Kafka ® is part of the apache foundation and is described as a distributed streaming platform that is used for building real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant, wicked fast, and runs in production in thousands of companies.

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Check the Kotlin Kafka examples for developers at sylhare/kafka

Introduction to Kafka Platform


Producer writes to a topic. Consumers poll and read the messages from topic. Messages can be read multiple times depending on your retention policy. (Which offsets when you are going to read from)

with Broker

Topics are more efficient when they are on multiple partitions. Those partitions are on one or multiple brokers, which are used for resiliency, because if one broker goes done the messages written / read on the Topic will still be able to be processed by other brokers.

With no keys the messages or record will be sent to the partition using round-robin. If there is a key, it will use this key to define the partition example:

5 partition, message with key = 7 -> <key> mod( <partition> ) = 2 
> Which mean message of key 7 will go to partition 2 in this case.


You can set a replica factor so that you will have a replica of each of the Topic partition in the other brokers. Ideally you set it to three, those wil create ISR in sync replicas.

The event is first sent to the leader then when it has committed on the transaction log, the event is sent to the next follower. Replicas are identical up to a specified point called the high-water mark which correspond to the last committed message. After each event processed by the leader it will then rise the high watermark for the replicas to process.

The data retention is made with the segments which are files that stores/logs the events that have been sent on the partition. Messages that are read are said to be committed to the log. So we know which message has been listened to and when. If a consumer fails to read a message, another consumer can review the log to start back at the right event in the queue.

Configuration Elements

ACK -> Acknowledge. There are three settings :

  • acks=0 will not wait for the acknowledgement of the server
  • acks=1 will wait for the leader to write on local log
  • ack=all Producer will wait for all in sync replicas (ISR) to have acknowledged the receipt of the record within delivery time

The retries for the amount of time it will retry (Until MAX_INT). You can set a retry.backoff.ms to pause in between retries. (default to 100ms)

The delivery.timeout.ms puts a limit to report the result or failure from a producer.

The offset is a simple integer number that is used by Kafka to maintain the current position of a consumer. The auto-offset-reset can be set to:

  • earliest Go through all the record since it lost the offset
  • latest Go from the latest record when the offset is lost

Interact with Kafka

There are usually two types of applications interacting with it:

  • Producers that writes data
  • Customers that read those data and react / transform them

Here is a simple diagram of the major components which you may interact with while using Kafka:

graph TD style Topic fill:#dbb498,stroke:#d98142 PR(Producer) PR -->|Send| PA subgraph Broker PA(Partionner) subgraph Topic P0(Partition 0) P1(Partition 1) P2(Partition 2) end end PA -->|Map| P0 PA -->|Map| P1 PA -->|Map| P2 subgraph Consumer Group 1 P0 --> C0(Consumer 1.0) P1 --> C1(Consumer 1.1) P2 --> C2(Consumer 1.2) end subgraph Consumer Group 2 P0 --> CC0(Consumer 2.0) P1 --> CC0 P2 --> CC0 end

With a Kafka Cluster, you can have multiple broker et replicate your topics.


Producers publish data (records) to the topics of their choice. The producer is responsible for choosing which record to assign to which partition within the topic. This can be done in a round-robin fashion simply to balance load or it can be done according to some semantic partition function (say based on some key in the record).


Consumers label themselves with a consumer group name/id using group.id. Consumer instances can be in separate processes or on separate machines.

Consumers get the data in the partitions that was published by the producer. Everything will be handled by Kafka and can be configurable.

Depending on the configuration the record will be either:

  • One consumer group: load balance between consumers within the group
  • Multiple different groups: broadcast to all groups then load balance to all consumer in each group

Stream API

Kafka allows you to read all the records as stream and perform operation on them. That Stream API allows making even more performing consumers.

If you are familiar with java stream() or functional programming, then you know how powerful streams can be.

Data Format

Kafka don’t do well with big files message.max.bytes is recommended to 1mb. Plain text is not very efficient, everything needs to be converted to text. We want to use a schema to structure the data.

Avro (another apache open source project) used for serialization of the data. It’s like an optimized json, faster to process and more robust to change.