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Apache Storm vs Apache Flink

January 25, 2022

Introduction

Stream processing has become an essential requirement for many organizations to handle real-time data analysis. Apache Storm and Apache Flink are two of the most popular open-source stream processing frameworks. Both tools are known for their ability to handle high data throughput and provide low-latency processing capabilities. In this article, we will compare Apache Storm and Apache Flink and provide insights into their strengths, weaknesses, and differences.

Overview

Apache Storm was initially released in September 2011, while Apache Flink was released in February 2014. Apache Storm processes data in real-time, whereas Apache Flink processes data in both batch and stream processing modes. Both tools support fault-tolerance, scalable, and distributed processing capabilities.

Performance

Apache Storm and Apache Flink are known for their exceptional performance. Apache Flink uses a pipelined execution engine that runs operations in parallel. It can perform joins, aggregations, and windows in one pass, which helps in reducing the processing time. In contrast, Apache Storm processes tuples (data items) in a sequence, providing a lower throughput as compared to Apache Flink.

Scalability

Apache Storm and Apache Flink are highly scalable, allowing users to increase cluster size as per their processing needs. Both tools provide horizontal scaling, which means adding nodes to the cluster to improve processing capabilities. However, Apache Flink supports dynamic scaling, which means users can add or remove nodes during runtime, while Apache Storm does not support dynamic scaling.

Language Support

Apache Storm supports multiple programming languages such as Java, Python, and Clojure, while Apache Flink supports Java, Scala and Python, making it easier for developers to choose their preferred language.

Use Cases

Apache Storm is best suited for real-time processing applications that require low-latency processing, such as fraud detection, monitoring social media, and analyzing website traffic. In contrast, Apache Flink is best suited for processing data in both batch and real-time, making it perfect for applications such as ETL pipelines, financial analytics, and predictive analytics.

Conclusion

Both Apache Storm and Apache Flink are excellent tools for stream processing. However, the choice between the two will depend on the specific use case, as both have their strengths and weaknesses. Apache Storm provides low-latency processing and is suitable for real-time stream processing applications, whereas Apache Flink supports both batch and stream processing and is great for applications that require versatile processing capabilities.

References

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