写更丰富多彩的文档。
@@ -159,7 +159,7 @@ WHERE time >= 1
进入页面 [http://127.0.0.1:8080/#/interpreter](http://127.0.0.1:8080/#/interpreter) 并配置 IoTDB 的连接参数：
-
+
可配置参数默认值和解释如下：
diff --git a/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md b/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md
index c5f172c0d..45a142903 100644
--- a/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md
+++ b/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Architecture.md
@@ -25,7 +25,7 @@ IoTDB 套件由若干个组件构成，共同形成“数据收集-数据写入-
如下图展示了使用 IoTDB 套件全部组件后形成的整体应用架构。下文称所有组件形成 IoTDB 套件，而 IoTDB 特指其中的时间序列数据库组件。
-
+
在上图中，用户可以通过 JDBC 将来自设备上传感器采集的时序数据、服务器负载和 CPU 内存等系统状态数据、消息队列中的时序数据、应用程序的时序数据或者其他数据库中的时序数据导入到本地或者远程的 IoTDB 中。用户还可以将上述数据直接写成本地（或位于 HDFS 上）的 TsFile 文件。
diff --git a/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md b/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md
index c963010c2..2eda6e899 100644
--- a/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/V0.13.x/IoTDB-Introduction/Scenario.md
@@ -29,11 +29,11 @@
此时可以采用 IoTDB 套件中的 TsFile 组件、TsFileSync 工具和 Hadoop/Spark 集成组件对数据进行存储：每新印刷一个芯片，就在 SPI 设备上使用 SDK 写一条数据，这些数据最终形成一个 TsFile 文件。通过 TsFileSync 工具，生成的 TsFile 文件将按一定规则（如每天）被同步到 Hadoop 数据中心，并由数据分析人员对其进行分析。
-
+
在场景 1 中，仅需要 TsFile、TsFileSync 部署在一台 PC 上，此外还需要部署 Hadoop/Spark 连接器用于数据中心端 Hadoop/Spark 集群的数据存储和分析。其示意图如上图所示。下图展示了此时的应用架构。
-
+
* 场景 2
@@ -43,11 +43,11 @@
此时可以采用 IoTDB 套件中的 IoTDB、TsFileSync 工具和 Hadoop/Spark 集成组件等。需要部署一个场控 PC 机，其上安装 IoTDB 和 TsFileSync 工具，用于支持读写数据、本地计算和分析以及上传数据到数据中心。此外还需要部署 Hadoop/Spark 连接器用于数据中心端 Hadoop/Spark 集群的数据存储和分析。如下图所示。
-
+
下图给出了此时的应用架构。
-
+
* 场景 3
@@ -57,11 +57,11 @@
此时，可以采用 IoTDB 套件中的 IoTDB、IoTDB-Client 工具、TsFileSync 工具和 Hadoop/Spark 集成组件等。将 IoTDB 服务器安装在工厂连接外网的服务器上，用户接收机械手传输的数据并将数据上传到数据中心。将 IoTDB-Client 工具安装在每一个连接工厂内网的机械手上，用于将传感器产生的实时数据上传到工厂内部服务器。再使用 TsFileSync 工具将原始数据上传到数据中心。此外还需要部署 Hadoop/Spark 连接器用于数据中心端 Hadoop/Spark 集群的数据存储和分析。如下图中间场景所示。
-
+
下图给出了此时的应用架构。
-
+
* 场景 4
@@ -73,4 +73,4 @@
此外还需要部署 Hadoop/Spark 集群用于数据中心端的数据存储和分析。如下图所示。
-
+
diff --git a/src/zh/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md b/src/zh/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md
index 884b7c813..d3b7c3ef3 100644
--- a/src/zh/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md
+++ b/src/zh/UserGuide/V0.13.x/Maintenance-Tools/JMX-Tool.md
@@ -49,11 +49,11 @@ JMX_IP="the_real_iotdb_server_ip" # 填写实际 IoTDB 的 IP 地址
1. 确保安装 jdk 8。jdk 8 以上需要 [下载 visualvm](https://visualvm.github.io/download.html)
2. 打开 jvisualvm
3. 在左侧导航栏空白处右键 -> 添加 JMX 连接
-
+
4. 填写信息进行登录，按下图分别填写，注意需要勾选”不要求 SSL 连接”。
例如：
连接：192.168.130.15:31999
用户名：iotdb
口令：passw!d
-
+
diff --git a/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md b/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md
index bf43f2831..ca905e4f1 100644
--- a/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md
+++ b/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Log-Tool.md
@@ -33,15 +33,15 @@ IoTDB 支持用户通过修改日志配置文件的方式对 IoTDB 系统日志
本节以 Jconsole 为例介绍连接 JMX 并进入动态系统日志配置模块的方法。启动 Jconsole 控制页面，在新建连接处建立与 IoTDB Server 的 JMX 连接（可以选择本地进程或给定 IoTDB 的 IP 及 PORT 进行远程连接，IoTDB 的 JMX 服务默认运行端口为 31999），如下图使用远程进程连接 Localhost 下运行在 31999 端口的 IoTDB JMX 服务。
-
+
连接到 JMX 后，您可以通过 MBean 选项卡找到名为`ch.qos.logback.classic`的`MBean`，如下图所示。
-
+
在`ch.qos.logback.classic`的 MBean 操作（Operations）选项中，可以看到当前动态系统日志配置支持的 6 种接口，您可以通过使用相应的方法，来执行相应的操作，操作页面如图。
-
+
#### 动态系统日志接口说明
diff --git a/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md b/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md
index d5b9a11e7..741ec608d 100644
--- a/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md
+++ b/src/zh/UserGuide/V0.13.x/Maintenance-Tools/Metric-Tool.md
@@ -320,7 +320,7 @@ mem{name="mtree",} 1328.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -358,7 +358,7 @@ static_configs:
### 1.4.3. Apache IoTDB Dashboard
我们提供了Apache IoTDB Dashboard，在Grafana中显示的效果图如下所示：
-
+
Apache IoTDB Dashboard的获取方式：
@@ -374,7 +374,7 @@ Apache IoTDB Dashboard的获取方式：
通过使用 JConsole 工具并与 JMX 连接，您可以查看一些系统统计信息和参数。
本节描述如何使用 JConsole 的 "Mbean" 选项卡来监视 IoTDB 的一些系统配置、写入数据统计等等。 连接到 JMX 后，您可以通过 "MBeans" 标签找到名为 "org.apache.iotdb.service" 的 "MBean"，如下图所示。
-
+
## 4.2. 配置参数
@@ -403,4 +403,4 @@ Apache IoTDB Dashboard的获取方式：
1. 通过端口 31999 连接 jconsole，并在上方菜单项中选择‘MBean’.
2. 展开侧边框并选择 'org.apache.iotdb.db.service'. 将会得到如下图所示结果：
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md b/src/zh/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md
index f57a6374d..8e05236c0 100644
--- a/src/zh/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md
+++ b/src/zh/UserGuide/V0.13.x/Operate-Metadata/Auto-Create-MetaData.md
@@ -50,7 +50,7 @@
* 当 default_storage_group_level=2 时，将使用 root.turbine1.d1、root.turbine1.d2、root.turbine2.d1 和 root.turbine2.d2 作为存储组。
-
+
### 自动创建序列的元数据（前端指定数据类型）
diff --git a/src/zh/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md b/src/zh/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md
index 872baf57a..8c7d3734a 100644
--- a/src/zh/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md
+++ b/src/zh/UserGuide/V0.13.x/Operate-Metadata/Timeseries.md
@@ -175,7 +175,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
diff --git a/src/zh/UserGuide/V0.13.x/Process-Data/Alerting.md b/src/zh/UserGuide/V0.13.x/Process-Data/Alerting.md
index 5c512b53f..8b263772f 100644
--- a/src/zh/UserGuide/V0.13.x/Process-Data/Alerting.md
+++ b/src/zh/UserGuide/V0.13.x/Process-Data/Alerting.md
@@ -371,4 +371,4 @@ INSERT INTO root.ln.wf01.wt01(timestamp, temperature) VALUES (5, 120);
会抑制 `severity` 为 `warning` 的告警，我们收到的告警邮件中只包含写入
`(5, 120)` 后触发的告警。
-
+
diff --git a/src/zh/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md b/src/zh/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md
index 6e0c3c367..43935b1c5 100644
--- a/src/zh/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md
+++ b/src/zh/UserGuide/V0.13.x/Process-Data/UDF-User-Defined-Function.md
@@ -166,7 +166,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
3 类参数的关系可见下图。策略的构造方法详见 Javadoc。
-
+
注意，最后的一些时间窗口的实际时间间隔可能小于规定的时间间隔参数。另外，可能存在某些时间窗口内数据行数量为 0 的情况，这种情况框架也会为该窗口调用一次`transform`方法。
diff --git a/src/zh/UserGuide/V0.13.x/Query-Data/Aggregate-Query.md b/src/zh/UserGuide/V0.13.x/Query-Data/Aggregate-Query.md
index ead7829c3..165cfcb76 100644
--- a/src/zh/UserGuide/V0.13.x/Query-Data/Aggregate-Query.md
+++ b/src/zh/UserGuide/V0.13.x/Query-Data/Aggregate-Query.md
@@ -178,7 +178,7 @@ GROUP BY 语句为用户提供三类指定参数：
三类参数的实际含义已经在下图中指出，这三类参数里，第三个参数是可选的。
-
+
接下来，我们将给出三种典型的降频聚合查询的例子：
diff --git a/src/zh/UserGuide/V0.13.x/Query-Data/Select-Expression.md b/src/zh/UserGuide/V0.13.x/Query-Data/Select-Expression.md
index 5c8aba16e..955d91162 100644
--- a/src/zh/UserGuide/V0.13.x/Query-Data/Select-Expression.md
+++ b/src/zh/UserGuide/V0.13.x/Query-Data/Select-Expression.md
@@ -781,7 +781,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
- 最小值点是拥有这个窗口内最小值的点（如果有多个这样的点，M4只返回其中一个）；
- 最大值点是拥有这个窗口内最大值的点（如果有多个这样的点，M4只返回其中一个）。
-
+
| 函数名 | 可接收的输入序列类型 | 属性参数 | 输出序列类型 | 功能类型 |
| ------ | ------------------------------ | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -794,7 +794,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `windowSize`: 一个窗口内的点数。Int数据类型。必需的属性参数。
+ `slidingStep`: 按照设定的点数来滑动窗口。Int数据类型。可选的属性参数；如果没有设置，默认取值和`windowSize`一样。
-
+
**(2) 滑动时间窗口（SlidingTimeWindowAccessStrategy）使用的属性参数:**
@@ -803,7 +803,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `displayWindowBegin`: 窗口滑动的起始时间戳位置（包含在内）。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MIN_VALUE，意为使用输入的时间序列的第一个点的时间戳作为窗口滑动的起始时间戳位置。
+ `displayWindowEnd`: 结束时间限制（不包含在内；本质上和`WHERE time < displayWindowEnd`起的效果是一样的)。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MAX_VALUE，意为除了输入的时间序列自身数据读取完毕之外没有增加额外的结束时间过滤条件限制。
-
+
#### 示例
diff --git a/src/zh/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md b/src/zh/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md
index 5647d8bd1..fa775bbcf 100644
--- a/src/zh/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md
+++ b/src/zh/UserGuide/V0.13.x/QuickStart/Command-Line-Interface.md
@@ -116,49 +116,49 @@ Shell >./standalone.sh
```
2、使用 https://ip:port/auth 登陆 keycloack, 首次登陆需要创建用户
-
+
3、点击 Administration Console 进入管理端
-
+
4、在左侧的 Master 菜单点击 add Realm, 输入 Name 创建一个新的 Realm
-
+
-
+
5、点击左侧菜单 Clients，创建 client
-
+
6、点击左侧菜单 User，创建 user
-
+
7、点击新创建的用户 id，点击 Credentials 导航输入密码和关闭 Temporary 选项，至此 keyclork 配置完成
-
+
8、创建角色，点击左侧菜单的 Roles然后点击Add Role 按钮添加角色
-
+
9、在Role Name 中输入`iotdb_admin`，点击save 按钮。提示：这里的`iotdb_admin`不能为其他名称否则即使登陆成功后也将无权限使用iotdb的查询、插入、创建存储组、添加用户、角色等功能
-
+
10、点击左侧的User 菜单然后点击用户列表中的Edit的按钮为该用户添加我们刚创建的`iotdb_admin`角色
-
+
11、选择Role Mappings ，在Available Role选择`iotdb_admin`角色然后点Add selected 按钮添加角色
-
+
12、如果`iotdb_admin`角色在Assigned Roles中并且出现`Success Role mappings updated`提示，证明角色添加成功
-
+
提示：如果用户角色有调整需要重新生成token并且重新登陆iotdb才会生效
diff --git a/src/zh/UserGuide/V0.13.x/Reference/TSDB-Comparison.md b/src/zh/UserGuide/V0.13.x/Reference/TSDB-Comparison.md
index 67b7a1e13..efd17378f 100644
--- a/src/zh/UserGuide/V0.13.x/Reference/TSDB-Comparison.md
+++ b/src/zh/UserGuide/V0.13.x/Reference/TSDB-Comparison.md
@@ -23,7 +23,7 @@
### Overview
-
+
**表格外观启发自 [Andriy Zabavskyy: How to Select Time Series DB](https://towardsdatascience.com/how-to-select-time-series-db-123b0eb4ab82)*
@@ -285,7 +285,7 @@ IoTDB 拥有许多其它时间序列数据库不支持的强大功能。
写入吞吐率（points/second）如下图所示：
-
+
+
## 内置 MQTT 服务
内置的 MQTT 服务提供了通过 MQTT 直接连接到 IoTDB 的能力。 它侦听来自 MQTT 客户端的发布消息，然后立即将数据写入存储。
@@ -57,7 +57,7 @@ MQTT 主题与 IoTDB 时间序列相对应。
```
或者以上两者的JSON数组形式。
-
+
## MQTT 配置
默认情况下，IoTDB MQTT 服务从`${IOTDB_HOME}/${IOTDB_CONF}/iotdb-common.properties`加载配置。
diff --git a/src/zh/UserGuide/V1.2.x/API/Programming-Python-Native-API.md b/src/zh/UserGuide/V1.2.x/API/Programming-Python-Native-API.md
index 03a53179f..36aea614f 100644
--- a/src/zh/UserGuide/V1.2.x/API/Programming-Python-Native-API.md
+++ b/src/zh/UserGuide/V1.2.x/API/Programming-Python-Native-API.md
@@ -562,7 +562,7 @@ SQLAlchemy 中的元数据有：
下图更加清晰的展示了二者的映射关系：
-
+
### 数据类型映射
| IoTDB 中的数据类型 | SQLAlchemy 中的数据类型 |
diff --git a/src/zh/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md b/src/zh/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md
index 943659ce2..8f744e672 100644
--- a/src/zh/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md
+++ b/src/zh/UserGuide/V1.2.x/Basic-Concept/Cluster-data-partitioning.md
@@ -23,7 +23,7 @@
IoTDB 以数据分区（DataRegion）为单位对元数据和数据进行管理，从序列和时间两个维度进行数据划分。
-
+
## 分区槽
@@ -33,7 +33,7 @@ IoTDB 以数据分区（DataRegion）为单位对元数据和数据进行管理
每个数据库持有固定数量的序列分区槽，默认为1000个。该数据库管理的每个时间序列都将通过序列分区算法（通常为某种哈希算法）被分配给唯一的序列分区槽管理。
-
+
### 时间分区槽
@@ -45,13 +45,13 @@ IoTDB 以数据分区（DataRegion）为单位对元数据和数据进行管理
单个数据库的元数据分区管理，会按照一定的负载均衡策略，将所有序列槽分配到相应的 SchemaRegionGroup 中，进而在集群中横向扩展。
-
+
## 数据分区
会按照一定的负载均衡策略，进行时间分区槽和序列分区槽的划分，并分配到相应的 DataRegionGroup 中，进而在集群中横向扩展。
-
+
## 负载均衡
diff --git a/src/zh/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md b/src/zh/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md
index 41ea664cb..e5e8007aa 100644
--- a/src/zh/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md
+++ b/src/zh/UserGuide/V1.2.x/Basic-Concept/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@
根据企业组织结构和设备实体层次结构，我们将其物联网数据模型表示为如下图所示的属性层级组织结构，即电力集团层-风电场层-实体层-物理量层。其中 ROOT 为根节点，物理量层的每一个节点为叶子节点。IoTDB 采用树形结构定义数据模式，以从 ROOT 节点到叶子节点的路径来命名一个时间序列，层次间以“.”连接。例如，下图最左侧路径对应的时间序列名称为`ROOT.ln.wf01.wt01.status`。
-
+
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)。
@@ -96,7 +96,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例
插入数据时，对齐的时间序列中某列的某些行允许有空值。
-
+
在后续数据定义语言、数据操作语言和 Java 原生接口章节，将对涉及到对齐时间序列的各种操作进行逐一介绍。
diff --git a/src/zh/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md b/src/zh/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md
index 3a5a86b14..f7cc82c46 100644
--- a/src/zh/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md
+++ b/src/zh/UserGuide/V1.2.x/Deployment-and-Maintenance/Monitoring-Board-Install-and-Deploy.md
@@ -83,8 +83,8 @@ scrape_configs:
4. 确认启动成功。在浏览器中输入 http://localhost:9090，进入Prometheus，点击进入Status下的Target界面（如下图1），当看到State均为Up时表示配置成功并已经联通（如下图2），点击左侧链接可以跳转到网页监控。
-
-
+
+
@@ -108,51 +108,51 @@ cd grafana-*
3. 在浏览器中输入 http://localhost:3000，进入Grafana，默认初始用户名和密码均为 admin。
4. 首先我们在 Configuration 中配置 Data Source 为 Prometheus
-
+
5. 在配置 Data Source 时注意 Prometheus 所在的URL，配置好后点击Save & Test 出现 Data source is working 提示则为配置成功
-
+
### Step2：使用IoTDB官方提供的Grafana看板
1. 进入 Grafana，选择 Dashboards 的 Browse
-
+
2. 点击右侧 Import 按钮
-
+
3. 选择一种方式导入 Dashboard
a. 上传本地已下载的 Dashboard 的 Json 文件
b. 输入 Grafana 官网获取到的 Dashboard 的 URL 或者 ID
c. 将 Dashboard 的 Json 文件内容直接粘贴进入
-
+
4. 选择 Dashboard 的 Prometheus 为刚刚配置好的 Data Source，然后点击 Import
-
+
5. 之后进入 Dashboard，选择 job 为 ConfigNode，就看到如下的监控面板
-
+
6. 同样地，我们可以导入 Apache DataNode Dashboard，选择 job 为 DataNode，就看到如下的监控面板：
-
+
### Step3：创建新的 Dashboard 进行数据可视化
1. 首先创建Dashboard，然后创建Panel
-
+
2. 之后就可以在面板根据自己的需求对监控相关的数据进行可视化（所有相关的监控指标可以先在job中选择confignode/datanode筛选）
-
+
3. 选择关注的监控指标可视化完成后，我们就得到了这样的面板：
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md
index 1b885e807..fd453fd3c 100644
--- a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md
+++ b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/DBeaver.md
@@ -46,24 +46,24 @@ DBeaver 是一个 SQL 客户端和数据库管理工具。DBeaver 可以使用 I
3. 打开 Driver Manager
- 
+ 
4. 为 IoTDB 新建一个驱动类型
- 
+ 
5. 下载 jdbc 驱动， 可点击下载 [地址1](https://maven.proxy.ustclug.org/maven2/org/apache/iotdb/iotdb-jdbc/1.2.1/iotdb-jdbc-1.2.1-jar-with-dependencies.jar) 或 [地址2](https://repo1.maven.org/maven2/org/apache/iotdb/iotdb-jdbc/1.2.1/iotdb-jdbc-1.2.1-jar-with-dependencies.jar)。
6. 添加刚刚下载的驱动包，点击 Find Class。
- 
+ 
7. 编辑驱动设置
- 
+ 
8. 新建 DataBase Connection， 选择 iotdb
- 
+ 
9. 编辑 JDBC 连接设置
@@ -72,12 +72,12 @@ DBeaver 是一个 SQL 客户端和数据库管理工具。DBeaver 可以使用 I
Username: root
Password: root
```
- 
+ 
10. 测试连接
- 
+ 
11. 可以开始通过 DBeaver 使用 IoTDB
- 
+ 
diff --git a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md
index a0623bbbb..3e44550ad 100644
--- a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md
+++ b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Connector.md
@@ -131,16 +131,16 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
### 添加 IoTDB 数据源
点击左上角的“Grafana”图标，选择`Data Source`选项，然后再点击`Add data source`。
-
+
在编辑数据源的时候，`Type`一栏选择`Simplejson`，`URL`一栏填写 http://\
+
### 操作 Grafana
进入 Grafana 可视化页面后，可以选择添加时间序列，如下图。您也可以按照 Grafana 官方文档进行相应的操作，详情可参看 Grafana 官方文档：http://docs.grafana.org/guides/getting_started/。
-
+
## 配置 grafana
diff --git a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md
index bb4d66ee0..c3deaf942 100644
--- a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Grafana-Plugin.md
@@ -117,9 +117,9 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
点击左侧的 `设置` 图标，选择 `Data Source` 选项，然后再点击 `Add data source`。
-
+
-
+
选择 `Apache IoTDB` 数据源，`URL` 一栏填写 `http://
+
@@ -135,15 +135,15 @@ Ip 为您的 IoTDB 服务器所在的宿主机 IP，port 为 REST 服务的运
点击左侧的 `Dashboards` 图标，选择 `Manage`，如下图所示：
-
+
点击右上方的 `New Dashboard` 图标，选择 `Add an empty panel`，如下图所示：
-
+
Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，默认是SQL: Full Customized方式。
-
+
#### SQL: Full Customized 输入方式
@@ -151,7 +151,7 @@ Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，
如果一个查询涉及多个表达式，我们可以点击 SELECT 输入框右侧的 `+` 来添加 SELECT 子句中的表达式，也可以点击 FROM 输入框右侧的 `+` 来添加路径前缀，如下图所示：
-
+
SELECT 输入框中的内容可以是时间序列的后缀，可以是函数或自定义函数，可以是算数表达式，也可以是它们的嵌套表达式。您还可以使用 as 子句来重命名需要显示的结果序列名字。
@@ -184,7 +184,7 @@ CONTROL 输入框为非必须填写项目，填写内容应当是控制查询类
#### SQL: Drop-down List 输入方式
在 TIME-SERIES 选择框中选择一条时间序列、FUNCTION 选择一个函数、SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL 输入框中输入内容，其中 TIME-SERIESL 为必填项其余为非必填项。
-
+
### 变量与模板功能的支持
@@ -192,32 +192,32 @@ SQL: Full Customized和SQL: Drop-down List两种输入方式都支持 Grafana
创建一个新的 Panel 后，点击右上角的设置按钮，如下图所示：
-
+
选择 `Variables`，点击 `Add variable` ，如下图所示：
-
+
示例一：输入 `Name`，`Label`，选择Type的`Query`、在Query 中输入show child paths xx ， 点击 `Update` 按钮，如下图所示：
-
+
应用 Variables，在 `grafana panel` 中输入变量点击 `save` 按钮，如下图所示
-
+
示例二：变量嵌套使用，如下图所示
-
+
-
+
-
+
示例三：函数变量使用，如下图所示
-
+
-
+
上图中Name 是变量名称也是将来我们在panel中使用的变量名称，Label是变量的展示名称如果为空就显示Name的变量反之则显示Label的名称，
Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、Ad hoc filters等这些都可以在IoTDB的Grafana Plugin 中使用
@@ -246,15 +246,15 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
