Information Density: Apache Hadoop – Signal Evidence & AI Readability

[H2] Latest news

Release 3.5.0 available 2026 Apr 2

This is the first stable release of Apache Hadoop 3.5 line.
It contains 485 bug fixes, improvements and enhancements since 3.4.
Users are encouraged to read the overview of major changes since 3.4.
For details of 485 bug fixes, improvements, and other enhancements since the previous 3.4.3 release,
please check release notes and changelog.

Release 3.4.3 available 2026 Feb 24

This is a release of Apache Hadoop 3.4.3 line.
Users of Apache Hadoop 3.4.2 and earlier should upgrade to
this release.
All users are encouraged to read the overview of major changes
since release 3.4.2.
For details of bug fixes, improvements, and other enhancements since
the previous 3.4.2 release, please check release notes
and changelog.
This release does not include the bundle.jar containing the AWS SDK, used by the s3a connector
in the hadoop-aws module.
To use it, download from Maven Central the version of the SDK you wish to use:
https://central.sonatype.com/artifact/software.amazon.awssdk/bundle/versions
For this release, the version to download is 2.35.4
https://repo1.maven.org/maven2/software/amazon/awssdk/bundle/2.35.4/)
Download the bundle-2.35.4.jar artifact and check its signature with
the accompanying bundle-2.35.4.jar.asc file.
Copy the JAR to share/hadoop/common/lib/
(Newer AWS SDK versions should work, though regressions are almost inevitable)

Release 3.4.2 available 2025 Aug 29

This is a release of Apache Hadoop 3.4.2 line.
Users of Apache Hadoop 3.4.1 and earlier should upgrade to
this release.
All users are encouraged to read the overview of major changes
since release 3.4.1.
For details of bug fixes, improvements, and other enhancements since
the previous 3.4.1 release, please check release notes
and changelog.

Release 3.4.1 available 2024 Oct 18

This is a release of Apache Hadoop 3.4.1 line.
Users of Apache Hadoop 3.4.0 and earlier should upgrade to
this release.
All users are encouraged to read the overview of major changes
since release 3.4.0.
We have also introduced a lean tar which is a small tar file that does not contain the AWS SDK
because the size of AWS SDK is itself 500 MB. This can ease usage for non AWS users.
Even AWS users can add this jar explicitly if desired.
For details of bug fixes, improvements, and other enhancements since
the previous 3.4.0 release, please check release notes
and changelog.

Release 3.4.0 available 2024 Mar 17

This is the first release of Apache Hadoop 3.4 line. It contains 2888 bug fixes, improvements and enhancements since 3.3.
Users are encouraged to read the overview of major changes.
For details of please check release notes and changelog.

Release archive →
News archive →

[H2] 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.

[H2] 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.

[H2] 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.

