• 时序数据库 TimescaleDB 安装与使用

    在这里插入图片描述

    TimescaleDB 是一个时间序列数据库，建立在 PostgreSQL 之上。然而，不仅如此，它还是时间序列的关系数据库。使用 TimescaleDB 的开发人员将受益于专门构建的时间序列数据库以及经典的关系数据库 (PostgreSQL)，所有这些都具有完整的 SQL 支持。本文介绍 TimescaleDB 的 CentOS 7 环境源码编译安装与使用。

    01 源码安装

    安装 TimescaleDB 之前确保你的机器上已经安装好了 PostgreSQL，并且检查安装的 PG 版本与 TimescaleDB 版本兼容情况：https://docs.timescale.com/self-hosted/latest/upgrades/upgrade-pg/
    在这里插入图片描述
    源码安装需要使用到 CMake 和 GUN 编译器 gcc 等工具，确保机器已经安装了这些工具

    1.1 源码安装 TimescaleDB

    1. 拉取 TimescaleDB 源码
    git clone https://github.com/timescale/timescaledb

    • 1
    1. 进入源码目录并切换分支到指定版本
    cd timescaledb
git checkout 2.11.1

    • 1
    • 2
    1. 执行 bootstrap 引导构建系统
    ./bootstrap 

    • 1

    遇到报错：PostgreSQL was built without OpenSSL support, which TimescaleDB needs for full compatibility
    解决方法：重新编译安装 postgresql，增加 --with-openssl 选项
    ./configure --prefix=/home/randy/soft/postgresql --with-openssl
    或者执行 ./bootstrap -DUSE_OPENSSL=0 不使用 openssl
    在这里插入图片描述

    1. 进入上一步生成的 build 目录，编译安装 timescaledb
    cd build
make && make install

    • 1
    • 2

    安装完成之后，就可以在 postgresql 插件目录下看到 timescaledb 了
    在这里插入图片描述

    1.2 修改 PG 配置

    完成 TimescaleDB 安装之后，需要修改 postgresql 配置文件增加 timescaledb 预加载库

    1. 找到 pg 配置文件
    $ psql -Upostgres -dpostgres -c "SHOW config_file;"
Password for user postgres:
                   config_file
--------------------------------------------------
 /home/randy/soft/postgresql/data/postgresql.conf
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    1. 修改 pg 配置文件
    vim /home/randy/soft/postgresql/data/postgresql.conf
# 做如下修改
shared_preload_libraries = 'timescaledb'

    • 1
    • 2
    • 3

    修改完成之后重启 pg

    1.3 安装 TimescaleDB 插件

    1. 执行如下命令创建 timescaledb 插件
    CREATE EXTENSION IF NOT EXISTS timescaledb;

    • 1
    1. 完成插件创建之后可以使用 \dx 命令查看插件是否成功安装
    testdb=# \dx
                                                List of installed extensions
    Name     | Version |   Schema   |                                      Description
-------------+---------+------------+---------------------------------------------------------------------------------------
 plpgsql     | 1.0     | pg_catalog | PL/pgSQL procedural language
 timescaledb | 2.11.1  | public     | Enables scalable inserts and complex queries for time-series data (Community Edition)
(2 rows)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7

    在这里插入图片描述
    至此，postgresql + timescaledb 时序数据库就安装完成了

    1. 不想再用这个插件的话，使用如下命令删除插件
    DROP EXTENSION IF EXISTS timescaledb;

    • 1

    02 Timescale 基础使用

    2.1 创建超表 hypertables

    1. CREATE 创建一个 pg 普通表
    testdb=# CREATE TABLE conditions (
  time        TIMESTAMPTZ       NOT NULL,
  location    TEXT              NOT NULL,
  device      TEXT              NOT NULL,
  temperature DOUBLE PRECISION  NULL,
  humidity    DOUBLE PRECISION  NULL
);
CREATE TABLE