1. 在Grafana panel中，点击alerting按钮，如下图所示：
-
+
2. 点击`Create alert rule from this panel`，如下图所示：
-
+
3. 在第1步中设置查询和警报条件，Conditions 表示查询条件，可以配置多个组合查询条件。如下图所示：
-
+
图中的查询条件：min() OF A IS BELOW 0，表示将A选项卡中的最小值在0一下就会触发条件，单击该函数可将其更改为另一个函数。
@@ -262,23 +262,23 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
例如，我们按以下顺序有 3 个条件： 条件：B（计算为：TRUE）或条件：C（计算为：FALSE）和条件：D（计算为：TRUE） 所以结果将计算为（（对或错）和对）=对。
4. 选择完指标及告警规则后点击`Preview`按钮，进行数据预览如下图所示：
-
+
5. 在第 2 步中，指定警报评估间隔，对于`Evaluate every`，指定评估频率。必须是 10 秒的倍数。例如，1m，30s。
对于`Evaluate for`，指定在警报触发之前的持续时间。如下图所示：
-
+
6. 在第 3 步中，添加存储位置、规则组以及与规则关联的其他元数据。 其中`Rule name`指定规则的名称。规则名称必须是唯一的。
-
+
7. 在第 4 步中，添加自定义标签。 从下拉列表中选择现有键值对添加自定义标签，或通过输入新键或值来添加新标签。如下图所示：
-
+
8. 单击保存以保存规则或单击保存并退出以保存规则并返回到警报页面。
9. 告警状态常用的有`Normal`、`Pending`、`Firing`等状态，如下图所示：
-
-
+
+
10. 我们也可以为告警配置`Contact points`用来接收告警通知，更加详细操作可以参考官方文档(https://grafana.com/docs/grafana/latest/alerting/alerting-rules/manage-contact-points/)
diff --git a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md
index b86843ab6..022a862bd 100644
--- a/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md
+++ b/src/zh/UserGuide/V1.2.x/Ecosystem-Integration/Zeppelin-IoTDB.md
@@ -25,7 +25,7 @@
Apache Zeppelin 是一个基于网页的交互式数据分析系统。用户可以通过 Zeppelin 连接数据源并使用 SQL、Scala 等进行交互式操作。操作可以保存为文档（类似于 Jupyter）。Zeppelin 支持多种数据源，包括 Spark、ElasticSearch、Cassandra 和 InfluxDB 等等。现在，IoTDB 已经支持使用 Zeppelin 进行操作。样例如下：
-
+
## Zeppelin-IoTDB 解释器
@@ -116,7 +116,7 @@ or
现在可以开始使用 Zeppelin 操作 IoTDB 了。
-
+
我们提供了一些简单的 SQL 来展示 Zeppelin-IoTDB 解释器的使用：
@@ -149,7 +149,7 @@ WHERE time >= 1
样例如下：
-
+
用户也可以参考 [[1]](https://zeppelin.apache.org/docs/0.9.0/usage/display_system/basic.html) 编写更丰富多彩的文档。
@@ -159,7 +159,7 @@ WHERE time >= 1
进入页面 [http://127.0.0.1:8080/#/interpreter](http://127.0.0.1:8080/#/interpreter) 并配置 IoTDB 的连接参数：
-
+
可配置参数默认值和解释如下：
diff --git a/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md b/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md
index f19cd6809..f901239f9 100644
--- a/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md
+++ b/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Architecture.md
@@ -25,7 +25,7 @@ IoTDB 套件由若干个组件构成，共同形成“数据收集-数据写入-
如下图展示了使用 IoTDB 套件全部组件后形成的整体应用架构。下文称所有组件形成 IoTDB 套件，而 IoTDB 特指其中的时间序列数据库组件。
-
+
在上图中，用户可以通过 JDBC 将来自设备上传感器采集的时序数据、服务器负载和 CPU 内存等系统状态数据、消息队列中的时序数据、应用程序的时序数据或者其他数据库中的时序数据导入到本地或者远程的 IoTDB 中。用户还可以将上述数据直接写成本地（或位于 HDFS 上）的 TsFile 文件。
diff --git a/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md b/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md
index 3fad46cf7..bd6db2a01 100644
--- a/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/V1.2.x/IoTDB-Introduction/Scenario.md
@@ -38,7 +38,7 @@
该车企以IoTDB为时序数据存储引擎的数据管理架构如下图所示。
-
+
车辆数据基于TCP和工业协议编码后发送至边缘网关，网关将数据发往消息队列Kafka集群，解耦生产和消费两端。Kafka将数据发送至Flink进行实时处理，处理后的数据写入IoTDB中，历史数据和最新数据均在IoTDB中进行查询，最后数据通过API流入可视化平台等进行应用。
@@ -61,7 +61,7 @@
下图为该钢厂的智能运维平台架构设计。
-.PNG)
+.PNG)
## 应用3——智能工厂
@@ -75,7 +75,7 @@
下图为该工厂的物联网系统架构，IoTDB贯穿公司、工厂、车间三级物联网平台，实现设备统一联调联控。车间层面的数据通过边缘层的IoTDB进行实时采集、处理和存储，并实现了一系列的分析任务。经过预处理的数据被发送至平台层的IoTDB，进行业务层面的数据治理，如设备管理、连接管理、服务支持等。最终，数据会被集成到集团层面的IoTDB中，供整个组织进行综合分析和决策。
-.PNG)
+.PNG)
## 应用4——工况监控
@@ -92,4 +92,4 @@
下图为该电厂的供热场景数据管理架构。
-
+
diff --git a/src/zh/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md
index d9ba5ca1c..dad2a60ac 100644
--- a/src/zh/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md
+++ b/src/zh/UserGuide/V1.2.x/SQL-Manual/SQL-Manual.md
@@ -250,7 +250,7 @@ IoTDB> count devices root.ln.**
### 4、设备模板管理
-
+
@@ -260,7 +260,7 @@ Create device root.sg.d1
-
+
#### 创建元数据模板
diff --git a/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md b/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
index bdffaab86..59e32120b 100644
--- a/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
@@ -27,13 +27,13 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
下图1-1囊括了测试基准流程及其他扩展功能。这些流程可以由IoT-benchmark 统一来完成。IoT Benchmark 支持多种工作负载，包括**纯写入、纯查询、写入查询混合**等，支持**软硬件系统监控、测试指标度量**等监控功能，还实现了**初始化数据库自动化、测试数据分析及系统参数优化**等功能。
-
+
图1-1
借鉴 YCSB 测试工具将工作负载生成、性能指标测量和数据库接口三个组件分离的设计思想，IoT-benchmark 的模块化设计如图1-2所示。与基于 YCSB 的测试工具系统不同的是，IoT-benchmark 增加了系统监控模块，支持测试数据和系统指标监控数据的持久化。此外也增加了一些特别针对时序数据场景的特殊负载测试功能，如支持物联网场景的批量写入和多种乱序数据写入模式。
-
+
图1-2
@@ -77,7 +77,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试包的目录结构如下图1-3所示。其中测试配置文件为conf/config.properties，测试启动脚本为benchmark\.sh (Linux & MacOS) 和 benchmark.bat (Windows)，详细文件用途见表1-2所示。
-
+
图1-3文件和文件夹列表
@@ -105,7 +105,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试的所有日志文件被存放于 logs 文件夹下，测试的结果在测试完成后被存放到 data/csvOutput 文件夹下，例如测试后我们得到了如下的结果矩阵：
-
+
- Result Matrix
- OkOperation：成功的对应操作次数
@@ -267,24 +267,24 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
首先在172.21.4.3和172.21.4.4上分别启动被测时间序列数据库Apache-IoTDB和KairosDB，之后在172.21.4.2、172.21.4.3和172.21.4.4上通过ser-benchamrk.sh脚本启动服务器资源监控（图2-1）。然后按照表2-3在172.21.4.2分别修改iotdb-0.13-0.0.1和kairosdb-0.0.1文件夹内的conf/config.properties文件满足测试需求。先后使用benchmark.sh启动对Apache-IoTDB和KairosDB的写入测试。
-
+
图2-1服务器监控任务
例如我们首先启动对KairosDB的测试，IoT-benchmark会在MySQL数据库中创建CONFIG数据表存放本次测试配置信息（图2-2），测试执行中会有日志输出当前测试进度（图2-3）。测试完成时会输出本次测试结果（图2-3），同时将结果写入FINAL_RESULT数据表中（图2-4）。
-
+
图2-2测试配置信息表
-
-
-
-
+
+
+
+
图2-3测试进度和结果
-
+
图2-4测试结果表
@@ -292,14 +292,14 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
依照测试结果信息我们知道同样的配置写入Apache-IoTDB和KairosDB写入延时时间分别为：55.98ms和1324.45ms；写入吞吐分别为：5,125,600.86点/秒和224,819.01点/秒；测试分别执行了585.30秒和11777.99秒。并且KairosDB有写入失败出现，排查后发现是数据磁盘使用率已达到100%，无磁盘空间继续接收数据。而Apache-IoTDB无写入失败现象，全部数据写入完毕后占用磁盘空间仅为4.7G（如图2-5所示）；从写入吞吐和磁盘占用情况上看Apache-IoTDB均优于KairosDB。当然后续还有其他测试来从多方面观察和对比，比如查询性能、文件压缩比、数据安全性等。
-
+
图2-5磁盘使用情况
那么测试过程中各个服务器资源使用情况如何呢？每个写操作具体的表现如何呢？这个时候我们就可以通过安装和使用Tableau来可视化服务器监控表和测试过程记录表内的数据了。Tableau的使用本文不展开介绍，通过它连接测试数据持久化的数据表后具体结果下如图（以Apache-IoTDB为例）：
-
-
+
+
图2-6Tableau可视化测试过程
@@ -326,7 +326,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
执行结果：
-
+
图2-7查询测试结果
diff --git a/src/zh/UserGuide/V1.2.x/Tools-System/CLI.md b/src/zh/UserGuide/V1.2.x/Tools-System/CLI.md
index 2ef4bb3fd..4e7b60744 100644
--- a/src/zh/UserGuide/V1.2.x/Tools-System/CLI.md
+++ b/src/zh/UserGuide/V1.2.x/Tools-System/CLI.md
@@ -132,47 +132,47 @@ Shell > ./kc.sh start-dev
```
2、使用 https://ip:port 登陆 keycloack, 首次登陆需要创建用户
-
+
3、点击 Administration Console 进入管理端
-
+
4、在左侧的 Master 菜单点击 Create Realm, 输入 Realm Name 创建一个新的 Realm
-
+
-
+
5、点击左侧菜单 Clients，创建 client
-
+
6、点击左侧菜单 User，创建 user
-
+
7、点击新创建的用户 id，点击 Credentials 导航输入密码和关闭 Temporary 选项，至此 keyclork 配置完成
-
+
8、创建角色，点击左侧菜单的 Roles然后点击Create Role 按钮添加角色
-
+
9、在Role Name 中输入`iotdb_admin`，点击save 按钮。提示：这里的`iotdb_admin`不能为其他名称否则即使登陆成功后也将无权限使用iotdb的查询、插入、创建 database、添加用户、角色等功能
-
+
10、点击左侧的User 菜单然后点击用户列表中的用户为该用户添加我们刚创建的`iotdb_admin`角色
-
+
11、选择Role Mappings ，在Assign role选择`iotdb_admin`增加角色
-
+
-
+
提示：如果用户角色有调整需要重新生成token并且重新登陆iotdb才会生效
@@ -188,7 +188,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
其中，需要将{my-access-token} （注意，包括{}）替换成你的 token，即 access_token 对应的值。密码为空需要再次确认。
-
+
如何获取 token 取决于你的 OIDC 设置。 最简单的一种情况是使用`password-grant`。例如，假设你在用 keycloack 作为你的 OIDC 服务，
并且你在 keycloack 中有一个被定义成 public 的`iotdb`客户的 realm，那么你可以使用如下`curl`命令获得 token。
diff --git a/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md b/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md
index e6c1e530d..93f7dc603 100644
--- a/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md
+++ b/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool.md
@@ -65,7 +65,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -104,9 +104,9 @@ static_configs:
`Apache IoTDB Dashboard`是 IoTDB 企业版的配套产品，支持统一集中式运维管理，可通过一个监控面板监控多个集群。你可以联系商务获取到 Dashboard 的 Json文件。
-
+
-
+
#### 集群概览
@@ -119,7 +119,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
#### 数据写入
@@ -128,7 +128,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
#### 数据查询
@@ -141,7 +141,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
#### 存储引擎
@@ -150,7 +150,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
#### 系统监控
@@ -160,9 +160,9 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md b/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md
index 4232582a4..9a5a773c0 100644
--- a/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md
+++ b/src/zh/UserGuide/V1.2.x/Tools-System/Monitor-Tool_timecho.md
@@ -65,7 +65,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -104,9 +104,9 @@ static_configs:
我们提供了Apache IoTDB Dashboard，支持统一集中式运维管理，可通过一个监控面板监控多个集群。
-
+
-
+
你可以在企业版中获取到 Dashboard 的 Json文件。
@@ -120,7 +120,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
#### 数据写入
@@ -129,7 +129,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
#### 数据查询
@@ -142,7 +142,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
#### 存储引擎
@@ -151,7 +151,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
#### 系统监控
@@ -161,9 +161,9 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md b/src/zh/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md
index e28a62c84..98ca65907 100644
--- a/src/zh/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md
+++ b/src/zh/UserGuide/V1.2.x/Tools-System/Workbench_timecho.md
@@ -22,10 +22,10 @@ IoTDB的可视化控制台包含以下功能模块：
主要功能展示：
* 首页
-
+
* 测点列表
-
+
* 数据查询
-
+
* 趋势
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/V1.2.x/User-Manual/Data-Sync.md b/src/zh/UserGuide/V1.2.x/User-Manual/Data-Sync.md
index 7f3dbbd3a..775264e2b 100644
--- a/src/zh/UserGuide/V1.2.x/User-Manual/Data-Sync.md
+++ b/src/zh/UserGuide/V1.2.x/User-Manual/Data-Sync.md
@@ -33,7 +33,7 @@
**Pipe 任务的模型如下：**
-
+
描述一个数据同步任务，本质就是描述 Pipe Extractor、Pipe Processor 和 Pipe Connector 插件的属性。用户可以通过 SQL 语句声明式地配置三个子任务的具体属性，通过组合不同的属性，实现灵活的数据 ETL 能力。
@@ -247,7 +247,7 @@ WHERE CONNECTOR USED BY
+
-
+
#### 示例
@@ -525,19 +525,19 @@ END
##### `
+
`SlidingTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 3 类参数：
@@ -1179,12 +1179,12 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
3 类参数的关系可见下图。策略的构造方法详见 Javadoc。
-
+
注意，最后的一些时间窗口的实际时间间隔可能小于规定的时间间隔参数。另外，可能存在某些时间窗口内数据行数量为 0 的情况，这种情况框架也会为该窗口调用一次`transform`方法。
如图是`SlidingSizeWindowAccessStrategy`的开窗示意图。
-
+
`SlidingSizeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 个参数：
@@ -1194,7 +1194,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
滑动步长参数不是必须的。当您不提供滑动步长参数时，滑动步长会被设定为窗口大小。
如图是`SessionTimeWindowAccessStrategy`的开窗示意图。**时间间隔小于等于给定的最小时间间隔 sessionGap 则分为一组。**
-
+
`SessionTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 类参数：
@@ -1202,7 +1202,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
2. 会话窗口之间的最小时间间隔。
如图是`StateWindowAccessStrategy`的开窗示意图。**对于数值型数据，状态差值小于等于给定的阈值 delta 则分为一组。**
-
+
`StateWindowAccessStrategy`有四种构造方法。
@@ -1598,8 +1598,8 @@ SHOW FUNCTIONS
mvn clean package -pl library-udf -am -DskipTests -Pget-jar-with-dependencies
```
2. 将在 target 下生成的带依赖的 jar 包复制到 IoTDB 程序目录的 `ext\udf` 目录下(若您使用的是集群，请将jar包复制到所有DataNode的该目录下),如下图所示；
-
-3. 下载注册脚本:[linux](https://alioss.timecho.com/docs/img/register-UDF.sh), [windows](https://alioss.timecho.com/docs/img/register-UDF.bat);
+
+3. 下载注册脚本:[linux](/img/register-UDF.sh), [windows](/img/register-UDF.bat);
4. 将注册脚本复制到 IoTDB 的`sbin`目录下，修改脚本中的参数（默认为host=127.0.0.1，rpcPort=6667，user=root，pass=root）；
5. 启动 IoTDB 服务；
6. 运行注册脚本`register-UDF.sh`以注册 UDF。
diff --git a/src/zh/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md b/src/zh/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md
index a7a70962c..d14367dbc 100644
--- a/src/zh/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md
+++ b/src/zh/UserGuide/V1.2.x/User-Manual/Operate-Metadata.md
@@ -728,7 +728,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
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 0ee29bef2..c1208d91a 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
@@ -866,7 +866,7 @@ IoTDB支持通过`GROUP BY`子句对序列进行分段或者分组聚合。
下图中指出了这三个参数的含义：
-
+
接下来，我们给出几个典型例子：
@@ -1115,7 +1115,7 @@ group by variation(controlExpression[,delta][,ignoreNull=true/false])
下图为差值分段的一个分段方式示意图，与组中第一个数据的控制列值的差值在delta内的控制列对应的点属于相同的分组。
-
+
##### 使用注意事项
1. `controlExpression`的结果应该为唯一值，如果使用通配符拼接后出现多列，则报错。
@@ -1288,7 +1288,7 @@ group by session(timeInterval)
下图为`group by session`下的一个分组示意图
-
+
##### 使用注意事项
1. 对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select `__endTime`的方式来使得结果输出分组的结束时间。
diff --git a/src/zh/UserGuide/V1.2.x/User-Manual/Security-Management_timecho.md b/src/zh/UserGuide/V1.2.x/User-Manual/Security-Management_timecho.md
index 54b48c4f8..0acaf6dbd 100644
--- a/src/zh/UserGuide/V1.2.x/User-Manual/Security-Management_timecho.md
+++ b/src/zh/UserGuide/V1.2.x/User-Manual/Security-Management_timecho.md
@@ -67,7 +67,7 @@ white.list:
5. 是否开启白名单功能的配置，可以热加载。
6. 使用Java 原生接口修改白名单，必须是root用户才能修改，拒绝非root用户修改；修改内容必须合法，否则会抛出StatementExecutionException异常。
-
+
## 审计日志
@@ -94,7 +94,7 @@ white.list:
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
diff --git a/src/zh/UserGuide/V1.2.x/User-Manual/Stage_Data-Sync_timecho.md b/src/zh/UserGuide/V1.2.x/User-Manual/Stage_Data-Sync_timecho.md
index 47997443a..a0bf01106 100644
--- a/src/zh/UserGuide/V1.2.x/User-Manual/Stage_Data-Sync_timecho.md
+++ b/src/zh/UserGuide/V1.2.x/User-Manual/Stage_Data-Sync_timecho.md
@@ -33,7 +33,7 @@
**Pipe 任务的模型如下：**
-
+
描述一个数据同步任务，本质就是描述 Pipe Extractor、Pipe Processor 和 Pipe Connector 插件的属性。用户可以通过 SQL 语句声明式地配置三个子任务的具体属性，通过组合不同的属性，实现灵活的数据 ETL 能力。
@@ -249,7 +249,7 @@ WHERE CONNECTOR USED BY
+
## 内置 MQTT 服务
内置的 MQTT 服务提供了通过 MQTT 直接连接到 IoTDB 的能力。 它侦听来自 MQTT 客户端的发布消息，然后立即将数据写入存储。
@@ -57,7 +57,7 @@ MQTT 主题与 IoTDB 时间序列相对应。
```
或者以上两者的JSON数组形式。
-
+
## MQTT 配置
默认情况下，IoTDB MQTT 服务从`${IOTDB_HOME}/${IOTDB_CONF}/iotdb-common.properties`加载配置。
diff --git a/src/zh/UserGuide/V1.3.0-2/API/Programming-ODBC.md b/src/zh/UserGuide/V1.3.0-2/API/Programming-ODBC.md
index df78dd589..08a7fc6b8 100644
--- a/src/zh/UserGuide/V1.3.0-2/API/Programming-ODBC.md
+++ b/src/zh/UserGuide/V1.3.0-2/API/Programming-ODBC.md
@@ -39,7 +39,7 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
1. **下载 Zappy-Sys ODBC-JDBC 桥插件**：
进入 https://zappysys.com/products/odbc-powerpack/odbc-jdbc-bridge-driver/ 网站，点击下载按钮并直接安装。
- 
+ 
2. **准备 IoTDB**：打开 IoTDB 集群，并任意写入一条数据。
```sql
@@ -49,11 +49,11 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
3. **部署及调试插件**：
1. 打开 ODBC 数据源 32/64 位，取决于 Windows 的位数，一个示例的位置是 `C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools`。
- 
+ 
2. 点击添加，选择 ZappySys JDBC Bridge。
- 
+ 
3. 填写如下配置：
@@ -65,15 +65,15 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
| User name | IoTDB 的用户名 | root |
| User password | IoTDB 的密码 | root |
- 
+ 
4. 点击 Test Connection 按钮，应该显示连接成功。
- 
+ 
5. 点击上方的 Preview， 将查询文本换为 `select * from root.**`，点击 Preview Data，应该正确显示查询结果。
- 
+ 
4. **使用 ODBC 操作数据**：正确部署后，就可以使用 Windows 的 ODBC 库，对 IoTDB 的数据进行操作。 这里给出 C# 语言的代码示例：
```C#
diff --git a/src/zh/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md b/src/zh/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md
index 9952cd77a..a012bbc6f 100644
--- a/src/zh/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md
+++ b/src/zh/UserGuide/V1.3.0-2/API/Programming-Python-Native-API.md
@@ -562,7 +562,7 @@ SQLAlchemy 中的元数据有：
下图更加清晰的展示了二者的映射关系：
-
+
### 数据类型映射
| IoTDB 中的数据类型 | SQLAlchemy 中的数据类型 |
diff --git a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md
index fa6019036..f853a8e05 100644
--- a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md
+++ b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-Concept.md
@@ -21,7 +21,7 @@
# 集群相关概念
下图展示了一个常见的 IoTDB 3C3D1A（3 个 ConfigNode、3 个 DataNode 和 1 个 AINode）的集群部署模式：
-
+
其中包括了 IoTDB 集群使用中用户常接触到的几个概念，包括：
- **节点**（ConfigNode、DataNode、AINode）；
diff --git a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md
index 3d188f07e..119e003a0 100644
--- a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md
+++ b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB 为时间序列数据实现了量身定制的分区算法。在此基础
### 分区策略
IoTDB 将生产环境中的每个传感器映射为一个时间序列。然后，使用序列分区算法对时间序列进行分区以管理其元数据，再结合时间分区算法来管理其数据。下图展示了 IoTDB 如何对时序数据进行分区。
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#### 分区算法
由于生产环境中通常部署大量设备和传感器，IoTDB 使用序列分区算法以确保分区信息的大小可控。由于生成的时间序列与时间戳相关联，IoTDB 使用时间分区算法来清晰区分冷热分区。
@@ -56,7 +56,7 @@ $$\left\lfloor\frac{\text{Timestamp}-\text{StartTimestamp}}{\text{TimePartitionI
