Why speed matters?

Raw speed is bound to queries per watt. Energy is a serious problem, not just for the environment, but also cost-wise. More computers running to serve your 1000 requests per second, the bigger your monthly bill.

Large systems are hard to handle, and hardware is not for free. If I can handle the same workload with 10 servers instead of 100 servers, everything will be cheaper and simpler to handle.

95% of all the web innovation today is driven by startups. Startups start very very small, they don't have budget for large powerful computers. A software system that is able to handle a reasonable amount of load with a cheap Linux box is going to win a lot of acceptance in the most fertile of the environments for new technology toys, that is, startups.

Scalable systems many times can work better with a smaller number of nodes, so even if there is the ability to scale it's better to run a 10 nodes cluster than a 100 nodes cluster. So the single node of a scalable system should be as fast as possible.

Systems for the toilet

All the tests are run using a single client into a busy loop . I love '80s busy loops but... well, let's be serious. I left a comment in the blog about this problem, and the author replied that in the real world things are different and threading is hard. Sorry but I was not thinking about multi thread programming: a DB under load receives many concurrent queries by different instances of the web server(s) (that can be threads, but much more easily different Apache processes). So it is not required that your web dude starts writing concurrent software to have N queries at the same time against your DB. If your DB is under serious load, you can bet that you are serving many requests at the same time. Sorry if this is obvious for most of the readers.

. I love '80s busy loops but... well, let's be serious. I left a comment in the blog about this problem, and the author replied that in the real world things are different and threading is hard. Sorry but I was not thinking about multi thread programming: a DB under load receives many concurrent queries by different instances of the web server(s) (that can be threads, but much more easily different Apache processes). So it is not required that your web dude starts writing concurrent software to have N queries at the same time against your DB. If your DB is under serious load, you can bet that you are serving many requests at the same time. Sorry if this is obvious for most of the readers. Not just the above, when you run single clients benchmarks what you are metering actually is, also: the round trip time between the client and the server, and all the other kind of latencies involved, and of course, the speed of the client library implementation .

. The test was performed with very different client libraries. For instance libmemcached accepted as input a buffer pointer and a length, while credis, the library used in the mentioned benchmark, was calling strlen() for every key. So in one benchmark there was a table of pre-computed lengths passed to the library, and in the other the lib was calling strlen() itself. This is just an example, there are much bigger parsing, buffering, and other implementation details affecting the outcome especially if you are using a single-client approach, that is perfect to meter this things instead of the actual server performance.

Redis VS Memcached

Apple to Apple, with the same mouth

@@ -213,8 +206,15 @@ char *tail = strstr(p,"\r

"); if (tail == NULL) return 0; - *tail = '\0'; - *len = atoi(p+1); + + if (p[0] == 'E') { /* END -- key not present */ + *len = -1; + } else { + char *x = tail; + while (*x != ' ') x--; + *tail = '\0'; + *len = atoi(x)+5; /* We add 5 bytes for the final "END\r

" */ + } return tail+2-p; }

Methodology

I used Redis master, the "betterparsing" branch to run the test. The result of the benchmark should be pretty similar to what you'll get with Redis 2.0, excluding the MGET test, that I'm not including now since it's not part of redis-benchmark currently.

I used the latest memcached stable release, that is, memcached-1.4.5.

Both systems were compiled with the same GCC, same optimization level (-O2).

I disabled persistence in Redis to avoid overhead due to the forking child in the benchmark.

I ran memcached with -m 5000 in order to avoid any kind of expire of old keys. This way both Redis and memcached were loading the whole dataset in memory.

The test was performed against real hardware, an oldish Linux box completely idle.

Every test was ran three times, the higher figure obtained was used.

The size of payload was 32 bytes. Sizes of 64, 128, 1024 bytes were tested without big differences in the results. Once you start to go out of the bounds of the MTU, performances will degradate a lot in both systems.

#!/bin/bash



# BIN=./redis-benchmark BIN=./mc-benchmark payload=32 iterations=100000 keyspace=100000



for clients in 1 5 10 20 30 40 50 60 70 80 90 100 200 300 do SPEED=0 for dummy in 0 1 2 do S=$($BIN -n $iterations -r $keyspace -d $payload -c $clients | grep 'per second' | tail -1 | cut -f 1 -d'.') if [ $(($S > $SPEED)) != "0" ] then SPEED=$S fi done echo "$clients $SPEED" done

