Difference between Hadoop MapReduce and Apache Spark
Hadoop MapReduce and Apache Spark are both Big Data processing tools. MapReduce and Spark share a mutual relationship. Both exhibit features which the other does not. It is difficult to take a side here. Both the tools taken together provide a very powerful and complete tool for processing Big Data and make the Hadoop cluster more robust.
Difference between Hadoop MapReduce and Apache Spark
Here, we have listed the main difference between Hadoop MapReduce and apache spark(two data processing engines) for you to review.
|Factors||Hadoop MapReduce||Apache Spark|
|Core Definition||MapReduce is a programming model that is implemented in processing huge amounts of data.
MapReduce has been developed using Java
MapReduce Programs work in two phases:
The entire MapReduce process goes through the following 4 phases:
|Apache Spark is an open-source, distributed processing system which is used for Big Data. Spark is an engine for large scale data processing.
Spark has been developed using Scala.
The main components of Apache Spark are as follows:
|Processing Speed||MapReduce reads and writes data from the disk. Though it is faster than traditional systems, it is substantially slower than Spark.||It runs on RAM, stores intermediate data in-memory reducing the number of read/write cycles to the disk. Hence it is faster than the classical MapReduce.|
|Data Processing||MapReduce was designed to perform Batch Processing for a voluminous amount of data. Hence, for extended data processing, it is dependent on different engines like Storm, Giraph, Impala, etc. Managing many different components adds to the hassle.
MapReduce cannot process data interactively
|Performs Batch Processing, Real-time processing, Iterative Processing, Graph Processing, Machine Learning and Streaming all in the same cluster. It thus accounts for a complete data analytics engine and is enough to handle all the requirements. Spark has the ability to process live streams efficiently.
Spark can process data interactively.
|Memory Usage||Does not support caching of Data.||Enhances the system performance by caching the data in-memory.|
|Coding||MapReduce requires handling low-level APIs due to which developers need to code each and every operation which makes it very difficult to work with.||Spark is easy to use and its Resilient Distributed Dataset helps to process data with its high-level operators. It provides rich APIs in Java, Scala, Python and R.|
Latency means Delay. It is the time the CPU has to wait to get a response after it makes a request to the RAM.
|MapReduce has a high-latency computing framework.||Spark provides a low latency computing.|
|Recovery From Failure||MapReduce is highly faulted tolerant and is resilient to system faults and failures. Here there is no need to restart the application from scratch in case of failure.||Spark is also fault tolerant. Resilient Distributed Dataset [RDDs] allow for recovery of partitions on failed nodes. It also supports recovery by checkpointing to reduce the dependencies of an RDD. Hence, here too there is no need to restart the application from scratch in case of failure.|
|Scheduler||MapReduce is dependant on external job scheduler like Oozie to schedule its complex flows.||Due to in-memory computation Spark acts like its own flow scheduler.|
|Security||MapReduce is comparatively more secure because of Kerberos. It also supports Access Control Lists (ACLs) which are traditional file permission model.||Spark supports only one authentication which is the shared secret password authentication.|
|Cost||MapReduce is a cheaper option in terms of cost.||Spark is costlier due to its in-memory processing power and RAM requirement.|
|Function||MapReduce is a Data Processing Engine.||Spark is a Data Analytics Engine hence a choice for Data Scientist.|
|Framework||It is an open-source framework for writing data into HDFS and processing structured and unstructured data present in HDFS.||Spark is an independent real-time processing engine that can be installed in any Distributed File System.|
|Programming Language Supported||Java, C, C++, Ruby, Groovy, Perl, Python||Scala, Java, Python, R, SQL|
|SQL Support||Runs SQL queries using Apache Hive||Runs SQL queries using Spark SQL|
|Hardware Requirement||MapReduce can be run on commodity hardware.||Apache Spark requires mid to high-level hardware configuration to run efficiently.|
|Hadoop requires a machine learning tool, one of which is Apache Mahout.||Spark has its own set of Machine Learning i.e. MLlib.|
|Redundancy Check||MapReduce does not support this feature.||Spark processes every record exactly once and hence eliminates duplication.|
From the above comparison, it is quite clear that Apache Spark is a more advanced cluster computing engine than MapReduce. Due to its advanced features, it is now replacing MapReduce very quickly. However, MapReduce is an economical option. Furthermore, the intent of the business is a major factor in deciding the software to be used. There is no one size fits all in today’s market. Hence, it is a combination of technical, non-technical, economic and business factors that ultimately influence the software selection.
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