### 分区分配
IoTDB 使用分片来实现时间序列的弹性存储，集群中分片的数量由所有数据节点的总资源决定。由于分片的数量是动态的，IoTDB 可以轻松扩展。元数据分片和数据分片都遵循相同的分区分配算法，即均匀划分所有序列分区。下图展示了分区分配过程，其中动态扩展的分片匹配不断扩展的时间序列和集群。
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#### 分片扩容
分片的数量由下式给出
@@ -76,7 +76,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 存储均衡
数据节点持有的副本数量反映了它的存储负载。如果数据节点之间的副本数量差异较大，拥有更多副本的数据节点可能成为存储瓶颈。尽管简单的轮询（Round Robin）放置算法可以通过确保每个数据节点持有等量副本来实现存储均衡，但它会降低集群的容错能力，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 将分片 $r_1$ 的 2 个副本放置在数据节点 $n_1$ 和 $n_2$ 上。
@@ -91,7 +91,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 计算均衡
数据节点持有的主副本数量反映了它的计算负载。如果数据节点之间持有主副本数量差异较大，拥有更多主副本的数据节点可能成为计算瓶颈。如果主副本选择过程使用直观的贪心算法，当副本以容错算法放置时，可能会导致主副本分布不均，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 选择分片 $r_5$ 在数据节点 $n_5$ 上的副本作为主副本。
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 5aca749d7..a42a895b8 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
@@ -25,7 +25,7 @@
根据企业组织结构和设备实体层次结构，我们将其物联网数据模型表示为如下图所示的属性层级组织结构，即电力集团层-风电场层-实体层-物理量层。其中 ROOT 为根节点，物理量层的每一个节点为叶子节点。IoTDB 采用树形结构定义数据模式，以从 ROOT 节点到叶子节点的路径来命名一个时间序列，层次间以“.”连接。例如，下图最左侧路径对应的时间序列名称为`ROOT.ln.wf01.wt01.status`。
-
+
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)。
@@ -94,7 +94,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例
插入数据时，对齐的时间序列中某列的某些行允许有空值。
-
+
在后续数据定义语言、数据操作语言和 Java 原生接口章节，将对涉及到对齐时间序列的各种操作进行逐一介绍。
diff --git a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md
index 96e9fdf9a..35f789271 100644
--- a/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md
+++ b/src/zh/UserGuide/V1.3.0-2/Basic-Concept/Navigating_Time_Series_Data.md
@@ -24,13 +24,13 @@
万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。
-
+
通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。
-
+
传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。
@@ -38,14 +38,14 @@
时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。
-
+
### 数据点
- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。
- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
-
+
### 测点
diff --git a/src/zh/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/zh/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md
index 94479b604..c77da0e83 100644
--- a/src/zh/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/Deployment-and-Maintenance/AINode_Deployment_timecho.md
@@ -31,8 +31,8 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，
### 部署模式
-
+
+
+
- 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用
- 建议在 `iotdb-enterprise-ainode-
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在编辑数据源的时候，`Type`一栏选择`Simplejson`，`URL`一栏填写 http://\
+
### 操作 Grafana
进入 Grafana 可视化页面后，可以选择添加时间序列，如下图。您也可以按照 Grafana 官方文档进行相应的操作，详情可参看 Grafana 官方文档：http://docs.grafana.org/guides/getting_started/。
-
+
## 配置 grafana
diff --git a/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md b/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md
index 604478f97..9f598df8a 100644
--- a/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Grafana-Plugin.md
@@ -115,9 +115,9 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
点击左侧的 `设置` 图标，选择 `Data Source` 选项，然后再点击 `Add data source`。
-
+
-
+
选择 `Apache IoTDB` 数据源，`URL` 一栏填写 `http://
+
@@ -133,15 +133,15 @@ Ip 为您的 IoTDB 服务器所在的宿主机 IP，port 为 REST 服务的运
点击左侧的 `Dashboards` 图标，选择 `Manage`，如下图所示：
-
+
点击右上方的 `New Dashboard` 图标，选择 `Add an empty panel`，如下图所示：
-
+
Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，默认是SQL: Full Customized方式。
-
+
#### SQL: Full Customized 输入方式
@@ -149,7 +149,7 @@ Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，
如果一个查询涉及多个表达式，我们可以点击 SELECT 输入框右侧的 `+` 来添加 SELECT 子句中的表达式，也可以点击 FROM 输入框右侧的 `+` 来添加路径前缀，如下图所示：
-
+
SELECT 输入框中的内容可以是时间序列的后缀，可以是函数或自定义函数，可以是算数表达式，也可以是它们的嵌套表达式。您还可以使用 as 子句来重命名需要显示的结果序列名字。
@@ -182,7 +182,7 @@ CONTROL 输入框为非必须填写项目，填写内容应当是控制查询类
#### SQL: Drop-down List 输入方式
在 TIME-SERIES 选择框中选择一条时间序列、FUNCTION 选择一个函数、SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL 输入框中输入内容，其中 TIME-SERIESL 为必填项其余为非必填项。
-
+
### 变量与模板功能的支持
@@ -190,32 +190,32 @@ SQL: Full Customized和SQL: Drop-down List两种输入方式都支持 Grafana
创建一个新的 Panel 后，点击右上角的设置按钮，如下图所示：
-
+
选择 `Variables`，点击 `Add variable` ，如下图所示：
-
+
示例一：输入 `Name`，`Label`，选择Type的`Query`、在Query 中输入show child paths xx ， 点击 `Update` 按钮，如下图所示：
-
+
应用 Variables，在 `grafana panel` 中输入变量点击 `save` 按钮，如下图所示
-
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示例二：变量嵌套使用，如下图所示
-
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示例三：函数变量使用，如下图所示
-
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上图中Name 是变量名称也是将来我们在panel中使用的变量名称，Label是变量的展示名称如果为空就显示Name的变量反之则显示Label的名称，
Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、Ad hoc filters等这些都可以在IoTDB的Grafana Plugin 中使用
@@ -244,15 +244,15 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
1. 在Grafana panel中，点击alerting按钮，如下图所示：
-
+
2. 点击`Create alert rule from this panel`，如下图所示：
-
+
3. 在第1步中设置查询和警报条件，Conditions 表示查询条件，可以配置多个组合查询条件。如下图所示：
-
+
图中的查询条件：min() OF A IS BELOW 0，表示将A选项卡中的最小值在0一下就会触发条件，单击该函数可将其更改为另一个函数。
@@ -260,23 +260,23 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
例如，我们按以下顺序有 3 个条件： 条件：B（计算为：TRUE）或条件：C（计算为：FALSE）和条件：D（计算为：TRUE） 所以结果将计算为（（对或错）和对）=对。
4. 选择完指标及告警规则后点击`Preview`按钮，进行数据预览如下图所示：
-
+
5. 在第 2 步中，指定警报评估间隔，对于`Evaluate every`，指定评估频率。必须是 10 秒的倍数。例如，1m，30s。
对于`Evaluate for`，指定在警报触发之前的持续时间。如下图所示：
-
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6. 在第 3 步中，添加存储位置、规则组以及与规则关联的其他元数据。 其中`Rule name`指定规则的名称。规则名称必须是唯一的。
-
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7. 在第 4 步中，添加自定义标签。 从下拉列表中选择现有键值对添加自定义标签，或通过输入新键或值来添加新标签。如下图所示：
-
+
8. 单击保存以保存规则或单击保存并退出以保存规则并返回到警报页面。
9. 告警状态常用的有`Normal`、`Pending`、`Firing`等状态，如下图所示：
-
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+
10. 我们也可以为告警配置`Contact points`用来接收告警通知，更加详细操作可以参考官方文档(https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/)。
diff --git a/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index 92c6fef6b..6798fc04f 100644
--- a/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -35,7 +35,7 @@
两个模块与Ignition的具体关系与内容如下图所示。
- 
+ 
## 安装要求
@@ -58,23 +58,23 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
步骤一：进入 `Config` - `System`- `Modules` 模块，点击最下方的`Install or Upgrade a Module...`
-
+
步骤二：选择获取到的 `modl`，选择文件并上传，点击 `Install`，信任相关证书。
-
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步骤三：安装完成后可以看到如下内容
-
+
步骤四：进入 `Config` - `Tags`- `History` 模块，点击下方的`Create new Historical Tag Provider...`
-
+
步骤五：选择 `IoTDB`并填写配置信息
-
+
配置内容如下：
@@ -161,21 +161,21 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
- 配置好 `Provider` 后就可以在 `Designer` 中使用 `IoTDB Tag Historian` 了，就跟使用其他的 `Provider` 一样，右键点击对应 `Tag` 选择 `Edit tag(s)`，在 Tag Editor 中选择 History 分类
- 
+ 
- 设置 `History Enabled` 为 `true`，并选择 `Storage Provider` 为上一步创建的 `Provider`，按需要配置其它参数，并点击 `OK`，然后保存项目。此时数据将会按照设置的内容持续的存入 `IoTDB` 实例中。
- 
+ 
#### 读取数据
- 也可以在 Report 的 Data 标签下面直接选择存入 IoTDB 的 Tags
- 
+ 
- 在 Components 中也可以直接浏览相关数据
- 
+ 
#### 脚本模块：该功能能够与 IoTDB 进行交互
@@ -242,15 +242,15 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
步骤一：进入 `Config` - `Databases` -`Drivers` 模块，创建 `Translator`
-
+
步骤二：进入 `Config` - `Databases` -`Drivers` 模块，创建 `JDBC Driver`，选择上一步配置的 `Translator`并上传下载的 `IoTDB-JDBC`，Classname 配置为 `org.apache.iotdb.jdbc.IoTDBDriver`
-
+
步骤三：进入 `Config` - `Databases` -`Connections` 模块，创建新的 `Connections`，`JDBC Driver` 选择上一步创建的 `IoTDB Driver`，配置相关信息后保存即可使用
-
+
### 使用说明
@@ -264,11 +264,11 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
不选择其他项，设置好字段，并 `Enabled` 后 数据会安装设置存入对应的 IoTDB
-
+
#### 数据查询
- 在 `Database Query Browser` 中选择`Data Source`选择之前创建的 `Connection`，即可编写 SQL 语句查询 IoTDB 中的数据
-
+
diff --git a/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md b/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md
index 4daa72af3..5d04a64e5 100644
--- a/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md
+++ b/src/zh/UserGuide/V1.3.0-2/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard 是一个开源物联网平台，可实现物联网项目的快速开发、管理和扩展。更多介绍详情请参考[ ThingsBoard 官网](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/)。
- 
+ 
2. ThingsBoard-IoTDB 简介
@@ -34,7 +34,7 @@
### 关系示意图
-
+
## 安装要求
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ##thingsboard数据写入iotdb所存储
1. 创建设备并接入数据：在 Thingsboard 的实体-设备中创建设备并通过工业网关将数据发送到 ThingsBoard 指定设备中
-
+
2. 设置规则链：在规则链库中对于“SD-032F 泵”设置告警规则并将该规则链设置为根链
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在上图中，用户可以通过 JDBC 将来自设备上传感器采集的时序数据、服务器负载和 CPU 内存等系统状态数据、消息队列中的时序数据、应用程序的时序数据或者其他数据库中的时序数据导入到本地或者远程的 IoTDB 中。用户还可以将上述数据直接写成本地（或位于 HDFS 上）的 TsFile 文件。
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 1d4633245..47795fec8 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
@@ -35,7 +35,7 @@ Apache IoTDB 是一款低成本、高性能的物联网原生时序数据库。
IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物联网产生的海量时序数据。
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IoTDB 性能监控
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+
+
| 函数名 | 可接收的输入序列类型 | 属性参数 | 输出序列类型 | 功能类型 |
| ------ | ------------------------------ | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2571,7 +2571,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `windowSize`: 一个窗口内的点数。Int数据类型。必需的属性参数。
+ `slidingStep`: 按照设定的点数来滑动窗口。Int数据类型。可选的属性参数；如果没有设置，默认取值和`windowSize`一样。
-
+
**(2) 滑动时间窗口（SlidingTimeWindowAccessStrategy）使用的属性参数:**
@@ -2580,7 +2580,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `displayWindowBegin`: 窗口滑动的起始时间戳位置（包含在内）。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MIN_VALUE，意为使用输入的时间序列的第一个点的时间戳作为窗口滑动的起始时间戳位置。
+ `displayWindowEnd`: 结束时间限制（不包含在内；本质上和`WHERE time < displayWindowEnd`起的效果是一样的)。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MAX_VALUE，意为除了输入的时间序列自身数据读取完毕之外没有增加额外的结束时间过滤条件限制。
-
+
#### 示例
diff --git a/src/zh/UserGuide/V1.3.0-2/Reference/UDF-development.md b/src/zh/UserGuide/V1.3.0-2/Reference/UDF-development.md
index d2ecb5dcd..24e0366ad 100644
--- a/src/zh/UserGuide/V1.3.0-2/Reference/UDF-development.md
+++ b/src/zh/UserGuide/V1.3.0-2/Reference/UDF-development.md
@@ -127,7 +127,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 3 类参数：
@@ -142,7 +142,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
3 类参数的关系可见下图。策略的构造方法详见 Javadoc。
-
+
> 注意，最后的一些时间窗口的实际时间间隔可能小于规定的时间间隔参数。另外，可能存在某些时间窗口内数据行数量为 0 的情况，这种情况框架也会为该窗口调用一次`transform`方法。
@@ -150,7 +150,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingSizeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 个参数：
@@ -163,7 +163,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**时间间隔小于等于给定的最小时间间隔 sessionGap 则分为一组。**
-
+
`SessionTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 类参数：
@@ -175,7 +175,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**对于数值型数据，状态差值小于等于给定的阈值 delta 则分为一组。**
-
+
`StateWindowAccessStrategy`有四种构造方法：
diff --git a/src/zh/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md
index 9779f8972..0cf3d8ca3 100644
--- a/src/zh/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md
+++ b/src/zh/UserGuide/V1.3.0-2/SQL-Manual/SQL-Manual.md
@@ -254,8 +254,8 @@ IoTDB> count devices where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:
### 设备模板管理
-
-
+
+
diff --git a/src/zh/UserGuide/V1.3.0-2/Tools-System/Benchmark.md b/src/zh/UserGuide/V1.3.0-2/Tools-System/Benchmark.md
index 1b8d52faf..e313c60ec 100644
--- a/src/zh/UserGuide/V1.3.0-2/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/V1.3.0-2/Tools-System/Benchmark.md
@@ -27,13 +27,13 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
下图1-1囊括了测试基准流程及其他扩展功能。这些流程可以由IoT-benchmark 统一来完成。IoT Benchmark 支持多种工作负载，包括**纯写入、纯查询、写入查询混合**等，支持**软硬件系统监控、测试指标度量**等监控功能，还实现了**初始化数据库自动化、测试数据分析及系统参数优化**等功能。
-
+
图1-1
借鉴 YCSB 测试工具将工作负载生成、性能指标测量和数据库接口三个组件分离的设计思想，IoT-benchmark 的模块化设计如图1-2所示。与基于 YCSB 的测试工具系统不同的是，IoT-benchmark 增加了系统监控模块，支持测试数据和系统指标监控数据的持久化。此外也增加了一些特别针对时序数据场景的特殊负载测试功能，如支持物联网场景的批量写入和多种乱序数据写入模式。
-
+
图1-2
@@ -77,7 +77,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试包的目录结构如下图1-3所示。其中测试配置文件为conf/config.properties，测试启动脚本为benchmark\.sh (Linux & MacOS) 和 benchmark.bat (Windows)，详细文件用途见表1-2所示。
-
+
图1-3文件和文件夹列表
@@ -105,7 +105,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试的所有日志文件被存放于 logs 文件夹下，测试的结果在测试完成后被存放到 data/csvOutput 文件夹下，例如测试后我们得到了如下的结果矩阵：
-
+
- Result Matrix
- OkOperation：成功的对应操作次数
@@ -267,24 +267,24 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
首先在172.21.4.3和172.21.4.4上分别启动被测时间序列数据库Apache-IoTDB和KairosDB，之后在172.21.4.2、172.21.4.3和172.21.4.4上通过ser-benchamrk.sh脚本启动服务器资源监控（图2-1）。然后按照表2-3在172.21.4.2分别修改iotdb-0.13-0.0.1和kairosdb-0.0.1文件夹内的conf/config.properties文件满足测试需求。先后使用benchmark.sh启动对Apache-IoTDB和KairosDB的写入测试。
-
+
图2-1服务器监控任务
例如我们首先启动对KairosDB的测试，IoT-benchmark会在MySQL数据库中创建CONFIG数据表存放本次测试配置信息（图2-2），测试执行中会有日志输出当前测试进度（图2-3）。测试完成时会输出本次测试结果（图2-3），同时将结果写入FINAL_RESULT数据表中（图2-4）。
-
+
图2-2测试配置信息表
-
-
-
-
+
+
+
+
图2-3测试进度和结果
-
+
图2-4测试结果表
@@ -292,14 +292,14 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
依照测试结果信息我们知道同样的配置写入Apache-IoTDB和KairosDB写入延时时间分别为：55.98ms和1324.45ms；写入吞吐分别为：5,125,600.86点/秒和224,819.01点/秒；测试分别执行了585.30秒和11777.99秒。并且KairosDB有写入失败出现，排查后发现是数据磁盘使用率已达到100%，无磁盘空间继续接收数据。而Apache-IoTDB无写入失败现象，全部数据写入完毕后占用磁盘空间仅为4.7G（如图2-5所示）；从写入吞吐和磁盘占用情况上看Apache-IoTDB均优于KairosDB。当然后续还有其他测试来从多方面观察和对比，比如查询性能、文件压缩比、数据安全性等。
-
+
图2-5磁盘使用情况
那么测试过程中各个服务器资源使用情况如何呢？每个写操作具体的表现如何呢？这个时候我们就可以通过安装和使用Tableau来可视化服务器监控表和测试过程记录表内的数据了。Tableau的使用本文不展开介绍，通过它连接测试数据持久化的数据表后具体结果下如图（以Apache-IoTDB为例）：
-
-
+
+
图2-6Tableau可视化测试过程
@@ -326,7 +326,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
执行结果：
-
+
图2-7查询测试结果
diff --git a/src/zh/UserGuide/V1.3.0-2/Tools-System/CLI.md b/src/zh/UserGuide/V1.3.0-2/Tools-System/CLI.md
index 2ef4bb3fd..4e7b60744 100644
--- a/src/zh/UserGuide/V1.3.0-2/Tools-System/CLI.md
+++ b/src/zh/UserGuide/V1.3.0-2/Tools-System/CLI.md
@@ -132,47 +132,47 @@ Shell > ./kc.sh start-dev
```
2、使用 https://ip:port 登陆 keycloack, 首次登陆需要创建用户
-
+
3、点击 Administration Console 进入管理端
-
+
4、在左侧的 Master 菜单点击 Create Realm, 输入 Realm Name 创建一个新的 Realm
-
+
-
+
5、点击左侧菜单 Clients，创建 client
-
+
6、点击左侧菜单 User，创建 user
-
+
7、点击新创建的用户 id，点击 Credentials 导航输入密码和关闭 Temporary 选项，至此 keyclork 配置完成
-
+
8、创建角色，点击左侧菜单的 Roles然后点击Create Role 按钮添加角色
-
+
9、在Role Name 中输入`iotdb_admin`，点击save 按钮。提示：这里的`iotdb_admin`不能为其他名称否则即使登陆成功后也将无权限使用iotdb的查询、插入、创建 database、添加用户、角色等功能
-
+
10、点击左侧的User 菜单然后点击用户列表中的用户为该用户添加我们刚创建的`iotdb_admin`角色
-
+
11、选择Role Mappings ，在Assign role选择`iotdb_admin`增加角色
-
+
-
+
提示：如果用户角色有调整需要重新生成token并且重新登陆iotdb才会生效
@@ -188,7 +188,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
其中，需要将{my-access-token} （注意，包括{}）替换成你的 token，即 access_token 对应的值。密码为空需要再次确认。
-
+
如何获取 token 取决于你的 OIDC 设置。 最简单的一种情况是使用`password-grant`。例如，假设你在用 keycloack 作为你的 OIDC 服务，
并且你在 keycloack 中有一个被定义成 public 的`iotdb`客户的 realm，那么你可以使用如下`curl`命令获得 token。
diff --git a/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md b/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md
index 92c4db0d9..f415ebea4 100644
--- a/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md
+++ b/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_apache.md
@@ -65,7 +65,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -105,9 +105,9 @@ static_configs:
`Apache IoTDB Dashboard`是 IoTDB 企业版的配套产品，支持统一集中式运维管理，可通过一个监控面板监控多个集群。你可以联系商务获取到 Dashboard 的 Json文件。
-
+
-
+
### 集群概览
@@ -120,7 +120,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -129,7 +129,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -142,7 +142,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -151,7 +151,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -161,8 +161,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md b/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md
index 961e57093..35bd0fac2 100644
--- a/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/Tools-System/Monitor-Tool_timecho.md
@@ -65,7 +65,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -104,9 +104,9 @@ static_configs:
我们提供了Apache IoTDB Dashboard，支持统一集中式运维管理，可通过一个监控面板监控多个集群。
-
+
-
+
你可以在企业版中获取到 Dashboard 的 Json文件。
@@ -120,7 +120,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -129,7 +129,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -142,7 +142,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -151,7 +151,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -161,8 +161,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md b/src/zh/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md
index 0dc256d31..50b3a988d 100644
--- a/src/zh/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/Tools-System/Workbench_timecho.md
@@ -22,10 +22,10 @@ IoTDB的可视化控制台包含以下功能模块：
主要功能展示：
* 首页
-
+
* 测点列表
-
+
* 数据查询
-
+
* 趋势
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md
index 291d2a1e1..32dfb729d 100644
--- a/src/zh/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
系统架构如下图所示：
::: center
-
+
:::
三种节点的职责如下：
@@ -41,7 +41,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
-
+
- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如：
- **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。
@@ -57,7 +57,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
::: center
-
+
:::
## 安装部署
@@ -301,13 +301,13 @@ window_function:
- **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期)
- **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理：
- **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪
- 
+ 
- **tail(window_size)**：获取数据中最后的window_size个点用于模型推，该窗口可用于数据裁剪
- 
+ 
- **count(window_size, sliding_step)**：基于点数的滑动窗口，每个窗口的数据会分别通过模型进行推理，如下图示例所示，window_size为2的窗口函数将输入数据集分为三个窗口，每个窗口分别进行推理运算生成结果。该窗口可用于连续推理
- 
+ 
**说明1: window可以用来解决sql查询结果和模型的输入行数要求不一致时的问题，对行进行裁剪。需要注意的是，当列数不匹配或是行数直接少于模型需求时，推理无法进行，会返回错误信息。**
@@ -456,7 +456,7 @@ Total line number = 4
在部分工业场景下，会存在预测电力负载的需求，预测结果可用于优化电力供应、节约能源和资源、支持规划和扩展以及增强电力系统的可靠性。
-我们所使用的 ETTh1 的测试集的数据为[ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv)。
+我们所使用的 ETTh1 的测试集的数据为[ETTh1](/img/ETTh1.csv)。
包含间隔1h采集一次的电力数据，每条数据由负载和油温构成，分别为：High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature。
@@ -519,7 +519,7 @@ Total line number = 48
图中10/24 00:00之前的数据为输入模型的过去数据，10/24 00:00后的蓝色线条为模型给出的油温预测结果，而红色为数据集中实际的油温数据（用于进行对比）。
-
+
可以看到，我们使用了过去96个小时（4天）的六个负载信息和对应时间油温的关系，基于之前学习到的序列间相互关系对未来48个小时（2天）的油温这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了较高程度的一致性。
@@ -529,7 +529,7 @@ Total line number = 48
我们利用某变电站中的电流、电压和功率等数据构成了真实场景下的数据集。该数据集包括变电站近四个月时间跨度，每5 - 6s 采集一次的 A相电压、B相电压、C相电压等数据。
-测试集数据内容为[data](https://alioss.timecho.com/docs/img/data.csv)。
+测试集数据内容为[data](/img/data.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过以往A相电压，B相电压和C相电压的数值和对应时间戳，预测未来一段时间内的C相电压，赋能变电站的监视管理。
@@ -584,7 +584,7 @@ Total line number = 48
图中 02/14 20:48 之前的数据为输入模型的过去数据， 02/14 20:48 后的蓝色线条为模型给出的C相电压预测结果，而红色为数据集中实际的C相电压数据（用于进行对比）。
-
+
可以看到，我们使用了过去10分钟的电压的数据，基于之前学习到的序列间相互关系对未来5分钟的C相电压这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了一定的同步性。
@@ -592,7 +592,7 @@ Total line number = 48
在民航交通运输业，存在着对乘机旅客数量进行异常检测的需求。异常检测的结果可用于指导调整航班的调度，以使得企业获得更大效益。
-Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](https://alioss.timecho.com/docs/img/airline.csv)。
+Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](/img/airline.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过捕捉序列的变化规律以对序列时间点进行异常检测，赋能交通运输业。
#### 步骤一：数据导入
@@ -645,6 +645,6 @@ Total line number = 144
我们将检测为异常的结果进行绘制，可以得到以下图像。其中蓝色曲线为原时间序列，用红色点特殊标注的时间点为算法检测为异常的时间点。
-
+
可以看到，Stray模型对输入序列变化进行了建模，成功检测出出现异常的时间点。
\ 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 ee116a082..b282f8a56 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
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -96,13 +96,13 @@ V1.3.0及之前版本:
在创建后不会立即启动，需要执行`START PIPE`语句启动任务。
-
+
V1.3.1及之后版本:
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
-
+
提供以下 SQL 语句对同步任务进行状态管理。
@@ -265,7 +265,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -281,7 +281,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -305,7 +305,7 @@ with SINK (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -355,7 +355,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md
index bd94fef6d..cca2529ee 100644
--- a/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Data-Sync_timecho.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -98,13 +98,13 @@ V1.3.0及之前版本:
在创建后不会立即启动，需要执行`START PIPE`语句启动任务。
-
+
V1.3.1及之后版本:
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
-
+
提供以下 SQL 语句对同步任务进行状态管理。
@@ -273,7 +273,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -289,7 +289,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -313,7 +313,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`forwarding-pipe-requests` 均设置为 `false`，表示不转发从另一 pipe 传输而来的数据，以及要保持两侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据。
@@ -350,7 +350,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -400,7 +400,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
@@ -431,7 +431,7 @@ with sink (
本例子用来演示将一个 IoTDB 的数据，经过单向网闸，同步至另一个 IoTDB 的场景，数据链路如下图所示：
-
+
在这个例子中，需要使用 sink 任务中的 iotdb-air-gap-sink 插件（目前支持部分型号网闸，具体型号请联系天谋科技工作人员确认），配置网闸后，在 A IoTDB 上执行下列语句，其中 node-urls 填写网闸配置的目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，详细语句如下：
diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md
index d69b9070e..1e61cce17 100644
--- a/src/zh/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md
+++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Database-Programming.md
@@ -207,9 +207,9 @@ insert into root.sg(time, a, b) values (1, 1, 1);
您可以参考下图辅助理解，其中 Trigger1 配置采用乐观策略，Trigger2 配置采用悲观策略。Trigger1 和 Trigger2 的触发时机是 BEFORE INSERT，Trigger3 和 Trigger4 的触发时机是 AFTER INSERT。 正常执行流程如下：
-
+
-
+
#### 示例
@@ -524,19 +524,19 @@ END
##### `
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata_timecho.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata_timecho.md
index b77a741df..070fdd7b8 100644
--- a/src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Operate-Metadata_timecho.md
@@ -304,9 +304,9 @@ It costs 0.058s
IoTDB 支持设备模板功能，实现同类型不同实体的物理量元数据共享，减少元数据内存占用，同时简化同类型实体的管理。
-
+
-
+
### 创建设备模板
@@ -733,7 +733,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Query-Data.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Query-Data.md
index a5e568e40..32205b27c 100644
--- a/src/zh/UserGuide/V1.3.0-2/User-Manual/Query-Data.md
+++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Query-Data.md
@@ -866,7 +866,7 @@ IoTDB支持通过`GROUP BY`子句对序列进行分段或者分组聚合。
下图中指出了这三个参数的含义：
-
+
接下来，我们给出几个典型例子：
@@ -1115,7 +1115,7 @@ group by variation(controlExpression[,delta][,ignoreNull=true/false])
下图为差值分段的一个分段方式示意图，与组中第一个数据的控制列值的差值在delta内的控制列对应的点属于相同的分组。
-
+
##### 使用注意事项
1. `controlExpression`的结果应该为唯一值，如果使用通配符拼接后出现多列，则报错。
@@ -1288,7 +1288,7 @@ group by session(timeInterval)
下图为`group by session`下的一个分组示意图
-
+
##### 使用注意事项
1. 对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select `__endTime`的方式来使得结果输出分组的结束时间。
diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md
index afc93412f..297b5f6ce 100644
--- a/src/zh/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md
+++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Security-Management_timecho.md
@@ -67,7 +67,7 @@ white.list:
5. 是否开启白名单功能的配置，可以热加载。
6. 使用Java 原生接口修改白名单，必须是root用户才能修改，拒绝非root用户修改；修改内容必须合法，否则会抛出StatementExecutionException异常。
-
+
## 审计日志
@@ -94,7 +94,7 @@ white.list:
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
diff --git a/src/zh/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md b/src/zh/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md
index f6653d6c0..3aff9500b 100644
--- a/src/zh/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md
+++ b/src/zh/UserGuide/V1.3.0-2/User-Manual/Streaming_apache.md
@@ -35,7 +35,7 @@ Pipe Source 用于抽取数据，Pipe Processor 用于处理数据，Pipe Sink
**Pipe 任务的模型如下：**
-
+
描述一个数据流处理任务，本质就是描述 Pipe Source、Pipe Processor 和 Pipe Sink 插件的属性。
用户可以通过 SQL 语句声明式地配置三个子任务的具体属性，通过组合不同的属性，实现灵活的数据 ETL 能力。
@@ -722,7 +722,7 @@ WHERE SINK USED BY
+
-
+
### 示例
diff --git a/src/zh/UserGuide/V1.3.3/API/Programming-MQTT.md b/src/zh/UserGuide/V1.3.3/API/Programming-MQTT.md
index bb8dd1a03..4a113cd13 100644
--- a/src/zh/UserGuide/V1.3.3/API/Programming-MQTT.md
+++ b/src/zh/UserGuide/V1.3.3/API/Programming-MQTT.md
@@ -30,7 +30,7 @@
IoTDB 支持 MQTT v3.1（OASIS 标准）协议。
IoTDB 服务器包括内置的 MQTT 服务，该服务允许远程设备将消息直接发送到 IoTDB 服务器。
-
+
## 内置 MQTT 服务
内置的 MQTT 服务提供了通过 MQTT 直接连接到 IoTDB 的能力。 它侦听来自 MQTT 客户端的发布消息，然后立即将数据写入存储。
@@ -57,7 +57,7 @@ MQTT 主题与 IoTDB 时间序列相对应。
```
或者以上两者的JSON数组形式。
-
+
## MQTT 配置
默认情况下，IoTDB MQTT 服务从`${IOTDB_HOME}/${IOTDB_CONF}/iotdb-system.properties`加载配置。
diff --git a/src/zh/UserGuide/V1.3.3/API/Programming-ODBC.md b/src/zh/UserGuide/V1.3.3/API/Programming-ODBC.md
index df78dd589..08a7fc6b8 100644
--- a/src/zh/UserGuide/V1.3.3/API/Programming-ODBC.md
+++ b/src/zh/UserGuide/V1.3.3/API/Programming-ODBC.md
@@ -39,7 +39,7 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
1. **下载 Zappy-Sys ODBC-JDBC 桥插件**：
进入 https://zappysys.com/products/odbc-powerpack/odbc-jdbc-bridge-driver/ 网站，点击下载按钮并直接安装。
- 
+ 
2. **准备 IoTDB**：打开 IoTDB 集群，并任意写入一条数据。
```sql
@@ -49,11 +49,11 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
3. **部署及调试插件**：
1. 打开 ODBC 数据源 32/64 位，取决于 Windows 的位数，一个示例的位置是 `C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools`。
- 
+ 
2. 点击添加，选择 ZappySys JDBC Bridge。
- 
+ 
3. 填写如下配置：
@@ -65,15 +65,15 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
| User name | IoTDB 的用户名 | root |
| User password | IoTDB 的密码 | root |
- 
+ 
4. 点击 Test Connection 按钮，应该显示连接成功。
- 
+ 
5. 点击上方的 Preview， 将查询文本换为 `select * from root.**`，点击 Preview Data，应该正确显示查询结果。
- 
+ 
4. **使用 ODBC 操作数据**：正确部署后，就可以使用 Windows 的 ODBC 库，对 IoTDB 的数据进行操作。 这里给出 C# 语言的代码示例：
```C#
diff --git a/src/zh/UserGuide/V1.3.3/API/Programming-OPC-UA_timecho.md b/src/zh/UserGuide/V1.3.3/API/Programming-OPC-UA_timecho.md
index ea23ccfbc..47e31ea4e 100644
--- a/src/zh/UserGuide/V1.3.3/API/Programming-OPC-UA_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/API/Programming-OPC-UA_timecho.md
@@ -30,7 +30,7 @@ OPC UA 是一种在自动化领域用于不同设备和系统之间进行通信
- **Client/Server 模式**：在这种模式下，IoTDB 的流处理引擎通过 OPC UA Sink 与 OPC UA 服务器（Server）建立连接。OPC UA 服务器在其地址空间(Address Space) 中维护数据，IoTDB可以请求并获取这些数据。同时，其他OPC UA客户端（Client）也能访问服务器上的数据。
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md b/src/zh/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md
index 77edf9ebb..4fbd9165d 100644
--- a/src/zh/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/Background-knowledge/Cluster-Concept_timecho.md
@@ -36,7 +36,7 @@
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
-
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md b/src/zh/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md
index 79c1c21b8..de2256cf7 100644
--- a/src/zh/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md
+++ b/src/zh/UserGuide/V1.3.3/Basic-Concept/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@
根据企业组织结构和设备实体层次结构，我们将其物联网数据模型表示为如下图所示的属性层级组织结构，即电力集团层-风电场层-实体层-物理量层。其中 ROOT 为根节点，物理量层的每一个节点为叶子节点。IoTDB 采用树形结构定义数据模式，以从 ROOT 节点到叶子节点的路径来命名一个时间序列，层次间以“.”连接。例如，下图最左侧路径对应的时间序列名称为`ROOT.ln.wf01.wt01.status`。
-
+
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)。
@@ -94,7 +94,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例
插入数据时，对齐的时间序列中某列的某些行允许有空值。
-
+
在后续数据定义语言、数据操作语言和 Java 原生接口章节，将对涉及到对齐时间序列的各种操作进行逐一介绍。
diff --git a/src/zh/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md b/src/zh/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md
index 96e9fdf9a..35f789271 100644
--- a/src/zh/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md
+++ b/src/zh/UserGuide/V1.3.3/Basic-Concept/Navigating_Time_Series_Data.md
@@ -24,13 +24,13 @@
万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。
-
+
通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。
-
+
传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。
@@ -38,14 +38,14 @@
时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。
-
+
### 数据点
- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。
- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
-
+
### 测点
diff --git a/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md b/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md
index 44e8df63a..ea83b049c 100644
--- a/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md
+++ b/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_apache.md
@@ -346,9 +346,9 @@ It costs 0.058s
IoTDB 支持设备模板功能，实现同类型不同实体的物理量元数据共享，减少元数据内存占用，同时简化同类型实体的管理。
-
+
-
+
### 创建设备模板
@@ -775,7 +775,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
diff --git a/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_timecho.md b/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_timecho.md
index 3c1ed63c8..0fa4e4ecc 100644
--- a/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/Basic-Concept/Operate-Metadata_timecho.md
@@ -345,9 +345,9 @@ It costs 0.058s
IoTDB 支持设备模板功能，实现同类型不同实体的物理量元数据共享，减少元数据内存占用，同时简化同类型实体的管理。
-
+
-
+
### 创建设备模板
@@ -774,7 +774,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
diff --git a/src/zh/UserGuide/V1.3.3/Basic-Concept/Query-Data.md b/src/zh/UserGuide/V1.3.3/Basic-Concept/Query-Data.md
index 060940e48..cc7895a9d 100644
--- a/src/zh/UserGuide/V1.3.3/Basic-Concept/Query-Data.md
+++ b/src/zh/UserGuide/V1.3.3/Basic-Concept/Query-Data.md
@@ -866,7 +866,7 @@ IoTDB支持通过`GROUP BY`子句对序列进行分段或者分组聚合。
下图中指出了这三个参数的含义：
-
+
接下来，我们给出几个典型例子：
@@ -1115,7 +1115,7 @@ group by variation(controlExpression[,delta][,ignoreNull=true/false])
下图为差值分段的一个分段方式示意图，与组中第一个数据的控制列值的差值在delta内的控制列对应的点属于相同的分组。
-
+
##### 使用注意事项
1. `controlExpression`的结果应该为唯一值，如果使用通配符拼接后出现多列，则报错。
@@ -1288,7 +1288,7 @@ group by session(timeInterval)
下图为`group by session`下的一个分组示意图
-
+
##### 使用注意事项
1. 对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select `__endTime`的方式来使得结果输出分组的结束时间。
diff --git a/src/zh/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/zh/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md
index 4c4710469..143b1eda8 100644
--- a/src/zh/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md
+++ b/src/zh/UserGuide/V1.3.3/Deployment-and-Maintenance/AINode_Deployment_apache.md
@@ -31,8 +31,8 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，
### 部署模式
-
+
+
+
- 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用
- 建议在 `iotdb-enterprise-ainode-
-
+
+
+
- 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用
- 建议在 `iotdb-enterprise-ainode-
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在编辑数据源的时候，`Type`一栏选择`Simplejson`，`URL`一栏填写 http://\
+
### 操作 Grafana
进入 Grafana 可视化页面后，可以选择添加时间序列，如下图。您也可以按照 Grafana 官方文档进行相应的操作，详情可参看 Grafana 官方文档：http://docs.grafana.org/guides/getting_started/。
-
+
## 配置 grafana
diff --git a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md
index 7e3853018..f7f3297f7 100644
--- a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Grafana-Plugin.md
@@ -115,9 +115,9 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
点击左侧的 `设置` 图标，选择 `Data Source` 选项，然后再点击 `Add data source`。
-
+
-
+
选择 `Apache IoTDB` 数据源，`URL` 一栏填写 `http://
+
@@ -133,15 +133,15 @@ Ip 为您的 IoTDB 服务器所在的宿主机 IP，port 为 REST 服务的运
点击左侧的 `Dashboards` 图标，选择 `Manage`，如下图所示：
-
+
点击右上方的 `New Dashboard` 图标，选择 `Add an empty panel`，如下图所示：
-
+
Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，默认是SQL: Full Customized方式。
-
+
#### SQL: Full Customized 输入方式
@@ -149,7 +149,7 @@ Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，
如果一个查询涉及多个表达式，我们可以点击 SELECT 输入框右侧的 `+` 来添加 SELECT 子句中的表达式，也可以点击 FROM 输入框右侧的 `+` 来添加路径前缀，如下图所示：
-
+
SELECT 输入框中的内容可以是时间序列的后缀，可以是函数或自定义函数，可以是算数表达式，也可以是它们的嵌套表达式。您还可以使用 as 子句来重命名需要显示的结果序列名字。
@@ -182,7 +182,7 @@ CONTROL 输入框为非必须填写项目，填写内容应当是控制查询类
#### SQL: Drop-down List 输入方式
在 TIME-SERIES 选择框中选择一条时间序列、FUNCTION 选择一个函数、SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL 输入框中输入内容，其中 TIME-SERIESL 为必填项其余为非必填项。
-
+
### 变量与模板功能的支持
@@ -190,32 +190,32 @@ SQL: Full Customized和SQL: Drop-down List两种输入方式都支持 Grafana
创建一个新的 Panel 后，点击右上角的设置按钮，如下图所示：
-
+
选择 `Variables`，点击 `Add variable` ，如下图所示：
-
+
示例一：输入 `Name`，`Label`，选择Type的`Query`、在Query 中输入show child paths xx ， 点击 `Update` 按钮，如下图所示：
-
+
应用 Variables，在 `grafana panel` 中输入变量点击 `save` 按钮，如下图所示
-
+
示例二：变量嵌套使用，如下图所示
-
+
-
+
-
+
示例三：函数变量使用，如下图所示
-
+
-
+
上图中Name 是变量名称也是将来我们在panel中使用的变量名称，Label是变量的展示名称如果为空就显示Name的变量反之则显示Label的名称，
Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、Ad hoc filters等这些都可以在IoTDB的Grafana Plugin 中使用
@@ -244,15 +244,15 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
1. 在Grafana panel中，点击alerting按钮，如下图所示：
-
+
2. 点击`Create alert rule from this panel`，如下图所示：
-
+
3. 在第1步中设置查询和警报条件，Conditions 表示查询条件，可以配置多个组合查询条件。如下图所示：
-
+
图中的查询条件：min() OF A IS BELOW 0，表示将A选项卡中的最小值在0一下就会触发条件，单击该函数可将其更改为另一个函数。
