Daily Archives: April 9, 2018

Apache Flink VS Apache Spark

In the Big Data landscape, Spark and Apache Flink are major technologies. There is a confusion about each of their work and purpose. Let us see them in detail:

  • Streaming Data

This implies that data there is an ongoing flow of data. In batch systems, processing streaming data offers technical complications that are not inherent. The technological advancement over here is the ability offered to do that by making streaming products similar to Spark, Flink, Kafka, and Storm which is quite important. The decisions can be made easily as it permits the organizations based on what is happening currently.

  • What is the purpose of Flink and Spark?

A replacement for the batch-oriented Hadoop System is none other than Apache Spark. It has a component termed as Apache Spark Streaming. Streaming can be done only with the help of Apache Flink. In-memory databases are done by both Flink and Spark that do not force their data to store. You cannot analyze the current data as there is no reason to write it for storage. There are other real-time systems like Spark/Flink that are very much sophisticated similar to a Syslog connection which is built over a TCP built inside every Linux system.

  • What is the difference between Apache Spark and Apache Flink and specifically Apache Spark streaming?

Ideally, Spark and Flink would make it same. The prime difference is that Flink was constructed from the ground up as a streaming product. Streaming has been included by Spark into their product.

Let us understand the technicalities of both

  • Spark Micro Batches

There are discrete chunks of data that is divided by Spark called micro batches. Then it again starts with a continuous loop. A checkpoint is done on streaming data for breaking the finite sets in Flink. At this checkpoint, the data which is coming is not lost when considering this checkpoint it is preserved for future in both the products. In most of the cases, you will find some lag time processing live data so ideally splitting it into sets does not matter.

  • Flink Usage for batch operations

Similar to Hadoop, a spark was built for running over static data sets. The streaming source is stopped when Flink comes into the picture. The data is processed by Flink in the same way irrespective of it being finite or infinite. Dstreams is used by Spark for streaming data and RDD for batch data.

  • The Flink Streaming Model

For projecting that a program processes streaming data implies that it unveils a file and never closes it. This is similar to keeping a TCP socket open and Syslog works for this purpose. On the contrary, batch programs, open a file, process it and then close it. It has created a way to checkpoint streams as per Flink without having to open or close them. After that simply try to run a type of iteration that assists their machine learning algorithms to keep on the run in a faster way when compared to Spark.

  • Flink versus Spark in Memory Management

When compared to memory management, flink has a different approach. When memory is full, Flink pages out to disk and this happens with Linux and Windows. When Spark runs out of memory, Spark crashes. But there are no means of losing the data as it is fault tolerant.

  • Spark for Other Streaming Products

Kafka is used for working on both Flink and Spark and the streaming product is written on LinkedIn. Flink combines its work with Storm topologies.

  • Cluster Operations

On a cluster, Spark or Flink can run locally. For running Flink or Hadoop on a cluster one normally uses Yarn. With the help of Mesos, Spark is usually run. You need to download the version of Spark if you want to use Spark with Yarn that has been compiled with Yarn support.

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Reference site: Developintelligence

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