[H1] Apache Hadoop 3.3.5
Apache Hadoop 3.3.5 is an update to the Hadoop 3.3.x release branch.
[H1] Overview of Changes
Users are encouraged to read the full set of release notes. This page provides an overview of the major changes.
[H2] Azure ABFS: Critical Stream Prefetch Fix
The abfs has a critical bug fix HADOOP-18546. ABFS. Disable purging list of in-progress reads in abfs stream close().
All users of the abfs connector in hadoop releases 3.3.2+ MUST either upgrade or disable prefetching by setting fs.azure.readaheadqueue.depth to 0
Consult the parent JIRA HADOOP-18521 ABFS ReadBufferManager buffer sharing across concurrent HTTP requests for root cause analysis, details on what is affected, and mitigations.
[H2] Vectored IO API
HADOOP-18103. High performance vectored read API in Hadoop
The PositionedReadable interface has now added an operation for Vectored IO (also known as Scatter/Gather IO):
void readVectored(List<? extends FileRange> ranges, IntFunction<ByteBuffer> allocate)
All the requested ranges will be retrieved into the supplied byte buffers -possibly asynchronously, possibly in parallel, with results potentially coming in out-of-order.
The default implementation uses a series of readFully() calls, so delivers equivalent performance.
The local filesystem uses java native IO calls for higher performance reads than readFully().
The S3A filesystem issues parallel HTTP GET requests in different threads.
Benchmarking of enhanced Apache ORC and Apache Parquet clients through file:// and s3a:// show significant improvements in query performance.
Further Reading: * FsDataInputStream. * Hadoop Vectored IO: Your Data Just Got Faster! Apachecon 2022 talk.
[H2] Mapreduce: Manifest Committer for Azure ABFS and google GCS
The new Intermediate Manifest Committer uses a manifest file to commit the work of successful task attempts, rather than renaming directories. Job commit is matter of reading all the manifests, creating the destination directories (parallelized) and renaming the files, again in parallel.
This is both fast and correct on Azure Storage and Google GCS, and should be used there instead of the classic v1/v2 file output committers.
It is also safe to use on HDFS, where it should be faster than the v1 committer. It is however optimized for cloud storage where list and rename operations are significantly slower; the benefits may be less.
More details are available in the manifest committer. documentation.
[H2] HDFS: Dynamic Datanode Reconfiguration
HDFS-16400, HDFS-16399, HDFS-16396, HDFS-16397, HDFS-16413, HDFS-16457.
A number of Datanode configuration options can be changed without having to restart the datanode. This makes it possible to tune deployment configurations without cluster-wide Datanode Restarts.
See DataNode.java for the list of dynamically reconfigurable attributes.
[H2] Transitive CVE fixes
A lot of dependencies have been upgraded to address recent CVEs. Many of the CVEs were not actually exploitable through the Hadoop so much of this work is just due diligence. However applications which have all the library is on a class path may be vulnerable, and the ugprades should also reduce the number of false positives security scanners report.
We have not been able to upgrade every single dependency to the latest version there is. Some of those changes are fundamentally incompatible. If you have concerns about the state of a specific library, consult the Apache JIRA issue tracker to see if an issue has been filed, discussions have taken place about the library in question, and whether or not there is already a fix in the pipeline. Please don’t file new JIRAs about dependency-X.Y.Z having a CVE without searching for any existing issue first
As an open-source project, contributions in this area are always welcome, especially in testing the active branches, testing applications downstream of those branches and of whether updated dependencies trigger regressions.
[H1] Security Advisory
Hadoop HDFS is a distributed filesystem allowing remote callers to read and write data.
Hadoop YARN is a distributed job submission/execution engine allowing remote callers to submit arbitrary work into the cluster.
Unless a Hadoop cluster is deployed with caller authentication with Kerberos, anyone with network access to the servers has unrestricted access to the data and the ability to run whatever code they want in the system.
In production, there are generally three deployment patterns which can, with care, keep data and computing resources private. 1. Physical cluster: configure Hadoop security, usually bonded to the enterprise Kerberos/Active Directory systems. Good. 1. Cloud: transient or persistent single or multiple user/tenant cluster with private VLAN and security. Good. Consider Apache Knox for managing remote access to the cluster. 1. Cloud: transient single user/tenant cluster with private VLAN and no security at all. Requires careful network configuration as this is the sole means of securing the cluster.. Consider Apache Knox for managing remote access to the cluster.
If you deploy a Hadoop cluster in-cloud without security, and without configuring a VLAN to restrict access to trusted users, you are implicitly sharing your data and computing resources with anyone with network access
If you do deploy an insecure cluster this way then port scanners will inevitably find it and submit crypto-mining jobs. If this happens to you, please do not report this as a CVE or security issue: it is utterly predictable. Secure your cluster if you want to remain exclusively your cluster.
Finally, if you are using Hadoop as a service deployed/managed by someone else, do determine what security their products offer and make sure it meets your requirements.
[H1] Getting Started
The Hadoop documentation includes the information you need to get started using Hadoop. Begin with the Single Node Setup which shows you how to set up a single-node Hadoop installation. Then move on to the Cluster Setup to learn how to set up a multi-node Hadoop installation.
Before deploying Hadoop in production, read Hadoop in Secure Mode, and follow its instructions to secure your cluster.