@@ -260,23 +260,23 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
例如，我们按以下顺序有 3 个条件： 条件：B（计算为：TRUE）或条件：C（计算为：FALSE）和条件：D（计算为：TRUE） 所以结果将计算为（（对或错）和对）=对。
4. 选择完指标及告警规则后点击`Preview`按钮，进行数据预览如下图所示：
-
+
5. 在第 2 步中，指定警报评估间隔，对于`Evaluate every`，指定评估频率。必须是 10 秒的倍数。例如，1m，30s。
对于`Evaluate for`，指定在警报触发之前的持续时间。如下图所示：
-
+
6. 在第 3 步中，添加存储位置、规则组以及与规则关联的其他元数据。 其中`Rule name`指定规则的名称。规则名称必须是唯一的。
-
+
7. 在第 4 步中，添加自定义标签。 从下拉列表中选择现有键值对添加自定义标签，或通过输入新键或值来添加新标签。如下图所示：
-
+
8. 单击保存以保存规则或单击保存并退出以保存规则并返回到警报页面。
9. 告警状态常用的有`Normal`、`Pending`、`Firing`等状态，如下图所示：
-
-
+
+
10. 我们也可以为告警配置`Contact points`用来接收告警通知，更加详细操作可以参考官方文档(https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/)。
diff --git a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index efbcbaaf9..42f5ed013 100644
--- a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -35,7 +35,7 @@ Ignition 是一个基于WEB的监控和数据采集工具（SCADA）- 一个开
两个模块与Ignition的具体关系与内容如下图所示。
- 
+ 
## 安装要求
@@ -58,23 +58,23 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
步骤一：进入 `Config` - `System`- `Modules` 模块，点击最下方的`Install or Upgrade a Module...`
-
+
步骤二：选择获取到的 `modl`，选择文件并上传，点击 `Install`，信任相关证书。
-
+
步骤三：安装完成后可以看到如下内容
-
+
步骤四：进入 `Config` - `Tags`- `History` 模块，点击下方的`Create new Historical Tag Provider...`
-
+
步骤五：选择 `IoTDB`并填写配置信息
-
+
配置内容如下：
@@ -161,21 +161,21 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
- 配置好 `Provider` 后就可以在 `Designer` 中使用 `IoTDB Tag Historian` 了，就跟使用其他的 `Provider` 一样，右键点击对应 `Tag` 选择 `Edit tag(s)`，在 Tag Editor 中选择 History 分类
- 
+ 
- 设置 `History Enabled` 为 `true`，并选择 `Storage Provider` 为上一步创建的 `Provider`，按需要配置其它参数，并点击 `OK`，然后保存项目。此时数据将会按照设置的内容持续的存入 `IoTDB` 实例中。
- 
+ 
#### 读取数据
- 也可以在 Report 的 Data 标签下面直接选择存入 IoTDB 的 Tags
- 
+ 
- 在 Components 中也可以直接浏览相关数据
- 
+ 
#### 脚本模块：该功能能够与 IoTDB 进行交互
@@ -242,15 +242,15 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
步骤一：进入 `Config` - `Databases` -`Drivers` 模块，创建 `Translator`
-
+
步骤二：进入 `Config` - `Databases` -`Drivers` 模块，创建 `JDBC Driver`，选择上一步配置的 `Translator`并上传下载的 `IoTDB-JDBC`，Classname 配置为 `org.apache.iotdb.jdbc.IoTDBDriver`
-
+
步骤三：进入 `Config` - `Databases` -`Connections` 模块，创建新的 `Connections`，`JDBC Driver` 选择上一步创建的 `IoTDB Driver`，配置相关信息后保存即可使用
-
+
### 使用说明
@@ -264,11 +264,11 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
不选择其他项，设置好字段，并 `Enabled` 后 数据会安装设置存入对应的 IoTDB
-
+
#### 数据查询
- 在 `Database Query Browser` 中选择`Data Source`选择之前创建的 `Connection`，即可编写 SQL 语句查询 IoTDB 中的数据
-
+
diff --git a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md
index 566007d21..a21e6df28 100644
--- a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md
+++ b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_apache.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 手动创建文件夹
@@ -398,7 +398,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. 扩容
@@ -406,7 +406,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
新增pv，必须有可用的pv才可以扩容。
-
+
**注意：DataNode重启后无法加入集群**
@@ -424,7 +424,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 扩容datanode
diff --git a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md
index 74947c84c..2a6847ff9 100644
--- a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Kubernetes_timecho.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 手动创建文件夹
@@ -398,7 +398,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. 扩容
@@ -406,7 +406,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
新增pv，必须有可用的pv才可以扩容。
-
+
**注意：DataNode重启后无法加入集群**
@@ -424,7 +424,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 扩容datanode
diff --git a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md
index 080b70483..a4b194800 100644
--- a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md
+++ b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Telegraf.md
@@ -38,7 +38,7 @@ Telegraf 有以下这些特点：
Telegraf-IoTDB 插件可以将保存在 Telegraf 中的监控信息输出存储到 IoTDB，输出插件使用了 IoTDB session 进行连接和数据写入。
-
+
## 2、安装要求
@@ -66,7 +66,7 @@ Telegraf支持多种操作系统，包括Linux、Windows、macOS，Telegraf 的
| name_prefix | 指定附加到测量名称的前缀 | |
| name_suffix | 指定附加到测量名称的后缀 | |
-
+
### 4.2 设置输出源
@@ -81,7 +81,7 @@ Telegraf支持多种操作系统，包括Linux、Windows、macOS，Telegraf 的
| timestamp_precision | 时间戳精度 | timestamp_precision = "millisecond" | timestamp_precision = "与 IoTDB 相同的时间戳精度" | 您可以通过查看"iotdb-system.properties"中"timestamp_precision"字段即可 |
| sanitize_tag | 数据库版本 | 无 | sanitize_tag = "0.13/1.0/1.1/1.2/1.3" | |
-
+
### 4.3 启动Telegraf服务
diff --git a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md
index 4761428cd..38728b6bb 100644
--- a/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md
+++ b/src/zh/UserGuide/V1.3.3/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard 是一个开源物联网平台，可实现物联网项目的快速开发、管理和扩展。更多介绍详情请参考[ ThingsBoard 官网](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/)。
- 
+ 
2. ThingsBoard-IoTDB 简介
@@ -34,7 +34,7 @@
### 关系示意图
-
+
## 安装要求
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ##thingsboard数据写入iotdb所存储
1. 创建设备并接入数据：在 Thingsboard 的实体-设备中创建设备并通过工业网关将数据发送到 ThingsBoard 指定设备中
-
+
2. 设置规则链：在规则链库中对于“SD-032F 泵”设置告警规则并将该规则链设置为根链
-
+
+
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+
+
+
+
IoTDB 性能监控
-
-
+
+
+
SQL 查询
-
-
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| 函数名 | 可接收的输入序列类型 | 属性参数 | 输出序列类型 | 功能类型 |
| ------ | ------------------------------ | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2574,7 +2574,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `windowSize`: 一个窗口内的点数。Int数据类型。必需的属性参数。
+ `slidingStep`: 按照设定的点数来滑动窗口。Int数据类型。可选的属性参数；如果没有设置，默认取值和`windowSize`一样。
-
+
**(2) 滑动时间窗口（SlidingTimeWindowAccessStrategy）使用的属性参数:**
@@ -2583,7 +2583,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `displayWindowBegin`: 窗口滑动的起始时间戳位置（包含在内）。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MIN_VALUE，意为使用输入的时间序列的第一个点的时间戳作为窗口滑动的起始时间戳位置。
+ `displayWindowEnd`: 结束时间限制（不包含在内；本质上和`WHERE time < displayWindowEnd`起的效果是一样的)。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MAX_VALUE，意为除了输入的时间序列自身数据读取完毕之外没有增加额外的结束时间过滤条件限制。
-
+
#### 示例
diff --git a/src/zh/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md
index 038c85164..279c3d1c9 100644
--- a/src/zh/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md
+++ b/src/zh/UserGuide/V1.3.3/SQL-Manual/SQL-Manual.md
@@ -254,8 +254,8 @@ IoTDB> count devices where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:
### 设备模板管理
-
-
+
+
diff --git a/src/zh/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md b/src/zh/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md
index 3d188f07e..119e003a0 100644
--- a/src/zh/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md
+++ b/src/zh/UserGuide/V1.3.3/Technical-Insider/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB 为时间序列数据实现了量身定制的分区算法。在此基础
### 分区策略
IoTDB 将生产环境中的每个传感器映射为一个时间序列。然后，使用序列分区算法对时间序列进行分区以管理其元数据，再结合时间分区算法来管理其数据。下图展示了 IoTDB 如何对时序数据进行分区。
-
+
#### 分区算法
由于生产环境中通常部署大量设备和传感器，IoTDB 使用序列分区算法以确保分区信息的大小可控。由于生成的时间序列与时间戳相关联，IoTDB 使用时间分区算法来清晰区分冷热分区。
@@ -56,7 +56,7 @@ $$\left\lfloor\frac{\text{Timestamp}-\text{StartTimestamp}}{\text{TimePartitionI
### 分区分配
IoTDB 使用分片来实现时间序列的弹性存储，集群中分片的数量由所有数据节点的总资源决定。由于分片的数量是动态的，IoTDB 可以轻松扩展。元数据分片和数据分片都遵循相同的分区分配算法，即均匀划分所有序列分区。下图展示了分区分配过程，其中动态扩展的分片匹配不断扩展的时间序列和集群。
-
+
#### 分片扩容
分片的数量由下式给出
@@ -76,7 +76,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 存储均衡
数据节点持有的副本数量反映了它的存储负载。如果数据节点之间的副本数量差异较大，拥有更多副本的数据节点可能成为存储瓶颈。尽管简单的轮询（Round Robin）放置算法可以通过确保每个数据节点持有等量副本来实现存储均衡，但它会降低集群的容错能力，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 将分片 $r_1$ 的 2 个副本放置在数据节点 $n_1$ 和 $n_2$ 上。
@@ -91,7 +91,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 计算均衡
数据节点持有的主副本数量反映了它的计算负载。如果数据节点之间持有主副本数量差异较大，拥有更多主副本的数据节点可能成为计算瓶颈。如果主副本选择过程使用直观的贪心算法，当副本以容错算法放置时，可能会导致主副本分布不均，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 选择分片 $r_5$ 在数据节点 $n_5$ 上的副本作为主副本。
diff --git a/src/zh/UserGuide/V1.3.3/Tools-System/Benchmark.md b/src/zh/UserGuide/V1.3.3/Tools-System/Benchmark.md
index 9c89db2a2..bf607f481 100644
--- a/src/zh/UserGuide/V1.3.3/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/V1.3.3/Tools-System/Benchmark.md
@@ -27,13 +27,13 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
下图1-1囊括了测试基准流程及其他扩展功能。这些流程可以由IoT-benchmark 统一来完成。IoT Benchmark 支持多种工作负载，包括**纯写入、纯查询、写入查询混合**等，支持**软硬件系统监控、测试指标度量**等监控功能，还实现了**初始化数据库自动化、测试数据分析及系统参数优化**等功能。
-
+
图1-1
借鉴 YCSB 测试工具将工作负载生成、性能指标测量和数据库接口三个组件分离的设计思想，IoT-benchmark 的模块化设计如图1-2所示。与基于 YCSB 的测试工具系统不同的是，IoT-benchmark 增加了系统监控模块，支持测试数据和系统指标监控数据的持久化。此外也增加了一些特别针对时序数据场景的特殊负载测试功能，如支持物联网场景的批量写入和多种乱序数据写入模式。
-
+
图1-2
@@ -77,7 +77,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试包的目录结构如下图1-3所示。其中测试配置文件为conf/config.properties，测试启动脚本为benchmark\.sh (Linux & MacOS) 和 benchmark.bat (Windows)，详细文件用途见表1-2所示。
-
+
图1-3文件和文件夹列表
@@ -105,7 +105,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试的所有日志文件被存放于 logs 文件夹下，测试的结果在测试完成后被存放到 data/csvOutput 文件夹下，例如测试后我们得到了如下的结果矩阵：
-
+
- Result Matrix
- OkOperation：成功的对应操作次数
@@ -267,24 +267,24 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
首先在172.21.4.3和172.21.4.4上分别启动被测时间序列数据库Apache-IoTDB和KairosDB，之后在172.21.4.2、172.21.4.3和172.21.4.4上通过ser-benchamrk.sh脚本启动服务器资源监控（图2-1）。然后按照表2-3在172.21.4.2分别修改iotdb-0.13-0.0.1和kairosdb-0.0.1文件夹内的conf/config.properties文件满足测试需求。先后使用benchmark.sh启动对Apache-IoTDB和KairosDB的写入测试。
-
+
图2-1服务器监控任务
例如我们首先启动对KairosDB的测试，IoT-benchmark会在MySQL数据库中创建CONFIG数据表存放本次测试配置信息（图2-2），测试执行中会有日志输出当前测试进度（图2-3）。测试完成时会输出本次测试结果（图2-3），同时将结果写入FINAL_RESULT数据表中（图2-4）。
-
+
图2-2测试配置信息表
-
-
-
-
+
+
+
+
图2-3测试进度和结果
-
+
图2-4测试结果表
@@ -292,14 +292,14 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
依照测试结果信息我们知道同样的配置写入Apache-IoTDB和KairosDB写入延时时间分别为：55.98ms和1324.45ms；写入吞吐分别为：5,125,600.86点/秒和224,819.01点/秒；测试分别执行了585.30秒和11777.99秒。并且KairosDB有写入失败出现，排查后发现是数据磁盘使用率已达到100%，无磁盘空间继续接收数据。而Apache-IoTDB无写入失败现象，全部数据写入完毕后占用磁盘空间仅为4.7G（如图2-5所示）；从写入吞吐和磁盘占用情况上看Apache-IoTDB均优于KairosDB。当然后续还有其他测试来从多方面观察和对比，比如查询性能、文件压缩比、数据安全性等。
-
+
图2-5磁盘使用情况
那么测试过程中各个服务器资源使用情况如何呢？每个写操作具体的表现如何呢？这个时候我们就可以通过安装和使用Tableau来可视化服务器监控表和测试过程记录表内的数据了。Tableau的使用本文不展开介绍，通过它连接测试数据持久化的数据表后具体结果下如图（以Apache-IoTDB为例）：
-
-
+
+
图2-6Tableau可视化测试过程
@@ -326,7 +326,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
执行结果：
-
+
图2-7查询测试结果
diff --git a/src/zh/UserGuide/V1.3.3/Tools-System/CLI.md b/src/zh/UserGuide/V1.3.3/Tools-System/CLI.md
index 80728257b..aaff80934 100644
--- a/src/zh/UserGuide/V1.3.3/Tools-System/CLI.md
+++ b/src/zh/UserGuide/V1.3.3/Tools-System/CLI.md
@@ -132,47 +132,47 @@ Shell > ./kc.sh start-dev
```
2、使用 https://ip:port 登陆 keycloack, 首次登陆需要创建用户
-
+
3、点击 Administration Console 进入管理端
-
+
4、在左侧的 Master 菜单点击 Create Realm, 输入 Realm Name 创建一个新的 Realm
-
+
-
+
5、点击左侧菜单 Clients，创建 client
-
+
6、点击左侧菜单 User，创建 user
-
+
7、点击新创建的用户 id，点击 Credentials 导航输入密码和关闭 Temporary 选项，至此 keyclork 配置完成
-
+
8、创建角色，点击左侧菜单的 Roles然后点击Create Role 按钮添加角色
-
+
9、在Role Name 中输入`iotdb_admin`，点击save 按钮。提示：这里的`iotdb_admin`不能为其他名称否则即使登陆成功后也将无权限使用iotdb的查询、插入、创建 database、添加用户、角色等功能
-
+
10、点击左侧的User 菜单然后点击用户列表中的用户为该用户添加我们刚创建的`iotdb_admin`角色
-
+
11、选择Role Mappings ，在Assign role选择`iotdb_admin`增加角色
-
+
-
+
提示：如果用户角色有调整需要重新生成token并且重新登陆iotdb才会生效
@@ -188,7 +188,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
其中，需要将{my-access-token} （注意，包括{}）替换成你的 token，即 access_token 对应的值。密码为空需要再次确认。
-
+
如何获取 token 取决于你的 OIDC 设置。 最简单的一种情况是使用`password-grant`。例如，假设你在用 keycloack 作为你的 OIDC 服务，
并且你在 keycloack 中有一个被定义成 public 的`iotdb`客户的 realm，那么你可以使用如下`curl`命令获得 token。
diff --git a/src/zh/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md b/src/zh/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md
index ed67d75ae..4c14acdf9 100644
--- a/src/zh/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md
+++ b/src/zh/UserGuide/V1.3.3/Tools-System/Data-Import-Tool.md
@@ -145,7 +145,7 @@ IoTDB 目前支持将 CSV、SQL、及TsFile（IoTDB底层开放式时间序列
TsFile 主动监听&加载功能能够主动监听指定目标路径（用户配置）下TsFile的文件变化，并将目标路径下的TsFile文件自动同步至指定接收路径（用户配置）。通过此功能，IoTDB 能自动检测并加载这些文件，无需手动执行任何额外的加载操作。这种自动化流程不仅简化了用户的操作步骤，还减少了操作过程中可能出现的错误，有效降低了用户在使用过程中的复杂性。
-
+
### 4.1 配置参数
diff --git a/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md b/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md
index ebb3b4f6c..3ef0730da 100644
--- a/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md
+++ b/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_apache.md
@@ -67,7 +67,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -107,9 +107,9 @@ static_configs:
`Apache IoTDB Dashboard`是 IoTDB 企业版的配套产品，支持统一集中式运维管理，可通过一个监控面板监控多个集群。你可以联系商务获取到 Dashboard 的 Json文件。
-
+
-
+
### 集群概览
@@ -122,7 +122,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -131,7 +131,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -144,7 +144,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -153,7 +153,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -163,8 +163,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md b/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md
index 8b6396ded..d3cc9dceb 100644
--- a/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/Tools-System/Monitor-Tool_timecho.md
@@ -67,7 +67,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -106,9 +106,9 @@ static_configs:
我们提供了Apache IoTDB Dashboard，支持统一集中式运维管理，可通过一个监控面板监控多个集群。
-
+
-
+
你可以在企业版中获取到 Dashboard 的 Json文件。
@@ -122,7 +122,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -131,7 +131,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -144,7 +144,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -153,7 +153,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -163,8 +163,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md b/src/zh/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md
index 2df219d05..2eb3f47b4 100644
--- a/src/zh/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/Tools-System/Workbench_timecho.md
@@ -25,10 +25,10 @@ IoTDB的可视化控制台包含以下功能模块：
主要功能展示：
* 首页
-
+
* 测点列表
-
+
* 数据查询
-
+
* 趋势
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/AINode_apache.md b/src/zh/UserGuide/V1.3.3/User-Manual/AINode_apache.md
index 2da3c824d..331365d1a 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/AINode_apache.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/AINode_apache.md
@@ -25,7 +25,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
系统架构如下图所示：
::: center
-
+
:::
三种节点的职责如下：
@@ -41,7 +41,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
-
+
- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如：
- **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。
@@ -57,7 +57,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
::: center
-
+
:::
## 安装部署
@@ -301,13 +301,13 @@ window_function:
- **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期)
- **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理：
- **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪
- 
+ 
- **tail(window_size)**：获取数据中最后的window_size个点用于模型推，该窗口可用于数据裁剪
- 
+ 
- **count(window_size, sliding_step)**：基于点数的滑动窗口，每个窗口的数据会分别通过模型进行推理，如下图示例所示，window_size为2的窗口函数将输入数据集分为三个窗口，每个窗口分别进行推理运算生成结果。该窗口可用于连续推理
- 
+ 
**说明1: window可以用来解决sql查询结果和模型的输入行数要求不一致时的问题，对行进行裁剪。需要注意的是，当列数不匹配或是行数直接少于模型需求时，推理无法进行，会返回错误信息。**
@@ -456,7 +456,7 @@ Total line number = 4
在部分工业场景下，会存在预测电力负载的需求，预测结果可用于优化电力供应、节约能源和资源、支持规划和扩展以及增强电力系统的可靠性。
-我们所使用的 ETTh1 的测试集的数据为[ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv)。
+我们所使用的 ETTh1 的测试集的数据为[ETTh1](/img/ETTh1.csv)。
包含间隔1h采集一次的电力数据，每条数据由负载和油温构成，分别为：High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature。
@@ -519,7 +519,7 @@ Total line number = 48
图中10/24 00:00之前的数据为输入模型的过去数据，10/24 00:00后的蓝色线条为模型给出的油温预测结果，而红色为数据集中实际的油温数据（用于进行对比）。
-
+
可以看到，我们使用了过去96个小时（4天）的六个负载信息和对应时间油温的关系，基于之前学习到的序列间相互关系对未来48个小时（2天）的油温这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了较高程度的一致性。
@@ -529,7 +529,7 @@ Total line number = 48
我们利用某变电站中的电流、电压和功率等数据构成了真实场景下的数据集。该数据集包括变电站近四个月时间跨度，每5 - 6s 采集一次的 A相电压、B相电压、C相电压等数据。
-测试集数据内容为[data](https://alioss.timecho.com/docs/img/data.csv)。
+测试集数据内容为[data](/img/data.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过以往A相电压，B相电压和C相电压的数值和对应时间戳，预测未来一段时间内的C相电压，赋能变电站的监视管理。
@@ -584,7 +584,7 @@ Total line number = 48
图中 02/14 20:48 之前的数据为输入模型的过去数据， 02/14 20:48 后的蓝色线条为模型给出的C相电压预测结果，而红色为数据集中实际的C相电压数据（用于进行对比）。
-
+
可以看到，我们使用了过去10分钟的电压的数据，基于之前学习到的序列间相互关系对未来5分钟的C相电压这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了一定的同步性。
