Best 15 Things You Need To Know About MapReduce vs Spark

 Difference Between MapReduce and Spark

Hadoop is a framework which helps us to store Big Data inefficient and distributed manner and also process the data in parallel and distributed manner. Two core components of Hadoop framework are HDFS and Map Reduce in addition we also have YARN(Yet Another Resource Negotiator) which does the resource management for better performance. We have many tools present in Hadoop ecosystem like(Hive, HBase, Pig, Sqoop and Zookeeper etc.)

SPARK is an independent processing engine for real-time processing which can be installed on any Distributed File system like Hadoop. Like YARN we have here a Own Cluster Resource manager commonly know as (Local Resource manager). The resource manager is not as mature as YARN so it is not used in Production environment. SPARK  provides a performance which is 10 times faster than Map Reduce on disk and 100 times faster than Map Reduce on a network in memory.

 Need For SPARK

• Iterative Analytics: Map reduce is not as efficient as SPARK to solve problems which require iterative analytics as it has to go to disk for every iteration.
• Interactive Analytics: Map reduce is often used to run ad-hoc queries for which it needs to get to on disk memory which again is not as efficient as SPARK because the latter refers in the in-memory which is faster.
• Not Suitable for OLTP: As it works on batch-oriented framework it is not suitable for a large number of the short transaction.
• Not Suitable for Graph: The Apache Graph library processes the graph which adds more complexity to Map Reduce.
• Not suitable for trivial operations: For operations like a filter and joins we might need to rewrite the jobs, which becomes more complex because of the key-value pattern.

Head To Head Comparison Between MapReduce vs Spark (Infographics)

Below is the top 15 Difference between MapReduce and Spark

Key Differences Between MapReduce vs Spark

Below are the lists of points, describe the key differences between MapReduce and Spark:

• Spark is suitable for real-time as it process using in memory whereas as MapReduce is limited to batch processing.
• Spark has RDD(Resilient Distributed Dataset) giving us high- level operators but in Map reduce we need to code each and every operation making it comparatively difficult.
• Spark can process graph’s and supports Machine learning tool.
• Below is the difference between MapReduce vs Spark ecosystem.

Example, where MapReduce vs Spark are suitable, are as follows

Spark: Credit Card fraud detection

MapReduce: Making of regular reports which require decision making.

MapReduce vs Spark Comparision Table

Conclusion – MapReduce vs Spark

As per the above Difference between MapReduce and Spark, it’s pretty clear that SPARK is much more advanced computing engine as compared to Map Reduce. Spark is compatible with any type of file format and also pretty faster than Map Reduce. The spark in addition also has Graph processing and machine learning capabilities.

On one hand, Map Reduce is limited to batch processing and on other Spark is able to do any type of processing (batch, interactive, iterative, streaming, graph). Due to big compatibility Spark is the favorite of Data Scientist and hence its replacing Map Reduce and growing rapidly. But still we need to store the data in HDFS and we also sometime may need HBase. So we need to run both Spark and Hadoop to get the best.

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This has been a guide to MapReduce vs Spark, their Meaning, Head to Head Comparison, Key Differences, Comparision Table, and Conclusion. You may also look at the following articles to learn more –

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