Spark vs MapReduce
The Spark framework and MapReduce architecture are both used by the Hadoop Distributed File System (HDFS) to process big data that has been broken down into multiple tasks that are spread out over several nodes in a cluster (also called “parallel processing”). This article will go over both frameworks, explaining the pros and cons of each, as well as their differences, and how it all applies to managing and retrieving big data on a Hadoop cluster.
Introduction to Spark
Apache Spark (not to be confused with the SPARK 2014 programming environment for Ada) is written in the Scala (“scalable language”) which is a statically-typed, compiled programming language that’s largely inspired by the Java programming language. Scala was even designed to be compiled into Java Byte Code and has several compiler options, such as scalac and sbt.
Spark also has wrappers and bindings in other languages, like PySpark for Python or EclairJS and Node.js packages, like
Why use Apache Spark with Hadoop
Spark is built upon the Hadoop’s open-source HDFS, and has a plethora of components, APIs, and libraries to help manage and access your big data stored on a Hadoop cluster. One of the more popular ones is its Spark SQL component that will allow you analyze data and run queries that closely resemble SQL syntax.
Using Spark without Hadoop
It’s not required to have Hadoop installed to use Spark, and both Spark and the Scala language can be used independent of Hadoop. However, some features of Spark, like the Parquet file format, will require Hadoop, and Spark doesn’t have its own storage, so it’s dependent on services such as Hadoop’s HDFS or Amazon S3.
Introduction to MapReduce
MapReduce is an older coding paradigm, created in 2004, that was originally designed to work with the Google File System (GFS), but was soon adapted by Apache to work with Hadoop. Its name comes from its designed functionality of mapping big data while performing “Reduce” operations in parallel programming.
Like Apache Spark, MapReduce can be used with Scala, as well as a myriad of other programming languages like C++, Python, Java, Ruby, Golang, as well as Scala, and it is used with RDBMS (Relational Database Management Systems) like Hadoop as well as NoSQL databases like MongoDB.
Why use MapReduce with Hadoop
Like Spark, MapReduce will break down the data processing tasks into chunks and distribute the work load through the various nodes on the Hadoop cluster. Before Apache developed Spark, MapReduce was the way to go as far as managing big data on Hadoop.
MapReduce is noted for using key-value pairs for its data input and output, but it should be noted that Spark has its own key-value pair equivalent called Spark Paired RDD.
Spark vs MapReduce
Both frameworks work well with Hadoop and the HDFS, but they each offer different features, and have their own sets of pros and cons. Let’s review some of the differences between the two.
Spark and MapReduce Comparison
Spark Spark is many, many times faster than MapReduce, is more efficiency, and has lower latency, but MapReduce is older and has more legacy code, support, and libraries. MapReduce is completely open-source and free, and Spark is free for use under the Apache licence.
|Easier to Learn||Apache Spark|
*Both MapReduce and Apache Spark are designed to be scalable, but MapReduce wins this round because it uses less RAM when scaling up. Apache Spark wins the category of evaluation because of its use of “lazy evaluation” so that it will not take action, and use up system resources, unless necessary.
Spark and MapReduce feature comparison
|Cost||Free under Apache License||Free and open-source|
|Native JSON support||✓|
|Written In||Scala, Java||originally inmostly C++|
|Languages Used||Scala mostly, but Java and others as well||Java mostly, as well as others|
Once of the biggest advantages that Apache Spark has over MapReduce is that it’s designed to handle real-time processing of data, where the latency of MapReduce make it virtually unthinkable.
Apache’s Spark framework is quickly gaining popularity over MapReduce, and is slowly replacing it, because of its speed, efficiency, and easy learning curve, and ease of use. If you’re wanting to maintain legacy code designed for MapReduce, use libraries that are dependent on MapReduce, stick with pure Java instead of using Scala, or if you just want to avoid being licensed under Apache, then you’re better off with MapReduce, otherwise Apache Spark is the way to go.
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