Kafka Streams vs Samza: What are the differences?

Introduction: Apache Kafka Streams and Apache Samza are both distributed stream processing frameworks that provide developers with the capability to process and analyze real-time data streams. While they have similarities, there are key differences between Kafka Streams and Samza that distinguish them in terms of use cases and features.

1. Architecture: Kafka Streams is a client library that allows developers to build stream processing applications that directly interface with Apache Kafka. In contrast, Samza is a full-fledged stream processing framework that provides a distributed processing engine, job coordination, and fault tolerance capabilities. The architecture of Samza is more suitable for complex processing requirements and high-throughput scenarios.

2. Integration with External Systems: Kafka Streams is tightly integrated with Apache Kafka, making it easier to build stream processing applications with data streams stored in Kafka topics. On the other hand, Samza offers more flexibility in terms of integration with external systems through its pluggable input/output system. This allows developers to connect Samza applications with various data sources and sinks beyond Kafka.

3. State Management: Kafka Streams provides built-in state management capabilities that allow developers to store and query state within the stream processing application. In comparison, Samza requires developers to manage states explicitly by providing custom state stores or leveraging external storage systems like Apache HBase or Apache Cassandra. This difference affects the complexity and scalability of stateful stream processing applications.

4. Processing Model: Kafka Streams leverages the concept of message timestamps to provide event-time processing semantics, allowing developers to handle out-of-order data events efficiently. In contrast, Samza's processing model is based on processing-time semantics by default, making it easier to implement real-time processing tasks that do not require event-time considerations. The choice of processing model impacts the consistency and correctness of stream processing results.

5. Deployment and Scalability: Kafka Streams applications can be deployed as standalone Java applications or as microservices within a larger ecosystem. This flexibility simplifies deployment and scaling of stream processing tasks. In contrast, Samza applications need to be deployed on a cluster managed by Apache YARN, which provides centralized resource management and scalability capabilities. This difference affects the operational complexity and resource utilization efficiency of stream processing deployments.

6. Community and Ecosystem: Kafka Streams benefits from the wide adoption of Apache Kafka and a vibrant community that actively contributes to its development and support. This results in a rich ecosystem of tools, libraries, and resources for Kafka Streams users. While Samza also has an active community, it may have a smaller user base compared to Kafka Streams, leading to differences in available resources and community support.

In Summary, Apache Kafka Streams and Apache Samza differ in their architecture, integration capabilities, state management, processing models, deployment options, and community ecosystems, catering to diverse stream processing requirements.