HP ConvergedSystem is a portfolio of system-based products from Hewlett-Packard (HP) that integrates preconfigured IT components into systems for virtualization , cloud computing , big data , collaboration , converged management, and client virtualization. Composed of servers, storage, networking, and integrated software and services, the systems are designed to address the cost and complexity of data center operations and maintenance by pulling the IT components together into a single resource pool so they are easier to manage and faster to deploy. Where previously it would take three to six months from the time of order to get a system up and running, it now reportedly takes as few as 20 days with the HP ConvergedSystem.
69-1093: HP ConvergedSystem uses a common Converged infrastructure architecture, the same common foundation used for all HP server, storage, and networking products. HP Converged Infrastructure pools resources so that they can be shared across different applications while being managed from a standardized management platform and security software. The convergence of server, storage, and networking can help user organizations save investment on equipment maintenance and management. HP Converged Systems includes HP ConvergedSystem for Virtualization, for developing and managing virtualized environments; HP CloudSystem, for building and managing cloud computing services across private, public and hybrid clouds; HP ConvergedSystem for Big Data, for loading, analyzing and managing vast quantities of data; HP ConvergedSystem for Collaboration, for configuration and deployment of Microsoft unified communications software; HP OneView for converged infrastructure management; and HP ConvergedSystem for Client Virtualization, for running Virtual desktop infrastructure. HP CloudSystem
138-609: A Filesystem in Userspace (FUSE) virtual file system on Linux and some other Unix systems. File access can be achieved through the native Java API, the Thrift API (generates a client in a number of languages e.g. C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa , Smalltalk, and OCaml ), the command-line interface , the HDFS-UI web application over HTTP , or via 3rd-party network client libraries. HDFS
207-418: A Software-defined data center . HP ConvergedSystem for Virtualization consists of a series of integrated server, storage and networking infrastructures that offer Scalability with modular virtualization systems that can support 50 to 1,000 Virtual machines each. The HP ConvergedSystem 300, designed to support 50 to 300 virtual machines, comes configured with HP ProLiant servers. The HP ConvergedSystem 700
276-468: A Virtual LAN (VLAN) through the network edge. An HP OneView Plug-in for the VMware vCenter virtualization console enables administrators to work directly through the vCenter console. Operations for VMware vSphere hosts and clusters can be managed and automated, reducing the number of steps it takes to carry out tasks like setting up a new cluster. HP ConvergedSystem 100 for Hosted Desktops works with
345-491: A data store due to its lack of POSIX compliance, but it does provide shell commands and Java application programming interface (API) methods that are similar to other file systems. A Hadoop instance is divided into HDFS and MapReduce. HDFS is used for storing the data and MapReduce is used for processing data. HDFS has five services as follows: Top three are Master Services/Daemons/Nodes and bottom two are Slave Services. Master Services can communicate with each other and in
414-569: A software framework for distributed storage and processing of big data using the MapReduce programming model . Hadoop was originally designed for computer clusters built from commodity hardware , which is still the common use. It has since also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by
483-423: A 2009 InformationWeek survey of executives in 500 companies with annual revenue over $ 250 million. That leaves just a third of the budget for new IT initiatives. This ratio prevents IT from supporting new business initiatives or responding to real application demands. A converged infrastructure addresses the problem of siloed architectures and IT sprawl by pooling and sharing IT resources. Rather than dedicating
552-514: A Heartbeat message to the Name node every 3 seconds and conveys that it is alive. In this way when Name Node does not receive a heartbeat from a data node for 2 minutes, it will take that data node as dead and starts the process of block replications on some other Data node. Secondary Name Node: This is only to take care of the checkpoints of the file system metadata which is in the Name Node. This
621-479: 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 namespaces served by separate namenodes. Moreover, there are some issues in HDFS such as small file issues, scalability problems, Single Point of Failure (SPoF), and bottlenecks in huge metadata requests. One advantage of using HDFS
