Flink Vs. Spark: Difference Between Flink and Spark

Introduction

A majority of successful businesses today are related to the field of technology and operate online. Their consumers’ activities create a large volume of data every second that needs to be processed at high speeds, as well as generate results at equal speed. These developments have created the need for data processing like stream and batch processing.

With this, big data can be stored, acquired, analyzed, and processed in numerous ways. Thus, continuous data streams or clusters can be queried, and conditions can be detected quickly, as soon as data is received. Apache Flink and Apache Spark are both open-source platforms created for this purpose.

However, as users are interested in studying Flink Vs. Spark, this article provides the differences in their features.

What is Apache Flink?

Apache Flink is an open-source framework for stream processing and it processes data quickly with high performance, stability, and accuracy on distributed systems. It provides low data latency and high fault tolerance. The significant feature of Flink is the ability to process data in real-time. It was developed by the Apache Software Foundation.

What is Apache Spark?

Apache Spark is an open-source cluster computing framework that works very fast and is used for large scale data processing. It is built around speed, ease of use, and sophisticated analytics, which has made it popular among enterprises in varied sectors.

It was originally developed by the University of California, Berkeley, and later donated to the Apache Software Foundation.

Flink Vs. Spark

Both Apache Flink and Apache Spark are general-purpose data processing platforms that have many applications individually. They can both be used in standalone mode, and have a strong performance.

They have some similarities, such as similar APIs and components, but they have several differences in terms of data processing. Given below is the list of differences when examining Flink Vs. Spark.

Flink	Spark
The computational model of Apache Flink is the operator-based streaming model, and it processes streaming data in real-time. It uses streams for all workloads, i.e., streaming, SQL, micro-batch, and batch.  In Flink, batch processing is considered as a special case of stream processing.	The computational model of Apache Spark is based on the micro-batch model, and so it processes data in batch mode for all workloads. It is operated by using third party cluster managers. It looks at streaming as fast batch processing. This is done with chunks of data called Resilient Distributed Datasets (RDDs).  It is not efficient to use Spark in cases where there is a need to process large streams of live data, or provide the results in real-time.
There is no minimum data latency in the process. It comes with an optimizer that is independent of the actual programming interface.	It has higher latency as compared to Flink. If there is a requirement of low-latency responsiveness, now there is no longer the need to turn to technology like Apache Storm.
The data processing is faster than Apache Spark due to pipelined execution.  By using native closed-loop operators, machine learning and graph processing is faster in Flink.	In Spark, jobs are manually optimized, and it takes a longer time for processing.
It also has lesser APIs than Spark.	It is easier to call and use APIs in this case.
The programming languages provided are Java and Scala.	High-level APIs are provided in various programming languages such as Java, Scala, Python, and R.
Flink provides two dedicated iterations- operation Iterate and Delta Iterate. It can iterate its data because of the streaming architecture. By supporting controlled cyclic dependency graphs in run time, Machine Learning algorithms are represented in an efficient way.	The iterative processing in Spark is based on non-native iteration that is implemented as normal for-loops outside the system, and it supports data iterations in batches. But each iteration has to be scheduled and executed separately.  The data flow is represented as a direct acyclic graph in Spark, even though the Machine Learning algorithm is a cyclic data flow.
The overall performance is great when compared to other data processing systems. The performance can further be increased by instructing it to process only the parts of data that have actually changed. Because of minimum efforts in configuration, Flink’s data streaming run-time can achieve low latency and high throughput. The user also has the benefit of being able to use the same algorithms in both modes of streaming and batch.	Spark takes a longer time to process as compared to Flink, as it uses micro-batch processing. But it has an excellent community background, and it is considered one of the most mature communities.
It also has its own memory management system, distinct from Java’s garbage collector. It can eliminate memory spikes by managing memory explicitly.	Spark now has automated memory management, and it provides configurable memory management. But the newer versions’ memory management system has not yet matured.
Apache Flink follows the fault tolerance mechanism based on Chandy-Lamport distributed snapshots. It is lightweight, which helps to maintain high throughput rates and provides a strong consistency guarantee.	With Spark Streaming, lost work can be recovered, and it can deliver exactly-once semantics out of the box without any extra code or configuration.
The Window criteria is record-based or any customer-defined.  Duplication is eliminated by processing every record exactly one time.	The Window criteria in Spark is time-based.  Even here, duplication is eliminated by processing every record only one time.

Also Read: Spark Project Ideas & Topics

Conclusion

Both Flink and Spark are big data technology tools that have gained popularity in the tech industry, as they provide quick solutions to big data problems. But when analyzing Flink Vs. Spark in terms of speed, Flink is better than Spark because of its underlying architecture.

On the other hand, Spark has strong community support, and a good number of contributors. When comparing the streaming capability of both, Flink is much better as it deals with streams of data, whereas Spark handles it in terms of micro-batches.

Through this article, the basics of data processing were covered, and a description of Apache Flink and Apache Spark was also provided. The features of both Flink and Spark were compared and explained briefly, giving the user a clear winner based on the speed of processing. However, the choice eventually depends on the user and the features they require. 

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