Redis - Cache DB Introduction

 Redis:  It stands for Remote Directory Server is an in-memory data structure store, used as a database, cache, and message broker. It is a disk-persistent key-value database with support for multiple data structures or data types

Redis is an open source (BSD licensed), highly replicated, performant, non-relational kind of database and caching server. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, geospatial indexes, and streams. 

Redis has built-in replication, Lua scripting, LRU eviction, transactions, and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.

You can run atomic operations on these types, like appending to a string; incrementing the value in a hash; pushing an element to a list; computing set intersection, union and difference; or getting the member with highest ranking in a sorted set.

To achieve top performance, Redis works with an in-memory dataset. Depending on your use case, you can persist your data either by periodically dumping the dataset to disk or by appending each command to a disk-based log. 

You can also disable persistence if you just need a feature-rich, networked, in-memory cache.

Redis is an open-source, highly replicated, performant, non-relational kind of database and caching server. It works by mapping keys to values with a sort of predefined data model. 

Its benefits include:

Mapped key-value-based caching system, almost comparable to me cached

No strict rules pertaining to defining strict schemas or tables for data (schema less)

Support for multiple data models or types

Offers more advanced features when compared to other kinds of database systems

Ability to withstand multiple concurrent write requests or transactions per second, via a technique known as sharding

Can be used together with other databases as a support to reduce load and improve performance, but can also be used as a primary database. Note that this is usually based on individual needs and use cases

Can come in handy in the areas of quick data ingestion with data integrity in the mix, were features like high efficiency and replication are paramount.