What are Redis memory management and eviction policies?

Redis memory management and eviction strategies: **Memory Policies:** # redis.conf maxmemory 2gb maxmemory-policy allkeys-lru **Eviction Policies:** • allkeys-lru**: Remove least recently used keys • allkeys-lfu**: Remove least frequently used keys • allkeys-random**: Remove random keys • volatile-lru**: Remove LRU keys with expire set • volatile-lfu**: Remove LFU keys with expire set • volatile-random**: Remove random keys with expire set • volatile-ttl**: Remove keys with shortest TTL • noeviction**: Return errors when memory limit reached **Memory Monitoring:** # Check memory usage INFO memory MEMORY USAGE key MEMORY STATS # Find memory-hungry keys redis-cli --bigkeys redis-cli --memkeys **Memory Optimization Techniques:** # 1. Use appropriate data structures # Hash for small objects (more memory efficient) r.hmset('user:1000', { 'name': 'John', 'email': 'john@example.com', 'age': 30 }) # 2. Set expiration on temporary data r.setex('session:abc123', 3600, session_data) r.expire('cache:product:456', 1800) # 3. Use compression for large values import gzip import json data = json.dumps(large_object) compressed = gzip.compress(data.encode()) r.set('large:object:1', compressed) # 4. Efficient key naming # Bad: "user:profile:personal:information:1000" # Good: "u:p:1000" **Memory Configuration:** # Compression settings hash-max-ziplist-entries 512 hash-max-ziplist-value 64 list-max-ziplist-size -2 set-max-intset-entries 512 zset-max-ziplist-entries 128 zset-max-ziplist-value 64 # Memory sampling maxmemory-samples 5 **Monitoring Script:** def monitor_memory(): info = r.info('memory') used_memory = info['used_memory'] max_memory = info.get('maxmemory', 0) fragmentation = info['mem_fragmentation_ratio'] if max_memory > 0: usage_percent = (used_memory / max_memory) * 100 if usage_percent > 80: print(f"High memory usage: {usage_percent:.1f}%") if fragmentation > 1.5: print(f"High fragmentation: {fragmentation:.2f}") # Check for large keys large_keys = [] for key in r.scan_iter(count=1000): size = r.memory_usage(key) if size and size > 1024 * 1024: # 1MB large_keys.append((key, size)) if large_keys: print(f"Found {len(large_keys)} large keys") **Best Practices:** • Set appropriate maxmemory limit • Choose eviction policy based on use case • Monitor memory usage and fragmentation • Use TTL on temporary data • Optimize data structures • Regular memory analysis