How to fix OOM Out of memory in Redis

Connect to Redis and view memory statistics to understand how much memory is being used:

redis-cli info memory

Look for used_memory_human and compare it to maxmemory. If used_memory is near or exceeding maxmemory, that confirms the OOM condition.

If this is a development or test environment, you can clear the cache:

redis-cli flushall

For production, selectively delete old or unnecessary keys:

redis-cli --bigkeys  # Find the largest keys
redis-cli DEL key_name  # Remove specific keys

Change from noeviction to an eviction policy that automatically removes keys when the limit is reached:

redis-cli CONFIG SET maxmemory-policy allkeys-lru

Common policies:
- allkeys-lru: Evict least recently used keys (recommended for most caches)
- volatile-lru: Evict least recently used keys with expiration only
- allkeys-lfu: Evict least frequently used keys
- volatile-ttl: Evict keys with shortest TTL first

To persist this change, update your redis.conf:

maxmemory-policy allkeys-lru

When using volatile policies, ensure keys have expiration times:

redis-cli SET mykey "value" EX 3600  # Expires in 1 hour
redis-cli EXPIRE existingkey 3600

In your application code:

# Python example
redis_client.setex("cache_key", 3600, "value")  # 1 hour TTL

# Node.js example
await redis.setex("cache_key", 3600, "value");  # 1 hour TTL

If your legitimate data exceeds the current limit:

redis-cli CONFIG SET maxmemory 512mb

For permanent changes, edit redis.conf:

maxmemory 512mb

Then restart Redis for the configuration to take effect. For managed Redis services (AWS ElastiCache, Google Cloud Memorystore, Azure Cache), scale up the node type through the service console.

Monitor memory usage continuously to catch issues before they cause OOM:

redis-cli --stat  # Real-time memory stats

Set up alerts when memory usage approaches the maxmemory limit (e.g., at 80% capacity). Most Redis managed services have built-in monitoring. For self-hosted Redis, use tools like Prometheus + Grafana or DataDog to track used_memory over time.