Apache Spark vs Apache Flink – 8 useful Things You Need To Know

Difference Between Apache Spark and Apache Flink

Spark is an open source cluster computing framework developed by Apache Software Foundation which was originally developed by the University of California Berkeley and was donated to Apache Foundation later to make it open source.

Apache Flink is an open source framework for stream processing of data streaming applications for high availability, high performance, stability and accuracy in distributed applications.

Apache Spark is very fast and can be used for large-scale data processing which is evolving great nowadays. It has become an alternative for many existing large-scale data processing tools in the area of big data technologies.

Apache Flink is an open source software framework developed by Apache Software Foundation. The core component of Flink is distributed streaming and data processing engine that was written in Java and Scala.

Apache Spark can be used to run programs 100 times faster than Map Reduce jobs in Hadoop environment making this more preferable.

Apache Flunk provides low latency, high throughput in the streaming engine with fault tolerance in the case of data engine or machine failure.

Spark can also be run on Hadoop or Amazon AWS cloud by creating Amazon EC2 (Elastic Cloud Compute) instance or standalone cluster mode and can also access different databases such as Cassandra, Amazon Dynamo DB etc.,

Head To Head Comparison between Apache Spark vs Apache Flink (Infographics)

Below is the Top 8 Comparison between Apache Spark vs Apache Flink

Key Differences between Apache Spark vs Apache Flink

1. Spark is a set of Application Programming Interfaces (APIs) out of all the existing Hadoop related projects more than 30. Apache Flink was previously a research project called Stratosphere before changing the name to Flink by its creators.
2. Spark provides high-level APIs in different programming languages such as Java, Python, Scala and R. In 2014 Apache Flink was accepted as Apache Incubator Project by Apache Projects Group.
3. Spark has core features such as Spark Core, Spark SQL, MLib (Machine Library), GraphX (for Graph processing) and Spark Streaming and Flink is used for performing cyclic and iterative processes by iterating collections.
4. Both Apache Spark and Apache Flink are general purpose streaming or data processing platforms in the big data environment. Spark cluster mode can be used to stream and process the data on different clusters for large-scale data in order to process fast and parallel.
5. Spark Cluster mode will have applications running as individual processes in the cluster. Flink is a strong an high performing tool for batch processing jobs and job scheduling processes.
6. The components of Spark cluster are Driver Manager, Driver Program, and Worker Nodes. Flink has another feature of good compatibility mode to support different Apache projects such as Apache storm and map reduce jobs on its execution engine to improve the data streaming performance.
7. Spark has different types of cluster managers available such as HADOOP Yarn cluster manager, standalone mode (already discussed above), Apache Mesos (a general cluster manager) and Kubernetes (experimental which is an open source system for automation deployment). Flink has only data processing engine compared to Spark which has different core components.
8. Spark cluster component functions have Tasks, Cache, and Executors inside a worker node where a cluster manager can have multiple worker nodes. Flink architecture works in such way that the streams need not be opened and closed every time.
9. Spark and Flink have in-memory management. Spark crashes the node when it runs out of memory but is having fault tolerance. Flink has a different approach to memory management. Flink writes to disk when the in-memory runs out.
10. Both the Apache Spark and Apache Flink work with Apache Kafka project developed by LinkedIn which is also a strong data streaming application with high fault tolerance.
11. Spark can have sharing capability of memory within different applications residing in it whereas Flink has explicit memory management that prevents the occasional spikes present in Apache Spark.
12. Spark has more configuration properties whereas Flink has less configuration properties.
13. Flink can approximate the batch processing techniques and Spark has unified engine that can be run independently on top of Hadoop by connecting to many other cluster managers and storage platforms or servers.
14. The network usage of Apache Spark is less in the beginning time of the job when it is triggered that causes some delay in the execution of a job. Apache Flink uses the network from the beginning which indicates that Flink uses its resource effectively.
15. The less resource utilization in Apache Spark causes less productive whereas in Apache Flunk resource utilization is effective causing it more productive with better results.

Apache Spark vs Apache Flink Comparision Table

Conclusion – Apache Spark vs Apache Flink

Apache Spark and Apache Flink both are general purpose data stream processing applications where the APIs provided by them and the architecture and core components are different. Spark has multiple core components to perform different application requirements whereas Flink has only data streaming and processing capacity.

Depending on the business requirements, the software framework can be chosen. Spark exists since few years whereas Flink is evolving gradually nowadays in the industry and there are chances that Apache Flink will overtake Apache Spark.

To integrate with multiple frameworks Spark is rather preferred compared to Flink in order to support multiple applications in a distributed environment.

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This has been a guide to Apache Spark vs Apache Flink, their Meaning, Head to Head Comparison, Key Differences, Comparision Table, and Conclusion. You may also look at the following articles to learn more –

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