The stream of open-source processing space is exploding currently, with several systems that are now becoming available presenting the users with various alternatives. In Apache Software Foundation, there are more than 10 streams for the processing projects, some are in the incubation and few of them graduated to the status of the top-level project.

In this post, we will mainly focus on discussing comparing the Flink and Kafka Streams with one other on the processing stream, and we also wish to attempt and provide clarity on a question that comes in your mind. The streams of Flink and Kafka were created with some different cases in your mind. While these also have some kind of overlap inapplicability, they usually are designed to solve the orthogonal problems and also have different sweet spots along with placement in a stack of data infrastructure. Before discussing the differences, let us quickly have a small glance about what are Apache Flink and Kafka Streams.

Apache Flink

The roots of Apache Flink are in the high-performance for the cluster computing, and the data processing the set of frameworks. The Flink also runs the self-contained computations of streaming which may also get deployed on the resources given by the resource manager. Flink jobs usually consume streams and it also produces data in the streams or databases. Flink is mainly used with the Kafka as an underlying layer of storage, but it is also is independent.

Before the coming up of Flink, the users related to the stream that are processing the frameworks need to make some of the hard choices and even trade-off either latency, all through and the result accuracy. Flink was the initial open framework of the source (and it is only one), which has been also demonstrated for delivering

(1) All through this in order of millions of events for each second in the moderate clusters.

(2) The latency of sub-second that may be as low as some 10s of the milliseconds.

(3) it is also exactly guaranteed that once semantics for the state of the application, and also exactly about end-to-end delivery through supported sources as well as sinks.

(4) Precise results in the presence of out of the order data arrival with their support for the time of the event.

Flink is mainly based on the cluster architecture having the master and the worker nodes. Moreover, the link clusters are available and they may be deployed standalone with resource managers like YARN and Mesos. Such kind of architecture allows Flink to use the lightweight as the checkpointing as the mechanism to guarantee the precise results if there are any kind of failures, as well it also allows simple and correct re-processing through the save points devoid of sacrificing latency and also the throughput. Flink has even proven to run much robustly in the production at quite a huge scale by various companies, powering the applications which are used each day by the end customers.


In contrast to the Flink, Streams API is known to be much powerful and embeddable stream for the processing engine to build the standard applications of Java for stream processing efficiently. Such kind of the Java applications is specifically well-suited, for instance, to build the reactive and wonderful applications, microservices along with the event-driven systems. As the native component of Kafka after version 0.10, it is a completely awesome stream for the processing solution which creates the top of the battle-tested foundation related to Kafka to make such stream processing applications much scalable, fault-tolerant, elastic, distributed as well as easy to build. On the other hand, a gap about Streams API fills are quite less to be focused analytics domain and others building the core applications along with the microservices that process the data streams.

Here, the goal of Streams API is mainly to simplify the processing of stream adequately to make it truly accessible as the mainstream model of application programming. To help in the goal, below mentioned are some of some deliberate decisions of design that are made in Streams API.

1) It is an embeddable library without any cluster, only Kafka and application. With Streams API you will be able to completely focus on building the applications which drive the business instead of building the clusters. It makes approachable to the application developers looking for stream processing, as it will seamlessly integrate along with the existing package of company.

2) It is completely integrated with the core abstractions in Kafka, and hence all strengths of Kafka such as failover, fault-tolerance, elasticity, security, and scalability— are available with the built-in to Streams API; However, Kafka is also battle-tested and it is even deployed at scale in several companies all around the world, permitting Streams API to create the great foundation

 3) It also simply introduces new concepts along with the functionality to permit for stream processing, like completely integrating abstractions of the streams and tables that you may simply use interchangeably to achieve, for instance, high performing join operations with regular queries.

How are Flink and Streams API Kafka different

The table below lists the most important differences between the two systems.

The fundamental and the key differences between the Flink and a Streams Kafka API program mainly lie in a way that they are usually deployed as well as managed (quite often it also has their set of implications about who owns such applications from the perspective of an organization and also how the parallel processing that includes the fault tolerance gets coordinated. They are mainly core differences between the Flink and a Streams Kafka API program as they are ingrained in the architecture of such two different systems.


Both, Flume systems and Apache Kafka offers the most reliable, scalable as well as high-performance for the purpose to handle some of the huge volumes of data with complete ease. On the other hand, Kafka is a much general kind of purpose system where various publishers, as well as subscribers, will be able to share diverse topics.

Kitty Gupta