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8

    然后使用 create_hypertable 函数将普通表转化成 hypertable

    testdb=# SELECT create_hypertable('conditions', 'time');
    create_hypertable
-------------------------
 (1,public,conditions,t)
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    1. INSERT 向 hypertable 中插入数据
    testdb=# INSERT INTO conditions(time, location, device, temperature, humidity)
SELECT now(), to_char(i, 'FM0000'), to_char(i, 'FM00000'), random()*i, random()*i FROM generate_series(1,10000) i;
INSERT 0 10000

    • 1
    • 2
    • 3
    1. SELECT 查询 hypertable 数据
    testdb=# SELECT * FROM conditions ORDER BY time DESC LIMIT 10;
             time              | location | device |     temperature     |       humidity
-------------------------------+----------+--------+---------------------+----------------------
 2023-08-03 12:37:00.345665+08 | 0001     | 00001  | 0.24349980974765373 |   0.5948687729797264
 2023-08-03 12:37:00.345665+08 | 0002     | 00002  |  1.8149739913052656 |    0.616265502369167
 2023-08-03 12:37:00.345665+08 | 0003     | 00003  |   2.400422475569293 |   0.6870057094407791
 2023-08-03 12:37:00.345665+08 | 0004     | 00004  |   2.639553072461581 |   1.9409034849705193
 2023-08-03 12:37:00.345665+08 | 0005     | 00005  |   2.127623537273031 |   3.8871503537982655
 2023-08-03 12:37:00.345665+08 | 0006     | 00006  |  2.3469833801156312 |    4.411529933527426
 2023-08-03 12:37:00.345665+08 | 0007     | 00007  |  1.0073460031664823 | 0.016827997740616496
 2023-08-03 12:37:00.345665+08 | 0008     | 00008  |   7.023014897212306 |   0.8679293544022073
 2023-08-03 12:37:00.345665+08 | 0009     | 00009  |   6.744935559863428 |    6.312948981297968
 2023-08-03 12:37:00.345665+08 | 0010     | 00010  |   9.279208258323166 |    9.451548543523778
(10 rows)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    1. UPDATE 更新 hypertable 数据

    更新数据的语句与标准 SQL 一致，如下示例修改指定记录的值

    testdb=# SELECT * FROM conditions
WHERE time = '2023-08-03 12:37:00.345665+08' AND location = '0001';
             time              | location | device |     temperature     |      humidity
-------------------------------+----------+--------+---------------------+--------------------
 2023-08-03 12:37:00.345665+08 | 0001     | 00001  | 0.24349980974765373 | 0.5948687729797264
(1 row)

testdb=# UPDATE conditions SET temperature = 70.2, humidity = 50.0
WHERE time = '2023-08-03 12:37:00.345665+08' AND location = '0001';
UPDATE 1
testdb=# SELECT * FROM conditions
WHERE time = '2023-08-03 12:37:00.345665+08' AND location = '0001';
             time              | location | device | temperature | humidity
-------------------------------+----------+--------+-------------+----------
 2023-08-03 12:37:00.345665+08 | 0001     | 00001  |        70.2 |       50
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16

