Which time-series database should I choose?

The skill provides guidance based on workload: TimescaleDB for SQL-heavy hybrid tasks, InfluxDB for DevOps metrics, ClickHouse for massive analytical queries, and QuestDB for extreme write throughput.

Can I use this for financial applications?

Absolutely. It contains specific patterns for high-frequency financial tick data and sub-millisecond query optimization often required for trading and fintech platforms.

How does this skill improve dashboard loading times?

It utilizes continuous aggregates to pre-compute rollups and the LTTB (Largest-Triangle-Three-Buckets) algorithm to downsample millions of points into a renderable format for the frontend.

Does it support automated data cleanup?

Yes, it includes patterns for implementing retention policies that automatically expire old raw data while preserving long-term historical aggregates.

Time-Series Database Implementation

Name: Time-Series Database Implementation
Author: ancoleman

byancoleman

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158

データベース管理

Implements high-performance storage and querying architectures for time-stamped metrics, IoT, and financial data.

概要

This skill equips Claude with specialized knowledge for architecting and optimizing time-series database (TSDB) backends. It provides comprehensive implementation patterns for leading technologies including TimescaleDB, InfluxDB, ClickHouse, and QuestDB, focusing on high-throughput ingestion and sub-millisecond query performance. Users can leverage this skill to implement advanced data management techniques such as hypertables for automatic partitioning, continuous aggregates for pre-computed rollups, LTTB downsampling for responsive data visualization, and automated retention policies to manage storage costs effectively.

主な機能

Pre-computed rollups using continuous aggregate policies
158 GitHub stars
Implementation of Hypertables and time-based partitioning
Visual fidelity preservation with LTTB downsampling algorithms
Database selection logic for TimescaleDB, InfluxDB, ClickHouse, and QuestDB
Automated data retention and multi-tier storage strategies

ユースケース

Building real-time observability and DevOps monitoring dashboards
Developing financial applications for tick data and OHLC aggregates
Architecting high-throughput IoT sensor data ingestion pipelines

概要

主な機能

Pre-computed rollups using continuous aggregate policies
158 GitHub stars
Implementation of Hypertables and time-based partitioning
Visual fidelity preservation with LTTB downsampling algorithms
Database selection logic for TimescaleDB, InfluxDB, ClickHouse, and QuestDB
Automated data retention and multi-tier storage strategies

ユースケース

Building real-time observability and DevOps monitoring dashboards
Developing financial applications for tick data and OHLC aggregates
Architecting high-throughput IoT sensor data ingestion pipelines