Real-world benchmarking of key/value stores

Photo by Stopnlook

Over the past year I've been doing a lot of work analyzing very large data sets, eg hundreds of millions of Twitter messages. I started with mysql, but wasn't able to get the performance I needed, so like a lot of other engineers I moved towards key/value databases offering far fewer features but much more control.

I found it very hard to pick a database, there's so many projects out there it's like a Cambrian explosion. To help me understand how they could meet my needs, I decided to take one of my typical data analysis jobs and turn it into a benchmark I could run against any key/value store. The benchmark takes 10,000 Facebook profiles, looks at the fan mentions and compiles a record of how fan pages are correlated, eg there were 406 Michael Jackson fans, and 22 of them were also fans of the Green Bay Packers.

I'm not claiming this is a definitive benchmark for everyone, but it's an accurate reflection of the work I need to do, with repeated updates of individual rows and large values. I've uploaded all the PHP files so you can also try this for yourself. Here are the results on my MacBook Pro 2.8GHz:

Null Store: 5.0s

RAM Store: 21.3s

Memcache: 35.7s

Redis Domain Socket: 37.3s

Tokyo Domain Socket: 40.8s

Redis TCP Socket: 42.6s

Mongo TCP Socket: 43.9s

Tokyo TCP Socket: 45.3s

MySQL: 543.5s

The 'Null Store' is a do-nothing interface, just to test the overhead of non-database work in my script. The 'RAM store' keeps the values in a PHP associative array, so it's a good control giving an upper limit on performance, since it never has to touch the disk. Memcache is another step towards the real world, it's in another process, but its lack of disk access also gives it an unrealistic advantage.

The 'Redis domain socket' gives the best results of the real database engines. The domain socket part refers to a patch I added to support local file sockets. It's impressively close to the Memcache performance, less than 2 seconds behind. The 'Tokyo domain socket' comes in next, also using file sockets, then Redis using TCP sockets on the local machine, and then Tokyo on TCP.

A long, long way behind is MySQL, at over 10 times the duration of any of the other solutions. This demonstrates pretty clearly why I had to abandon it as an engine for my purposes, despite its rich features and stability. I also tested MongoDB, but was getting buggy results out of the analysis so I was unable to get meaningful timings. I've included the file if anyone's able to tell me where I'm going wrong?

[Updated: thanks to Kristina who fixed the bug in my PHP code in the comments I now have timings of 43.9s for Mongo via TCP, and I've updated the code download. Bearing in mind I'm doing a lot of unnecessary work for Mongo, like serializing, this is an impressive result and I should be able to do better if I adapt my code to its features. I'm also trying to find info on their support for domain sockets.]

To try this on your own machine, download the PHP files, go to the directory and run it from the command line like this:

time php fananalyze.php -f data.txt -s redis -h localhost -p 6379

You can replace redis and the host arguments with the appropriate values for the database you want to test. To make sure the analysis has worked, try running a query, eg

php fananalyze.php -q "" -s redis -h localhost -p 6379

You should see a large JSON dump showing which pages are correlated with Jackson.

I would love to hear feedback on ways I can improve this benchmark, especially since they will also improve my analysis! I'll also be rolling in some RAM caching in a later article, since I've found the locality of my data access makes delaying writes and keeping a fast in-memory cache layer around helps a lot.

Here's the versions of everything I tested:

Redis 1.02 (+ my domain socket patch)
libevent 1.4.13
memcache 1.4.4
PECL/memcache 2.2.5
Tokyo Cabinet 1.4.9
Tokyo Tyrant 1.1.7 (+an snprintf bug fix for OS X)
MySQL 5.1.41, InnoDB
Mongo 64 bit v1.1.4 and PHP driver v1.0.1

MacBook Pro specs

I’m thankful for Ed Pulaski

Photo from the Santa Monica Mountains Trails Council

For years one of my favorite trail work tools has been the Pulaski ax, similar to a small pick-ax with a blade on one side and a grubbing hoe on the other. It works great for digging out stubborn stumps and then chopping the roots so they can be pulled out, as you can see in the shot above.

I vaguely knew it was named after a fire-fighter who popularized it, but I never knew his amazing life-story until I read The Big Burn. It's one of the best books I've read in a long time, weaving the political story of the early days of the Forest Service with the personal nightmares and heroics of the biggest wildfire in American history.

Ed Pulaski was a grizzled woodsman in his 40s when he joined the new-born Forest Service as a Ranger in Idaho. In those early days Rangers were mostly young college boys from the East, so as a respected local who'd spent years outdoors as a prospector, rancher and railroad worker, Ed brought more credibility to the job than most.

In the summer of 1910, Teddy Roosevelt was out of office and without his support the Forest Service was being starved of funds. Since the year was unusually dry, there were spot fires throughout the Rockies, and not enough man-power to control them. It was so desperate that Pulaski, like many Rangers, ended up paying fire-fighter's wages out of his own savings.

On August 20th, hurricane-force winds whipped up the mass of spot fires into an immense burn, covering over 3 million acres, about the size of Connecticut. Ed Pulaski was leading a crew of 50 men trying to save the town of Wallace, and when it became clear the winds made the blaze unstoppable he tried to lead them out. The fire moved too fast, and they became cut off. Remembering a small mine nearby, he found the tunnel and led his crew inside as the forest was burning around them.

Standing at the entrance, he desperately tried to keep breathable air from being sucked out of the mine by hanging wet clothes as a barrier, and trying to extinguish the supports as they kept igniting. Finally out of cloth, and badly burned and blinded by the flames, he ordered his crew to get low to the ground to make the most of the breathable air. He had to threaten the panicked men with his pistol to get them to obey, and shortly afterward he fell unconscious to the floor.

After the fire had passed, the men stumbled to their feet. Five were dead of asphyxiation, and Ed was thought to be gone too until he was woken by the fresher air. Forty-five men survived thanks to his leadership, and they stumbled the five miles back to down on feet burned raw, in shoes with the soles melted off.

They found a third of Wallace burned, but most lives saved by evacuation. Other fire-fighting crews throughout the Rockies were not as lucky, with 28 killed in just one spot, and over 125 dead in total.

Ed never fully recovered from his injuries, but went back to work as a Ranger, with medical treatment paid for by donations from his colleagues since the Service refused to help. He fought for years to get a memorial built for the fire-fighters who lost their lives, and created the Pulaski ax as a better tool for future crews.

Ironically, the great fire was a public relations coup for the Forest Service. They used the heroic story of Ed Pulaski to push through increased funding, with the promise of a zero-tolerance approach to wild fires. This use of fire as a justification for largely unrelated policies set a terrible precedent that's come back to haunt us. Now most debates around how we should use our National Forests are fought by invoking the specter of wild fires on both sides. The lack of both regular smaller fires and logging has left us with a tinder box, and from my time with forest and fire professionals, there are no simple solutions. The only approach that makes commercial sense for loggers is clear-cutting easily accessible areas, and simply letting fires burn when there's so much fuel results in far more devastation than when they were smaller but more frequent. I'm in favor of letting the professionals figure out good management plans without too much political pressure to lean towards a pre-judged outcome. I'd imagine that would involve more selective logging, which wouldn't go down well with many environmentalists, but it's also obvious that's only going to address a small part of the problem.

Despite this political knot, I'm grateful that people back in 1905 put so much of America into National Forests. After growing up in a country where every square inch has been used and reused for thousands of years, I fell in love with the immense wildernesses over here. Even just a few miles from LA you can wander for hours in beautiful mountains without seeing another soul. I'm thankful we had dedicated Rangers like Ed Pulaski to preserve that for us.

Boosting Redis performance with Unix sockets

Photo by SomeDriftwood

I've been searching for a way to speed up my analysis runs, and Redis's philosophy of keeping everything in RAM looks very promising for my uses. As I mentioned previously, I hit a lot of speed-bumps trying to get it working with PHP, but I finally got up and running and ran some tests. The results were good, faster than using the purely disk-based Tokyo Tyrant setup I have been relying on.

The only niggling issue was that I knew from Tokyo that Unix file (aka domain) sockets have a lot less overhead and help performance compared to TCP sockets, even using localhost. Since the interface is almost identical, I decided to dive in and spend a couple of hours patching my copy of Redis 1.02 to support file sockets. The files I've changed are available at

My initial results using the redis-benchmark app that ships with the code show a noticeable performance boost across the board, sometimes up to 2x. Since this is an artificial benchmark it's unlikely to be quite this dramatic in real-world situations, but with Tokyo 30%-50% increases in speed were common.

I hope these changes will get merged into newer versions of Redis, it's a comparatively small change to get a big performance boost in situations when the client and server are on the same machine.

Using Redis on PHP

Photo by Bowbrick

There's a saying that pioneers tend to come back stuck full of arrows. After a couple of days trying to get Redis working with PHP, I know that feeling!

I'm successfully using Tokyo Tyrant/Cabinet for my key/value store, but I do find for a lot of my uses disk access is a major bottleneck on performance. I do lots of application-level RAM caching to work around this, but Redis's philosophy of keeping everything in main memory looked like a much lower-maintenance solution.

Getting started is simple, on OS X I was able to simply download the stable 1.02 source, make and then run the default redis-server executable. Its interface is through a TCP socket, so I then grabbed the native PHP module from the project's front page and started running some tests.

The first problem I hit was that the PHP interface silently failed whenever a value longer than 1024 characters was set. Looking into the source of the module, it was using fixed-length C arrays (they were even local stack variables with pointers returned from leaf functions!) and failing to check if the passed-in arguments were longer. This took me an hour or two to figure out in unfamiliar code, so I was a bit annoyed that there weren't more 'danger! untested!' signs around the module, though the README did state it was experimental.

Happily a couple of other developers had already run into this problem, and once I brought up the issue on the mailing list, Nicolas Favre-Félix and Nasreddine Bouafif made their fork of PHPRedis available with bug fixes for this and a lot of other issues.

The next day I downloaded and ran the updated version. This time I was able to get a lot further, but on my real data runs I was seeing intermittent empty string being returned for keys which should have had values. This was tough to track down, and even when I uncovered the underlying cause it didn't make any sense. It happened seemingly at random, and I wasn't able to reproduce it in a simple test case. An email to the list didn't get any response, so the following day I heavily instrumented the PHP interface module to understand what was going wrong.

Finally I spotted a pattern. The command before the one that returned an empty string was always

SET 100
<1000 character-long value>

It turned out that some digit-counting code thought the number 1000 only had three digits, and truncated it to 100. The other 900 characters in the value remained in the buffer and were misinterpreted as a second command. That meant the real next command received a -ERR result. I coded up a fix and submitted a patch, and now it seems to be working at last.

Hitting this many problems so quickly has certainly made me hesitate to move forward using Redis in PHP. It's definitely not a well-trodden path, and while the list was able bring me a solution to my first problem, I was left to debug the second one on my own, and a question about Unix domain sockets versus TCP was left unanswered as well. If you are looking at Redis yourself in PHP, make sure you're mentally prepared for something pretty experimental, and don't count on much hand-holding from the developer community.

Of course, the same goes for almost any key/value store right now, it's the wild west out there compared to the stability of the SQL world. My next stop will be MongoDB to see if having a well-supported company behind the product improves the experience.

Nonsensical Infographics

Nonsensical Infographic 1 by Chad Hagan

20×200 is an awesome concept, an online gallery selling limited editions of works by new artists starting at $20 each. While I strive to follow Tufte and make all my visualizations tell a clear story, I'm aware they sometimes turn out more pretty than functional, so I'm in love with Chad Hagan's 'Nonsensical Infographic' series on there. Now I just need to convert them into flash animations to make them even more beautiful and confusing.

