librato-rails

librato-rails will report key statistics for your Rails app to Librato and allow you to easily track your own custom metrics. Metrics are delivered asynchronously behind the scenes so they won't affect performance of your requests.

Rails versions 3.0 or greater are supported on Ruby 1.9.2 and above.

Verified combinations of Ruby/Rails are available in our build matrix.

Quick Start

Installing librato-rails and relaunching your application will automatically start the reporting of metrics to your Librato account.

After installation librato-rails will detect your environment and start reporting available performance information for your application.

Custom metrics can also be added easily:

# keep counts of key events
Librato.increment 'user.signup'

# easily benchmark sections of code to verify production performance
Librato.timing 'my.complicated.work' do
  # do work
end

# track averages across requests
Librato.measure 'user.social_graph.nodes', user.social_graph.size

Installation

In your Gemfile add:

gem 'librato-rails'

Then run bundle install.

Configuration

If you don't have a Librato account already, sign up. In order to send measurements to Librato you need to provide your account credentials to librato-rails. You can provide these one of two ways:

Use a config file

Create a config/librato.yml like the following:

production:
  user: <your-email>
  token: <your-api-key>

The librato.yml file is parsed via ERB in case you need to add some host or environment-specific magic.

Note that using a configuration file allows you to specify different configurations per-environment. Submission will be disabled in any environment without credentials.

Use environment variables

Alternately you can provide LIBRATO_USER and LIBRATO_TOKEN environment variables. Unlike config file settings, environment variables will be used in all non-test environments (development, production, etc).

Note that if a config file is present, all environment variables will be ignored.

For more information on combining config files and environment variables, see the full configuration docs.

Running on Heroku

If you are using the Librato Heroku addon, your user and token environment variables will already be set in your Heroku environment. If you are running without the addon you will need to provide them yourself.

In either case you will need to specify a custom source for your app to track properly. If librato-rails does not detect an explicit source it will not start. You can set the source in your environment:

heroku config:add LIBRATO_SOURCE=myappname

If you are using a config file, add your source entry to that instead.

Full information on configuration options is available on the configuration wiki page.

Note that if Heroku idles your application measurements will not be sent until it receives another request and is restarted. If you see intermittent gaps in your measurements during periods of low traffic this is the most likely cause.

Automatic Measurements

After installing librato-rails and restarting your app and you will see a number of new metrics appear in your Librato account. These track request performance, sql queries, mail handling, and other key stats.

Built-in performance metrics will start with either rack or rails, depending on the level they are being sampled from. For example: rails.request.total is the total number of requests rails has received each minute.

Custom Measurements

Tracking anything that interests you is easy with Librato. There are four primary helpers available to use anywhere in your application:

increment

Use for tracking a running total of something across requests, examples:

# increment the 'sales_completed' metric by one
Librato.increment 'sales_completed'

# increment by five
Librato.increment 'items_purchased', by: 5

# increment with a custom source
Librato.increment 'user.purchases', source: user.id

Other things you might track this way: user signups, requests of a certain type or to a certain route, total jobs queued or processed, emails sent or received

Sporadic Increment Reporting

Note that increment is primarily used for tracking the rate of occurrence of some event. Given this increment metrics are continuous by default: after being called on a metric once they will report on every interval, reporting zeros for any interval when increment was not called on the metric.

Especially with custom sources you may want the opposite behavior - reporting a measurement only during intervals where increment was called on the metric:

# report a value for 'user.uploaded_file' only during non-zero intervals
Librato.increment 'user.uploaded_file', source: user.id, sporadic: true

measure

Use when you want to track an average value per-request. Examples:

Librato.measure 'user.social_graph.nodes', 212

# report from a custom source
Librato.measure 'jobs.queued', 3, source: 'worker.12'

timing

Like Librato.measure this is per-request, but specialized for timing information:

Librato.timing 'twitter.lookup.time', 21.2

The block form auto-submits the time it took for its contents to execute as the measurement value:

Librato.timing 'twitter.lookup.time' do
  @twitter = Twitter.lookup(user)
end

group

There is also a grouping helper, to make managing nested metrics easier. So this:

Librato.measure 'memcached.gets', 20
Librato.measure 'memcached.sets', 2
Librato.measure 'memcached.hits', 18

Can also be written as:

Librato.group 'memcached' do |g|
  g.measure 'gets', 20
  g.measure 'sets', 2
  g.measure 'hits', 18
end

Symbols can be used interchangably with strings for metric names.

