pg_mooncake
PostgreSQL列式存储表
	Module:
Categories:
扩展总览
PIGSTY 第三方扩展: pg_mooncake : PostgreSQL列式存储表
基本信息
- 扩展编号: 2440
- 扩展名称: pg_mooncake
- 标准包名: pg_mooncake
- 扩展类目: OLAP
- 开源协议: MIT
- 官方网站: https://github.com/Mooncake-Labs/pg_mooncake
- 编程语言: C++
- 其他标签: duckdb
- 备注信息: 无
元数据
- 默认版本: 0.1.2
- PG大版本: 17,16,15,14
- 动态加载: 无需动态加载
- 需要DDL:  需要执行 CREATE EXTENSIONDDL
- 可重定位: 可以重定位安装至其他模式下
- 信任程度: 未受信任,创建扩展需要超级用户权限
- 所需模式: 无
- 所需扩展: 无
软件包
- RPM仓库:PIGSTY
- RPM包名:pg_mooncake_$v*
- RPM版本:0.1.2
- RPM依赖:无
- DEB仓库:PIGSTY
- DEB包名:postgresql-$v-pg-mooncake
- DEB版本:0.1.2
- DEB依赖:无
最新版本
扩展安装
使用 pig 命令行工具安装 pg_mooncake 扩展:
pig ext install pg_mooncake
使用 Pigsty剧本 安装 pg_mooncake 扩展:
./pgsql.yml -t pg_extension -e '{"pg_extensions": ["pg_mooncake"]}' # -l <集群名>
从 YUM仓库 手工安装 pg_mooncake RPM 包:
dnf install pg_mooncake_17*;
dnf install pg_mooncake_16*;
dnf install pg_mooncake_15*;
dnf install pg_mooncake_14*;
从 APT仓库 手工安装 pg_mooncake DEB 包:
apt install postgresql-17-pg-mooncake;
apt install postgresql-16-pg-mooncake;
apt install postgresql-15-pg-mooncake;
apt install postgresql-14-pg-mooncake;
使用以下 SQL 命令在已经安装此扩展插件的 PG 集群上 启用 pg_mooncake 扩展:
CREATE EXTENSION pg_mooncake;
使用方法
THIS EXTENSION IS CONFLICT WITH pg_duckdb & duckdb_fdw, if it is under maintained, we may remove this extension in the future
Beware that this package is conflict with the official pg_duckdb extension due to use the same libduckdb.so under same path.
And this function will block the duckdb_fdw functioning.
-- Create a columnstore table in PostgreSQL
CREATE TABLE user_activity (....) USING columnstore;
-- Insert data into a columnstore table
INSERT INTO user_activity VALUES ....;
-- Query a columnstore table in PostgreSQL
SELECT * FROM user_activity LIMIT 5;
Example
Use mooncake with S3:
SELECT mooncake.create_secret('<name>', 'S3', '<key_id>', '<secret>', '{"REGION": "<s3-region>"}');
SET mooncake.default_bucket = 's3://<bucket>';
SET mooncake.enable_local_cache = false; -- (if you are using Neon)
Use mooncake with local columnstore:
CREATE TABLE user_activity(
  user_id BIGINT,
  activity_type TEXT,
  activity_timestamp TIMESTAMP,
  duration INT
) USING columnstore;
INSERT INTO user_activity VALUES
  (1, 'login', '2024-01-01 08:00:00', 120),
  (2, 'page_view', '2024-01-01 08:05:00', 30),
  (3, 'logout', '2024-01-01 08:30:00', 60),
  (4, 'error', '2024-01-01 08:13:00', 60);
SELECT * FROM user_activity;
Run analytic queries
SELECT
    user_id,
    activity_type,
    SUM(duration) AS total_duration,
    COUNT(*) AS activity_count
FROM
    user_activity
GROUP BY
    user_id, activity_type
ORDER BY
    user_id, activity_type;
The explain result could be:
postgres@u22:5432/postgres=# explain SELECT
    user_id,
    activity_type,
    SUM(duration) AS total_duration,
    COUNT(*) AS activity_count
FROM
    user_activity
GROUP BY
    user_id, activity_type
ORDER BY
    user_id, activity_type;
                         QUERY PLAN
------------------------------------------------------------
 Custom Scan (DuckDBScan)  (cost=0.00..0.00 rows=0 width=0)
   DuckDB Execution Plan:
 ┌───────────────────────────┐
 │         PROJECTION        │
 │    ────────────────────   │
 │__internal_decompress_integ│
 │     ral_bigint(#0, 1)     │
 │             #1            │
 │             #2            │
 │             #3            │
 │                           │
 │          ~2 Rows          │
 └─────────────┬─────────────┘
 ┌─────────────┴─────────────┐
 │          ORDER_BY         │
 │    ────────────────────   │
 │ user_activity.user_id ASC │
 │       user_activity       │
 │     .activity_type ASC    │
 └─────────────┬─────────────┘
 ┌─────────────┴─────────────┐
 │         PROJECTION        │
 │    ────────────────────   │
 │__internal_compress_integra│
 │     l_utinyint(#0, 1)     │
 │             #1            │
 │             #2            │
 │             #3            │
 │                           │
 │          ~2 Rows          │
 └─────────────┬─────────────┘
 ┌─────────────┴─────────────┐
 │         PROJECTION        │
 │    ────────────────────   │
 │__internal_decompress_integ│
 │     ral_bigint(#0, 1)     │
 │             #1            │
 │             #2            │
 │             #3            │
 │                           │
 │          ~2 Rows          │
 └─────────────┬─────────────┘
 ┌─────────────┴─────────────┐
 │       HASH_GROUP_BY       │
 │    ────────────────────   │
 │          Groups:          │
 │             #0            │
 │             #1            │
 │                           │
 │        Aggregates:        │
 │          sum(#2)          │
 │        count_star()       │
 │                           │
 │          ~2 Rows          │
 └─────────────┬─────────────┘
 ┌─────────────┴─────────────┐
 │         PROJECTION        │
 │    ────────────────────   │
 │          user_id          │
 │       activity_type       │
 │          duration         │
 │                           │
 │          ~4 Rows          │
 └─────────────┬─────────────┘
 ┌─────────────┴─────────────┐
 │         PROJECTION        │
 │    ────────────────────   │
 │__internal_compress_integra│
 │     l_utinyint(#0, 1)     │
 │             #1            │
 │             #2            │
 │                           │
 │          ~4 Rows          │
 └─────────────┬─────────────┘
 ┌─────────────┴─────────────┐
 │     COLUMNSTORE_SCAN      │
 │    ────────────────────   │
 │         Function:         │
 │      COLUMNSTORE_SCAN     │
 │                           │
 │        Projections:       │
 │          user_id          │
 │       activity_type       │
 │          duration         │
 │                           │
 │          ~4 Rows          │
 └───────────────────────────┘
(90 rows)