690-438: A business email system in a modular, simplified manner. HP Converged Systems Solutions for Microsoft Lync Server streamlines deployment of Microsoft Lync communications for voice, instant messaging and video and includes a reference architecture for small to medium enterprises. HP OneView for converged infrastructure management enables data center administrators to perform automated lifecycle management for HP servers, as part of
759-405: A collective manner using policy-driven processes. IT vendors and IT industry analysts use various terms to describe the concept of a converged infrastructure. These include "converged system", "unified computing" , "fabric-based computing", and " dynamic infrastructure ". Historically, to keep pace with the growth of business applications and the data they generate, IT resources were deployed in
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#1732801034535828-461: A default pool. Pools have to specify the minimum number of map slots, reduce slots, as well as a limit on the number of running jobs. The capacity scheduler was developed by Yahoo. The capacity scheduler supports several features that are similar to those of the fair scheduler. There is no preemption once a job is running. The biggest difference between Hadoop 1 and Hadoop 2 is the addition of YARN (Yet Another Resource Negotiator), which replaced
897-470: A joint effort with AMD and Citrix, the system is reported to provide graphics that are six times faster than those of competing products. Client virtualization also provides a way for users to more securely access business applications and data from mobile devices . HP ConvergedSystem at the Wayback Machine (archived 2014-04-15) Converged infrastructure Converged infrastructure
966-476: A service (SaaS) offerings. Several characteristics make converged infrastructure well suited to cloud deployments. These include the ability to pool IT resources, to automate resource provisioning and to scale up and down capacity quickly to meet the needs of dynamic computing workloads. Apache Hadoop Apache Hadoop ( / h ə ˈ d uː p / ) is a collection of open-source software utilities for reliable, scalable, distributed computing . It provides
1035-408: A set of resources to a particular computing technology, application or line of business, converged infrastructure creates a pool of virtualized servers, storage and networking capacity that is shared by multiple applications and lines of business. Converged infrastructure provides both technical and business efficiencies, according to industry researchers and observers. These gains stem in part from
1104-524: A silo-like fashion. One set of resources has been devoted to one particular computing technology, business application or line of business . These resources support a single set of assumptions and cannot be optimized or reconfigured to support varying usage loads. The proliferation of IT sprawl in data centers has contributed to rising operations costs, reducing productivity, and stifling agility and flexibility. Maintenance and operations can consume two-thirds of an organization's technology budget, according to
1173-509: A single framework, help to transform the economics [of] running the datacenter thus accelerating the transition to IP storage to help build infrastructures that are "cloud-ready". The combination of storage and compute into a single entity is known as converged storage . Decreased complexity, through the use of pre-integrated hardware with virtualization and automation management tools, is another important value proposition for converged infrastructure as noted in an IDC study. In April 2012,
1242-402: A single namenode plus a cluster of datanodes, although redundancy options are available for the namenode due to its criticality. Each datanode serves up blocks of data over the network using a block protocol specific to HDFS. The file system uses TCP/IP sockets for communication. Clients use remote procedure calls (RPC) to communicate with each other. HDFS stores large files (typically in
1311-490: A software-defined data center. Unlike legacy converged infrastructure management software, which focuses on managing devices, HP OneView is designed to help administrators enable tasks. Administrators can view resources and their relationships through a visual map and use the Representational state transfer (REST) application programming interface (API) to automate system management tasks. The HP OneView interface
1380-534: A standalone JobTracker server can manage job scheduling across nodes. When Hadoop MapReduce is used with an alternate file system, the NameNode, secondary NameNode, and DataNode architecture of HDFS are replaced by the file-system-specific equivalents. The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file system written in Java for the Hadoop framework. Some consider it to instead be
