Update Highlight big features and improvements.

hadoop-project/src/site/markdown/index.md.vm

@@ -23,109 +23,164 @@ Overview of Changes
 Users are encouraged to read the full set of release notes.
 This page provides an overview of the major changes.
 
-DataNode FsDatasetImpl Fine-Grained Locking via BlockPool
+S3A: Upgrade AWS SDK to V2
 ----------------------------------------
-[HDFS-15180](https://issues.apache.org/jira/browse/HDFS-15180) Split FsDatasetImpl datasetLock via blockpool to solve the issue of heavy FsDatasetImpl datasetLock
-When there are many namespaces in a large cluster.
+
+[HADOOP-18073](https://issues.apache.org/jira/browse/HADOOP-18073) S3A: Upgrade AWS SDK to V2
+
+The S3A connector now uses the V2 AWS SDK.  This is a significant change at the source code level.
+
+Any applications using the internal extension/override points in the filesystem connector are likely to break.
+
+Consult the document aws\_sdk\_upgrade for the full details.
+
+HDFS DataNode Split one FsDatasetImpl lock to volume grain locks
+----------------------------------------
+
+[HDFS-15382](https://issues.apache.org/jira/browse/HDFS-15382) Split one FsDatasetImpl lock to volume grain locks.
+
+Throughput is one of the core performance evaluation for DataNode instance.
+
+However, it does not reach the best performance  especially for Federation deploy all the time although there are different improvement,
+
+because of the global coarse-grain lock.
+
+These series issues (include [HDFS-16534](https://issues.apache.org/jira/browse/HDFS-16534), [HDFS-16511](https://issues.apache.org/jira/browse/HDFS-16511), [HDFS-15382](https://issues.apache.org/jira/browse/HDFS-15382) and [HDFS-16429](https://issues.apache.org/jira/browse/HDFS-16429).) try to split the global coarse-grain lock to
+
+fine-grain lock which is double level lock for blockpool and volume, to improve the throughput and avoid lock impacts between
+
+blockpools and volumes.
 
 YARN Federation improvements
 ----------------------------------------
-[YARN-5597](https://issues.apache.org/jira/browse/YARN-5597) brings many improvements, including the following:
 
-1. YARN Router now boasts a full implementation of all relevant interfaces including the ApplicationClientProtocol, ResourceManagerAdministrationProtocol, and RMWebServiceProtocol.
-2. Enhanced support for Application cleanup and automatic offline mechanisms for SubCluster are now facilitated by the YARN Router.
-3. Code optimization for Router and AMRMProxy was undertaken, coupled with improvements to previously pending functionalities.
+[YARN-5597](https://issues.apache.org/jira/browse/YARN-5597) YARN Federation improvements.
+
+We have enhanced the YARN Federation functionality for improved usability. The enhanced features are as follows:
+
+1. YARN Router now boasts a full implementation of all interfaces including the ApplicationClientProtocol, ResourceManagerAdministrationProtocol, and RMWebServiceProtocol.
+2. YARN Router support for application cleanup and automatic offline mechanisms for subCluster.
+3. Code improvements were undertaken for the Router and AMRMProxy, along with enhancements to previously pending functionalities.
 4. Audit logs and Metrics for Router received upgrades.
 5. A boost in cluster security features was achieved, with the inclusion of Kerberos support.
 6. The page function of the router has been enhanced.
+7. A set of commands has been added to the Router side for operating on SubClusters and Policies.
 
-Upgrade AWS SDK to V2
+HDFS RBF: Code Enhancements, New Features, and Bug Fixes
 ----------------------------------------
-[HADOOP-18073](https://issues.apache.org/jira/browse/HADOOP-18073)
-The S3A connector now uses the V2 AWS SDK.  This is a significant change at the source code level.
-Any applications using the internal extension/override points in the filesystem connector are likely to break.
-Consult the document aws\_sdk\_upgrade for the full details.
 
