The Apache Hadoop framework is composed of the following modules :
Hadoop Common - contains libraries and utilities needed by other Hadoop modules
Hadoop Distributed File System (HDFS) - a distributed file-system that stores data on the commodity machines, providing very high aggregate bandwidth across the cluster.
Hadoop YARN - a resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users' applications.
Hadoop MapReduce - a programming model for large scale data processing.
All the modules in Hadoop are designed with a fundamental assumption that hardware failures (of individual machines, or racks of machines) are common and thus should be automatically handled in software by the framework. Apache Hadoop's MapReduce and HDFS components originally derived respectively from Google's MapReduce and Google File System (GFS) papers.
Beyond HDFS, YARN and MapReduce, the entire Apache Hadoop “platform” is now commonly considered to consist of a number of related projects as well – Apache Pig, Apache Hive, Apache HBase, and others.
For the end-users, though MapReduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program.Apache Pig, Apache Hive among other related projects expose higher level user interfaces like Pig latin and a SQL variant respectively. The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell-scripts.
Hadoop was created by Doug Cutting and Mike Cafarella in 2005. Cutting, who was working at Yahoo! at the time, named it after his son's toy elephant. It was originally developed to support distribution for the Nutch search engine project.
Hadoop consists of the Hadoop Common package, which provides filesystem and OS level abstractions, a MapReduce engine (either MapReduce/MR1 or YARN/MR2) and the Hadoop Distributed File System (HDFS). The Hadoop Common package contains the necessary Java ARchive (JAR) files and scripts needed to start Hadoop. The package also provides source code, documentation and a contribution section that includes projects from the Hadoop Community.
For effective scheduling of work, every Hadoop-compatible file system should provide location awareness: the name of the rack (more precisely, of the network switch) where a worker node is. Hadoop applications can use this information to run work on the node where the data is, and, failing that, on the same rack/switch, reducing backbone traffic. HDFS uses this method when replicating data to try to keep different copies of the data on different racks. The goal is to reduce the impact of a rack power outage or switch failure, so that even if these events occur, the data may still be readable.
A multi-node Hadoop cluster
A small Hadoop cluster includes a single master and multiple worker nodes. The master node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node acts as both a DataNode and TaskTracker, though it is possible to have data-only worker nodes and compute-only worker nodes. These are normally used only in nonstandard applications. Hadoop requires Java Runtime Environment (JRE) 1.6 or higher. The standard start-up and shutdown scripts require Secure Shell (ssh) to be set up between nodes in the cluster.
In a larger cluster, the HDFS is managed through a dedicated NameNode server to host the file system index, and a secondary NameNode that can generate snapshots of the namenode's memory structures, thus preventing file-system corruption and reducing loss of data. Similarly, a standalone JobTracker server can manage job scheduling. In clusters where the Hadoop MapReduce engine is deployed against an alternate file system, the NameNode, secondary NameNode and DataNode architecture of HDFS is replaced by the file-system-specific equivalent.
The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written in Java for the Hadoop framework. Each node in a Hadoop instance typically has a single namenode; a cluster of datanodes form the HDFS cluster. The situation is typical because each node does not require a datanode to be present. Each datanode serves up blocks of data over the network using a block protocol specific to HDFS. The file system uses the TCP/IP layer for communication. Clients use Remote procedure call (RPC) to communicate between each other.
HDFS stores large files (typically in the range of gigabytes to terabytes) across multiple machines. It achieves reliability by replicating the data across multiple hosts, and hence does not require RAID storage on hosts. With the default replication value, 3, data is stored on three nodes: two on the same rack, and one on a different rack. Data nodes can talk to each other to rebalance data, to move copies around, and to keep the replication of data high. HDFS is not fully POSIX-compliant, because the requirements for a POSIX file-system differ from the target goals for a Hadoop application. The tradeoff of not having a fully POSIX-compliant file-system is increased performance for data throughput and support for non-POSIX operations such as Append.
HDFS added the high-availability capabilities, as announced for release 2.0 in May 2012, allowing the main metadata server (the NameNode) to be failed over manually to a backup in the event of failure. The project has also started developing automatic fail-over.
The HDFS file system includes a so-called secondary namenode, which misleads some people into thinking that when the primary namenode goes offline, the secondary namenode takes over. In fact, the secondary namenode regularly connects with the primary namenode and builds snapshots of the primary namenode's directory information, which the system then saves to local or remote directories. These checkpointed images can be used to restart a failed primary namenode without having to replay the entire journal of file-system actions, then to edit the log to create an up-to-date directory structure. Because the namenode is the single point for storage and management of metadata, it can become a bottleneck for supporting a huge number of files, especially a large number of small files. HDFS Federation, a new addition, aims to tackle this problem to a certain extent by allowing multiple name-spaces served by separate namenodes.
