Tag: apache kafka

Inside Kafka: Enhancing Data Reliability Through Transactional Offsets with Karafka

Karafka is a Ruby and Rails framework that simplifies the development of Apache Kafka-based applications. Among its varied features, the Filtering API provides enhanced control over the data flow.

The crux of this article is about managing offsets - unique identifiers for messages within Kafka's partitions. Often, there's a need to manage offsets alongside database operations within a transaction, especially when handling potential process crashes and anomalies, minimizing the risk of double processing.

For instance, if a SQL operation concludes successfully but the offset commit fails due to a crash, data could be processed again, leading to potential duplications or data integrity issues. Integrating offset management with database transactions using Karafka's Filtering API can help tackle this problem. It ensures the offset progress is tracked within the database transaction, maintaining data integrity even in crashes.

We'll explore this concept further in the coming sections, highlighting its practical implications and benefits.

The Importance of Offset Management in Kafka

In a world of streaming data, Kafka has cemented its role as an industry-standard platform for handling high-volume, real-time data feeds. At the heart of Kafka's functionality lies the concept of offsets, which are crucial in ensuring data consistency and reliability.

Offsets are unique identifiers assigned to each message within a Kafka partition. They serve as checkpoints that allow Kafka to track which messages have been consumed and which haven't. In other words, they are the mechanism by which Kafka maintains the state across distributed data streams, marking the position of every consumer in the stream. With them, it is possible to keep track of the data flowing through Kafka at any given time.

However, Kafka offset management has its challenges. Because it is entirely independent of database operations, there may be cases where a SQL operation finishes successfully, but the offset commit fails due to a process crash or an involuntary rebalance. This can lead to issues like data duplication, as when the system recovers, the data already processed by the SQL operation may be consumed again.

Below you can find an an example code and a graph that illustrates this problem:

def consume
  Event.transaction do
    messages.each do |message|

  # Karafka does that automatically after batch is successfully processed
  # however we do it here as well to better illustrate this scenario

When using the #mark_as_consume, Karafka will store the offset locally and commit it periodically. This means there may be cases where the partition is lost, but the process still needs to be made aware of it. If that happens, while the database operation finishes, the offset won't be committed, and a different process may already be working with the same messages. This will result in inserting some of the events multiple times.

One way to partially mitigate would be to use mark_as_consumed! at the end of the transaction as follows:

def consume
  Event.transaction do
    messages.each do |message|

    # Stop the transaction if we no longer own the partition
    raise(ActiveRecord::Rollback) unless mark_as_consumed!(messages.last)

This, however, creates a new problem: what if the offset is committed, but the transaction fails?

Wouldn't it be amazing if we could store the offsets of processed messages or batches within the same DB transactions, ensuring that both always succeed or fail together?

Note 1: By default, Karafka will wait for the consumer to finish work and commit the offsets during rebalances unless the process is forcefully evicted from the consumer group.

Note 2: Yes this could be solved also by using unique keys for events, but this is not always the case. The example was reduced in complexity to focus on the transactional offset management and not a sophisticated SQL operations case.

Transactional Offset Management with the Filtering API

With Karafkas' Filtering API you can achieve exactly that!

By integrating offset management with the transactional integrity of the database using Karafka's Filtering API, we ensure that the offset progress is tracked within the database transaction itself. This approach helps maintain data integrity, even when crashes occur, by providing atomicity to the operation - meaning that all parts of the operation must succeed for the transaction to be committed. If any part fails, the entire transaction is rolled back, avoiding inconsistencies.

Karafka Filtering API is a powerful tool that allows developers to perform various actions around the consumption process. With the Filtering API, users can register multiple filters to validate, filter out messages as they arrive, and alter the polling process by pausing or starting from a different offset.

This time we will elevate the ability exposed by the Filtering API to inject an offset taken from the database in case it would not match the one stored in Kafka.

Defining the flow expectations

There are a few things we need to take into consideration to build a transactional offset management filter for Karafka:

  • All SQL operations should have a timeout shorter than max.poll.interval.ms to ensure we do not end up with an endless cycle of forced rebalances.
  • Upon a conflict between the offset present in the database and Kafka, database offset should have the higher priority.
  • Number of partitions is known (to simplify our code)
  • Each topic partition has a pre-existing row in an appropriate table
  • Our per-partition rows are always accessed with the FOR UPDATE lock since they should be only used by the consumers that claim partition ownership. Those rows should not be used for anything else.
  • Our per-partition row is used as a lock around the transaction happening during the consumption, ensuring that in case of reassignment, the other process is blocked on the initial offset selection until the transaction is finalized.

