Mathias Meyer
Mathias Meyer


The need to measure everything that moves in a distributed system or even simple web apps is becoming the basis for thorough monitoring of an application.

However, there is one thing that’s starting to get in the way of of getting good measurements of all layers in a system: client libraries used to talk to network services, be it the database, an API, a message bus, anything that’s bound to the intricate latency variances of the network stack.

Without full instrumentation of all parts of the application’s stack, it’s going to be very hard to figure out where exactly a problems boils down to. Measuring client access to a network service in addition to collecting data on the other end, e.g. the slow query log, allows you to pinpoint issues to the network, to increased latency, or to parsing responses.

If the other end is not under your control, it’s just as important to have this data available. Having good metrics on request latencies to an external service, even a database hosted by a third party, gives you a minimum amount of confidence that while you maybe can’t fix the underlying problem, you at least have the data to show where the problem is most likely to be. Useful data to have when approaching the third party vendor or hosting company about the issue.

Rails has set a surprisingly good example, by way of ActiveSupport::Notifications. Controller requests are instrumented just as database queries of any kind.

You can subscribe to the notifications and start collecting them in your own metrics tool. StatsD, Graphite and Librato Metrics are pretty great tools for this purpose.

There’s not much a client library needs to do to emit measurements of network requests. The ones for Ruby could start by adding optional instrumentation based on AS::Notifications. That’d ensure that ActiveSupport itself doesn’t turn into a direct dependency. I’d love to see the notifications bit being extracted into a separate library that’s easier to integrate than pulling in the entire ActiveSupport ball of mud.

Node.js has EventEmitters, which are similar to AS::Notifications, and they lend themselves quite nicely for this purpose.

I’ve dabbled with this for riak-js, the Node.js library for Riak. There’s an example that shows how to register and collect the metrics from the events emitted. The library itself just emits the events at the right spot, adds some timestamps so that event listeners can reconstruct the trail of a request.

It worked out pretty well and is just as easy to plug into a metrics library or to report measurements directly to StatsD.

The thing that matters is that any library for a network service you write or maintain, should have some sort of instrumentation built in. Your users and I will be forever grateful.

This goes both ways, too. Network servers need to be just as diligent in collecting and exposing data as the client libraries talking to them. Historically, though, a lot of servers already expose a lot of data, not always in a convenient format, but at least it’s there.

Build every layer of your application and library with instrumentation in mind. Next time you have to tackle an issue in any part of the stack, you’ll be glad you did.

Now go and measure everything!