Study & Implementation Using Haddop

Experiment No: DA-05

TITLE: - Write Hadoop Program to count number of words in file.

Problem Statement: - Implementation of Hadoop(Single Node) Map-Reduce technique to count numbers of words in text file.

Objective:

• To study fundamental concept of Hadoop & Map Reduce technique.
• To study word count process using Hadoop framework.
• To study HDFS

Requirements (Hw/Sw): PC, Jdk1.5 & above ,Hadoop, Eclipse

Theory:-

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

MapReduce

Hadoop MapReduce is a software framework for easily writing applications which process big amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

The term MapReduce actually refers to the following two different tasks that Hadoop programs perform:

• The Map Task: This is the first task, which takes input data and converts it into a set of data, where individual elements are broken down into tuples (key/value pairs).
• The Reduce Task: This task takes the output from a map task as input and combines those data tuples into a smaller set of tuples. The reduce task is always performed after the map task.

Typically both the input and the output are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster-node. The master is responsible for resource management, tracking resource consumption/availability and scheduling the jobs component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves TaskTracker execute the tasks as directed by the master and provide task-status information to the master periodically.

The JobTracker is a single point of failure for the Hadoop MapReduce service which means if JobTracker goes down, all running jobs are halted.

What is MapReduce?

MapReduce is a programming framework that allows us to perform distributed and parallel processing on large data sets in a distributed environment.

• MapReduce consists of two distinct tasks – Map and Reduce.
• As the name MapReduce suggests, reducer phase takes place after mapper phase has been completed.
• So, the first is the map job, where a block of data is read and processed to produce key-value pairs as intermediate outputs.
• The output of a Mapper or map job (key-value pairs) is input to the Reducer.
• The reducer receives the key-value pair from multiple map jobs.
• Then, the reducer aggregates those intermediate data tuples (intermediate key-value pair) into a smaller set of tuples or key-value pairs which is the final output.

MapReduce Tutorial: A Word Count Example of MapReduce

Let us understand, how a MapReduce works by taking an example where I have a text file called example.txt whose contents are as follows:

Dear, Bear, River, Car, Car, River, Deer, Car and Bear

Now, suppose, we have to perform a word count on the sample.txt using MapReduce. So, we will be finding the unique words and the number of occurrences of those unique words.

• First, we divide the input in three splits as shown in the figure. This will distribute the work among all the map nodes.
• Then, we tokenize the words in each of the mapper and give a hardcoded value (1) to each of the tokens or words. The rationale behind giving a hardcoded value equal to 1 is that every word, in itself, will occur once.
• Now, a list of key-value pair will be created where the key is nothing but the individual words and value is one. So, for the first line (Dear Bear River) we have 3 key-value pairs – Dear, 1; Bear, 1; River, 1. The mapping process remains the same on all the nodes.
• After mapper phase, a partition process takes place where sorting and shuffling happens so that all the tuples with the same key are sent to the corresponding reducer.
• So, after the sorting and shuffling phase, each reducer will have a unique key and a list of values corresponding to that very key. For example, Bear, [1,1]; Car, [1,1,1].., etc. 
• Now, each Reducer counts the values which are present in that list of values. As shown in the figure, reducer gets a list of values which is [1,1] for the key Bear. Then, it counts the number of ones in the very list and gives the final output as – Bear, 2.
• Finally, all the output key/value pairs are then collected and written in the output file.

MapReduce Tutorial: Advantages of MapReduce

The two biggest advantages of MapReduce are:

      1. Parallel Processing:

In MapReduce, we are dividing the job among multiple nodes and each node works with a part of the job simultaneously. So, MapReduce is based on Divide and Conquer paradigm which helps us to process the data using different machines. As the data is processed by multiple machine instead of a single machine in parallel, the time taken to process the data gets reduced by a tremendous amount as shown in the figure below (2).

Fig.: Traditional Way Vs. MapReduce Way – MapReduce Tutorial 

2. Data Locality: 

Instead of moving data to the processing unit, we are moving processing unit to the data in the MapReduce Framework.  In the traditional system, we used to bring data to the processing unit and process it. But, as the data grew and became very huge, bringing this huge amount of data to the processing unit posed following issues: 

• Moving huge data to processing is costly and deteriorates the network performance. 
• Processing takes time as the data is processed by a single unit which becomes the bottleneck.
• Master node can get over-burdened and may fail.  

Now, MapReduce allows us to overcome above issues by bringing the processing unit to the data. So, as you can see in the above image that the data is distributed among multiple nodes where each node processes the part of the data residing on it. This allows us to have the following advantages:

• It is very cost effective to move processing unit to the data.
• The processing time is reduced as all the nodes are working with their part of the data in parallel.
• Every node gets a part of the data to process and therefore, there is no chance of a node getting overburdened. 

