How do you implement Redis performance benchmarking?

Redis benchmarking measures performance characteristics, identifies bottlenecks, and validates system capacity under various load conditions.\n\n• **Why Benchmark Redis?**\nValidate performance expectations, identify optimal configurations, plan capacity, compare hardware options, and establish performance baselines.\n\n• **Built-in Benchmarking Tool:**\nredis-benchmark provides comprehensive testing with various parameters and realistic workload simulation.\n\nredis-benchmark -h localhost -p 6379 -n 100000 -c 50\nredis-benchmark -t set,get -n 100000 -q # Quiet mode\nredis-benchmark -P 16 -n 100000 -t set,get # Pipeline\n\n• **Key Parameters:**\n-c clients (concurrent connections)\n-n requests (total operations)\n-d data size (payload size)\n-t tests (specific operations)\n-P pipeline (batch requests)\n\n• **Custom Benchmarking:**\nimport redis\nimport time\n\ndef benchmark_operation(redis_client, operation, iterations=10000):\n start_time = time.time()\n \n for i in range(iterations):\n if operation == "set":\n redis_client.set(f"bench:key:{i}", f"value:{i}")\n elif operation == "get":\n redis_client.get(f"bench:key:{i % 1000}")\n \n total_time = time.time() - start_time\n ops_per_second = iterations / total_time\n \n return ops_per_second\n\n• **Pipeline Benchmarking:**\npipe = redis_client.pipeline()\nfor i in range(1000):\n pipe.set(f"pipe:key:{i}", f"value:{i}")\nresults = pipe.execute()\n\n• **Load Testing Tools:**\nmemtier_benchmark -s localhost -p 6379 -n 100000 -c 50\n# Advanced load testing with realistic patterns\n\n• **Metrics to Measure:**\nThroughput (ops/sec), latency (response time), memory usage, CPU utilization, and network bandwidth.\n\n• **Best Practices:**\nTest on production-like hardware, use realistic data sizes, measure under various loads, establish baselines, and automate regression testing.