Hadoop Distributed File System – HDFS
The Hadoop Distributed File System (HDFS) is the primary storage system used by Hadoop applications. HDFS is a distributed file system that provides high-performance access to data across Hadoop clusters. Like other Hadoop-related technologies, HDFS has become a key tool for managing pools of big data and supporting big data analytics applications.
Because HDFS typically is deployed on low-cost commodity hardware, server failures are common. The file system is designed to be highly fault-tolerant, however, by facilitating the rapid transfer of data between compute nodes and enabling Hadoop systems to continue running if a node fails. That decreases the risk of catastrophic failure, even in the event that numerous nodes fail.
When HDFS takes in data, it breaks the information down into separate pieces and distributes them to different nodes in a cluster, allowing for parallel processing. The file system also copies each piece of data multiple times and distributes the copies to individual nodes, placing at least one copy on a different server rack than the others. As a result, the data on nodes that crash can be found elsewhere within a cluster, which allows processing to continue while the failure is resolved.
HDFS is built to support applications with large data sets, including individual files that reach into the terabytes. It uses a master/slave architecture, with each cluster consisting of a single NameNode that manages file system operations and supporting DataNodes that manage data storage on individual compute nodes.
An example of HDFS
Think of a file that contains the phone numbers for everyone in the United States; the people with a last name starting with A might be stored on server 1, B on server 2, and so on. In a Hadoop world, pieces of this phonebook would be stored across the cluster, and to reconstruct the entire phonebook, your program would need the blocks from every server in the cluster. To achieve availability as components fail, HDFS replicates these smaller pieces onto two additional servers by default. (This redundancy can be increased or decreased on a per-file basis or for a whole environment; for example, a development Hadoop cluster typically doesn’t need any data redundancy.) This redundancy offers multiple benefits, the most obvious being higher availability.
In addition, this redundancy allows the Hadoop cluster to break work up into smaller chunks and run those jobs on all the servers in the cluster for better scalability. Finally, you get the benefit of data locality, which is critical when working with large data sets.
What is Apache Hadoop ?
Hadoop, formally called Apache Hadoop, is an Apache Software Foundation project and open source software platform for scalable,distributed computing. Hadoop can provide fast and reliable analysis of both structured data and unstructured data. Given its capabilities to handle large data sets, it’s often associated with the phrase big data.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
The Apache Hadoop software library can detect and handle failures at the application layer, so it can deliver a highly-available service on top of a cluster of computers, each of which may be prone to failures.
As listed on the The Apache Hadoop project Web site, the Apache Hadoop project includes a number of subprojects, including:
Hadoop Map Reduce
Hadoop MapReduce (Hadoop Map/Reduce) is a software framework for distributed processing of large data sets on compute clusters of commodity hardware. It is a sub-project of the Apache Hadoop project. The framework takes care of scheduling tasks, monitoring them and re-executing any failed tasks.
According to The Apache Software Foundation, the primary objective of Map/Reduce is to split the input data set into independent chunks that are processed in a completely parallel manner. The Hadoop MapReduce framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically, both the input and the output of the job are stored in a file system.
What is MapReduce?
MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job.
The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers. Decomposing a data processing application into mappers and reducers is sometimes nontrivial. But, once we write an application in the MapReduce form, scaling the application to run over hundreds, thousands, or even tens of thousands of machines in a cluster is merely a configuration change. This simple scalability is what has attracted many programmers to use the MapReduce model.
The Algorithm
- Generally MapReduce paradigm is based on sending the computer to where the data resides!
- MapReduce program executes in three stages, namely map stage, shuffle stage, and reduce stage.
- Map stage : The map or mapper’s job is to process the input data. Generally the input data is in the form of file or directory and is stored in the Hadoop file system (HDFS). The input file is passed to the mapper function line by line. The mapper processes the data and creates several small chunks of data.
- Reduce stage : This stage is the combination of the Shufflestage and the Reduce stage. The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS.
- During a MapReduce job, Hadoop sends the Map and Reduce tasks to the appropriate servers in the cluster.
- The framework manages all the details of data-passing such as issuing tasks, verifying task completion, and copying data around the cluster between the nodes.
- Most of the computing takes place on nodes with data on local disks that reduces the network traffic.
- After completion of the given tasks, the cluster collects and reduces the data to form an appropriate result, and sends it back to the Hadoop server.
- NamedNode – Node that manages the Hadoop Distributed File System (HDFS).
- DataNode – Node where data is presented in advance before any processing takes place.
- MasterNode – Node where JobTracker runs and which accepts job requests from clients.
- SlaveNode – Node where Map and Reduce program runs.
Reference :
- https://www-01.ibm.com/software/data/infosphere/hadoop/hdfs/
- http://www.tutorialspoint.com/hadoop/hadoop_mapreduce.htm
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