An advantage of using HDFS is data awareness between the job tracker and task tracker. The job tracker schedules map or reduce jobs to task trackers with an awareness of the data location. For example: if node A contains data (x,y,z) and node B contains data (a,b,c), the job tracker schedules node B to perform map or reduce tasks on (a,b,c) and node A would be scheduled to perform map or reduce tasks on (x,y,z). This reduces the amount of traffic that goes over the network and prevents unnecessary data transfer. When Hadoop is used with other file systems this advantage is not always available. This can have a significant impact on job-completion times, which has been demonstrated when running data-intensive jobs.
HDFS was designed[by whom?] for mostly immutable files and may not be suitable for systems requiring concurrent write-operations.
Another limitation of HDFS is that it cannot be mounted directly by an existing operating system. Getting data into and out of the HDFS file system, an action that often needs to be performed before and after executing a job, can be inconvenient. A Filesystem in Userspace (FUSE) virtual file system has been developed to address this problem, at least for Linux and some other Unix systems.
File access can be achieved through the native Java API, the Thrift API to generate a client in the language of the users' choosing (C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, Smalltalk, and OCaml), the command-line interface, or browsed through the HDFS-UI webapp over HTTP.
By May 2011, the list of supported file systems included:
Amazon S3 file system. This is targeted at clusters hosted on the Amazon Elastic Compute Cloud server-on-demand infrastructure. There is no rack-awareness in this file system, as it is all remote.
CloudStore (previously Kosmos Distributed File System), which is rack-aware.
FTP File system: this stores all its data on remotely accessible FTP servers.
HDFS: Hadoop's own rack-aware file system. This is designed to scale to tens of petabytes of storage and runs on top of the file systems of the underlying operating systems.
MapR's maprfs file system. This system provides inherent high availability, transactionally correct snapshots and mirrors while offering higher scaling than HDFS while giving higher performance. Maprfs is available as part of the MapR distribution and as a native option on Elastic Map Reduce from Amazon's web services, as well as on Google Compute Engine.
Hadoop can work directly with any distributed file system that can be mounted by the underlying operating system simply by using a file:// URL; however, this comes at a price: the loss of locality. To reduce network traffic, Hadoop needs to know which servers are closest to the data; this is information that Hadoop-specific file system bridges can provide.
Out-of-the-box, this includes Amazon S3, and the CloudStore filestore, through s3:// and kfs:// URLs directly.
A number of third-party file system bridges have also been written, none of which are currently in Hadoop distributions.
In May 2011, MapR Technologies, Inc. announced the availability of an alternative file system for Hadoop, which replaced the HDFS file system with a full random-access read/write file system, with advanced features like snaphots and mirrors, and got rid of the single point of failure issue of the default HDFS NameNode.
JobTracker and TaskTracker: the MapReduce engine
Above the file systems comes the MapReduce engine, which consists of one JobTracker, to which client applications submit MapReduce jobs. The JobTracker pushes work out to available TaskTracker nodes in the cluster, striving to keep the work as close to the data as possible. With a rack-aware file system, the JobTracker knows which node contains the data, and which other machines are nearby. If the work cannot be hosted on the actual node where the data resides, priority is given to nodes in the same rack. This reduces network traffic on the main backbone network. If a TaskTracker fails or times out, that part of the job is rescheduled. The TaskTracker on each node spawns off a separate Java Virtual Machine process to prevent the TaskTracker itself from failing if the running job crashes the JVM. A heartbeat is sent from the TaskTracker to the JobTracker every few minutes to check its status. The Job Tracker and TaskTracker status and information is exposed by Jetty and can be viewed from a web browser.
If the JobTracker failed on Hadoop 0.20 or earlier, all ongoing work was lost. Hadoop version 0.21 added some checkpointing to this process; the JobTracker records what it is up to in the file system. When a JobTracker starts up, it looks for any such data, so that it can restart work from where it left off.
Known limitations of this approach are:
The allocation of work to TaskTrackers is very simple. Every TaskTracker has a number of available slots (such as "4 slots"). Every active map or reduce task takes up one slot. The Job Tracker allocates work to the tracker nearest to the data with an available slot. There is no consideration of the current system load of the allocated machine, and hence its actual availability.
If one TaskTracker is very slow, it can delay the entire MapReduce job - especially towards the end of a job, where everything can end up waiting for the slowest task. With speculative execution enabled, however, a single task can be executed on multiple slave nodes.
By default Hadoop uses FIFO, and optional 5 scheduling priorities to schedule jobs from a work queue. In version 0.19 the job scheduler was refactored out of the JobTracker, and added the ability to use an alternate scheduler (such as the Fair scheduler or the Capacity scheduler).
The fair scheduler was developed by Facebook. The goal of the fair scheduler is to provide fast response times for small jobs and QoS for production jobs. The fair scheduler has three basic concepts.
The HDFS file system is not restricted to MapReduce jobs. It can be used for other applications, many of which are under development at Apache. The list includes the HBase database, the Apache Mahoutmachine learning system, and the Apache HiveData Warehouse system. Hadoop can in theory be used for any sort of work that is batch-oriented rather than real-time, that is very data-intensive, and able to work on pieces of the data in parallel. As of October 2009, commercial applications of Hadoop included:
Log and/or clickstream analysis of various kinds
Machine learning and/or sophisticated data mining
Processing of XML messages
Web crawling and/or text processing
General archiving, including of relational/tabular data, e.g. for compliance
On February 19, 2008, Yahoo! Inc. launched what it claimed was the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on a more than 10,000 coreLinuxcluster and produces data that is used in every Yahoo! Web search query.
There are multiple Hadoop clusters at Yahoo! and no HDFS file systems or MapReduce jobs are split across multiple datacenters. Every Hadoop cluster node bootstraps the Linux image, including the Hadoop distribution. Work that the clusters perform is known to include the index calculations for the Yahoo! search engine.
On June 10, 2009, Yahoo! made the source code of the version of Hadoop it runs in production available to the public. Yahoo! contributes all the work it does on Hadoop to the open-source community. The company's developers also fix bugs, provide stability improvements internally and release this patched source code so that other users may benefit from their effort.
In 2010 Facebook claimed that they had the largest Hadoop cluster in the world with 21 PB of storage. On June 13, 2012 they announced the data had grown to 100 PB. On November 8, 2012 they announced the warehouse grows by roughly half a PB per day.
There is support for the S3 file system in Hadoop distributions, and the Hadoop team generates EC2 machine images after every release. From a pure performance perspective, Hadoop on S3/EC2 is inefficient, as the S3 file system is remote and delays returning from every write operation until the data is guaranteed not to be lost. This removes the locality advantages of Hadoop, which schedules work near data to save on network load.
Elastic MapReduce (EMR) was introduced by Amazon in April 2009. Provisioning of the Hadoop cluster, running and terminating jobs, and handling data transfer between EC2 and S3 are automated by Elastic MapReduce. Apache Hive, which is built on top of Hadoop for providing data warehouse services, is also offered in Elastic MapReduce.
Support for using Spot Instances was later added in August 2011. Elastic MapReduce is fault tolerant for slave failures, and it is recommended to only run the Task Instance Group on spot instances to take advantage of the lower cost while maintaining availability.
In June 2012, premium options for EMR were added that replace ordinary Hadoop with MapR's M3 and M5 versions. These options provide additional capabilities over and above what the default EMR offering provides.
IBM and Google announced an initiative in 2007 to use Hadoop to support university courses in distributed computer programming.
In 2008 this collaboration, the Academic Cloud Computing Initiative (ACCI), partnered with the National Science Foundation to provide grant funding to academic researchers interested in exploring large-data applications. This resulted in the creation of the Cluster Exploratory (CLuE) program.
Hadoop can also be used in compute farms and high-performance computing environments. Instead of setting up a dedicated Hadoop cluster, an existing compute farm can be used if the resource manager of the cluster is aware of the Hadoop jobs, and thus Hadoop jobs can be scheduled like other jobs in the cluster.
Integration with Sun Grid Engine was released in 2008, and running Hadoop on Sun Grid (Sun's on-demand utility computing service) was possible. In the initial implementation of the integration, the CPU-time scheduler has no knowledge of the locality of the data. Unfortunately, this means that the processing is not always done on the same rack as the data; this was a key feature of the Hadoop Runtime. An improved integration with data-locality was announced during the Sun HPC Software Workshop '09.
In 2008-2009 Sun released the Hadoop Live CDOpenSolaris project, which allows running a fully functional Hadoop cluster using a live CD. This distribution includes Hadoop 0.19 -as of April 2010 there has not been an updated release.
There are a number of companies offering commercial implementations and/or providing support for Hadoop.
