Redis 2.6 is near, and a few more updates

Friday, 24 February 12
Redis 2.6 was expected to go live in the first weeks of 2012, but today is 24th of February and there are still no 2.6-rc1 tags around, what happened to it you may ask!?

Well, for one time, a delay is not a signal that something is wrong. What happened is simply that we put a lot more than expected inside this release, so without further delays here is a list of new features:

There are two features still to merge, but already implemented into branches:

And new internals...

Redis 2.6 offers the above new features, but another interesting fact is that it is also a spinoff of the unstable branch, the one that is going to be Redis 3.0 soon or later. Instead 2.4 was a spinoff of Redis 2.2 code base. This means that we now are working with a better code base that makes implementing certain features simpler.

It will also make it much easier for us in the future to backport stuff from the unstable branch to 2.6. This means that we can either backport stuff from time to time into 2.6 releases, or to create a 2.8 branch to merge all the interesting features that are already stable to create an intermediate release in a few months from now.

Redis benchmarks with pipelining support, impressive numbers, and stupid benchmarks

After looking to the next set of benchmarks that were actually measuring everything but actual DB performances, I decided to go ahead and implement pipelining in the Redis-benchmark tool to show some good numbers.

Redis-benchmark used to create 50 clients, and perform something like: send request, wait for reply, send request, wait for reply, with all those 50 clients. However Redis supports pipelining, that is, if you have N queries to do where you don't need the reply of the previous to perform the next request, you can send N queries at once to Redis, and then read all the replies. This dramatically improve performances because there are less syscall required, less context switches, less TCP packets, and so forth.

Most real world Redis applications use pipelining, often you need to do things like paginate a list of objets, so you do LRANGE to get the IDs, and then a pipeling with all the GET or HGETALL and so forth. Or you want to write an object on the database and update it's position into a sorted set.

But still redis-benchmark was not able to test pipelining, so when we saw Redis can do 150k requests per second in entry level hardware we were actually saying ... if you never use pipelining at all. But how it can perform if you can use it?

Let's check with pipelining, using my glorious MBA 11" running OSX:
$ redis-benchmark -P 64 -q -n 1000000
PING_INLINE: 540540.56 requests per second
PING_BULK: 636942.62 requests per second
SET: 301204.81 requests per second
GET: 430848.75 requests per second
INCR: 341530.06 requests per second
LPUSH: 305623.47 requests per second
LPOP: 296120.81 requests per second
SADD: 313774.72 requests per second
SPOP: 418060.22 requests per second
Wow, 430k GETS/sec requests per second with a macbook air, and finally with this new benchmark not everything is the same, PING is faster than GET that is faster than SET, and so forth. This also means: more ability to optimize commands in our side.

If you test this into a Xeon, you get 650k GETs easily, or other impressive numbers even reducing the pipeling from 64 to 32 or 16.

Now to show how benchmarks can easily be turned into everything you want, we have this numbers of Redis performing 500k operations per second, per core, but now in the web site of HyperDex I read: With 32 servers and sufficient clients to create a heavy workload, HyperDex is able to sustain 3.2 million ops/s..

Hey dudes, I can do 1/6th of the ops/sec you do with 32 servers using just 1 core of my Xeon desktop. What this means? nothing.

Long story short, don't show benchmarks unless you have a very good methodology explained in the web site, and your methodology makes sense, otherwise it is just marketing that does not provide any value to the user.

A better way to do benchmarks is to isolate a common real-world problem, and write a real world implementation of this problem using different databases, in the idiomatic way for every database, mixing both writes and reads in the same benchmark. Then test the different implementations with many simultaneous clients, with millions of objects.

Those tests, performed independently by smart programmers, is what is making Redis very popular across guys that have serious requests per second, and I hope that 2.6 with built-in server side scripting will allow them to get more out of Redis.
Posted at 16:00:03 | permalink | discuss | print