I’m spending days trying to get a couple of APIs right. New APIs about modules, and a new Redis data type. I really mean it when I say *days*, just for the API. Writing drafts, starting the implementation shaping data structures and calls, and then restarting from scratch to iterate again in a better way, to improve the design and the user facing part. Why I do that, delaying features for weeks? Is it really so important? Programmers are engineers, maybe they should just adapt to whatever API is better to export for the system exporting it.
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It was a matter of time but it eventually happened. In the Redis 1.0 release notes, 7 years ago, I mentioned that one of the interesting features for the future was “loadable modules”. I was really interested in such a feature back then, but over the years I became more and more skeptic about the idea of adding loadable modules in Redis. And probably for good reasons. Modules can be the most interesting feature of a system and the most problematic one at the same time: API incompatibilities between versions, low quality modules crashing the system, a lack
I’m aboard of a flight bringing me to San Francisco. Eventually I purchased the slowest internet connection of my life (well at least for a good reason), but for several hours I was without internet, as usually when I fly. I don’t mind staying disconnected for some time usually. It’s a good time to focus, write some code, or a blog post like this one. However when I’m disconnected, what makes the most difference is not Facebook or Twitter or Github, but the lack of text messages. At this point text messages are a fundamental thing in my life. They are also probably the main source of distraction. I use messages to talk with my family, even just to communicate between different floors. I use messages with friends to organize dinners and vacations. I even use messages with the plumber or the doctor.
It took more than expected, but finally we have it, Redis 3.2.0 stable is out with changes that may be useful to a big number of Redis users. At this point I covered the changes multiple time, but the big ones are: * The GEO API. Index whatever you want by latitude and longitude, and query by radius, with the same speed and easy of use of the other Redis data structures. Here you can find the API documentation: http://redis.io/commands/#geo. Thank you to Matt Stancliff for the initial implementation, that was reworked but is still at the core of the GEO API, and to the developers of ARDB for providing the geo indexing code that Matt used.
Today Redis is 7 years old, so to commemorate the event a bit I passed the latest couple of days doing a fun coding marathon to implement a new crazy command called BITFIELD. The essence of this command is not new, it was proposed in the past by me and others, but never in a serious way, the idea always looked a bit strange. We already have bit operations in Redis: certain users love it, it’s a good way to represent a lot of data in a compact way. However so far we handle each bit separately, setting, testing, getting bits, counting all the bits that are set in a range, and so forth.
Yesterday night I was re-reading Redlock analysis Martin Kleppmann wrote (http://martin.kleppmann.com/2016/02/08/how-to-do-distributed-locking.html). At some point Martin wonders if there is some good way to generate monotonically increasing IDs with Redis. This apparently simple problem can be more complex than it looks at a first glance, considering that it must ensure that, in all the conditions, there is a safety property which is always guaranteed: the ID generated is always greater than all the past IDs generated, and the same ID cannot be generated multiple times. This must hold during network partitions and other failures. The system may just become unavailable if there are less than the majority of nodes that can be reached, but never provide the wrong answer (note: as we'll see this algorithm has another liveness issue that happens during high load of requests).
Martin Kleppmann, a distributed systems researcher, yesterday published an analysis of Redlock (http://redis.io/topics/distlock), that you can find here: http://martin.kleppmann.com/2016/02/08/how-to-do-distributed-locking.html Redlock is a client side distributed locking algorithm I designed to be used with Redis, but the algorithm orchestrates, client side, a set of nodes that implement a data store with certain capabilities, in order to create a multi-master fault tolerant, and hopefully safe, distributed lock with auto release capabilities.
Today I’m happy to announce that the first release candidate for Disque 1.0 is available. If you don't know what Disque is, the best starting point is to read the README in the Github project page at http://github.com/antirez/disque. Disque is a just piece of software, so it has a material value which can be zero or more, depending on its ability to make useful things for people using it. But for me there is an huge value that goes over what Disque, materially, is. It is the value of designing and doing something you care about. It’s the magic of programming: where there was nothing, now there is something that works, that other people may potentially analyze, run, use.
Two days ago Mike Malone published an interesting post on Medium about the V8 implementation of Math.random(), and how weak is the quality of the PRNG used: http://bit.ly/1SPDraN. The post was one of the top news on Hacker News today. It’s pretty clear and informative from the point of view of how Math.random() is broken and how should be fixed, so I’ve nothing to add to the matter itself. But since the author discovered the weakness of the PRNG in the context of generating large probably-non-colliding IDs, I want to share with you an alternative that I used multiple times in the past, which is fast and extremely reliable.
Today I was curious about plotting all the Redis commits we have on Git, which are 90% of all the Redis commits. There was just an initial period where I used SVN but switched very soon. Full size image here: http://antirez.com/misc/commitsvis.png Each commit is a rectangle. The height is the number of affected lines (a logarithmic scale is used). The gray labels show release tags. There are little surprises since the amount of commit remained pretty much the same over the time, however now that we no longer backport features back into 3.0 and future releases, the rate at which new patchlevel versions are released diminished.