How do you implement Redis cache warming strategies?

Cache warming pre-populates Redis with frequently accessed data to ensure optimal performance, reduce database load, and improve user experience.\n\n• **Why Cache Warming is Important:**\nPrevents cache misses after restarts, ensures consistent performance, reduces database load spikes, and improves user experience during peak times.\n\n• **Proactive Warming Strategies:**\nScheduled warming during low-traffic periods, predictive warming based on usage patterns, and application startup warming.\n\n• **Application Startup Warming:**\nclass CacheWarmer:\n def warm_critical_data(self):\n # Warm user data\n active_users = db.get_active_users(limit=10000)\n for user in active_users:\n user_data = db.get_user(user.id)\n redis.setex(f"user:{user.id}", 3600, json.dumps(user_data))\n \n # Warm product catalog\n popular_products = db.get_popular_products(limit=5000)\n for product in popular_products:\n redis.setex(f"product:{product.id}", 7200, json.dumps(product))\n\n• **Scheduled Warming:**\nimport schedule\n\ndef daily_cache_warm():\n warmer.warm_product_catalog()\n warmer.warm_configuration_data()\n\nschedule.every().day.at("02:00").do(daily_cache_warm)\nschedule.every().hour.do(warmer.warm_user_data)\n\n• **Intelligent Warming:**\nAnalyze access patterns, predict future requests, warm seasonal data, and prioritize high-value content.\n\n• **Background Refresh:**\nMonitor TTL values, schedule refresh before expiration, use background workers, and implement non-blocking updates.\n\n• **Progressive Warming:**\nBatch processing with rate limiting, priority-based loading, progress tracking, and resource usage monitoring.\n\n• **Best Practices:**\nWarm during low-traffic periods, prioritize frequently accessed data, monitor warming effectiveness, implement progress tracking, and measure cache hit ratio improvements.