Kafka vs Kestrel: What are the differences?

Introduction

In this article, we will highlight the key differences between Kafka and Kestrel.

1. Architecture: Kafka is developed by LinkedIn and is a distributed messaging queue system, designed to handle large amounts of data efficiently. It stores data on disk and allows for data replication across multiple servers. On the other hand, Kestrel is a simple, distributed message queuing system developed by Twitter which stores messages in memory, making it faster but with limited storage capacity.

2. Message Persistence: Kafka persists messages on disk, providing durability and fault tolerance. This makes Kafka suitable for handling large volumes of data where data loss is unacceptable. In contrast, Kestrel stores messages in memory, which can lead to message loss if the server crashes before messages are written to disk.

3. Scalability: Kafka is designed for horizontal scalability, allowing it to handle high message throughput across multiple servers. It can distribute messages across partitions for parallel processing. Kestrel, while also scalable, may require sharding to distribute the message load efficiently, which can add complexity to the system.

4. Message Retention: Kafka allows customizable message retention policies, where messages can be stored for a defined period or until a certain storage limit is reached. Kestrel, being memory-based, has limited storage capacity and may drop messages once the limit is reached, leading to message loss.

5. Consumer Groups: Kafka supports the concept of consumer groups, allowing multiple consumers to read from different partitions of a topic simultaneously. This feature enables Kafka to scale efficiently and handle high message traffic. Kestrel lacks native support for consumer groups, making it less suited for scenarios requiring parallel message processing by multiple consumers.

6. Ecosystem: Kafka has a robust ecosystem with support for various programming languages, connectors, and integrations with other data processing frameworks like Apache Spark and Flink. Kestrel, being a simpler system, may have limited integrations and ecosystem support compared to Kafka.

In Summary, Kafka and Kestrel differ in architecture, message persistence, scalability, message retention, consumer group support, and ecosystem, making them suitable for different use cases based on specific requirements.