    也可以修改该指定范围内的多行记录，如下例子中修改时间 2023-08-03 12:37:00 到 2023-08-03 12:37:05 这 5 秒内的数据如下

    testdb=# SELECT * FROM conditions ORDER BY time DESC LIMIT 10;
             time              | location | device |    temperature     |       humidity
-------------------------------+----------+--------+--------------------+----------------------
 2023-08-03 12:37:00.345665+08 | 0002     | 00002  | 1.8149739913052656 |    0.616265502369167
 2023-08-03 12:37:00.345665+08 | 0003     | 00003  |  2.400422475569293 |   0.6870057094407791
 2023-08-03 12:37:00.345665+08 | 0004     | 00004  |  2.639553072461581 |   1.9409034849705193
 2023-08-03 12:37:00.345665+08 | 0005     | 00005  |  2.127623537273031 |   3.8871503537982655
 2023-08-03 12:37:00.345665+08 | 0006     | 00006  | 2.3469833801156312 |    4.411529933527426
 2023-08-03 12:37:00.345665+08 | 0007     | 00007  | 1.0073460031664823 | 0.016827997740616496
 2023-08-03 12:37:00.345665+08 | 0008     | 00008  |  7.023014897212306 |   0.8679293544022073
 2023-08-03 12:37:00.345665+08 | 0009     | 00009  |  6.744935559863428 |    6.312948981297968
 2023-08-03 12:37:00.345665+08 | 0010     | 00010  |  9.279208258323166 |    9.451548543523778
 2023-08-03 12:37:00.345665+08 | 0011     | 00011  |  1.994685462372594 |    7.361677356344085
(10 rows)

testdb=# UPDATE conditions SET temperature = temperature + 0.1
WHERE time >= '2023-08-03 12:37:00' AND time < '2023-08-03 12:37:05';
UPDATE 10000
testdb=# SELECT * FROM conditions ORDER BY time DESC LIMIT 10;
             time              | location | device |    temperature     |       humidity
-------------------------------+----------+--------+--------------------+----------------------
 2023-08-03 12:37:00.345665+08 | 0002     | 00002  | 1.9149739913052657 |    0.616265502369167
 2023-08-03 12:37:00.345665+08 | 0003     | 00003  |  2.500422475569293 |   0.6870057094407791
 2023-08-03 12:37:00.345665+08 | 0004     | 00004  |  2.739553072461581 |   1.9409034849705193
 2023-08-03 12:37:00.345665+08 | 0005     | 00005  | 2.2276235372730313 |   3.8871503537982655
 2023-08-03 12:37:00.345665+08 | 0006     | 00006  | 2.4469833801156313 |    4.411529933527426
 2023-08-03 12:37:00.345665+08 | 0007     | 00007  | 1.1073460031664824 | 0.016827997740616496
 2023-08-03 12:37:00.345665+08 | 0008     | 00008  |  7.123014897212306 |   0.8679293544022073
 2023-08-03 12:37:00.345665+08 | 0009     | 00009  |  6.844935559863428 |    6.312948981297968
 2023-08-03 12:37:00.345665+08 | 0010     | 00010  |  9.379208258323166 |    9.451548543523778
 2023-08-03 12:37:00.345665+08 | 0011     | 00011  |  2.094685462372594 |    7.361677356344085
(10 rows)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    1. Upsert 插入新数据或更新已存在的数据

    Upsert 只能在存在唯一索引或唯一约束的表中生效，可以使用 ALTER TABLE … ADD CONSTRAINT … UNIQUE 语句为已经存在的 hypertable 创建唯一约束

    testdb=# ALTER TABLE conditions
  ADD CONSTRAINT conditions_time_location
    UNIQUE (time, location);
ALTER TABLE
testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
Indexes:
    "conditions_time_location" UNIQUE CONSTRAINT, btree ("time", location)
    "conditions_time_idx" btree ("time")
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19

    插入新数据或更新已存在的数据使用 INSERT INTO … VALUES … ON CONFLICT … DO UPDATE 语句实现

    testdb=# SELECT * FROM conditions
WHERE time = '2023-08-03 12:37:00.345665+08' AND location = '0001';
             time              | location | device | temperature | humidity
-------------------------------+----------+--------+-------------+----------
 2023-08-03 12:37:00.345665+08 | 0001     | 00001  |        70.3 |       50
(1 row)

testdb=# INSERT INTO conditions
VALUES ('2023-08-03 12:37:00.345665+08', '0001', '00001', 70.2, 50.1)
ON CONFLICT (time, location) DO UPDATE
  SET temperature = excluded.temperature,
        humidity = excluded.humidity;
INSERT 0 1
testdb=# SELECT * FROM conditions
WHERE time = '2023-08-03 12:37:00.345665+08' AND location = '0001';
             time              | location | device | temperature | humidity
-------------------------------+----------+--------+-------------+----------
 2023-08-03 12:37:00.345665+08 | 0001     | 00001  |        70.2 |     50.1
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19

    也可以只执行插入操作，如果记录已存在则直接跳过，该操作使用 INSERT INTO … VALUES … ON CONFLICT DO NOTHING 语句实现

    testdb=# INSERT INTO conditions
  VALUES (NOW(), 'new', '00001', 70.1, 50.0)
  ON CONFLICT DO NOTHING;
INSERT 0 1
testdb=# SELECT * FROM conditions WHERE location = 'new';
             time              | location | device | temperature | humidity
-------------------------------+----------+--------+-------------+----------
 2023-08-04 10:33:16.698018+08 | new      | 00001  |        70.1 |       50
(1 row)

testdb=# INSERT INTO conditions
VALUES ('2023-08-04 10:33:16.698018+08', '0001', '00001', 71, 50.1)
ON CONFLICT DO NOTHING;
INSERT 0 1
testdb=# SELECT * FROM conditions WHERE location = 'new';
             time              | location | device | temperature | humidity
-------------------------------+----------+--------+-------------+----------
 2023-08-04 10:33:16.698018+08 | new      | 00001  |        70.1 |       50
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    1. DELETE 删除数据

    删除数据的语句与标准 SQL 一致 DELETE FROM …

    testdb=# SELECT * FROM conditions WHERE location = 'new';
             time              | location | device | temperature | humidity
-------------------------------+----------+--------+-------------+----------
 2023-08-04 10:33:16.698018+08 | new      | 00001  |        70.1 |       50
(1 row)

testdb=# DELETE FROM conditions WHERE location = 'new';
DELETE 1
testdb=# SELECT * FROM conditions WHERE location = 'new';
 time | location | device | temperature | humidity
------+----------+--------+-------------+----------
(0 rows)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    1. DROP 删除 hypertable 只需要使用 PG 命令删除基表（普通表）即可
    testdb=# DROP TABLE conditions;
DROP TABLE

    • 1
    • 2

    2.2 编辑超表 hypertable

    hypertable 的修改和标准 SQL 一致，可以使用 ALTER TABLE 相关语句实现：http://www.postgres.cn/docs/12/ddl-alter.html

    1. 增删列

    增加列 column 时，如果没有默认值即 NULL 时，新增操作可以很快完成；但是默认值为非空的时候，就需要花大量时间填充所有分区中每条记录新增列的值。

    另外，无法对已经被压缩的 hypertable 进行增删列操作，如果要进行该操作需要先解压

    testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
Indexes:
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

testdb=# ALTER TABLE conditions
ADD COLUMN test_column DOUBLE PRECISION NULL;
ALTER TABLE
testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
 test_column | double precision         |           |          |
Indexes:
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

testdb=# ALTER TABLE conditions
DROP COLUMN test_column;
ALTER TABLE
testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
Indexes:
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49
    • 50
    • 51
    1. 重命名列或表
    testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
 test_column | double precision         |           |          |
Indexes:
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

testdb=# ALTER TABLE conditions RENAME COLUMN test_column TO ts_column;
ALTER TABLE
testdb=# ALTER TABLE conditions RENAME TO weather;
ALTER TABLE
testdb=# \d conditions
Did not find any relation named "conditions".
testdb=# \d weather
                         Table "public.weather"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
 ts_column   | double precision         |           |          |
Indexes:
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON weather FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37

