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November 29, 2023

Apache Kafka vs. Redis: Are They Truly Comparable?

Apache Kafka

Apache Kafka vs. Redis, while an often-searched topic, isn't the 1:1 comparison a lot of people seem to think it is. In this blog, we clear up some of that confusion with a comparison of Kafka vs. Redis, and an explanation of the unique problems they can solve.

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Overview of Apache Kafka and Redis

Kafka is an open source stream-processing platform, while Redis is a general purpose in-memory data store, so they serve very different functions within application data infrastructure.

What Is Apache Kafka?

Apache Kafka is an open-source stream-processing platform designed for high throughout, low latency processing.

Whereas traditional message brokers focus on a pattern of allowing consumer applications to work on one piece of data at a time, Kafka allows for the analysis of lots of individual pieces of inflight data.

This makes Kafka an ideal candidate for implementing streaming analytics and big data applications. Designed at LinkedIn to scale to hundreds of millions of messages, though not as lightweight a solution as ActiveMQ, it is ideal for working on astronomically large data sets.

What Is Redis?

Redis (Remote Dictionary Server) is a general purpose, in-memory data store that supports a variety of data types, including strings, hashes, lists, sets, and stores sets.

Redis was one of the first open source key caching solutions, which has led to its widespread adoption as a database cache. While Redis is primarily used for highly-available in-memory caching, it can also be used for chat, messaging, queues, session stores, and rich media streaming.

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Kafka vs. Redis: Features Comparison

Data PersistenceNonePersistent, with options for data retention
Message DeliveryAt-most-onceAt-least-once, exactly-once support possible via Transactional API
Message OrderingLimited (within a single channel)Guaranteed within a partition
ScalabilityHorizontal scaling via sharding/Redis clusterHorizontal scaling via partitioning
Message RetentionNo native persistenceConfigurable retention period
Message AcknowledgmentNo explicit acknowledgment mechanismAcknowledgment mechanism for consumers
Pub/Sub SupportNative support within channelsNative support with topics and partitions
Distributed SystemDistributed, sharding/Redis clusterDistributed, ZooKeeper/KRaft
Use CasesCaching, real-time analytics, leaderboardsEvent streaming, log aggregation, analytics
ComplexitySimpler, lightweightMore complex, designed for event streaming

A head-to-head features comparison, as shown in the table above, further underlines how the different problems Redis and Kafka were designed to solve is reflected in their differing capabilities and attributes.

Redis is often used when low latency and simplicity are crucial, such as in caching scenarios or real-time analytics. Redis does not inherently provide the same level of durability or fault tolerance as Kafka, and its use as a message broker may be more suitable for scenarios where these features are less critical. And while Redis itself doesn't support it, Redis Streams does provide support persistence and can support at-least-once delivery semantics. 

Kafka is designed for event streaming scenarios where durability, fault tolerance, and scalable message processing are key requirements, such as in log aggregation and building data pipelines. Kafka's partitioning and distributed nature make it well-suited for handling large volumes of data and supporting high through-put, fault-tolerant architectures. And Kafka Connect provides a robust plug-and-play client framework that supports numerous "sync" types.  

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Apache Kafka vs. Redis Use Cases

When teams think about Kafka vs. Redis in terms of use cases, they need to think about them as serving completely different purposes within their data infrastructure. In some cases, Kafka and Redis can even be used as complimentary pieces within that infrastructure (e.g. Kafka and Redis can be deployed together for real-time analytics use cases).

When to Use Kafka

Kafka an ideal candidate for implementing streaming analytics and big data applications. Designed at LinkedIn to scale to hundreds of millions of messages, though not as lightweight a solution as ActiveMQ, it is ideal for working on astronomically large data sets.

Kafka has a number of potential use cases that span across many verticals, including Internet of things (IoT), e-commerce, security, and financial institutions — all of which can benefit from real-time data analysis.  For those who only need to process a small amount of messages (up to several thousand), Kafka might be overkill.

It is also important to note that Kafka, while powerful, is also complex. It’s not easy to perform data transformations that involve a complex pipeline of producers and consumers. If your needs require Kafka, then you have a substantial amount of work ahead of you! 

When to Use Redis

Redis is highly scalable, and can scale well in most deployment architectures. Because it provides sharding and multiple types of replication, Redis offers a highly flexible and reliable option for teams who need an in-memory data store.

Historically, Redis has primarily been used as a enterprise-grade session cache, but more recently Redis has found a home in a variety of new use cases -- like inventory systems or fraud analysis. Because Redis was designed as a general purpose key storage, there are some areas where it can be outshined by alternatives (e.g. ElasticSearch for more search-focused datastores).

For teams considering Redis as a pub/sub message broker, it's important to note that it should not be considered a fully-functional message broker service due to it's lack of support for exactly-once delivery semantics.

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Final Thoughts

Finding the right open source software for your use case can be hard. That's especially true when you consider the sheer amount of open source projects available for use today. This tyranny of choice can be further complicated by the advertised features for projects that often don't match their ideal use cases.

At the end of the day, it's up to teams to perform their due diligence when selecting new pieces of open source software to ensure that the functionalities in that software are the ones best equipped to solve their specific use cases. For teams that don't have that expertise in-house, there are companies, like OpenLogic, that can provide guidance and support.

Editor's Note: This blog was originally published on October 31, 2022 and was updated with new content on November 29, 2023. 

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