What is the difference between Flink and Spark?
Flink and Spark are two popular big data processing frameworks, and they have the following differences:
- Data processing model: Flink is an event-driven streaming processing framework that can process data streams in real-time and support stateful computation. Spark, on the other hand, is a batch processing framework that can handle offline datasets. Despite Spark also having streaming capabilities, it is implemented through micro-batch processing and not as real-time as Flink.
- Processing Engine: Flink leverages an underlying architecture called a “stream processing engine”, which allows Flink to provide low-latency processing, whereas Spark uses an abstract model called “Resilient Distributed Dataset (RDD)”. RDD is an immutable distributed collection of objects that can handle data efficiently in memory. However, due to the nature of RDD, Spark has relatively higher latency.
- State Management: Flink comes with a built-in distributed stream processing engine that can manage state information during stream computation processes. This allows Flink to handle stateful computations and support semantics for event time and processing time. In contrast, Spark requires the use of external storage to manage state.
- Scalability: Flink is able to horizontally scale on large clusters and handle very large data streams. Spark also has the capability to scale, but its performance relative to Flink’s is not as strong when processing large-scale stream data.
- Spark has a broader ecosystem, which includes modules like Spark SQL, Spark Streaming, MLlib, and GraphX. This allows users to perform a variety of data processing tasks in a unified framework. Flink’s ecosystem, although smaller, is also continuously growing.
In conclusion, Flink and Spark have some differences in data processing model, processing engine, state management, scalability, and ecosystem. Choosing the framework that best fits your needs requires considering specific application scenarios and requirements.
