Mapreduce Tutorial covers Introduction of MapReduce, Definition, Why Map Reduce, Algorithm,Examples, Installation, API (Application Programming interface), Implementation of Mapreduce, Mapreduce Partitioner, Mapreduce Combiner, Administration.
Initially created by Google, MapReduce soon gained immense popularity because of its unmatched qualities mandating big data players to deploy it. Some of it unique features are as follows:
|Flexibility||Can be developed in any language like java, c++, python, etc.|
|Scalability||Able to process petabytes of data on single cluster|
|Recovery||Takes care of failure by storing the replica on another machine|
|Lesser data motion||Processing tasks appear on physical nodes which increases the speed in turn.|
Apart from the above key features some of the key highlights of this technology are:
This blog will help you get a better understanding of Hadoop MapReduce – What it Refers To?
Last year MapReduce received the first place at “TeraByte Sort Benchmark”. They used 910 nodes, every node with two cores, i.e., a total of 1820 cores and were able to store the entire data in memory across the nodes. By implementation of MapReduce they were able to arrange entire one terabyte of data in 209 seconds. Users program, i.e., map and reduce functions in ANSI C.
MapReduce is a patented software framework introduced by Google to support distributed computing on large data sets on clusters of computers. MapReduce is a functional programming model. It runs in the Hadoop background to provide scalability, simplicity, speed, recovery and easy solutions for data processing. Here is a Mapreduce Tutorial Video by Intellipaat [videothumb class="col-md-12" id="aonriEk5IbU" alt="Mapreduce Read More
In the MapReduce bulk tasks are divided into smaller tasks, they are then alloted to many systems. The two important tasks in MapReduce algorithm Map Reduce Map task is always performed first which is then followed by Reduce job. One data set converts into another data set in map, and individual element is broken into tuples. Read More
On a daily basis the micro-blogging site Twitter receives nearly 500 million tweets, i.e., 3000 tweets per second. We can see the illustration on Twitter with the help of MapReduce. In the above example Twitter data is an input, and MapReduce Training performs the actions like Tokenize, filter, count and aggregate counters. Read More
MapReduce Tutorial supports only the Linux based OS, and it comes default with a Hadoop framework. So, we need to perform following steps to install the Hadoop framework. We have to install Java first in our system, before installing Hadoop. So using the below command we have to check whether Java is installed in our Read More
Classes and methods are involved in the operations of MapReduce programming. We focus on the following concepts. Job context interface Job class Mapper class Reducer class Here is a Mapreduce Tutorial Video by Intellipaat [videothumb class="col-md-12" id="aonriEk5IbU" alt="Mapreduce Tutorial" title="MAPREDUCE Tutorial"] Job context interface It is the super-interface for all the classes, which defines different jobs in Read More
The following table shows the data about customer visited the Intellipaat.com page. The table includes the monthly visitors of intellipaat.com page and annual average of five years. JAN FEB MAR APR MAY JUN JULY AUG SEP OCT NOV DEC AVG 2008 23 23 2 43 24 25 26 26 26 25 26 26 25 2009 26 Read More
Intermediate-outputs in the key-value pairs partitioned by a partitioner. The number of reducer tasks is equal to the number of partitions in the job. Implementation Let us take some employee details from the intellipaat company as an input table with the name employee. Emp_id name age gender salary 6001 aaaaa 45 Male 50,000 6002 bbbbb 40 Female Read More
It is a localized optional reducer. It used mapper intermediate keys and applies a user method to combine the values in smaller segment of that particular mapper. Many repeated keys are produced by maps. It is often useful to do a local aggregation process done by specifying combiner. The goal of the combiner is to decrease Read More
Hdfs administration and MapReduce administration, both concepts come under Hadoop administration. Hdfs administration: It includes monitoring the HDFS file structure, location and updated files. MapReduce administration: it includes monitoring the list of applications, configuration of nodes, application status. Here is a Mapreduce Tutorial Video by Intellipaat [videothumb class="col-md-12" id="1OFFAr8zYEY" alt="Mapreduce Tutorial" title="MAPREDUCE Tutorial"] HDFS administration: We are Read More
Download Interview Questions asked by top MNCs in 2019?