1449-469: Is a Hadoop application that runs on a Linux cluster with more than 10,000 cores and produced data that was 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 data centers. Every Hadoop cluster node bootstraps the Linux image, including the Hadoop distribution. Work that the clusters perform is known to include
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#17328010345351518-689: Is a way of structuring an information technology (IT) system which groups multiple components into a single optimized computing package. Components of a converged infrastructure may include servers , data storage devices , networking equipment and software for IT infrastructure management, automation and orchestration . IT organizations use converged infrastructure to centralize the management of IT resources, to consolidate systems, to increase resource-utilization rates, and to lower costs. Converged infrastructures foster these objectives by implementing pools of computers, storage and networking resources that can be shared by multiple applications and managed in
1587-411: Is also known as the checkpoint Node. It is the helper Node for the Name Node. The secondary name node instructs the name node to create & send fsimage & editlog file, upon which the compacted fsimage file is created by the secondary name node. Job Tracker: Job Tracker receives the requests for Map Reduce execution from the client. Job tracker talks to the Name Node to know about the location of
1656-411: Is an integrated cloud infrastructure for delivering private, public, and hybrid cloud services. It integrates HP cloud software with HP servers, storage, and networking technologies into a single system. The HP OneView converged infrastructure management product provides a unified interface that lets users automate formerly labor-intensive manual Data center management and maintenance tasks, as part of
1725-427: Is batch-oriented rather than real-time, is very data-intensive, and benefits from parallel processing . It can also be used to complement a real-time system, such as lambda architecture , Apache Storm , Flink , and Spark Streaming . Commercial applications of Hadoop include: On 19 February 2008, Yahoo! Inc. launched what they claimed was the world's largest Hadoop production application. The Yahoo! Search Webmap
1794-497: Is commonly known as cloud bursting. HP CloudSystem has lifecycle management tools to automate the process of application provisioning, customization and configuration, patch management and retirement. HP CloudSystem Foundation features the core components. Additional management capabilities and a simplified installation are included with HP CloudSystem Enterprise. HP ConvergedSystem for Big Data provides workload-specific, single-purpose systems that support tasks for which performance
1863-549: Is critical, such as Data analytics , Data warehousing , and business applications. Software applications are pre-integrated and designed for the integrated hardware, networking, and storage components. The HP ConvergedSystem 300 for Vertica is designed for businesses that run the HP Vertica data analytics solution. The system works with the Cloudera, Hortonworks, and MapR versions of Apache Hadoop . It has been reported that
1932-409: 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 (a, b, c) and node X contains data (x, y, z), the job tracker schedules node A to perform map or reduce tasks on (a, b, c) and node X would be scheduled to perform map or reduce tasks on (x, y, z). This reduces
2001-523: Is designed for larger enterprise installations of 100 to more than 1,000 virtual machines and comes configured with HP BladeSystem servers. For both models, customers can choose between VMware or Microsoft virtualization environments. Customers of the 700 model can also install their own virtualization software. All of the models are managed from a single console. It has been reported that HP ConvergedSystem for Virtualization performs twice as fast and costs 25 percent less than competing systems. HP CloudSystem
2070-583: Is designed for portability across various hardware platforms and for compatibility with a variety of underlying operating systems. The HDFS design introduces portability limitations that result in some performance bottlenecks, since the Java implementation cannot use features that are exclusive to the platform on which HDFS is running. Due to its widespread integration into enterprise-level infrastructure, monitoring HDFS performance at scale has become an increasingly important issue. Monitoring end-to-end performance requires tracking metrics from datanodes, namenodes, and
2139-644: Is modeled after consumer applications, to more efficiently manage HP BladeSystem and HP ProLiant generation 7 and 8 servers, with support for HP Moonshot Arm servers planned. It has been reported that because of HP OneView’s deep knowledge of these servers and HP Virtual Connect virtualization technology, administrators can deploy a 16-node Computer cluster 12 times faster than they could using manual operations. Administrators can also use HP OneView to provision and maintain Firmware across HP BladeSystem enclosures, servers, and Virtual Connect modules, as well as manage
