timescaledb_toolkit

Overview

PIGSTY 3rd Party Extension: timescaledb_toolkit : Library of analytical hyperfunctions, time-series pipelining, and other SQL utilities

Information

• Extension ID: 1010
• Extension Name: timescaledb_toolkit
• Package Name: timescaledb_toolkit
• Category: TIME
• License: Timescale
• Website: https://github.com/timescale/timescaledb-toolkit
• Language: Rust
• Extra Tags: N/A
• Comment: N/A

Metadata

• Latest Version: 1.19.0
• Postgres Support: 17,16,15,14
• Need Load: Shared library do not need explicit loading
• Need DDL: Need CREATE EXTENSION DDL
• Relocatable: Can be installed into other schemas
• Trusted: Trusted, Can be created by user with CREATE Privilege
• Schemas: N/A
• Requires: N/A

RPM / DEB

• RPM Repo: PIGSTY
• RPM Name: timescaledb-toolkit_$v
• RPM Ver : 1.19.0
• RPM Deps: N/A
• DEB Repo: PIGSTY
• DEB Name: postgresql-$v-timescaledb-toolkit
• DEB Ver : 1.19.0
• DEB Deps: N/A

Packages

Installation

Install timescaledb_toolkit via the pig CLI tool:

pig ext install timescaledb_toolkit

Install timescaledb_toolkit via Pigsty playbook:

./pgsql.yml -t pg_extension -e '{"pg_extensions": ["timescaledb_toolkit"]}' # -l <cls>

Install timescaledb_toolkit RPM from YUM repo directly:

dnf install timescaledb-toolkit_17;
dnf install timescaledb-toolkit_16;
dnf install timescaledb-toolkit_15;
dnf install timescaledb-toolkit_14;

Install timescaledb_toolkit DEB from APT repo directly:

apt install postgresql-17-timescaledb-toolkit;
apt install postgresql-16-timescaledb-toolkit;
apt install postgresql-15-timescaledb-toolkit;
apt install postgresql-14-timescaledb-toolkit;

Enable timescaledb_toolkit extension on PostgreSQL cluster：

CREATE EXTENSION timescaledb_toolkit;

Usage

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:

  • T-Digest – A quantile estimate sketch optimized to provide more accurate estimates near the tails (i.e. 0.001 or 0.995) than conventional approaches. (Methods)
  • UddSketch – A quantile estimate sketch which provides a guaranteed maximum relative error. (Methods)