MapReduce is pivotal to big
data as it has allowed the processing of massive datasets, which the earlier
preferred format for data storage and analysis, RBDMS was not capable of doing.
In terms of big data analytics, the open-source
framework of Hadoop has been a game changer. It has enabled storage of large datasets
going up to petabytes in a cluster format, and faster processing of this distributed
data. An essential feature of Hadoop is its ability for parallel processing which
is executed via MapReduce.
MapReduce is defined as the distributed data
processing and querying programming engine that effectively splits and spreads
around the necessary computation activities on a dataset across a wide range of
servers which are known as data clusters.
A query that needs to run through a mega data set may take hours if
situated in one computer server. This is however cut down to minutes when done
in parallel over a spread of servers.
The term MapReduce refers to two
critical tasks it handles on the Hadoop Distributed File System (HDFS) – the Map Job and the Reduce Job. The Map
function takes the different input data elements available and processes them
into an output data element, creating key value pairs. The Reduce function aggregates outputs created under the key value pairs, put them
back together quickly and reliably in order to produce the required end-result.
Structurally, MapReduce has a single master Job Tracker and several slaves Task Tracker, one each per
cluster. The master distributes and schedules the tasks to these slaves and
keeps track of the assigned jobs, redoing any that fail. The slave tracker ensures that the assigned
task is executed and communicates with the master
There are number of benefits of MapReduce that has
made it an important element of Hadoop.
- It allows developers to use any language like Java or C++ to write the applications, although Java is most preferred.
- MapReduce can handle all forms of data whether structured or unstructured.
- Another core feature of MapReduce is that it can easily run through petabytes of data stored in a data center due to its construct.
- The MapReduce framework is also highly flexible in case of failures. If one dataset fails but is available in another machine, it can index and use the alternate location.
Today, there are several additional data processing
engines like Pig or Hive that can be used to extract data from a Hadoop
framework. These eliminate some of the complexity of MapReduce and make it
easier to generate insights.
Will discuss more about Map reduce in our upcoming
post.
Learn more about Pig and Hive here
