What would you say if I told you that you could get more precise, actionable, and useful information about how your Rails application is performing than any third party service or log parsing tool with just a few hours of work?

For years, we’ve used third party tools like New Relic in all of our apps, and while we still use some of those tools today, we found ourselves wanting more – more information about the distribution of timing, more control over what’s being measured, a more intuitive user interface, and more real-time access to data when something’s going wrong.

Fortunately, there are simple, minimally-invasive options that are available virtually for “free” in Rails. If you’ve ever looked through Rails log files, you’ve probably seen lines like:

Feb  7 11:27:49 bc-06 basecamp[16760]: [projects]   Person Load (0.5ms)  SELECT `people`.* FROM `people` WHERE `people`.`id` = ? LIMIT 1
Feb  7 11:27:49 bc-06 basecamp[16760]: [projects] Rendered events/_post.rhtml (0.4ms)
Feb  7 11:27:50 bc-06 basecamp[16760]: [projects] Rendered project/index.erb within layouts/in_global (447.2ms)
Feb  7 11:27:50 bc-06 basecamp[16760]: [projects] Completed 200 OK in 529ms (Views: 421.7ms | ActiveRecord: 58.0ms)

You could try to parse these log files, or you could tap into Rails’ internals to extract just the numbers, but both of those are somewhat difficult and open up a lot of areas for things to go wrong. Fortunately, in Rails 3, you can get all this information and more in whatever form you want with just a few lines of code.

All the details you could want to know, after the jump…

Dipping into the well of knowledge

In Rails 3, ActiveSupport::Notifications provide you with direct access to this information. Rails generates instrumentation events that include information about each part of a request/response cycle, including how long it took. Rails 3+ logs are generated by subscribing to these events and writing them to log files in a specified format.

In order to get the performance data we’re interested in, we add a set of listeners that subscribe to these performance events, format them the way we want, and then re-emit them. Our typical usage looks like:

ActiveSupport::Notifications.subscribe /process_action.action_controller/ do |*args|
  event = ActiveSupport::Notifications::Event.new(*args)
  controller = event.payload[:controller]
  action = event.payload[:action]
  format = event.payload[:format] || "all"
  format = "all" if format == "*/*"
  status = event.payload[:status]
  key = "#{controller}.#{action}.#{format}.#{ENV["INSTRUMENTATION_HOSTNAME"]}"
  ActiveSupport::Notifications.instrument :performance, :action => :timing, :measurement => "#{key}.total_duration", :value => event.duration
  ActiveSupport::Notifications.instrument :performance, :action => :timing, :measurement => "#{key}.db_time", :value => event.payload[:db_runtime]
  ActiveSupport::Notifications.instrument :performance, :action => :timing, :measurement => "#{key}.view_time", :value => event.payload[:view_runtime]
  ActiveSupport::Notifications.instrument :performance, :measurement => "#{key}.status.#{status}"

In just a dozen or so lines of code, we’ve subscribed to action_controller instrumentation events, extracted the information that we’re interested in (time spent in database queries, rendering views, total time and status code, segmented by controller, action, format, and hostname) and re-emitted that information as “performance” events. We do something similar for action_mailer, active_record, etc., in each case emitting similarly formatted performance events.

Sidenote: what about Rails 2.3.x?

Rails got ActiveSupport::Notification events in version 3.0. For those applications of ours that are still on Rails 2.3.x, we resort to the less elegant approach of around_filter’s and aliasing methods like render, deliver, and log to _with_instrumentation methods which time the original method and pass the results along.

Storing the data

We’re big fans of statsd, which is a data-over-UDP approach to handling timeseries data (we use our own Redis-backed implementation which is ‘wireline’ compatible with Etsy’s original implementation). Statsd lets us send as much information as we want without having a substantial impact on app performance, and without requiring that the receiver be up for the apps to remain available. Since we send data via UDP, we can take down the statsd server for maintenance and only incur data loss, with no impact on the applications themselves.

Statsd stores several aggregates of data, so we’ve configured our setup to store near real-time data for the last hour, per-minute aggregates for the last week, and per-10-minute data indefinitely.

To get those performance events that we generated above into statsd, we subscribe to those events (via an initializer after setting up MyApp.statsd with the statsd host and port) and send them along to statsd, doing something like:

def send_event_to_statsd(name, payload)
  action = payload[:action] || :increment
  measurement = payload[:measurement]
  value = payload[:value]
  key_name = ::Statsd.clean_name("#{name.to_s.capitalize}.#{measurement}")
  MyApp.statsd.__send__ action.to_s, key_name, (value || 1)

ActiveSupport::Notifications.subscribe /performance/ do |name, start, finish, id, payload|
  send_event_to_statsd(name, payload)

This converts the instrumentation events into a key and value to be sent to statsd, either as a “counter”, a “gauge”, or a “timer”.

We also supplement the information from these instrumentation events by feeding HAProxy events, bluepill health checks on our unicorns, and statistics from memcached and redis into statsd via parser scripts.

Using the data

Once we have the data in statsd, we do a few things with it:

  • We have Nagios alerts that monitor error rates, total request time, etc. on an aggregate and per-host basis, and alert when they cross certain thresholds that indicate something unusual is going on.
  • The most commonly used information gets presented in a standardized dashboard for each application and host, with additional details on mail delivery, database performance, uptime, and background services like Resque. For example, below is what our application overview for the dash application itself looks like:
  • Every datapoint that gets into statsd, as well as data from third party services, Nagios checks, and our internal financial systems ends up in a tool we call “Flyash”, which does one thing—it graphs timeseries data. Flyash has some support for saving views, but its real strength comes when you’re digging deep into something and want to look at information in a way that’s different from how you might normally look at it, Flyash is our way to do that – for example, if I wanted to compare the number of application exceptions with average daily Smiley score, I could do that:

    In all, we have 457,739 different metrics available in Flyash right now.

Why would you do this yourself?

To some extent, we’re reinventing the wheel. There are services out there that monitor your applications’ performance, and as long as you don’t try to get too fancy or look too closely at the data, those are fine.

There are a few reasons that we’ve migrated to doing something like this:

  1. Non-invasive and lightweight. Almost all of our homegrown instrumentation – especially on Rails 3 applications – is fairly non-invasive. In most cases, we listen to existing instrumentation events and parse them rather than redefining existing functions. This lowers the “surface area” for bugs in the instrumentation to cause customer-facing problems, and the fact that we use UDP means we don’t have to worry much about the failure of a remote service impacting application availability.
  2. No manipulations. Most third party services report mean, and if you’re lucky, some approximate measure of distribution like apdex. This is better than nothing, but still leaves a lot to be desired – mean is often a pretty poor indicator of true performance. With our setup, we store min, max, mean, 90th percentile, and standard deviation for all timing information in a given time period.
  3. Everything in one place. When you’re using third party services, you end up bouncing from site-to-site to try to understand what’s happening, and it can be hard to get a full picture, especially in a crisis like an outage. Flyash started for us as a way to bring all this data into one place.

So sure, we’re reinventing the wheel a little bit, but I think we’re ending up with a better wheel as a result of it. We have a better understanding of what’s happening with our applications, and as a result, we’ve been able to respond to and fix problems more quickly when they occur.

Have questions about what we’re measuring or what we’re doing with that? Feel free to ask below, and we’ll try to answer as many questions as we can.