Hadoop has been a media darling for years. When a technology solution is touted as the “be all end all solution” to manage big-data, heads turn and eyes roll. I believe it is time where we appreciate hadoop for what it is and understand what it is not.

Hadoop is an Apache open source framework written in java that allows distributed processing of large data sets 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.

What do I mean when I say Hadoop?

Hadoop can mean different things to different people but in the context of this doc, hadoop means “The Hadoop Eco-system” (http://hadoop.apache.org/). The Hadoop Eco-system is a family of open source and increasingly, not so open source projects that help acquire, store, transform, query and analyze data. The verbs/capabilities are expected to grow over time.

Hadoop dwells in the world of terabytes if not petabytes of data. The data must come from somewhere. Most common source of data is the frontier databases, which keep the websites and apps alive. As table sizes grow, the queries slow down and you end up archiving this data. This data is valuable and can yield insights if we analyze it instead of archiving it. Sqoop is a member of the Hadoop Eco system which is designed to pull in data from relational databases pushes it into hadoop. It is highly configurable and will not burn down your precious oracle with connection requests.

Another common source is logs. Oh the tons and tons of logs. Push them right into hadoop. Multiple solutions exist in the Hadoop Eco system to capture logs from the army of enterprise apps and feed them to hadoop. Apache flume and Chukwa are great options to explore and stream logs into hadoop, where we can archive/analyze them.

So Basically “Hadoop is a software framework which supports data intensive processes and enables applications to work with Big Data. Hadoop is based on Mapper-reducer technology”

Hadoop framework includes following four modules:

  • Hadoop Common: These are Java libraries and utilities required by other Hadoop modules. These libraries provides filesystem and OS level abstractions and contains the necessary Java files and scripts required to start Hadoop.
  • Hadoop YARN: This is a framework for job scheduling and cluster resource management.
  • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
  • Hadoop MapReduce: This is YARN-based system for parallel processing of large data sets.

We can use following diagram to depict these four components available in Hadoop framework.


Since 2012, the term “Hadoop” often refers not just to the base modules mentioned above but also to the collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Spark etc.


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.

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 singleNameNode that manages the file system metadata and one or more slaveDataNodes 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.

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.


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