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From "Misha Dmitriev (JIRA)" <j...@apache.org>
Subject [jira] [Created] (HDFS-12042) Reduce memory used by snapshot diff data structures
Date Mon, 26 Jun 2017 20:19:00 GMT
Misha Dmitriev created HDFS-12042:
-------------------------------------

             Summary: Reduce memory used by snapshot diff data structures
                 Key: HDFS-12042
                 URL: https://issues.apache.org/jira/browse/HDFS-12042
             Project: Hadoop HDFS
          Issue Type: Improvement
            Reporter: Misha Dmitriev
            Assignee: Misha Dmitriev


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%), 12784
310 of empty 399,509K (0.8%)
     <-- org.apache.hadoop.hdfs.server.namenode.snapshot.FileDiffList.diffs <-- org.apache.hadoop.hdfs.server.nameno
de.snapshot.FileWithSnapshotFeature.diffs <-- org.apache.hadoop.hdfs.server.namenode.INode$Feature[]
<-- org.apache.
hadoop.hdfs.server.namenode.INodeFile.features <-- org.apache.hadoop.hdfs.server.blockmanagement.BlockInfo.bc
<-- or
g.apache.hadoop.util.LightWeightGSet$LinkedElement[] <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.e
ntries <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <-- org.apache.hadoop.hdfs.server.blockman
agement.BlocksMap$1.entries <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks
<-- org.apache.hadoop
.hdfs.server.blockmanagement.BlockManager.blocksMap <-- org.apache.hadoop.hdfs.server.blockmanagement.BlockManager$B
lockReportProcessingThread.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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