[jira] [Updated] (HDFS-12042) Reduce memory used by snapshot diff data structures
[
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)
> 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
> Attachments: HDFS-12042.01.patch, HDFS-12042.02.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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[jira] [Updated] (HDFS-12042) Reduce memory used by snapshot diff data structures
[
https://issues.apache.org/jira/browse/HDFS-12042?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Misha Dmitriev updated HDFS-12042:
--
Attachment: HDFS-12042.02.patch
> 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
> Attachments: HDFS-12042.01.patch, HDFS-12042.02.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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[jira] [Updated] (HDFS-12042) Reduce memory used by snapshot diff data structures
[
https://issues.apache.org/jira/browse/HDFS-12042?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Misha Dmitriev updated HDFS-12042:
--
Status: In Progress (was: Patch Available)
> 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
> Attachments: HDFS-12042.01.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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[jira] [Updated] (HDFS-12042) Reduce memory used by snapshot diff data structures
[
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: Open)
> 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
> Attachments: HDFS-12042.01.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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[jira] [Updated] (HDFS-12042) Reduce memory used by snapshot diff data structures
[
https://issues.apache.org/jira/browse/HDFS-12042?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Misha Dmitriev updated HDFS-12042:
--
Attachment: HDFS-12042.01.patch
> 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
> Attachments: HDFS-12042.01.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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[jira] [Updated] (HDFS-12042) Reduce memory used by snapshot diff data structures
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https://issues.apache.org/jira/browse/HDFS-12042?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Misha Dmitriev updated HDFS-12042:
--
Description:
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%.
was:
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.Ha
