From e34006401312132b597e9ad357e2048a8ac232cf Mon Sep 17 00:00:00 2001 From: Andrew Wang Date: Wed, 20 Jul 2016 16:57:55 -0700 Subject: [PATCH] HADOOP-13383. Update release notes for 3.0.0-alpha1. --- hadoop-project/src/site/markdown/index.md.vm | 180 +++++++++++++++---- 1 file changed, 146 insertions(+), 34 deletions(-) diff --git a/hadoop-project/src/site/markdown/index.md.vm b/hadoop-project/src/site/markdown/index.md.vm index c3a93ad345..8c766b4cd3 100644 --- a/hadoop-project/src/site/markdown/index.md.vm +++ b/hadoop-project/src/site/markdown/index.md.vm @@ -15,50 +15,162 @@ Apache Hadoop ${project.version} ================================ -Apache Hadoop ${project.version} consists of significant -improvements over the previous stable release (hadoop-1.x). +Apache Hadoop ${project.version} incorporates a number of significant +enhancements over the previous major release line (hadoop-2.x). -Here is a short overview of the improvments to both HDFS and MapReduce. +This is an alpha release to facilitate testing and the collection of +feedback from downstream application developers and users. There are +no guarantees regarding API stability or quality. -* HDFS Federation +Overview +======== - In order to scale the name service horizontally, federation uses - multiple independent Namenodes/Namespaces. The Namenodes are - federated, that is, the Namenodes are independent and don't require - coordination with each other. The datanodes are used as common storage - for blocks by all the Namenodes. Each datanode registers with all the - Namenodes in the cluster. Datanodes send periodic heartbeats and block - reports and handles commands from the Namenodes. +Users are encouraged to read the full set of release notes. +This page provides an overview of the major changes. - More details are available in the - [HDFS Federation](./hadoop-project-dist/hadoop-hdfs/Federation.html) - document. +Minimum required Java version increased from Java 7 to Java 8 +------------------ -* MapReduce NextGen aka YARN aka MRv2 +All Hadoop JARs are now compiled targeting a runtime version of Java 8. +Users still using Java 7 or below must upgrade to Java 8. - The new architecture introduced in hadoop-0.23, divides the two major - functions of the JobTracker: resource management and job life-cycle - management into separate components. - - The new ResourceManager manages the global assignment of compute - resources to applications and the per-application - ApplicationMaster manages the application‚ scheduling and - coordination. +Support for erasure encoding in HDFS +------------------ - An application is either a single job in the sense of classic - MapReduce jobs or a DAG of such jobs. +Erasure coding is a method for durably storing data with significant space +savings compared to replication. Standard encodings like Reed-Solomon (10,4) +have a 1.4x space overhead, compared to the 3x overhead of standard HDFS +replication. - The ResourceManager and per-machine NodeManager daemon, which - manages the user processes on that machine, form the computation - fabric. +Since erasure coding imposes additional overhead during reconstruction +and performs mostly remote reads, it has traditionally been used for +storing colder, less frequently accessed data. Users should consider +the network and CPU overheads of erasure coding when deploying this +feature. - The per-application ApplicationMaster is, in effect, a framework - specific library and is tasked with negotiating resources from the - ResourceManager and working with the NodeManager(s) to execute and - monitor the tasks. +More details are available in the +[HDFS Erasure Coding](./hadoop-project-dist/hadoop-hdfs/HDFSErasureCoding.html) +documentation. - More details are available in the - [YARN](./hadoop-yarn/hadoop-yarn-site/YARN.html) document. +YARN Timeline Service v.2 +------------------- + +We are introducing an early preview (alpha 1) of a major revision of YARN +Timeline Service: v.2. YARN Timeline Service v.2 addresses two major +challenges: improving scalability and reliability of Timeline Service, and +enhancing usability by introducing flows and aggregation. + +YARN Timeline Service v.2 alpha 1 is provided so that users and developers +can test it and provide feedback and suggestions for making it a ready +replacement for Timeline Service v.1.x. It should be used only in a test +capacity. Most importantly, security is not enabled. Do not set up or use +Timeline Service v.2 until security is implemented if security is a +critical