How do you troubleshoot Redis performance bottlenecks?

Redis performance troubleshooting involves systematic analysis of metrics, identifying bottlenecks, and applying targeted optimizations to restore optimal performance.\n\n• **Common Performance Issues:**\nHigh memory usage, slow queries, network latency, blocking operations, memory fragmentation, and inadequate hardware resources.\n\n• **Essential Monitoring Commands:**\nINFO stats - Operations per second and hit ratio\nINFO memory - Memory usage and fragmentation\nSLOWLOG GET 10 - Identify slow queries\nCLIENT LIST - Check connection status\n\n• **Memory Analysis:**\nredis-cli --bigkeys # Find large keys\nMEMORY USAGE keyname # Check specific key size\nINFO memory | grep fragmentation # Check fragmentation ratio\n\n• **Performance Profiling:**\nimport redis\nimport time\n\ndef measure_latency(redis_client):\n start = time.time()\n redis_client.ping()\n return (time.time() - start) * 1000\n\n# Monitor latency trends\nlatencies = [measure_latency(r) for _ in range(100)]\navg_latency = sum(latencies) / len(latencies)\n\n• **Optimization Strategies:**\nUse pipelining for bulk operations, implement proper key expiration, optimize data structures, enable compression, and tune memory settings.\n\n• **System-Level Checks:**\nCPU usage (top, htop), memory pressure (free -h), disk I/O (iostat), network latency (ping), and swap usage (swapon -s).\n\n• **Configuration Tuning:**\nmaxmemory-policy allkeys-lru\ntcp-keepalive 300\nstop-writes-on-bgsave-error no\n\n• **Best Practices:**\nEstablish performance baselines, monitor key metrics continuously, test optimizations in staging, document changes, and maintain runbooks for common issues.