Results

memcache: user 8.640 seconds, system 19.370 seconds

redis: user 8.95 seconds, system 20.59 seconds

Atomic operations can be traded for speed

Conclusions

Disclaimer

The internet is full of things: trolls, awesome programming threads, p0rn, proofs of how cool and creative the human beings can be, and of course. In this blog post I want to focus my attention to the latter, and at the same time I'll try to show how good methodology to perform a benchmark is supposed to be, at least from my point of view.In theera everybody is much more obsessed with scalability than speed. That is, if I've something that is able to do handle a (not better defined) load of L, I want it to handle a load of L*N if I use N instances of this thing. Automagically, Fault tolerantly, Bullet proofly.Stressing scalability so much put somewhat in shadow what in the 80's was one of the most interesting meters of software coolness, that is: raw speed. I mean old-fashion speed, that is, how many operations is this single thing able to perform? How many triangles can you trace in a second? How many queries can you answer? How many square roots can you compute?Back in the initial days of home computing this was so important since CPU power was a limited resource. Now computers are way, way faster, but is still speed important, or should we just care about scalability, given that we can handle any load after all just adding more? My bet is that raw speed is very important, and this are a few arguments:This does not mean we don't need scalable systems. Scalability is completely central for the future of Redis. I'll start working at Redis cluster in the next months almost full time. Just the right order is, IMHO, to get a very good single node that is very CPU/memory efficient, and then build a scalable system upon this efficient node.Apparently I'm not alone in reconsidering the importance of raw speed. The sys/toilet blog published a benchmark comparing the raw speed of Redis and Memcached . The author of this blog firmly believes that many open source programs (and not only) should better go to the toilet instead of running in real systems, since they are not good. I can't agree more with this sentiment. Unfortunately I also think that our dude is part of the problem, since running crappy benchmarks is not going to help our pop culture.You should check it yourself reading the original post, but this is why the sys/toilet benchmark is ill conceived.Still this benchmark captured my interest. Even with all this problems, why on the earth Redis was showing so low numbers in multi-get (MGET) operations?I checked the implementation better and found a problem in the query parsing code that resulted in a quadratic algorithm due to a wrong positioned strchr() call. The problem is now fixed in this branch , that is what I'm using to run the benchmarks, and that will be merged in Redis master within days. After this fixthan before.But at this point I started to be curious...Even if Redis provides much more features than memcached, including persistence, complex data types, replication, and so forth, it's easy to say that it is an almost strict superset of memcached. There are GET and SET operations, timeouts, a networking layer, check-and-set operations and so forth. This two systems are very, very similar in their essence, so comparing the two is definitely an apple to apple comparison.Not only this, but memcached is a truly mature software, that undertook a lot of real world stress testing and changes to work well in the practice. It's definitely a good meter to have for Redis. So given the great similarity between the systems if there are huge discrepancies likely there is a bug somewhere, either in Redis or memcached. And this actually was the case with MGET! I fixed a bug thanks to the comparison of numbers with memcached.But even comparing Apples to Apples is not trivial. You need to really have the very same semantics client side, do sensible things about the testing environment, number of simulated clients and so forth.In Redis land we already havethat assisted us in the course of the latest one and half years, and worked as a great advertising tool: we were able to show that our system was performing well.Memcached and Redis protocols are not so different, both are simple ASCII protocols with similar formats. So I used a few hours of my time to port redis-benchmark to the memcached protocol. The result is mc-benchmark If you compare redis-benchmark and mc-benchmark with diff, you'll notice that apart from a lot of code removed since memcached does not support many of the operations that redis-benchmark was running against Redis,Basically in the memcached protocol the length of the payload was one of the space separated arguments, while in the Redis protocol is at fixed offset (there is just a "$" character before). So there was to add a short while loop (that will scan just two bytes in our actual benchmark), and an explicit very fast test for the "END" condition of the memcached protocol.so the actual processing is just the absolute minimal required to read the reply. Also note that the few lines of code added to turn redis-benchmark into mc-benchmark are inherently needed for the memcached protocol design. This means thatThe following is the methodology used for this test:In order to automatically run the tests collecting data I used the following bash script:Sorry, the comparison withis surely lame but I did not remembered the right way to do it with shell scripting :)Finally... here are the results, in form of a graph generated with gnuplot:As you can see there are no huge differences in the range of one order of magnitude, but surely Redis is considerably faster in this test. If you check the left side of the graph you'll notice that the speed is about the same with a single client. The more we add simultaneous clients the more Redis starts to go faster.It's worth to note that I measured the CPU used by both the systems in a 1 million keys GET/SET test, obtaining the following results:As you can see both the systems used the same amount of CPU power. So actually the number of queries per watt are the same. For some reason memcached was not able to exploit all the CPU power available, as the same amount of CPU was used in the course of more real elapsed time to serve the same number of queries.To really understand what is the potential of the two systems as caching systems, there is to take into account another aspect. Redis can run complex atomic operations against hashes, lists, sets and sorted sets at the same speed it can run GET and SET operations (you can check this fact using redis-benchmark). The atomic operations and more advanced data structures allow to do much more in a single operation, avoiding check-and-set operations most of the time. So I think that once we drop the limit of just using GET and SET and uncover the full power of the Redis semantic there is a lot you can do with Redis to run a lot of concurrent users with very little hardware.This is a limited test that can't be interpreted as. It is surely faster under the tested conditions, but different real world work loads can lead to different results. For sure after this tests I've the impressions that the two systems are well designed, and that in Redis we are not overlooking any obvious optimization.As a result of this new branch of investigation in the next days I'll create speed.redis.io that will be something like a continuous speed testing of the different branches of Redis, with history. This way we can be sure that our changes will not modify performances, or we can try to enhance performances while checking how the numbers will change after every commit.I used a limited amount of time to setup and run this tests. Please if you find any kind of flaw or error drop me a note or a comment in this blog post, so that I can fix it and publish updated results.