Hadoop Distributed File System

Hadoop can work directly with any mountable distributed file system such as Local FS, HFTP FS, S3 FS, and others, but the most common file system used by Hadoop is the Hadoop Distributed File System (HDFS).

The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on large clusters (thousands of computers) of small computer machines in a reliable, fault-tolerant manner.

HDFS uses a master/slave architecture where master consists of a single NameNode that manages the file system metadata and one or more slave DataNodes that store the actual data.

A file in an HDFS namespace is split into several blocks and those blocks are stored in a set of DataNodes. The NameNode determines the mapping of blocks to the DataNodes. The DataNodes takes care of read and write operation with the file system. They also take care of block creation, deletion and replication based on instruction given by NameNode.

HDFS provides a shell like any other file system and a list of commands are available to interact with the file system. These shell commands will be covered in a separate chapter along with appropriate examples.

ADD FIGURE OF HDFS ARCHITECTURE HERE.

Functions of NameNode:

• It is the master daemon that maintains and manages the DataNodes (slave nodes)
• It records the metadata of all the files stored in the cluster, e.g. The location of blocks stored, the size of the files, permissions, hierarchy, etc. There are two files associated with the metadata:
  • FsImage: It contains the complete state of the file system namespace since the start of the NameNode.
  • EditLogs: It contains all the recent modifications made to the file system with respect to the most recent FsImage.
• It records each change that takes place to the file system metadata. For example, if a file is deleted in HDFS, the NameNode will immediately record this in the EditLog.
• It regularly receives a Heartbeat and a block report from all the DataNodes in the cluster to ensure that the DataNodes are live.
• It keeps a record of all the blocks in HDFS and in which nodes these blocks are located.
• The NameNode is also responsible to take care of the replication factor of all the blocks which we will discuss in detail later in this HDFS tutorial blog.
• In case of the DataNode failure, the NameNode chooses new DataNodes for new replicas, balance disk usage and manages the communication traffic to the DataNodes.

DataNode:

DataNodes are the slave nodes in HDFS. Unlike NameNode, DataNode is a commodity hardware, that is, a non-expensive system which is not of high quality or high-availability. The DataNode is a block server that stores the data in the local file ext3 or ext4.

Functions of DataNode:

• These are slave daemons or process which runs on each slave machine.
• The actual data is stored on DataNodes.
• The DataNodes perform the low-level read and write requests from the file system’s clients.
• They send heartbeats to the NameNode periodically to report the overall health of HDFS, by default, this frequency is set to 3 seconds.

Till now, you must have realized that the NameNode is pretty much important to us. If it fails, we are doomed.  But don’t worry, we will be talking about how Hadoop solved this single point of failure problem in the next Apache Hadoop HDFS Architecture blog. So, just relax for now and let’s take one step at a time.

Secondary NameNode:

Apart from these two daemons, there is a third daemon or a process called Secondary NameNode. The Secondary NameNode works concurrently with the primary NameNode as a helper daemon. And don’t be confused about the Secondary NameNode being a backup NameNode because it is not.

Functions of Secondary NameNode:

• The Secondary NameNode is one which constantly reads all the file systems and metadata from the RAM of the NameNode and writes it into the hard disk or the file system.
• It is responsible for combining the EditLogs with FsImage from the NameNode. 
• It downloads the EditLogs from the NameNode at regular intervals and applies to FsImage. The new FsImage is copied back to the NameNode, which is used whenever the NameNode is started the next time.

How Does Hadoop Work?

Stage 1

A user/application can submit a job to the Hadoop (a hadoop job client) for required process by specifying the following items:

1. The location of the input and output files in the distributed file system.
2. The java classes in the form of jar file containing the implementation of map and reduce functions.
3. The job configuration by setting different parameters specific to the job.

Stage 2

The Hadoop job client then submits the job (jar/executable etc) and configuration to the JobTracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

Stage 3

The TaskTrackers on different nodes execute the task as per MapReduce implementation and output of the reduce function is stored into the output files on the file system.

Advantages of Hadoop

• Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
• Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
• Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
• Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based.

Sample Algorithm For Map Reduce:

map(String input_key, String input_value):

// input_key: document name

// input_value: document contents

for each word w in input_value:

EmitIntermediate(w, "1");

reduce(String output_key, Iterator intermediate_values):

// output_key: a word

// output_values: a list of counts

int result = 0;

for each v in intermediate_values:

result += ParseInt(v);

Emit(AsString(result));

Input:

Output:

STEPS FOR HADOOP 

INSTALLATIO AND

 IMPLEMENTATION

• Extra Jar Files   

• WordCount Java Program

Experiment No: DA-06

TITLE: - Write Hadoop Program to search & count of words in file.

Problem Statement: - Implementation of Hadoop(Single Node) Map-Reduce technique to search & count of words in file.

• WordSearch Java Program

Conclusion: