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Kafka: Introduction to core concepts


Apache Kafka was developed by LinkedIn and donated to Apache.
Apache Kafka is a distributed streaming platform that can handle high volume of data.

Pull or Push?

I initially misunderstood Kafka as a push based messaging system. However Kafka has chosen traditional pull approach.
In Kafka, data is pushed to the broker by producers and pulled from the broker by the consumers.

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Why Kafka?

  • Kafka is a reliable messaging system which is fast and durable. We can list it's benifits as;
  • Scalable - Kafka's partion model allows data to distributed across multipel servers, making it highly scalable. 
  • Durable - Kafka's data is written to disk making it highly durable agaisnt server failures.
  • Multiple producers - Kafka can handle multpile producers which publish to the same topic.
  • Multiple consumers - Kafka is designed so that multipel consumers can read messages without interfering with each other.
  • High performance - All these features allows high performace distributed messaging system.

What are use cases?

  • Website activity tracking - this is the original use case for Kafka. It helps to track user activities in real time.
  • Messaging - Kafka can replace traditional messaging systems.
  • Metrics - Kafka is used for centralized data monitoring.
  • Log aggregation - Ideal for collecting application logs and analize.
  • Stream processing - Operates on data in real time, as quickly as messages are produces.

Core concepts

  • Kafka runs as a cluster.
  • Can span to multiple data centers.
  • Store data in topics.
  • Topics has partitions.
  • Each record consists of a key, a value and a timestamp.

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Messages

  • The unit of data within Kafka is called a message. (similar to a database row)
  • A message is simply an array of bytes.
  • A message can have an optional key.
  • Messages are written into Kafka in batches (collection of messages) for efficiency.

Brokers and clusters

  • A single Kafka server is called a broker.
  • The broker receives messages form producers and also serves consumers.
  • Each broker is identified with its ID (integer).
  • After connecting to any broker (called a bootstrap broker), you will be connected to the entire broker.
  • A good number to get started is 3 brokers.
  • A Kafka cluster is composed of multiple brokers called a cluster.
  • Within a cluster of brokers, one broker will also function as the cluster controller.
  • A partition is owned by a single broker in the cluster, and that broker is called the leader of the partition.

Topics

  • Messages are categorized into topics. (similar to database table)
  • Topic is identified by its name.
  • Topics are additionally brokwn down into a number of partitions.
  • Most often, a stream is considered to be a single topic of data.
  • Data is kept only for a limited time (default is one week).
  • Data is immutable, once written it can't be changed.
  • Topics should have a replication factor (usually between 2 to 3). This way if a broker is down, another broker can serve the data.

Partition and offset

  • Topics are split in partitions.
  • Each partition is ordered. However, between 2 partitions we cannot guarantee any order.
  • Data is assigned randomly to a partition unless a key is provided.
  • Each message within a partiton get an incremental id which is called as Offset.
  • Offset only have a meaning for a specific partition.

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Leader for a partition

  • At any time only one broker can be a leader for a given partition.
  • Only that leader can receive and serve data for a partiton.
  • The other brokers will synchronize the data.

Producers

  • Producres create new messages.
  • producers automatically know to which broker and parttion to write to.
  • In case of broker failures, produces will automatically recover.
  • Producers can choose to receive ack of data writes.

Consumers

  • Consumers read messages.
  • Consumer subscribes to one or more topics and reads the messages.
  • Consumers know which broker to read from.
  • In case of broker failures, consumers know how to recover.
  • Data is read in order within each partitions.

Consumer groups

  • Consumers read data in consumer groups.
  • Each consumers within a group reads from exclusive partitions.
  • If you have more consumers than partitions, some consumers will be inactive.

Consumer offsets

  • Kafka stores the offsets at which a consumer group has been reading. (similar to bookmarking)
  • When a consumer in a group has processed data received from Kafka, it should be commiting the offsets.
  • If a consumer dies, it will be able to read back from where it left off thanks to the committed consumer offsets.

Zookeeper

  • Apache Kafka uses Zookeeper to store metadata about the Kafka cluster, as well as consumer client details.
  • A Zookeeper cluster is called an ensemble.

Further reading

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