This tutorial is the continuation of Hadoop Tutorial 1 -- Running WordCount. It is based on the excellent tutorial by Michael Noll 'Writing an Hadoop MapReduce Program in Python'[1]
Top 50 Interview Quiz for MapReduce. Q2 Explain a MapReduce program. Answer: A MapReduce program consists of 3 parts namely, Driver, Mapper, and Reducer. The Driver code runs on the client machine and is responsible for building the configuration of the job and submitting it to the Hadoop Cluster. Writing an Hadoop MapReduce Program in Python ขั้นตอนการ. An introduction to the basics of MapReduce, along with a tutorial to create a word count app using Hadoop and Java. Join the DZone community and get the full member experience. Hortonworks Sandbox for HDP and HDF is your chance to get started on learning, developing, testing and trying out new features. MapReduce and the Hadoop Distributed File System (HDFS) are now separate subprojects. This is the first time that either a Java or an open source program has won. Write a Hadoop MapReduce job in any programming language. This lets you write your distributed job in Perl. Writing An Hadoop MapReduce Program In Python. Writing An Hadoop MapReduce.
Dataflow of information between streaming process and taskTracker processes Image taken from [2].
All we have to do in write a mapper and a reducer function in Python, and make sure they exchange tuples with the outside world through stdin and stdout. Furthermore, the format of the data in the tuples should be that of strings.
Mapper
Map Reduce
The mapper code is shown below. It is stored in a file called mapper.py, and does not even contain a function. All it needs to do is receive data on its stdin input and output data on its stdout.
Make sure you make the program executable:
Reducer Code
Typically the reducer gets the tuples generated by the mapper, after the shuffle and sort phases.
The code is stored in a file called reducer.py.
Make sure the file is executable:
Testing
Make sur your two programs work. Here's a simple series of test you can run:
This will make mapper.py output all the words that make up its code.
This will generate the (unsorted) frequencies of all the unique words (punctuated or not) in mapper.py.
Running on the Hadoop Cluster
Writing An Hadoop Mapreduce Program In Perlmutter
Let's run the Python code on the Ulysses.txt file.
We'll assume that the Python code is stored in ~hadoop/352/dft/python
We'll assume that the streaming java library is in ~hadoop/contrib/streaming/streaming-0.19.2-streaming.jar
We'll also assume that ulysses.txt is in dft and that we want the output in dft-output:
Changing the number of Reducers
To change the number of reducers, simply add this switch -jobconf mapred.reduce.tasks=16 to the command line:
↑Michael Noll, Writing an Hadoop MapReduce Program in Python, www.michael-noll.com.
↑Hadoop, the definitive guide, Tim White, O'Reilly Media, June 2009, ISBN 0596521979. The Web site for the book is http://www.hadoopbook.com/
Mapreduce Examples In Java
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