Entries tagged with walrus
walrus is my go-to toolkit for working with Redis in Python, and hopefully this post will convince you that it can be your go-to as well. I've tried to include lots of high-level Python APIs built on Redis primitives and the result is quite a lot of functionality. In this post I'll take you on a tour of the library and show examples of how it might be useful in your next project.
Redis is one of the more unique NoSQL offerings to have become popular over the past five years. It seems that there is no limit to the use-cases one can find for Redis. It's fantastic as a cache, doubles as a task-queue, can provide fast type-ahead search, and much more. The idea that you can store data-structures instead of rows and columns, keys and values, or JSON documents strikes me as particularly innovative. A while back I released walrus, a collection of Python utilities I'd built to simplify some of these use-cases and provide Pythonic APIs for the data-structures Redis natively supports. If you're a Python developer you might check it out.
Recently I've learned about a few new Redis-like databases: Rlite, Vedis and LedisDB. Each of these projects offers a slightly different take on the data-structure server you find in Redis, so I thought that I'd take some time and see how they worked. In this post I'll share what I've learned, and also show you how to use these databases with Walrus, as I've added support for them in the latest 0.3.0 release.
A couple weekends ago I got it into my head that I would build a thin Python wrapper for working with Redis. Andy McCurdy's redis-py is a fantastic low-level client library with built-in support for connection-pooling and pipelining, but it does little more than provide an interface to Redis' built-in commands (and rightly so). I decided to build a project on top of redis-py that exposed pythonic containers for the Redis data-types. I went on to add a few extras, including a cache and a declarative model layer. The result is walrus.