🚀 Enhancement: Lower memory footprint by using compression for Redis

### 🔖 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? - [X] I checked and didn't find similar issue ### 🏢 Have you read the Code of Conduct? - [X] I have read the [Code of Conduct](https://github.com/appwrite/.github/blob/main/CODE_OF_CONDUCT.md)