Nonsensical Infographic 2 by Chad Hagan

Amazing scams are earning $1000+ CPMs for

Photo by Toasty Ken

I'm a cheerful pessimist about human nature, I generally expect the worst but don't let it get me down. This report from the US Senate is beyond the pale though, it really makes me mad. It details how companies like Affinion, Webloyalty and Vertrue pay immense amounts of money (CPM rates of up to $2650!) to well-known firms like and 1-800-flowers to get links inserted into their checkout process. These links look like discount offers, but clicking on them passes your credit card information to the scammers, and lets them set up a recurring monthly payment on your account without asking for permission, hoping you won't notice, at least for a while.

How much money is there to be made here? The report estimates just those three firms have earned over $1.4 billion so far! And how much of a scam is it? Vertrue estimates 98% of their call volume is cancellation requests, and Webloyalty admit that 90% of their members have no idea they're enrolled.

As an entrepreneur I know how much over-regulation can hurt startups and economic growth, but scams like these drive people down that path. As an industry we need to have enough sense to avoid crazily short-sighted schemes like these if we want to have a long-term future. All three companies are owned by big-name private equity firms, and big-name websites are hosting their ads. Everyone involved should be ashamed of themselves, and nobody else should touch them with a barge pole. Sadly, $1.4 billion is very persuasive…

Get new users for $2 each with Facebook Ads

Photo by Intermayer

This is one of those posts I hesitate about writing, because it's tempting to hoard an advantage like this, but sharing always seems to benefit me more in the long run. I'm able to get new users for my (in-testing, very unfinished) Facebook app for as little as $2 each using Facebook Ads. Here's my most successful ad so far:


It's short and simple, and around 0.07% of people who see it clicking. Naively I started off expecting click rates of around 1%, but since then I've talked to people with more experience in the ad world, and outside of search ads mine is actually pretty respectable. It's also cheap – I set my cost-per-click bid to 50 cents, but actually ended up paying 37 cents each.

This is only the start of my funnel, the landing page is the install dialog for my Facebook app. The only thing I have control over there is the app description and logo, and currently only 30% of the visitors click accept to install it. That means my cost per installation is around $1 per user.

After they've made it through that screen, they're finally on a page I control. Here I ask them to give me their email address, accept extended permissions and authorize me to access their Twitter account. I lose between 50% and 70% of users there, bumping my final cost per true user to between $2 and $3 each.

So what are the secrets to achieving similar results?

Land in Facebook. I have a massive advantage in that I've moved my service over to run as a Facebook app. It's low friction for users when they're staying within the same site, I doubt you could achieve the same CPC for external pages. I'm now a hostage to Facebook's whims of course, but for me the gain in user trust far outweighs the risks.

Start small. I'm still paying only $15 a day spread over several campaigns, that gives me enough data to tell what's working and refine my ads and landing pages before I ramp up to collect larger numbers of users. It's also a great way of flushing out bugs and scaling issues while annoying a relatively small number of users.

Test, test, test! I'm terrible at writing ad copy, really, really bad, and my first versions had awful click-through rates around 0.01%. I was able to use Facebook's statistics panel to tell which ads were the least-worst, and spot the patterns. In my case the shorter ones worked much better, as did the ones that focused on a single feature, which is how I ended up with the one above. I'm also constantly trying new versions of the landing page and sign-up flow to measure how I can improve the rest of the funnel.

Foreigners are cheap. There must be a lot less competition for UK and commonwealth Facebook views, because I'm able to get CPCs of 37 cents if I specify the English-speaking non-US in my targeting, versus around 60 cents each in the US. If you're in the testing phase, I would expect you could get representative data for almost half the price if you use non-Americans as guinea pigs.

I still think there's a lot of room for improvement in my funnel, so I'm hopeful I can keep driving the cost down, even if the ad market overall becomes more expensive with competition. I'm also not doing much with the targeting possibilities beyond picking countries, I think localized ads could get a strong response, and I need to run a census of my users to understand what demographics the service appeals to most and target them.