@@ -592,7 +592,7 @@ Total line number = 48
在民航交通运输业，存在着对乘机旅客数量进行异常检测的需求。异常检测的结果可用于指导调整航班的调度，以使得企业获得更大效益。
-Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](https://alioss.timecho.com/docs/img/airline.csv)。
+Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](/img/airline.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过捕捉序列的变化规律以对序列时间点进行异常检测，赋能交通运输业。
#### 步骤一：数据导入
@@ -645,6 +645,6 @@ Total line number = 144
我们将检测为异常的结果进行绘制，可以得到以下图像。其中蓝色曲线为原时间序列，用红色点特殊标注的时间点为算法检测为异常的时间点。
-
+
可以看到，Stray模型对输入序列变化进行了建模，成功检测出出现异常的时间点。
\ No newline at end of file
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/AINode_timecho.md b/src/zh/UserGuide/V1.3.3/User-Manual/AINode_timecho.md
index 291d2a1e1..32dfb729d 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
系统架构如下图所示：
::: center
-
+
:::
三种节点的职责如下：
@@ -41,7 +41,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
-
+
- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如：
- **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。
@@ -57,7 +57,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
::: center
-
+
:::
## 安装部署
@@ -301,13 +301,13 @@ window_function:
- **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期)
- **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理：
- **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪
- 
+ 
- **tail(window_size)**：获取数据中最后的window_size个点用于模型推，该窗口可用于数据裁剪
- 
+ 
- **count(window_size, sliding_step)**：基于点数的滑动窗口，每个窗口的数据会分别通过模型进行推理，如下图示例所示，window_size为2的窗口函数将输入数据集分为三个窗口，每个窗口分别进行推理运算生成结果。该窗口可用于连续推理
- 
+ 
**说明1: window可以用来解决sql查询结果和模型的输入行数要求不一致时的问题，对行进行裁剪。需要注意的是，当列数不匹配或是行数直接少于模型需求时，推理无法进行，会返回错误信息。**
@@ -456,7 +456,7 @@ Total line number = 4
在部分工业场景下，会存在预测电力负载的需求，预测结果可用于优化电力供应、节约能源和资源、支持规划和扩展以及增强电力系统的可靠性。
-我们所使用的 ETTh1 的测试集的数据为[ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv)。
+我们所使用的 ETTh1 的测试集的数据为[ETTh1](/img/ETTh1.csv)。
包含间隔1h采集一次的电力数据，每条数据由负载和油温构成，分别为：High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature。
@@ -519,7 +519,7 @@ Total line number = 48
图中10/24 00:00之前的数据为输入模型的过去数据，10/24 00:00后的蓝色线条为模型给出的油温预测结果，而红色为数据集中实际的油温数据（用于进行对比）。
-
+
可以看到，我们使用了过去96个小时（4天）的六个负载信息和对应时间油温的关系，基于之前学习到的序列间相互关系对未来48个小时（2天）的油温这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了较高程度的一致性。
@@ -529,7 +529,7 @@ Total line number = 48
我们利用某变电站中的电流、电压和功率等数据构成了真实场景下的数据集。该数据集包括变电站近四个月时间跨度，每5 - 6s 采集一次的 A相电压、B相电压、C相电压等数据。
-测试集数据内容为[data](https://alioss.timecho.com/docs/img/data.csv)。
+测试集数据内容为[data](/img/data.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过以往A相电压，B相电压和C相电压的数值和对应时间戳，预测未来一段时间内的C相电压，赋能变电站的监视管理。
@@ -584,7 +584,7 @@ Total line number = 48
图中 02/14 20:48 之前的数据为输入模型的过去数据， 02/14 20:48 后的蓝色线条为模型给出的C相电压预测结果，而红色为数据集中实际的C相电压数据（用于进行对比）。
-
+
可以看到，我们使用了过去10分钟的电压的数据，基于之前学习到的序列间相互关系对未来5分钟的C相电压这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了一定的同步性。
@@ -592,7 +592,7 @@ Total line number = 48
在民航交通运输业，存在着对乘机旅客数量进行异常检测的需求。异常检测的结果可用于指导调整航班的调度，以使得企业获得更大效益。
-Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](https://alioss.timecho.com/docs/img/airline.csv)。
+Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](/img/airline.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过捕捉序列的变化规律以对序列时间点进行异常检测，赋能交通运输业。
#### 步骤一：数据导入
@@ -645,6 +645,6 @@ Total line number = 144
我们将检测为异常的结果进行绘制，可以得到以下图像。其中蓝色曲线为原时间序列，用红色点特殊标注的时间点为算法检测为异常的时间点。
-
+
可以看到，Stray模型对输入序列变化进行了建模，成功检测出出现异常的时间点。
\ No newline at end of file
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md b/src/zh/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md
index 0165a99d8..1912bc5e6 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/Audit-Log_timecho.md
@@ -45,7 +45,7 @@
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
#### 可关闭部分用户连接的审计日志
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md b/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md
index 3c84f50a2..21a7eb8e4 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_apache.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -92,7 +92,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -263,7 +263,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -279,7 +279,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -303,7 +303,7 @@ with SINK (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -353,7 +353,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md b/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md
index 7d6b2d91c..7d9b059a5 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/Data-Sync_timecho.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -94,7 +94,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -271,7 +271,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -287,7 +287,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -311,7 +311,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`forwarding-pipe-requests` 均设置为 `false`，表示不转发从另一 pipe 传输而来的数据，以及要保持两侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据。
@@ -348,7 +348,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -398,7 +398,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
@@ -429,7 +429,7 @@ with sink (
本例子用来演示将一个 IoTDB 的数据，经过单向网闸，同步至另一个 IoTDB 的场景，数据链路如下图所示：
-
+
在这个例子中，需要使用 sink 任务中的 iotdb-air-gap-sink 插件，配置网闸后，在 A IoTDB 上执行下列语句，其中 node-urls 填写网闸配置的目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，详细语句如下：
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/Data-subscription.md b/src/zh/UserGuide/V1.3.3/User-Manual/Data-subscription.md
index 6b6be6063..cb46617a5 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/Data-subscription.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/Data-subscription.md
@@ -14,7 +14,7 @@ IoTDB 数据订阅模块（又称 IoTDB 订阅客户端）是IoTDB V1.3.3 版本
IoTDB 订阅客户端包含 3 个核心概念：Topic、Consumer、Consumer Group，具体关系如下图
+
+
+
-
+
### 示例
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/UDF-development.md b/src/zh/UserGuide/V1.3.3/User-Manual/UDF-development.md
index d2ecb5dcd..24e0366ad 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/UDF-development.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/UDF-development.md
@@ -127,7 +127,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 3 类参数：
@@ -142,7 +142,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
3 类参数的关系可见下图。策略的构造方法详见 Javadoc。
-
+
> 注意，最后的一些时间窗口的实际时间间隔可能小于规定的时间间隔参数。另外，可能存在某些时间窗口内数据行数量为 0 的情况，这种情况框架也会为该窗口调用一次`transform`方法。
@@ -150,7 +150,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingSizeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 个参数：
@@ -163,7 +163,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**时间间隔小于等于给定的最小时间间隔 sessionGap 则分为一组。**
-
+
`SessionTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 类参数：
@@ -175,7 +175,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**对于数值型数据，状态差值小于等于给定的阈值 delta 则分为一组。**
-
+
`StateWindowAccessStrategy`有四种构造方法：
diff --git a/src/zh/UserGuide/V1.3.3/User-Manual/White-List_timecho.md b/src/zh/UserGuide/V1.3.3/User-Manual/White-List_timecho.md
index 4b43a03fc..33245504e 100644
--- a/src/zh/UserGuide/V1.3.3/User-Manual/White-List_timecho.md
+++ b/src/zh/UserGuide/V1.3.3/User-Manual/White-List_timecho.md
@@ -66,5 +66,5 @@ white.list:
5. 是否开启白名单功能的配置，可以热加载。
6. 使用Java 原生接口修改白名单，必须是root用户才能修改，拒绝非root用户修改；修改内容必须合法，否则会抛出StatementExecutionException异常。
-
+
diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_apache.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_apache.md
index 92cc35c3a..971aa995c 100644
--- a/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_apache.md
+++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_apache.md
@@ -52,7 +52,7 @@ IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
-
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_timecho.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_timecho.md
index 21dadde38..ad0e513f4 100644
--- a/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Cluster-Concept_timecho.md
@@ -52,7 +52,7 @@ IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
-
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology.md b/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology.md
index bf396459a..65fc48659 100644
--- a/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology.md
+++ b/src/zh/UserGuide/latest-Table/Background-knowledge/Data-Model-and-Terminology.md
@@ -38,7 +38,7 @@
示例：下图是一个风电场的建模管理，通过多个层级【集团】-【风电场】-【风机】-【物理量】可以唯一确定一个实体测点。
+
+
+
+
+
+
+
+
|
+| **数据点（Data Point）** | 由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。如下图表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。 |
| **采集频率（Frequency）** | 指物理量在一定时间内产生数据的次数。例如，一个温度传感器可能每秒钟采集一次温度数据，那么它的采集频率就是1Hz（赫兹），即每秒1次。 |
| **数据保存时间（TTL）** | TTL 指定表中数据的保存时间，超过 TTL 的数据将自动删除。IoTDB 支持对不同的表设定不同的数据存活时间，便于 IoTDB 定期、自动地删除一定时间之前的数据。合理使用 TTL 可以控制 IoTDB 占用的总磁盘空间，避免磁盘写满等异常，并维持较高的查询性能和减少内存资源占用。 |
\ No newline at end of file
diff --git a/src/zh/UserGuide/latest-Table/Basic-Concept/Write-Updata-Data.md b/src/zh/UserGuide/latest-Table/Basic-Concept/Write-Updata-Data.md
index a546a9fa8..2b82430ae 100644
--- a/src/zh/UserGuide/latest-Table/Basic-Concept/Write-Updata-Data.md
+++ b/src/zh/UserGuide/latest-Table/Basic-Concept/Write-Updata-Data.md
@@ -43,7 +43,7 @@ INSERT INTO
+
+
+
+
+
-
+
+
-
-
+
+
+
+
+
## 产品特性
diff --git a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md
index 03ede2159..8bbd61f78 100644
--- a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md
+++ b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_timecho.md
@@ -32,7 +32,7 @@ TimechoDB 是一款低成本、高性能的物联网原生时序数据库，是
天谋产品体系由若干个组件构成，覆盖由【数据采集】到【数据管理】到【数据分析&应用】的全时序数据生命周期，做到“采-存-用”一体化时序数据解决方案，帮助用户高效地管理和分析物联网产生的海量时序数据。
+
+
## 产品特性
@@ -230,9 +230,9 @@ TimechoDB 将为用户提供更简单、易用的工具体系，通过集群监
IoTDB 性能监控
-
-
+
+
+
SQL 查询
-
-
+
+
+
+
+
#### 1.1.1 分区算法
由于生产环境中通常部署大量设备和传感器，IoTDB 使用序列分区算法以确保分区信息的大小可控。由于生成的时间序列与时间戳相关联，IoTDB 使用时间分区算法来清晰区分冷热分区。
@@ -57,7 +57,7 @@ $$\left\lfloor\frac{\text{Timestamp}-\text{StartTimestamp}}{\text{TimePartitionI
### 1.2 分区分配
IoTDB 使用分片来实现时间序列的弹性存储，集群中分片的数量由所有数据节点的总资源决定。由于分片的数量是动态的，IoTDB 可以轻松扩展。元数据分片和数据分片都遵循相同的分区分配算法，即均匀划分所有序列分区。下图展示了分区分配过程，其中动态扩展的分片匹配不断扩展的时间序列和集群。
-
+
#### 1.2.1 分片扩容
分片的数量由下式给出
@@ -77,7 +77,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 2.1 存储均衡
数据节点持有的副本数量反映了它的存储负载。如果数据节点之间的副本数量差异较大，拥有更多副本的数据节点可能成为存储瓶颈。尽管简单的轮询（Round Robin）放置算法可以通过确保每个数据节点持有等量副本来实现存储均衡，但它会降低集群的容错能力，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 将分片 $r_1$ 的 2 个副本放置在数据节点 $n_1$ 和 $n_2$ 上。
@@ -92,7 +92,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 2.2 计算均衡
数据节点持有的主副本数量反映了它的计算负载。如果数据节点之间持有主副本数量差异较大，拥有更多主副本的数据节点可能成为计算瓶颈。如果主副本选择过程使用直观的贪心算法，当副本以容错算法放置时，可能会导致主副本分布不均，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 选择分片 $r_5$ 在数据节点 $n_5$ 上的副本作为主副本。
diff --git a/src/zh/UserGuide/latest-Table/Tools-System/CLI.md b/src/zh/UserGuide/latest-Table/Tools-System/CLI.md
index fa72ab417..36a5d5f0b 100644
--- a/src/zh/UserGuide/latest-Table/Tools-System/CLI.md
+++ b/src/zh/UserGuide/latest-Table/Tools-System/CLI.md
@@ -67,7 +67,7 @@ Shell> sbin\start-cli.bat -h 127.0.0.1 -p 6667 -u root -pw root -sql_dialect tab
启动后出现如图提示即为启动成功。
-
+
## 2 在 CLI 中执行语句
@@ -80,7 +80,7 @@ Shell> sbin\start-cli.bat -h 127.0.0.1 -p 6667 -u root -pw root -sql_dialect tab
create database test
```
-
+
- 查看数据库
@@ -89,7 +89,7 @@ create database test
show databases
```
-
+
## 3 CLI 退出
diff --git a/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_apache.md b/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_apache.md
index cbad5d698..bc4dbdf81 100644
--- a/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_apache.md
+++ b/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_apache.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -46,7 +46,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -210,7 +210,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -226,7 +226,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode.streaming。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -250,7 +250,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`source.mode.double-living` 均设置为 `true`，表示不转发从另一 pipe 传输而来的数据。
@@ -285,7 +285,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置database-name 和 table-name 限制范围，详细语句如下：
@@ -335,7 +335,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `source.mode.double-living` 配置为`true`，详细语句如下：
diff --git a/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_timecho.md b/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_timecho.md
index 2acd17dd1..bd7f6661f 100644
--- a/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_timecho.md
+++ b/src/zh/UserGuide/latest-Table/User-Manual/Data-Sync_timecho.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -46,7 +46,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -215,7 +215,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -231,7 +231,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode.streaming。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -255,7 +255,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`source.mode.double-living` 均设置为 `true`，表示不转发从另一 pipe 传输而来的数据。
@@ -290,7 +290,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置database-name 和 table-name 限制范围，详细语句如下：
@@ -340,7 +340,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `source.mode.double-living` 配置为`true`，详细语句如下：
@@ -371,7 +371,7 @@ with sink (
本例子用来演示将一个 IoTDB 的数据，经过单向网闸，同步至另一个 IoTDB 的场景，数据链路如下图所示：
-
+
在这个例子中，需要使用 sink 任务中的 iotdb-air-gap-sink 插件，配置网闸后，在 A IoTDB 上执行下列语句，其中 node-urls 填写网闸配置的目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，详细语句如下：
diff --git a/src/zh/UserGuide/latest/API/Programming-MQTT.md b/src/zh/UserGuide/latest/API/Programming-MQTT.md
index bb8dd1a03..4a113cd13 100644
--- a/src/zh/UserGuide/latest/API/Programming-MQTT.md
+++ b/src/zh/UserGuide/latest/API/Programming-MQTT.md
@@ -30,7 +30,7 @@
IoTDB 支持 MQTT v3.1（OASIS 标准）协议。
IoTDB 服务器包括内置的 MQTT 服务，该服务允许远程设备将消息直接发送到 IoTDB 服务器。
-
+
## 内置 MQTT 服务
内置的 MQTT 服务提供了通过 MQTT 直接连接到 IoTDB 的能力。 它侦听来自 MQTT 客户端的发布消息，然后立即将数据写入存储。
@@ -57,7 +57,7 @@ MQTT 主题与 IoTDB 时间序列相对应。
```
或者以上两者的JSON数组形式。
-
+
## MQTT 配置
默认情况下，IoTDB MQTT 服务从`${IOTDB_HOME}/${IOTDB_CONF}/iotdb-system.properties`加载配置。
diff --git a/src/zh/UserGuide/latest/API/Programming-ODBC.md b/src/zh/UserGuide/latest/API/Programming-ODBC.md
index df78dd589..08a7fc6b8 100644
--- a/src/zh/UserGuide/latest/API/Programming-ODBC.md
+++ b/src/zh/UserGuide/latest/API/Programming-ODBC.md
@@ -39,7 +39,7 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
1. **下载 Zappy-Sys ODBC-JDBC 桥插件**：
进入 https://zappysys.com/products/odbc-powerpack/odbc-jdbc-bridge-driver/ 网站，点击下载按钮并直接安装。
- 
+ 
2. **准备 IoTDB**：打开 IoTDB 集群，并任意写入一条数据。
```sql
@@ -49,11 +49,11 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
3. **部署及调试插件**：
1. 打开 ODBC 数据源 32/64 位，取决于 Windows 的位数，一个示例的位置是 `C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools`。
- 
+ 
2. 点击添加，选择 ZappySys JDBC Bridge。
- 
+ 
3. 填写如下配置：
@@ -65,15 +65,15 @@ mvn clean package -pl iotdb-client/jdbc -am -DskipTests -P get-jar-with-dependen
| User name | IoTDB 的用户名 | root |
| User password | IoTDB 的密码 | root |
- 
+ 
4. 点击 Test Connection 按钮，应该显示连接成功。
- 
+ 
5. 点击上方的 Preview， 将查询文本换为 `select * from root.**`，点击 Preview Data，应该正确显示查询结果。
- 
+ 
4. **使用 ODBC 操作数据**：正确部署后，就可以使用 Windows 的 ODBC 库，对 IoTDB 的数据进行操作。 这里给出 C# 语言的代码示例：
```C#
diff --git a/src/zh/UserGuide/latest/API/Programming-OPC-UA_timecho.md b/src/zh/UserGuide/latest/API/Programming-OPC-UA_timecho.md
index ea23ccfbc..47e31ea4e 100644
--- a/src/zh/UserGuide/latest/API/Programming-OPC-UA_timecho.md
+++ b/src/zh/UserGuide/latest/API/Programming-OPC-UA_timecho.md
@@ -30,7 +30,7 @@ OPC UA 是一种在自动化领域用于不同设备和系统之间进行通信
- **Client/Server 模式**：在这种模式下，IoTDB 的流处理引擎通过 OPC UA Sink 与 OPC UA 服务器（Server）建立连接。OPC UA 服务器在其地址空间(Address Space) 中维护数据，IoTDB可以请求并获取这些数据。同时，其他OPC UA客户端（Client）也能访问服务器上的数据。
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md b/src/zh/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md
index 21dadde38..ad0e513f4 100644
--- a/src/zh/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md
+++ b/src/zh/UserGuide/latest/Background-knowledge/Cluster-Concept_timecho.md
@@ -52,7 +52,7 @@ IoTDB 在数据编码后，使用压缩技术进一步压缩二进制数据，
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
