What is Apache Kafka

What is Kafka?

Apache Kafka is a distributed and robust queue that can handle high volume data and enables you to pass messages from one end-point to another.

Kafka is

  • Fast
  • Scalable
  • Durable

When used in the right way and for the right use case, Kafka has unique attributes that make it a highly attractive option for data integration.

Publish subscribe messaging system

  • Kafka maintains feeds of messages in categories called topics
  • Producers are processes that publish messages to one or more topics
  • Consumers are processes that subscribe to topics and process the feed of published messages


Kafka Cluster

  • Since Kafka is distributed in nature, Kafka is run as a cluster.
  • Kafka use Zookeper as distribution centralizer.
  • A cluster is typically comprised multiple servers; each of which is called a broker.
  • Communication between the clients and the servers takes place over TCP protocol.



  • Kafka uses Topic conception which comes to organize messages flow.
  • To balance the load, a topic may be divided into multiple partitions and replicated across brokers.
  • Partitions are ordered, immutable sequences of messages that’s continually appended i.e. a commit log.
  • Messages in the partition have a sequential id number that uniquely identifies each message within the partition.



  • Partitions allow a topic’s log to scale beyond a size that will fit on a single server (a broker) and act as the unit of parallelism.
  • The partitions of a topic are distributed over the brokers in the Kafka cluster where each broker handles data and requests for a share of the partitions.
  • Each partition is replicated across a configurable number of brokers to insure fault tolerance.


Fault Tolerance

  • Each partition has one server which acts as the leader and zero or more servers which act as followers.
  • The leader handles all read and write requests for the partition while the followers passively replicate the leader.
  • If the leader fails, one of the followers will automatically become the new leader.
  • Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster.


  • The Kafka cluster retains all published messages (whether or not they have been consumed) for a configurable period of time; after which it will be discarded to free up space.
  • Metadata retained on a per-consumer basis is the position of the consumer in the log, called offset, which is controlled by consumer.
  • Usually a consumer advances its offset linearly as it reads messages, but it can consume messages in any order.
  • Kafka consumers can come and go without much impact on the cluster or on other consumers.


Producers publish data to the topics by assigning messages to a partition within the topic either in a round-robin fashion or according to some semantic partition function.



  • Kafka offers a single consumer abstraction called consumer group that generalizes both queue and topic.
  • Consumers label themselves with a consumer group name.
  • Each message published to a topic is delivered to one consumer instance within each subscribing consumer group.
  • If all the consumer instances have the same consumer group, then Kafka acts just like a traditional queue balancing load over the consumers.
  • If all the consumer instances have different consumer groups, then Kafka acts like publish-subscribe and all messages are broadcast to all consumers.

Consumer Groups

  • Topics have a small number of consumer groups, one for each logical subscriber.
  • Each group is composed of many consumer instances to insure scalability and fault tolerance.


Ordering guarantees

  • Kafka assigns partitions in a topic to consumers in a consumer group so, each partition is consumed by exactly one consumer in the group, but there cannot be more consumer instances in a consumer group than partitions.
  • Kafka provides a total order over messages within a partition, not between different partitions in a topic.

Why Kafka?

Horizontally scalable

  • Kafka is a distributed system that can be elastically and transparently expanded with no downtime.

High throughput

  • High throughput is provided for both publishing and subscribing, due to disk structures that provide constant performance even with many terabytes of stored messages.

Reliable delivery

  • Persists messages on disk, and provides cluster replication.
  • Supports large number of subscribers and automatically balances consumers in case of failure.

Common Use Cases

  • Stream processing, event sourcing, traditional message broker replacement.
  • Website activity tracking – original use case for Kafka.
  • Metrics collection and monitoring – centralized feeds of operational data.
  • Log aggregation.


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