Analytics for Ruby

Segment’s Ruby library lets you record analytics data from your ruby code. The requests hit Segment servers, and then Segment routes your data to any analytics service you enable on your destinations page.

This library is open-source, so you can check it out on GitHub.

All of Segment’s server-side libraries are built for high-performance, so you can use them in your web server controller code. This library uses an internal queue to make identify and track calls non-blocking and fast. It also batches messages and flushes asynchronously to our servers.

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Getting Started

If you’re using Bundler, add the following line to your project’s Gemfile:

gem 'analytics-ruby', '~> 2.4.0', :require => 'segment/analytics'

Or, if you’re using the gem directly from your application, you’ll need to:

gem install analytics-ruby

Then initialize the gem with your Segment source’s Write Key and an optional error handler, like so:

require 'segment/analytics'

Analytics = Segment::Analytics.new({
    write_key: 'YOUR_WRITE_KEY',
    on_error: Proc.new { |status, msg| print msg }
})

That will create an instance of Analytics that you can use to send data to Segment for your source.

If you’re using Rails, you can stick that initialization logic in config/initializers/analytics_ruby.rb and omit the require call.

Regional configuration

For Business plans with access to Regional Segment, you can use the host configuration parameter to send data to the desired region:

1. Oregon (Default) — api.segment.io/
2. Dublin — events.eu1.segmentapis.com/

Identify

The identify method is how you associate your users and their actions to a recognizable userId and traits. You can find details on the identify method payload in the Spec.

The identify call has the following fields:

Example identify:

Analytics.identify(
    user_id: '019mr8mf4r',
    traits: { email: "#{ user.email }", friends: 872 },
    context: {ip: '8.8.8.8'})

This example call will identify your user by their unique User ID (the one you know him by in your database) and label them with email and friends traits.

Track

The track method lets you record any actions your users perform. You can find details on the track method payload.

The track call has the following fields:

You should track events that are indicators of success for your site, like Signed Up, Item Purchased or Article Bookmarked.

To get started, we recommend tracking just a few important events. You can always add more later!

Example track call:

Analytics.track(
    user_id: '019mr8mf4r',
    event: 'Item Purchased',
    properties: { revenue: 39.95, shipping: '2-day' })

This example track call tells us that your user just triggered the Item Purchased event with a revenue of $39.95 and chose your hypothetical ‘2-day’ shipping.

track event properties can be anything you want to record, for example:

Analytics.track(
    user_id: 'f4ca124298',
    event: 'Article Bookmarked',
    properties: {
      title: 'Snow Fall',
      subtitle: 'The Avalance at Tunnel Creek',
      author: 'John Branch'
    })

For more information about choosing which events to track, event naming and more, check out Analytics Academy

Page

The page method lets you record page views on your website, along with optional extra information about the page being viewed.

If you’re using our client-side set up in combination with the Ruby library, page calls are already tracked for you by default. However, if you want to record your own page views manually and aren’t using our client-side library, read on!

The page call has the following fields:

Example page call:

Analytics.page(
  user_id: user_id,
  category: 'Docs',
  name: 'Ruby library',
  properties: { url: 'https://segment.com/libraries/ruby/' })

Find details on the page payload in our Spec.

Group

The group method associates an identified user with a company, organization, project, workspace, team, tribe, platoon, assemblage, cluster, troop, gang, party, society or any other name you came up with for the same concept.

This is useful for tools like Intercom, Preact and Totango, as it ties the user to a group of other users.

The group call has the following fields:

Example group call:

Analytics.group(
  user_id: '019mr8mf4r',
  group_id: '56',
  traits: { name: 'Initech', description: 'Accounting Software'})

Find more details about group including the group payload in our Spec.

Alias

alias is how you associate one identity with another. This is an advanced method, but it is required to manage user identities successfully in some of our destinations.

In Mixpanel it’s used to associate an anonymous user with an identified user once they sign up. For Kissmetrics, if your user switches IDs, you can use ‘alias’ to rename the ‘userId’.

alias method definition:

Analytics.alias(previous_id: 'previous id', user_id: 'new id')

The alias call has the following fields:

Here’s a full example of how we might use the alias call:

# the anonymous user does actions ...
Analytics.track(user_id: 'anonymous_user', event: 'Anonymous Event')
# the anonymous user signs up and is aliased
Analytics.alias(previous_id: 'anonymous id', user_id: 'user id')
# the identified user is identified
Analytics.identify(user_id: 'user id', traits: { plan: 'Free' })
# the identified user does actions ...
Analytics.track(user_id: 'user id', event: 'Identified Action')

For more details about alias, including the alias call payload, check out our Spec.

Historical Import

You can import historical data by adding the timestamp argument to any of your method calls. This can be helpful if you’ve just switched to Segment.

Historical imports can only be done into destinations that can accept historical timestamped data. Most analytics tools like Mixpanel, Amplitude, Kissmetrics, etc. can handle that type of data just fine. One common destination that does not accept historical data is Google Analytics since their API cannot accept historical data.

Note: If you’re tracking things that are happening right now, leave out the timestamp and our servers will timestamp the requests for you.

Selecting Destinations

The alias, group, identify, page and track calls can all be passed an object of integrations that lets you turn certain destinations on or off. By default all destinations are enabled.

Here’s an example track call with the integrations object shown.

Analytics.track({
  user_id: '83489',
  event: 'Song Paused',
  integrations: { All: false, Kissmetrics: true }
})

In this case, we’re specifying that we want this identify to only go to Kissmetrics. all: false says that no destination should be enabled unless otherwise specified. Kissmetrics: true turns on Kissmetrics, etc.

Destination flags are case sensitive and match the destination’s name in the docs (i.e. “AdLearn Open Platform”, “awe.sm”, “MailChimp”, etc.).

Note:

• Available at the business level, filtering track calls can be done right from the Segment UI on your source schema page. We recommend using the UI if possible since it’s a much simpler way of managing your filters and can be updated with no code changes on your side.

• If you are on a grandfathered plan, events sent server-side that are filtered through the Segment dashboard will still count towards your API usage.

Performance

Our libraries are built to support high performance environments. That means it is safe to use analytics-ruby on a web server that’s serving hundreds of requests per second.

Every method you call does not result in an HTTP request, but is queued in memory instead. Messages are flushed in batch in the background, which allows for much faster operation.

By default, our library will flush:

• the very first time it gets a message
• whenever messages are queued and there is no outstanding request

There is a maximum of 500KB per batch request and 32KB per call.

The queue consumer makes only a single outbound request at a time to avoid saturating your server’s resources. If multiple messages are in the queue, they are sent together in a batch call.

You can specify the following additional options to determine how the queue operates and to help debug possible errors. None of them are required for normal operation.

# Error handler to log statements

Segment::Analytics.new({
  write_key: 'YOUR_WRITE_KEY',
  on_error: Proc.new { |status, msg| print msg },
  max_queue_size: 10000,
  batch_size: 100,
  stub: true
})

Flush

If you’re running a script or internal queue system to upload data, you should call Analytics.flush at the end of execution to ensure that all of your messages are sent to our servers. Segment also recommendeds that you call flush on shutdown, so that all queued messages are sent to Segment instead of waiting for the next launch.

AppAnalytics = Segment::Analytics.new({
  write_key: 'ONE_WRITE_KEY'
})
AppAnalytics.flush

success “” Tip: When you call flush, the call blocks execution on the thread until it finishes processing all the messages in the queue. If you want to call flush during the normal operation of your application, you can spawn a local worker thread and call flush on the worker. This prevents the call from blocking the main thread.

Turbolinks

If you’re using Ruby on Rails with the Turbolinks setting enabled, and you’re adding Analytics.js on your website, you’ll need to tweak the default configuration.

Instead of having the entire snippet in the <head> of your site, you need to move the analytics.page() call that is included in the snippet by default into the <body> so that it will get triggered on every new page load. But you must have the first part of the snippet in the <head> or the library will fail to load properly.

Serialization

The Ruby library will automatically handle serializating your data into JSON for our servers. It uses JSON.generate under the hood. Note that BigDecimal values are intentionally sent as Strings rather than floats so that our Node servers don’t lose precision. If you’d prefer to use a float, you can coerce values to a float before sending the data to Segment.

Multiple Clients

Different parts of your application may require different types of batching, or even sending to multiple Segment sources. In that case, you can initialize multiple instances of Analytics with different settings:

AppAnalytics = Segment::Analytics.new({
  write_key: 'ONE_WRITE_KEY'
})

MarketingAnalytics = Segment::Analytics.new({
  write_key: 'ANOTHER_WRITE_KEY'
})

Troubleshooting

The following tips often help resolve common issues.

No events in my debugger

1. Double check that you’ve followed all the steps in the Quickstart.

2. Make sure that you’re calling a Segment API method once the library is successfully installed—identify, track, etc.

3. Make sure your application isn’t shutting down before the Analytics.Client local queue events are pushed to Segment. You can manually call Analytics.Client.Flush() to ensure the queue is fully processed before shutdown.

No events in my end tools

1. Double check your credentials for that destination.

2. Make sure that the destination you are troubleshooting can accept server-side API calls. Compatibility is shown on the destination docs pages and on the sheets on your Segment source Destinations page.

3. Check out the destination’s documentation to see if there are other requirements for using the method and destination you’re trying to get working.