Redis Memory Fragmentation Causing Slow Performance

Redis can sometimes experience memory fragmentation, especially in long-running instances that deal with large data sets.

Fragmentation occurs when the memory allocator has trouble efficiently using available memory, causing Redis to run out of contiguous blocks of memory.

This can lead to slow performance or even crashes.

The first step to diagnose this issue is to use the MEMORY STATS command, which provides insight into memory usage, fragmentation, and other metrics.

Check for high values in the fragmentation_ratio field.

If this ratio is above 1.5, it suggests that fragmentation is an issue.

To mitigate fragmentation, try restarting the Redis server to free up fragmented memory.

If you’re using Redis with AOF (Append-Only File) persistence, try disabling AOF or switching to RDB snapshots for persistence, as AOF writes to disk in a way that can exacerbate fragmentation.

If disabling AOF is not an option, consider optimizing the AOF rewrite process by adjusting the auto-aof-rewrite-min-size and auto-aof-rewrite-percentage configuration options to make AOF rewrites more frequent and less memory-intensive.

Another strategy is to adjust the maxmemory directive to limit the maximum amount of memory Redis can use, forcing it to evict data once the limit is reached.

This can help prevent Redis from allocating excessive memory over time.

Additionally, consider using the volatile-lru or allkeys-lru eviction policies to remove less frequently accessed keys and free up memory.

Finally, if you’re using Redis on a virtualized environment, ensure that the host has sufficient memory resources, as memory allocation issues on the host machine can exacerbate fragmentation.

If the problem persists, consider running Redis on a dedicated physical server or use Redis modules such as Redis-ML or RedisGraph that are optimized for memory management.