BMC Software provides BMC Control-M for Hadoop, which adds capabilities to monitor and manage Hadoop workflows with predictive analytics and automated alerts.
Cloudera offers CDH (Cloudera's Distribution including Apache Hadoop) and Cloudera Enterprise.
Dell added Pentaho Business Analytics to the Dell Apache Hadoop solution for big data analytics. This consists of Dell servers, Dell networking components, Dell's Crowbar cloud deployment framework open source software, and Cloudera Distribution including Apache Hadoop (CDH).
EMC released EMC Greenplum Community Edition and EMC Greenplum HD Enterprise Edition in May 2011. The community edition, with optional for-fee technical support, consists of Hadoop, HDFS, HBase, Hive, and the ZooKeeper configuration service. The enterprise edition is an offering based on the MapR product, and offers proprietary features such as snapshots and wide area replication.
ETU Asia's largest Hadoop platform - owned by Systex Corp and represented in Europe by GNR Corporation 
EMC Isilon announced support for HDFS in its OneFS clustered file system.
IBM offers WebSphere eXtreme Scale (formerly ObjectGrid), which includes two styles of the HADOOP map-reduce pattern in its "agents" a.k.a. DataGrid APIs. Together with its scalable distributed data cache capability, it gives both map-reduce's ability to parallelize function and the ability to store plenty of data (in memory) for the function to quickly access. It's transactional and highly available, too.
IBM offers InfoSphere BigInsights based on Hadoop in both a basic and enterprise edition.
Intel released its own Hadoop distribution that takes advantage of capabilities in Intel Xeon chips, such as its processor instructions for accelerating AES encryption.
MapR Technologies, Inc. announced the availability of their distributed file system and MapReduce engine, the MapR Distribution for Apache Hadoop. The MapR product includes most Hadoop eco-system components and adds capabilities such as snapshots, mirrors, NFS access and full read-write file semantics. MapR's distribution was selected by Amazon to provide premium versions of the Elastic Map Reduce (EMR) service.
MetaScale, a wholly owned subsidiary of Sears offers vendor neutral and platform independent Hadoop consulting and implementation services.
OceanSync Hadoop Management and Visualization Software allows users to control, monitor, and visualize all aspects of a Hadoop cluster including data analytic workflow management and output data processing visualization. The package is offered in three versions, OceanSync Free Desktop Edition, OceanSync Enterprise Edition with Visualization, and OceanSync Mobile for iPhone/Android devices, by Dovestech.
Pentaho announced support for Apache Hadoop allowing companies to access data integration and business analytics directly against Apache Hadoop based distributions of Hadoop. In January 2012, Pentaho announced they made their big data integration capabilities freely under open source, and moved the entire Pentaho Kettle (data integration engine) project from the LGPL license to the Apache License.
Pivotal offers Pivotal HD, a distribution of Hadoop that includes HAWQ, with 100% ANSI SQL compatibility.
Splunk offers a Hadoop integration product called Hadoop Connect, which is certified on MapR, Cloudera, Hortonworks, and Apache Hadoop. This integration allows users to search Hadoop data from Splunk and import data from Hadoop into Splunk and vice versa.
sqrrl offers sqrrl enterprise, which extends hadoop with Apache accumulo and combines the features of several datastores (Column + Document + Graph).
Syncsort provides an ETL Solution, which extends the capabilities of Hadoop, turning it into a highly scalable, affordable, and easy-to-use data integration environment.
Talend offers Talend Open Studio for Big Data, released under the Apache Software License, that includes native support for Apache Hadoop.
Teradata offers Teradata Appliance for Hadoop, as well as customer support for software-only Hadoop deployments. 
TIBCO supports Hadoop with their Businessworks Hadoop plugin and the TIBCO Spotfire Client.
WANdisco offers WDD (WANdisco's Distribution including Apache Hadoop).
Zettaset offers new version of its Big Data Mgt Platform based on Hadoop. Zettaset's Big Data Platform delivers High Availability via NameNode Failover, a streamlined UI, network Time Protocol and built in security via Kerberos Authentication
ASF's view on the use of "Hadoop" in product names
The Apache Software Foundation has stated that only software officially released by the Apache Hadoop Project can be called Apache Hadoop or Distributions of Apache Hadoop. The naming of products and derivative works from other vendors and the term "compatible" are somewhat controversial within the Hadoop developer community.
^"Hadoop contains the distributed computing platform that was formerly a part of Nutch. This includes the Hadoop Distributed Filesystem (HDFS) and an implementation of MapReduce." About Hadoop[dead link]