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The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. Īpache Hadoop's MapReduce and HDFS components were inspired by Google papers on MapReduce and Google File System.
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The term Hadoop is often used for both base modules and sub-modules and also 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, Cloudera Impala, Apache Flume, Apache Sqoop, Apache Oozie, and Apache Storm.
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This approach takes advantage of data locality, where nodes manipulate the data they have access to. It then transfers packaged code into nodes to process the data in parallel. Hadoop splits files into large blocks and distributes them across nodes in a cluster. 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. 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. It has since also found use on clusters of higher-end hardware. Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. 2.7.7 / May 31, 2018 3 years ago ( ) Ģ.8.5 / September 15, 2018 3 years ago ( ) Ģ.9.2 / November 9, 2018 3 years ago ( ) Ģ.10.1 / September 21, 2020 15 months ago ( ) ģ.1.4 / August 3, 2020 16 months ago ( ) ģ.2.2 / January 9, 2021 11 months ago ( ) ģ.3.1 / June 15, 2021 6 months ago ( ) Īpache Hadoop ( / h ə ˈ d uː p/) is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation.