Does it offer guidance on indexing strategy?

Absolutely. It covers complex indexing scenarios including composite indexes, partial indexes for filtered data, and covering indexes to improve data retrieval speeds.

Can this skill help with batch processing performance?

Yes, it includes optimized patterns for bulk inserts and updates to reduce network round trips and improve transaction throughput in high-volume environments.

Does this skill support both PostgreSQL and MySQL?

Yes, it provides specific optimization techniques, query syntax, and performance monitoring commands tailored for both PostgreSQL and MySQL database systems.

How does this skill help identify slow queries?

It provides a structured framework to analyze query plans using EXPLAIN ANALYZE, identifies missing indexes, and suggests specific rewriting patterns to minimize execution time.

SQL Query Optimizer

Name: SQL Query Optimizer
Author: aj-geddes

byaj-geddes

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数据库管理

Analyzes and optimizes SQL queries for PostgreSQL and MySQL to improve performance and reduce execution time.

关于

The SQL Query Optimization skill provides a comprehensive framework for identifying and resolving performance bottlenecks in relational database systems. It equips developers with specialized strategies for analyzing query execution plans, implementing advanced indexing techniques like partial and covering indexes, and refactoring inefficient SQL patterns into performant code. Whether you are dealing with slow-running joins, complex aggregations, or high-volume bulk operations, this skill helps you establish performance baselines and maintain highly responsive database architectures through proven optimization methodologies.

主要功能

Advanced indexing strategies including composite, partial, and covering indexes.
Performance monitoring techniques for identifying slow-running queries.
Automated query plan analysis for PostgreSQL and MySQL using EXPLAIN.
Query rewriting patterns for window functions and join optimization.
Bulk operation patterns to reduce network round trips and overhead.
18 GitHub stars

使用场景

Designing efficient indexing strategies for high-traffic database tables.
Analyzing and fixing performance bottlenecks in complex analytical joins.
Refactoring slow-running legacy queries to reduce server latency.

关于

主要功能

Advanced indexing strategies including composite, partial, and covering indexes.
Performance monitoring techniques for identifying slow-running queries.
Automated query plan analysis for PostgreSQL and MySQL using EXPLAIN.
Query rewriting patterns for window functions and join optimization.
Bulk operation patterns to reduce network round trips and overhead.
18 GitHub stars

使用场景

Designing efficient indexing strategies for high-traffic database tables.
Analyzing and fixing performance bottlenecks in complex analytical joins.
Refactoring slow-running legacy queries to reduce server latency.