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From "Mostafa Mokhtar (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HIVE-8292) Reading from partitioned bucketed tables has high overhead in MapOperator.cleanUpInputFileChangedOp
Date Mon, 29 Sep 2014 19:11:39 GMT

     [ https://issues.apache.org/jira/browse/HIVE-8292?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Mostafa Mokhtar updated HIVE-8292:
----------------------------------
    Description: 
Reading from bucketed partitioned tables has significantly higher overhead compared to non-bucketed
non-partitioned files.


50% of the profile is spent in MapOperator.cleanUpInputFileChangedOp

5% the CPU in 
{code}
 Path onepath = normalizePath(onefile);
{code}

And 
45% the CPU in 
{code}
 onepath.toUri().relativize(fpath.toUri()).equals(fpath.toUri());
{code}

>From the profiler 
{code}
Stack Trace	Sample Count	Percentage(%)
hive.ql.exec.tez.MapRecordSource.processRow(Object)	5,327	62.348
   hive.ql.exec.vector.VectorMapOperator.process(Writable)	5,326	62.336
      hive.ql.exec.Operator.cleanUpInputFileChanged()	4,851	56.777
         hive.ql.exec.MapOperator.cleanUpInputFileChangedOp()	4,849	56.753
                                 java.net.URI.relativize(URI)	3,903	45.681
                                    java.net.URI.relativize(URI, URI)	3,903	45.681
                                       java.net.URI.normalize(String)	2,169	25.386
                                       java.net.URI.equal(String, String)	526	6.156
                                       java.net.URI.equalIgnoringCase(String, String)	1	0.012
                                       java.lang.String.substring(int)	1	0.012
            hive.ql.exec.MapOperator.normalizePath(String)	506	5.922
            org.apache.commons.logging.impl.Log4JLogger.info(Object)	32	0.375
                                 java.net.URI.equals(Object)	12	0.14
                                 java.util.HashMap$KeySet.iterator()	5	0.059
                                 java.util.HashMap.get(Object)	4	0.047
                                 java.util.LinkedHashMap.get(Object)	3	0.035
         hive.ql.exec.Operator.cleanUpInputFileChanged()	1	0.012
      hive.ql.exec.Operator.forward(Object, ObjectInspector)	473	5.536
      hive.ql.exec.mr.ExecMapperContext.inputFileChanged()	1	0.012
{code}


  was:
Reading from bucketed partitioned tables has significantly higher overhead compared to non-bucketed
non-partitioned files.


50% of the profile is spent in MapOperator.cleanUpInputFileChangedOp

5% the CPU in 
{code}
 Path onepath = normalizePath(onefile);
{code}

And 
45% the CPU in 
{code}
 onepath.toUri().relativize(fpath.toUri()).equals(fpath.toUri());
{code}

>From the profiler 
{code}
Stack Trace	Sample Count	Percentage(%)
org.apache.hadoop.hive.ql.exec.tez.MapRecordSource.processRow(Object)	978	28.613
   org.apache.hadoop.hive.ql.exec.vector.VectorMapOperator.process(Writable)	978	28.613
      org.apache.hadoop.hive.ql.exec.Operator.cleanUpInputFileChanged()	866	25.336
         org.apache.hadoop.hive.ql.exec.MapOperator.cleanUpInputFileChangedOp()	866	25.336
            java.net.URI.relativize(URI)	655	19.163
               java.net.URI.relativize(URI, URI)	655	19.163
                  java.net.URI.normalize(String)	517	15.126
					java.net.URI.needsNormalization(String)	372	10.884
					   java.lang.String.charAt(int)	235	6.875
									  java.net.URI.equal(String, String)	27	0.79
									  java.lang.StringBuilder.toString()	1	0.029
									  java.lang.StringBuilder.<init>()	1	0.029
									  java.lang.StringBuilder.append(String)	1	0.029
								org.apache.hadoop.hive.ql.exec.MapOperator.normalizePath(String)	167	4.886
								   org.apache.hadoop.fs.Path.<init>(String)	162	4.74
									  org.apache.hadoop.fs.Path.initialize(String, String, String, String)	162	4.74
	org.apache.hadoop.fs.Path.normalizePath(String, String)	97	2.838
	   org.apache.commons.lang.StringUtils.replace(String, String, String)	97	2.838
		  org.apache.commons.lang.StringUtils.replace(String, String, String, int)	97	2.838
			 java.lang.String.indexOf(String, int)	97	2.838
		java.net.URI.<init>(String, String, String, String, String)	65	1.902
{code}



> Reading from partitioned bucketed tables has high overhead in MapOperator.cleanUpInputFileChangedOp
> ---------------------------------------------------------------------------------------------------
>
>                 Key: HIVE-8292
>                 URL: https://issues.apache.org/jira/browse/HIVE-8292
>             Project: Hive
>          Issue Type: Bug
>          Components: Query Processor
>    Affects Versions: 0.14.0
>         Environment: cn105
>            Reporter: Mostafa Mokhtar
>            Assignee: Prasanth J
>             Fix For: 0.14.0
>
>         Attachments: 2014_09_29_14_46_04.jfr
>
>
> Reading from bucketed partitioned tables has significantly higher overhead compared to
non-bucketed non-partitioned files.
> 50% of the profile is spent in MapOperator.cleanUpInputFileChangedOp
> 5% the CPU in 
> {code}
>  Path onepath = normalizePath(onefile);
> {code}
> And 
> 45% the CPU in 
> {code}
>  onepath.toUri().relativize(fpath.toUri()).equals(fpath.toUri());
> {code}
> From the profiler 
> {code}
> Stack Trace	Sample Count	Percentage(%)
> hive.ql.exec.tez.MapRecordSource.processRow(Object)	5,327	62.348
>    hive.ql.exec.vector.VectorMapOperator.process(Writable)	5,326	62.336
>       hive.ql.exec.Operator.cleanUpInputFileChanged()	4,851	56.777
>          hive.ql.exec.MapOperator.cleanUpInputFileChangedOp()	4,849	56.753
>                                  java.net.URI.relativize(URI)	3,903	45.681
>                                     java.net.URI.relativize(URI, URI)	3,903	45.681
>                                        java.net.URI.normalize(String)	2,169	25.386
>                                        java.net.URI.equal(String, String)	526	6.156
>                                        java.net.URI.equalIgnoringCase(String, String)
1	0.012
>                                        java.lang.String.substring(int)	1	0.012
>             hive.ql.exec.MapOperator.normalizePath(String)	506	5.922
>             org.apache.commons.logging.impl.Log4JLogger.info(Object)	32	0.375
>                                  java.net.URI.equals(Object)	12	0.14
>                                  java.util.HashMap$KeySet.iterator()	5	0.059
>                                  java.util.HashMap.get(Object)	4	0.047
>                                  java.util.LinkedHashMap.get(Object)	3	0.035
>          hive.ql.exec.Operator.cleanUpInputFileChanged()	1	0.012
>       hive.ql.exec.Operator.forward(Object, ObjectInspector)	473	5.536
>       hive.ql.exec.mr.ExecMapperContext.inputFileChanged()	1	0.012
> {code}



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