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https://issues.apache.org/jira/browse/MAHOUT-1226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13664606#comment-13664606
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Dmitriy Lyubimov commented on MAHOUT-1226:
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use smaller decomposition rank. whatever you do, I bet 100 is more than
reasonable. For LSA errors are dwarfed by variation in the training corpus
itself; for more exact inference the tail will likely start encoding nothing
but noise in the data anyway much sooner. the running time will not scale to -k
parameter (it will still be somewhat O(n^3/2)) even if you manage to have
enough available task resources.
> mahout ssvd Bt-job bug
> ----------------------
>
> Key: MAHOUT-1226
> URL: https://issues.apache.org/jira/browse/MAHOUT-1226
> Project: Mahout
> Issue Type: Bug
> Affects Versions: 0.7
> Environment: mahout-0.7
> hadoop-0.20.205.0
> Reporter: Jakub
> Attachments: core-site.xml, hdfs-site.xml, mapred-site.xml
>
>
> when using mahout ssvd job, Bt-job creates lots of spills to disk.
> Those might be minimized by tuning hadoop io.sort.mb parameter.
> However, when io.sort.mb is bigger than ~ 1100 , ie. 1500 I'm getting that
> exception:
> java.io.IOException: Spill failed
> at
> org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1029)
> at
> org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:691)
> at
> org.apache.hadoop.mapreduce.TaskInputOutputContext.write(TaskInputOutputContext.java:80)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.BtJob$BtMapper$1.collect(BtJob.java:261)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.BtJob$BtMapper$1.collect(BtJob.java:255)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.SparseRowBlockAccumulator.flushBlock(SparseRowBlockAccumulator.java:65)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.SparseRowBlockAccumulator.collect(SparseRowBlockAccumulator.java:75)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.BtJob$BtMapper.map(BtJob.java:158)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.BtJob$BtMapper.map(BtJob.java:102)
> at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:144)
> at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:764)
> at org.apache.hadoop.mapred.MapTask.run(MapTask.java:370)
> at org.apache.hadoop.mapred.Child$4.run(Child.java:255)
> at java.security.AccessController.doPrivileged(Native Method)
> at javax.security.auth.Subject.doAs(Subject.java:415)
> at
> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1059)
> at org.apache.hadoop.mapred.Child.main(Child.java:249)
> Caused by: java.lang.RuntimeException: next value iterator failed
> at
> org.apache.hadoop.mapreduce.ReduceContext$ValueIterator.next(ReduceContext.java:166)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.BtJob$OuterProductCombiner.reduce(BtJob.java:322)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.BtJob$OuterProductCombiner.reduce(BtJob.java:302)
> at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:176)
> at org.apache.hadoop.mapred.Task$NewCombinerRunner.combine(Task.java:1502)
> at
> org.apache.hadoop.mapred.MapTask$MapOutputBuffer.sortAndSpill(MapTask.java:1436)
> at
> org.apache.hadoop.mapred.MapTask$MapOutputBuffer.access$1800(MapTask.java:853)
> at
> org.apache.hadoop.mapred.MapTask$MapOutputBuffer$SpillThread.run(MapTask.java:1344)
> Caused by: java.io.EOFException
> at java.io.DataInputStream.readByte(DataInputStream.java:267)
> at org.apache.mahout.math.Varint.readUnsignedVarInt(Varint.java:159)
> at
> org.apache.mahout.math.hadoop.stochasticsvd.SparseRowBlockWritable.readFields(SparseRowBlockWritable.java:60)
> at
> org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:67)
> at
> org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:40)
> at
> org.apache.hadoop.mapreduce.ReduceContext.nextKeyValue(ReduceContext.java:116)
> at
> org.apache.hadoop.mapreduce.ReduceContext$ValueIterator.next(ReduceContext.java:163)
> ... 7 more
> by changing this value I've already managed to reduce spills from 100 (for
> default io.sort.mb value) to 10, disk usage dropped from around 7 gigabytes
> for my small data set to around 900 mb. repairing this issue might bring big
> performance improvements.
> I've got lots of free ram, that's not some lack of memory issue.
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