Apache Storm vs Kafka Streams: What are the differences?

Introduction

Apache Storm and Kafka Streams are both widely used open-source frameworks for processing real-time data in big data applications. While they have some similarities in terms of their ability to handle streaming data, there are several key differences between them.

1. Architecture: Apache Storm follows a distributed and fault-tolerant architecture known as the master/worker model, where a cluster manager (master) assigns tasks to a set of worker nodes. On the other hand, Kafka Streams provides a simple library that runs on top of the Kafka broker, enabling applications to process data directly within the Kafka cluster. This difference in architecture impacts various aspects of their use cases, scalability, and fault tolerance.

2. Data Processing Paradigm: Apache Storm focuses on stream processing and provides a low-level API for handling complex event processing, allowing users to build custom processing logic. In contrast, Kafka Streams is designed as a lightweight stream processing library that leverages Kafka's messaging model and provides high-level abstractions such as streams and tables. This makes Kafka Streams more user-friendly for developers who prefer a declarative programming approach.

3. Fault Tolerance: Both Apache Storm and Kafka Streams offer fault tolerance capabilities, but the mechanisms differ. Apache Storm ensures fault tolerance through parallelism and replication of processing tasks across worker nodes. In case of failures, the failed tasks are automatically reassigned to other available workers. On the other hand, Kafka Streams leverages the fault tolerance provided by Kafka itself, which includes replication and data durability through distributed commit logs. If a Kafka Streams application fails, it can restart and resume processing from where it left off, ensuring fault tolerance.

4. Scalability: Apache Storm allows users to scale their processing capabilities by adding more worker nodes to the cluster dynamically. This horizontal scalability is a crucial feature in scenarios where the rate of incoming data increases. Kafka Streams, being tightly integrated with Kafka, leverages Kafka's scalability by automatically parallelizing the processing tasks based on the number of Kafka partitions. This makes it easy to scale the Kafka Streams application simply by increasing the number of Kafka partitions.

5. State Management: Apache Storm does not provide built-in support for state management. Instead, users have to rely on external systems like Apache HBase or Apache Cassandra to handle the state storage. In comparison, Kafka Streams offers built-in state management, allowing the applications to maintain and query the internal state. This simplifies the overall system architecture as users do not need to manage an external state store separately.

6. Integration with Ecosystem: Apache Storm is designed to work with various data sources and sinks, including Kafka, Hadoop, and databases, making it a versatile solution for data ingestion and processing. Kafka Streams, as a part of the Kafka ecosystem, integrates seamlessly with Kafka, enabling users to read from Kafka topics and write back to Kafka topics. This tight integration makes Kafka Streams an ideal choice for applications where Kafka is already being used as a messaging platform.

In summary, Apache Storm and Kafka Streams differ in their architecture, data processing paradigm, fault tolerance mechanisms, scalability options, state management, and integration with the broader ecosystem. While Apache Storm provides a low-level framework for complex event processing, Kafka Streams offers a lightweight stream processing library with high-level abstractions, making it easier to use. Both frameworks have their strengths and can be chosen based on specific application requirements and the existing technology stack.