🚀 Enhancement: Lower memory footprint by using compression for Redis

[JoshiJoshiJoshi]

🔖 Enhancement description

Before storing any data to redis Appwrite should compress it first. For example with GZip, LZ4 or use a more efficient format like Msgpack. Appwrite will decompress upon retrieval of the data.

References
*LZ4 compression
https://deepinder.me/speeding-up-redis-with-compression
https://doordash.engineering/2019/01/02/speeding-up-redis-with-compression/
*Msgpack
https://www.linkedin.com/pulse/json-vs-messagepack-battle-data-efficiency-akshay-singh-kanawat-7dx2c#:~:text=While%20JSON%20is%20great%20for,with%20less%20fuel%20(MessagePack).
*Redisconf18
https://de.slideshare.net/slideshow/redisconf18-redis-memory-optimization/99431767

🎤 Pitch

Redis is used for realtime, tracking events and for the appwrite workers. This can add up real fast. For example everytime a document is created, updated or deleted its action and data gets saved to redis uncompressed. It not only consumes more memory but also congest the network a lot more (ex. when scaling horizontally).

Compressing the data can

• reduce memory and disk space
• reduce network bandwidth and congestion
• improve latency

👀 Have you spent some time to check if this issue has been raised before?

• I checked and didn't find similar issue

🏢 Have you read the Code of Conduct?

• I have read the Code of Conduct