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| 3 | + Licensed to the Apache Software Foundation (ASF) under one |
| 4 | + or more contributor license agreements. See the NOTICE file |
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| 6 | + regarding copyright ownership. The ASF licenses this file |
| 7 | + to you under the Apache License, Version 2.0 (the |
| 8 | + "License"); you may not use this file except in compliance |
| 9 | + with the License. You may obtain a copy of the License at |
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| 11 | + http://www.apache.org/licenses/LICENSE-2.0 |
| 12 | + |
| 13 | + Unless required by applicable law or agreed to in writing, |
| 14 | + software distributed under the License is distributed on an |
| 15 | + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| 16 | + KIND, either express or implied. See the License for the |
| 17 | + specific language governing permissions and limitations |
| 18 | + under the License. |
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| 20 | +--> |
| 21 | + |
| 22 | +# Sample Data |
| 23 | + |
| 24 | +This chapter mainly introduces a simple temporal data application scenario and the modeling and example data in this scenario. All the example SQL statements in the IoTDB table model user manual can be executed under this modeling and example data. |
| 25 | + |
| 26 | +## Data Structure |
| 27 | + |
| 28 | + |
| 29 | +Table 1 and Table 2 both have the following table structure: |
| 30 | + |
| 31 | + |
| 32 | + |
| 33 | +## Import Statement |
| 34 | + |
| 35 | +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. |
| 36 | + |
| 37 | +```SQL |
| 38 | +-- Create a table with table names that are close to business semantics. Here, we use table1 instead |
| 39 | +-- The time column does not need to be manually specified, IoTDB will automatically create it |
| 40 | +-- The unit of TTL is ms, so 1 year is 31536000000 ms |
| 41 | +create database database1; |
| 42 | +use database1; |
| 43 | +CREATE TABLE table1 ( |
| 44 | + time TIMESTAMP TIME, |
| 45 | + region STRING TAG, |
| 46 | + plant_id STRING TAG, |
| 47 | + device_id STRING TAG, |
| 48 | + model_id STRING ATTRIBUTE, |
| 49 | + maintenance STRING ATTRIBUTE, |
| 50 | + temperature FLOAT FIELD, |
| 51 | + humidity FLOAT FIELD, |
| 52 | + status Boolean FIELD, |
| 53 | + arrival_time TIMESTAMP FIELD |
| 54 | +) WITH (TTL=31536000000); |
| 55 | + |
| 56 | +CREATE TABLE table2 ( |
| 57 | + time TIMESTAMP TIME, |
| 58 | + region STRING TAG, |
| 59 | + plant_id STRING TAG, |
| 60 | + device_id STRING TAG, |
| 61 | + model_id STRING ATTRIBUTE, |
| 62 | + maintenance STRING ATTRIBUTE, |
| 63 | + temperature FLOAT FIELD, |
| 64 | + humidity FLOAT FIELD, |
| 65 | + status Boolean FIELD, |
| 66 | + arrival_time TIMESTAMP FIELD |
| 67 | +) WITH (TTL=31536000000); |
| 68 | + |
| 69 | + |
| 70 | + |
| 71 | +INSERT INTO table1(region, plant_id, device_id, model_id, maintenance, time, temperature, humidity, status, arrival_time) VALUES |
| 72 | + ('北京', '1001', '100', 'A', '180', '2024-11-26 13:37:00', 90.0, 35.1, true, '2024-11-26 13:37:34'), |
| 73 | + ('北京', '1001', '100', 'A', '180', '2024-11-26 13:38:00', 90.0, 35.1, true, '2024-11-26 13:38:25'), |
| 74 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 16:38:00', NULL, 35.1, true, '2024-11-27 16:37:01'), |
| 75 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 16:39:00', 85.0, 35.3, NULL, Null), |
| 76 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 16:40:00', 85.0, NULL, NULL, '2024-11-27 16:37:03'), |
| 77 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 16:41:00', 85.0, NULL, NULL, '2024-11-27 16:37:04'), |
| 78 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 16:42:00', NULL, 35.2, false, Null), |
| 79 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 16:43:00', NULL, Null, false, Null), |
| 80 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 16:44:00', NULL, Null, false, '2024-11-27 16:37:08'), |
| 81 | + ('上海', '3001', '100', 'C', '90', '2024-11-28 08:00:00', 85.0, Null, NULL, '2024-11-28 08:00:09'), |
| 82 | + ('上海', '3001', '100', 'C', '90', '2024-11-28 09:00:00', NULL, 40.9, true, NULL), |
| 83 | + ('上海', '3001', '100', 'C', '90', '2024-11-28 10:00:00', 85.0, 35.2, NULL, '2024-11-28 10:00:11'), |
| 84 | + ('上海', '3001', '100', 'C', '90', '2024-11-28 11:00:00', 88.0, 45.1, true, '2024-11-28 11:00:12'), |
| 85 | + ('上海', '3001', '101', 'D', '360', '2024-11-29 10:00:00', 85.0, NULL, NULL, '2024-11-29 10:00:13'), |
| 86 | + ('上海', '3002', '100', 'E', '180', '2024-11-29 11:00:00', NULL, 45.1, true, NULL), |
| 87 | + ('上海', '3002', '100', 'E', '180', '2024-11-29 18:30:00', 90.0, 35.4, true, '2024-11-29 18:30:15'), |
| 88 | + ('上海', '3002', '101', 'F', '360', '2024-11-30 09:30:00', 90.0, 35.2, true, NULL), |
| 89 | + ('上海', '3002', '101', 'F', '360', '2024-11-30 14:30:00', 90.0, 34.8, true, '2024-11-30 14:30:17'); |
| 90 | + |
| 91 | + INSERT INTO table2(region, plant_id, device_id, model_id, maintenance, time, temperature, humidity, status, arrival_time) VALUES |
| 92 | + ('北京', '1001', '100', 'A', '180', '2024-11-26 13:37:00', 90.0, 35.1, true, '2024-11-26 13:37:34'), |
| 93 | + ('北京', '1001', '101', 'B', '180', '2024-11-27 00:00:00', 85.0, 35.1, true, '2024-11-27 16:37:01'), |
| 94 | + ('上海', '3001', '100', 'C', '90', '2024-11-28 08:00:00', 85.0, 35.2, false, '2024-11-28 08:00:09'), |
| 95 | + ('上海', '3001', '101', 'D', '360', '2024-11-29 00:00:00', 85.0, 35.1, NULL, '2024-11-29 10:00:13'), |
| 96 | + ('上海', '3002', '100', 'E', '180', '2024-11-29 11:00:00', NULL, 45.1, true, NULL), |
| 97 | + ('上海', '3002', '101', 'F', '360', '2024-11-30 00:00:00', 90.0, 35.2, true, NULL); |
| 98 | +``` |
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