[00:01] Redis, an in-memory multi-model database famous for its sub-millisecond latency. It was created in 2009 based on the idea that a cache can also be a durable data store. Around this time, apps like Twitter were growing exponentially and needed a way to deliver data to their end users faster than a relational database could handle.
[00:19] Redis, which means Remote Dictionary Server, was adopted by some of the most heavily trafficked sites in the world because it changed the database game by creating a system where data is always modified or read from the main computer memory as opposed to the much slower disk.
[00:33] But at the same time, it stores its data on the disk so it can be reconstructed as needed, which means the database is fully durable, supporting things like snapshots and backups. Every data point in the database is a key followed by one of many different data structures,
[00:47] like a string, list, hash, or stream, so you can store data naturally just like you would with your favorite programming language, as opposed to squeezing it into a bunch of tables or JSON documents. Interacting with the database is handled with a simple set of commands, like set followed by a key
[01:03] and value to create data then get followed by a key to read data Historically Redis has been described as a key value store and is often used as a cache to make relational databases faster at scale But here where your mind is about to be blown Redis is perfectly suitable as a primary database and can dramatically reduce complexity because making
[01:22] things go fast at scale is the primary cause of complexity in the first place. With Redis, your database is already fast. No need for a complex caching layer. But you might be wondering, how do I model complex relationships? Today, Redis is what you would call a multi-model
[01:37] database, supporting a variety of different database paradigms with add-on modules that you can opt into as needed. If your data contains relationships, bring in Redis Graph and query it with Cypher. Or use the JSON module to structure your data as a hierarchy, just like a document-oriented
[01:52] database. Then use the Search module to turn your database into a full-text search engine. And it even has modules for AI workloads, time series data, and more. The quickest way to get started is the free tier on Redis Enterprise Cloud. That'll give you a free database to play
[02:06] around with, and you can attach one extra module to it. Then you can connect to it with the Redis CLI or with the Redis Insight tool, which contains an interactive dashboard to manage your data. This has been Redis in 100 seconds. But what do you guys think? Would you use Redis as your
[02:20] primary database? Let me know why or why not in the comments. Thanks for watching, and I will see you in the next one.