Spark vs. Hadoop MapReduce: Which Big Data Framework to Choose
Choosing the most suitable one is a challenge when several big data frameworks are available in the market. The traditional approach of comparing the strength and weaknesses of each platform is to be of less help, as businesses should consider each framework with their needs in mind.
Here we are attempting to answer a pressing issue: which to choose-Hadoop MapReduce or Spark.
A Quick Review of the Market Situation
Hadoop and Spark are big wigs in big data analytics. Both are open source projects by Apache Software. Hadoop has been a market leader for the past five years. Based on recent market research, Hadoop’s installed base includes more than fifty thousand, while Spark has ten thousand installations only.
Nevertheless, Spark’s reputation soared in 2013 to beat Hadoop in only a year. To make the comparison equitable and fair, we will compare Spark with Hadoop MapReduce, as both are responsible for data processing.
The Major Difference Between Hadoop MapReduce and Spark
In fact, the major difference between Hadoop MapReduce and Spark is in the method of data processing: Spark does its processing in memory, while Hadoop MapReduce has to read from and write to a disk. Hence, the speed of processing differs significantly- Spark maybe a hundred times faster.
However, the processed data volume also differs. Hadoop MapReduce can work with far larger data sets than Spark.
Now, let us examine the tasks each framework is best suited for.
Tasks Hadoop MapReduce is Ideal For
Parallel Processing of Huge Data Sets: Apache Hadoop MapReduce processes large data sets in parallel for analysis across a Hadoop cluster. It breaks large data sets into small chunks to be processed separately on different data nodes and automatically collects the analysis from different data nodes and returns as a single result. In instances of data sets being larger than available RAM, Hadoop MapReduce may outshine Spark.
Cost-Effective If Speed Processing Is Not Critical
MapReduce is a good solution if the processing speed is not critical to the application. For example, if data processing can be carried out during the night, it would be logical to consider using Hadoop MapReduce.
Tasks Hadoop Spark is Ideal For
- Fast Data Processing: Spark is a framework known for real-time data analytics. It performs in-memory data processing to increase speed which makes it faster than Hadoop MapReduce. It is best suited for businesses that require immediate insights.
- Iterative Processing: If the task has to process data over and over- Spark outperforms Hadoop MapReduce. Spark’s Resilient Distributed Datasets (RDDs) enables several map operations to be run in memory, without writing the interim results to a disk.
- Graph Processing: Support from Spark’s inbuilt graph computation library called GraphX along with in-memory calculation improves the performance of Spark by a magnitude of two or more degrees over Apache Hadoop MapReduce.
- Machine Learning: Spark has MLlibe- a built-in machine learning library with out of box algorithms that also run in memory. It caches the intermediate dataset which reduces the I/O and helps to run algorithm faster in a fault resilient manner.
Which Data framework is best suited?
The choice of data framework should be based on the business needs in hand. Parallel processing of huge datasets is the advantage offered by Hadoop MapReduce. On the other hand, Spark boasts of faster performance, iterative processing, graph processing machine learning, and many more. In many cases, Spark may outdo Hadoop MapReduce. The good news is that Spark works fine with Hadoop Distributed File System, Apache Hive, etc.