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If Engineering at Etsy has a religion, it’s the Church of Graphs. If it moves, we track it. Sometimes we’ll draw a graph of something that isn’t moving yet, just in case it decides to make a run for it. In general, we tend to measure at three levels: network, machine, and application. (You can read more about our graphs in Mike’s Tracking Every Release post.)
Application metrics are usually the hardest, yet most important, of the three. They’re very specific to your business, and they change as your applications change (and Etsy changes a lot). Instead of trying to plan out everything we wanted to measure and putting it in a classical configuration management system, we decided to make it ridiculously simple for any engineer to get anything they can count or time into a graph with almost no effort. (And, because we can push code anytime, anywhere, it’s easy to deploy the code too, so we can go from “how often does X happen?” to a graph of X happening in about half an hour, if we want to.)
StatsD is a simple NodeJS daemon (and by “simple” I really mean simple — NodeJS makes event-based systems like this ridiculously easy to write) that listens for messages on a UDP port. (See Flickr’s “Counting & Timing” for a previous description and implementation of this idea, and check out the open-sourced code on github to see our version.) It parses the messages, extracts metrics data, and periodically flushes the data to graphite.
We like graphite for a number of reasons: it’s very easy to use, and has very powerful graphing and data manipulation capabilities. We can combine data from StatsD with data from our other metrics-gathering systems. Most importantly for StatsD, you can create new metrics in graphite just by sending it data for that metric. That means there’s no management overhead for engineers to start tracking something new: simply tell StatsD you want to track “grue.dinners” and it’ll automagically appear in graphite. (By the way, because we flush data to graphite every 10 seconds, our StatsD metrics are near-realtime.)
Not only is it super easy to start capturing the rate or speed of something, but it’s very easy to view, share, and brag about them.
So, why do we use UDP to send data to StatsD? Well, it’s fast — you don’t want to slow your application down in order to track its performance — but also sending a UDP packet is fire-and-forget. Either StatsD gets the data, or it doesn’t. The application doesn’t care if StatsD is up, down, or on fire; it simply trusts that things will work. If they don’t, our stats go a bit wonky, but the site stays up. Because we also worship at the Church of Uptime, this is quite alright. (The Church of Graphs makes sure we graph UDP packet receipt failures though, which the kernel usefully provides.)
Here’s how we do it using our PHP StatsD library:
StatsD::increment("grue.dinners");
That’s it. That line of code will create a new counter on the fly and increment it every time it’s executed. You can then go look at your graph and bask in the awesomeness, or for that matter, spot someone up to no good in the middle of the night:
We can use graphite’s data-processing tools to take the the data above and make a graph that highlights deviations from the norm:
(We sometimes use the “rawData=true” option in graphite to get a stream of numbers that can feed into automatic monitoring systems. Graphs like this are very “monitorable.”)
We don’t just track trivial things like how many people are signing into the site — we also track really important stuff, like how much coffee is left in the kitchen:
In addition to plain counters, we can track times too:
$start = microtime(true);
eat_adventurer();
StatsD::timing("grue.dinners", (microtime(true) - $start) * 1000);
StatsD automatically tracks the count, mean, maximum, minimum, and 90th percentile times (which is a good measure of “normal” maximum values, ignoring outliers). Here, we’re measuring the execution times of part of our search infrastructure:
One thing we found early on is that if we want to track something that happens really, really frequently, we can start to overwhelm StatsD with UDP packets. To cope with that, we added the option to sample data, i.e. to only send packets a certain percentage of the time. For very frequent events, this still gives you a statistically accurate view of activity.
To record only one in ten events:
StatsD::increment(“adventurer.heartbeat”, 0.1);
What’s important here is that the packet sent to StatsD includes the sample rate, and so StatsD then multiplies the numbers to give an estimate of a 100% sample rate before it sends the data on to graphite. This means we can adjust the sample rate at will without having to deal with rescaling the y-axis of the resulting graph.
We’ve found that tracking everything is key to moving fast, but the only way to do it is to make tracking anything easy. Using StatsD, we enable engineers to track what they need to track, at the drop of a hat, without requiring time-sucking configuration changes or complicated processes.
Try StatsD for yourself: grab the open-sourced code from github and start measuring. We’d love to hear what you think of it.