模拟 IoT 传感器数据集

物联网 (IoT) 描述了一种趋势，其中计算能力被嵌入到 IoT 设备中。这些设备可以是物理对象，范围从灯泡到油井。许多 IoT 设备收集关于其环境的传感器数据，并生成带有关系元数据的时间序列数据集。

通常有必要模拟 IoT 数据集。例如，当您测试新系统时。本教程展示了如何在您的 Timescale Cloud 服务中模拟一个基本数据集，然后在上面运行简单的查询。

要模拟更高级的数据集，请参阅时间序列基准测试套件 (TSBS)。

先决条件

要学习本教程，您需要

创建一个目标 Timescale Cloud 服务。
连接到您的服务.

模拟数据集

要模拟数据集，请运行以下查询

创建 sensors 和 sensor_data 表:


CREATE TABLE sensors(
  id SERIAL PRIMARY KEY,
  type VARCHAR(50),
  location VARCHAR(50)
);


CREATE TABLE sensor_data (
  time TIMESTAMPTZ NOT NULL,
  sensor_id INTEGER,
  temperature DOUBLE PRECISION,
  cpu DOUBLE PRECISION,
  FOREIGN KEY (sensor_id) REFERENCES sensors (id)
);

将 sensor_data 转换为超表:


SELECT create_hypertable('sensor_data', 'time');

填充 sensors 表:


INSERT INTO sensors (type, location) VALUES
('a','floor'),
('a', 'ceiling'),
('b','floor'),
('b', 'ceiling');

验证传感器是否已正确添加:


SELECT * FROM sensors;

示例输出


id | type | location
----+------+----------
  1 | a    | floor
  2 | a    | ceiling
  3 | b    | floor
  4 | b    | ceiling
(4 rows)

为所有传感器生成并插入数据集


INSERT INTO sensor_data (time, sensor_id, cpu, temperature)
SELECT
  time,
  sensor_id,
  random() AS cpu,
  random()*100 AS temperature
FROM generate_series(now() - interval '24 hour', now(), interval '5 minute') AS g1(time), generate_series(1,4,1) AS g2(sensor_id);

验证模拟的数据集:


SELECT * FROM sensor_data ORDER BY time;

示例输出


time              | sensor_id |    temperature     |         cpu         
-------------------------------+-----------+--------------------+---------------------
 2020-03-31 15:56:25.843575+00 |         1 |   6.86688972637057 |   0.682070567272604
 2020-03-31 15:56:40.244287+00 |         2 |    26.589260622859 |   0.229583469685167
 2030-03-31 15:56:45.653115+00 |         3 |   79.9925176426768 |   0.457779890391976
 2020-03-31 15:56:53.560205+00 |         4 |   24.3201029952615 |   0.641885648947209
 2020-03-31 16:01:25.843575+00 |         1 |   33.3203678019345 |  0.0159163917414844
 2020-03-31 16:01:40.244287+00 |         2 |   31.2673618085682 |   0.701185956597328
 2020-03-31 16:01:45.653115+00 |         3 |   85.2960689924657 |   0.693413889966905
 2020-03-31 16:01:53.560205+00 |         4 |   79.4769988860935 |   0.360561791341752
...

运行基本查询

在您模拟数据集后，您可以在上面运行一些基本查询。例如

每 30 分钟窗口的平均温度和 CPU


SELECT
  time_bucket('30 minutes', time) AS period,
  AVG(temperature) AS avg_temp,
  AVG(cpu) AS avg_cpu
FROM sensor_data
GROUP BY period;

示例输出


period         |     avg_temp     |      avg_cpu      
------------------------+------------------+-------------------
 2020-03-31 19:00:00+00 | 49.6615830013373 | 0.477344429974134
 2020-03-31 22:00:00+00 | 58.8521540844037 | 0.503637770501276
 2020-03-31 16:00:00+00 | 50.4250325243144 | 0.511075591299838
 2020-03-31 17:30:00+00 | 49.0742547437549 | 0.527267253802468
 2020-04-01 14:30:00+00 | 49.3416377226822 | 0.438027751864865
 ...

每 30 分钟窗口的平均温度和最后温度、平均 CPU


SELECT
  time_bucket('30 minutes', time) AS period,
  AVG(temperature) AS avg_temp,
  last(temperature, time) AS last_temp,
  AVG(cpu) AS avg_cpu
FROM sensor_data
GROUP BY period;

示例输出


period         |     avg_temp     |    last_temp     |      avg_cpu      
------------------------+------------------+------------------+-------------------
 2020-03-31 19:00:00+00 | 49.6615830013373 | 84.3963081017137 | 0.477344429974134
 2020-03-31 22:00:00+00 | 58.8521540844037 | 76.5528806950897 | 0.503637770501276
 2020-03-31 16:00:00+00 | 50.4250325243144 | 43.5192013625056 | 0.511075591299838
 2020-03-31 17:30:00+00 | 49.0742547437549 |  22.740753274411 | 0.527267253802468
 2020-04-01 14:30:00+00 | 49.3416377226822 | 59.1331578791142 | 0.438027751864865
...

查询元数据


SELECT
  sensors.location,
  time_bucket('30 minutes', time) AS period,
  AVG(temperature) AS avg_temp,
  last(temperature, time) AS last_temp,
  AVG(cpu) AS avg_cpu
FROM sensor_data JOIN sensors on sensor_data.sensor_id = sensors.id
GROUP BY period, sensors.location;

示例输出


location |         period         |     avg_temp     |     last_temp     |      avg_cpu      
----------+------------------------+------------------+-------------------+-------------------
 ceiling  | 20120-03-31 15:30:00+00 | 25.4546818090603 |  24.3201029952615 | 0.435734559316188
 floor    | 2020-03-31 15:30:00+00 | 43.4297036845237 |  79.9925176426768 |  0.56992522883229
 ceiling  | 2020-03-31 16:00:00+00 | 53.8454438598516 |  43.5192013625056 | 0.490728285357666
 floor    | 2020-03-31 16:00:00+00 | 47.0046211887772 |  23.0230117216706 |  0.53142289724201
 ceiling  | 2020-03-31 16:30:00+00 | 58.7817596504465 |  63.6621567420661 | 0.488188337767497
 floor    | 2020-03-31 16:30:00+00 |  44.611586847653 |  2.21919436007738 | 0.434762630766879
 ceiling  | 2020-03-31 17:00:00+00 | 35.7026890735142 |  42.9420990403742 | 0.550129583687522
 floor    | 2020-03-31 17:00:00+00 | 62.2794370166957 |  52.6636955793947 | 0.454323202022351
...

您现在已成功模拟并在 IoT 数据集上运行了查询。

模拟 IoT 传感器数据集

先决条件

模拟数据集

运行基本查询

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