Kafka vs Redis: A Comprehensive Comparison for 2024

With the ever-changing landscape of real-time data processing, the requirement for robust and highly scalable data streaming platforms has increased more than ever. Apache Kafka vs Redis are two critical contenders in this space.

In this article, we provide a fuller comparison between Kafka and Redis that touches on features, use cases, performance, and suitability for different applications in 2024.

Overview of Kafka and Redis

What is Kafka?

Apache Kafka is an open-source stream-processing platform accommodating real-time data processing and event streaming. It is written in Scala and Java and was developed by the Apache Software Foundation. In other words, it was created to establish one real-time, unified, high-throughput, and low-latency platform dealing with real-time data feeds. The system ingests data streams from multiple sources, processes them based on specified requirements, and then reliably and scalably distributes the processed data to many consumers.

What is Redis?

Redis is an in-memory, open-source data structure store used as a database management system and message broker. Redis is also used as a cache. Due to its fast performance and flexibility, Redis natively supports several data structures: strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, geospatial indexes, and streams. Redis finds application in scenarios often requiring fast or real-time data processing and analytics.

Redis vs Kafka: Core Differences

Architecture

• Kafka: The significant components of Kafka architecture include producers, consumers, topics, and brokers. Messages for the producer are published in Topics. Consumers subscribe to these topics to receive notifications. Kafka Brokers save messages while ensuring high availability and fault tolerance through replication.
• Redis: It has a master-slave architecture, where the master node processes write operations and slave nodes process read operations. Redis hosting supports clustering, which allows data partitioning across multiple nodes to improve scalability and availability.

Data Storage and Persistence

• Kafka: Kafka extensively uses durable message storage, so messages persist on a disk with configurable retention policies. These policies guarantee data persistence for a specified time or until the data reaches a specific size. This makes it applicable to use cases needing reliable and sustained data storage. 
• Redis: Being an in-memory store, Redis provides very fast read and write operations. In Redis, data persistence is optional; it provides data persistence by taking snapshots (RDB) or append-only files (AOF). This makes Redis a perfect fit for apps that need high speed and can afford to lose some data in return for speed.

Performance

• Kafka: Kafka streams high volumes of data with high throughput, is scalable, and fault-tolerant. It supports millions of messages per second with latency as low as milliseconds, making it perfect for handling real-time analytics, log aggregation, and event sourcing.
• Redis: Redis is swift, having sub-millisecond response times. Also, being an in-memory solution enables handling large numbers of operations per second, so it’s perfect for caching, real-time web analytics, and leaderboard applications.

Kafka and Redis Streams

Kafka Streams

This is a Java client library that builds real-time, scalable applications and microservices—connected or standalone—that store their input and output data in Kafka clusters. It exposes a high-level Application Programming Interface for stream processing: transformation, aggregation, joining, and windowing. Due to tight integration with the Kafka functionality, it inherits its features of being highly scalable and fault-tolerant with stream processing.

Redis Streams

Redis Stream is a data structure that debuted in Redis 5.0 to handle data streams. It ingests, processes, and delivers considerable messages in chronological order. Support from Redis Streams includes consumer groups; these involve multiple clients reading messages from a stream concurrently with built-in mechanisms for message acknowledgment and fault tolerance.