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2208-487: Is mostly written in the Java programming language , with some native code in C and command line utilities written as shell scripts . 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. Other projects in the Hadoop ecosystem expose richer user interfaces. According to its co-founders, Doug Cutting and Mike Cafarella ,
2277-406: Is one single namenode in Hadoop 2, Hadoop 3, enables having multiple name nodes, which solves the single point of failure problem. In Hadoop 3, there are containers working in principle of Docker , which reduces time spent on application development. One of the biggest changes is that Hadoop 3 decreases storage overhead with erasure coding . Also, Hadoop 3 permits usage of GPU hardware within
2346-447: Is the name of the rack, specifically the network switch where a worker node is. Hadoop applications can use this information to execute code on the node where the data is, and, failing that, on the same rack/switch to reduce backbone traffic. HDFS uses this method when replicating data for data redundancy across multiple racks. This approach reduces the impact of a rack power outage or switch failure; if any of these hardware failures occurs,
2415-403: Is used by enterprises and service providers to build and manage private, public and hybrid cloud services. The system includes a consumer-style user interface and streamlined management tools. Based on HP Converged Infrastructure, HP CloudSystem modular infrastructure and HP Cloud Service Automation Software , CloudSystem integrates servers, storage, networking, security, and management to automate
2484-621: The Hadoop Common package, which provides file system and operating system level abstractions, a MapReduce engine (either MapReduce/MR1 or YARN/MR2) and the Hadoop Distributed File System (HDFS). The Hadoop Common package contains the Java Archive (JAR) files and scripts needed to start Hadoop. For effective scheduling of work, every Hadoop-compatible file system should provide location awareness, which
2553-518: The Java Runtime Environment (JRE) 1.6 or higher. The standard startup and shutdown scripts require that Secure Shell (SSH) be set up between nodes in the cluster. In a larger cluster, HDFS nodes are 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, thereby preventing file-system corruption and loss of data. Similarly,
2622-462: The ecosystem , or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig , Apache Hive , Apache HBase , Apache Phoenix , Apache Spark , Apache ZooKeeper , Apache Impala , Apache Flume , Apache Sqoop , Apache Oozie , and Apache Storm . Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on MapReduce and Google File System . The Hadoop framework itself
2691-485: The HP Collaboration Solutions for Microsoft SharePoint Server . These include HP SharePoint Business Decision for business intelligence and combines HP ProLiant Gen8 servers with SharePoint 2013 and SQL Server’s Power View. It has been reported that the system takes less than an hour to install and launch. HP ConvergedSystems Solutions for Microsoft Exchange Server is designed for users to deploy
2760-590: The JobTracker, while adding the ability to use an alternate scheduler (such as the Fair scheduler or the Capacity scheduler , described next). The fair scheduler was developed by Facebook . The goal of the fair scheduler is to provide fast response times for small jobs and Quality of service (QoS) for production jobs. The fair scheduler has three basic concepts. By default, jobs that are uncategorized go into
2829-477: The MapReduce engine in the first version of Hadoop. YARN strives to allocate resources to various applications effectively. It runs two daemons, which take care of two different tasks: the resource manager , which does job tracking and resource allocation to applications, the application master , which monitors progress of the execution. There are important features provided by Hadoop 3. For example, while there
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2898-609: The SAP HANA in-memory data management platform for data analytics and data warehousing. It has been reported that the system can process data analytics twice as fast as competing systems for SAP HANA. The HP ConvergedSystem 500 for SAP HANA includes ConvergedSystem 500 hardware and HP ServiceGuard for SAP HANA, a data management tool that protects against unscheduled downtime by providing the capability for automatic failover. The HP ConvergedSystem 500 for SAP HANA enables users to run data analytics and Enterprise resource planning (ERP) on
2967-465: 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. Known limitations of this approach are: By default Hadoop uses FIFO scheduling, and optionally 5 scheduling priorities to schedule jobs from a work queue. In version 0.19 the job scheduler was refactored out of