    2.3 修改 hypertable 分区间隔

    1. 普通表转化成 hypertable 时指定分区时间间隔 chunk_time_interval

    如果创建 hypertable 是没有指定分区间隔，默认值是 7 天；可以通过查询 _timescaledb.catalog 查看分区间隔的当前设置

    testdb=# CREATE TABLE conditions (
  time        TIMESTAMPTZ       NOT NULL,
  location    TEXT              NOT NULL,
  device      TEXT              NOT NULL,
  temperature DOUBLE PRECISION  NULL,
  humidity    DOUBLE PRECISION  NULL
);
CREATE TABLE
testdb=# SELECT create_hypertable('conditions', 'time');
    create_hypertable
-------------------------
 (2,public,conditions,t)
(1 row)

testdb=# SELECT h.table_name, c.interval_length
  FROM _timescaledb_catalog.dimension c
  JOIN _timescaledb_catalog.hypertable h
    ON h.id = c.hypertable_id;
 table_name | interval_length
------------+-----------------
 conditions |    604800000000
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22

    可以看到没有指定 chunk_time_interval 的 hypertable 分区间隔为 604800000000 / 1000 / 1000 / 60 / 60 / 24 = 7，interval_length 的单位为微秒（microsecond）

    创建 hypertable 时我们通过 chunk_time_interval 指定分区间隔为 1 天

    testdb=# DROP TABLE conditions;
DROP TABLE
testdb=# CREATE TABLE conditions (
  time        TIMESTAMPTZ       NOT NULL,
  location    TEXT              NOT NULL,
  device      TEXT              NOT NULL,
  temperature DOUBLE PRECISION  NULL,
  humidity    DOUBLE PRECISION  NULL
);
CREATE TABLE
testdb=# SELECT create_hypertable(
  'conditions',
  'time',
  chunk_time_interval => INTERVAL '1 day'
);
    create_hypertable
-------------------------
 (3,public,conditions,t)
(1 row)

testdb=# SELECT h.table_name, c.interval_length
  FROM _timescaledb_catalog.dimension c
  JOIN _timescaledb_catalog.hypertable h
    ON h.id = c.hypertable_id;
 table_name | interval_length
------------+-----------------
 conditions |     86400000000
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    1. 通过 set_chunk_time_interval 函数修改 hypertable 分区间隔

    需要注意的是修改分区时间间隔只会再新建的分区中生效，已经创建的分区不会受到影响。所以如果创建了一个很长时间的分区间隔例如 1 年，然后你想修改成一个更小的分区间隔，那这个更小间隔只会在一年后生效了，这时候只能新建一个表设置分区间隔，然后迁移数据了。

    testdb=# SELECT set_chunk_time_interval('conditions', INTERVAL '12 hours');
 set_chunk_time_interval
-------------------------

(1 row)

testdb=# SELECT h.table_name, c.interval_length
  FROM _timescaledb_catalog.dimension c
  JOIN _timescaledb_catalog.hypertable h
    ON h.id = c.hypertable_id;
 table_name | interval_length
------------+-----------------
 conditions |     43200000000
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14

    2.4 hypertable 创建索引

    在一个 hypertable 中创建索引分为两步：

    1. 确定该超表的分区列有哪些，其中 time 列是所有 hypertable 表的分区列，所以创建索引必须包含该列；另外，在创建 hypertable 表时可以通过 partitioning_column 字段指定空间分区列。
    2. 创建的索引组合必须包括所有分区列，在此基础上增加其他列
    1. 创建索引

    hypertable 中索引的创建仍是使用标准的 SQL 语句 CREATE INDEX / CREATE UNIQUE INDEX，例如，如下例子中在 conditions 超表的 time 和 device 列上建立索引 idx_device_time

    testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
Indexes:
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

testdb=# CREATE UNIQUE INDEX idx_device_time
ON conditions(device, time);
CREATE INDEX
testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
Indexes:
    "idx_device_time" UNIQUE, btree (device, "time")
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33

    idx_device_time 索引中包含了所有分区列 time，而 device 不是分区列，如果创建的索引组合不包含分区列，会抛出错误，如下 idx_device 索引仅建立在 device 列上

    testdb=# CREATE UNIQUE INDEX idx_device
ON conditions(device);
ERROR:  cannot create a unique index without the column "time" (used in partitioning)
testdb=#

    • 1
    • 2
    • 3
    • 4
    1. 删除索引

    hypertable 中删除索引仍是使用标准的 SQL 语句 DROP INDEX，删除 conditions 超表中的索引 idx_device_time

    需要注意的是DBMS为主键约束和唯一约束自动创建的索引是无法删除的

    testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
Indexes:
    "idx_device_time" UNIQUE, btree (device, "time")
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

testdb=# DROP INDEX idx_device_time;
DROP INDEX
testdb=# \d conditions
                        Table "public.conditions"
   Column    |           Type           | Collation | Nullable | Default
-------------+--------------------------+-----------+----------+---------
 time        | timestamp with time zone |           | not null |
 location    | text                     |           | not null |
 device      | text                     |           | not null |
 temperature | double precision         |           |          |
 humidity    | double precision         |           |          |
Indexes:
    "conditions_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON conditions FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()
Number of child tables: 1 (Use \d+ to list them.)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    1. 已建索引的普通表转化为 hypertable，空间分区列 partitioning_column 必须包含在已建的索引中

    例如，我们在普通表 basic_table 中已经在 device_id 和 time 列上建立了索引 idx_deviceid_time，那么创建以该表为基表的 hypertable 的分区列需要包含在索引列中，即分区列仅能为 time 与 device_id 的组合。

    下例中，将已经创建好索引的 basic_table 表转化为仅以 time 为分区列的 hypertable

    testdb=# CREATE TABLE basic_table(
  time TIMESTAMPTZ,
  user_id BIGINT,
  device_id BIGINT,
  value FLOAT
);
CREATE TABLE
testdb=# CREATE UNIQUE INDEX idx_deviceid_time
  ON basic_table(device_id, time);
CREATE INDEX
testdb=# \d basic_table
                      Table "public.basic_table"
  Column   |           Type           | Collation | Nullable | Default
-----------+--------------------------+-----------+----------+---------
 time      | timestamp with time zone |           |          |
 user_id   | bigint                   |           |          |
 device_id | bigint                   |           |          |
 value     | double precision         |           |          |
Indexes:
    "idx_deviceid_time" UNIQUE, btree (device_id, "time")

testdb=# SELECT * from create_hypertable('basic_table', 'time');
NOTICE:  adding not-null constraint to column "time"
DETAIL:  Time dimensions cannot have NULL values.
 hypertable_id | schema_name | table_name  | created
---------------+-------------+-------------+---------
             4 | public      | basic_table | t
(1 row)

testdb=# \d basic_table
                      Table "public.basic_table"
  Column   |           Type           | Collation | Nullable | Default
-----------+--------------------------+-----------+----------+---------
 time      | timestamp with time zone |           | not null |
 user_id   | bigint                   |           |          |
 device_id | bigint                   |           |          |
 value     | double precision         |           |          |
Indexes:
    "idx_deviceid_time" UNIQUE, btree (device_id, "time")
    "basic_table_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON basic_table FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42

    也可以将已经创建好索引的 basic_table 表转化为以 time 和 device_id 为分区列的 hypertable

    testdb=# SELECT * FROM create_hypertable(
  'basic_table',
  'time',
  partitioning_column => 'device_id',
  number_partitions => 4
);
NOTICE:  adding not-null constraint to column "time"
DETAIL:  Time dimensions cannot have NULL values.
 hypertable_id | schema_name | table_name  | created
---------------+-------------+-------------+---------
             5 | public      | basic_table | t
(1 row)

testdb=# \d basic_table
                      Table "public.basic_table"
  Column   |           Type           | Collation | Nullable | Default
-----------+--------------------------+-----------+----------+---------
 time      | timestamp with time zone |           | not null |
 user_id   | bigint                   |           |          |
 device_id | bigint                   |           |          |
 value     | double precision         |           |          |
Indexes:
    "idx_deviceid_time" UNIQUE, btree (device_id, "time")
    "basic_table_time_idx" btree ("time" DESC)
Triggers:
    ts_insert_blocker BEFORE INSERT ON basic_table FOR EACH ROW EXECUTE FUNCTION _timescaledb_internal.insert_blocker()