requirement. + +More details are available in the +[YARN Timeline Service v.2](./hadoop-yarn/hadoop-yarn-site/TimelineServiceV2.html) +documentation. + +Shell script rewrite +------------------- + +The Hadoop shell scripts have been rewritten to fix many long-standing +bugs and include some new features. While an eye has been kept towards +compatibility, some changes may break existing installations. + +Incompatible changes are documented in the release notes, with related +discussion on [HADOOP-9902](https://issues.apache.org/jira/browse/HADOOP-9902). + +More details are available in the +[Unix Shell Guide](./hadoop-project-dist/hadoop-common/UnixShellGuide.html) +documentation. Power users will also be pleased by the +[Unix Shell API](./hadoop-project-dist/hadoop-common/UnixShellAPI.html) +documentation, which describes much of the new functionality, particularly +related to extensibility. + +MapReduce task-level native optimization +-------------------- + +MapReduce has added support for a native implementation of the map output +collector. For shuffle-intensive jobs, this can lead to a performance +improvement of 30% or more. + +See the release notes for +[MAPREDUCE-2841](https://issues.apache.org/jira/browse/MAPREDUCE-2841) +for more detail. + +Support for more than 2 NameNodes. +-------------------- + +The initial implementation of HDFS NameNode high-availability provided +for a single active NameNode and a single Standby NameNode. By replicating +edits to a quorum of three JournalNodes, this architecture is able to +tolerate the failure of any one node in the system. + +However, some deployments require higher degrees of fault-tolerance. +This is enabled by this new feature, which allows users to run multiple +standby NameNodes. For instance, by configuring three NameNodes and +five JournalNodes, the cluster is able to tolerate the failure of two +nodes rather than just one. + +The [HDFS high-availability documentation](./hadoop-project-dist/hadoop-hdfs/HDFSHighAvailabilityWithQJM.html) +has been updated with instructions on how to configure more than two +NameNodes. + +Default ports of multiple services have been changed. +------------------------ + +Previously, the default ports of multiple Hadoop services were in the +Linux ephemeral port range (32768-61000). This meant that at startup, +services would sometimes fail to bind to the port due to a conflict +with another application. + +These conflicting ports have been moved out of the ephemeral range, +affecting the NameNode, Secondary NameNode, DataNode, and KMS. Our +documentation has been updated appropriately, but see the release +notes for [HDFS-9427](https://issues.apache.org/jira/browse/HDFS-9427) and +[HADOOP-12811](https://issues.apache.org/jira/browse/HADOOP-12811) +for a list of port changes. + +Support for Microsoft Azure Data Lake filesystem connector +--------------------- + +Hadoop now supports integration with Microsoft Azure Data Lake as +an alternative Hadoop-compatible filesystem. + +Intra-datanode balancer +------------------- + +A single DataNode manages multiple disks. During normal write operation, +disks will be filled up evenly. However, adding or replacing disks can +lead to significant skew within a DataNode. This situation is not handled +by the existing HDFS balancer, which concerns itself with inter-, not intra-, +DN skew. + +This situation is handled by the new intra-DataNode balancing +functionality, which is invoked via the `hdfs diskbalancer` CLI. +See the disk balancer section in the +[HDFS Commands Guide](./hadoop-project-dist/hadoop-hdfs/HDFSCommands.html) +for more information. + +Reworked daemon and task heap management +--------------------- + +A series of changes have been made to heap management for Hadoop daemons +as well as MapReduce tasks. + +[HADOOP-10950](https://issues.apache.org/jira/browse/HADOOP-10950) introduces +new methods for configuring daemon heap sizes. +Notably, auto-tuning is now possible based on the memory size of the host, +and the `HADOOP_HEAPSIZE` variable has been deprecated. +See the full release notes of HADOOP-10950 for more detail. + +[MAPREDUCE-5785](https://issues.apache.org/jira/browse/MAPREDUCE-5785) +simplifies the configuration of map and reduce task +heap sizes, so the desired heap size no longer needs to be specified +in both the task configuration and as a Java option. +Existing configs that already specify both are not affected by this change. +See the full release notes of MAPREDUCE-5785 for more details. Getting Started ===============