3036-409: 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 a separate Java virtual machine (JVM) process to prevent the TaskTracker itself from failing if the running job crashes its JVM. A heartbeat is sent from
3105-436: 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 as demonstrated with data-intensive jobs. HDFS was designed for mostly immutable files and may not be suitable for systems requiring concurrent write operations. HDFS can be mounted directly with
3174-725: The application and infrastructure lifecycle for service delivery via private and hybrid clouds. HP CloudSystem is a component of HP Converged Cloud, which combines software and cloud services into a unified set of packages and under a single unified architecture. HP CloudSystem is built on HP Cloud OS, which features OpenStack technology and enables users to choose from multiple Hypervisors and operating systems, including those from Microsoft and VMware. HP CloudSystem can automatically shift workloads to external clouds during busy periods, based on pre-defined business policies. Supported public clouds include those from Arsys and SFR, as well as Amazon Web Services and Microsoft Windows Azure. This function
3243-597: The cluster, which is a very substantial benefit to execute deep learning algorithms on a Hadoop cluster. The HDFS 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 Mahout machine learning system, and the Apache Hive data warehouse . Theoretically, Hadoop could be used for any workload that
3312-538: The data that will be used in processing. The Name Node responds with the metadata of the required processing data. Task Tracker: It is the Slave Node for the Job Tracker and it will take the task from the Job Tracker. It also receives code from the Job Tracker. Task Tracker will take the code and apply on the file. The process of applying that code on the file is known as Mapper. Hadoop cluster has nominally
3381-434: The data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking. The base Apache Hadoop framework is composed of the following modules: The term Hadoop is often used for both base modules and sub-modules and also
3450-404: The data will remain available. A small Hadoop cluster includes a single master and multiple worker nodes. The master node consists of a Job Tracker, Task Tracker, NameNode, and DataNode. A slave or worker node acts as both a DataNode and TaskTracker, though it is possible to have data-only and compute-only worker nodes. These are normally used only in nonstandard applications. Hadoop requires
3519-435: The data, information that Hadoop-specific file system bridges can provide. In May 2011, the list of supported file systems bundled with Apache Hadoop were: A number of third-party file system bridges have also been written, none of which are currently in Hadoop distributions. However, some commercial distributions of Hadoop ship with an alternative file system as the default – specifically IBM and MapR . Atop
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#17328010345353588-430: The details of the number of blocks, locations of the data node that the data is stored in, where the replications are stored, and other details. The name node has direct contact with the client. Data Node: A Data Node stores data in it as blocks. This is also known as the slave node and it stores the actual data into HDFS which is responsible for the client to read and write. These are slave daemons. Every Data node sends
3657-486: The file systems comes the MapReduce Engine, which consists of one JobTracker , to which client applications submit MapReduce jobs. The JobTracker pushes work 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
3726-419: The framework. The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality , where nodes manipulate
3795-681: The genesis of Hadoop was the Google File System paper that was published in October 2003. This paper spawned another one from Google – "MapReduce: Simplified Data Processing on Large Clusters". Development started on the Apache Nutch project, but was moved to the new Hadoop subproject in January 2006. Doug Cutting, who was working at Yahoo! at the time, named it after his son's toy elephant. The initial code that
3864-453: The index calculations for the Yahoo! search engine. In June 2009, Yahoo! made the source code of its Hadoop version available to the open-source community. In 2010, Facebook claimed that they had the largest Hadoop cluster in the world with 21 PB of storage. In June 2012, they announced the data had grown to 100 PB and later that year they announced that the data was growing by roughly half
3933-426: The main metadata server called the NameNode manually fail-over onto a backup. The project has also started developing automatic fail-overs . The HDFS file system includes a so-called secondary namenode , a misleading term that some might incorrectly interpret as a backup namenode when the primary namenode goes offline. In fact, the secondary namenode regularly connects with the primary namenode and builds snapshots of
