Apache Kafka Cluster Explained: Core Concepts and Architectures

In today’s data-driven world, the ability to process and analyze data in real time is crucial for many applications. Apache Kafka, an open-source distributed streaming platform, has emerged as a leading solution for handling real-time data feeds. This guide aims to provide a comprehensive understanding of Kafka, including its architecture, key terminologies, and how it solves various data streaming problems. Additionally, we will delve into the role of Zookeeper in Kafka and the transition to the new KRaft architecture.

Origins of Kafka

Apache Kafka was originally developed by LinkedIn to address the need for a robust, scalable messaging system. It was open-sourced in early 2011 and subsequently donated to the Apache Software Foundation. The creators of Kafka, including Jay Kreps, Neha Narkhede, and Jun Rao, designed it to handle real-time data streams with high throughput, fault tolerance, and scalability.

What is Apache Kafka?

Apache Kafka is an open-source platform used for building real-time data pipelines and streaming applications. It allows you to publish, subscribe to, store, and process streams of records in a fault-tolerant manner.

Problems Solved by Kafka

🔸Real-Time Data Processing

Traditional systems often rely on batch processing, which means data is collected over a period, processed, and then results are delivered. This approach introduces latency, making it unsuitable for applications requiring real-time insights. Kafka enables continuous data ingestion and processing, allowing businesses to react to events as they occur.

Example: In an online retail platform, Kafka can process user actions (clicks, purchases, etc.) in real-time, enabling immediate inventory updates, personalized recommendations, and dynamic pricing adjustments.

🔸Scalability and Fault Tolerance

As businesses grow, the volume of data they generate increases exponentially. Kafka’s architecture is designed to scale horizontally, allowing the addition of more brokers to handle increased load. Moreover, Kafka’s data replication ensures fault tolerance, meaning that even if a broker fails, data is not lost.

Example: A financial institution using Kafka to process stock trade information can scale its infrastructure as the number of trades increases, ensuring no data loss even during broker failures.

🔸Decoupling Data Streams

Kafka decouples data producers and consumers, allowing them to operate independently. This decoupling makes systems more modular and easier to manage.

Example: In a microservices architecture, different services can produce and consume data from Kafka topics without being directly dependent on each other. This setup enables independent scaling, development, and deployment of microservices.

Related read: Empowering Your Data with Apache Kafka and Kafka Connect

Key Kafka Terminologies

Understanding Kafka requires familiarity with its core components and concepts. Here’s a detailed look at each:

1. Producer: Producers are client applications that publish (write) events to Kafka topics. Producers send data to the Kafka broker, which then writes the data to a specific partition within a topic.
2. Consumer: Consumers are client applications that subscribe to (read) events from Kafka topics. They read data from Kafka partitions in a distributed and scalable manner.
3. Broker: A broker is a Kafka server that stores data and serves client requests. Kafka brokers manage the persistence and replication of data.
4. Topic: Topics are categories or feed names to which records are published. Topics in Kafka are multi-subscriber, meaning data written to a topic is available to be read by multiple consumers.
5. Partition: A topic is divided into multiple partitions to allow for parallel processing of data. Each partition is an ordered, immutable sequence of records, and each record within a partition has an offset, a unique identifier.
6. Offset: Offsets are unique identifiers for each record within a partition. They enable consumers to track their position in the stream of data.
7. Consumer Group: A group of consumers that work together to consume data from a topic. Each partition in a topic is consumed by only one consumer in a consumer group, allowing for parallel processing of data.
8. Replication: Kafka replicates data across multiple brokers to ensure fault tolerance. Each partition has one leader and several followers. The leader handles all reads and writes, while followers replicate the data.

Kafka Architecture with Zookeeper

In a Kafka cluster, Zookeeper is used as a coordination service to manage the metadata and state of the Kafka brokers. This includes keeping track of topics, partitions, brokers, and leader elections. Let’s break down how this works step by step.

