Kafka vs Zookeeper: What are the differences?

Introduction

Here is the comparison between Kafka and Zookeeper.

1. Scalability and Fault-tolerance: Kafka is designed to provide high scalability and fault-tolerance by distributing and replicating data across multiple nodes. It can handle large amounts of data and easily scale horizontally by adding more brokers to the cluster. On the other hand, Zookeeper is a centralized service for maintaining configuration information and providing distributed coordination. It is not designed to store large amounts of data but rather focus on providing highly reliable and available coordination services.

2. Data Handling: Kafka is a distributed streaming platform that handles real-time data streams and stores them in a fault-tolerant way. It allows users to publish and subscribe to streams of records, process them in real-time, and store them in a distributed manner. Zookeeper, on the other hand, is not primarily focused on data handling but instead provides coordination services such as distributed locks, leader election, and configuration management.

3. Message Ordering: Kafka guarantees the order of messages within a partition, meaning that messages published to the same partition will be stored and delivered in the same order. This makes it suitable for applications that require strict message ordering. Zookeeper, on the other hand, does not provide ordering guarantees for its nodes. Each node has its own state and can change independently, which makes it less suitable for applications that require strict ordering.

4. Use Cases: Kafka is commonly used for building real-time streaming applications and data pipelines, as it can handle high throughput and low-latency requirements. It is often used for log aggregation, event sourcing, and stream processing. Zookeeper, on the other hand, is primarily used for maintaining configuration information and providing coordination services. It is commonly used in distributed systems for tasks such as service discovery, distributed locks, and leader election.

5. Client Interaction: Kafka interacts with clients through a publish-subscribe model, where producers publish messages to topics and consumers subscribe to topics to receive messages. It provides a high-level API with various client libraries available in different programming languages. Zookeeper, on the other hand, provides a simple file-system-like API for clients to create, read, update, and delete znodes (nodes in the Zookeeper namespace). It also supports notifications and watches for changes to znodes.

6. Architecture: Kafka uses a distributed, partitioned, and replicated commit log design. It consists of multiple brokers organized into clusters, with each broker handling a subset of the total load and replicating data across multiple brokers for fault-tolerance. Zookeeper, on the other hand, follows a centralized architecture with a leader node and multiple follower nodes. It stores its data in memory for faster access and uses a consensus algorithm called ZAB (Zookeeper Atomic Broadcast) for maintaining consistency among the nodes.

In summary, Kafka is a distributed streaming platform suitable for handling real-time data streams and building data pipelines, while Zookeeper is a centralized service for providing distributed coordination and maintaining configuration information. Kafka focuses on scalability, fault-tolerance, and high throughput, while Zookeeper focuses on reliability, availability, and coordination services.