Performance

A comprehensive reference guide for optimizing Apache Spark jobs. Covers partitioning strategies, join optimization (broadcast, sort-merge, bucket joins), caching patterns, memory tuning, shuffle optimization, and data format best practices with PySpark code examples.

A comprehensive guide to profiling and optimizing Python code. Covers CPU and memory profiling tools (cProfile, line_profiler, memory_profiler, py-spy), optimization patterns for loops, data structures, caching, multiprocessing, async I/O, and database operations with practical code examples.

This skill provides comprehensive guidance for designing PostgreSQL database schemas. It covers data type selection, indexing strategies, partitioning approaches, constraints, and performance patterns for various workload types including insert-heavy, update-heavy, and upsert scenarios.

This skill guides developers through React modernization, covering version upgrades from React 16 to 18, migrating class components to functional components with hooks, and adopting concurrent features like Suspense and transitions. It includes practical code examples, codemod automation guidance, and comprehensive migration checklists.

A comprehensive guide to systematic debugging techniques that transforms bug-hunting from guesswork into methodical problem-solving. Covers debugging tools and strategies for JavaScript/TypeScript, Python, and Go including profilers, debuggers, memory leak detection, and best practices for tracking down intermittent bugs and production issues.

A comprehensive reference guide for SQL query optimization that covers EXPLAIN plan analysis, indexing strategies, pagination optimization, and common performance anti-patterns. The skill provides practical SQL examples for PostgreSQL and MySQL to help developers identify and fix slow queries.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.