How can I optimize memory use in Redis for storing numerous JSON API results?

Other Questions API
1

I’m a beginner with Redis and I’m currently testing it for caching API JSON responses. I’m utilizing the ServiceStack.Redis library and I’m concerned about the memory usage I’m observing.

public class CacheHandler
{
    private readonly IRedisClient redisClient;
    
    public void SaveApiResponse(string key, ApiResponse response)
    {
        var jsonString = JsonSerializer.Serialize(response);
        redisClient.Set(key, jsonString);
    }
    
    public ApiResponse RetrieveCachedResponse(string key)
    {
        var jsonData = redisClient.Get<string>(key);
        return JsonSerializer.Deserialize<ApiResponse>(jsonData);
    }
}

Currently, with 25,000 cache entries, my RAM usage is around 250MB, but the dump file is only 100MB. Each JSON item is about 4KB, but appears to be utilizing 10KB in memory. The significant disparity between memory and disk size troubles me.

I’m operating on a 64-bit environment, which seems to consume more memory for pointers. My caching pattern leans heavily towards reading, with less frequent batch updates. Is there a built-in way to configure compression in the client library, or will I need to manually manage compression when saving data?

I’m looking for suggestions on maximizing cache entries within the same memory limits.

2

redis has built-in memory optimizations you might not be using. try running redis-cli memory doctor to see what’s hogging ur RAM. also, adjust hash-max-ziplist settings in redis.conf - it can save a ton of memory for small objects like yours. i’ve seen good drops just by tweaking those configs.

3

Yeah, that memory overhead is super common with Redis when you’ve got tons of small keys. I’ve dealt with similar cache volumes - the way you structure your key namespaces makes a huge difference on memory usage. Instead of storing each API response as its own key, try using Redis hashes to group related responses under fewer top-level keys. This cuts down on the per-key overhead Redis has to maintain. For compression, ServiceStack.Redis doesn’t do it out of the box, but I’ve had good luck implementing gzip compression before storing. You’ll use a bit more CPU during serialization, but with read-heavy workloads like yours, the memory savings are usually worth it. Also check your TTL strategy - shorter expiration times for data that doesn’t get hit much helps keep your memory budget in check while keeping the hot data fast.

4

That memory overhead is totally normal for Redis on 64-bit systems. Each string key comes with metadata that can double or triple your actual data size. I’ve had good luck switching to ZLIB compression with custom serialization before it hits the ServiceStack client. Just wrap your JSON serialization with something like GZipStream - you’ll usually see 60-70% smaller payloads. Yeah, it uses more CPU during cache ops, but since you’re mostly reading, it’s worth it. Also check your key names. Shorter keys save tons of memory when you’re dealing with 25k entries. And definitely monitor your hit rates - you might be able to set up smarter eviction to keep only the frequently accessed stuff in memory.