在简单的 postgres 和 timescaleDB 查询性能测试中没有看到任何提升？

Question

我尝试了 postgres 和 timescaleDB 之间的简单性能测试。这是我的结果：-

总行数 403,204

使用 Postgres

聚合查询 176 行的获取时间：203 毫秒 - 240 毫秒

连接查询 102 行的获取时间：660 毫秒 - 720 毫秒

使用TimescaleDB

聚合查询 176 行的获取时间：175 毫秒 - 200 毫秒

连接查询 102 行的获取时间：614 毫秒 - 650 毫秒

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

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

--drop table public.sensor_data;

-- TimescaleDB table
CREATE TABLE sensor_data_ts (
  time TIMESTAMPTZ NOT NULL,
  sensor_id INTEGER,
  temperature DOUBLE PRECISION,
  cpu DOUBLE PRECISION,
  FOREIGN KEY (sensor_id) REFERENCES sensors (id)
);
SELECT create_hypertable('sensor_data_ts', 'time');

-- Insert Data

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


-- Postgres 

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 '50 week', now(), interval '5 minute') AS g1(time), generate_series(1,4,1) AS g2(sensor_id);

-- TimescaleDB
INSERT INTO sensor_data_ts (time, sensor_id, cpu, temperature)
SELECT
  time,
  sensor_id,
  random() AS cpu,
  random()*100 AS temperature
FROM generate_series(now() - interval '50 week', now(), interval '5 minute') AS g1(time), generate_series(1,4,1) AS g2(sensor_id);


--truncate table public.sensor_data;
--truncate table public.sensor_data_ts;

select count(*) from public.sensor_data sd ;
select count(*) from public.sensor_data_ts sd ;

--Postgres

--Aggregate queries
SELECT 
  floor(extract(epoch from "time")/(60*60*24*2)) as period,
  AVG(temperature) AS avg_temp, 
  AVG(cpu) AS avg_cpu 
FROM sensor_data 
GROUP BY period;
--ORDER BY PERIOD;

--Join Queries
SELECT 
  sensors.location,
  floor(extract(epoch from "time")/(60*60*24*7)) 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;

--Timescale DB

--Aggregate Queries
SELECT 
  time_bucket('2 day', time) AS period, 
  AVG(temperature) AS avg_temp, 
  AVG(cpu) AS avg_cpu 
FROM sensor_data_ts 
GROUP BY period;
--ORDER BY PERIOD;

--Join Queries
SELECT 
  sensors.location,
  time_bucket('1 week', 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;

我期待查询性能有明显的提升。我还能做些什么来提高查询性能？

Answer 1

几件事：

我有点困惑。 time_bucket 是一个 TimescaleDB 函数，不是 Postgres 函数，所以它可能是运行我们的一些代码。
您仍在对所有数据执行完整 table 扫描。这里没有太多的优化方法。而且数据集很小（400K），所以可以全部放入缓冲区缓存；如果您想查看一些 insert/query 性能，可能需要 (a) 更多数据，(b) 更复杂的查询类型。
但是TimescaleDB还有其他的特点。例如，打开压缩，您可能会发现这些“完整 table 扫描”速度更快（尽管一旦您进入 disk-bound 工作负载）。或者打开连续聚合，以便您可以 continuously/incrementally 实现这些结果以提供服务，例如 user-facing 仪表板。

在简单的 postgres 和 timescaleDB 查询性能测试中没有看到任何提升？

Didn't see any boost in a simple postgres and timescaleDB query performance test?

postgresql

timescaledb