[
https://issues.apache.org/jira/browse/HDFS-12051?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16067460#comment-16067460
]
Misha Dmitriev commented on HDFS-12051:
---------------------------------------
Upon a closer analysis of the problem, it looks like we will need to apply a
very specialized byte[] array interning solution here.
What makes this case special is that the number of byte[] arrays is very high
(~100M total arrays, ~25M unique arrays), but the average duplication factor is
not very high (~4). Some byte[] arrays are replicated in an extremely high
number, e.g. per the jxray report there are 3.5M copies of one 17-element array
and so on. But that means that the vast majority of arrays actually don't have
any duplicates. So if we use a standard dedupe solution, a WeakHashMap for all
byte[] arrays, it will end up taking a lot of memory. A java.util.HashMap would
use ~60 bytes per entry, a WeakHashMap even more, so this table will take at
least 25M * 60 bytes = 1.5GB. That's comparable with the amount of memory that
we try to save, and will result in multiple extra objects and the associated GC
overhead - all because of the vast number of no-dupe arrays that the table
would contain.
To address this problem, I suggest to use a solution that worked for me in the
past in similar situations. It's a small (probably a few thousand elements in
our case) fixed-size cache for byte[] arrays. It is organized as a simple
hashmap. If a new element is added to it and its hashcode puts it into an
already occupied slot, the old element is thrown away and replaced with the new
one. In this way the cache stays fixed, but the elements that are duplicated
most, have the highest chance to occupy its slots. This cache will not
eliminate all the duplicates, but it will eliminate most of them, at a very
small cost.
> Intern INOdeFileAttributes$SnapshotCopy.name byte[] arrays to save memory
> -------------------------------------------------------------------------
>
> Key: HDFS-12051
> URL: https://issues.apache.org/jira/browse/HDFS-12051
> Project: Hadoop HDFS
> Issue Type: Improvement
> Reporter: Misha Dmitriev
> Assignee: Misha Dmitriev
>
> When snapshot diff operation is performed in a NameNode that manages several
> million HDFS files/directories, NN needs a lot of memory. Analyzing one heap
> dump with jxray (www.jxray.com), we observed that duplicate byte[] arrays
> result in 6.5% memory overhead, and most of these arrays are referenced by
> {{org.apache.hadoop.hdfs.server.namenode.INodeFileAttributes$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.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