    Keeping all of the above in mind, we can draw the expected flow of the initial offset selection:

    We still have to remember that consumption may happen with a delay and that the partition may be lost between the messages' delivery and their consumption. However, this is a separate issue we will tackle soon.

Because of the DB lock, we now know that:

  • no one else owns the lock, which means there are no currently running operations on any other processes operating on the same topic partition (it does not mean there won't be any before the consumption in our process, but as mentioned, we will tackle this as well).
  • we have the current Kafka offset and the DB one, and we can ensure that we start from the transactional one in case of a conflict.

What about the consumption itself? Can we just run it as previously? Well almost. We need a way to ensure that at the moment of locking the row, we own the partition. Yes, we may lose it during the processing, but as long as we hold the lock, any other process attempting to establish its starting offset will have to wait.

While the processing may end when we no longer own the partition, it was started with ownership confirmed. Hence, as long as we hold the lock, no other process can fetch the DB offset. This means that we can safely finish our DB operations and ignore potential Kafka offset commit failure.


Partitions table

There's not much in our table design. We need to make sure we have a row per each topic partition and that we have a way to store the offset.

class CreatePartitions < ActiveRecord::Migration[6.1]
  def change
    create_table :kafka_partitions do |t|
      t.string :topic_with_partition, unique: true, null: false
      t.integer :offset, limit: 8, default: 0, null: fase


Locking code

Code to ensure, that we can work with a given partition fully locked looks as followed:

class Partition < ApplicationRecord
  self.table_name = :kafka_partitions

  class << self
    def locked(topic, partition, &block)
      partition = find_by!(topic_with_partition: "#{topic}-#{partition}")

      partition.with_lock('FOR UPDATE') do

  def mark_as_consumed(message)
    update!(offset: message.offset + 1)

Filter for offset management

The most complex code resides in the filter. For the sake of simplicity, I left the lock timeout handling out:

class OffsetManager < Karafka::Pro::Processing::Filters::Base
  def initialize(topic, partition)
    @topic = topic
    @partition = partition
    @executed = false
    @analyze = false

  def apply!(messages)
    # This filter should resolve sattes only on the first run because it's the first
    # one after the partition assignment
    # Every Karafka filter instance is reinitialized after a rebalance
    if @executed
      @analyze = false

    # Care only on first run
    @executed = true
    @analyze = true

    ::Partition.locked(@topic, @partition) do |partition|
      kafka_offset = messages.first.offset

      # Selecting max will ensure that we always prioritize the DB one and since
      # we always commit the transactional offset first, no risk in max
      @start_offset = [partition.offset, kafka_offset].max
      @mismatch = partition.offset != kafka_offset

    # This will ensure that we do not  pass any messages for consumption when seek will run
    messages.clear if @mismatch

  def applied?

  def action
    @analyze && @mismatch ? :seek : :skip

  def cursor

You can register this filter as follows:

topic :my_topic do
  consumer Consumer
  filter ->(topic, partition) { OffsetManager.new(topic, partition) }

Note that it is crucial to make sure this is the first filter that runs, as it needs to be aware of the initial offset received alongside the first message from Kafka.

Consumption alignment

The last remaining thing is the alignment of our consumption process. Similarly to our initial code, we do need to run in a transaction, however now it is being taken care of by our Partition#locked wrapper.

We use a synchronous #revoked? method that will return false in case our consumer lost the assignments it was working with.

def consume
  successful = false

  ) do |partition|
    # Do not proceed if we have lost the assignment
    raise(ActiveRecord::Rollback) if revoked?

    # Do the work
    messages.each do |message|

    # Store the DB offset
    successful = true

  return unless successful

  # Store Kafka offset


My focus in this article was the careful and efficient management of Kafka's offsets, which are crucial for maintaining data integrity and consistency.

We explored how to integrate offset management with database transactions for handling scenarios involving process crashes. By doing so, the offset progress is meticulously tracked within the database transaction, significantly reducing the risk of data duplication or loss.

However, it's important to note that the examples and strategies discussed in this article have been simplified for clarity and understanding. In a real-world, production-grade environment, some extra development and adjustments may be required.

Kafka topics as code – declarative Kafka topics management in Ruby

Kafka topics are a fundamental concept in Apache Kafka. Topics are logical names or labels representing a stream of messages that Kafka clients can produce and consume.

What makes them interesting is the variety of settings that can be applied to them. These settings, amongst others include:

  • Partition count: The number of partitions that a topic should be split into.
  • Replication factor: The number of replicas that should be maintained for each partition.
  • Retention period: The time that messages should be retained in the topic.
  • Minimum and maximum in-sync replicas: The minimum number of replicas that must be in sync before a producer can receive acknowledgment for a write operation.
  • Cleanup policy: The policy used for deleting old messages from the topic.

When looking from a management perspective, topics are similar to database tables. They have names, a set of settings that apply to them, and their constraints. And on top of all of that, they need to be managed.

Declarative topics management

The management approach that I like and support in Karafka is called Declarative Topics Management. It allows for automatic topic creation and configuration based on predefined rules. It is a way to automate the process of managing Kafka topics by defining the desired topic properties and configurations in a declarative manner rather than manually creating and managing topics.

With Declarative Topics Management, you can define a set of rules that specify how topics should be created and configured. These rules can be based on various factors, such as the topic's name, number of partitions, replication factor, retention policy, and more.

Example of a declarative repartitioning using karafka topics migrate command

Keeping Kafka topics configuration as code has several benefits:

  • Version Control: By keeping the topic settings as code, you can track changes over time and easily understand historical changes related to the topics. This is particularly important in a production environment where changes need to be carefully managed.

  • Reproducibility: When you define Kafka topics settings as code, you can easily recreate the same topic with the same settings in multiple environments. This ensures that your development, staging, and production environments are consistent, which can help prevent unexpected issues and bugs.

  • Automation: If you use code to define Kafka topics settings, you can automate the process of creating and updating topics. This can save time and reduce the risk of human error.

  • Collaboration: When you keep Kafka topics settings as code, you can collaborate with other developers on the configuration. You can use tools like Git to manage changes and merge different configurations.

  • Documentation: Code is self-documenting, meaning anyone can look at the configuration and understand what is happening. This can make it easier for new team members to get up to speed and help troubleshoot issues.

In-app topics management

There are many ways to manage declaratively Kafka topics. For complex systems, you may want to look into tools like topicctl.

Often, however, your setup won't be overcomplicated. The primary thing that needs to happen is to ensure that all of your environments and developers use topics with the same configuration.

Partition count is a simple example where a config difference can impact the business logic and create hard-to-track issues. By default, topics created automatically by Kafka always have one partition. Assume a developer is working on something that requires strong ordering. If his development and test environments operate on only one partition, problems emerging from invalid partition key selection may only occur once the code hits production. Those types of race conditions can be both critical and hard to detect.

To mitigate risks of that nature, Karafka ships with a Declarative Topics feature. This feature lets you describe your topics' configuration in your routing, ensuring that the set of settings is the same across all the managed environments.

Defining topic configuration

All the configuration for a given topic needs to be defined using the topic scope #config method:

class KarafkaApp < Karafka::App
  routes.draw do
    topic :events do
        partitions: 6,
        replication_factor: Rails.env.production? ? 3 : 1,
        'retention.ms': 86_400_000 # 1 day in ms,
        'cleanup.policy': 'delete'

      consumer EventsConsumer

Such a configuration can be then applied by running the following command: bundle exec karafka topics migrate. This command will create the topic if missing or repartition in case there are not enough partitions.

Karafka ships with following topics management related commands:

  • karafka topics create - creates topics with appropriate settings.
  • karafka topics delete - deletes all the topics defined in the routes.
  • karafka topics repartition - adds additional partitions to topics with fewer partitions than expected.
  • karafka topics reset - deletes and re-creates all the topics.
  • karafka topics migrate - creates missing topics and repartitions existing to match expected partitions count.

The below example illustrates the usage of the migrate command to align the number of partitions and to add one additional topic:


This API has few limitations about which you can read here. There are two primary things you need to keep in mind:

  • Topics management API does not provide any means of concurrency locking when CLI commands are being executed. This means it is up to you to ensure that two topic CLI commands are not running in parallel during the deployments.
  • Karafka currently does not update settings different than the partition count on existing topics. This feature is under development.


Karafka declarative topics management API is an excellent solution for low and medium-complexity systems to ensure consistency of their topics across multiple environments, and that is available out-of-the-box with the framework itself.

Getting started with Kafka and Karafka

If you want to get started with Karafka as fast as possible, then the best idea is to visit our Getting started guides and the example Rails app repository.

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