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https://issues.apache.org/jira/browse/HDFS-12051?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16314331#comment-16314331
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Tsz Wo Nicholas Sze commented on HDFS-12051:
--------------------------------------------
I am very surprise from the Summary and Description of this JIRA that the patch
changes not only SnapshotCopy but also all other INode as well as FSImage/Edit.
Does this suppose to improve the performance of Namenode? If yes, could you
measure it?
Please hold on committing the patch. It definitely needs more discussion.
The current summary and description sound like a minor change but the patch is
a major change. Please update them or restrict the patch to what is described
here. Thanks.
> Intern INOdeFileAttributes$SnapshotCopy.name byte[] arrays to save memory
> -------------------------------------------------------------------------
>
> Key: HDFS-12051
> URL: https://issues.apache.org/jira/browse/HDFS-12051
> Project: Hadoop HDFS
> Issue Type: Improvement
> Reporter: Misha Dmitriev
> Assignee: Misha Dmitriev
> Attachments: HDFS-12051.01.patch, HDFS-12051.02.patch,
> HDFS-12051.03.patch, HDFS-12051.04.patch, HDFS-12051.05.patch,
> HDFS-12051.06.patch
>
>
> When snapshot diff operation is performed in a NameNode that manages several
> million HDFS files/directories, NN needs a lot of memory. Analyzing one heap
> dump with jxray (www.jxray.com), we observed that duplicate byte[] arrays
> result in 6.5% memory overhead, and most of these arrays are referenced by
> {{org.apache.hadoop.hdfs.server.namenode.INodeFileAttributes$SnapshotCopy.name}}
> and {{org.apache.hadoop.hdfs.server.namenode.INodeFile.name}}:
> {code}
> 19. DUPLICATE PRIMITIVE ARRAYS
> Types of duplicate objects:
> Ovhd Num objs Num unique objs Class name
> 3,220,272K (6.5%) 104749528 25760871 byte[]
> ....
> 1,841,485K (3.7%), 53194037 dup arrays (13158094 unique)
> 3510556 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 2228255
> of byte[8](48, 48, 48, 48, 48, 48, 95, 48), 357439 of byte[17](112, 97, 114,
> 116, 45, 109, 45, 48, 48, 48, ...), 237395 of byte[8](48, 48, 48, 48, 48, 49,
> 95, 48), 227853 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...),
> 179193 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 169487
> of byte[8](48, 48, 48, 48, 48, 50, 95, 48), 145055 of byte[17](112, 97, 114,
> 116, 45, 109, 45, 48, 48, 48, ...), 128134 of byte[8](48, 48, 48, 48, 48, 51,
> 95, 48), 108265 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...)
> ... and 45902395 more arrays, of which 13158084 are unique
> <--
> org.apache.hadoop.hdfs.server.namenode.INodeFileAttributes$SnapshotCopy.name
> <-- org.apache.hadoop.hdfs.server.namenode.snapshot.FileDiff.snapshotINode
> <-- {j.u.ArrayList} <--
> org.apache.hadoop.hdfs.server.namenode.snapshot.FileDiffList.diffs <--
> org.apache.hadoop.hdfs.server.namenode.snapshot.FileWithSnapshotFeature.diffs
> <-- org.apache.hadoop.hdfs.server.namenode.INode$Feature[] <--
> org.apache.hadoop.hdfs.server.namenode.INodeFile.features <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockInfo.bc <-- ... (1
> elements) ... <--
> 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
> 409,830K (0.8%), 13482787 dup arrays (13260241 unique)
> 430 of byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 353 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 352 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 350 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 342 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 341 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 341 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 340 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 337 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 334 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...)
> ... and 13479257 more arrays, of which 13260231 are unique
> <-- org.apache.hadoop.hdfs.server.namenode.INodeFile.name <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockInfo.bc <--
> org.apache.hadoop.util.LightWeightGSet$LinkedElement[] <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.blocksMap <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager$BlockReportProcessingThread.this$0
> <-- j.l.Thread[] <--
> 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}
> To eliminate this duplication and reclaim memory, we will need to write a
> small class similar to StringInterner, but designed specifically for byte[]
> arrays.
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