How do you optimize Redis connection pooling strategies?

Connection pooling reduces overhead by reusing connections, improves performance, and manages resource usage efficiently in high-traffic applications.\n\n• **Why Connection Pooling Matters:**\nReduces connection establishment overhead, manages concurrent access, prevents connection exhaustion, and improves application response times.\n\n• **Optimal Pool Configuration:**\nMax connections: 2x CPU cores for CPU-bound apps, higher for I/O-bound\nMin idle: 25% of max connections\nConnection timeout: 5-10 seconds\nIdle timeout: 300 seconds\n\n• **Python Connection Pool:**\nimport redis\n\npool = redis.ConnectionPool(\n host="localhost",\n port=6379,\n max_connections=50,\n retry_on_timeout=True,\n socket_keepalive=True,\n health_check_interval=30\n)\n\nredis_client = redis.Redis(connection_pool=pool)\n\n• **Node.js Optimization:**\nconst redis = new Redis({\n host: "localhost",\n maxRetriesPerRequest: 3,\n lazyConnect: true,\n keepAlive: 30000,\n connectTimeout: 10000\n});\n\n• **Monitoring Pool Health:**\nTrack active vs idle connections, monitor connection creation rate, alert on pool exhaustion, and measure connection latency.\n\n• **Performance Tips:**\nUse pipelining to reduce connection usage, implement connection warming, optimize network settings, and use Unix sockets for local connections.\n\n• **Best Practices:**\nSize pools based on load testing, monitor utilization, implement circuit breakers, use separate pools for different workloads, and validate connections regularly.