Apache Hadoop MapReduce Tutorial

MapReduce
Farzad Nozarian
4/11/15 @AUT

Purpose
This document describes how to set up and configure a single-node Hadoop
installation so that you can quickly perform simple operations using Hadoop
MapReduce
2

Supported Platforms
• GNU/Linux is supported as a development and production platform.
Hadoop has been demonstrated on GNU/Linux clusters with 2000 nodes.
• Windows is also a supported platform but the followings steps are for
Linux only.
3

Required Software
• Java™ must be installed. Recommended Java versions are described at
http://wiki.apache.org/hadoop/HadoopJavaVersions
• ssh must be installed and sshd must be running to use the Hadoop scripts
that manage remote Hadoop daemons.
• To get a Hadoop distribution, download a recent stable release from one
of the Apache Download Mirrors
$ sudo apt-get install ssh
$ sudo apt-get install rsync
4

Prepare to Start the Hadoop Cluster
• Unpack the downloaded Hadoop distribution. In the distribution, edit the
file etc/hadoop/hadoop-env.sh to define some parameters as follows:
• Try the following command:
This will display the usage documentation for the hadoop script.
# set to the root of your Java installation
export JAVA_HOME=/usr/lib/jvm/jdk1.7.0
# Assuming your installation directory is
/usr/local/hadoop
export HADOOP_PREFIX=/usr/local/hadoop
$ bin/hadoop
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Prepare to Start the Hadoop Cluster (Cont.)
• Now you are ready to start your Hadoop cluster in one of the three
supported modes:
• Local (Standalone) Mode
• By default, Hadoop is configured to run in a non-distributed mode, as a single Java
process. This is useful for debugging.
• Pseudo-Distributed Mode
• Hadoop can also be run on a single-node in a pseudo-distributed mode where each
Hadoop daemon runs in a separate Java process.
• Fully-Distributed Mode
6

Pseudo-Distributed Configuration
• etc/hadoop/core-site.xml:
• etc/hadoop/hdfs-site.xml:
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>
7

MapReduce Execution Pipeline
8

Main components of the MapReduce
execution pipeline
• Driver:
• The main program that initializes a MapReduce job.
• It defines job-specific configuration, and specifies all of its components:
• input and output formats
• mapper and reducer
• use of a combiner
• use of a custom partitioner
• The driver can also get back the status of the job execution.
9

execution pipeline
• Context:
• The driver, mappers, and reducers are executed in different processes, typically
on multiple machines.
• A context object is available at any point of MapReduce execution.
• It provides a convenient mechanism for exchanging required system and job-
wide information.
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execution pipeline
• Input data:
• This is where the data for a MapReduce task is initially stored
• This data can reside in HDFS, HBase, or other storage.
• InputFormat:
• This defines how input data is read and split.
• InputFormat is a class that defines the InputSplits that break input data into
tasks.
• It provides a factory for RecordReader objects that read the file.
• Several InputFormats are provided by Hadoop
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execution pipeline
• InputSplit:
• An InputSplit defines a unit of work for a single map task in a MapReduce
program.
• The InputFormat (invoked directly by a job driver) defines the number of map
tasks that make up the mapping phase.
• Each map task is given a single InputSplit to work on
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execution pipeline
• RecordReader:
• Although the InputSplit defines a data subset for a map task, it does not
describe how to access the data.
• The RecordReader class actually reads the data from its source, converts it into
key/value pairs suitable for processing by the mapper, and delivers them to the
map method.
• The RecordReader class is defined by the InputFormat.
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execution pipeline
• Mapper:
• Performs the user-defined work of the first phase of the MapReduce program.
• It takes input data in the form of a series of key/value pairs (k1, v1), which are
used for individual map execution.
• The map typically transforms the input pair into an output pair (k2, v2), which is
used as an input for shuffle and sort.
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execution pipeline
• Partition:
• A subset of the intermediate key space (k2, v2) produced by each individual
mapper is assigned to each reducer.
• These subsets (or partitions) are the inputs to the reduce tasks.
• Each map task may emit key/value pairs to any partition.
• The Partitioner class determines which reducer a given key/value pair will go to.
• The default Partitioner computes a hash value for the key, and assigns the
partition based on this result.
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execution pipeline
• Shuffle:
• Once at least one map function for a given node is completed, and the keys’
space is partitioned, the run time begins moving the intermediate outputs
from the map tasks to where they are required by the reducers.
• This process of moving map outputs to the reducers is known as shuffling.
• Sort:
• The set of intermediate key/value pairs for a given reducer is automatically
sorted by Hadoop to form keys/values (k2, {v2, v2,…}) before they are presented
to the reducer.
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execution pipeline
• Reducer:
• A reducer is responsible for an execution of user-provided code for the second
phase of job-specific work.
• For each key assigned to a given reducer, the reducer’s reduce() method is called
once.
• This method receives a key, along with an iterator over all the values associated
with the key.
• The reducer typically transforms the input key/value pairs into
output pairs (k3, v3).
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execution pipeline
• OutputFormat:
• The responsibility of the OutputFormat is to define a location of the output
data and RecordWriter used for storing the resulting data.
• RecordWriter:
• A RecordWriter defines how individual output records are written.
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Let’s try it with simple example!
Word Count
(the Hello World! for MapReduce, available in Hadoop sources)
We want to count the occurrences of every word
of a text file
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Driver
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public class WordCount {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
…
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
FileOutputFormat.setOutputPath(job, new Path(
otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
} }

Mapper class
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//inside WordCount class
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}

Reducer class
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//inside WordCount class
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

References:
• hadoop.apache.org
• Professional Hadoop Solutions - Boris Lublinsky, Kevin T.
Smith, Alexey Yakubovich - WILEY
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Apache Hadoop MapReduce Tutorial

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