Why you should almost always choose Redis as your database

Edit (9/7/2015): after three years, I have a single (but powerful) counterargument to the case for redis I make below.
Whenever the topic of databases/persistence arises, I almost always recommend using Redis instead of MySQL or even any other NoSQL solution.
There are two reasons for choosing Redis almost always:
1) Redis data structures are far more intuitive and versatile means of storing data than relational databases.
To me, relational databases are a very limiting and antinatural way of structuring data. I’ve always felt that mapping the concepts of your program (whatever your style of programming, but especially if it is object-oriented or functional) to relational databases is both painful and frustrating. This is for two reasons:
– Relational databases have no concept of hierarchy – that is, no nesting. What you have is a set of arrays, instead of having a tree. There’s nothing bigger than a table, and nothing smaller than a field.
– The links between nodes are of a weak and limited type: foreign keys. So you have to bend over backwards to implement some sort of network model for your data.
(BTW, this is why ORM is a rabbit hole of the kind that nothing of real beauty can come from. No matter how good the solution is, it’s always a variant of fitting a square peg in a round hole).
Redis, although it isn’t a true tree or a graph, is far closer to either of them, because it has a rich set of data structures which are very similar to those of today’s high level programming languages. From what I’ve looked around, no other NoSQL tool offers a comparable set of data structures.
This means you’ll do far less violence to the concepts of your program when you persist its data with Redis. This makes for faster development and will considerably improve the quality of your code. More importantly, your code will be more beautiful.
2) Redis runs in RAM
Although it persists to disk, Redis data is read from and written to RAM. Since RAM is about an order of magnitude faster than a disk, this translates to queries and write operations that are roughly an order of magnitude faster. Sure, many caveats and exceptions apply, but that’s the essence of Redis’ blazing performance.
So, to sum up:
Redis will make your application 1) easier and more enjoyable to program, because it maps better to the concepts of your program; and 2) faster.
You should not use Redis if your dataset is large (more than 2gb).
This is because it is non-trivial (though possible) to create a cluster of Redis instances, each of them holding up to 2gb (or 4, or 8). Also, if your application stores larges volumes of data, then probably Redis will never be your option because of economics (can you afford terabytes of RAM?). In that case, you should give a deep, meaningful look to Amazon S3.
(Did you notice I’m implying you should never use MySQL?)
These counterarguments to using Redis are invalid:
MySQL is the default and Redis is not production-ready: there’s much to argue against using the default technological choice for anything – and unless your clients insist of vanilla-grade software, you should seek something better than the median tool. And Redis is very, very production ready. Just look around and see who’s using it.
– Redis is not truly persistent because it runs in RAM: both of Redis’ persistence operations (journal and snapshotting) are good enough. For me, the ideal would be to have a reverse journal, where you store the negative changes (what you should apply to go back instead of starting from 0 and going forwards) – if you combine this with snapshotting, you’d have something that’s virtually lossless and fast. But going back to the main point, Redis persistence to disk is secure and reasonably fast.
Further arguments, counterarguments and objections
I’ll be glad to hear them: write me at fpereiro@gmail.com