What is a TimescaleDB hypertable?

A hypertable is an abstraction layer in TimescaleDB that automatically partitions data into 'chunks' based on time or other dimensions, enabling massive scale and easier management of time-series datasets.

How does this skill help with database compression?

It provides specific logic for choosing segment_by and order_by columns to maximize storage savings and query performance on compressed data, including sparse index configuration.

Is pgvector compatible with the compression settings suggested?

The skill explicitly advises disabling columnstore compression if the table uses vector columns, as indexes on vector columns are currently incompatible with TimescaleDB's columnstore.

Can I use this for non-time-series data?

Yes, while optimized for time-series, it includes patterns for sequential data using auto-incrementing IDs and append-only datasets like audit logs and transaction records.

Does it support continuous aggregates?

Yes, it includes templates for creating both short-term (hourly) and long-term (daily) materialized views with automated refresh policies and real-time aggregation options.

TimescaleDB Setup & Hypertable Configuration

Name: TimescaleDB Setup & Hypertable Configuration
Author: Activer007

byActiver007

データベース管理

Configures high-performance TimescaleDB hypertables with optimized compression, retention policies, and continuous aggregates.

概要

This skill provides a comprehensive framework for architecting and implementing TimescaleDB schemas optimized for insert-heavy time-series data. It guides developers through critical architectural decisions, such as selecting optimal partition columns, defining segment and order keys for maximum compression, and establishing automated maintenance policies for data retention. By following these industry-standard implementation patterns, users can ensure their PostgreSQL-based time-series databases scale efficiently while maintaining high query performance for IoT sensors, financial logs, and system metrics.

主な機能

Strategic selection of partition, segment_by, and order_by columns
Configuration of sparse indexes for accelerated query filtering
Setup of short-term and long-term continuous aggregates
0 GitHub stars
Automated compression and data retention policy implementation
Guided hypertable creation with optimized schema design patterns

ユースケース

Optimizing IoT and sensor data storage for high-frequency metrics
Building scalable financial transaction and audit trail logs
Creating real-time observability dashboards with continuous aggregates

概要

主な機能

Strategic selection of partition, segment_by, and order_by columns
Configuration of sparse indexes for accelerated query filtering
Setup of short-term and long-term continuous aggregates
0 GitHub stars
Automated compression and data retention policy implementation
Guided hypertable creation with optimized schema design patterns

ユースケース

Optimizing IoT and sensor data storage for high-frequency metrics
Building scalable financial transaction and audit trail logs
Creating real-time observability dashboards with continuous aggregates