-
+
IoTDB 的集群包括如下常见概念：
diff --git a/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology.md b/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology.md
index 25e7083f8..8b4d3733c 100644
--- a/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology.md
+++ b/src/zh/UserGuide/latest/Background-knowledge/Data-Model-and-Terminology.md
@@ -25,7 +25,7 @@
根据企业组织结构和设备实体层次结构，我们将其物联网数据模型表示为如下图所示的属性层级组织结构，即电力集团层-风电场层-实体层-物理量层。其中 ROOT 为根节点，物理量层的每一个节点为叶子节点。IoTDB 采用树形结构定义数据模式，以从 ROOT 节点到叶子节点的路径来命名一个时间序列，层次间以“.”连接。例如，下图最左侧路径对应的时间序列名称为`ROOT.ln.wf01.wt01.status`。
-
+
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)。
@@ -94,7 +94,7 @@ Database 节点名只支持中英文字符、数字和下划线的组合。例
插入数据时，对齐的时间序列中某列的某些行允许有空值。
-
+
在后续数据定义语言、数据操作语言和 Java 原生接口章节，将对涉及到对齐时间序列的各种操作进行逐一介绍。
diff --git a/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md
index 96e9fdf9a..35f789271 100644
--- a/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md
+++ b/src/zh/UserGuide/latest/Background-knowledge/Navigating_Time_Series_Data.md
@@ -24,13 +24,13 @@
万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。
-
+
通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。
-
+
传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。
@@ -38,14 +38,14 @@
时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。
-
+
### 数据点
- 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。
- 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
-
+
### 测点
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 44e8df63a..ea83b049c 100644
--- a/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md
+++ b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_apache.md
@@ -346,9 +346,9 @@ It costs 0.058s
IoTDB 支持设备模板功能，实现同类型不同实体的物理量元数据共享，减少元数据内存占用，同时简化同类型实体的管理。
-
+
-
+
### 创建设备模板
@@ -775,7 +775,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
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 5ba32f82a..11ea81ef3 100644
--- a/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md
+++ b/src/zh/UserGuide/latest/Basic-Concept/Operate-Metadata_timecho.md
@@ -346,9 +346,9 @@ It costs 0.058s
IoTDB 支持设备模板功能，实现同类型不同实体的物理量元数据共享，减少元数据内存占用，同时简化同类型实体的管理。
-
+
-
+
### 创建设备模板
@@ -775,7 +775,7 @@ It costs 0.004s
那么 Metadata Tree 如下所示：
-
+
可以看到，`root`被定义为`LEVEL=0`。那么当你输入如下语句时：
diff --git a/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md b/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md
index 060940e48..cc7895a9d 100644
--- a/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md
+++ b/src/zh/UserGuide/latest/Basic-Concept/Query-Data.md
@@ -866,7 +866,7 @@ IoTDB支持通过`GROUP BY`子句对序列进行分段或者分组聚合。
下图中指出了这三个参数的含义：
-
+
接下来，我们给出几个典型例子：
@@ -1115,7 +1115,7 @@ group by variation(controlExpression[,delta][,ignoreNull=true/false])
下图为差值分段的一个分段方式示意图，与组中第一个数据的控制列值的差值在delta内的控制列对应的点属于相同的分组。
-
+
##### 使用注意事项
1. `controlExpression`的结果应该为唯一值，如果使用通配符拼接后出现多列，则报错。
@@ -1288,7 +1288,7 @@ group by session(timeInterval)
下图为`group by session`下的一个分组示意图
-
+
##### 使用注意事项
1. 对于一个分组，默认Time列输出分组的开始时间，查询时可以使用select `__endTime`的方式来使得结果输出分组的结束时间。
diff --git a/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md
index 4c4710469..143b1eda8 100644
--- a/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md
+++ b/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md
@@ -31,8 +31,8 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，
### 部署模式
-
+
+
+
- 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用
- 建议在 `iotdb-enterprise-ainode-
-
+
+
+
- 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用
- 建议在 `iotdb-enterprise-ainode-
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在编辑数据源的时候，`Type`一栏选择`Simplejson`，`URL`一栏填写 http://\
+
### 操作 Grafana
进入 Grafana 可视化页面后，可以选择添加时间序列，如下图。您也可以按照 Grafana 官方文档进行相应的操作，详情可参看 Grafana 官方文档：http://docs.grafana.org/guides/getting_started/。
-
+
## 配置 grafana
diff --git a/src/zh/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md b/src/zh/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md
index 7e3853018..f7f3297f7 100644
--- a/src/zh/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md
+++ b/src/zh/UserGuide/latest/Ecosystem-Integration/Grafana-Plugin.md
@@ -115,9 +115,9 @@ Grafana 以网页的 dashboard 形式为您展示数据，在使用时请您打
点击左侧的 `设置` 图标，选择 `Data Source` 选项，然后再点击 `Add data source`。
-
+
-
+
选择 `Apache IoTDB` 数据源，`URL` 一栏填写 `http://
+
@@ -133,15 +133,15 @@ Ip 为您的 IoTDB 服务器所在的宿主机 IP，port 为 REST 服务的运
点击左侧的 `Dashboards` 图标，选择 `Manage`，如下图所示：
-
+
点击右上方的 `New Dashboard` 图标，选择 `Add an empty panel`，如下图所示：
-
+
Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，默认是SQL: Full Customized方式。
-
+
#### SQL: Full Customized 输入方式
@@ -149,7 +149,7 @@ Grafana Plugin 支持SQL: Full Customized和SQL: Drop-down List 两种方式，
如果一个查询涉及多个表达式，我们可以点击 SELECT 输入框右侧的 `+` 来添加 SELECT 子句中的表达式，也可以点击 FROM 输入框右侧的 `+` 来添加路径前缀，如下图所示：
-
+
SELECT 输入框中的内容可以是时间序列的后缀，可以是函数或自定义函数，可以是算数表达式，也可以是它们的嵌套表达式。您还可以使用 as 子句来重命名需要显示的结果序列名字。
@@ -182,7 +182,7 @@ CONTROL 输入框为非必须填写项目，填写内容应当是控制查询类
#### SQL: Drop-down List 输入方式
在 TIME-SERIES 选择框中选择一条时间序列、FUNCTION 选择一个函数、SAMPLING INTERVAL、SLIDING STEP、LEVEL、FILL 输入框中输入内容，其中 TIME-SERIESL 为必填项其余为非必填项。
-
+
### 变量与模板功能的支持
@@ -190,32 +190,32 @@ SQL: Full Customized和SQL: Drop-down List两种输入方式都支持 Grafana
创建一个新的 Panel 后，点击右上角的设置按钮，如下图所示：
-
+
选择 `Variables`，点击 `Add variable` ，如下图所示：
-
+
示例一：输入 `Name`，`Label`，选择Type的`Query`、在Query 中输入show child paths xx ， 点击 `Update` 按钮，如下图所示：
-
+
应用 Variables，在 `grafana panel` 中输入变量点击 `save` 按钮，如下图所示
-
+
示例二：变量嵌套使用，如下图所示
-
+
-
+
-
+
示例三：函数变量使用，如下图所示
-
+
-
+
上图中Name 是变量名称也是将来我们在panel中使用的变量名称，Label是变量的展示名称如果为空就显示Name的变量反之则显示Label的名称，
Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、Ad hoc filters等这些都可以在IoTDB的Grafana Plugin 中使用
@@ -244,15 +244,15 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
1. 在Grafana panel中，点击alerting按钮，如下图所示：
-
+
2. 点击`Create alert rule from this panel`，如下图所示：
-
+
3. 在第1步中设置查询和警报条件，Conditions 表示查询条件，可以配置多个组合查询条件。如下图所示：
-
+
图中的查询条件：min() OF A IS BELOW 0，表示将A选项卡中的最小值在0一下就会触发条件，单击该函数可将其更改为另一个函数。
@@ -260,23 +260,23 @@ Type下拉中有Query、Custom、Text box、Constant、DataSource、Interval、A
例如，我们按以下顺序有 3 个条件： 条件：B（计算为：TRUE）或条件：C（计算为：FALSE）和条件：D（计算为：TRUE） 所以结果将计算为（（对或错）和对）=对。
4. 选择完指标及告警规则后点击`Preview`按钮，进行数据预览如下图所示：
-
+
5. 在第 2 步中，指定警报评估间隔，对于`Evaluate every`，指定评估频率。必须是 10 秒的倍数。例如，1m，30s。
对于`Evaluate for`，指定在警报触发之前的持续时间。如下图所示：
-
+
6. 在第 3 步中，添加存储位置、规则组以及与规则关联的其他元数据。 其中`Rule name`指定规则的名称。规则名称必须是唯一的。
-
+
7. 在第 4 步中，添加自定义标签。 从下拉列表中选择现有键值对添加自定义标签，或通过输入新键或值来添加新标签。如下图所示：
-
+
8. 单击保存以保存规则或单击保存并退出以保存规则并返回到警报页面。
9. 告警状态常用的有`Normal`、`Pending`、`Firing`等状态，如下图所示：
-
-
+
+
10. 我们也可以为告警配置`Contact points`用来接收告警通知，更加详细操作可以参考官方文档(https://grafana.com/docs/grafana/latest/alerting/manage-notifications/create-contact-point/)。
diff --git a/src/zh/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md b/src/zh/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
index efbcbaaf9..42f5ed013 100644
--- a/src/zh/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
+++ b/src/zh/UserGuide/latest/Ecosystem-Integration/Ignition-IoTDB-plugin_timecho.md
@@ -35,7 +35,7 @@ Ignition 是一个基于WEB的监控和数据采集工具（SCADA）- 一个开
两个模块与Ignition的具体关系与内容如下图所示。
- 
+ 
## 安装要求
@@ -58,23 +58,23 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
步骤一：进入 `Config` - `System`- `Modules` 模块，点击最下方的`Install or Upgrade a Module...`
-
+
步骤二：选择获取到的 `modl`，选择文件并上传，点击 `Install`，信任相关证书。
-
+
步骤三：安装完成后可以看到如下内容
-
+
步骤四：进入 `Config` - `Tags`- `History` 模块，点击下方的`Create new Historical Tag Provider...`
-
+
步骤五：选择 `IoTDB`并填写配置信息
-
+
配置内容如下：
@@ -161,21 +161,21 @@ Ignition-IoTDB连接器提供了将 Ignition 采集到的数据存入 IoTDB 的
- 配置好 `Provider` 后就可以在 `Designer` 中使用 `IoTDB Tag Historian` 了，就跟使用其他的 `Provider` 一样，右键点击对应 `Tag` 选择 `Edit tag(s)`，在 Tag Editor 中选择 History 分类
- 
+ 
- 设置 `History Enabled` 为 `true`，并选择 `Storage Provider` 为上一步创建的 `Provider`，按需要配置其它参数，并点击 `OK`，然后保存项目。此时数据将会按照设置的内容持续的存入 `IoTDB` 实例中。
- 
+ 
#### 读取数据
- 也可以在 Report 的 Data 标签下面直接选择存入 IoTDB 的 Tags
- 
+ 
- 在 Components 中也可以直接浏览相关数据
- 
+ 
#### 脚本模块：该功能能够与 IoTDB 进行交互
@@ -242,15 +242,15 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
步骤一：进入 `Config` - `Databases` -`Drivers` 模块，创建 `Translator`
-
+
步骤二：进入 `Config` - `Databases` -`Drivers` 模块，创建 `JDBC Driver`，选择上一步配置的 `Translator`并上传下载的 `IoTDB-JDBC`，Classname 配置为 `org.apache.iotdb.jdbc.IoTDBDriver`
-
+
步骤三：进入 `Config` - `Databases` -`Connections` 模块，创建新的 `Connections`，`JDBC Driver` 选择上一步创建的 `IoTDB Driver`，配置相关信息后保存即可使用
-
+
### 使用说明
@@ -264,11 +264,11 @@ system.iotdb.query("IoTDB", "select * from root.db.Sine where time > 17095634272
不选择其他项，设置好字段，并 `Enabled` 后 数据会安装设置存入对应的 IoTDB
-
+
#### 数据查询
- 在 `Database Query Browser` 中选择`Data Source`选择之前创建的 `Connection`，即可编写 SQL 语句查询 IoTDB 中的数据
-
+
diff --git a/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md b/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md
index 566007d21..a21e6df28 100644
--- a/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md
+++ b/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_apache.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 手动创建文件夹
@@ -398,7 +398,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. 扩容
@@ -406,7 +406,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
新增pv，必须有可用的pv才可以扩容。
-
+
**注意：DataNode重启后无法加入集群**
@@ -424,7 +424,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 扩容datanode
diff --git a/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md b/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md
index 74947c84c..2a6847ff9 100644
--- a/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md
+++ b/src/zh/UserGuide/latest/Ecosystem-Integration/Kubernetes_timecho.md
@@ -102,7 +102,7 @@ kubectl apply -f pv-02.yaml
```Bash
kubectl get pv
```
-
+
### 3.4 手动创建文件夹
@@ -398,7 +398,7 @@ start-cli.sh -h 172.20.31.87 -p 31895
start-cli.sh -h 172.20.31.88 -p 31895
```
-
+
## 10. 扩容
@@ -406,7 +406,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
新增pv，必须有可用的pv才可以扩容。
-
+
**注意：DataNode重启后无法加入集群**
@@ -424,7 +424,7 @@ start-cli.sh -h 172.20.31.88 -p 31895
helm upgrade iotdb . -n iotdb-ns
```
-
+
### 10.3 扩容datanode
diff --git a/src/zh/UserGuide/latest/Ecosystem-Integration/Telegraf.md b/src/zh/UserGuide/latest/Ecosystem-Integration/Telegraf.md
index 47d7bd078..52250fb5d 100644
--- a/src/zh/UserGuide/latest/Ecosystem-Integration/Telegraf.md
+++ b/src/zh/UserGuide/latest/Ecosystem-Integration/Telegraf.md
@@ -38,7 +38,7 @@ Telegraf 有以下这些特点：
Telegraf-IoTDB 插件可以将保存在 Telegraf 中的监控信息输出存储到 IoTDB，输出插件使用了 IoTDB session 进行连接和数据写入。
-
+
## 2、安装要求
@@ -66,7 +66,7 @@ Telegraf支持多种操作系统，包括Linux、Windows、macOS，Telegraf 的
| name_prefix | 指定附加到测量名称的前缀 | |
| name_suffix | 指定附加到测量名称的后缀 | |
-
+
### 4.2 设置输出源
@@ -81,7 +81,7 @@ Telegraf支持多种操作系统，包括Linux、Windows、macOS，Telegraf 的
| timestamp_precision | 时间戳精度 | timestamp_precision = "millisecond" | timestamp_precision = "与 IoTDB 相同的时间戳精度" | 您可以通过查看"iotdb-system.properties"中"timestamp_precision"字段即可 |
| sanitize_tag | 数据库版本 | 无 | sanitize_tag = "0.13/1.0/1.1/1.2/1.3" | |
-
+
### 4.3 启动Telegraf服务
diff --git a/src/zh/UserGuide/latest/Ecosystem-Integration/Thingsboard.md b/src/zh/UserGuide/latest/Ecosystem-Integration/Thingsboard.md
index 4761428cd..38728b6bb 100644
--- a/src/zh/UserGuide/latest/Ecosystem-Integration/Thingsboard.md
+++ b/src/zh/UserGuide/latest/Ecosystem-Integration/Thingsboard.md
@@ -26,7 +26,7 @@
ThingsBoard 是一个开源物联网平台，可实现物联网项目的快速开发、管理和扩展。更多介绍详情请参考[ ThingsBoard 官网](https://thingsboard.io/docs/getting-started-guides/what-is-thingsboard/)。
- 
+ 
2. ThingsBoard-IoTDB 简介
@@ -34,7 +34,7 @@
### 关系示意图
-
+
## 安装要求
@@ -77,23 +77,23 @@ export IoTDB_DATABASE=root.thingsboard ##thingsboard数据写入iotdb所存储
1. 创建设备并接入数据：在 Thingsboard 的实体-设备中创建设备并通过工业网关将数据发送到 ThingsBoard 指定设备中
-
+
2. 设置规则链：在规则链库中对于“SD-032F 泵”设置告警规则并将该规则链设置为根链
-
+
+
-
+
+
+
+
## 产品特性
diff --git a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_timecho.md b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_timecho.md
index 03ede2159..8bbd61f78 100644
--- a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_timecho.md
+++ b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_timecho.md
@@ -32,7 +32,7 @@ TimechoDB 是一款低成本、高性能的物联网原生时序数据库，是
天谋产品体系由若干个组件构成，覆盖由【数据采集】到【数据管理】到【数据分析&应用】的全时序数据生命周期，做到“采-存-用”一体化时序数据解决方案，帮助用户高效地管理和分析物联网产生的海量时序数据。
+
+
## 产品特性
@@ -230,9 +230,9 @@ TimechoDB 将为用户提供更简单、易用的工具体系，通过集群监
IoTDB 性能监控
-
-
+
+
+
SQL 查询
-
-
+
+
+
+
+
| 函数名 | 可接收的输入序列类型 | 属性参数 | 输出序列类型 | 功能类型 |
| ------ | ------------------------------ | ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
@@ -2574,7 +2574,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `windowSize`: 一个窗口内的点数。Int数据类型。必需的属性参数。
+ `slidingStep`: 按照设定的点数来滑动窗口。Int数据类型。可选的属性参数；如果没有设置，默认取值和`windowSize`一样。
-
+
**(2) 滑动时间窗口（SlidingTimeWindowAccessStrategy）使用的属性参数:**
@@ -2583,7 +2583,7 @@ M4用于在窗口内采样第一个点（`first`）、最后一个点（`last`
+ `displayWindowBegin`: 窗口滑动的起始时间戳位置（包含在内）。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MIN_VALUE，意为使用输入的时间序列的第一个点的时间戳作为窗口滑动的起始时间戳位置。
+ `displayWindowEnd`: 结束时间限制（不包含在内；本质上和`WHERE time < displayWindowEnd`起的效果是一样的)。Long数据类型。可选的属性参数；如果没有设置，默认取值为Long.MAX_VALUE，意为除了输入的时间序列自身数据读取完毕之外没有增加额外的结束时间过滤条件限制。
-
+
#### 示例
diff --git a/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md
index 038c85164..279c3d1c9 100644
--- a/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md
+++ b/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md
@@ -254,8 +254,8 @@ IoTDB> count devices where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:
### 设备模板管理
-
-
+
+
diff --git a/src/zh/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md b/src/zh/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md
index 3d188f07e..119e003a0 100644
--- a/src/zh/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md
+++ b/src/zh/UserGuide/latest/Technical-Insider/Cluster-data-partitioning.md
@@ -28,7 +28,7 @@ IoTDB 为时间序列数据实现了量身定制的分区算法。在此基础
### 分区策略
IoTDB 将生产环境中的每个传感器映射为一个时间序列。然后，使用序列分区算法对时间序列进行分区以管理其元数据，再结合时间分区算法来管理其数据。下图展示了 IoTDB 如何对时序数据进行分区。
-
+
#### 分区算法
由于生产环境中通常部署大量设备和传感器，IoTDB 使用序列分区算法以确保分区信息的大小可控。由于生成的时间序列与时间戳相关联，IoTDB 使用时间分区算法来清晰区分冷热分区。
@@ -56,7 +56,7 @@ $$\left\lfloor\frac{\text{Timestamp}-\text{StartTimestamp}}{\text{TimePartitionI
### 分区分配
IoTDB 使用分片来实现时间序列的弹性存储，集群中分片的数量由所有数据节点的总资源决定。由于分片的数量是动态的，IoTDB 可以轻松扩展。元数据分片和数据分片都遵循相同的分区分配算法，即均匀划分所有序列分区。下图展示了分区分配过程，其中动态扩展的分片匹配不断扩展的时间序列和集群。
-
+
#### 分片扩容
分片的数量由下式给出
@@ -76,7 +76,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 存储均衡
数据节点持有的副本数量反映了它的存储负载。如果数据节点之间的副本数量差异较大，拥有更多副本的数据节点可能成为存储瓶颈。尽管简单的轮询（Round Robin）放置算法可以通过确保每个数据节点持有等量副本来实现存储均衡，但它会降低集群的容错能力，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 将分片 $r_1$ 的 2 个副本放置在数据节点 $n_1$ 和 $n_2$ 上。
@@ -91,7 +91,7 @@ $$\text{RegionGroupNumber}=\left\lfloor\frac{\sum_{i=1}^{DataNodeNumber}\text{Re
### 计算均衡
数据节点持有的主副本数量反映了它的计算负载。如果数据节点之间持有主副本数量差异较大，拥有更多主副本的数据节点可能成为计算瓶颈。如果主副本选择过程使用直观的贪心算法，当副本以容错算法放置时，可能会导致主副本分布不均，如下所示：
-
+
+ 假设集群有 4 个数据节点，4 个分片，并且副本因子为 2。
+ 选择分片 $r_5$ 在数据节点 $n_5$ 上的副本作为主副本。
diff --git a/src/zh/UserGuide/latest/Tools-System/Benchmark.md b/src/zh/UserGuide/latest/Tools-System/Benchmark.md
index 9c89db2a2..bf607f481 100644
--- a/src/zh/UserGuide/latest/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/latest/Tools-System/Benchmark.md
@@ -27,13 +27,13 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
下图1-1囊括了测试基准流程及其他扩展功能。这些流程可以由IoT-benchmark 统一来完成。IoT Benchmark 支持多种工作负载，包括**纯写入、纯查询、写入查询混合**等，支持**软硬件系统监控、测试指标度量**等监控功能，还实现了**初始化数据库自动化、测试数据分析及系统参数优化**等功能。
-
+
图1-1
借鉴 YCSB 测试工具将工作负载生成、性能指标测量和数据库接口三个组件分离的设计思想，IoT-benchmark 的模块化设计如图1-2所示。与基于 YCSB 的测试工具系统不同的是，IoT-benchmark 增加了系统监控模块，支持测试数据和系统指标监控数据的持久化。此外也增加了一些特别针对时序数据场景的特殊负载测试功能，如支持物联网场景的批量写入和多种乱序数据写入模式。
-
+
图1-2
@@ -77,7 +77,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试包的目录结构如下图1-3所示。其中测试配置文件为conf/config.properties，测试启动脚本为benchmark\.sh (Linux & MacOS) 和 benchmark.bat (Windows)，详细文件用途见表1-2所示。
-
+
图1-3文件和文件夹列表
@@ -105,7 +105,7 @@ IoT-benchmark 是基于 Java 和大数据环境开发的时序数据库基准测
测试的所有日志文件被存放于 logs 文件夹下，测试的结果在测试完成后被存放到 data/csvOutput 文件夹下，例如测试后我们得到了如下的结果矩阵：
-
+
- Result Matrix
- OkOperation：成功的对应操作次数
@@ -267,24 +267,24 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
首先在172.21.4.3和172.21.4.4上分别启动被测时间序列数据库Apache-IoTDB和KairosDB，之后在172.21.4.2、172.21.4.3和172.21.4.4上通过ser-benchamrk.sh脚本启动服务器资源监控（图2-1）。然后按照表2-3在172.21.4.2分别修改iotdb-0.13-0.0.1和kairosdb-0.0.1文件夹内的conf/config.properties文件满足测试需求。先后使用benchmark.sh启动对Apache-IoTDB和KairosDB的写入测试。
-
+
图2-1服务器监控任务
例如我们首先启动对KairosDB的测试，IoT-benchmark会在MySQL数据库中创建CONFIG数据表存放本次测试配置信息（图2-2），测试执行中会有日志输出当前测试进度（图2-3）。测试完成时会输出本次测试结果（图2-3），同时将结果写入FINAL_RESULT数据表中（图2-4）。
-
+
图2-2测试配置信息表
-
-
-
-
+
+
+
+
图2-3测试进度和结果
-
+
图2-4测试结果表
@@ -292,14 +292,14 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
依照测试结果信息我们知道同样的配置写入Apache-IoTDB和KairosDB写入延时时间分别为：55.98ms和1324.45ms；写入吞吐分别为：5,125,600.86点/秒和224,819.01点/秒；测试分别执行了585.30秒和11777.99秒。并且KairosDB有写入失败出现，排查后发现是数据磁盘使用率已达到100%，无磁盘空间继续接收数据。而Apache-IoTDB无写入失败现象，全部数据写入完毕后占用磁盘空间仅为4.7G（如图2-5所示）；从写入吞吐和磁盘占用情况上看Apache-IoTDB均优于KairosDB。当然后续还有其他测试来从多方面观察和对比，比如查询性能、文件压缩比、数据安全性等。
-
+
图2-5磁盘使用情况
那么测试过程中各个服务器资源使用情况如何呢？每个写操作具体的表现如何呢？这个时候我们就可以通过安装和使用Tableau来可视化服务器监控表和测试过程记录表内的数据了。Tableau的使用本文不展开介绍，通过它连接测试数据持久化的数据表后具体结果下如图（以Apache-IoTDB为例）：
-
-
+
+
图2-6Tableau可视化测试过程
@@ -326,7 +326,7 @@ IoT-benchmark目前支持通过配置参数“TEST_DATA_PERSISTENCE”将测试
执行结果：
-
+
图2-7查询测试结果
diff --git a/src/zh/UserGuide/latest/Tools-System/CLI.md b/src/zh/UserGuide/latest/Tools-System/CLI.md
index 80728257b..aaff80934 100644
--- a/src/zh/UserGuide/latest/Tools-System/CLI.md
+++ b/src/zh/UserGuide/latest/Tools-System/CLI.md
@@ -132,47 +132,47 @@ Shell > ./kc.sh start-dev
```
2、使用 https://ip:port 登陆 keycloack, 首次登陆需要创建用户
-
+
3、点击 Administration Console 进入管理端
-
+
4、在左侧的 Master 菜单点击 Create Realm, 输入 Realm Name 创建一个新的 Realm
-
+
-
+
5、点击左侧菜单 Clients，创建 client
-
+
6、点击左侧菜单 User，创建 user
-
+
7、点击新创建的用户 id，点击 Credentials 导航输入密码和关闭 Temporary 选项，至此 keyclork 配置完成
-
+
8、创建角色，点击左侧菜单的 Roles然后点击Create Role 按钮添加角色
-
+
9、在Role Name 中输入`iotdb_admin`，点击save 按钮。提示：这里的`iotdb_admin`不能为其他名称否则即使登陆成功后也将无权限使用iotdb的查询、插入、创建 database、添加用户、角色等功能
-
+
10、点击左侧的User 菜单然后点击用户列表中的用户为该用户添加我们刚创建的`iotdb_admin`角色
-
+
11、选择Role Mappings ，在Assign role选择`iotdb_admin`增加角色
-
+
-
+
提示：如果用户角色有调整需要重新生成token并且重新登陆iotdb才会生效
@@ -188,7 +188,7 @@ Shell > bash sbin/start-cli.sh -h 10.129.187.21 -p 6667 -u {my-access-token} -pw
其中，需要将{my-access-token} （注意，包括{}）替换成你的 token，即 access_token 对应的值。密码为空需要再次确认。
-
+
如何获取 token 取决于你的 OIDC 设置。 最简单的一种情况是使用`password-grant`。例如，假设你在用 keycloack 作为你的 OIDC 服务，
并且你在 keycloack 中有一个被定义成 public 的`iotdb`客户的 realm，那么你可以使用如下`curl`命令获得 token。
diff --git a/src/zh/UserGuide/latest/Tools-System/Data-Import-Tool.md b/src/zh/UserGuide/latest/Tools-System/Data-Import-Tool.md
index ed67d75ae..4c14acdf9 100644
--- a/src/zh/UserGuide/latest/Tools-System/Data-Import-Tool.md
+++ b/src/zh/UserGuide/latest/Tools-System/Data-Import-Tool.md
@@ -145,7 +145,7 @@ IoTDB 目前支持将 CSV、SQL、及TsFile（IoTDB底层开放式时间序列
TsFile 主动监听&加载功能能够主动监听指定目标路径（用户配置）下TsFile的文件变化，并将目标路径下的TsFile文件自动同步至指定接收路径（用户配置）。通过此功能，IoTDB 能自动检测并加载这些文件，无需手动执行任何额外的加载操作。这种自动化流程不仅简化了用户的操作步骤，还减少了操作过程中可能出现的错误，有效降低了用户在使用过程中的复杂性。
-
+
### 4.1 配置参数
diff --git a/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_apache.md b/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_apache.md
index ebb3b4f6c..3ef0730da 100644
--- a/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_apache.md
+++ b/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_apache.md
@@ -67,7 +67,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -107,9 +107,9 @@ static_configs:
`Apache IoTDB Dashboard`是 IoTDB 企业版的配套产品，支持统一集中式运维管理，可通过一个监控面板监控多个集群。你可以联系商务获取到 Dashboard 的 Json文件。
-
+
-
+
### 集群概览
@@ -122,7 +122,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -131,7 +131,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -144,7 +144,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -153,7 +153,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -163,8 +163,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md b/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md
index 8b6396ded..d3cc9dceb 100644
--- a/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md
+++ b/src/zh/UserGuide/latest/Tools-System/Monitor-Tool_timecho.md
@@ -67,7 +67,7 @@ file_count{name="seq",} 2.0
IoTDB、Prometheus、Grafana三者的关系如下图所示:
-
+
1. IoTDB在运行过程中持续收集监控指标数据。
2. Prometheus以固定的间隔（可配置）从IoTDB的HTTP接口拉取监控指标数据。
@@ -106,9 +106,9 @@ static_configs:
我们提供了Apache IoTDB Dashboard，支持统一集中式运维管理，可通过一个监控面板监控多个集群。
-
+
-
+
你可以在企业版中获取到 Dashboard 的 Json文件。
@@ -122,7 +122,7 @@ static_configs:
- 集群各节点当前CPU、内存、磁盘使用率
- 分节点的信息
-
+
### 数据写入
@@ -131,7 +131,7 @@ static_configs:
- WAL文件数量与尺寸
- 节点 WAL flush SyncBuffer 耗时
-
+
### 数据查询
@@ -144,7 +144,7 @@ static_configs:
- 节点查询按照Chunk元数据过滤耗时
- 节点查询构造Chunk Reader耗时的平均值
-
+
### 存储引擎
@@ -153,7 +153,7 @@ static_configs:
- 处于各阶段的TsFile数量、大小
- 各类任务的数量与耗时
-
+
### 系统监控
@@ -163,8 +163,8 @@ static_configs:
- JVM GC时间占比、分类型的GC次数、GC数据量、各年代的堆内存占用
- 网络传输速率、包发送速率
-
+
-
+
-
+
diff --git a/src/zh/UserGuide/latest/Tools-System/Workbench_timecho.md b/src/zh/UserGuide/latest/Tools-System/Workbench_timecho.md
index 2df219d05..2eb3f47b4 100644
--- a/src/zh/UserGuide/latest/Tools-System/Workbench_timecho.md
+++ b/src/zh/UserGuide/latest/Tools-System/Workbench_timecho.md
@@ -25,10 +25,10 @@ IoTDB的可视化控制台包含以下功能模块：
主要功能展示：
* 首页
-
+
* 测点列表
-
+
* 数据查询
-
+
* 趋势
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/latest/User-Manual/AINode_apache.md b/src/zh/UserGuide/latest/User-Manual/AINode_apache.md
index 2da3c824d..331365d1a 100644
--- a/src/zh/UserGuide/latest/User-Manual/AINode_apache.md
+++ b/src/zh/UserGuide/latest/User-Manual/AINode_apache.md
@@ -25,7 +25,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
系统架构如下图所示：
::: center
-
+
:::
三种节点的职责如下：
@@ -41,7 +41,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
-
+
- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如：
- **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。
@@ -57,7 +57,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
::: center
-
+
:::
## 安装部署
@@ -301,13 +301,13 @@ window_function:
- **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期)
- **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理：
- **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪
- 
+ 
- **tail(window_size)**：获取数据中最后的window_size个点用于模型推，该窗口可用于数据裁剪
- 
+ 
- **count(window_size, sliding_step)**：基于点数的滑动窗口，每个窗口的数据会分别通过模型进行推理，如下图示例所示，window_size为2的窗口函数将输入数据集分为三个窗口，每个窗口分别进行推理运算生成结果。该窗口可用于连续推理
- 
+ 
**说明1: window可以用来解决sql查询结果和模型的输入行数要求不一致时的问题，对行进行裁剪。需要注意的是，当列数不匹配或是行数直接少于模型需求时，推理无法进行，会返回错误信息。**
@@ -456,7 +456,7 @@ Total line number = 4
在部分工业场景下，会存在预测电力负载的需求，预测结果可用于优化电力供应、节约能源和资源、支持规划和扩展以及增强电力系统的可靠性。
-我们所使用的 ETTh1 的测试集的数据为[ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv)。
+我们所使用的 ETTh1 的测试集的数据为[ETTh1](/img/ETTh1.csv)。
包含间隔1h采集一次的电力数据，每条数据由负载和油温构成，分别为：High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature。
@@ -519,7 +519,7 @@ Total line number = 48
图中10/24 00:00之前的数据为输入模型的过去数据，10/24 00:00后的蓝色线条为模型给出的油温预测结果，而红色为数据集中实际的油温数据（用于进行对比）。
-
+
可以看到，我们使用了过去96个小时（4天）的六个负载信息和对应时间油温的关系，基于之前学习到的序列间相互关系对未来48个小时（2天）的油温这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了较高程度的一致性。
@@ -529,7 +529,7 @@ Total line number = 48
我们利用某变电站中的电流、电压和功率等数据构成了真实场景下的数据集。该数据集包括变电站近四个月时间跨度，每5 - 6s 采集一次的 A相电压、B相电压、C相电压等数据。
-测试集数据内容为[data](https://alioss.timecho.com/docs/img/data.csv)。
+测试集数据内容为[data](/img/data.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过以往A相电压，B相电压和C相电压的数值和对应时间戳，预测未来一段时间内的C相电压，赋能变电站的监视管理。
@@ -584,7 +584,7 @@ Total line number = 48
图中 02/14 20:48 之前的数据为输入模型的过去数据， 02/14 20:48 后的蓝色线条为模型给出的C相电压预测结果，而红色为数据集中实际的C相电压数据（用于进行对比）。
-
+
可以看到，我们使用了过去10分钟的电压的数据，基于之前学习到的序列间相互关系对未来5分钟的C相电压这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了一定的同步性。
@@ -592,7 +592,7 @@ Total line number = 48
在民航交通运输业，存在着对乘机旅客数量进行异常检测的需求。异常检测的结果可用于指导调整航班的调度，以使得企业获得更大效益。
-Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](https://alioss.timecho.com/docs/img/airline.csv)。
+Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](/img/airline.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过捕捉序列的变化规律以对序列时间点进行异常检测，赋能交通运输业。
#### 步骤一：数据导入
@@ -645,6 +645,6 @@ Total line number = 144
我们将检测为异常的结果进行绘制，可以得到以下图像。其中蓝色曲线为原时间序列，用红色点特殊标注的时间点为算法检测为异常的时间点。
-
+
可以看到，Stray模型对输入序列变化进行了建模，成功检测出出现异常的时间点。
\ No newline at end of file
diff --git a/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md b/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
index 291d2a1e1..32dfb729d 100644
--- a/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
@@ -25,7 +25,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
系统架构如下图所示：
::: center
-
+
:::
三种节点的职责如下：
@@ -41,7 +41,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
-
+
- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如：
- **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。
@@ -57,7 +57,7 @@ AINode 是 IoTDB 在ConfigNode、DataNode后提供的第三种内生节点，该
- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
::: center
-
+
:::
## 安装部署
@@ -301,13 +301,13 @@ window_function:
- **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期)
- **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理：
- **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪
- 
+ 
- **tail(window_size)**：获取数据中最后的window_size个点用于模型推，该窗口可用于数据裁剪
- 
+ 
- **count(window_size, sliding_step)**：基于点数的滑动窗口，每个窗口的数据会分别通过模型进行推理，如下图示例所示，window_size为2的窗口函数将输入数据集分为三个窗口，每个窗口分别进行推理运算生成结果。该窗口可用于连续推理
- 
+ 
**说明1: window可以用来解决sql查询结果和模型的输入行数要求不一致时的问题，对行进行裁剪。需要注意的是，当列数不匹配或是行数直接少于模型需求时，推理无法进行，会返回错误信息。**
@@ -456,7 +456,7 @@ Total line number = 4
在部分工业场景下，会存在预测电力负载的需求，预测结果可用于优化电力供应、节约能源和资源、支持规划和扩展以及增强电力系统的可靠性。
-我们所使用的 ETTh1 的测试集的数据为[ETTh1](https://alioss.timecho.com/docs/img/ETTh1.csv)。
+我们所使用的 ETTh1 的测试集的数据为[ETTh1](/img/ETTh1.csv)。
包含间隔1h采集一次的电力数据，每条数据由负载和油温构成，分别为：High UseFul Load, High UseLess Load, Middle UseLess Load, Low UseFul Load, Low UseLess Load, Oil Temperature。
@@ -519,7 +519,7 @@ Total line number = 48
图中10/24 00:00之前的数据为输入模型的过去数据，10/24 00:00后的蓝色线条为模型给出的油温预测结果，而红色为数据集中实际的油温数据（用于进行对比）。
-
+
可以看到，我们使用了过去96个小时（4天）的六个负载信息和对应时间油温的关系，基于之前学习到的序列间相互关系对未来48个小时（2天）的油温这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了较高程度的一致性。
@@ -529,7 +529,7 @@ Total line number = 48
我们利用某变电站中的电流、电压和功率等数据构成了真实场景下的数据集。该数据集包括变电站近四个月时间跨度，每5 - 6s 采集一次的 A相电压、B相电压、C相电压等数据。
-测试集数据内容为[data](https://alioss.timecho.com/docs/img/data.csv)。
+测试集数据内容为[data](/img/data.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过以往A相电压，B相电压和C相电压的数值和对应时间戳，预测未来一段时间内的C相电压，赋能变电站的监视管理。
@@ -584,7 +584,7 @@ Total line number = 48
图中 02/14 20:48 之前的数据为输入模型的过去数据， 02/14 20:48 后的蓝色线条为模型给出的C相电压预测结果，而红色为数据集中实际的C相电压数据（用于进行对比）。
-
+
可以看到，我们使用了过去10分钟的电压的数据，基于之前学习到的序列间相互关系对未来5分钟的C相电压这一数据的可能变化进行了建模，可以看到可视化后预测曲线与实际结果在趋势上保持了一定的同步性。
@@ -592,7 +592,7 @@ Total line number = 48
在民航交通运输业，存在着对乘机旅客数量进行异常检测的需求。异常检测的结果可用于指导调整航班的调度，以使得企业获得更大效益。
-Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](https://alioss.timecho.com/docs/img/airline.csv)。
+Airline Passengers一个时间序列数据集，该数据集记录了1949年至1960年期间国际航空乘客数量，间隔一个月进行一次采样。该数据集共含一条时间序列。数据集为[airline](/img/airline.csv)。
在该数据集上，IoTDB-ML的模型推理功能可以通过捕捉序列的变化规律以对序列时间点进行异常检测，赋能交通运输业。
#### 步骤一：数据导入
@@ -645,6 +645,6 @@ Total line number = 144
我们将检测为异常的结果进行绘制，可以得到以下图像。其中蓝色曲线为原时间序列，用红色点特殊标注的时间点为算法检测为异常的时间点。
-
+
可以看到，Stray模型对输入序列变化进行了建模，成功检测出出现异常的时间点。
\ No newline at end of file
diff --git a/src/zh/UserGuide/latest/User-Manual/Audit-Log_timecho.md b/src/zh/UserGuide/latest/User-Manual/Audit-Log_timecho.md
index 0165a99d8..1912bc5e6 100644
--- a/src/zh/UserGuide/latest/User-Manual/Audit-Log_timecho.md
+++ b/src/zh/UserGuide/latest/User-Manual/Audit-Log_timecho.md
@@ -45,7 +45,7 @@
- Cli、workbench、Zeppelin、Grafana、通过 Session/JDBC/MQTT 等协议传入的请求
-
+
#### 可关闭部分用户连接的审计日志
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 3c84f50a2..21a7eb8e4 100644
--- a/src/zh/UserGuide/latest/User-Manual/Data-Sync_apache.md
+++ b/src/zh/UserGuide/latest/User-Manual/Data-Sync_apache.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -92,7 +92,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -263,7 +263,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -279,7 +279,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -303,7 +303,7 @@ with SINK (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -353,7 +353,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
diff --git a/src/zh/UserGuide/latest/User-Manual/Data-Sync_timecho.md b/src/zh/UserGuide/latest/User-Manual/Data-Sync_timecho.md
index 7d6b2d91c..7d9b059a5 100644
--- a/src/zh/UserGuide/latest/User-Manual/Data-Sync_timecho.md
+++ b/src/zh/UserGuide/latest/User-Manual/Data-Sync_timecho.md
@@ -28,7 +28,7 @@
一个数据同步任务包含 3 个阶段：
-
+
- 抽取（Source）阶段：该部分用于从源 IoTDB 抽取数据，在 SQL 语句中的 source 部分定义
- 处理（Process）阶段：该部分用于处理从源 IoTDB 抽取出的数据，在 SQL 语句中的 processor 部分定义
@@ -94,7 +94,7 @@
数据同步任务有三种状态：RUNNING、STOPPED 和 DROPPED。任务状态转换如下图所示：
-
+
创建后任务会直接启动，同时当任务发生异常停止后，系统会自动尝试重启任务。
@@ -271,7 +271,7 @@ IoTDB> SHOW PIPEPLUGINS
本例子用来演示将一个 IoTDB 的所有数据同步至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务，用来同步 A IoTDB 到 B IoTDB 间的全量数据，这里需要用到用到 sink 的 iotdb-thrift-sink 插件（内置插件），需通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，如下面的示例语句：
@@ -287,7 +287,7 @@ with sink (
本例子用来演示同步某个历史时间范围（ 2023 年 8 月 23 日 8 点到 2023 年 10 月 23 日 8 点）的数据至另一个 IoTDB，数据链路如下图所示：
-
+
在这个例子中，我们可以创建一个名为 A2B 的同步任务。首先我们需要在 source 中定义传输数据的范围，由于传输的是历史数据（历史数据是指同步任务创建之前存在的数据），需要配置数据的起止时间 start-time 和 end-time 以及传输的模式 mode。通过 node-urls 配置目标端 IoTDB 中 DataNode 节点的数据服务端口的 url。
@@ -311,7 +311,7 @@ with SINK (
本例子用来演示两个 IoTDB 之间互为双活的场景，数据链路如下图所示：
-
+
在这个例子中，为了避免数据无限循环，需要将 A 和 B 上的参数`forwarding-pipe-requests` 均设置为 `false`，表示不转发从另一 pipe 传输而来的数据，以及要保持两侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据。
@@ -348,7 +348,7 @@ with sink (
本例子用来演示多个 IoTDB 之间边云传输数据的场景，数据由 B 、C、D 集群分别都同步至 A 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 B 、C、D 集群的数据同步至 A，在 BA 、CA、DA 之间的 pipe 需要配置`path`限制范围，以及要保持边侧和云侧的数据一致 pipe 需要配置`inclusion=all`来同步全量数据和元数据，详细语句如下：
@@ -398,7 +398,7 @@ with sink (
本例子用来演示多个 IoTDB 之间级联传输数据的场景，数据由 A 集群同步至 B 集群，再同步至 C 集群，数据链路如下图所示：
-
+
在这个例子中，为了将 A 集群的数据同步至 C，在 BC 之间的 pipe 需要将 `forwarding-pipe-requests` 配置为`true`，详细语句如下：
@@ -429,7 +429,7 @@ with sink (
本例子用来演示将一个 IoTDB 的数据，经过单向网闸，同步至另一个 IoTDB 的场景，数据链路如下图所示：
-
+
在这个例子中，需要使用 sink 任务中的 iotdb-air-gap-sink 插件，配置网闸后，在 A IoTDB 上执行下列语句，其中 node-urls 填写网闸配置的目标端 IoTDB 中 DataNode 节点的数据服务端口的 url，详细语句如下：
diff --git a/src/zh/UserGuide/latest/User-Manual/Data-subscription.md b/src/zh/UserGuide/latest/User-Manual/Data-subscription.md
index 6b6be6063..cb46617a5 100644
--- a/src/zh/UserGuide/latest/User-Manual/Data-subscription.md
+++ b/src/zh/UserGuide/latest/User-Manual/Data-subscription.md
@@ -14,7 +14,7 @@ IoTDB 数据订阅模块（又称 IoTDB 订阅客户端）是IoTDB V1.3.3 版本
IoTDB 订阅客户端包含 3 个核心概念：Topic、Consumer、Consumer Group，具体关系如下图
+
+
+
-
+
### 示例
diff --git a/src/zh/UserGuide/latest/User-Manual/UDF-development.md b/src/zh/UserGuide/latest/User-Manual/UDF-development.md
index d2ecb5dcd..24e0366ad 100644
--- a/src/zh/UserGuide/latest/User-Manual/UDF-development.md
+++ b/src/zh/UserGuide/latest/User-Manual/UDF-development.md
@@ -127,7 +127,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 3 类参数：
@@ -142,7 +142,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
3 类参数的关系可见下图。策略的构造方法详见 Javadoc。
-
+
> 注意，最后的一些时间窗口的实际时间间隔可能小于规定的时间间隔参数。另外，可能存在某些时间窗口内数据行数量为 0 的情况，这种情况框架也会为该窗口调用一次`transform`方法。
@@ -150,7 +150,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图:
-
+
`SlidingSizeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 个参数：
@@ -163,7 +163,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**时间间隔小于等于给定的最小时间间隔 sessionGap 则分为一组。**
-
+
`SessionTimeWindowAccessStrategy`有多种构造方法，您可以向构造方法提供 2 类参数：
@@ -175,7 +175,7 @@ void beforeStart(UDFParameters parameters, UDTFConfigurations configurations) th
开窗示意图：**对于数值型数据，状态差值小于等于给定的阈值 delta 则分为一组。**
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`StateWindowAccessStrategy`有四种构造方法：
diff --git a/src/zh/UserGuide/latest/User-Manual/White-List_timecho.md b/src/zh/UserGuide/latest/User-Manual/White-List_timecho.md
index 4b43a03fc..33245504e 100644
--- a/src/zh/UserGuide/latest/User-Manual/White-List_timecho.md
+++ b/src/zh/UserGuide/latest/User-Manual/White-List_timecho.md
@@ -66,5 +66,5 @@ white.list:
5. 是否开启白名单功能的配置，可以热加载。
6. 使用Java 原生接口修改白名单，必须是root用户才能修改，拒绝非root用户修改；修改内容必须合法，否则会抛出StatementExecutionException异常。
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