4002-451: The open source analyst firm Wikibon released the first market forecast for converged infrastructure, with a projected $ 402 billion total available market (TAM) by 2017 of which, nearly 2/3 of the infrastructure that supports enterprise applications will be packaged in some type of converged solution by 2017. InformationWeek highlighted the promise of two long-term advantages of a unified data center infrastructure: Data centers around
4071-524: The pre-integration of technology components, the pooling of IT resources and the automation of IT processes. Converged infrastructure further contributes to efficient data centers by enhancing the ability of cloud computing systems to handle enormous data sets, using only a single integrated IT management system Writing in CIO magazine , Forrester Research analyst Robert Whiteley noted that converged infrastructures, combining server, storage, and networks into
4140-409: 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
4209-409: The range of gigabytes to terabytes ) across multiple machines. It achieves reliability by replicating the data across multiple hosts, and hence theoretically does not require redundant array of independent disks (RAID) storage on hosts (but to increase input-output (I/O) performance some RAID configurations are still useful). With the default replication value, 3, data is stored on three nodes: two on
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#17328010345354278-539: 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 of a Hadoop application. The trade-off of not having a fully POSIX-compliant file-system is increased performance for data throughput and support for non-POSIX operations such as Append. In May 2012, high-availability capabilities were added to HDFS, letting
4347-461: The same way Slave services can communicate with each other. Name Node is a master node and Data node is its corresponding Slave node and can talk with each other. Name Node: HDFS consists of only one Name Node that is called the Master Node. The master node can track files, manage the file system and has the metadata of all of the stored data within it. In particular, the name node contains
4416-653: The same workload-based system. It has been reported that data analytics processes that used to take days or weeks to run can now be accessed in real-time. The HP ConvergedSystem 900 for SAP HANA is designed to manage and analyze very large and varied data sets. It has been reported that the HP ConvergedSystem 900 for SAP HANA is capable of supplying 12 terabytes of data in one memory pool. HP ConvergedSystem for Collaboration integrates hardware, software and services to provide Unified communications for specific platforms. The pre-supplied workload configurations include
4485-540: The software-defined HP Moonshot m700 microserver systems and includes software, hardware and services to provide users with an environment for running Virtual desktops . Each user gets a dedicated CPU and GPU , which removes the need for a hypervisor. According to published reports, the Moonshot server consumes up to 63 percent less power than competing systems, and can lower the total cost of ownership by 44 percent compared to standard desktop computer systems. Built as
4554-438: The system can operate from 50 to 1,000 times faster than competitors’ data warehouse offerings. The HP ConvergedSystem 300 for Microsoft Analytics Platform System (APS) includes SQL Server Parallel Data Warehouse (PDW) and can be configured with HDInsight Hadoop nodes. It is possible to run PolyBase queries against SQL Server PDW and Cloudera, Apache, or Hortonworks HDP nodes. The HP ConvergedSystem 500 for SAP HANA operates
4623-440: The underlying operating system. There are currently several monitoring platforms to track HDFS performance, including Hortonworks , Cloudera , and Datadog . Hadoop works directly with any distributed file system that can be mounted by the underlying operating system by simply 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
4692-429: The world are reaching limits in power, cooling and space. At the same time, capital constraints are requiring organizations to rethink data center strategy. Converged infrastructure offers a solution to these challenges. Converged infrastructure can serve as an enabling platform for private and public cloud computing services, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as
4761-593: Was factored out of Nutch consisted of about 5,000 lines of code for HDFS and about 6,000 lines of code for MapReduce. In March 2006, Owen O'Malley was the first committer to add to the Hadoop project; Hadoop 0.1.0 was released in April 2006. It continues to evolve through contributions that are being made to the project. The first design document for the Hadoop Distributed File System was written by Dhruba Borthakur in 2007. Hadoop consists of
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