-Azure ABFS: Critical Stream Prefetch Fix
+The HDFS RBF functionality has undergone significant enhancements, encompassing over 200 commits for feature
+
+improvements, new functionalities, and bug fixes.
+
+Important features and improvements are as follows:
+
+**Feature**
+
+[HDFS-15294](https://issues.apache.org/jira/browse/HDFS-15294) Federation balance tool introduces a new HDFS federation balance tool to balance data across different federation
+
+namespaces. It uses Distcp to copy data from the source path to the target path.
+
+**Improvement**
+
+[HDFS-17128](https://issues.apache.org/jira/browse/HDFS-17128) RBF: SQLDelegationTokenSecretManager should use version of tokens updated by other routers.
+
+The SQLDelegationTokenSecretManager enhances performance by maintaining processed tokens in memory. However, there is
+
+a potential issue of router cache inconsistency due to token loading and renewal. This issue has been addressed by the
+
+resolution of HDFS-17128.
+
+[HDFS-17148](https://issues.apache.org/jira/browse/HDFS-17148) RBF: SQLDelegationTokenSecretManager must cleanup expired tokens in SQL.
+
+SQLDelegationTokenSecretManager, while fetching and temporarily storing tokens from SQL in a memory cache with a short TTL,
+
+faces an issue where expired tokens are not efficiently cleaned up, leading to a buildup of expired tokens in the SQL database.
+
+This issue has been addressed by the resolution of HDFS-17148.
+
+**Others**
+
+Other changes to HDFS RBF include WebUI, command line, and other improvements. Please refer to the release document.
+
+HDFS EC: Code Enhancements and Bug Fixes
 ----------------------------------------
 
-The abfs has a critical bug fix
-[HADOOP-18546](https://issues.apache.org/jira/browse/HADOOP-18546).
-*ABFS. Disable purging list of in-progress reads in abfs stream close().*
+HDFS EC has made code improvements and fixed some bugs.
+
+Important improvements and bugs are as follows:
+
+**Improvement**
+
+[HDFS-16613](https://issues.apache.org/jira/browse/HDFS-16613) EC: Improve performance of decommissioning dn with many ec blocks.
+
+In a hdfs cluster with a lot of EC blocks, decommission a dn is very slow. The reason is unlike replication blocks can be replicated
+
+from any dn which has the same block replication, the ec block have to be replicated from the decommissioning dn.
+
+The configurations `dfs.namenode.replication.max-streams` and `dfs.namenode.replication.max-streams-hard-limit` will limit
+
+the replication speed, but increase these configurations will create risk to the whole cluster's network. So it should add a new
+
+configuration to limit the decommissioning dn, distinguished from the cluster wide max-streams limit.
+
+[HDFS-16663](https://issues.apache.org/jira/browse/HDFS-16663) Allow block reconstruction pending timeout refreshable to increase decommission performance.
+
+In [HDFS-16613](https://issues.apache.org/jira/browse/HDFS-16613), increase the value of `dfs.namenode.replication.max-streams-hard-limit` would maximize the IO
+
+performance of the decommissioning DN, which has a lot of EC blocks. Besides this, we also need to decrease the value of
+
+`dfs.namenode.reconstruction.pending.timeout-sec`, default is 5 minutes, to shorten the interval time for checking
+
+pendingReconstructions. Or the decommissioning node would be idle to wait for copy tasks in most of this 5 minutes.
+
+In decommission progress, we may need to reconfigure these 2 parameters several times. In [HDFS-14560](https://issues.apache.org/jira/browse/HDFS-14560), the
+
+`dfs.namenode.replication.max-streams-hard-limit` can already be reconfigured dynamically without namenode restart. And
 
-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`
+the `dfs.namenode.reconstruction.pending.timeout-sec` parameter also need to be reconfigured dynamically.
 
-Consult the parent JIRA [HADOOP-18521](https://issues.apache.org/jira/browse/HADOOP-18521)
-*ABFS ReadBufferManager buffer sharing across concurrent HTTP requests*
-for root cause analysis, details on what is affected, and mitigations.
+**Bug**
 
+[HDFS-16456](https://issues.apache.org/jira/browse/HDFS-16456) EC: Decommission a rack with only on dn will fail when the rack number is equal with replication.
 
