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Posted to user@hive.apache.org by Rajit Saha <rs...@lendingclub.com> on 2016/02/27 02:34:18 UTC
Running hive queries in different queue
Hi
I want to run hive query in a queue others than "default" queue from hive client command line . Can anybody please suggest a way to do it.
Regards
Rajit
On Feb 26, 2016, at 07:36, Patrick Duin <pa...@gmail.com>> wrote:
Hi Prasanth.
Thanks for the quick reply!
The logs don't show much more of the stacktrace I'm afraid:
java.lang.NullPointerException
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
The stacktrace isn't really the issue though. The NullPointer is a symptom caused by not being able to return any stripes, if you look at the line in the code it is because the 'stripes' field is null which should never happen. This, we think, is caused by failing namenode network traffic. We would have lots of IO warning in the logs saying block's cannot be found or e.g.:
16/02/01 13:20:34 WARN hdfs.BlockReaderFactory: I/O error constructing remote block reader.
java.io.IOException: java.lang.InterruptedException
at org.apache.hadoop.ipc.Client.call(Client.java:1448)
at org.apache.hadoop.ipc.Client.call(Client.java:1400)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:232)
at com.sun.proxy.$Proxy32.getServerDefaults(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getServerDefaults(ClientNamenodeProtocolTranslatorPB.java:268)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy33.getServerDefaults(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient.getServerDefaults(DFSClient.java:1007)
at org.apache.hadoop.hdfs.DFSClient.shouldEncryptData(DFSClient.java:2062)
at org.apache.hadoop.hdfs.DFSClient.newDataEncryptionKey(DFSClient.java:2068)
at org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.checkTrustAndSend(SaslDataTransferClient.java:208)
at org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.peerSend(SaslDataTransferClient.java:159)
at org.apache.hadoop.hdfs.net.TcpPeerServer.peerFromSocketAndKey(TcpPeerServer.java:90)
at org.apache.hadoop.hdfs.DFSClient.newConnectedPeer(DFSClient.java:3123)
at org.apache.hadoop.hdfs.BlockReaderFactory.nextTcpPeer(BlockReaderFactory.java:755)
at org.apache.hadoop.hdfs.BlockReaderFactory.getRemoteBlockReaderFromTcp(BlockReaderFactory.java:670)
at org.apache.hadoop.hdfs.BlockReaderFactory.build(BlockReaderFactory.java:337)
at org.apache.hadoop.hdfs.DFSInputStream.blockSeekTo(DFSInputStream.java:576)
at org.apache.hadoop.hdfs.DFSInputStream.readWithStrategy(DFSInputStream.java:800)
at org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:848)
at java.io.DataInputStream.readFully(DataInputStream.java:195)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.extractMetaInfoFromFooter(ReaderImpl.java:407)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:311)
at org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:228)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.populateAndCacheStripeDetails(OrcInputFormat.java:885)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:771)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.InterruptedException
at java.util.concurrent.FutureTask.awaitDone(FutureTask.java:400)
at java.util.concurrent.FutureTask.get(FutureTask.java:187)
at org.apache.hadoop.ipc.Client$Connection.sendRpcRequest(Client.java:1047)
at org.apache.hadoop.ipc.Client.call(Client.java:1442)
... 33 more
Our job doesn't always fail sometimes splits get calculated. We suspect when the namenode is too busy our job maybe hits some time-outs and the whole thing fails.
Our intuition has been the same as you suggest, bigger files is better. But we see a degradation in performance as soon as our files get bigger than the ORC block size. Keeping file size within ORC block size sounds silly but when looking at the code (OrcInputFormat) we think it cuts out a bunch of code that is causing us problems. The code we are trying to hit is: https://github.com/apache/hive/blob/release-0.14.0/ql/src/java/org/apache/hadoop/hive/ql/io/orc/OrcInputFormat.java#L656. Avoiding the scheduling.
In our case we are not using any SARG but we do use column projection.
Any idea why if we query the data via Hive we don't have this issue?
Let me know if you need more information. Thanks for the insights, much appreciated.
Kind regards,
Patrick
2016-02-25 22:20 GMT+01:00 Prasanth Jayachandran <pj...@hortonworks.com>>:
> On Feb 25, 2016, at 3:15 PM, Prasanth Jayachandran <pj...@hortonworks.com>> wrote:
>
> Hi Patrick
>
> Can you paste entire stacktrace? Looks like NPE happened during split generation but stack trace is incomplete to know what caused it.
>
> In Hive 0.14.0, the stripe size is changed to 64MB. The default block size for ORC files is 256MB. 4 stripes can fit a block. ORC does padding to avoid stripes straddling HDFS blocks. During split calculation, ORC footer which contains stripe level column statistics is read to perform split pruning based on predicate condition specified via SARG(Search Argument).
>
> For example: Assume column ‘state’ is sorted and the predicate condition is ‘state’=“CA"
> Stripe 1: min = AZ max = FL
> Stripe 2: min = GA max = MN
> Stripe 3: min = MS max = SC
> Stripe 4: min = SD max = WY
>
> In this case, only stripe 1 satisfies the above predicate condition. So only 1 split with stripe 1 will be created.
