hadoop-logo Apache Hadoop

The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

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Latest news

Hadoop is not susceptible to log4shell vulnerability 2021 Dec 17

Hadoop, as of today depends on log4j 1.x, which is NOT susceptible to the attack (CVE-2021-44228).

For more information check the published CVEs page.

Release 3.3.1 available 2021 Jun 15

This is the first stable release of Apache Hadoop 3.3.x line. It contains 697 bug fixes, improvements and enhancements since 3.3.0.

Users are encouraged to read the overview of major changes since 3.3.0. For details of 697 bug fixes, improvements, and other enhancements since the previous 3.3.0 release, please check release notes and changelog detail the changes since 3.3.0.

Ozone 1.1.0 is released 2021 Apr 17

General available(GA) release of Apache Hadoop Ozone with Volume/Bucket Quota Support, Security related enhancements, ofs/o3fs performance improvements, Recon improvements etc.

For more information check the ozone site.

Release 3.2.2 available 2021 Jan 9

This is the second stable release of Apache Hadoop 3.2 line. It contains 516 bug fixes, improvements and enhancements since 3.2.1.

Users are encouraged to read the overview of major changes since 3.2.1. For details of 516 bug fixes, improvements, and other enhancements since the previous 3.2.1 release, please check release notes and changelog detail the changes since 3.2.1.

Release 2.10.1 available 2020 Sep 21

This is the second stable release of Apache Hadoop 2.10 line. It contains 218 bug fixes, improvements and enhancements since 2.10.0.

Users are encouraged to read the overview of major changes since 2.10.0. For details of 218 bug fixes, improvements, and other enhancements since the previous 2.10.0 release, please check release notes and changelog detail the changes since 2.10.0.

Modules

The project includes these modules:

  • Hadoop Common: The common utilities that support the other Hadoop modules.
  • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
  • Hadoop YARN: A framework for job scheduling and cluster resource management.
  • Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Who Uses Hadoop?

A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page.

Related projects

Other Hadoop-related projects at Apache include:

  • Ambari™: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
  • Avro™: A data serialization system.
  • Cassandra™: A scalable multi-master database with no single points of failure.
  • Chukwa™: A data collection system for managing large distributed systems.
  • HBase™: A scalable, distributed database that supports structured data storage for large tables.
  • Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
  • Mahout™: A Scalable machine learning and data mining library.
  • Ozone™: A scalable, redundant, and distributed object store for Hadoop.
  • Pig™: A high-level data-flow language and execution framework for parallel computation.
  • Spark™: A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation.
  • Submarine: A unified AI platform which allows engineers and data scientists to run Machine Learning and Deep Learning workload in distributed cluster.
  • Tez™: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.
  • ZooKeeper™: A high-performance coordination service for distributed applications.