🔸Kafka with Zookeeper Architecture

1. Kafka Cluster Setup:
   ▫️ A Kafka cluster consists of multiple brokers (servers).
   ▫️ Zookeeper is deployed as a separate ensemble, usually consisting of 3 or more nodes to ensure high availability and fault tolerance.
2. Zookeeper’s Role:
   ▫️ Zookeeper manages the metadata of the Kafka cluster. This metadata includes information about brokers, topics, partitions, and their respective leaders.
   ▫️ Zookeeper handles configuration management and keeps track of the state of Kafka brokers.
3. Broker Metadata Management:
   ▫️ When a broker starts, it registers itself with Zookeeper.
   ▫️ Zookeeper keeps track of all active brokers and their status.
4. Topic and Partition Management:
   ▫️ When a topic is created, Zookeeper stores the metadata about that topic, including the number of partitions and the replication factor.
   ▫️ Zookeeper maintains information about which brokers are responsible for each partition.
5. Leader Election:
   ▫️ For each partition, Zookeeper helps in electing a leader broker. The leader is responsible for handling all read and write requests for that partition.
   ▫️ Followers replicate data from the leader to ensure high availability and fault tolerance.
6. How a Write Request is Handled:
   ▫️ A producer sends a written request to the Kafka cluster, targeting a specific topic.
   ▫️ The broker receiving the request checks with the Zookeeper to identify the leader for the partition.
   ▫️ The written request is forwarded to the leader broker.
   ▫️ The leader writes the data to its local log and replicates it to follower brokers.
   ▫️ Once the followers acknowledge the writer, the leader confirms the successful writing to the producer.
7. How a Read Request is Handled:
   ▫️ A consumer sends a read request to the Kafka cluster, targeting a specific topic.
   ▫️ The broker receiving the request checks with the Zookeeper to identify the leader for the partition.
   ▫️ The read request is directed to the leader broker, which serves the data from its log.
8. Handling Broker Failures:
   ▫️ If a broker fails, Zookeeper detects the failure through its heartbeat mechanism.
   ▫️ Zookeeper triggers a leader re-election process for the partitions handled by the failed broker.
   ▫️New leaders are elected from the available followers, ensuring that the partitions remain available.

Visual Representation of Kafka with Zookeeper

Understanding Kafka with KRaft

KRaft (Kafka Raft) is Kafka’s new consensus protocol, designed to replace Zookeeper. It integrates metadata management directly within Kafka, leveraging the Raft consensus algorithm.

🔸KRaft Architecture

1. Kafka Cluster Setup:
   ▫️ A Kafka cluster consists of multiple brokers, similar to the Zookeeper setup.
   ▫️ Instead of a separate Zookeeper ensemble, Kafka brokers coordinate among themselves using the Raft protocol.
2. Controller Role:
   ▫️ In KRaft, one of the brokers acts as the controller, responsible for managing the cluster metadata and coordinating updates.
   ▫️ The controller is elected using the Raft consensus algorithm.
3. Integrated Metadata Management:
   ▫️ Metadata about brokers, topics, partitions, and replicas is stored and managed directly within the Kafka cluster.
   ▫️ This metadata is replicated across all brokers to ensure consistency and availability.
4. How a Write Request is Handled:
   ▫️ A producer sends a written request to the Kafka cluster, targeting a specific topic.
   ▫️ The broker receiving the request uses the metadata managed by the KRaft controller to identify the leader for the partition.
   ▫️ The written request is forwarded to the leader broker.
   ▫️ The leader writes the data to its local log and replicates it to follower brokers.
   ▫️ Once the followers acknowledge the writer, the leader confirms the successful write to the producer.
5. How a Read Request is Handled:
   ▫️ A consumer sends a read request to the Kafka cluster, targeting a specific topic.
   ▫️ The broker receiving the request uses the metadata managed by the KRaft controller to identify the leader for the partition.
   ▫️ The read request is directed to the leader broker, which serves the data from its log.
6. Handling Broker Failures:
   ▫️ If a broker fails, the KRaft controller detects the failure through the Raft protocol.
   ▫️ The Raft consensus algorithm triggers a leader re-election process for the partitions handled by the failed broker.
   ▫️ New leaders are elected from the available followers, ensuring that the partitions remain available.

How KRaft Overcomes Zookeeper Limitations

1. Operational Simplification:
   ▫️ With KRaft, there is no need for a separate Zookeeper ensemble. Metadata management is integrated within the Kafka brokers, reducing operational complexity.
2. Enhanced Scalability:
   ▫️ KRaft is designed to handle larger Kafka clusters more efficiently. By using the Raft consensus algorithm, KRaft ensures that metadata updates are consistent and scalable across the cluster.
3. Performance Improvements:
   ▫️ Direct management of leader elections and metadata within Kafka reduces latency. There is no need to communicate with an external Zookeeper service, which can be a performance bottleneck.
4. Improved Reliability:
   ▫️ The Raft consensus algorithm provides strong consistency guarantees. In the event of broker failures, KRaft quickly re-elects new leaders, ensuring high availability and minimal disruption.