timescaledb

Name: timescaledb
Availability: InStock
Author: bearflower

Extend PostgreSQL with TimescaleDB hypertables and continuous aggregates for time-series analytics.

Updated Apr 27, 2026

Search Tags:#real-time #compression #postgres #timescaledb #continuous-aggregates #timeseries #hypertable

Ready to run

npx skills add https://github.com/Bearflower/stockfiter --skill timescaledb-bearflower

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: timescaledb
Source: https://github.com/Bearflower/stockfiter/tree/main/skills/vibe-coding-cn-main/i18n/zh/skills/timescaledb
Command: npx skills add https://github.com/Bearflower/stockfiter --skill timescaledb-bearflower

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

TimescaleDB extends PostgreSQL to efficiently store and analyze time-series data using hypertables, continuous aggregates, and compression, enabling real-time analytics at scale.

Core Features & Use Cases

Hypertables for scalable time-series storage and fast ingestion
Continuous aggregates for real-time materialized views over time-series data
Columnar compression of historical data to save space and speed queries
Time-based analytics and tiered storage for large datasets
Use cases include monitoring metrics, IoT telemetry, and financial time-series analyses

Quick Start

Install TimescaleDB on PostgreSQL, create a hypertable, and begin ingesting time-series data.

Frequently Asked Questions about timescaledb

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is a hypertable in PostgreSQL for time-series data?▼

A hypertable is a PostgreSQL table partitioned by time to scale time-series data storage and enable fast ingestion. TimescaleDB creates hypertables to efficiently manage large datasets across time intervals.

How do I create continuous aggregates for real-time analytics?▼

Continuous aggregates in TimescaleDB materialize time-series views automatically for real-time analytics. You create them using PostgreSQL extension functions to continuously calculate and refresh summarized metrics over time intervals.

Does TimescaleDB support columnar compression for historical time-series data?▼

TimescaleDB supports columnar compression for historical time-series data to save storage space and speed up queries. It compresses data within hypertables based on time intervals to optimize analytical performance.

Can I use PostgreSQL for IoT telemetry and financial time-series monitoring?▼

PostgreSQL with the TimescaleDB extension handles IoT telemetry and financial time-series monitoring by using hypertables and continuous aggregates. It ingests metrics at scale and analyzes events across large datasets in real-time.

Do I need a separate database to build a scalable time-series database with PostgreSQL?▼

You do not need a separate database to build a scalable time-series database. You install the TimescaleDB extension on an existing PostgreSQL instance to create hypertables and perform real-time analytics.

What is the best way to store and query time-series metrics in real-time?▼

The best way to store and query time-series metrics in real-time is extending PostgreSQL with TimescaleDB. It uses hypertables for scalable ingestion and continuous aggregates for fast materialized analytical views.