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Community Blog Open Source PolarDB Enhances Time Series Data with TimescaleDB

Open Source PolarDB Enhances Time Series Data with TimescaleDB

This article describes how open source PolarDB uses TimescaleDB to implement high-speed writing, compression, real-time aggregation computing, and automatic aging of time series data.

Background

PolarDB's cloud-native computing-storage separation architecture offers cost-effective data storage, scalable operations, high-speed multi-machine parallel computing, and efficient data search and processing. By combining PolarDB with computing algorithms, it enables businesses to derive value from their data and turn it into productivity.

This article describes how open source PolarDB uses TimescaleDB to implement high-speed writing, compression, real-time aggregation computing, and automatic aging of time series data.

The test environment used for this demonstration is macOS + Docker. For detailed instructions on PolarDB deployment, please refer to the following article: Simple Deployment of PolarDB

Deploy Timescale DB

Open source PolarDB is compatible with PostgreSQL 11. Therefore, you can only use TimescaleDB 1.7.x. After PolarDB is upgraded to PostgreSQL 14, you can use TimescaleDB 2.x.

cd ~  
  
git clone -b 1.7.x --depth 1 https://github.com/timescale/timescaledb  
cd timescaledb  
  
./bootstrap  -DREGRESS_CHECKS=OFF  
  
cd build && make  
  
sudo make install  

Modify the configurations of a PolarDB instance.

vi ~/tmp_master_dir_polardb_pg_1100_bld/postgresql.conf  
  
vi ~/tmp_replica_dir_polardb_pg_1100_bld1/postgresql.conf  
vi ~/tmp_replica_dir_polardb_pg_1100_bld2/postgresql.conf  
  
shared_preload_libraries = 'timescaledb,......'  

For more suggestions about parameter configuration and optimization, see: https://github.com/timescale/timescaledb-tune

Use TimescaleD

postgres=# create extension timescaledb ;  
WARNING:   
WELCOME TO  
 _____ _                               _     ____________    
|_   _(_)                             | |    |  _  \ ___ \   
  | |  _ _ __ ___   ___  ___  ___ __ _| | ___| | | | |_/ /   
  | | | |  _ ` _ \ / _ \/ __|/ __/ _` | |/ _ \ | | | ___ \   
  | | | | | | | | |  __/\__ \ (_| (_| | |  __/ |/ /| |_/ /  
  |_| |_|_| |_| |_|\___||___/\___\__,_|_|\___|___/ \____/  
               Running version 1.7.4  
For more information on TimescaleDB, please visit the following links:  
  
 1. Getting started: https://docs.timescale.com/getting-started  
 2. API reference documentation: https://docs.timescale.com/api  
 3. How TimescaleDB is designed: https://docs.timescale.com/introduction/architecture  
  
Note: TimescaleDB collects anonymous reports to better understand and assist our users.  
For more information and how to disable, please see our docs https://docs.timescaledb.com/using-timescaledb/telemetry.  
  
CREATE EXTENSION  
postgres=# \dx  
                                      List of installed extensions  
    Name     | Version |   Schema   |                            Description                              
-------------+---------+------------+-------------------------------------------------------------------  
 plpgsql     | 1.0     | pg_catalog | PL/pgSQL procedural language  
 postgis     | 3.3.2   | public     | PostGIS geometry and geography spatial types and functions  
 timescaledb | 1.7.5   | public     | Enables scalable inserts and complex queries for time-series data  
(3 rows)  

Create a normal time series table.

-- We start by creating a regular SQL table  
  
CREATE TABLE conditions (  
  time        TIMESTAMPTZ       NOT NULL,  
  location    TEXT              NOT NULL,  
  temperature DOUBLE PRECISION  NULL,  
  humidity    DOUBLE PRECISION  NULL  
);  

Convert a standard table to a timescale time series table.

-- This creates a hypertable that is partitioned by time  
--   using the values in the `time` column.  
  
SELECT create_hypertable('conditions', 'time');  

Write test data to the table.

INSERT INTO conditions(time, location, temperature, humidity)  
VALUES (NOW(), 'office', 70.0, 50.0);  

Query the content of the base table of the time series table.

SELECT * FROM conditions ORDER BY time DESC LIMIT 100;  

Automatic sharding of the base table.

postgres=# \d+ conditions  
                                            Table "public.conditions"  
   Column    |           Type           | Collation | Nullable | Default | Storage  | Stats target | Description   
-------------+--------------------------+-----------+----------+---------+----------+--------------+-------------  
 time        | timestamp with time zone |           | not null |         | plain    |              |   
 location    | text                     |           | not null |         | extended |              |   
 temperature | double precision         |           |          |         | plain    |              |   
 humidity    | double precision         |           |          |         | plain    |              |   
Indexes:  
    "conditions_time_idx" btree ("time" DESC)  
Triggers:  
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE PROCEDURE _timescaledb_internal.insert_blocker()  
Child tables: _timescaledb_internal._hyper_1_1_chunk  
INSERT INTO conditions(time, location, temperature, humidity)   
select now()+(id||' second')::interval,   
  md5((random()*1000)::int::text),   
  random()*100, random()*100   
  from generate_series(1,1000000) id;  
  
  
postgres=# \d+ conditions  
                                            Table "public.conditions"  
   Column    |           Type           | Collation | Nullable | Default | Storage  | Stats target | Description   
-------------+--------------------------+-----------+----------+---------+----------+--------------+-------------  
 time        | timestamp with time zone |           | not null |         | plain    |              |   
 location    | text                     |           | not null |         | extended |              |   
 temperature | double precision         |           |          |         | plain    |              |   
 humidity    | double precision         |           |          |         | plain    |              |   
Indexes:  
    "conditions_time_idx" btree ("time" DESC)  
Triggers:  
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE PROCEDURE _timescaledb_internal.insert_blocker()  
Child tables: _timescaledb_internal._hyper_1_1_chunk,  
              _timescaledb_internal._hyper_1_2_chunk  
  
  
postgres=# SELECT * FROM conditions ORDER BY time DESC LIMIT 100;  
             time              |             location             |    temperature    |     humidity        
-------------------------------+----------------------------------+-------------------+-------------------  
 2023-01-16 16:27:12.442233+00 | 70efdf2ec9b086079795c442636b55fb | 0.917297508567572 |  45.0286225881428  
 2023-01-16 16:27:11.442233+00 | b056eb1587586b71e2da9acfe4fbd19e |  59.0947337448597 |  49.3321735877544  
 2023-01-16 16:27:10.442233+00 | 28dd2c7955ce926456240b2ff0100bde |  26.5667649917305 |  88.5223139543086  
 2023-01-16 16:27:09.442233+00 | 1ecfb463472ec9115b10c292ef8bc986 |  12.9402264486998 |   23.304360313341  
 2023-01-16 16:27:08.442233+00 | 82161242827b703e6acf9c726942a1e4 |  48.1451884843409 |  97.9283190798014  
 2023-01-16 16:27:07.442233+00 | 812b4ba287f5ee0bc9d43bbf5bbe87fb |  76.0097410064191 |  20.2729247976094  
 2023-01-16 16:27:06.442233+00 | d645920e395fedad7bbbed0eca3fe2e0 |  97.6623016409576 |  22.9934238363057  
 2023-01-16 16:27:05.442233+00 | 0d0fd7c6e093f7b804fa0150b875b868 |  7.43439155630767 |  96.3830435648561  
 2023-01-16 16:27:04.442233+00 | 6e2713a6efee97bacb63e52c54f0ada0 |  30.4179009050131 |  36.7151976097375  
 2023-01-16 16:27:03.442233+00 | fb7b9ffa5462084c5f4e7e85a093e6d7 |  22.1182454843074 |  23.0733227450401  
 2023-01-16 16:27:02.442233+00 | d1f255a373a3cef72e03aa9d980c7eca |  95.6964490003884 |  43.6015542596579  
 2023-01-16 16:27:01.442233+00 | 89f0fd5c927d466d6ec9a21b9ac34ffa |  60.8098595868796 |  26.7892859410495  
 ...  

Automatically create indexes for sharding fields.

postgres=# explain SELECT * FROM conditions ORDER BY time DESC LIMIT 100;  
                                                            QUERY PLAN                                                               
-----------------------------------------------------------------------------------------------------------------------------------  
 Limit  (cost=0.42..7.77 rows=100 width=56)  
   ->  Custom Scan (ChunkAppend) on conditions  (cost=0.42..32778.88 rows=446297 width=56)  
         Order: conditions."time" DESC  
         ->  Index Scan using _hyper_1_2_chunk_conditions_time_idx on _hyper_1_2_chunk  (cost=0.42..32778.88 rows=446297 width=56)  
         ->  Index Scan using _hyper_1_1_chunk_conditions_time_idx on _hyper_1_1_chunk  (cost=0.42..48162.05 rows=656108 width=56)  
(5 rows)  

Example of Aggregating Base Table Data in Real Time

https://legacy-docs.timescale.com/v1.7/using-timescaledb/continuous-aggregates

CREATE TABLE conditions (  
      time TIMESTAMPTZ NOT NULL,  
      device INTEGER NOT NULL,  
      temperature FLOAT NOT NULL,  
      PRIMARY KEY(time, device)  
);  
SELECT create_hypertable('conditions', 'time');  

Create an automatic aggregate view.

CREATE VIEW conditions_summary_hourly  
WITH (timescaledb.continuous) AS  
SELECT device,  
       time_bucket(INTERVAL '1 hour', time) AS bucket,  
       AVG(temperature),  
       MAX(temperature),  
       MIN(temperature)  
FROM conditions  
GROUP BY device, bucket;  
  
  
CREATE VIEW conditions_summary_daily  
WITH (timescaledb.continuous) AS  
SELECT device,  
       time_bucket(INTERVAL '1 day', time) AS bucket,  
       AVG(temperature),  
       MAX(temperature),  
       MIN(temperature)  
FROM conditions  
GROUP BY device, bucket;  

Write test data to the table.

INSERT INTO conditions(time, device, temperature)   
select now()+(id||' second')::interval,   
  (random()*100)::int,   
  random()*100    
  from generate_series(1,1000000) id;  

Query the aggregate view.

SELECT * FROM conditions_summary_daily  
WHERE device = 5  
  AND bucket >= '2023-01-01' AND bucket < '2023-01-10';  
  
  
 device |         bucket         |       avg        |       max        |        min           
--------+------------------------+------------------+------------------+--------------------  
      5 | 2023-01-05 00:00:00+00 | 52.8728757359047 | 99.9651623424143 |  0.113607617095113  
      5 | 2023-01-06 00:00:00+00 | 50.9738177677259 | 99.9353400431573 | 0.0549898017197847  
      5 | 2023-01-07 00:00:00+00 | 49.2079831483183 | 99.9868880026042 | 0.0576195307075977  
      5 | 2023-01-08 00:00:00+00 | 48.3715454505876 | 99.9165495857596 |  0.242615444585681  
      5 | 2023-01-09 00:00:00+00 | 49.0718302013499 | 99.7824223246425 | 0.0885920133441687  
(5 rows)  
SELECT * FROM conditions_summary_daily  
WHERE max - min > 1800  
  AND bucket >= '2023-01-01' AND bucket < '2023-04-01'  
ORDER BY bucket DESC, device DESC LIMIT 20;  

Modify the automatic refresh delay and retention time window, and manually maintain the retained data based on the time window for aggregate views.

ALTER VIEW conditions_summary_hourly SET (  
  timescaledb.refresh_lag = '1 hour'  
);  
  
  
ALTER VIEW conditions_summary_daily SET (  
  timescaledb.ignore_invalidation_older_than = '30 days'  
);  
  
  
SELECT drop_chunks(INTERVAL '30 days', 'conditions_summary_daily');  

Modify the style of automatic aggregation views and decide whether to query only aggregated content or include unaggregated content (you need to query the base table in real time for calculation):

ALTER VIEW conditions_summary_hourly SET (  
    timescaledb.materialized_only = false  
);  
  
ALTER VIEW conditions_summary_daily SET (  
    timescaledb.materialized_only = false  
);  

References

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