Big Data Tutorial: Map Reduce

NOTICE: This page refers to the Peel Hadoop Cluster which was retired in 2023 and is in the process of being archived. Its contents should not be referenced for Dataproc Courses.

What is Hadoop?

Hadoop is an open-source software framework for storing and processing big data in a distributed/parallel fashion on large clusters of commodity hardware. Essentially, it accomplishes two tasks: massive data storage and faster processing. The core Hadoop consists of HDFS and Hadoop's implementation of MapReduce.

What is HDFS? 

HDFS stands for Hadoop Distributed File System. HDFS is a highly fault-tolerant file system and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets.

What is Map-Reduce?

MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster.

Let's go to the slide deck for more information:  Mapreduce

Phases in MapReduce

A MapReduce job splits a large data set into independent chunks and organizes them into key-value pairs for parallel processing. A key-value pair (KVP) is a set of two linked data items: a key, which is a unique identifier for some item of data, and the value, which is either the data that is identified or a pointer to the location of that data. The mapping and reducing functions receive not just values, but (key, value) pairs.This parallel processing improves the speed and reliability of the cluster, returning solutions more quickly and with greater reliability.  

Every MapReduce job consists of at least three parts:

• • The driver 

  • The Mapper 

  • The Reducer 

Mapping Phase

The first phase of a MapReduce program is called mapping. A list of data elements are provided, one at a time, to a function called the Mapper, which transforms each element individually to an output data element.

The Map function divides the input into ranges by the InputFormat and creates a map task for each range in the input. The JobTracker distributes those tasks to the worker nodes. The output of each map task is partitioned into a group of key-value pairs for each reduce.

Reducing Phase

Reducing lets you aggregate values together. A reducer function receives an iterator of input values from an input list. It then combines these values together, returning a single output value.

The Reduce function then collects the various results and combines them to answer the larger problem that the master node needs to solve. Each reduce pulls the relevant partition from the machines where the maps executed, then writes its output back into HDFS. Thus, the reduce is able to collect the data from all of the maps for the keys and combine them to solve the problem.

MapReduce Data Flow

What is Peel?

Peel is the stand-alone Hadoop cluster running on Cloudera CDH version 6.3.4. Cloudera Enterprise(CDH) combines Apache Hadoop 3.0.0 and Apache Spark 2.4.0 with a number of other open-source projects to create a single, massively scalable system where you can unite storage with an array of powerful processing and analytic frameworks.

Commands for HDFS & MapReduce

To access peel the Hadoop cluster 

Windows:

• Step - 1: Connect to NYU VPN

• Step - 2: User hostname "peel.hpc.nyu.edu" with port 22 in putty. Provide your credentials. 

Mac:

• Step - 1: Connect to NYU VPN

• Step - 2: Then from terminal, use "ssh <net id>@peel.hpc.nyu.edu". It will connect to the peel cluster. 

For more information, please follow the instructions on the Hadoop User Guide.

HDFS COMMANDS

TO UPLOAD DATA TO HDFS

TO GET DATA FROM HDFS  

TO CHECK HDFS FOR YOUR FILE 

MAPREDUCE COMMANDS

To trigger the  job 

To check running jobs

To kill a job 

Examples for Map-Reduce Jobs

Example 1:

Word Count: The objective here is to count the number of occurrences of each word by using key-value pairs.

Step 1:

Mac Users: ssh <NYUID>@peel.hpc.nyu.edu

                    or

Windows Users: Use Putty

Step 2: 

Copy example1 folder to /home/<net_id>/ 

Step 3:

Place the book.txt file on to hdfs 

Step 4: 

Compile code with java compiler and create a jar file using generated class files.

Step 5:

Run the MapReduce job using WordCount.jar

Step 6: 

Check output by accessing HDFS directories

Example 2: 

Standard Deviation: The objective is to find the standard deviation of the length of the words. 

Step 1: 

copy example2 folder to /home/<net_id>/

example2.txt - Input file

StandardDeviation.jar - compiled jar file

Step 2:

Place the example2.txt file on to hdfs 

Step 3:

Run the MapReduce job using StandardDeviation.jar

Step 4:

Check output by accessing HDFS directories

Example 3:

Step 1:  

Create a directory to work with example3

Step 2: 

Copy the input to the local directory, then to HDFS.

Step 3: 

Clone a git repository to create a local copy of the code 

Step 4: 

Build/compile using maven. Make sure pom.xml is present in the same directory. This command will generate the "target" directory.

Step 5: 

Extract jar file. This command creates directory "com".

Step 6: 

Execute the process using the class files created in directory "com".

Step 7:

Check output by accessing HDFS directories. 

MapReduce Streaming

Even though the Hadoop framework is written in Java, programs for Hadoop need not be coded in Java but can also be developed in other languages like Python, shell scripts or C++. Hadoop streaming is a utility that comes with the Hadoop distribution. This utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer. 

Streaming runs a MapReduce job from the command line. You specify a map script, a reduce script, an input, and an output. Streaming takes care of the Map-Reduce details such as making sure that your job is split into separate tasks, that the map tasks are executed where the data is stored. Hadoop Streaming works a little differently (your program is not presented with one record at a time, you have to iterate yourself)

• • -input – The data in hdfs that you want to process

  • -output – The directory in hdfs where you want to store the output

  • -mapper script – the program script command line or process that you want to use for your mapper

  • -reducer script – the program script command or process that you want to use for your reducer.

  • -file – Make the mapper, reducer, or combiner executable available locally on the compute nodes.

Command used to run a MapReduce job using streaming:

An example of how to run a Hadoop-streaming job is: