Single Node Setup Commands Reference FileSystem Shell Compatibility Specification Downstream Developer's Guide Admin Compatibility Guide Interface Classification FileSystem Specification CLI Mini Cluster Fair Call Queue Native Libraries Service Level Authorization HTTP Authentication Credential Provider API Unix Shell Guide Commands Reference NameNode HA With QJM NameNode HA With NFS Observer NameNode ViewFsOverloadScheme Permissions and HDFS Quotas and HDFS libhdfs (C API) WebHDFS (REST API) Short Circuit Local Reads Centralized Cache Management Rolling Upgrade Extended Attributes Transparent Encryption Storage Policies Memory Storage Support Synthetic Load Generator Router Federation Provided Storage Commands Reference Compatibility with 1.x Encrypted Shuffle Pluggable Shuffle/Sort Distributed Cache Deploy Support for YARN Shared Cache MR Application Master MR History Server Commands Reference Capacity Scheduler ResourceManager Restart ResourceManager HA Node Attributes Web Application Proxy Timeline Server Timeline Service V.2 Writing YARN Applications YARN Application Security Running Applications in Docker Containers Running Applications in runC Containers Secure Containers Reservation System Graceful Decommission Opportunistic Containers YARN Federation Placement Constraints

Resource Manager Timeline Server Timeline Service V.2 Yarn Service API Service Discovery System Services Azure Blob Storage Azure Data Lake Storage Hadoop Streaming Hadoop Archives Hadoop Archive Logs Resource Estimator Service Scheduler Load Simulator Hadoop Benchmarking Changelog and Release Notes core-default.xml hdfs-default.xml hdfs-rbf-default.xml mapred-default.xml yarn-default.xml kms-default.xml httpfs-default.xml Deprecated Properties Apache Hadoop 3.3.5 is an update to the Hadoop 3.3.x release branch. Users are encouraged to read the full set of release notes. This page provides an overview of the major changes. 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. 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): 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. 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. 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. 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. 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. 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.