It’s an old question. And one that can vary quite a bit from domain to domain.
But for Web Operations there are some definite patterns I’ve come across. Most of these come from the last two years I spent working as a Reliability Engineer at Netflix. Even though we had hundreds of discrete applications when something broke you often found yourself looking at similar sets of metrics.
So without further ado, here they are, broken down per category and in a roughly-prioritized order.
Successful requests in the HTTP world take the form of something in the 100s-300s. Failed HTTP requests would usually be anything 400 and above. Most services have some way of designating “yes it worked” vs “no it didn’t”. Count those.
Ideally count them from both the client and server side. For middle-tier services the client-side counting can often go in a common client library, edge services may be able to count at the load balancer. If counting at the client isn’t feasible you can count at the server side only, but in that case you run the risk of missing unseen failures due to networking trouble.
You can then make a ratio out of these
(failures / successes) * 100
and start tracking your availability against an error budget.
If possible, break these down on a per-resource basis (in the REST-resource sense). Not all monitoring tools can provide this sort of tagging but being able to say which resource is very valuable when trying to deduce what’s wrong. This will often take the form of the first part of the HTTP path or the controller object that’s servicing the request.
Not all error conditions manifest as a failed request. Typically a service has circuit breakers, fallbacks, or other mechanisms to deal with internal failures without causing a user-visible error. But you’ll want to count them even if the user doesn’t see them.
Watching for changes in deeper error trends can tell you when something is about to go wrong or when something is going wrong in a way that you can’t see from your basic metrics.
For example, you could have a failed remote call which causes key data to be omitted from the returned data. This would still be a 200 but the client may not be able to use the response.
Count these on a per-second basis with some low-cardinality break down. Many systems have a set of internal error codes which will likely be reusable as a way to break down deeper error counts. Or in the case of circuit breakers, the name or class of the circuit is typically a good way to segment these errors.
Now you may have a system which is always returning 200s, but if it goes from 150ms response times to 4000ms response times, you have a problem.
In addition to a simple max, min and average per second, percentile distributions are common here. You can start by looking at the 50th, 95th and 99.5th percentile which many tools (e.g., statsd) support. Bucketing by time (e.g., < 10ms, 100-500ms, > 500ms) is also useful since bucketed counters can be averaged across many servers and still retain some accuracy. Averaging the 95th percentile across a hundred machines is still useful, but doesn’t tell you your actual 95th percentile for the whole fleet. Unfortunately this bucketed approach is a bit less common probably since it requires you to have a sense of how your service will perform ahead of time.
Much like the success counters, you’ll want to break these down by resource if possible. Knowing where the latency is coming from can point you in the right direction a lot faster. If all resources are effected equally, investigate the system showing the latency. If it’s a single resource, investigate the systems used to build that resource’s response data.
Of course, these are important to keep an eye on as well. But how their used will vary quite a bit between services.
On the plus side there are many good tools and resources for monitoring this stuff since most any environment will be capable of providing metrics on it.
Rather that get into details here, I’ll refer you to the USE method from Brendan Gregg which is a really solid place to start when examining OS level resources.
In some respects the metrics discussed above are Brendan’s USE method re-applied to distributed systems.
So if you should find yourself in an unknown domain wondering what to monitor, try the USE method there too.
There are a number of software packages that can help you track the sort of metrics covered here so I won’t try to cover them in detail. But if you’re really just getting started here are a few options I’ve had experience with:
I did a screencast on this approach almost a year ago. It’s a fully open-source approach that’s quite common and has a large ecosystem.
The Stellar setup is mainly using this right now. It’s also open source and has a rich feature set, but the surrouding ecosystem a a bit limited.
I’m not as familiar with MRTG, but it’s been around a very long time and is still cited as one of the easier monitoring setups to get started with.
The system I’m most familiar with. I’ve seen it cover all of the above items while working at Netflix, but the open source offering is still new and somewhat raw. I look forward to more experimentation and blogging around Atlas in the near future.
As stated above, metrics and monitoring can be a very application-specific thing so consider everything in the context of your application and infrastructure. My hope is that for those of you still defining your operational metrics, this will provide a good basis for growing your own distributed system.
If you have any comments or examples from your own system, I’d love to hear them. Feel free to leave a comment below or come find matschaffer in #stellar-dev on freenode.