timescaledb_toolkit
Module:
Categories:
扩展总览
PIGSTY 第三方扩展: timescaledb_toolkit
: 超表分析查询,时间序列流式处理,以及其他SQL工具
基本信息
- 扩展编号: 1010
- 扩展名称:
timescaledb_toolkit
- 标准包名:
timescaledb_toolkit
- 扩展类目:
TIME
- 开源协议: Timescale
- 官方网站: https://github.com/timescale/timescaledb-toolkit
- 编程语言: Rust
- 其他标签:
pgrx
- 备注信息: 无
元数据
- 默认版本: 1.19.0
- 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 EXTENSION
DDL - 可重定位: 可以重定位安装至其他模式下
- 信任程度: 受信任,无需超级用户,带
CREATE
权限的用户可以直接创建 - 所需模式: 无
- 所需扩展: 无
软件包
- RPM仓库:PIGSTY
- RPM包名:
timescaledb-toolkit_$v
- RPM版本:
1.19.0
- RPM依赖:无
- DEB仓库:PIGSTY
- DEB包名:
postgresql-$v-timescaledb-toolkit
- DEB版本:
1.19.0
- DEB依赖:无
最新版本
扩展安装
使用 pig
命令行工具安装 timescaledb_toolkit
扩展:
pig ext install timescaledb_toolkit
使用 Pigsty剧本 安装 timescaledb_toolkit 扩展:
./pgsql.yml -t pg_extension -e '{"pg_extensions": ["timescaledb_toolkit"]}' # -l <集群名>
从 YUM仓库 手工安装 timescaledb_toolkit
RPM 包:
dnf install timescaledb-toolkit_17;
dnf install timescaledb-toolkit_16;
dnf install timescaledb-toolkit_15;
dnf install timescaledb-toolkit_14;
从 APT仓库 手工安装 timescaledb_toolkit
DEB 包:
apt install postgresql-17-timescaledb-toolkit;
apt install postgresql-16-timescaledb-toolkit;
apt install postgresql-15-timescaledb-toolkit;
apt install postgresql-14-timescaledb-toolkit;
使用以下 SQL 命令在已经安装此扩展插件的 PG 集群上 启用 timescaledb_toolkit
扩展:
CREATE EXTENSION timescaledb_toolkit;
使用方法
This extension provide experimental features for timescaledb, check the docs for details.
Features
The following links lead to pages for the different features in the TimescaleDB Toolkit repository.
-
ASAP Smoothing experimental - A data smoothing algorithm designed to generate human readable graphs which maintain any erratic data behavior while smoothing away the cyclic noise.
-
Hyperloglog experimental – An approximate
COUNT DISTINCT
based on hashing that provides reasonable accuracy in constant space. (Methods) -
LTTB experimental – A downsample method that preserves visual similarity. (Methods)
-
Percentile Approximation - A simple percentile approximation interface [(Methods)], wraps and simplifies the lower level algorithms: