Racecar is a friendly and easy-to-approach Kafka consumer framework. It allows you to write small applications that process messages stored in Kafka topics while optionally integrating with your Rails models.

The framework is based on ruby-kafka, which, when used directly, can be a challenge: it's a flexible library with lots of knobs and options. Most users don't need that level of flexibility, though. Racecar provides a simple and intuitive way to build and configure Kafka consumers.

NOTE: Racecar requires Kafka 0.10 or higher.

Table of content

  1. Installation
  2. Usage
    1. Creating consumers
    2. Running consumers
    3. Producing messages
    4. Configuration
    5. Testing consumers
    6. Deploying consumers
    7. Handling errors
    8. Logging
    9. Operations
  3. Development
  4. Contributing
  5. Support and Discussion
  6. Copyright and license


Add this line to your application's Gemfile:

gem 'racecar'

And then execute:

$ bundle

Or install it yourself as:

$ gem install racecar

Then execute (if you're in a Rails application):

$ bundle exec rails generate racecar:install

This will add a config file in config/racecar.yml.


Racecar is built for simplicity of development and operation. If you need more flexibility, it's quite straightforward to build your own Kafka consumer executables using ruby-kafka directly.

First, a short introduction to the Kafka consumer concept as well as some basic background on Kafka.

Kafka stores messages in so-called partitions which are grouped into topics. Within a partition, each message gets a unique offset.

In Kafka, consumer groups are sets of processes that collaboratively process messages from one or more Kafka topics; they divide up the topic partitions amongst themselves and make sure to reassign the partitions held by any member of the group that happens to crash or otherwise becomes unavailable, thus minimizing the risk of disruption. A consumer in a group is responsible for keeping track of which messages in a partition it has processed – since messages are processed in-order within a single partition, this means keeping track of the offset into the partition that has been processed. Consumers periodically commit these offsets to the Kafka brokers, making sure that another consumer can resume from those positions if there is a crash.

Creating consumers

A Racecar consumer is a simple Ruby class that inherits from Racecar::Consumer:

class UserBanConsumer < Racecar::Consumer
  subscribes_to "user_banned"

  def process(message)
    data = JSON.parse(message.value)
    user = User.find(data["user_id"])
    user.banned = true

In order to create your own consumer, run the Rails generator racecar:consumer:

$ bundle exec rails generate racecar:consumer TapDance

This will create a file at app/consumers/tap_dance_consumer.rb which you can modify to your liking. Add one or more calls to subscribes_to in order to have the consumer subscribe to Kafka topics.

Now run your consumer with bundle exec racecar TapDanceConsumer.

Note: if you're not using Rails, you'll have to add the file yourself. No-one will judge you for copy-pasting it.

Initializing consumers

You can optionally add an initialize method if you need to do any set-up work before processing messages, e.g.

class PushNotificationConsumer < Racecar::Consumer
  subscribes_to "notifications"

  def initialize
    @push_service = PushService.new # pretend this exists.

  def process(message)
    data = JSON.parse(message.value)

      recipient: data.fetch("recipient"),
      notification: data.fetch("notification"),

This is useful to do any one-off work that you wouldn't want to do for each and every message.

Setting the starting position

When a consumer is started for the first time, it needs to decide where in each partition to start. By default, it will start at the beginning, meaning that all past messages will be processed. If you want to instead start at the end of each partition, change your subscribes_to like this:

subscribes_to "some-topic", start_from_beginning: false

Note that once the consumer has started, it will commit the offsets it has processed until and in the future will resume from those.

Processing messages in batches

If you want to process whole batches of messages at a time, simply rename your #process method to #process_batch. The method will now be called with a "batch" object rather than a message:

class ArchiveEventsConsumer < Racecar::Consumer
  subscribes_to "events"

  def process_batch(batch)
    file_name = [
      batch.topic, # the topic this batch of messages came from.
      batch.partition, # the partition this batch of messages came from.
      batch.first_offset, # offset of the first message in the batch.
      batch.last_offset, # offset of the last message in the batch.

    File.open(file_name, "w") do |file|
      # the messages in the batch.
      batch.messages.each do |message|
        file << message.value

An important detail is that, if an exception is raised while processing a batch, the whole batch is re-processed.

Message headers

Any headers set on the message will be available when consuming the message:

message.headers #=> { "Header-A" => 42, ... }


In order to avoid your consumer being kicked out of its group during long-running message processing operations, it may be a good idea to periodically send so-called heartbeats back to Kafka. This is done automatically for you after each message has been processed, but if the processing of a single message takes a long time you may run into group stability issues.

If possible, intersperse heartbeat calls in between long-running operations in your consumer, e.g.

def process(message)

  # Signals back to Kafka that we're still alive!


Tearing down resources when stopping

When a Racecar consumer shuts down, it gets the opportunity to tear down any resources held by the consumer instance. For example, it may make sense to close any open files or network connections. Doing so is simple: just implement a #teardown method in your consumer class and it will be called during the shutdown procedure.

class ArchiveConsumer < Racecar::Consumer
  subscribes_to "events"

  def initialize
    @file = File.open("archive", "a")

  def process(message)
    @file << message.value

  def teardown

Running consumers

Racecar is first and foremost an executable consumer runner. The racecar executable takes as argument the name of the consumer class that should be run. Racecar automatically loads your Rails application before starting, and you can load any other library you need by passing the --require flag, e.g.

$ bundle exec racecar --require dance_moves TapDanceConsumer

The first time you execute racecar with a consumer class a consumer group will be created with a group id derived from the class name (this can be configured). If you start racecar with the same consumer class argument multiple times, the processes will join the existing group – even if you start them on other nodes. You will typically want to have at least two consumers in each of your groups – preferably on separate nodes – in order to deal with failures.

Producing messages

WARNING: This is an alpha feature, and could cause weird and unpredictable errors. Use with caution.

Consumers can produce messages themselves, allowing for powerful stream processing applications that transform and filter message streams. The API for this is simple:

class GeoCodingConsumer < Racecar::Consumer
  subscribes_to "pageviews"

  def process(message)
    pageview = JSON.parse(message.value)
    ip_address = pageview.fetch("ip_address")

    country = GeoCode.country(ip_address)

    # Enrich the original message:
    pageview["country"] = country

    # The `produce` method enqueues a message to be delivered after #process
    # returns. It won't actually deliver the message.
    produce(JSON.dump(pageview), topic: "pageviews-with-country")

You can set message headers by passing a headers: option with a Hash of headers.


Racecar provides a flexible way to configure your consumer in a way that feels at home in a Rails application. If you haven't already, run bundle exec rails generate racecar:install in order to generate a config file. You'll get a separate section for each Rails environment, with the common configuration values in a shared common section.

Note: many of these configuration keys correspond directly to similarly named concepts in ruby-kafka; for more details on low-level operations, read that project's documentation.

It's also possible to configure Racecar using environment variables. For any given configuration key, there should be a corresponding environment variable with the prefix RACECAR_, in upper case. For instance, in order to configure the client id, set RACECAR_CLIENT_ID=some-id in the process in which the Racecar consumer is launched. You can set brokers by passing a comma-separated list, e.g. RACECAR_BROKERS=kafka1:9092,kafka2:9092,kafka3:9092.

Finally, you can configure Racecar directly in Ruby. The file config/racecar.rb will be automatically loaded if it exists; in it, you can configure Racecar using a simple API:

Racecar.configure do |config|
  # Each config variable can be set using a writer attribute.
  config.brokers = ServiceDiscovery.find("kafka-brokers")

Basic configuration

  • brokers – A list of Kafka brokers in the cluster that you're consuming from. Defaults to localhost on port 9092, the default Kafka port.
  • client_id – A string used to identify the client in logs and metrics.
  • group_id – The group id to use for a given group of consumers. Note that this must be different for each consumer class. If left blank a group id is generated based on the consumer class name.
  • group_id_prefix – A prefix used when generating consumer group names. For instance, if you set the prefix to be kevin. and your consumer class is named BaconConsumer, the resulting consumer group will be named kevin.bacon_consumer.


  • logfile – A filename that log messages should be written to. Default is nil, which means logs will be written to standard output.
  • log_level – The log level for the Racecar logs, one of debug, info, warn, or error. Default is info.

Consumer checkpointing

The consumers will checkpoint their positions from time to time in order to be able to recover from failures. This is called committing offsets, since it's done by tracking the offset reached in each partition being processed, and committing those offset numbers to the Kafka offset storage API. If you can tolerate more double-processing after a failure, you can increase the interval between commits in order to better performance. You can also do the opposite if you prefer less chance of double-processing.

  • offset_commit_interval – How often to save the consumer's position in Kafka. Default is every 10 seconds.
  • offset_commit_threshold – How many messages to process before forcing a checkpoint. Default is 0, which means there's no limit. Setting this to e.g. 100 makes the consumer stop every 100 messages to checkpoint its position.
  • offset_retention_time - How long committed offsets will be retained. Defaults to the broker setting.

Timeouts & intervals

All timeouts are defined in number of seconds.

  • session_timeout – The idle timeout after which a consumer is kicked out of the group. Consumers must send heartbeats with at least this frequency.
  • heartbeat_interval – How often to send a heartbeat message to Kafka.
  • pause_timeout – How long to pause a partition for if the consumer raises an exception while processing a message. Default is to pause for 10 seconds. Set this to zero in order to disable automatic pausing of partitions.
  • connect_timeout – How long to wait when trying to connect to a Kafka broker. Default is 10 seconds.
  • socket_timeout – How long to wait when trying to communicate with a Kafka broker. Default is 30 seconds.
  • max_wait_time – How long to allow the Kafka brokers to wait before returning messages. A higher number means larger batches, at the cost of higher latency. Default is 1 second.

Memory & network usage

Kafka is really good at throwing data at consumers, so you may want to tune these variables in order to avoid ballooning your process' memory or saturating your network capacity.

Racecar uses ruby-kafka under the hood, which fetches messages from the Kafka brokers in a background thread. This thread pushes fetch responses, possible containing messages from many partitions, into a queue that is read by the processing thread (AKA your code). The main way to control the fetcher thread is to control the size of those responses and the size of the queue.

  • max_bytes — The maximum size of message sets returned from a single fetch request.
  • max_fetch_queue_size — The maximum number of fetch responses to keep in the queue. Once reached, the fetcher will back off until the queue gets back down under to limit.

The memory usage limit is roughly estimated as max_bytes * max_fetch_queue_size, plus whatever your application uses.

SSL encryption, authentication & authorization

  • ssl_ca_cert – A valid SSL certificate authority, as a string.
  • ssl_ca_cert_file_path - The path to a valid SSL certificate authority file.
  • ssl_client_cert – A valid SSL client certificate, as a string.
  • ssl_client_cert_key – A valid SSL client certificate key, as a string.

SASL encryption, authentication & authorization

Racecar has support for using SASL to authenticate clients using either the GSSAPI or PLAIN mechanism.

If using GSSAPI:

  • sasl_gssapi_principal – The GSSAPI principal.
  • sasl_gssapi_keytab – Optional GSSAPI keytab.

If using PLAIN:

  • sasl_plain_authzid – The authorization identity to use.
  • sasl_plain_username – The username used to authenticate.
  • sasl_plain_password – The password used to authenticate.

If using SCRAM:

  • sasl_scram_username – The username used to authenticate.
  • sasl_scram_password – The password used to authenticate.
  • sasl_scram_mechanism – The SCRAM mechanism to use, either sha256 or sha512.

Producing messages

These settings are related to consumers that produce messages to Kafka.

  • producer_compression_codec – If defined, Racecar will compress messages before writing them to Kafka. The codec needs to be one of gzip, lz4, or snappy, either as a Symbol or a String.

Datadog monitoring

Racecar supports configuring ruby-kafka's Datadog monitoring integration. If you're running a normal Datadog agent on your host, you just need to set datadog_enabled to true, as the rest of the settings come with sane defaults.

  • datadog_enabled – Whether Datadog monitoring is enabled (defaults to false).
  • datadog_host – The host running the Datadog agent.
  • datadog_port – The port of the Datadog agent.
  • datadog_namespace – The namespace to use for Datadog metrics.
  • datadog_tags – Tags that should always be set on Datadog metrics.

Testing consumers

Since consumers are merely classes that implement a simple interface, they're dead simple to test.

Here's an example of testing a consumer class using RSpec and Rails:

# app/consumers/create_contacts_consumer.rb
# Creates a Contact whenever an email address is written to the
# `email-addresses` topic.
class CreateContactsConsumer < Racecar::Consumer
  subscribes_to "email-addresses"

  def process(message)
    email = message.value

    Contact.create!(email: email)

# spec/consumers/create_contacts_consumer_spec.rb
describe CreateContactsConsumer do
  it "creates a Contact for each email address in the topic" do
    message = double("message", value: "[email protected]")
    consumer = CreateContactsConsumer.new


    expect(Contact.where(email: "[email protected]")).to exist

Deploying consumers

If you're already deploying your Rails application using e.g. Capistrano, all you need to do to run your Racecar consumers in production is to have some process supervisor start the processes and manage them for you.

Foreman is a very straightford tool for interfacing with several process supervisor systems. You define your process types in a Procfile, e.g.

racecar-process-payments: bundle exec racecar ProcessPaymentsConsumer
racecar-resize-images: bundle exec racecar ResizeImagesConsumer

If you've ever used Heroku you'll recognize the format – indeed, deploying to Heroku should just work if you add Racecar invocations to your Procfile.

With Foreman, you can easily run these processes locally by executing foreman run; in production you'll want to export to another process management format such as Upstart or Runit. capistrano-foreman allows you to do this with Capistrano.

Running consumers in the background

While it is recommended that you use a process supervisor to manage the Racecar consumer processes, it is possible to daemonize the Racecar processes themselves if that is more to your liking. Note that this support is currently in alpha, as it hasn't been tested extensively in production settings.

In order to daemonize Racecar, simply pass in --daemonize when executing the command:

$ bundle exec racecar --daemonize ResizeImagesConsumer

This will start the consumer process in the background. A file containing the process id (the "pidfile") will be created, with the file name being constructed from the consumer class name. If you want to specify the name of the pidfile yourself, pass in --pidfile=some-file.pid.

Since the process is daemonized, you need to know the process id (PID) in order to be able to stop it. Use the racecarctl command to do this:

$ bundle exec racecarctl stop --pidfile=some-file.pid

Again, the recommended approach is to manage the processes using process managers. Only do this if you have to.

Handling errors

When processing messages from a Kafka topic, your code may encounter an error and raise an exception. The cause is typically one of two things:

  1. The message being processed is somehow malformed or doesn't conform with the assumptions made by the processing code.
  2. You're using some external resource such as a database or a network API that is temporarily unavailable.

In the first case, you'll need to either skip the message or deploy a new version of your consumer that can correctly handle the message that caused the error. In order to skip a message, handle the relevant exception in your #process method:

def process(message)
  data = JSON.parse(message.value)
  # ...
rescue JSON::ParserError => e
  puts "Failed to process message in #{message.topic}/#{message.partition} at offset #{message.offset}: #{e}"
  # It's probably a good idea to report the exception to an exception tracker service.

Since the exception is handled by your #process method and is no longer raised, Racecar will consider the message successfully processed. Tracking these errors in an exception tracker or some other monitoring system is highly recommended, as you otherwise will have little insight into how many messages are being skipped this way.

If, on the other hand, the exception was cause by a temporary network or database problem, you will probably want to retry processing of the message after some time has passed. By default, if an exception is raised by the #process method, the consumer will pause all processing of the message's partition for some number of seconds, configured by setting the pause_timeout configuration variable. This allows the consumer to continue processing messages from other partitions that may not be impacted by the problem while still making sure to not drop the original message. Since messages in a single Kafka topic partition must be processed in order, it's not possible to keep processing other messages in that partition.

In addition to retrying the processing of messages, Racecar also allows defining an error handler callback that is invoked whenever an exception is raised by your #process method. This allows you to track and report errors to a monitoring system:

Racecar.config.on_error do |exception, info|
  MyErrorTracker.report(exception, {
    topic: info[:topic],
    partition: info[:partition],
    offset: info[:offset],

It is highly recommended that you set up an error handler.


By default, Racecar will log to STDOUT. If you're using Rails, your application code will use whatever logger you've configured there.

In order to make Racecar log its own operations to a log file, set the logfile configuration variable or pass --log filename.log to the racecar command.


In order to gracefully shut down a Racecar consumer process, send it the SIGTERM signal. Most process supervisors such as Runit and Kubernetes send this signal when shutting down a process, so using those systems will make things easier.

In order to introspect the configuration of a consumer process, send it the SIGUSR1 signal. This will make Racecar print its configuration to the standard error file descriptor associated with the consumer process, so you'll need to know where that is written to.


After checking out the repo, run bin/setup to install dependencies. Then, run rspec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.


Bug reports and pull requests are welcome on GitHub. Feel free to join our Slack team and ask how best to contribute!

Support and Discussion

If you've discovered a bug, please file a Github issue, and make sure to include all the relevant information, including the version of Racecar, ruby-kafka, and Kafka that you're using.

If you have other questions, or would like to discuss best practises, how to contribute to the project, or any other ruby-kafka related topic, join our Slack team!

Copyright 2017 Daniel Schierbeck & Zendesk

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.

You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.