Kafka Education Series: Kafka Internals

Kafka is a distributed streaming platform that allows you to collect, store, and process large streams of data in real-time. It is designed to handle high throughput, low latency, and high availability.

Here is a detailed guide on Kafka internals:

1. Topics and Partitions: Kafka organizes data into topics, which are essentially streams of data. Each topic is divided into one or more partitions, which are ordered, immutable sequences of records. Each partition is a self-contained unit of data that can be stored on a different machine, allowing for horizontal scalability.
2. Producers and Consumers: Producers write data to a topic, and consumers read data from a topic. Producers can write to a specific partition or use a partitioning strategy to automatically distribute data across multiple partitions. Consumers can read from a specific partition or read from all partitions in a topic.
3. Brokers: A Kafka cluster consists of one or more brokers, which are responsible for managing the storage and retrieval of data. Each broker is responsible for one or more partitions, and each partition is replicated across multiple brokers for fault tolerance.
4. Zookeeper: Kafka uses Zookeeper to maintain configuration information, such as the location of partitions and replicas, and to coordinate leader elections for partitions. Zookeeper also helps in maintaining the state of the Kafka cluster and in detecting the failures of Brokers.
5. Replication: Kafka uses a replicator to replicate data between brokers. Each partition has a leader broker, which handles all read and write requests for that partition, and one or more follower brokers, which replicate data from the leader. If the leader broker fails, one of the followers is automatically elected as the new leader.
6. Compaction: When the number of records in a partition exceeds a certain threshold, a process called compaction is triggered to remove duplicate records and expired records. This allows the partition to reclaim space and improve query performance.
7. Log Compaction: log compaction is a feature of Kafka that allows you to retain only the latest version of each record in a topic. Log compaction is useful for use cases where you want to retain only the most recent state of a record, such as for maintaining a materialized view.
8. Offsets: Consumers keep track of their current position in a topic using offsets. Offsets are used to track the location of the last consumed message in a topic. Consumers use offsets to determine where to start consuming data when they first join a topic or when they are recovering from a failure.
9. Consumer Group: Consumers can be organized into consumer groups, which allow multiple consumers to work together to consume data from a topic. Each consumer group is assigned a unique group ID, and each consumer in the group is assigned a unique consumer ID.

Conclusion

Kafka is a powerful and flexible platform that allows you to collect, store, and process large streams of data in real-time. It’s designed to handle high throughput, low latency, and high availability. Understanding Kafka internals, such as topics and partitions, producers and consumers, brokers, replication, compaction, and offsets, is essential for understanding how to use Kafka effectively and for troubleshooting issues that may arise.