Spark Vs Flink
Businesses today need to pick up the pace of changing market conditions, wherein the data and information are being processed at rocket speed. Since processing the big volumes of data is not the only thing that can suffice, it is also necessary to get the results faster. This has increased the demand for stream processing tremendously.
One can store, acquire, analyze and process big data in many ways. Every big data source has different characteristics, like the frequency, volume, velocity, type, and veracity of the data. When Big Data is processed and stored, additional dimensions come into existence. Often, masses of structured and semi-structured historical data is stored (Volume + Variety). On the other side, stream processing is used for fast data requirements (Velocity + Variety). Both complement each other very well.
Stream processing is designed to analyze and act on real-time streaming data, using “continuous queries”. Essential to this is Streaming Analytics, or the ability to continuously calculate mathematical or statistical analytics within the stream.
Stream processing solutions are designed to handle high volume in real time with a scalable, highly available and fault-tolerant architecture. This enables analysis of data in motion.
The demand for faster data processing has increased tremendously and so is the way real-time streaming data processing appears to be. Giving way to make Apache Spark and Apache Flink a point of discussion.
What is Apache Spark?
Apache Spark is a powerful open source processing engine built around speed, ease of use, and sophisticated analytics. Since its release, Spark is being rapidly adopted by enterprises across a wide range of industries. Internet powerhouses such as Yahoo, Baidu, and Tencent, have eagerly deployed Spark at massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. It has quickly become the largest open source community in big data, with over 750 contributors from 200+ organizations.
What is Apache Flink?
Apache Flink is a big data processing tool and it is known to process big data quickly with low data latency and high fault tolerance on distributed systems on a large scale. Its defining feature is its ability to process streaming data in real time. The name Flink is appropriate because it means agile. Even its logo has the squirrel which suggests the virtues of agility, nimbleness and speed.
Spark Vs Flink: Comparison between Spark and Flink
Though there are a few similarities between Spark and Flink, for example, their APIs and components are almost similar, the similarities do not matter much when it comes to data processing. Spark and Flink are both general-purpose data processing platforms and top level projects of the Apache Software Foundation (ASF). They have a wide field of application and are usable for dozens of big data scenarios. Both are capable of running in standalone mode and share a strong performance.
#### Given below is a comparison between Spark vs Flink
While Apache Spark is still being used in a lot of organizations for big data processing, Apache Flink has been coming up fast as an alternative. As the matter of fact, the debate revolves around whether it has the potential to replace Apache Spark because of its ability to process streaming data real time. So far the grounds look stronger for Spark as Flink is yet to be put to widespread tests. Also, considerably enough the batch processing capabilities of Apache Spark is still relevant. So to say, it still is too early to decide which one is stronger and which might falter.
||Processes data in batch mode. And does it with chunks of data, known as RDDs. So, a minimum data latency is always there with Spark.
||Processes streaming data in real time. And does it, rows after rows of data in real time. And there is no minimum data latency.
||Takes more time to process.
||Processes faster because of its pipelined execution.
||Supports data iterations in batches.
||It can natively iterate its data by using its streaming architecture.
||It has adopted microbatching.
||It has adopted a continuous flow, operatorbased streaming model.
||Looks at streaming as fast batch processing.
||Looks at batch processing as the special case of stream processing.
||Though ML algorithm is a cyclic data flow it is represented as direct acyclic graph inside the Spark.
||But for Flink, it takes little bit different approach. It supports controlled cyclic dependency graph in run time. This makes them to represent the ML algorithms in a very efficient way.
||In Spark one no longer has to turn to a technology like Apache Storm if one requires low-latency responsiveness
||By Flink one gets the benefit of being able to use the same algorithms in both streaming and batch modes. In addition, it takes the approach that a cluster should manage itself rather than user tuning it.
||Newer versions of Spark have their memory management system, however, they yet to get mature.
||It has its own memory management system, separate from Java’s garbage collector. By managing memory explicitly, Flink almost eliminates the memory spikes.
||In Spark, for iterative processing, each iteration has to be scheduled and executed separately.
||It can be instructed to only process the parts of the data that has actually changed, thus significantly increasing the performance of job.