vibe-rules-collection — Windsurf Rules

• Measure First: Don't optimize prematurely. Identify actual bottlenecks using profiling tools, monitoring metrics (CPU, memory, I/O, network, latency), and load testing before making changes. • Identify Bottlenecks: Determine whether the system is CPU-bound, memory-bound, I/O-bound (disk or network), or limited by lock contention. Focus optimization efforts on the primary bottleneck. • Optimize Algorithms & Data Structures: The biggest gains often come from choosing more efficient algorithms (e.g., O(log n) vs O(n^2)) and appropriate data structures for the task (e.g., HashMap/Dictionary for lookups, List/Vector for iteration). • Reduce Work: Eliminate unnecessary computations, data transfers, or operations. Can the work be avoided, done less frequently, or done more efficiently? • Database Performance: • Indexing: Ensure appropriate database indexes exist for common query patterns. • Query Optimization: Write efficient SQL queries. Analyze execution plans (EXPLAIN, EXPLAIN ANALYZE). Avoid SELECT *. Fetch only necessary data. • Connection Pooling: Use connection pools to reuse database connections, reducing connection overhead. • N+1 Problem: Avoid fetching related data in loops; use joins or batch loading techniques. • Caching: Implement caching at appropriate levels (database query cache, application-level cache, CDN, browser cache) to store and reuse frequently accessed or computationally expensive data. • Concurrency & Parallelism: Utilize multiple cores/threads/processes or asynchronous operations (non-blocking I/O) to perform tasks in parallel where appropriate. Be mindful of synchronization overhead and complexity. • Network Optimization: • Reduce Round Trips: Minimize the number of network calls between components (e.g., client-server, server-database). • Payload Size: Reduce the size of data transferred (compression, selective fetching, efficient serialization formats). • HTTP/2 or HTTP/3: Leverage newer protocols for multiplexing and header compression. • CDNs: Use Content Delivery Networks to serve static assets closer to users. • Memory Management: • Reduce Allocations: Minimize object creation/allocations, especially in hot paths. • Memory Leaks: Identify and fix memory leaks. • Efficient Data Representation: Use memory-efficient data structures and types. • System Configuration: Tune operating system, database, web server, runtime (JVM, CLR, Node.js) configurations based on workload and hardware. • Code Level Optimizations: Focus on hot spots identified by profilers. Optimize loops, reduce function call overhead, use efficient library functions.

• Profiling Reminder: Suggest using profiling tools (language-specific profilers, database profilers, browser dev tools) to identify bottlenecks before generating optimization code. • Algorithmic Suggestions: Suggest alternative algorithms or data structures if a potentially inefficient pattern is detected. • Query Optimization: Generate optimized SQL queries (using joins, appropriate WHERE clauses, avoiding SELECT *). Suggest relevant indexes. • Caching Patterns: Generate code implementing common caching strategies (e.g., memoization, using caching libraries/frameworks). • Concurrency/Async: Generate code using threading, async/await, or relevant concurrency primitives/libraries. • Network Efficiency: Suggest batching API calls, using specific fields in GraphQL, or enabling compression. • Memory Allocation: Highlight potential areas of high memory allocation in generated code. • N+1 Detection: Identify potential N+1 query patterns in ORM or data access code and suggest eager loading or batching alternatives.