Apache Spark vs Apache Flink vs Apache Storm
- Apache Spark: It is a powerful, in-memory data processing engine that is designed to be fast, easy to use, and flexible. It can handle batch, interactive, and streaming workloads, and can also be used for SQL, machine learning, and graph processing. Spark also has a built-in cluster manager, making it easy to set up and scale.
- Apache Flink: It is a stream processing framework that is designed to handle both batch and streaming workloads. It is known for its low-latency and high-throughput performance, making it well-suited for use cases such as real-time analytics and complex event processing. Flink also provides a feature-rich API and robust state management, making it easy to develop and maintain streaming applications.
- Apache Storm: It is a distributed real-time computation system that is designed to handle high-throughput, low-latency data streams. It is based on a simple programming model and can process millions of events per second. Storm is well-suited for use cases such as real-time analytics, online machine learning, and continuous computation. Storm also provides built-in support for fault-tolerance, making it easy to build reliable, real-time applications.
Here’s a comparison of Apache Spark, Apache Flink, and Apache Storm in tabular format:
| Feature | Apache Spark | Apache Flink | Apache Storm |
|---|---|---|---|
| Type of Processing | Batch | Batch & Stream | Stream |
| Latency | High | Low | Low |
| Data Processing Model | Micro-batch | Stream | Stream |
| Memory Management | Automatic | Manual | Automatic |
| Fault Tolerance | Yes | Yes | Yes |
| State Management | In-memory | In-memory | Zookeeper |
| API | RDD, DataFrame, SQL | DataStream, Table | Trident |
| Machine Learning | MLlib | Flink MLlib | N/A |
| Use Cases | Batch processing, SQL, Streaming, Machine Learning | Streaming, Event-Driven, Machine Learning, Complex Event Processing | Streaming, Real-time Processing, Complex Event Processing |