[H1] Release 3.5.0 available
This is the first stable release of Apache Hadoop 3.5 line.
It contains 485 bug fixes, improvements and enhancements since 3.4.
Users are encouraged to read the overview of major changes since 3.4.
For details of 485 bug fixes, improvements, and other enhancements since the previous 3.4.3 release,
please check release notes and changelog.
2026 Apr 2
Download tar.gz
(checksum
signature)

Download aarch64 tar.gz
(checksum
signature)

Download src
(checksum
signature)
Documentation

[H1] Apache Hadoop 3.5.0

Apache Hadoop 3.5.0 is the first stable release of the Apache Hadoop 3.5 line.
[H1] Overview of Changes

Users are encouraged to read the full set of release notes. This page provides an overview of the major changes.
[H2] Java 17 Support

This is the first Hadoop release with full support for Java 17. On the server side, Java 17 is required. On the client side, Java 17 and Java 21 are supported.
[H2] HDFS Optimizations

HDFS has implemented new optimizations for finer-grained locking on concurrent NameNode operations and asynchronous RPC processing in router-based federation.
[H2] GCS FileSystem

Apache Hadoop now includes a FileSystem implementation that integrates with Google Cloud Storage buckets.
[H2] WASB Removed

The WASB FileSystem that was previously deprecated has been removed from the codebase. Use ABFS instead.
[H2] YARN Capacity Scheduler UI

This release adds a new modern UI for monitoring the Capacity Scheduler and dynamically editing its configuration.
[H2] JUnit 5 Upgrade

For developers working on the codebase, all tests have been upgraded from JUnit 4 to JUnit 5. All new tests must utilize JUnit 5 going forward.
[H2] Transitive CVE fixes

A lot of dependencies have been upgraded to address recent CVEs. Many of the CVEs were not actually exploitable through Hadoop so much of this work is just due diligence. However, applications which have all the libraries on a class path may be vulnerable, and the upgrades should also reduce the number of false positives security scanners report.
We have not been able to upgrade every single dependency to the latest version there is. Some of those changes are fundamentally incompatible. If you have concerns about the state of a specific library, consult the Apache JIRA issue tracker to see if an issue has been filed, discussions have taken place about the library in question, and whether or not there is already a fix in the pipeline. Please don’t file new JIRAs about dependency-X.Y.Z having a CVE without searching for any existing issue first
As an open-source project, contributions in this area are always welcome, especially in testing the active branches, testing applications downstream of those branches and of whether updated dependencies trigger regressions.
[H1] Security Advisory

Hadoop HDFS is a distributed filesystem allowing remote callers to read and write data.
Hadoop YARN is a distributed job submission/execution engine allowing remote callers to submit arbitrary work into the cluster.
Unless a Hadoop cluster is deployed with caller authentication with Kerberos, anyone with network access to the servers has unrestricted access to the data and the ability to run whatever code they want in the system.
In production, there are generally three deployment patterns which can, with care, keep data and computing resources private. 1. Physical cluster: configure Hadoop security, usually bonded to the enterprise Kerberos/Active Directory systems. Good. 2. Cloud: transient or persistent single or multiple user/tenant cluster with private VLAN and security. Good. Consider Apache Knox for managing remote access to the cluster. 3. Cloud: transient single user/tenant cluster with private VLAN and no security at all. Requires careful network configuration as this is the sole means of securing the cluster.. Consider Apache Knox for managing remote access to the cluster.
If you deploy a Hadoop cluster in-cloud without security, and without configuring a VLAN to restrict access to trusted users, you are implicitly sharing your data and computing resources with anyone with network access
If you do deploy an insecure cluster this way then port scanners will inevitably find it and submit crypto-mining jobs. If this happens to you, please do not report this as a CVE or security issue: it is utterly predictable. Secure your cluster if you want to remain exclusively your cluster.
Finally, if you are using Hadoop as a service deployed/managed by someone else, do determine what security their products offer and make sure it meets your requirements.
[H1] Getting Started

The Hadoop documentation includes the information you need to get started using Hadoop. Begin with the Single Node Setup which shows you how to set up a single-node Hadoop installation. Then move on to the Cluster Setup to learn how to set up a multi-node Hadoop installation.
Before deploying Hadoop in production, read Hadoop in Secure Mode, and follow its instructions to secure your cluster.