How do you monitor and debug Redis performance issues?

Redis monitoring and debugging strategies: • Built-in Monitoring Commands:** # General information INFO all INFO memory INFO stats INFO replication INFO clients # Real-time monitoring MONITOR # Shows all commands (use carefully in production) # Client connections CLIENT LIST CLIENT INFO # Slow query log SLOWLOG GET 10 SLOWLOG LEN SLOWLOG RESET • Key Performance Metrics:** import redis import time def get_redis_metrics(r): info = r.info() metrics = { # Memory 'used_memory': info['used_memory'], 'used_memory_human': info['used_memory_human'], 'mem_fragmentation_ratio': info['mem_fragmentation_ratio'], # Performance 'total_commands_processed': info['total_commands_processed'], 'instantaneous_ops_per_sec': info['instantaneous_ops_per_sec'], 'keyspace_hits': info['keyspace_hits'], 'keyspace_misses': info['keyspace_misses'], # Connections 'connected_clients': info['connected_clients'], 'blocked_clients': info['blocked_clients'] } # Calculate hit ratio total_hits = metrics['keyspace_hits'] + metrics['keyspace_misses'] if total_hits > 0: metrics['hit_ratio'] = metrics['keyspace_hits'] / total_hits return metrics • Memory Analysis:** # Find memory usage by key MEMORY USAGE mykey # Find largest keys redis-cli --bigkeys # Memory stats MEMORY STATS MEMORY DOCTOR • Performance Debugging:** # Latency monitoring def measure_latency(r, iterations=1000): start_time = time.time() for i in range(iterations): r.ping() end_time = time.time() avg_latency = (end_time - start_time) / iterations * 1000 print(f"Average latency: {avg_latency:.2f}ms") return avg_latency **Alerting Thresholds:** • Memory usage > 80% • Hit ratio < 90% • Slow queries > 10ms • Connected clients > 1000 • Memory fragmentation > 1.5 • Blocked clients > 0 **Best Practices:** • Set up comprehensive monitoring • Use slow query log • Monitor memory usage and fragmentation • Track key performance metrics • Set up alerts for critical thresholds