Is this skill compatible with all SQL databases?

It focuses on industry-standard SQL partitioning practices compatible with major relational systems like PostgreSQL, MySQL, and similar SQL-based engines.

Can it help with existing large tables already in production?

Yes, it is designed to analyze tables exceeding 100GB and generate scripts to migrate existing data into a partitioned structure safely.

What types of partitioning does this skill support?

The skill supports Range, List, and Hash partitioning strategies, selecting the best fit based on your data distribution and query patterns.

Can it automate data archival?

Yes, it provides strategies for data lifecycle management, including automating the creation of new partitions and the archival of old data.

How does this skill improve query performance?

It implements partition pruning, which allows the database engine to scan only relevant data segments, significantly reducing I/O and query execution time.

Database Partition Manager

Name: Database Partition Manager
Author: intent-solutions-io

byintent-solutions-io

데이터베이스 관리

Automates the design and implementation of database partitioning strategies to optimize query performance and manage large-scale data sets.

소개

This skill empowers Claude to architect and deploy advanced database partitioning schemes, specifically targeting tables exceeding 100GB or high-velocity time-series data. It analyzes query patterns and data volume to recommend range, list, or hash partitioning strategies, generating production-ready SQL scripts while providing guidance on partition pruning and lifecycle management to reduce maintenance windows and improve system responsiveness.

주요 기능

Bottleneck analysis for massive datasets exceeding 100GB
Automated partitioning strategy design (Range, List, Hash)
Production-ready SQL script generation for new and existing tables
Time-series data archival and lifecycle management workflows
0 GitHub stars
Query optimization via partition pruning and indexing recommendations

사용 사례

Optimizing IoT and log data using time-based range partitioning
Managing multi-region order history using list partitioning
Reducing database maintenance windows for legacy massive tables

소개

주요 기능

Bottleneck analysis for massive datasets exceeding 100GB
Automated partitioning strategy design (Range, List, Hash)
Production-ready SQL script generation for new and existing tables
Time-series data archival and lifecycle management workflows
0 GitHub stars
Query optimization via partition pruning and indexing recommendations

사용 사례

Optimizing IoT and log data using time-based range partitioning
Managing multi-region order history using list partitioning
Reducing database maintenance windows for legacy massive tables