Redis
2 minutes of reading
Redis is a versatile in-memory database that is becoming increasingly popular in the development community. It is a NoSQL database that allows for fast processing and storage of data.
Redis allows storing data in memory with fast access times. It is used where time and simplicity of use are crucial. In Redis we mostly store simple data structures, although unlike solutions such as DynamoDB from AWS, the data size is much larger because it is 512mb. Redis is an open-source solution, so you can use it unhindered in your software. Redis has an SDK API available in many programming languages such as:
- C++ ( Redis-plus-plus )
- Go ( GoRedis )
- Java ( Jedis )
- NodeJS ( IORedis )
- Perl ( Redis )
- Python ( Redis-py )
- R ( Rccp-redis )
- Ruby ( Redis-rb )
- Rust ( Redis-rs )
- Scala ( Scala-redis )
- Swift ( RediStack )
Redis is highly scalable
Redis, due to its architecture, is a highly scalable solution. It is used most often as the front line, where we need to skip SQL or even NoSQL type databases directly and get the fastest response times. Redis uses partitioning because of its easy separation of data and lack of interconnection, as well as master-slave replication.
Redis is used in many types of applications
The most common use of Redis is caching application data. Take, for example, a web application that repeatedly reaches for the same data. Instead of querying the database directly, which is not the fastest solution, you can query the cache, which in this case will be Redis. This will reduce the response time of the server, as well as significantly increase the scalability of the application.
Other solutions where Redis is used are:
data analytics
chat (pub/sub)
data queue
user data session management
artificial intelligence
Downsides of a Redis-type solution
The downside of an in-memory solution can be over-reliance on it, and in the event of a loss of server availability, redirecting traffic to the database can end up with the service unavailable. You need to remember to prevent this type of situation by maintaining systems that restart resolved data, or run a backup server. You can also keep a copy of your Redis data on your hard drive and take regular memory dumps, which will avoid the situation of losing the entire cache.
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