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26

    但是，我们无法将不在索引组合中的 user_id 作为创建 hypertable 时的分区列

    要解决这个问题，要确保在转化为超表之前建立的所有索引都要包含 hypertable 的分区列，所以将已存在的索引中加入 user_id 即可（重建索引可以重建一个索引，然后删除老的索引，避免使用 reindex 重建索引，因为该过程是阻塞的，一般大表不建议使用这个命令 ）

    testdb=# \d basic_table
                      Table "public.basic_table"
  Column   |           Type           | Collation | Nullable | Default
-----------+--------------------------+-----------+----------+---------
 time      | timestamp with time zone |           |          |
 user_id   | bigint                   |           |          |
 device_id | bigint                   |           |          |
 value     | double precision         |           |          |
Indexes:
    "idx_deviceid_time" UNIQUE, btree (device_id, "time")

testdb=# SELECT * FROM create_hypertable(
  'basic_table',
  'time',
  partitioning_column => 'user_id',
  number_partitions => 4
);
NOTICE:  adding not-null constraint to column "time"
DETAIL:  Time dimensions cannot have NULL values.
ERROR:  cannot create a unique index without the column "user_id" (used in partitioning)
testdb=# DROP INDEX idx_deviceid_time;
DROP INDEX
testdb=# CREATE UNIQUE INDEX idx_userid_deviceid_time
  ON basic_table(user_id, device_id, time);
CREATE INDEX
testdb=# \d basic_table
                      Table "public.basic_table"
  Column   |           Type           | Collation | Nullable | Default
-----------+--------------------------+-----------+----------+---------
 time      | timestamp with time zone |           |          |
 user_id   | bigint                   |           |          |
 device_id | bigint                   |           |          |
 value     | double precision         |           |          |
Indexes:
    "idx_userid_deviceid_time" UNIQUE, btree (user_id, device_id, "time")

testdb=# SELECT * FROM create_hypertable(
  'basic_table',
  'time',
  partitioning_column => 'user_id',
  number_partitions => 4
);
NOTICE:  adding not-null constraint to column "time"
DETAIL:  Time dimensions cannot have NULL values.
 hypertable_id | schema_name | table_name  | created
---------------+-------------+-------------+---------
            12 | public      | basic_table | t
(1 row)

    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48

    如果文章对你有帮助，欢迎一键三连 👍 ⭐️ 💬 。如果还能够点击关注，那真的是对我最大的鼓励 🔥 🔥 🔥 。

    参考资料

    https://docs.timescale.com/self-hosted/latest/install/installation-source/
    https://docs.timescale.com/use-timescale/latest/hypertables/create/
    PolarDB 开源版 使用TimescaleDB 实现时序数据高速写入、压缩、实时聚合计算、自动老化等-阿里云开发者社区
    https://zhuanlan.zhihu.com/p/57120174

  • 相关阅读:
    [附源码]java毕业设计购买车票系统
    安道亮相深圳国际全触与显示展，展示最新商显研发成果！
    【Linux】《Linux命令行与shell脚本编程大全 (第4版) 》笔记-Chapter25-井井有条
    超小型射频接头SMP/SSMP
    怎样判定一个可执行文件是否是PIE 格式的文件
    Java版本spring cloud + spring boot企业电子招投标系统源代码
    华为ERP，包含哪些内容？技术的先进性体现在哪里？
    Java8 为什么在接口中引入default方法，以及default方法的使用
    设计模式: 模板方法模式
    ChatGPT⼊门到精通(5):ChatGPT 和Claude区别
  • 原文地址：https://blog.csdn.net/qq_41773806/article/details/132677234