Open Source > statsd-instrument


A StatsD client for Ruby apps. Provides metaprogramming methods to inject StatsD instrumentation into your code.

Built on Travis

This is a ruby client for statsd ( It provides a lightweight way to track and measure metrics in your application.

We call out to statsd by sending data over a UDP socket. UDP sockets are fast, but unreliable, there is no guarantee that your data will ever arrive at its location. In other words, fire and forget. This is perfect for this use case because it means your code doesn’t get bogged down trying to log statistics. We send data to statsd several times per request and haven’t noticed a performance hit.

The fact that all of your stats data may not make it into statsd is no issue. Graphite (the graph database that statsd is built on) will only show you trends in your data. Internally it only keeps enough data to satisfy the levels of granularity we specify. As well as satisfying its requirement as a fixed size database. We can throw as much data at it as we want it and it will do its best to show us the trends over time and get rid of the fluff.

For Shopify, our retention periods are:

  1. 10 seconds of granularity for the last 6 hours
  2. 60 seconds of granularity for the last week
  3. 10 minutes of granularity for the last 5 years

This is the same as what Etsy uses (mentioned in the README for


The library comes with different backends. Based on your environment (detected using environment variables), it will select one of the following backends by default:

You can override the currently active backend by setting StatsD.backend:

# Sets up a UDP backend. First argument is the UDP address to send StatsD packets to, 
# second argument specifies the protocol variant (i.e. `:statsd`, `:statsite`, or `:datadog`).
StatsD.backend ="", :statsite)

# Sets up a logger backend
StatsD.backend =

The other available settings, with their default, are

# Logger to which commands are logged when using the LoggerBackend, which is
# the default in development environment. Also, any errors or warnings will 
# be logged here.
StatsD.logger = defined?(Rails) ? Rails.logger :$stderr)

# An optional prefix to be added to each metric.
StatsD.prefix = nil # but can be set to any string

# Sample 10% of events. By default all events are reported, which may overload your network or server.
# You can, and should vary this on a per metric basis, depending on frequency and accuracy requirements
StatsD.default_sample_rate = (ENV['STATSD_SAMPLE_RATE'] || 0.1 ).to_f

StatsD keys

StatsD keys look like ‘admin.logins.api.success’. Dots are used as namespace separators. In Graphite, they will show up as folders.


You can either use the basic methods to submit stats over StatsD, or you can use the metaprogramming methods to instrument your methods with some basic stats (call counts, successes & failures, and timings).


Lets you benchmark how long the execution of a specific method takes.

# You can pass a key and a ms value
StatsD.measure('GoogleBase.insert', 2.55)

# or more commonly pass a block that calls your code
StatsD.measure('GoogleBase.insert') do


Lets you increment a key in statsd to keep a count of something. If the specified key doesn’t exist it will create it for you.

# increments default to +1
# you can also specify how much to increment the key by
StatsD.increment('GoogleBase.insert', 10)
# you can also specify a sample rate, so only 1/10 of events
# actually get to statsd. Useful for very high volume data
StatsD.increment('GoogleBase.insert', 1, sample_rate: 0.1)


A gauge is a single numerical value value that tells you the state of the system at a point in time. A good example would be the number of messages in a queue.

StatsD.gauge('GoogleBase.queued', 12, sample_rate: 1.0)

Normally, you shouldn’t update this value too often, and therefore there is no need to sample this kind metric.


A set keeps track of the number of unique values that have been seen. This is a good fit for keeping track of the number of unique visitors. The value can be a string.

# Submit the customer ID to the set. It will only be counted if it hasn't been seen before.
StatsD.set('GoogleBase.customers', "12345", sample_rate: 1.0)

Because you are counting unique values, the results of using a sampling value less than 1.0 can lead to unexpected, hard to interpret results.

Metaprogramming Methods

As mentioned, it’s most common to use the provided metaprogramming methods. This lets you define all of your instrumentation in one file and not litter your code with instrumentation details. You should enable a class for instrumentation by extending it with the StatsD::Instrument class.

GoogleBase.extend StatsD::Instrument

Then use the methods provided below to instrument methods in your class.


This will measure how long a method takes to run, and submits the result to the given key.

GoogleBase.statsd_measure :insert, 'GoogleBase.insert'


This will increment the given key even if the method doesn’t finish (ie. raises).

GoogleBase.statsd_count :insert, 'GoogleBase.insert'

Note how I used the ‘GoogleBase.insert’ key above when measuring this method, and I reused here when counting the method calls. StatsD automatically separates these two kinds of stats into namespaces so there won’t be a key collision here.


This will only increment the given key if the method executes successfully.

GoogleBase.statsd_count_if :insert, 'GoogleBase.insert'

So now, if GoogleBase#insert raises an exception or returns false (ie. result == false), we won’t increment the key. If you want to define what success means for a given method you can pass a block that takes the result of the method.

GoogleBase.statsd_count_if :insert, 'GoogleBase.insert' do |response|
  response.code == 200

In the above example we will only increment the key in statsd if the result of the block returns true. So the method is returning a Net::HTTP response and we’re checking the status code.


Similar to statsd_count_if, except this will increment one key in the case of success and another key in the case of failure.

GoogleBase.statsd_count_success :insert, 'GoogleBase.insert'

So if this method fails execution (raises or returns false) we’ll increment the failure key (‘GoogleBase.insert.failure’), otherwise we’ll increment the success key (‘GoogleBase.insert.success’). Notice that we’re modifying the given key before sending it to statsd.

Again you can pass a block to define what success means.

GoogleBase.statsd_count_success :insert, 'GoogleBase.insert' do |response|
  response.code == 200

Instrumenting Class Methods

You can instrument class methods, just like instance methods, using the metaprogramming methods. You simply have to configure the instrumentation on the singleton class of the Class you want to instrument.

AWS::S3::Base.singleton_class.statsd_measure :request, 'S3.request'

Dynamic Metric Names

You can use a lambda function instead of a string dynamically set the name of the metric. The lambda function must accept two arguments: the object the function is being called on and the array of arguments passed.

GoogleBase.statsd_count :insert, lambda{|object, args| object.class.to_s.downcase + "." + args.first.to_s + ".insert" }


The Datadog implementation support tags, which you can use to slice and dice metrics in their UI. You can specify a list of tags as an option, either standalone tag (e.g. "mytag"), or key value based, separated by a colon: "env:production".

StatsD.increment('my.counter', tags: ['env:production', 'unicorn'])
GoogleBase.statsd_count :insert, 'GoogleBase.insert', tags: ['env:production']

If implementation is not set to :datadog, tags will not be included in the UDP packets, and a warning is logged to StatsD.logger.


This library comes with a module called StatsD::Instrument::Assertions to help you write tests to verify StatsD is called properly.

class MyTestcase < Minitest::Test
  include StatsD::Instrument::Assertions

  def test_some_metrics
    # This will pass if there is exactly one matching StatsD call
    # it will ignore any other, non matching calls.
    assert_statsd_increment('', sample_rate: 1.0) do
      StatsD.increment('unrelated') # doesn't match
      StatsD.increment('', sample_rate: 1.0) # matches
      StatsD.increment('', sample_rate: 0.1) # doesn't match

    # Set `times` if there will be multiple matches:
    assert_statsd_increment('', times: 2) do
      StatsD.increment('unrelated') # doesn't match
      StatsD.increment('', sample_rate: 1.0) # matches
      StatsD.increment('', sample_rate: 0.1) # matches too

  def test_no_udp_traffic
    # Verifies no StatsD calls occured at all.
    assert_no_statsd_calls do

    # Verifies no StatsD calls occured for the given metric.
    assert_no_statsd_calls('metric_name') do

  def test_more_complicated_stuff
    # capture_statsd_calls will capture all the StatsD calls in the
    # given block, and returns them as an array. You can then run your 
    # own assertions on it.
    metrics = capture_statsd_calls do
      StatsD.increment('mycounter', sample_rate: 0.01)

    assert_equal 1, metrics.length
    assert_equal 'mycounter', metrics[0].name
    assert_equal :c, metrics[0].type
    assert_equal 1, metrics[0].value
    assert_equal 0.01, metrics[0].sample_rate

Reliance on DNS

Out of the box StatsD is set up to be unidirectional fire-and-forget over UDP. Configuring the StatsD host to be a non-ip will trigger a DNS lookup (i.e. a synchronous TCP round trip). This can be particularly problematic in clouds that have a shared DNS infrastructure such as AWS.

Common Workarounds

  1. Using an IP avoids the DNS lookup but generally requires an application deploy to change.
  2. Hardcoding the DNS/IP pair in /etc/hosts allows the IP to change without redeploying your application but fails to scale as the number of servers increases.
  3. Installing caching software such as nscd that uses the DNS TTL avoids most DNS lookups but makes the exact moment of change indeterminate.


Tested on several Ruby versions using Travis CI:


This project welcomes outside contributions.

  1. Fork the repository, and create a feature branch.
  2. Implement the feature, and add tests that cover the new changes functionality.
  3. Update the README.
  4. Create a pull request. Make sure that you get a Travis CI pass on it.
  5. Ping @jstorimer and/or @wvanbergen for review.


Copyright (c) 2011 Shopify. Released under the MIT-LICENSE.