This article aims to compare and contrast Akka Stream Kafka and Kafka Streams, two frameworks commonly used for stream processing.

While both frameworks serve the purpose of processing data streams, they exhibit distinct characteristics.

Kafka Streams is a client library designed specifically for handling unbounded data streams, utilizing RockDB as its underlying datastore. However, it lacks native support for back-pressure and may encounter scalability and memory consumption issues.

Conversely, Akka Streams offers greater flexibility in choosing persistence options and better support for the Command Query Responsibility Segregation (CQRS) pattern and back pressure.

Although it requires a deeper understanding of stream processing concepts, Akka Streams demonstrates improved scalability and performance, particularly for larger datasets.

Consequently, Kafka Streams may be more suitable for beginners seeking an introduction to streaming concepts, while Akka Streams may be preferred for advanced usage, particularly when working exclusively with Kafka.

Comparison

When comparing Kafka Streams and Akka Streams, it is important to note the following:

Kafka Streams:

• Suitable for beginners to understand streaming concepts.
• Limitations in querying data with RockDB.
• Challenging to implement a CQRS solution, as it lacks native back-pressure support.

Akka Streams:

• Provides more flexibility in choosing persistence options.
• Better support for CQRS and back pressure.
• Better scalability and back-pressure handling.
• Requires a deeper understanding of stream processing.
• May require switching from Kafka Streams at some point.
• Better performance after a dataset size of 100,000.
• Has committed code to improve performance in Kafka.

Features

The features of the two stream processing frameworks can be compared.

Kafka Streams is a client library that processes unbounded data by reading from Kafka topics, performing computations, and writing the results to new topics. It utilizes RockDB as its datastore for stateful scenarios, but this can limit querying capabilities.

Kafka Streams lacks native back-pressure support, which can lead to performance issues and unresponsiveness to HTTP requests.

On the other hand, Akka Streams offers a more flexible approach as it is not coupled with a single persistence option. It has direct support for the CQRS pattern and provides better scalability and back-pressure handling.

Akka Streams also has better performance after a dataset size of 100,000 and has committed code to improve performance specifically in Kafka.

Performance

In terms of performance, a comparison between the two stream processing frameworks can be made.

Kafka Streams uses RockDB as its datastore, which can result in high memory consumption and GC latencies. Additionally, Kafka Streams lacks native back-pressure support, causing performance issues and potential unresponsiveness to HTTP requests due to queue growth.

On the other hand, Akka Streams has better scalability and back-pressure handling. It provides better performance after a dataset size of 100,000 and has committed code to improve performance specifically in Kafka. Akka Streams also offers more flexibility in choosing persistence options and has direct support for the CQRS pattern.

However, it should be noted that Akka Streams requires a deeper understanding of stream processing concepts compared to Kafka Streams.