1296:, 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 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 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 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 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.
1832:
3827:
213:
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27:
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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 the underlying operating system. There are currently several monitoring platforms to track HDFS performance, including
1128:
1352:
HDFS 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
464:
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
1483:
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
1277:
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 the same rack, and one on a different rack. Data nodes can talk to each other to rebalance data, to move copies
1208:
Top three are Master
Services/Daemons/Nodes and bottom two are Slave Services. Master Services can communicate with each other and in 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.
1736:
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
1123:
For effective scheduling of work, every Hadoop-compatible file system should provide location awareness, which 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
1601:
The biggest difference between Hadoop 1 and Hadoop 2 is the addition of YARN (Yet
Another Resource Negotiator), which replaced 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
1233:
This is only to take care of the checkpoints of the file system metadata which is in the Name Node. This 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
1226:
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 a
Heartbeat message to the Name node every 3 seconds and conveys that it is
1153:
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, a standalone JobTracker
1124:
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, the data will remain available.
1278:
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
3330:
Chintapalli, Sanket; Dagit, Derek; Evans, Bobby; Farivar, Reza; Graves, Thomas; Holderbaugh, Mark; Liu, Zhuo; Nusbaum, Kyle; Patil, Kishorkumar; Peng, Boyang Jerry; Poulosky, Paul (May 2016). "Benchmarking
Streaming Computation Engines: Storm, Flink and Spark Streaming".
1219:
of all of the stored data within it. In particular, the name node contains 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.
292:
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
1510:
If one TaskTracker is very slow, it can delay the entire MapReduce job – especially towards the end, when everything can end up waiting for the slowest task. With speculative execution enabled, however, a single task can be executed on multiple slave
284:, 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 the framework.
1488:(JVM) process to prevent the TaskTracker itself from failing if the running job crashes its 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
1415:
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
1240:
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 the data that will be used in processing. The Name Node responds with the metadata of the required processing data.
1247:
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.
453:, 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
1154:
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.
437:. 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.
3512:
1524:
scheduling, and optionally 5 scheduling priorities to schedule jobs from a work queue. In version 0.19 the job scheduler was refactored out of the JobTracker, while adding the ability to use an alternate scheduler (such as the
1731:
On 19 February 2008, Yahoo! Inc. launched what they claimed was the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on a Linux cluster with more than 10,000
1377:
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, information that Hadoop-specific file system bridges can provide.
1187:
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:
1503:(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
1411:
Windows Azure
Storage Blobs (WASB) file system: This is an extension of HDFS that allows distributions of Hadoop to access data in Azure blob stores without moving the data permanently into the cluster.
1285:
In May 2012, high-availability capabilities were added to HDFS, letting the main metadata server called the NameNode manually fail-over onto a backup. The project has also started developing automatic
1227:
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.
1484:
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
1737:
clusters perform is known to include 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.
461:
at the time, named it after his son's toy elephant. The initial code that was factored out of Nutch consisted of about 5,000 lines of code for HDFS and about 6,000 lines of code for MapReduce.
2172:
3115:
3394:
3468:
1806:. The authors highlight the need for storage systems to accept all data formats and to provide APIs for data access that evolve based on the storage system's understanding of the data.
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By default, jobs that are uncategorized go into 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.
1255:
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
6038:
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1139:
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.
1135:
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
2845:
1799:, Google. This paper inspired Doug Cutting to develop an open-source implementation of the Map-Reduce framework. He named it Hadoop, after his son's toy elephant.
1744:
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 a PB per day.
1639:
Also, Hadoop 3 permits usage of GPU hardware within the cluster, which is a very substantial benefit to execute deep learning algorithms on a Hadoop cluster.
1810:
1783:. The naming of products and derivative works from other vendors and the term "compatible" are somewhat controversial within the Hadoop developer community.
3854:
3177:
1385:
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
2426:
2131:
Wang, Yandong; Goldstone, Robin; Yu, Weikuan; Wang, Teng (October 2014). "Characterization and Optimization of Memory-Resident MapReduce on HPC Systems".
1647:
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
257:
software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a
2180:
3155:
2669:
1796:
3513:"Altior's AltraSTAR – Hadoop Storage Accelerator and Optimizer Now Certified on CDH4 (Cloudera's Distribution Including Apache Hadoop Version 4)"
1575:
The capacity scheduler was developed by Yahoo. The capacity scheduler supports several features that are similar to those of the fair scheduler.
3119:
335:– (introduced in 2012) is a platform responsible for managing computing resources in clusters and using them for scheduling users' applications;
3402:
1665:. Theoretically, Hadoop could be used for any workload that is batch-oriented rather than real-time, is very data-intensive, and benefits from
2452:
6018:
3300:
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package, which provides file system and operating system level abstractions, a MapReduce engine (either MapReduce/MR1 or YARN/MR2) and the
2735:
3625:
2871:
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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
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6033:
4507:
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2790:
1373:
Hadoop works directly with any distributed file system that can be mounted by the underlying operating system by simply using a
1269:
HDFS stores large files (typically in the range of gigabytes to terabytes) across multiple machines. It achieves reliability by
4737:
4712:
329:– a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster;
5396:
3801:
3780:
3755:
3718:
3693:
3348:
2148:
1775:
The Apache Software Foundation has stated that only software officially released by the Apache Hadoop Project can be called
2849:
2721:
2699:
1837:
2974:
2935:
2521:
5969:
5444:
4678:
3840:
2895:
Pessach, Yaniv (2013). "Distributed Storage" (Distributed Storage: Concepts, Algorithms, and Implementations ed.).
1299:
HDFS was designed for mostly immutable files and may not be suitable for systems requiring concurrent write operations.
5959:
5130:
4939:
2230:"Continuuity Raises $ 10 Million Series A Round to Ignite Big Data Application Development Within the Hadoop Ecosystem"
5337:
6028:
5581:
2291:
2229:
5495:
3704:
1759:. The cloud allows organizations to deploy Hadoop without the need to acquire hardware or specific setup expertise.
5247:
4969:
4929:
1521:
1429:
175:
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1972:
3070:
2418:
3863:
2757:
1329:
API (generates a client in a number of languages e.g. C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#,
1270:
2105:
6043:
5026:
4450:
1622:
in Hadoop 2, Hadoop 3, enables having multiple name nodes, which solves the single point of failure problem.
1401:
1184:
359:, or collection of additional software packages that can be installed on top of or alongside Hadoop, such as
2203:
5964:
5380:
4954:
3027:
1747:
As of 2013, Hadoop adoption had become widespread: more than half of the Fortune 50 companies used Hadoop.
1405:
139:
1438:
In April 2010, Appistry released a Hadoop file system driver for use with its own CloudIQ Storage product.
5885:
5733:
5665:
4959:
4766:
3088:"HADOOP-6330: Integrating IBM General Parallel File System implementation of Hadoop Filesystem interface"
2677:
1354:
422:
156:
84:
2777:
HDFS is not a file system in the traditional sense and isn't usually directly mounted for a user to view
2025:
1863:, a database that uses JSON for documents, JavaScript for MapReduce queries, and regular HTTP for an API
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5770:
5760:
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55:
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project, but was moved to the new Hadoop subproject in January 2006. Doug Cutting, who was working at
2314:
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server-on-demand infrastructure. There is no rack-awareness in this file system, as it is all remote.
3001:
2921:
5835:
5688:
5591:
5536:
5411:
5267:
5036:
4455:
2337:
1874:
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The allocation of work to TaskTrackers is very simple. Every TaskTracker has a number of available
1435:
In April 2010, Parascale published the source code to run Hadoop against the Parascale file system.
1252:
1143:
426:
3218:
1791:
Some papers influenced the birth and growth of Hadoop and big data processing. Some of these are:
1392:
Apache Hadoop Ozone: HDFS-compatible object store targeting optimized for billions of small files.
5934:
5890:
5872:
5571:
5561:
5016:
3929:
3275:
2820:
1554:
1307:
1172:
180:
3308:
1803:
1183:
compliance, but it does provide shell commands and Java application programming interface (API)
5780:
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5683:
5162:
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4846:
4776:
4470:
1901:
1590:
1338:
1176:
430:
302:
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3141:
2281:
1479:, to which client applications submit MapReduce jobs. The JobTracker pushes work to available
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5434:
5385:
5237:
5210:
5087:
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4890:
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1263:
262:
3647:
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Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
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5459:
5100:
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4907:
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There are important features provided by Hadoop 3. For example, while there is one single
8:
5995:
5941:
5880:
5469:
5137:
5078:
4994:
4334:
3469:"Under the Hood: Hadoop Distributed File system reliability with Namenode and Avatarnode"
2958:"Improving MapReduce performance through data placement in heterogeneous Hadoop Clusters"
1670:
1311:
3333:
2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
1998:
1740:
In 2010, Facebook claimed that they had the largest Hadoop cluster in the world with 21
1585:
Within a queue, a job with a high level of priority has access to the queue's resources.
5985:
5847:
5810:
5723:
5449:
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5424:
5365:
5195:
4858:
4851:
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3924:
3354:
2154:
1666:
1546:
1489:
1460:, which replaced the HDFS file system with a full random-access read/write file system.
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341:– an implementation of the MapReduce programming model for large-scale data processing.
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258:
192:
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1545:. The goal of the fair scheduler is to provide fast response times for small jobs and
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5262:
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5224:
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5068:
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4349:
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3989:
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3768:
Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools
3751:
3747:
3714:
3689:
3559:
3534:
3344:
3171:
2936:"Version 2.0 provides for manual failover and they are working on automatic failover"
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2144:
1626:
1326:
1303:
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Hadoop cluster has nominally a single namenode plus a cluster of datanodes, although
1117:
301:, where nodes manipulate the data they have access to. This allows the dataset to be
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273:
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The Lucene PMC has voted to split part of Nutch into a new sub-project named Hadoop
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1504:
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H-store: a high-performance, distributed main memory transaction processing system
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In May 2011, the list of supported file systems bundled with Apache Hadoop were:
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450:
372:
168:
43:
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A number of companies offer commercial implementations or support for Hadoop.
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into nodes to process the data in parallel. This approach takes advantage of
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One of the biggest changes is that Hadoop 3 decreases storage overhead with
1146:(JRE) 1.6 or higher. The standard startup and shutdown scripts require that
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written in Java for the Hadoop framework. Some consider it to instead be a
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39:
3002:"The Hadoop Distributed Filesystem: Balancing Portability and Performance"
2140:
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2014 IEEE 28th International Parallel and Distributed Processing Symposium
1950:
1804:
From Databases to Dataspaces: A New Abstraction for Information Management
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file system: This stores all its data on remotely accessible FTP servers.
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the data across multiple hosts, and hence theoretically does not require
364:
3491:"Under the Hood: Scheduling MapReduce jobs more efficiently with Corona"
1755:
Hadoop can be deployed in a traditional onsite datacenter as well as in
1549:(QoS) for production jobs. The fair scheduler has three basic concepts.
1475:
Atop the file systems comes the MapReduce Engine, which consists of one
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1606:, which does job tracking and resource allocation to applications, the
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The base Apache Hadoop framework is composed of the following modules:
50:
34:
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Archival work for compliance, including of relational and tabular data
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where computation and data are distributed via high-speed networking.
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announced the availability of an alternative file system for Hadoop,
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faster and more efficiently than it would be in a more conventional
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4199:
4179:
3071:"Cloud analytics: Do we really need to reinvent the storage stack?"
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Free resources are allocated to queues beyond their total capacity.
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1358:
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266:
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1007:
988:
323:– contains libraries and utilities needed by other Hadoop modules;
26:
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is often used for both base modules and sub-modules and also the
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Apache HCatalog, a table and storage management layer for Hadoop
1669:. It can also be used to complement a real-time system, such as
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5541:
5345:
5046:
4690:
3772:
3710:
3395:"Yahoo! Launches World's Largest Hadoop Production Application"
2179:. Apache Software Foundation. 12 September 2014. Archived from
1579:
Queues are allocated a fraction of the total resource capacity.
1256:
458:
407:
406:
Apache Hadoop's MapReduce and HDFS components were inspired by
3158:. 17 August 2011. Archived from the original on 17 August 2011
2453:"[RESULT] VOTE: add Owen O'Malley as Hadoop committer"
1234:
compacted fsimage file is created by the secondary name node.
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5370:
5323:
5318:
5311:
5306:
5301:
5296:
5252:
5242:
5147:
5115:
5009:
5004:
4999:
4895:
4823:
4771:
4623:
4538:
4517:
4209:
2522:"The Hadoop Distributed File System: Architecture and Design"
2336:
Cutting, Mike; Cafarella, Ben; Lorica, Doug (31 March 2016).
2259:
1857:, a column-oriented database that supports access from Hadoop
1648:
1334:
1325:
File access can be achieved through the native Java API, the
1315:
1180:
392:
242:
3820:
3515:(Press release). Eatontown, NJ: Altior Inc. 18 December 2012
3329:
2050:
1507:
of the allocated machine, and hence its actual availability.
203:
5566:
5531:
5521:
5516:
5454:
5390:
5360:
5355:
5291:
5286:
5257:
5166:
5157:
5110:
5051:
5021:
4912:
4883:
4873:
4868:
4833:
4815:
4608:
4399:
2722:"Running Hadoop on Ubuntu Linux System(Multi-Node Cluster)"
2419:"Hadoop, a Free Software Program, Finds Uses Beyond Search"
2362:
Ghemawat, Sanjay; Gobioff, Howard; Leung, Shun-Tak (2003).
1879:
1613:
1453:
1417:
1404:
object storage: This is targeted at clusters hosted on the
1346:
1319:
1274:
1127:
233:
1811:
Bigtable: A Distributed Storage System for Structured Data
1625:
In Hadoop 3, there are containers working in principle of
1073:
5172:
5152:
5041:
4943:
3226:
2379:"MapReduce: Simplified Data Processing on Large Clusters"
2083:
1711:
1425:
1395:
1061:
1056:
3447:"HDFS: Facebook has the world's largest Hadoop cluster!"