-Vectored IO API
----------------
+In below scenario, decommission will fail by `TOO_MANY_NODES_ON_RACK` reason:
 
-[HADOOP-18103](https://issues.apache.org/jira/browse/HADOOP-18103).
-*High performance vectored read API in Hadoop*
+- Enable EC policy, such as RS-6-3-1024k.
 
-The `PositionedReadable` interface has now added an operation for
-Vectored IO (also known as Scatter/Gather IO):
+- The rack number in this cluster is equal with or less than the replication number(9)
 
-```java
-void readVectored(List<? extends FileRange> ranges, IntFunction<ByteBuffer> allocate)
-```
+- A rack only has one DN, and decommission this DN.
 
-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.
+This issue has been addressed by the resolution of HDFS-16456.
 
-1. The default implementation uses a series of `readFully()` calls, so delivers
-   equivalent performance.
-2. The local filesystem uses java native IO calls for higher performance reads than `readFully()`.
-3. The S3A filesystem issues parallel HTTP GET requests in different threads.
+[HDFS-17094](https://issues.apache.org/jira/browse/HDFS-17094) EC: Fix bug in block recovery when there are stale datanodes.
 
-Benchmarking of enhanced Apache ORC and Apache Parquet clients through `file://` and `s3a://`
-show significant improvements in query performance.
+During block recovery, the `RecoveryTaskStriped` in the datanode expects a one-to-one correspondence between
 
-Further Reading:
-* [FsDataInputStream](./hadoop-project-dist/hadoop-common/filesystem/fsdatainputstream.html).
-* [Hadoop Vectored IO: Your Data Just Got Faster!](https://apachecon.com/acasia2022/sessions/bigdata-1148.html)
-  Apachecon 2022 talk.
+`rBlock.getLocations()` and `rBlock.getBlockIndices()`. However, if there are stale locations during a NameNode heartbeat,
 
-Mapreduce: Manifest Committer for Azure ABFS and google GCS
-----------------------------------------------------------
+this correspondence may be disrupted. Specifically, although there are no stale locations in `recoveryLocations`, the block indices
 
-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.
+array remains complete. This discrepancy causes `BlockRecoveryWorker.RecoveryTaskStriped#recover` to generate an incorrect
 
-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.
+internal block ID, leading to a failure in the recovery process as the corresponding datanode cannot locate the replica.
 
-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.
+This issue has been addressed by the resolution of HDFS-17094.
 
-More details are available in the
-[manifest committer](./hadoop-mapreduce-client/hadoop-mapreduce-client-core/manifest_committer.html).
-documentation.
+[HDFS-17284](https://issues.apache.org/jira/browse/HDFS-17284). EC: Fix int overflow in calculating numEcReplicatedTasks and numReplicationTasks during block recovery.
 
+Due to an integer overflow in the calculation of numReplicationTasks or numEcReplicatedTasks, the NameNode's configuration
 
-HDFS: Dynamic Datanode Reconfiguration
---------------------------------------
+parameter `dfs.namenode.replication.max-streams-hard-limit` failed to take effect. This led to an excessive number of tasks
 
-HDFS-16400, HDFS-16399, HDFS-16396, HDFS-16397, HDFS-16413, HDFS-16457.
+being sent to the DataNodes, consequently occupying too much of their memory.
 
-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.
+This issue has been addressed by the resolution of HDFS-17284.
 
-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)
-for the list of dynamically reconfigurable attributes.
+**Others**
 
+Other improvements and fixes for HDFS EC, Please refer to the release document.
 
 Transitive CVE fixes
 --------------------
@@ -133,8 +188,8 @@ 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
+However, applications which have all the library is 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
@@ -170,12 +225,12 @@ 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
+2. Cloud: transient or persistent single or multiple user/tenant cluster
    with private VLAN *and security*.
    Good.
    Consider [Apache Knox](https://knox.apache.org/) for managing remote
    access to the cluster.
-1. Cloud: transient single user/tenant cluster with private VLAN
+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..