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Posted to hdfs-issues@hadoop.apache.org by "Hudson (JIRA)" <ji...@apache.org> on 2017/07/01 00:53:00 UTC
[jira] [Commented] (HDFS-12042) Lazy initialize
AbstractINodeDiffList#diffs for snapshots to reduce memory consumption
[ https://issues.apache.org/jira/browse/HDFS-12042?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16070913#comment-16070913 ]
Hudson commented on HDFS-12042:
-------------------------------
SUCCESS: Integrated in Jenkins build Hadoop-trunk-Commit #11959 (See [https://builds.apache.org/job/Hadoop-trunk-Commit/11959/])
HDFS-12042. Lazy initialize AbstractINodeDiffList#diffs for snapshots to (weichiu: rev bcba844d1144cc334e2babbc34c9d42eac1c203a)
* (edit) hadoop-hdfs-project/hadoop-hdfs/src/main/java/org/apache/hadoop/hdfs/server/namenode/snapshot/AbstractINodeDiffList.java
* (edit) hadoop-hdfs-project/hadoop-hdfs/src/main/java/org/apache/hadoop/hdfs/server/namenode/INodeDirectory.java
* (edit) hadoop-hdfs-project/hadoop-hdfs/src/test/java/org/apache/hadoop/hdfs/server/namenode/TestTruncateQuotaUpdate.java
> Lazy initialize AbstractINodeDiffList#diffs for snapshots to reduce memory consumption
> --------------------------------------------------------------------------------------
>
> Key: HDFS-12042
> URL: https://issues.apache.org/jira/browse/HDFS-12042
> Project: Hadoop HDFS
> Issue Type: Improvement
> Reporter: Misha Dmitriev
> Assignee: Misha Dmitriev
> Fix For: 2.9.0, 3.0.0-beta1, 2.8.2
>
> Attachments: HDFS-12042.01.patch, HDFS-12042.02.patch, HDFS-12042.03.patch, HDFS-12042.04.patch
>
>
> When snapshot diff operation is performed in a NameNode that manages several million HDFS files/directories, NN needs a lot of memory. Some of that memory is wasted due to suboptimal data structures, such as empty or under-populated ArrayLists, etc. Analyzing one heap dump with jxray (www.jxray.com), we observed the following problems with data structures:
> {code}
> 9. BAD COLLECTIONS
> Total collections: 99,707,902 Bad collections: 88,799,760 Overhead: 9,063,898K (18.2%)
> Top bad collections:
> Ovhd Problem Num objs Type
> -------------------------------------------------
> 3,056,014K (6.1%) small 29435572 j.u.ArrayList
> 2,641,373K (5.3%) 1-elem 21837906 j.u.ArrayList
> 864,215K (1.7%) 1-elem 5291813 j.u.TreeSet
> 808,456K (1.6%) 1-elem 3045847 j.u.HashMap
> 602,470K (1.2%) empty 18549109 j.u.ArrayList
> 441,563K (0.9%) empty 4356975 j.u.TreeSet
> 373,088K (0.7%) empty 5297007 j.u.HashMap
> 270,324K (0.5%) small 931394 j.u.HashMap
> {code}
> The data structures created by HDFS code that suffer from the above problems are, in particular:
> {code}
> 4,228,182K (8.5%): j.u.ArrayList: 19412263 of small 2,111,087K (4.2%), 12932408 of 1-elem 1,717,585K (3.4%), 12784310 of empty 399,509K (0.8%)
> <-- org.apache.hadoop.hdfs.server.namenode.snapshot.FileDiffList.diffs <-- org.apache.hadoop.hdfs.server.namenode.snapshot.FileWithSnapshotFeature.diffs <-- org.apache.hadoop.hdfs.server.namenode.INode$Feature[] <-- org.apache.hadoop.hdfs.server.namenode.INodeFile.features <-- org.apache.hadoop.hdfs.server.blockmanagement.BlockInfo.bc <-- org.apache.hadoop.util.LightWeightGSet$LinkedElement[] <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <-- org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.blocksMap <-- org.apache.hadoop.hdfs.server.blockmanagement.BlockManager$BlockReportProcessingThread.this$0 <-- j.l.Thread[] <-- j.l.ThreadGroup.threads <-- j.l.Thread.group <-- Java Static: org.apache.hadoop.fs.FileSystem$Statistics.STATS_DATA_CLEANER
> {code}
> and
> {code}
> 575,557K (1.2%): j.u.ArrayList: 4363271 of 1-elem 409,056K (0.8%), 2439001 of small 166,482K (0.3%)
> <-- org.apache.hadoop.hdfs.server.namenode.INodeDirectory.children <-- org.apache.hadoop.util.LightWeightGSet$LinkedElement[] <-- org.apache.hadoop.util.LightWeightGSet.entries <-- org.apache.hadoop.hdfs.server.namenode.INodeMap.map <-- org.apache.hadoop.hdfs.server.namenode.FSDirectory.inodeMap <-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem.dir <-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem$NameNodeResourceMonitor.this$0 <-- org.apache.hadoop.util.Daemon.target <-- org.apache.hadoop.hdfs.server.namenode.FSDirectory.inodeMap <-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem.dir <-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem$NameNodeResourceMonitor.this$0 <-- org.apache.hadoop.util.Daemon.target <-- j.l.Thread[] <-- j.l.ThreadGroup.threads <-- j.l.Thread.group <-- Java Static: org.apache.hadoop.fs.FileSystem$Statistics.STATS_DATA_CLEANER
> {code}
> There are several different reference chains that all lead to FileDiffList.diffs or INodeDirectory.children. The total percentage of memory wasted by these data structures in the analyzed dump is about 12%. By creating these lists lazily and/or with capacity that better matches their actual size, we should be able to reclaim a significant part of these 12%.
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