2286:. John Wiley & Sons. 19 December 2014. p. 300.
6039:
Free software programmed in Java (programming language)
1797:
MapReduce: Simplified Data Processing on Large Clusters
1629:, which reduces time spent on application development.
5276:
2736:"Running Hadoop on Ubuntu Linux (Single-Node Cluster)"
2335:
3301:"How Apache Hadoop 3 Adds Value Over Apache Hadoop 2"
2791:"Managing Files with the Hadoop File System Commands"
1282:
and support for non-POSIX operations such as Append.
421:
The Hadoop framework itself is mostly written in the
2361:
2315:"[nlpatumd] Adventures with Hadoop and Perl"
2076:"What is the Hadoop Distributed File System (HDFS)?"
1827:
245:
239:
3102:"HADOOP-6704: add support for Parascale filesystem"
2130:
1113:
1095:
236:
230:
3862:
3582:"Why the Pace of Hadoop Innovation Has to Pick Up"
3373:""How 30+ enterprises are using Hadoop", in DBMS2"
2728:
2714:
1802:Michael Franklin, Alon Halevy, David Maier (2005)
1886:Risk Solutions High Performance Computing Cluster
1560:Each pool is assigned a guaranteed minimum share.
1162:
347:– (introduced in 2020) An object store for Hadoop
6010:
3176:: CS1 maint: bot: original URL status unknown (
2204:"Apache Hadoop YARN – Concepts and Applications"
1898:– Open source distributed storage and processing
1465:JobTracker and TaskTracker: the MapReduce engine
1432:. The source code was published in October 2009.
1171:(HDFS) is a distributed, scalable, and portable
2890:
2888:
1597:Difference between Hadoop 1 and Hadoop 2 (YARN)
1116:(HDFS). The Hadoop Common package contains the
3192:"Refactor the scheduler out of the JobTracker"
1750:
1150:(SSH) be set up between nodes in the cluster.
4706:
3848:
1349:, or via 3rd-party network client libraries.
1066:
3425:"Hadoop and Distributed Computing at Yahoo!"
3118:. Appistry, Inc. 6 July 2010. Archived from
2885:
2376:
1610:, which monitors progress of the execution.
3028:"How to Collect Hadoop Performance Metrics"
3000:Shafer, Jeffrey; Rixner, Scott; Cox, Alan.
2999:
1688:Commercial applications of Hadoop include:
1275:redundant array of independent disks (RAID)
4713:
4699:
3855:
3841:
3825:
3626:"Defining Hadoop Compatibility: revisited"
3025:
1945:
1943:
1292:The HDFS file system includes a so-called
1120:files and scripts needed to start Hadoop.
211:
25:
3789:
2758:"Big data storage: Hadoop storage basics"
2667:
2519:
2173:"Resource (Apache Hadoop Main 2.5.1 API)"
2106:"Data Locality: HPC vs. Hadoop vs. Spark"
3049:"HDFS Users Guide – Rack Awareness"
2928:
2846:"Apache Hadoop 2.7.5 – HDFS Users Guide"
2821:"Big Data Hadoop Tutorial for Beginners"
2377:Dean, Jeffrey; Ghemawat, Sanjay (2004).
2026:"Cray Launches Hadoop into HPC Airspace"
1614:Difference between Hadoop 2 and Hadoop 3
1495:Known limitations of this approach are:
1126:
1078:
3648:"Apache Accumulo User Manual: Security"
3628:. Mail-archives.apache.org. 10 May 2011
3248:"Hadoop Fair Scheduler Design Document"
2894:
2447:
2391:
2043:
2023:
1940:
6011:
3702:
2201:
1996:
1726:
1563:Excess capacity is split between jobs.
1492:and can be viewed from a web browser.
1266:(RPC) to communicate with each other.
4694:
3836:
3764:
3739:
3449:. Hadoopblog.blogspot.com. 9 May 2010
3216:
2755:
2413:
2103:
1973:"Doug Cutting: Big Data Is No Bubble"
1970:
1795:Jeffrey Dean, Sanjay Ghemawat (2004)
1762:
1642:
1570:
1368:
327:Hadoop Distributed File System (HDFS)
276:. Hadoop was originally designed for
16:Distributed data processing framework
2668:Chouraria, Harsh (21 October 2012).
2338:"The next 10 years of Apache Hadoop"
2104:Malak, Michael (19 September 2014).
1838:Free and open-source software portal
1541:The fair scheduler was developed by
6019:Apache Software Foundation projects
3677:
2561:"Release 3.0.0 generally available"
2429:from the original on 30 August 2011
2394:"new mailing lists request: hadoop"
2024:Hemsoth, Nicole (15 October 2014).
13:
3790:Wiktorski, Tomasz (January 2019).
3401:. 19 February 2008. Archived from
2818:
2788:
1428:discussed running Hadoop over the
1402:Amazon S3 (Simple Storage Service)
973:Old version, no longer maintained:
956:Old version, no longer maintained:
939:Old version, yet still maintained:
922:Old version, no longer maintained:
905:Old version, no longer maintained:
888:Old version, no longer maintained:
871:Old version, no longer maintained:
854:Old version, no longer maintained:
837:Old version, no longer maintained:
821:Old version, no longer maintained:
805:Old version, no longer maintained:
788:Old version, no longer maintained:
771:Old version, no longer maintained:
754:Old version, no longer maintained:
737:Old version, no longer maintained:
720:Old version, no longer maintained:
703:Old version, no longer maintained:
687:Old version, no longer maintained:
671:Old version, no longer maintained:
654:Old version, no longer maintained:
637:Old version, no longer maintained:
620:Old version, no longer maintained:
603:Old version, no longer maintained:
586:Old version, no longer maintained:
569:Old version, no longer maintained:
552:Old version, no longer maintained:
535:Old version, no longer maintained:
518:Old version, no longer maintained:
502:Old version, no longer maintained:
486:Old version, no longer maintained:
14:
6065:
6054:Software using the Apache license
6034:Free software for cloud computing
3812:
3307:. 7 February 2018. Archived from
3217:Jones, M. Tim (6 December 2011).
3026:Mouzakitis, Evan (21 July 2016).
2392:Cutting, Doug (28 January 2006).
1536:
4720:
4673:
4672:
3606:. Wiki.apache.org. 30 March 2013
2670:"MR2 and YARN Briefly Explained"
1971:Judge, Peter (22 October 2012).
1830:
1430:IBM General Parallel File System
226:
3796:. Cham, Switzerland: Springer.
3670:
3640:
3618:
3596:
3574:
3552:
3527:
3505:
3483:
3461:
3439:
3417:
3387:
3365:
3323:
3293:
3268:
3240:
3210:
3184:
3148:
3134:
3108:
3094:
3080:
3063:
3041:
3019:
2993:
2967:
2950:
2864:
2838:
2812:
2782:
2749:
2692:
2661:
2643:
2625:
2607:
2589:
2571:
2553:
2535:
2513:
2488:
2463:
2441:
2407:
2385:
2370:
2355:
2329:
2307:
2274:
2252:
2222:
2202:Murthy, Arun (15 August 2012).
