Report key app statistics to the Librato Metrics service and easily track your own custom metrics
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NOTE: Starting with version 2.0.0 librato-rails requires a Librato account that supports tagged metrics.

If your Librato account doesn't yet support tagged metrics or you are using a heroku addon, please use the 1.x.x version.

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 1.9.3 and above.

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


Upgrading from version 1.x to 2.x introduces breaking changes for legacy sources. Please contact to migrate an existing Librato account.

Quick Start

Note: If you have not yet enabled Rails on the Librato integrations page within your account, do this first. This will automatically set up Rails and Rack Spaces, displaying many useful performance metrics.

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

Once installed, 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 '' do
  # do work

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


In your Gemfile add:

gem 'librato-rails'

Then run bundle install.


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:

  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.

To see all config options or for 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.

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.

If you are using Librato as a Heroku addon, a paid plan is required for reporting custom metrics with librato-rails. You can view more about available addon levels here.

Default Tags

Librato Metrics supports tagged measurements that are associated with a metric, one or more tag pairs, and a point in time. For more information on tagged measurements, visit our API documentation.

Detected Tags

By default, service, environment and host are detected and applied as default tags for submitted measurements. Optionally, you can override the detected values in your configuration file:

  user: <your-email>
  token: <your-api-key>
    service: 'myapp'
    environment: 'production'
    host: 'myapp-prod-1'
Custom Tags

In addition to the default tags, you can also provide custom tags:

  user: <your-email>
  token: <your-api-key>
    region: 'us-east-1'

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

Automatic Measurements

After installing librato-rails and restarting your app 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 rails or rack, depending on the level they are being sampled from. For example: is the total number of requests rails has received each minute.

The metrics automatically recorded by librato-rails are organized into named metric suites that can be selectively enabled/disabled:

Rails Suites

Request Metrics
  • rails_controller: Metrics which provide a high level overview of request performance including, rails.request.time, rails.request.time.db, rails.request.time.view, and rails.request.slow
  • rails_method: rails.request.method metric with method tag name and HTTP method tag value, e.g. method=POST
  • rails_status: rails.request.status metric with status tag name and HTTP status code tag value, e.g. status=200
System-Specific Metrics
  • rails_cache: rails.cache.* metrics including reads, writes, hits & deletes
  • rails_job: rails.job.* metrics including jobs queued, started & performed (Rails 4.2+)
  • rails_mail: rails.mail.* metrics including mail sent / received
  • rails_render: rails.view.* metrics including time to render individual templates & partials
  • rails_sql: rails.sql.* metrics, including SELECT / INSERT / UPDATE / DELETEs as well as total query operations

Rack Suites

Rack measurements are taken from the very beginning of your rack middleware stack. They include all time spent in your ruby process (not just in Rails proper). They will also show requests that are handled entirely in middleware and don't appear in the rails suites above.

  • rack: The, rack.request.time, rack.request.slow, and rack.request.queue.time metrics
  • rack_method: rack.request.method metric with method tag name and HTTP method tag value, e.g. method=POST
  • rack_status: rack.request.status metric with status tag name and HTTP status code tag value, e.g. status=200
Queue Time

The rack.request.queue.time metric will show you queuing time (the time between your load balancer receiving a request & your application process starting to work on it) if your load balancer sets HTTP_X_REQUEST_START or HTTP_X_QUEUE_START.

Default Suites

All of the rails & rack suites listed above are enabled by default.

Suite Configuration

Suites can be configured via either the LIBRATO_SUITES environment variable or the suites setting in a config/librato.yml configuration file. The syntax is the same regardless of configuration method.

  LIBRATO_SUITES="rails_controller,rails_sql"  # use ONLY the rails_controller & rails_sql suites
  LIBRATO_SUITES="+foo,+bar"                   # + prefix: default suites plus foo & bar
  LIBRATO_SUITES="-rails_render"               # - prefix: default suites removing rails_render
  LIBRATO_SUITES="+foo,-rack_status"           # Default suites except for rack_status, also add foo
  LIBRATO_SUITES="all"                         # Enable all suites
  LIBRATO_SUITES="none"                        # Disable all suites
  LIBRATO_SUITES=""                            # Use only the default suites (same as if env var is absent)

Note that you should specify either an explicit list of suites to enable or add/subtract individual suites from the default list (using the +/- prefixes). If you try to mix these two forms a Librato::Rack::InvalidSuiteConfiguration error will be raised.

Configuring the metric suites via the config/librato.yml file would look like this:

  token: abc123
  suites: 'rails_controller,rails_status,rails_sql,rack'

Custom Measurements

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


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

# increment the 'sales_completed' metric by one
Librato.increment 'sales.completed'
# => {:service=>"myapp", :environment=>"production", :host=>"myapp-prod-1"}

# increment by five
Librato.increment 'items.purchased', by: 5
# => {:service=>"myapp", :environment=>"production", :host=>"myapp-prod-1"}

# increment with custom per-measurement tags
Librato.increment 'user.purchases', tags: { user_id:, currency: 'USD' }
# => {:user_id=>43, :currency=>"USD"}

# increment with custom per-measurement tags and inherited default tags
Librato.increment 'user.purchases', tags: { user_id:, currency: 'USD' }, inherit_tags: true
# => {:service=>"myapp", :environment=>"production", :host=>"myapp-prod-1", :user_id=>43, :currency=>"USD"}

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', tags: { user:, bucket: }, sporadic: true


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

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

# report from custom per-measurement tags
Librato.measure 'jobs.queued', 3, tags: { priority: 'high', worker: 'worker.12' }


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

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

# report from custom per-measurement tags
Librato.measure 'api.response.time', time, tags: { node: node_name, db: 'rr1' }

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)

By defaults timings will send the average, sum, max and min for every minute. If you want to send percentiles as well you can specify them inline while instrumenting:

# track a single percentile
Librato.timing 'api.request.time', time, percentile: 95

# track multiple percentiles
Librato.timing 'api.request.time', time, percentile: [95, 99]

You can also use percentiles with the block form of timings:

Librato.timing 'my.important.event', percentile: 95 do
  # do work


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: 'memcached' do |g|
  g.measure 'gets', 20
  g.measure 'sets', 2
  g.measure 'hits', 18

Symbols can be used interchangably with strings for metric names.

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..

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

ActiveSupport::Notifications.subscribe 'my.event' do |*args|
  event =*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', tags: { user: }, sporadic: true

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

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

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 default tags (detects service, environment and host) 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.


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.


  • 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.


Copyright (c) 2012-2017 Librato Inc. See LICENSE for details.