How do you implement Redis time series data management?

Redis handles time series data using RedisTimeSeries module or native data structures for storing and querying time-based data.\n\n• **RedisTimeSeries Module:**\nPurpose-built for time series data, supports automatic downsampling, retention policies, and efficient range queries.\n\nTS.CREATE temperature RETENTION 86400000 LABELS sensor_id 1\nTS.ADD temperature * 23.5\nTS.RANGE temperature 1609459200000 1609545600000\n\n• **Native Approach with Sorted Sets:**\nUse timestamps as scores, store values as members, leverage Redis built-in commands for time-based operations.\n\nZADD metrics:cpu:20210101 1609459200 75.5\nZRANGEBYSCORE metrics:cpu:20210101 1609459200 1609545600\n\n• **Data Aggregation:**\nCreate downsampled views, calculate averages over time windows, and maintain multiple resolution levels.\n\nTS.CREATERULE source_key dest_key AGGREGATION avg 3600000\n\n• **Python Implementation:**\nimport redis\nfrom redistimeseries.client import Client\n\nrts = Client()\nrts.create("cpu_usage", retention_msecs=86400000)\nrts.add("cpu_usage", "*", 75.5)\ndata = rts.range("cpu_usage", from_time="-1h")\n\n• **Use Cases:**\nIoT sensor data, application metrics, financial market data, system monitoring, and real-time analytics.\n\n• **Performance Benefits:**\nOptimized storage format, fast range queries, automatic data expiration, and built-in aggregation functions.\n\n• **Best Practices:**\nSet appropriate retention periods, use consistent naming conventions, implement proper indexing, and monitor memory usage.