Optimization

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

A comprehensive guide to profiling and optimizing Python code. Covers CPU and memory profiling tools (cProfile, memory_profiler, py-spy), optimization patterns like list comprehensions, caching with lru_cache, NumPy vectorization, and parallel processing with multiprocessing and asyncio.

This skill provides comprehensive guidance for using pymoo, a Python framework for multi-objective optimization. It covers evolutionary algorithms (NSGA-II, NSGA-III, MOEA/D), benchmark problems (ZDT, DTLZ), constraint handling, custom problem definition, and multi-criteria decision making for selecting preferred solutions from Pareto fronts.

This skill provides detailed PostgreSQL performance optimization guidelines and best practices from Supabase. It covers query performance, connection pooling, Row-Level Security, schema design, and more with concrete SQL examples showing incorrect vs correct patterns.

This skill provides comprehensive React Native performance optimization guidelines from Callstack's 'Ultimate Guide to React Native Optimization'. It covers JavaScript/React optimizations (FPS, re-renders, animations), native performance (Turbo Modules, memory management), and bundling optimizations (tree shaking, code splitting) with practical code patterns and debugging commands.

This skill provides comprehensive guidelines for diagnosing and fixing UI animation performance problems. It covers rendering optimization rules across compositing, paint, and layout phases, with specific guidance for scroll-linked motion, blur effects, view transitions, and avoiding common performance pitfalls like layout thrashing.