How does this help with storage costs?

It implements advanced compression policies and tiered storage patterns to significantly reduce the storage footprint of your time-series data without sacrificing query performance.

Can it help with slow update queries on compressed data?

Absolutely. It leverages the TimescaleDB 2.24.0 recompression feature which allows for updates to compressed chunks that are up to 100x faster than previous versions.

What version of TimescaleDB does this skill support?

This skill specifically targets TimescaleDB 2.24.0 and later, including new features like Direct Compress and lightning-fast recompression.

When should I use this skill?

Apply this skill whenever you are working with hypertables, time-series metrics, logs, sensor data, or continuous aggregates in your database schema or application code.

Does it support PostgreSQL 18 features?

Yes, it includes native support for PostgreSQL 18 features such as UUIDv7 primary keys and their integration with continuous aggregates.

TimescaleDB Time-Series Optimization

Name: TimescaleDB Time-Series Optimization
Author: troykelly

bytroykelly

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Database Management

Optimizes time-series data management by enforcing TimescaleDB 2.24.0 best practices for hypertables, continuous aggregates, and compression.

About

This skill provides specialized guidance for working with TimescaleDB, focusing on the latest 2.24.0 features like lightning-fast recompression and Direct Compress. It ensures developers implement efficient hypertable designs, effective compression strategies, and robust data retention policies. By automating the application of domain-specific patterns for metrics, logs, and IoT data, it helps maintain high-performance time-series databases with reduced storage overhead and faster query speeds while leveraging native PostgreSQL 18 features like UUIDv7.

Key Features

UUIDv7 support in continuous aggregates for modern primary key patterns
Automated hypertable design with optimized chunk interval selection
Advanced compression strategies using segment-by and order-by optimization
Lightning-fast recompression for 100x faster updates to compressed data
Direct Compress integration for continuous aggregates to reduce storage overhead
5 GitHub stars

Use Cases

Managing financial market data with complex continuous aggregates and retention policies
Building high-performance IoT sensor data monitoring systems
Optimizing log storage and retrieval for large-scale distributed applications

About

Key Features

UUIDv7 support in continuous aggregates for modern primary key patterns
Automated hypertable design with optimized chunk interval selection
Advanced compression strategies using segment-by and order-by optimization
Lightning-fast recompression for 100x faster updates to compressed data
Direct Compress integration for continuous aggregates to reduce storage overhead
5 GitHub stars

Use Cases

Managing financial market data with complex continuous aggregates and retention policies
Building high-performance IoT sensor data monitoring systems
Optimizing log storage and retrieval for large-scale distributed applications