HADOOP-18470. index.md update for 3.3.5 release
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Steve Loughran
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@ -22,7 +22,17 @@ Purpose
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This document describes how to install and configure Hadoop clusters ranging from a few nodes to extremely large clusters with thousands of nodes. To play with Hadoop, you may first want to install it on a single machine (see [Single Node Setup](./SingleCluster.html)).
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This document does not cover advanced topics such as [Security](./SecureMode.html) or High Availability.
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This document does not cover advanced topics such as High Availability.
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*Important*: all production Hadoop clusters use Kerberos to authenticate callers
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and secure access to HDFS data as well as restriction access to computation
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services (YARN etc.).
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These instructions do not cover integration with any Kerberos services,
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-everyone bringing up a production cluster should include connecting to their
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organisation's Kerberos infrastructure as a key part of the deployment.
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See [Security](./SecureMode.html) for details on how to secure a cluster.
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Prerequisites
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-------------
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@ -26,6 +26,15 @@ Purpose
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This document describes how to set up and configure a single-node Hadoop installation so that you can quickly perform simple operations using Hadoop MapReduce and the Hadoop Distributed File System (HDFS).
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*Important*: all production Hadoop clusters use Kerberos to authenticate callers
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and secure access to HDFS data as well as restriction access to computation
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services (YARN etc.).
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These instructions do not cover integration with any Kerberos services,
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-everyone bringing up a production cluster should include connecting to their
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organisation's Kerberos infrastructure as a key part of the deployment.
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Prerequisites
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-------------
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@ -33,8 +42,6 @@ $H3 Supported Platforms
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* GNU/Linux is supported as a development and production platform. Hadoop has been demonstrated on GNU/Linux clusters with 2000 nodes.
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* Windows is also a supported platform but the followings steps are for Linux only. To set up Hadoop on Windows, see [wiki page](http://wiki.apache.org/hadoop/Hadoop2OnWindows).
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$H3 Required Software
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Required software for Linux include:
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@ -15,226 +15,99 @@
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Apache Hadoop ${project.version}
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================================
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Apache Hadoop ${project.version} incorporates a number of significant
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enhancements over the previous major release line (hadoop-2.x).
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Apache Hadoop ${project.version} is an update to the Hadoop 3.3.x release branch.
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This release is generally available (GA), meaning that it represents a point of
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API stability and quality that we consider production-ready.
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Overview
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========
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Overview of Changes
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===================
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Users are encouraged to read the full set of release notes.
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This page provides an overview of the major changes.
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Minimum required Java version increased from Java 7 to Java 8
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------------------
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All Hadoop JARs are now compiled targeting a runtime version of Java 8.
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Users still using Java 7 or below must upgrade to Java 8.
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Support for erasure coding in HDFS
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------------------
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Erasure coding is a method for durably storing data with significant space
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savings compared to replication. Standard encodings like Reed-Solomon (10,4)
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have a 1.4x space overhead, compared to the 3x overhead of standard HDFS
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replication.
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Since erasure coding imposes additional overhead during reconstruction
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and performs mostly remote reads, it has traditionally been used for
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storing colder, less frequently accessed data. Users should consider
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the network and CPU overheads of erasure coding when deploying this
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feature.
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More details are available in the
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[HDFS Erasure Coding](./hadoop-project-dist/hadoop-hdfs/HDFSErasureCoding.html)
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documentation.
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YARN Timeline Service v.2
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-------------------
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We are introducing an early preview (alpha 2) of a major revision of YARN
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Timeline Service: v.2. YARN Timeline Service v.2 addresses two major
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challenges: improving scalability and reliability of Timeline Service, and
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enhancing usability by introducing flows and aggregation.
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YARN Timeline Service v.2 alpha 2 is provided so that users and developers
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can test it and provide feedback and suggestions for making it a ready
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replacement for Timeline Service v.1.x. It should be used only in a test
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capacity.
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More details are available in the
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[YARN Timeline Service v.2](./hadoop-yarn/hadoop-yarn-site/TimelineServiceV2.html)
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documentation.
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Shell script rewrite
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-------------------
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The Hadoop shell scripts have been rewritten to fix many long-standing
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bugs and include some new features. While an eye has been kept towards
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compatibility, some changes may break existing installations.
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Incompatible changes are documented in the release notes, with related
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discussion on [HADOOP-9902](https://issues.apache.org/jira/browse/HADOOP-9902).
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More details are available in the
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[Unix Shell Guide](./hadoop-project-dist/hadoop-common/UnixShellGuide.html)
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documentation. Power users will also be pleased by the
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[Unix Shell API](./hadoop-project-dist/hadoop-common/UnixShellAPI.html)
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documentation, which describes much of the new functionality, particularly
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related to extensibility.
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Shaded client jars
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------------------
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The `hadoop-client` Maven artifact available in 2.x releases pulls
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Hadoop's transitive dependencies onto a Hadoop application's classpath.
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This can be problematic if the versions of these transitive dependencies
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conflict with the versions used by the application.
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[HADOOP-11804](https://issues.apache.org/jira/browse/HADOOP-11804) adds
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new `hadoop-client-api` and `hadoop-client-runtime` artifacts that
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shade Hadoop's dependencies into a single jar. This avoids leaking
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Hadoop's dependencies onto the application's classpath.
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Support for Opportunistic Containers and Distributed Scheduling.
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--------------------
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A notion of `ExecutionType` has been introduced, whereby Applications can
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now request for containers with an execution type of `Opportunistic`.
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Containers of this type can be dispatched for execution at an NM even if
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there are no resources available at the moment of scheduling. In such a
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case, these containers will be queued at the NM, waiting for resources to
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be available for it to start. Opportunistic containers are of lower priority
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than the default `Guaranteed` containers and are therefore preempted,
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if needed, to make room for Guaranteed containers. This should
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improve cluster utilization.
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Opportunistic containers are by default allocated by the central RM, but
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support has also been added to allow opportunistic containers to be
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allocated by a distributed scheduler which is implemented as an
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AMRMProtocol interceptor.
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Please see [documentation](./hadoop-yarn/hadoop-yarn-site/OpportunisticContainers.html)
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for more details.
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MapReduce task-level native optimization
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--------------------
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MapReduce has added support for a native implementation of the map output
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collector. For shuffle-intensive jobs, this can lead to a performance
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improvement of 30% or more.
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See the release notes for
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[MAPREDUCE-2841](https://issues.apache.org/jira/browse/MAPREDUCE-2841)
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for more detail.
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Support for more than 2 NameNodes.
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--------------------
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The initial implementation of HDFS NameNode high-availability provided
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for a single active NameNode and a single Standby NameNode. By replicating
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edits to a quorum of three JournalNodes, this architecture is able to
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tolerate the failure of any one node in the system.
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However, some deployments require higher degrees of fault-tolerance.
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This is enabled by this new feature, which allows users to run multiple
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standby NameNodes. For instance, by configuring three NameNodes and
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five JournalNodes, the cluster is able to tolerate the failure of two
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nodes rather than just one.
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The [HDFS high-availability documentation](./hadoop-project-dist/hadoop-hdfs/HDFSHighAvailabilityWithQJM.html)
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has been updated with instructions on how to configure more than two
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NameNodes.
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Default ports of multiple services have been changed.
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------------------------
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Previously, the default ports of multiple Hadoop services were in the
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Linux ephemeral port range (32768-61000). This meant that at startup,
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services would sometimes fail to bind to the port due to a conflict
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with another application.
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These conflicting ports have been moved out of the ephemeral range,
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affecting the NameNode, Secondary NameNode, DataNode, and KMS. Our
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documentation has been updated appropriately, but see the release
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notes for [HDFS-9427](https://issues.apache.org/jira/browse/HDFS-9427) and
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[HADOOP-12811](https://issues.apache.org/jira/browse/HADOOP-12811)
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for a list of port changes.
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Support for Microsoft Azure Data Lake and Aliyun Object Storage System filesystem connectors
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---------------------
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Hadoop now supports integration with Microsoft Azure Data Lake and
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Aliyun Object Storage System as alternative Hadoop-compatible filesystems.
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Intra-datanode balancer
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-------------------
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A single DataNode manages multiple disks. During normal write operation,
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disks will be filled up evenly. However, adding or replacing disks can
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lead to significant skew within a DataNode. This situation is not handled
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by the existing HDFS balancer, which concerns itself with inter-, not intra-,
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DN skew.
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This situation is handled by the new intra-DataNode balancing
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functionality, which is invoked via the `hdfs diskbalancer` CLI.
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See the disk balancer section in the
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[HDFS Commands Guide](./hadoop-project-dist/hadoop-hdfs/HDFSCommands.html)
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for more information.
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Reworked daemon and task heap management
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---------------------
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A series of changes have been made to heap management for Hadoop daemons
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as well as MapReduce tasks.
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[HADOOP-10950](https://issues.apache.org/jira/browse/HADOOP-10950) introduces
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new methods for configuring daemon heap sizes.
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Notably, auto-tuning is now possible based on the memory size of the host,
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and the `HADOOP_HEAPSIZE` variable has been deprecated.
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See the full release notes of HADOOP-10950 for more detail.
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[MAPREDUCE-5785](https://issues.apache.org/jira/browse/MAPREDUCE-5785)
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simplifies the configuration of map and reduce task
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heap sizes, so the desired heap size no longer needs to be specified
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in both the task configuration and as a Java option.
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Existing configs that already specify both are not affected by this change.
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See the full release notes of MAPREDUCE-5785 for more details.
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HDFS Router-Based Federation
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---------------------
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HDFS Router-Based Federation adds a RPC routing layer that provides a federated
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view of multiple HDFS namespaces. This is similar to the existing
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[ViewFs](./hadoop-project-dist/hadoop-hdfs/ViewFs.html)) and
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[HDFS Federation](./hadoop-project-dist/hadoop-hdfs/Federation.html)
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functionality, except the mount table is managed on the server-side by the
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routing layer rather than on the client. This simplifies access to a federated
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cluster for existing HDFS clients.
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See [HDFS-10467](https://issues.apache.org/jira/browse/HDFS-10467) and the
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HDFS Router-based Federation
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[documentation](./hadoop-project-dist/hadoop-hdfs-rbf/HDFSRouterFederation.html) for
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more details.
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API-based configuration of Capacity Scheduler queue configuration
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----------------------
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The OrgQueue extension to the capacity scheduler provides a programmatic way to
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change configurations by providing a REST API that users can call to modify
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queue configurations. This enables automation of queue configuration management
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by administrators in the queue's `administer_queue` ACL.
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See [YARN-5734](https://issues.apache.org/jira/browse/YARN-5734) and the
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[Capacity Scheduler documentation](./hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html) for more information.
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YARN Resource Types
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Vectored IO API
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---------------
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The YARN resource model has been generalized to support user-defined countable resource types beyond CPU and memory. For instance, the cluster administrator could define resources like GPUs, software licenses, or locally-attached storage. YARN tasks can then be scheduled based on the availability of these resources.
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The `PositionedReadable` interface has now added an operation for
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Vectored (also known as Scatter/Gather IO):
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See [YARN-3926](https://issues.apache.org/jira/browse/YARN-3926) and the [YARN resource model documentation](./hadoop-yarn/hadoop-yarn-site/ResourceModel.html) for more information.
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```java
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void readVectored(List<? extends FileRange> ranges, IntFunction<ByteBuffer> allocate)
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```
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All the requested ranges will be retrieved into the supplied byte buffers -possibly asynchronously,
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possibly in parallel, with results potentially coming in out-of-order.
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1. The default implementation uses a series of `readFully()` calls, so delivers
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equivalent performance.
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2. The local filesystem uses java native IO calls for higher performance reads than `readFully()`
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3. The S3A filesystem issues parallel HTTP GET requests in different threads.
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Benchmarking of (modified) ORC and Parquet clients through `file://` and `s3a://`
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show tangible improvements in query times.
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Further Reading: [FsDataInputStream](./hadoop-project-dist/hadoop-common/filesystem/fsdatainputstream.html).
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Manifest Committer for Azure ABFS and google GCS performance
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------------------------------------------------------------
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A new "intermediate manifest committer" uses a manifest file
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to commit the work of successful task attempts, rather than
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renaming directories.
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Job commit is matter of reading all the manifests, creating the
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destination directories (parallelized) and renaming the files,
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again in parallel.
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This is fast and correct on Azure Storage and Google GCS,
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and should be used there instead of the classic v1/v2 file
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output committers.
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It is also safe to use on HDFS, where it should be faster
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than the v1 committer. It is however optimized for
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cloud storage where list and rename operations are significantly
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slower; the benefits may be less.
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More details are available in the
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[manifest committer](./hadoop-mapreduce-client/hadoop-mapreduce-client-core/manifest_committer.html).
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documentation.
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Transitive CVE fixes
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--------------------
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A lot of dependencies have been upgraded to address recent CVEs.
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Many of the CVEs were not actually exploitable through the Hadoop
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so much of this work is just due diligence.
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However applications which have all the library is on a class path may
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be vulnerable, and the ugprades should also reduce the number of false
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positives security scanners report.
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We have not been able to upgrade every single dependency to the latest
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version there is. Some of those changes are just going to be incompatible.
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If you have concerns about the state of a specific library, consult the apache JIRA
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issue tracker to see what discussions have taken place about the library in question.
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As an open source project, contributions in this area are always welcome,
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especially in testing the active branches, testing applications downstream of
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those branches and of whether updated dependencies trigger regressions.
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HDFS: Router Based Federation
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-----------------------------
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A lot of effort has been invested into stabilizing/improving the HDFS Router Based Federation feature.
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1. HDFS-13522, HDFS-16767 & Related Jiras: Allow Observer Reads in HDFS Router Based Federation.
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2. HDFS-13248: RBF supports Client Locality
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HDFS: Dynamic Datanode Reconfiguration
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--------------------------------------
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HDFS-16400, HDFS-16399, HDFS-16396, HDFS-16397, HDFS-16413, HDFS-16457.
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A number of Datanode configuration options can be changed without having to restart
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the datanode. This makes it possible to tune deployment configurations without
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cluster-wide Datanode Restarts.
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See [DataNode.java](https://github.com/apache/hadoop/blob/branch-3.3.5/hadoop-hdfs-project/hadoop-hdfs/src/main/java/org/apache/hadoop/hdfs/server/datanode/DataNode.java#L346-L361)
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for the list of dynamically reconfigurable attributes.
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Getting Started
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===============
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