November 21, 2022

Kafka Streams vs. Apache Flink

Apache Kafka

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While there are a lot of stream processing frameworks available, two of the most popular (and fast-growing) are Apache Flink and Kafka Streams API. Although initially created for different use cases, Apache Flink and Kafka Streams API have a lot of overlap in their application. While both can solve stateful and streaming challenges, their differences in deployment, architecture, and more are worth considering before making a decision.

As a user, it can be challenging to determine the right solution that meets your needs now, and in the future. That’s because switching between stream processing tools is not a trivial decision, and can set you back later.

Learn more about high-level differences between Kafka Streams vs. Flink to make the right choice for your team.

The main difference between Flink vs. Kafka Streams is that Flink is a data processing framework that uses a cluster model, whereas the Kafka Streams API is an embeddable library that eliminates the need for building clusters.

While both Kafka Streams and Flink come from the open source world and offer native stream processing, each has unique strengths and limitations. 

What Is Kafka Streams?

Kafka Streams API is a powerful, lightweight library and stream processing engine, designed to build standard java applications. This powerful and embeddable system can be used for microservices, reactive stateful applications, and event-driven systems. It is a native component of Apache Kafka, which allows it to be scalable and fault tolerant, as it relies on its distributed architecture.

Kafka Streams API has an embeddable library that eliminates the need for building clusters and allows it to be seamlessly integrated into your existing toolstack. Developers can focus on their application without needing to worry about deployment. Plus, teams get all the benefits of Kafka including, failover, scalability, and security.

Apache Flink was built from scratch as a large-scale data processing engine and stream framework. It was designed to focus on real-time data and stateful processing, making it an ideal solution for processing large amounts of data. It was the first open source framework that could deliver on throughput at scale (up to tens of millions of events per second), sub-second latency as low as 10s of milliseconds, and accurate results.

Flink runs self-contained streams in a cluster model that can be deployed using resource managers or standalone. Flink can consume streams and ingest data into streams and databases. With APIs and libraries available, Flink can act as a batch processing framework, which has been proven to run well, even at scale. Most commonly, Flink is used in combination with Apache Kafka as the storage layer. Flink is managed independently, allowing teams to get the best out of both tools.

Kafka Streams vs. Flink: Key Differences

To choose the right stream processing system, it is important to evaluate your choices across several criteria — deployment, ease of use, architecture, performance (throughput and latency), and more.

Architecture and Deployment

Apache Kafka uses a persistent publish/subscribe message broker system. The Kafka Streams API, which also uses a distributed computing solution using brokers, has an embedded database. The API library can integrate into an existing application. It can also be deployed over a cluster environment as a standalone, using containers, resource managers, or deployment automation tools like Puppet.

Flink uses a distributed computing framework. Its cluster network handles the deployment of an application either as a standalone cluster or using YARN, Mesos, or other container services, such as Docker or Kubernetes. Although you need a dedicated master node for coordination, which can add to Flink’s complexity.

Complexity and Accessibility

When it comes to ease of use, it all depends on who is using it. Kafka Streams and Flink are used by developers and data analysts, making their complexities relative.

Kafka Streams usually requires less expertise to get started and manage overtime for developers. It is very accessible to deploy standard Java and Scala applications with Kafka Streams. Additionally, Kafka Streams works out-of-the-box. Teams do not have to integrate any cluster manager to get started, reducing its overall complexity. For non-developers, this can be a pretty sharp learning curve.

Flink’s interface is easy to navigate and its intuitive documentation makes it quick to get started. But Flink is deployed on the cluster, which can be more complex. Usually this is managed by the infrastructure team. This can alleviate some of the complexity around set up for both developers and data scientists. Due to its flexible nature, Flink can be easily customized to support a variety of data sources, and comes with built-in support for multiple third-party sources.

Other Notable Differences

Some other considerations when looking at Kafka Streams vs. Flink:

  • Stream type: Kafka Streams only supports unbounded streams (streams with a start but no defined end). Flink, on the other hand, supports both bounded streams (defined start and end) and unbounded streams.
  • Maintenance: As mentioned earlier, Flink is deployed at the infrastructure level, meaning it is usually not maintained by developers and instead is managed by an infrastructure team. Kafka Streams are integrated into the application, and are usually managed by a business team.
  • Data sources: Where Flink can ingest data from multiple sources, like external files or other message queues, Kafka Streams are shackled to Kafka topics as the source. Both support multiple data types for sink/output.

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Kafka Streams vs. Flink Use Cases

Stream processing can be used across an organization, from user-facing applications to data analytics. While both Kafka Streams and Apache Flink can be used, their main difference comes down to where these frameworks reside — in a cluster with Flink or inside microservices with Kafka Streams.

Because they occupy different spaces, they can be used together. Kafka Stream's works well for microservices and IoT applications. Companies can build applications with the API to help them make real-time decisions. It lacks in analytics capability, which is where Flink can excel. This is why big companies like Uber and Alibaba are deploying it in their environments. Data can be processed quickly, allowing teams to make better decisions, fast.

When evaluating Kafka Streams vs. Flink, make sure to:

  • Review your use case and examine your tech stack. If you are looking to do something simple, there is no need to get a complicated stream framework. For example, if you are wanting a simple event-based alerting system, Kafka Streams works. But if you are looking at managing several data types across sources, Flink would be a better solution.
  • Look to your future. Sure, your team might only have a few features that need stream processing, but what about the future? If you are planning on introducing more event processing, aggregation, or streams, Flink offers a more advanced streaming framework. It is important to note, once you have invested and implemented a technology, it can be hard to change it later (not to mention costly).

Final Thoughts

There are reasons to compare Kafka Streams vs. Apache Flink. But it’s not a one-to-one comparison. Due to architecture differences, Kafka and Flink live in different areas of an organization. For many, this makes them complementary systems that can be deployed together. Users can get the best of both worlds.

Kafka Streams includes all the benefits of Kafka (performance, scale, reliability) and the API can be leveraged to create real-time applications. Flink can be deployed in existing clusters, giving teams the benefits of latency, throughput, checkpoints and other operational features, plus an intuitive UI.

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Additional Resources

Headshot for Rajesh Krishnamurthy

Rajesh Krishnamurthy

Enterprise Architect, OpenLogic by Perforce

Rajesh has been working in IT for the past 25 years, mostly focused on enterprise back-end data solutions, including data architecture. For the last 10 years, he has been working on various open source, big data stacks and has been instrumental in migrating legacy data pipelines over to big data stacks for various enterprises.