2195:
2165:
1262:for communication. Clients use
1157:
1089:
3864:The Apache Software Foundation
3765:Vohra, Deepak (October 2016).
3703:Venner, Jason (22 June 2009).
2317:. Mail-archive.com. 2 May 2010
2124:
2097:
2068:
2017:
1990:
1964:
1914:
1781:Distributions of Apache Hadoop
1169:Hadoop distributed file system
1163:Hadoop distributed file system
1114:Hadoop Distributed File System
1096:Hadoop Distributed File System
445:According to its co-founders,
1:
2756:Evans, Chris (October 2013).
2529:Apache Hadoop Code Repository
1907:
1816:Robert Kallman et al. (2008)
1515:
1062:Old version, still maintained
3743:Hadoop: The Definitive Guide
3654:. Apache Software Foundation
3375:. Dbms2.com. 10 October 2009
3335:. IEEE. pp. 1789–1792.
3198:. Apache Software Foundation
2963:. Eng.auburn.ed. April 2010.
2471:"Index of /dist/hadoop/core"
2260:"Hadoop-related projects at"
1997:Woodie, Alex (12 May 2014).
1928:. Apache Software Foundation
1406:Amazon Elastic Compute Cloud
7:
5886:Filesystem-level encryption
3740:White, Tom (16 June 2009).
3678:Lam, Chuck (28 July 2010).
3584:. Gigaom.com. 25 April 2011
3116:"HDFS with CloudIQ Storage"
3104:. Parascale. 14 April 2010.
2051:"Welcome to Apache Hadoop!"
1823:
1770:
1751:Hadoop hosting in the cloud
1445:discussed a location-aware
1131:A multi-node Hadoop cluster
1057:Old version, not maintained
425:, with some native code in
287:
115:3.4.0 / March 17, 2024
10:
6070:
4733:Comparison of file systems
3535:"Hadoop - Microsoft Azure"
3142:"High Availability Hadoop"
2543:"Release 2.10.2 available"
2520:Borthakur, Dhruba (2006).
2135:. IEEE. pp. 799–808.
2112:. Data Science Association
1468:
1093:
440:
307:supercomputer architecture
97:2.10.2 / May 31, 2022
56:Apache Software Foundation
5978:
5950:
5915:
5871:
5796:
5789:
5711:
5664:
5610:
5512:
5505:
5410:
5336:
5219:
5186:
4762:
4753:
4728:
4668:
4652:
4526:
4495:
4479:
4441:
3870:
3276:"CapacityScheduler Guide"
2651:"Release 3.4.0 available"
2633:"Release 3.3.6 available"
2615:"Release 3.2.4 available"
2597:"Release 3.1.4 available"
2579:"Release 3.0.3 available"
1999:"Why Hadoop on IBM Power"
1786:
1045:
423:Java programming language
198:
186:
174:
162:
152:
138:
134:
111:
93:
83:
79:
61:
49:
33:
24:
6029:Distributed file systems
5836:Extended file attributes
5537:Compact Disc File System
2364:"The Google File System"
1875:Data-intensive computing
1809:Fay Chang et al. (2006)
1144:Java Runtime Environment
5935:Installable File System
3427:. Yahoo!. 20 April 2011
3341:10.1109/IPDPSW.2016.138
3090:. IBM. 23 October 2009.
1593:once a job is running.
1520:By default Hadoop uses
1308:Filesystem in Userspace
1108:Hadoop consists of the
1028:Current stable version:
1009:Current stable version:
990:Current stable version:
181:Distributed file system
4983:TiVo Media File System
4847:Encrypting File System
3793:Data-intensive Systems
3219:"Scheduling in Hadoop"
2916:Cite journal requires
1902:Slurm Workload Manager
1553:Jobs are grouped into
1454:MapR Technologies Inc.
1339:command-line interface
1264:remote procedure calls
1132:
1074:Latest preview version
474:Original release date
117:; 6 months ago
67:; 18 years ago
4978:Macintosh File System
3562:. Azure.microsoft.com
2141:10.1109/IPDPS.2014.87
1702:Machine learning and
1602:different tasks: the
1130:
433:utilities written as
253:) is a collection of
99:; 2 years ago
6044:Free system software
5991:GUID Partition Table
5338:Distributed parallel
5086:Shared File System (
3686:Manning Publications
1486:Java virtual machine
1231:Secondary Name Node:
1142:Hadoop requires the
311:parallel file system
5996:Apple Partition Map
5942:Virtual file system
5881:Access-control list
4995:NetWare File System
3539:azure.microsoft.com
3311:on 16 November 2018
3051:. Hadoop.apache.org
2938:. Hadoop.apache.org
2872:"HDFS Architecture"
2702:. Hadoop.apache.org
2381:. pp. 137–150.
2262:. Hadoop.apache.org
2110:datascienceassn.org
1892:– HBase alternative
1727:Prominent use cases
1699:Marketing analytics
1671:lambda architecture
1667:parallel processing
1533:, described next).
1449:file system driver.
1312:virtual file system
1195:Secondary Name Node
1179:due to its lack of
263:distributed storage
21:
5986:Master Boot Record
5811:Data deduplication
5450:Google File System
5366:Google File System
4852:Extent File System
4814:Byte File System (
3925:Apache HTTP Server
3727:on 5 December 2010
3144:. HP. 9 June 2010.
2852:on 23 October 2019
2762:computerweekly.com
2680:on 22 October 2013
2475:archive.apache.org
2423:The New York Times
2240:. 14 November 2012
1763:Commercial support
1714:message processing
1643:Other applications
1608:application master
1571:Capacity scheduler
1547:Quality of service
1531:Capacity scheduler
1369:Other file systems
1294:secondary namenode
1133:
1118:Java Archive (JAR)
416:Google File System
282:commodity hardware
265:and processing of
259:software framework
193:Apache License 2.0
65:April 1, 2006
35:Original author(s)
19:
6024:Big data products
6004:
6003:
5911:
5910:
5801:Case preservation
5707:
5706:
5406:
5405:
5332:
5331:
5094:Smart File System
4688:
4687:
3803:978-3-030-04603-3
3782:978-1-4842-2199-0
3757:978-0-596-52197-4
3720:978-1-430-21942-2
3695:978-1-935-18219-1
3604:"Defining Hadoop"
3350:978-1-5090-3682-0
3280:Hadoop.apache.org
3076:. IBM. June 2009.
3007:. Rice University
2700:"HDFS User Guide"
2655:hadoop.apache.org
2637:hadoop.apache.org
2619:hadoop.apache.org
2601:hadoop.apache.org
2583:hadoop.apache.org
2565:hadoop.apache.org
2547:hadoop.apache.org
2500:hadoop.apache.org
2457:hadoop-common-dev
2451:(30 March 2006).
2417:(17 March 2009).
2398:issues.apache.org
2366:. pp. 20–43.
2234:finance.yahoo.com
2183:on 6 October 2014
2150:978-1-4799-3800-1
2055:hadoop.apache.org
1922:"Hadoop Releases"
1387:operating systems
1333:, Smalltalk, and
1087:
1086:
1083:
309:that relies on a
278:computer clusters
274:programming model
219:
218:
146:Hadoop Repository
129:
128:
6061:
5821:Execute in place
5794:
5793:
5527:Boot File System
5510:
5509:
5274:
5273:
4810:Boot File System
4760:
4759:
4715:
4708:
4701:
4692:
4691:
4676:
4675:
3857:
3850:
3843:
3834:
3833:
3829:
3824:
3823:
3821:Official website
3807:
3786:
3771:(1st ed.).
3761:
3746:(1st ed.).
3736:
3734:
3732:
3723:. Archived from
3709:(1st ed.).
3699:
3684:(1st ed.).
3681:Hadoop in Action
3664:
3663:
3661:
3659:
3644:
3638:
3637:
3635:
3633:
3622:
3616:
3615:
3613:
3611:
3600:
3594:
3593:
3591:
3589:
3578:
3572:
3571:
3569:
3567:
3556:
3550:
3549:
3547:
3545:
3531:
3525:
3524:
3522:
3520:
3509:
3503:
3502:
3500:
3498:
3487:
3481:
3480:
3478:
3476:
3465:
3459:
3458:
3456:
3454:
3443:
3437:
3436:
3434:
3432:
3421:
3415:
3414:
3412:
3410:
3391:
3385:
3384:
3382:
3380:
3369:
3363:
3362:
3327:
3321:
3320:
3318:
3316:
3297:
3291:
3290:
3288:
3286:
3272:
3266:
3265:
3263:
3261:
3252:
3244:
3238:
3237:
3235:
3233:
3214:
3208:
3207:
3205:
3203:
3188:
3182:
3181:
3175:
3167:
3165:
3163:
3156:"Commands Guide"
3152:
3146:
3145:
3138:
3132:
3131:
3129:
3127:
3112:
3106:
3105:
3098:
3092:
3091:
3084:
3078:
3077:
3075:
3067:
3061:
3060:
3058:
3056:
3045:
3039:
3038:
3036:
3034:
3023:
3017:
3016:
3014:
3012:
3006:
2997:
2991:
2990:
2988:
2986:
2977:. Archived from
2971:
2965:
2964:
2962:
2954:
2948:
2947:
2945:
2943:
2932:
2926:
2925:
2919:
2914:
2912:
2904:
2892:
2883:
2882:
2880:
2878:
2868:
2862:
2861:
2859:
2857:
2848:. Archived from
2842:
2836:
2835:
2833:
2831:
2816:
2810:
2809:
2807:
2805:
2786:
2780:
2779:
2774:
2772:
2753:
2747:
2746:
2744:
2742:
2732:
2726:
2725:
2718:
2712:
2711:
2709:
2707:
2696:
2690:
2689:
2687:
2685:
2676:. Archived from
2665:
2659:
2658:
2647:
2641:
2640:
2629:
2623:
2622:
2611:
2605:
2604:
2593:
2587:
2586:
2575:
2569:
2568:
2557:
2551:
2550:
2539:
2533:
2532:
2526:
2517:
2511:
2510:
2508:
2506:
2492:
2486:
2485:
2483:
2481:
2467:
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2445:
2439:
2438:
2436:
2434:
2411:
2405:
2404:
2389:
2383:
2382:
2374:
2368:
2367:
2359:
2353:
2352:
2350:
2348:
2333:
2327:
2326:
2324:
2322:
2311:
2305:
2304:
2302:
2300:
2278:
2272:
2271:
2269:
2267:
2256:
2250:
2249:
2247:
2245:
2226:
2220:
2219:
2217:
2215:
2199:
2193:
2192:
2190:
2188:
2169:
2163:
2162:
2128:
2122:
2121:
2119:
2117:
2101:
2095:
2094:
2092:
2090:
2072:
2066:
2065:
2063:
2061:
2047:
2041:
2040:
2038:
2036:
2021:
2015:
2014:
2012:
2010:
1994:
1988:
1987:
1985:
1983:
1968:
1962:
1961:
1959:
1957:
1947:
1938:
1937:
1935:
1933:
1918:
1855:Apache Cassandra
1840:
1835:
1834:
1833:
1708:Image processing
1658:system, and the
1656:machine learning
1604:resource manager
1376:
1306:directly with a
1080:
1075:
1070:
1063:
1058:
1053:
1046:
1029:
1010:
991:
974:
957:
940:
923:
906:
889:
872:
855:
838:
822:
806:
789:
772:
755:
738:
721:
704:
688:
672:
655:
638:
621:
604:
587:
570:
553:
536:
519:
503:
487:
468:
467:
381:Apache ZooKeeper
339:Hadoop MapReduce
252:
251:
248:
247:
244:
241:
238:
235:
232:
215:
210:
207:
205:
164:Operating system
148:
125:
123:
118:
107:
105:
100:
91:
90:
75:
73:
68:
29:
22:
18:
6069:
6068:
6064:
6063:
6062:
6060:
6059:
6058:
6009:
6008:
6005:
6000:
5974:
5946:
5930:File system API
5907:
5867:
5843:File change log
5785:
5761:Record-oriented
5734:Self-certifying
5703:
5660:
5606:
5501:
5402:
5328:
5272:
5215:
5182:
4755:
4749:
4745:Unix filesystem
4724:
4719:
4689:
4684:
4664:
4648:
4522:
4491:
4475:
4437:
3872:
3866:
3861:
3819:
3818:
3815:
3810:
3804:
3783:
3775:. p. 429.
3758:
3750:. p. 524.
3730:
3728:
3721:
3713:. p. 440.
3696:
3688:. p. 325.
3673:
3668:
3667:
3657:
3655:
3646:
3645:
3641:
3631:
3629:
3624:
3623:
3619:
3609:
3607:
3602:
3601:
3597:
3587:
3585:
3580:
3579:
3575:
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3563:
3558:
3557:
3553:
3543:
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3533:
3532:
3528:
3518:
3516:
3511:
3510:
3506:
3496:
3494:
3489:
3488:
3484:
3474:
3472:
3467:
3466:
3462:
3452:
3450:
3445:
3444:
3440:
3430:
3428:
3423:
3422:
3418:
3408:
3406:
3405:on 7 March 2016
3393:
3392:
3388:
3378:
3376:
3371:
3370:
3366:
3351:
3328:
3324:
3314:
3312:
3305:hortonworks.com
3299:
3298:
3294:
3284:
3282:
3274:
3273:
3269:
3259:
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3250:
3246:
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3241:
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3229:
3215:
3211:
3201:
3199:
3190:
3189:
3185:
3169:
3168:
3161:
3159:
3154:
3153:
3149:
3140:
3139:
3135:
3125:
3123:
3122:on 5 April 2014
3114:
3113:
3109:
3100:
3099:
3095:
3086:
3085:
3081:
3073:
3069:
3068:
3064:
3054:
3052:
3047:
3046:
3042:
3032:
3030:
3024:
3020:
3010:
3008:
3004:
2998:
2994:
2984:
2982:
2975:"Mounting HDFS"
2973:
2972:
2968:
2960:
2956:
2955:
2951:
2941:
2939:
2934:
2933:
2929:
2917:
2915:
2906:
2905:
2893:
2886:
2876:
2874:
2870:
2869:
2865:
2855:
2853:
2844:
2843:
2839:
2829:
2827:
2825:www.gyansetu.in
2817:
2813:
2803:
2801:
2787:
2783:
2770:
2768:
2766:Computer Weekly
2754:
2750:
2740:
2738:
2734:
2733:
2729:
2720:
2719:
2715:
2705:
2703:
2698:
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2613:
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2541:
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2524:
2518:
2514:
2504:
2502:
2494:
2493:
2489:
2479:
2477:
2469:
2468:
2464:
2459:(Mailing list).
2446:
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2412:
2408:
2390:
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2228:
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2213:
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2208:hortonworks.com
2200:
2196:
2186:
2184:
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2151:
2129:
2125:
2115:
2113:
2102:
2098:
2088:
2086:
2074:
2073:
2069:
2059:
2057:
2049:
2048:
2044:
2034:
2032:
2022:
2018:
2008:
2006:
1995:
1991:
1981:
1979:
1969:
1965:
1955:
1953:
1951:"Apache Hadoop"
1949:
1948:
1941:
1931:
1929:
1920:
1919:
1915:
1910:
1846:Apache Accumulo
1836:
1831:
1829:
1826:
1789:
1773:
1765:
1753:
1729:
1683:Spark Streaming
1645:
1616:
1599:
1573:
1539:
1518:
1473:
1467:
1374:
1371:
1343:web application
1318:and some other
1165:
1160:
1106:
1092:
1082:
1081:
1076:
1071:
1064:
1059:
1054:
1049:
1027:
1008:
989:
972:
955:
938:
921:
904:
887:
870:
853:
836:
820:
804:
787:
770:
753:
736:
719:
702:
686:
670:
653:
636:
619:
602:
585:
568:
551:
534:
517:
501:
485:
477:Latest version
443:
290:
229:
225:
202:
144:
130:
121:
119:
116:
103:
101:
98:
71:
69:
66:
62:Initial release
17:
12:
11:
5:
6067:
6057:
6056:
6051:
6046:
6041:
6036:
6031:
6026:
6021:
6002:
6001:
5999:
5998:
5993:
5988:
5982:
5980:
5976:
5975:
5973:
5972:
5970:Log-structured
5967:
5962:
5956:
5954:
5948:
5947:
5945:
5944:
5939:
5938:
5937:
5927:
5921:
5919:
5913:
5912:
5909:
5908:
5906:
5905:
5904:
5903:
5898:
5888:
5883:
5877:
5875:
5873:Access control
5869:
5868:
5866:
5865:
5864:
5863:
5858:
5850:
5845:
5840:
5839:
5838:
5831:File attribute
5828:
5823:
5818:
5816:Data scrubbing
5813:
5808:
5803:
5797:
5791:
5787:
5786:
5784:
5783:
5778:
5773:
5771:Steganographic
5768:
5763:
5758:
5753:
5751:Log-structured
5748:
5743:
5738:
5737:
5736:
5731:
5726:
5715:
5713:
5709:
5708:
5705:
5704:
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5701:
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5676:
5670:
5668:
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5635:
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5599:
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5579:
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5128:
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5081:
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5074:Reliance Nitro
5071:
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5049:
5044:
5039:
5034:
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5024:
5019:
5014:
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4997:
4992:
4987:
4986:
4985:
4980:
4972:
4967:
4962:
4957:
4952:
4947:
4937:
4934:Classic Mac OS
4927:
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4915:
4910:
4905:
4900:
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4898:
4888:
4887:
4886:
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4854:
4849:
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4826:
4821:
4820:
4819:
4812:
4807:
4805:Be File System
4799:
4794:
4789:
4784:
4779:
4774:
4769:
4763:
4757:
4751:
4750:
4748:
4747:
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4726:
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4710:
4703:
4695:
4686:
4685:
4683:
4682:
4669:
4666:
4665:
4663:
4662:
4660:Apache License
4656:
4654:
4650:
4649:
4647:
4646:
4641:
4636:
4631:
4626:
4621:
4616:
4611:
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4536:
4530:
4528:
4524:
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4520:
4515:
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4505:
4499:
4497:
4496:Other projects
4493:
4492:
4490:
4489:
4483:
4481:
4477:
4476:
4474:
4473:
4468:
4463:
4458:
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4445:
4439:
4438:
4436:
4435:
4430:
4427:
4422:
4417:
4412:
4407:
4402:
4397:
4395:Traffic Server
4392:
4387:
4382:
4377:
4372:
4367:
4362:
4357:
4352:
4347:
4342:
4337:
4332:
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4312:
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4297:
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4287:
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4262:
4257:
4252:
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4237:
4232:
4227:
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4212:
4207:
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4182:
4177:
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4137:
4132:
4127:
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4112:
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4102:
4097:
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4087:
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4067:
4062:
4057:
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4017:
4012:
4007:
4002:
3997:
3992:
3987:
3982:
3977:
3972:
3967:
3962:
3957:
3952:
3947:
3942:
3937:
3932:
3927:
3922:
3917:
3912:
3907:
3902:
3897:
3892:
3887:
3882:
3876:
3874:
3868:
3867:
3860:
3859:
3852:
3845:
3837:
3831:
3830:
3814:
3813:External links
3811:
3809:
3808:
3802:
3787:
3781:
3762:
3756:
3748:O'Reilly Media
3737:
3719:
3700:
3694:
3674:
3672:
3669:
3666:
3665:
3639:
3617:
3595:
3573:
3551:
3526:
3504:
3482:
3460:
3438:
3416:
3386:
3364:
3349:
3322:
3292:
3267:
3239:
3209:
3183:
3147:
3133:
3107:
3093:
3079:
3062:
3040:
3018:
2992:
2981:on 14 May 2014
2966:
2949:
2927:
2918:|journal=
2884:
2863:
2837:
2811:
2789:deRoos, Dirk.
2781:
2748:
2727:
2713:
2691:
2660:
2642:
2624:
2606:
2588:
2570:
2552:
2534:
2512:
2487:
2462:
2440:
2406:
2384:
2369:
2354:
2342:O'Reilly Media
2328:
2306:
2292:
2273:
2251:
2221:
2194:
2164:
2149:
2123:
2096:
2067:
2042:
2016:
1989:
1963:
1939:
1912:
1911:
1909:
1906:
1905:
1904:
1899:
1893:
1887:
1877:
1872:
1867:
1864:
1861:Apache CouchDB
1858:
1852:
1842:
1841:
1825:
1822:
1821:
1820:
1814:
1807:
1800:
1788:
1785:
1772:
1769:
1764:
1761:
1752:
1749:
1728:
1725:
1724:
1723:
1720:
1715:
1709:
1706:
1700:
1697:
1663:data warehouse
1651:database, the
1644:
1641:
1634:erasure coding
1615:
1612:
1598:
1595:
1587:
1586:
1583:
1580:
1572:
1569:
1565:
1564:
1561:
1558:
1538:
1537:Fair scheduler
1535:
1527:Fair scheduler
1517:
1514:
1513:
1512:
1508:
1469:Main article:
1466:
1463:
1462:
1461:
1450:
1441:In June 2010,
1439:
1436:
1433:
1413:
1412:
1409:
1399:
1393:
1390:
1370:
1367:
1341:, the HDFS-UI
1206:
1205:
1202:
1199:
1196:
1193:
1164:
1161:
1159:
1156:
1091:
1088:
1085:
1084:
1079:Future release
1077:
1072:
1068:Latest version
1065:
1060:
1055:
1048:
1047:
1043:
1042:
1039:
1036:
1033:
1024:
1023:
1020:
1017:
1014:
1005:
1004:
1001:
998:
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986:
985:
982:
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968:
965:
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948:
945:
942:
935:
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931:
928:
925:
918:
917:
914:
911:
908:
901:
900:
897:
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884:
883:
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830:
827:
824:
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791:
784:
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763:
760:
757:
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746:
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582:
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558:
555:
548:
547:
544:
541:
538:
531:
530:
527:
524:
521:
514:
513:
510:
507:
505:
498:
497:
494:
491:
489:
482:
481:
478:
475:
472:
451:Mike Cafarella
442:
439:
373:Apache Phoenix
349:
348:
342:
336:
330:
324:
289:
286:
217:
216:
200:
196:
195:
190:
184:
183:
178:
172:
171:
169:Cross-platform
166:
160:
159:
154:
150:
149:
142:
136:
135:
132:
131:
127:
126:
113:
109:
108:
95:
89:
87:
85:Stable release
81:
80:
77:
76:
63:
59:
58:
53:
47:
46:
44:Mike Cafarella
37:
31:
30:
15:
9:
6:
4:
3:
2:
6066:
6055:
6052:
6050:
6047:
6045:
6042:
6040:
6037:
6035:
6032:
6030:
6027:
6025:
6022:
6020:
6017:
6016:
6014:
6007:
5997:
5994:
5992:
5989:
5987:
5984:
5983:
5981:
5977:
5971:
5968:
5966:
5963:
5961:
5960:Cryptographic
5958:
5957:
5955:
5953:
5949:
5943:
5940:
5936:
5933:
5932:
5931:
5928:
5926:
5923:
5922:
5920:
5918:
5914:
5902:
5899:
5897:
5894:
5893:
5892:
5889:
5887:
5884:
5882:
5879:
5878:
5876:
5874:
5870:
5862:
5859:
5857:
5854:
5853:
5851:
5849:
5846:
5844:
5841:
5837:
5834:
5833:
5832:
5829:
5827:
5824:
5822:
5819:
5817:
5814:
5812:
5809:
5807:
5806:Copy-on-write
5804:
5802:
5799:
5798:
5795:
5792:
5788:
5782:
5779:
5777:
5774:
5772:
5769:
5767:
5764:
5762:
5759:
5757:
5754:
5752:
5749:
5747:
5744:
5742:
5739:
5735:
5732:
5730:
5727:
5725:
5722:
5721:
5720:
5717:
5716:
5714:
5710:
5700:
5697:
5695:
5692:
5690:
5687:
5685:
5682:
5680:
5677:
5675:
5672:
5671:
5669:
5667:
5663:
5657:
5654:
5652:
5649:
5647:
5644:
5641:
5639:
5636:
5634:
5631:
5629:
5626:
5624:
5621:
5619:
5616:
5615:
5613:
5609:
5603:
5600:
5598:
5595:
5593:
5590:
5588:
5585:
5583:
5580:
5578:
5575:
5573:
5570:
5568:
5565:
5563:
5560:
5558:
5555:
5553:
5550:
5548:
5545:
5543:
5540:
5538:
5535:
5533:
5530:
5528:
5525:
5523:
5520:
5518:
5515:
5514:
5511:
5508:
5504:
5498:
5497:
5493:
5491:
5488:
5486:
5483:
5481:
5478:
5476:
5473:
5471:
5468:
5466:
5463:
5461:
5458:
5456:
5453:
5451:
5448:
5446:
5443:
5441:
5438:
5436:
5433:
5430:
5426:
5423:
5421:
5418:
5417:
5415:
5413:
5409:
5399:
5398:
5394:
5392:
5389:
5387:
5384:
5382:
5379:
5377:
5374:
5372:
5369:
5367:
5364:
5362:
5359:
5357:
5354:
5352:
5349:
5347:
5344:
5343:
5341:
5339:
5335:
5325:
5322:
5320:
5317:
5313:
5310:
5309:
5308:
5305:
5303:
5300:
5298:
5295:
5293:
5290:
5288:
5285:
5284:
5282:
5280:
5279:wear leveling
5275:
5269:
5266:
5264:
5261:
5259:
5256:
5254:
5251:
5249:
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3281:
3277:
3271:
3256:
3249:
3243:
3228:
3224:
3220:
3213:
3197:
3196:Hadoop Common
3193:
3187:
3179:
3173:
3157:
3151:
3143:
3137:
3121:
3117:
3111:
3103:
3097:
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2530:
2523:
2516:
2501:
2497:
2491:
2476:
2472:
2466:
2458:
2454:
2450:
2449:Cutting, Doug
2444:
2428:
2424:
2420:
2416:
2415:Vance, Ashlee
2410:
2403:
2399:
2395:
2388:
2380:
2373:
2365:
2358:
2343:
2339:
2332:
2316:
2310:
2295:
2293:9781118876220
2289:
2285:
2284:
2277:
2261:
2255:
2239:
2235:
2231:
2225:
2210:. Hortonworks
2209:
2205:
2198:
2182:
2178:
2174:
2168:
2160:
2156:
2152:
2146:
2142:
2138:
2134:
2127:
2111:
2107:
2100:
2085:
2081:
2077:
2071:
2056:
2052:
2046:
2031:
2027:
2020:
2004:
2000:
1993:
1978:
1977:silicon.co.uk
1974:
1967:
1952:
1946:
1944:
1927:
1923:
1917:
1913:
1903:
1900:
1897:
1896:Sector/Sphere
1894:
1891:
1888:
1885:
1881:
1878:
1876:
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1862:
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1812:
1808:
1805:
1801:
1798:
1794:
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1784:
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1778:
1777:Apache Hadoop
1768:
1760:
1758:
1748:
1745:
1743:
1738:
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1721:
1719:
1716:
1713:
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1690:
1689:
1686:
1684:
1680:
1676:
1672:
1668:
1664:
1661:
1657:
1654:
1653:Apache Mahout
1650:
1640:
1637:
1635:
1630:
1628:
1623:
1621:
1611:
1609:
1605:
1594:
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1502:
1498:
1497:
1496:
1493:
1491:
1487:
1482:
1478:
1472:
1459:
1455:
1452:In May 2011,
1451:
1448:
1444:
1440:
1437:
1434:
1431:
1427:
1423:
1422:
1421:
1419:
1410:
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1403:
1400:
1397:
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1360:
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1305:
1300:
1297:
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1288:
1283:
1281:
1276:
1272:
1267:
1265:
1261:
1258:
1254:
1249:
1246:
1245:Task Tracker:
1242:
1239:
1235:
1232:
1228:
1225:
1221:
1218:
1214:
1210:
1203:
1200:
1197:
1194:
1191:
1190:
1189:
1186:
1182:
1178:
1174:
1170:
1155:
1151:
1149:
1145:
1140:
1138:
1129:
1125:
1121:
1119:
1115:
1111:
1110:Hadoop Common
1105:
1101:
1097:
1069:
1052:
1044:
1040:
1037:
1034:
1032:
1025:
1021:
1018:
1015:
1013:
1006:
1002:
999:
996:
994:
987:
983:
980:
977:
970:
966:
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960:
953:
949:
946:
943:
936:
932:
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926:
919:
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902:
898:
895:
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885:
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815:
812:
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764:
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730:
727:
724:
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684:
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664:
661:
658:
651:
647:
644:
641:
634:
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627:
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613:
610:
607:
600:
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593:
590:
583:
579:
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573:
566:
562:
559:
556:
549:
545:
542:
539:
532:
528:
525:
522:
515:
511:
508:
506:
499:
495:
492:
490:
483:
480:Release date
479:
476:
473:
470:
469:
466:
462:
460:
456:
452:
448:
438:
436:
435:shell scripts
432:
428:
424:
419:
417:
413:
409:
404:
402:
398:
394:
390:
386:
385:Apache Impala
382:
378:
374:
370:
366:
362:
358:
354:
346:
343:
340:
337:
334:
331:
328:
325:
322:
321:Hadoop Common
319:
318:
317:
314:
312:
308:
304:
300:
299:data locality
296:
295:packaged code
285:
283:
279:
275:
272:
268:
264:
260:
256:
250:
223:
222:Apache Hadoop
214:
209:
201:
197:
194:
191:
189:
185:
182:
179:
177:
173:
170:
167:
165:
161:
158:
155:
151:
147:
143:
141:
137:
133:
114:
110:
96:
92:
88:
86:
82:
78:
64:
60:
57:
54:
52:
48:
45:
41:
38:
36:
32:
28:
23:
20:Apache Hadoop
6006:
5925:File manager
5494:
5479:
5395:
5221:Flash memory
5188:Optical disc
5126:soft updates
5106:Soup (Apple)
4756:non-rotating
4722:File systems
4677:
4335:SpamAssassin
4089:
3792:
3767:
3742:
3729:. Retrieved
3725:the original
3705:
3680:
3671:Bibliography
3656:. Retrieved
3651:
3642:
3630:. Retrieved
3620:
3608:. Retrieved
3598:
3586:. Retrieved
3576:
3564:. Retrieved
3554:
3542:. Retrieved
3538:
3529:
3517:. Retrieved
3507:
3495:. Retrieved
3485:
3475:13 September
3473:. Retrieved
3463:
3451:. Retrieved
3441:
3429:. Retrieved
3419:
3407:. Retrieved
3403:the original
3398:
3389:
3377:. Retrieved
3367:
3332:
3325:
3313:. Retrieved
3309:the original
3304:
3295:
3283:. Retrieved
3279:
3270:
3258:. Retrieved
3254:
3242:
3230:. Retrieved
3222:
3212:
3200:. Retrieved
3195:
3186:
3160:. Retrieved
3150:
3136:
3124:. Retrieved
3120:the original
3110:
3096:
3082:
3065:
3053:. Retrieved
3043:
3031:. Retrieved
3021:
3011:19 September
3009:. Retrieved
2995:
2983:. Retrieved
2979:the original
2969:
2952:
2940:. Retrieved
2930:
2909:cite journal
2875:. Retrieved
2866:
2854:. Retrieved
2850:the original
2840:
2828:. Retrieved
2824:
2814:
2802:. Retrieved
2794:
2784:
2776:
2769:. Retrieved
2761:
2751:
2739:. Retrieved
2730:
2716:
2704:. Retrieved
2694:
2682:. Retrieved
2678:the original
2674:Cloudera.com
2673:
2663:
2654:
2645:
2636:
2627:
2618:
2609:
2600:
2591:
2582:
2573:
2564:
2555:
2546:
2537:
2528:
2515:
2503:. Retrieved
2499:
2496:"Who We Are"
2490:
2478:. Retrieved
2474:
2465:
2456:
2443:
2431:. Retrieved
2422:
2409:
2401:
2397:
2387:
2372:
2357:
2345:. Retrieved
2341:
2331:
2319:. Retrieved
2309:
2297:. Retrieved
2282:
2276:
2264:. Retrieved
2254:
2242:. Retrieved
2233:
2224:
2214:30 September
2212:. Retrieved
2207:
2197:
2187:30 September
2185:. Retrieved
2181:the original
2176:
2167:
2132:
2126:
2114:. Retrieved
2109:
2099:
2087:. Retrieved
2079:
2070:
2058:. Retrieved
2054:
2045:
2033:. Retrieved
2029:
2019:
2007:. Retrieved
2003:datanami.com
2002:
1992:
1980:. Retrieved
1976:
1966:
1956:27 September
1954:. Retrieved
1930:. Retrieved
1925:
1916:
1790:
1780:
1776:
1774:
1766:
1754:
1746:
1739:
1730:
1718:Web crawling
1687:
1675:Apache Storm
1646:
1638:
1631:
1624:
1619:
1617:
1607:
1603:
1600:
1589:There is no
1588:
1574:
1566:
1540:
1530:
1526:
1519:
1500:
1494:
1480:
1476:
1474:
1447:IBRIX Fusion
1414:
1380:
1372:
1351:
1324:
1302:HDFS can be
1301:
1298:
1293:
1291:
1284:
1268:
1250:
1244:
1243:
1238:Job Tracker:
1237:
1236:
1230:
1229:
1223:
1222:
1212:
1211:
1207:
1204:Task Tracker
1168:
1166:
1158:File systems
1152:
1148:Secure Shell
1141:
1136:
1134:
1122:
1109:
1107:
1100:Apache HBase
1090:Architecture
1067:
1050:
1030:
1011:
992:
779:2.0.6-alpha
463:
455:Apache Nutch
447:Doug Cutting
444:
431:command line
420:
405:
401:Apache Storm
397:Apache Oozie
393:Apache Sqoop
389:Apache Flume
377:Apache Spark
369:Apache HBase
356:
352:
350:
345:Hadoop Ozone
344:
338:
332:
326:
320:
315:
291:
221:
220:
51:Developer(s)
40:Doug Cutting
5891:Permissions
5506:Specialized
4738:distributed
3544:11 December
3409:31 December
3285:31 December
3232:20 November
3162:11 December
3126:10 December
2877:1 September
2799:For Dummies
2795:dummies.com
2706:4 September
2505:11 December
2480:11 December
2238:Marketwired
2030:hpcwire.com
1704:data mining
1694:clickstream
1660:Apache Hive
1505:system load
1481:TaskTracker
1355:Hortonworks
1271:replicating
1198:Job tracker
1173:file system
1137:worker node
1041:2024-07-17
1035:2024-03-17
1022:2023-06-23
1016:2020-07-14
1003:2022-07-22
997:2019-01-16
984:2020-08-03
978:2018-04-06
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