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Date: Tue, 27 Jun 2017 19:15:00 +0000 (UTC)
From: "Misha Dmitriev (JIRA)" <jira@apache.org>
To: hdfs-issues@hadoop.apache.org
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Subject: [jira] [Updated] (HDFS-12042) Lazy initialize
 AbstractINodeDiffList#diffs for snapshots to reduce memory consumption
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archived-at: Tue, 27 Jun 2017 19:15:06 -0000


     [ https://issues.apache.org/jira/browse/HDFS-12042?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Misha Dmitriev updated HDFS-12042:
----------------------------------
    Status: Patch Available  (was: In Progress)

> 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
>         Attachments: HDFS-12042.01.patch, HDFS-12042.02.patch, HDFS-12042.03.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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