Controller Helpers

librato-rails also has special helpers which are available inside your controllers:

instrument_action (experimental)

Use when you want to profile execution time or request volume for a specific controller action:

class CommentController < ApplicationController
  instrument_action :create # can accept a list

  def create
    # ...
  end
end

Once you instrument an action, librato-rails will start reporting a set of metrics specific to that action including:

rails.action.request.total (# of requests)
rails.action.request.slow (requests >= 200ms to produce)
rails.action.request.exceptions
rails.action.request.time (total time spent in action)
rails.action.request.time.db (db interaction time)
rails.action.request.time.view (view rendering time)

Each instrumented action will appear as a source for the rails.action.* metrics, for example mycontroller.action.html.

IMPORTANT NOTE: Metrics from instrument_action take into account all time spent in the ActionController stack for that action, including before/after filters and any global processing. They are not equivalent to using a Librato.timing block inside the method body.

Use with ActiveSupport::Notifications

librato-rails and ActiveSupport::Notifications work great together. In fact, many of the Rails metrics provided are produced by subscribing to the instrumentation events built into Rails.

Assume you have a custom event:

ActiveSupport::Notifications.instrument 'my.event', user: user do
  # do work..
end

Writing a subscriber to capture that event and its outcomes is easy:

ActiveSupport::Notifications.subscribe 'my.event' do |*args|
  event = ActiveSupport::Notifications::Event.new(*args)
  user = event.payload[:user]

  # track every time the event happens
  Librato.increment 'my.event'

  # track how long the event is taking
  Librato.timing 'my.event.time', event.duration

  # use payload data to do user-specific tracking
  Librato.increment 'user.did.event', source: user.id, sporadic: true

  # do conditional tracking
  if user.feature_on?(:sample_group)
    Librato.increment 'user.sample.event'
  end

  # track slow events
  if event.duration >= 50.0
    Librato.increment 'my.event.slow'
  end
end

These are just a few examples. Combining ActiveSupport::Notifications instrumentation with Librato can be extremely powerful. As an added benefit, using the instrument/subscribers model allows you to isolate complex instrumentation code from your main application codebase.

Custom Prefixes

You can set an optional prefix to all metrics reported by librato-rails in your configuration. This can be helpful for isolating test data or forcing different apps to use different metric names.

Tracking Deploys

It can be very useful to track your deploys using annotations so you can use them to monitor the impact of changes to your app. Take a look at the librato-rake-deploytrack gem for an easy install option or this ticket for examples of how you can write your own.

Use with Background Workers / Cron Jobs

librato-rails is designed to run within a long-running process and report periodically. Intermittently running rake tasks and most background job tools (delayed job, resque, queue_classic) don't run long enough for this to work.

Never fear, we have some guidelines for how to instrument your workers properly.

Cross-Process Aggregation

librato-rails submits measurements back to the Librato platform on a per-process basis. By default these measurements are then combined into a single measurement per source (default is your hostname) before persisting the data.

For example if you have 4 hosts with 8 unicorn instances each (i.e. 32 processes total), on the Librato site you'll find 4 data streams (1 per host) instead of 32. Current pricing applies after aggregation, so in this case you will be charged for 4 streams instead of 32.

If you want to report per-process instead, you can set source_pids to true in your config, which will append the process id to the source name used by each thread.

Troubleshooting

Note that it may take 2-3 minutes for the first results to show up in your Librato account after you have started your servers with librato-rails enabled and the first request has been received.

Verbose Logging

If you want to get more information about librato-rails submissions to the Librato service you can set your log_level to debug (see configuration) to get detailed information added to your logs about the settings librato-rails is seeing at startup and when it is submitting.

Be sure to tail your logs manually (tail -F <logfile>) as the log output you get when using the rails server command often skips startup log lines.

If you are having an issue with a specific metric, using a log_level of trace will add the exact measurements being sent to your logs along with lots of other information about librato-rails as it executes. Neither of these modes are recommended long-term in production as they will add quite a bit of volume to your log file and will slow operation somewhat. Note that submission I/O is non-blocking, submission times are total time - your process will continue to handle requests during submissions.

Console Mode

By default the librato-rails reporter will not start in console mode, even if librato-rails is configured. If you want to force the reporter to run in console mode, set LIBRATO_AUTORUN to 1 in your environment:

$ LIBRATO_AUTORUN=1 rails console

Custom Flush Interval

If you are debugging setting up librato-rails locally you can set flush_interval to something shorter (say 10s) to force submission more frequently. Don't change your flush_interval in production as it will not result in measurements showing up more quickly, but may affect performance.

Contribution

Check out the latest master to make sure the feature hasn't been implemented or the bug hasn't been fixed yet.
Check out the issue tracker to make sure someone already hasn't requested it and/or contributed it.
Fork the project and submit a pull request from a feature or bugfix branch.
Please include tests. This is important so we don't break your changes unintentionally in a future version.
Please don't modify the gemspec, Rakefile, version, or changelog. If you do change these files, please isolate a separate commit so we can cherry-pick around it.

jipiboily / librato-rails