Apache Hadoop 3.5.0 is the first stable release of the Apache Hadoop 3.5 line.
Users are encouraged to read the full set of release notes. This page provides an overview of the major changes.
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.
HDFS has implemented new optimizations for finer-grained locking on concurrent NameNode operations and asynchronous RPC processing in router-based federation.
Apache Hadoop now includes a FileSystem implementation that integrates with Google Cloud Storage buckets.
The WASB FileSystem that was previously deprecated has been removed from the codebase. Use ABFS instead.
This release adds a new modern UI for monitoring the Capacity Scheduler and dynamically editing its configuration.
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.
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.
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.
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.