You are viewing a plain text version of this content. The canonical link for it is here.
Posted to hdfs-issues@hadoop.apache.org by "Misha Dmitriev (JIRA)" <ji...@apache.org> on 2018/01/17 02:24:01 UTC
[jira] [Commented] (HDFS-12051) Intern duplicate byte[] arrays
(mainly those denoting file/directory names) to save memory
[ https://issues.apache.org/jira/browse/HDFS-12051?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16328189#comment-16328189 ]
Misha Dmitriev commented on HDFS-12051:
---------------------------------------
I've just modified the ticket name and description.
> Intern duplicate byte[] arrays (mainly those denoting file/directory names) 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
> Priority: Major
> 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$SnapshotCopy.name}} and {{org.apache.hadoop.hdfs.server.namenode.INodeFile.name}}:
> {code:java}
> 19. DUPLICATE PRIMITIVE ARRAYS
> Types of duplicate objects:
> Ovhd Num objs Num unique objs Class name
> 3,220,272K (6.5%) 104749528 25760871 byte[]
> ....
> 1,841,485K (3.7%), 53194037 dup arrays (13158094 unique)
> 3510556 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 2228255 of byte[8](48, 48, 48, 48, 48, 48, 95, 48), 357439 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 237395 of byte[8](48, 48, 48, 48, 48, 49, 95, 48), 227853 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 179193 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 169487 of byte[8](48, 48, 48, 48, 48, 50, 95, 48), 145055 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 128134 of byte[8](48, 48, 48, 48, 48, 51, 95, 48), 108265 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...)
> ... and 45902395 more arrays, of which 13158084 are unique
> <-- org.apache.hadoop.hdfs.server.namenode.INodeFileAttributes$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}
> There are several other places in NameNode code which also produce duplicate {{byte[]}} arrays.
> To eliminate this duplication and reclaim memory, we will need to write a small class similar to StringInterner, but designed specifically for byte[] arrays.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: hdfs-issues-unsubscribe@hadoop.apache.org
For additional commands, e-mail: hdfs-issues-help@hadoop.apache.org