> So if there are huge number of small files, then footers from all files has to be read to do split pruning. If there are few number of large files then only few footers have to be read. Also the minimum splittable position is stripe boundary. So having fewer large files has the advantage of reading less data during split pruning.
>
> If you can send me the full stacktrace, I can tell what is causing the exception here. I will also let you know of any workaround/next hive version with the fix.
>
> In more recent hive versions, hive 1.2.0 onwards. OrcInputFormat is has strategies to decided when to read footers and when not to read footers automatically. You can configure the strategy that you want based on the workload. In case of many small files, footers will not be read and with large files footers will be read for split pruning.
The default strategy does it automatically (choosing between when to read and when not to footers). It is configurable as well.
>
> Thanks
> Prasanth
>
>> On Feb 25, 2016, at 7:08 AM, Patrick Duin <pa...@gmail.com>> wrote:
>>
>> Hi,
>>
>> We've recently moved one of our datasets to ORC and we use Cascading and Hive to read this data. We've had problems reading the data via Cascading, because of the generation of splits.
>> We read in a large number of files (thousands) and they are about 1GB each. We found that the split calculation took minutes on our cluster and often didn't succeed at all (when our namenode was busy).
>> When digging through the code of the 'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.class' we figured out that if we make the files less then the ORC block size (256MB) the code would avoid lots of namenode calls. We applied this solution and made our files smaller and that solved the problem. Split calculation in our job went from 10+ mins to a couple of seconds and always succeeds.
>> We feel it is counterintuitive as bigger files are usually better in HDFS. We've also seen that doing a hive query on the data does not present this problem. Internally Hive seem to take a completely different execution path and is not using the OrcInputFormat but uses 'org.apache.hadoop.hive.ql.io.CombineHiveInputFormat.class'.
>>
>> Can someone explain the reason for this difference or shed some light on the behaviour we are seeing? Any help will be greatly appreciated. We are using hive-0.14.0.
>>
>> Kind regards,
>> Patrick
>>
>> Here is the stack-trace that we would see when our Cascading job failed to calculate the splits:
>> Caused by: java.lang.RuntimeException: serious problem
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$Context.waitForTasks(OrcInputFormat.java:478)
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:949)
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:974)
>> at com.hotels.corc.mapred.CorcInputFormat.getSplits(CorcInputFormat.java:201)
>> at cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:200)
>> at cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:142)
>> at org.apache.hadoop.mapreduce.JobSubmitter.writeOldSplits(JobSubmitter.java:624)
>> at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:616)
>> at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:492)
>> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1296)
>> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1293)
>> 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:1628)
>> at org.apache.hadoop.mapreduce.Job.submit(Job.java:1293)
>> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:585)
>> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:580)
>> at java.security.AccessController.doPrivileged(Native Method)
>> at javax.security.auth.Subject.doAs(Subject.java:415)
>> at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
>> at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:580)
>> at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:571)
>> at cascading.flow.hadoop.planner.HadoopFlowStepJob.internalNonBlockingStart(HadoopFlowStepJob.java:106)
>> at cascading.flow.planner.FlowStepJob.blockOnJob(FlowStepJob.java:265)
>> at cascading.flow.planner.FlowStepJob.start(FlowStepJob.java:184)
>> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:146)
>> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:48)
>> ... 4 more
>> Caused by: java.lang.NullPointerException
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
>
________________________________
DISCLAIMER: The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review, re-transmission, dissemination or other use of, or taking of any action in reliance upon this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender and destroy any copies of this document and any attachments.
Re: Running hive queries in different queue
Posted by Mich Talebzadeh <mi...@gmail.com>.
Hello.
What Hive client are you using? beeline
Dr Mich Talebzadeh
LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
<https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
http://talebzadehmich.wordpress.com
On 27 February 2016 at 01:34, Rajit Saha <rs...@lendingclub.com> wrote:
> Hi
>
> I want to run hive query in a queue others than "default" queue from hive
> client command line . Can anybody please suggest a way to do it.
>
> Regards
> Rajit
>
> On Feb 26, 2016, at 07:36, Patrick Duin <pa...@gmail.com> wrote:
>
> Hi Prasanth.
>
> Thanks for the quick reply!
>
> The logs don't show much more of the stacktrace I'm afraid:
> java.lang.NullPointerException
> at
> org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
>
>
> The stacktrace isn't really the issue though. The NullPointer is a symptom
> caused by not being able to return any stripes, if you look at the line in
> the code it is because the 'stripes' field is null which should never
> happen. This, we think, is caused by failing namenode network traffic. We
> would have lots of IO warning in the logs saying block's cannot be found or
> e.g.:
> 16/02/01 13:20:34 WARN hdfs.BlockReaderFactory: I/O error constructing
> remote block reader.
> java.io.IOException: java.lang.InterruptedException
> at org.apache.hadoop.ipc.Client.call(Client.java:1448)
> at org.apache.hadoop.ipc.Client.call(Client.java:1400)
> at
> org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:232)
> at com.sun.proxy.$Proxy32.getServerDefaults(Unknown Source)
> at
> org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getServerDefaults(ClientNamenodeProtocolTranslatorPB.java:268)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at
> org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
> at
> org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
> at com.sun.proxy.$Proxy33.getServerDefaults(Unknown Source)
> at
> org.apache.hadoop.hdfs.DFSClient.getServerDefaults(DFSClient.java:1007)
> at
> org.apache.hadoop.hdfs.DFSClient.shouldEncryptData(DFSClient.java:2062)
> at
> org.apache.hadoop.hdfs.DFSClient.newDataEncryptionKey(DFSClient.java:2068)
> at
> org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.checkTrustAndSend(SaslDataTransferClient.java:208)
> at
> org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.peerSend(SaslDataTransferClient.java:159)
> at
> org.apache.hadoop.hdfs.net.TcpPeerServer.peerFromSocketAndKey(TcpPeerServer.java:90)
> at
> org.apache.hadoop.hdfs.DFSClient.newConnectedPeer(DFSClient.java:3123)
> at
> org.apache.hadoop.hdfs.BlockReaderFactory.nextTcpPeer(BlockReaderFactory.java:755)
> at
> org.apache.hadoop.hdfs.BlockReaderFactory.getRemoteBlockReaderFromTcp(BlockReaderFactory.java:670)
> at
> org.apache.hadoop.hdfs.BlockReaderFactory.build(BlockReaderFactory.java:337)
> at
> org.apache.hadoop.hdfs.DFSInputStream.blockSeekTo(DFSInputStream.java:576)
> at
> org.apache.hadoop.hdfs.DFSInputStream.readWithStrategy(DFSInputStream.java:800)
> at
> org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:848)
> at java.io.DataInputStream.readFully(DataInputStream.java:195)
> at
> org.apache.hadoop.hive.ql.io.orc.ReaderImpl.extractMetaInfoFromFooter(ReaderImpl.java:407)
> at
> org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:311)
> at
> org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:228)
> at
> org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.populateAndCacheStripeDetails(OrcInputFormat.java:885)
> at
> org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:771)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.lang.InterruptedException
> at java.util.concurrent.FutureTask.awaitDone(FutureTask.java:400)
> at java.util.concurrent.FutureTask.get(FutureTask.java:187)
> at
> org.apache.hadoop.ipc.Client$Connection.sendRpcRequest(Client.java:1047)
> at org.apache.hadoop.ipc.Client.call(Client.java:1442)
> ... 33 more
>
> Our job doesn't always fail sometimes splits get calculated. We suspect
> when the namenode is too busy our job maybe hits some time-outs and the
> whole thing fails.
>
> Our intuition has been the same as you suggest, bigger files is better.
> But we see a degradation in performance as soon as our files get bigger
> than the ORC block size. Keeping file size within ORC block size sounds
> silly but when looking at the code (OrcInputFormat) we think it cuts out a
> bunch of code that is causing us problems. The code we are trying to hit
> is:
> https://github.com/apache/hive/blob/release-0.14.0/ql/src/java/org/apache/hadoop/hive/ql/io/orc/OrcInputFormat.java#L656.
> Avoiding the scheduling.
>
> In our case we are not using any SARG but we do use column projection.
>
> Any idea why if we query the data via Hive we don't have this issue?
>
> Let me know if you need more information. Thanks for the insights, much
> appreciated.
>
> Kind regards,
> Patrick
>
>
> 2016-02-25 22:20 GMT+01:00 Prasanth Jayachandran <
> pjayachandran@hortonworks.com>:
>
>>
>> > On Feb 25, 2016, at 3:15 PM, Prasanth Jayachandran <
>> pjayachandran@hortonworks.com> wrote:
>> >
>> > Hi Patrick
>> >
>> > Can you paste entire stacktrace? Looks like NPE happened during split
>> generation but stack trace is incomplete to know what caused it.
>> >
>> > In Hive 0.14.0, the stripe size is changed to 64MB. The default block
>> size for ORC files is 256MB. 4 stripes can fit a block. ORC does padding to
>> avoid stripes straddling HDFS blocks. During split calculation, ORC footer
>> which contains stripe level column statistics is read to perform split
>> pruning based on predicate condition specified via SARG(Search Argument).
>> >
>> > For example: Assume column ‘state’ is sorted and the predicate
>> condition is ‘state’=“CA"
>> > Stripe 1: min = AZ max = FL
>> > Stripe 2: min = GA max = MN
>> > Stripe 3: min = MS max = SC
>> > Stripe 4: min = SD max = WY
>> >
>> > In this case, only stripe 1 satisfies the above predicate condition. So
>> only 1 split with stripe 1 will be created.
>> > So if there are huge number of small files, then footers from all files
>> has to be read to do split pruning. If there are few number of large files
>> then only few footers have to be read. Also the minimum splittable position
>> is stripe boundary. So having fewer large files has the advantage of
>> reading less data during split pruning.
>> >
>> > If you can send me the full stacktrace, I can tell what is causing the
>> exception here. I will also let you know of any workaround/next hive
>> version with the fix.
>> >
>> > In more recent hive versions, hive 1.2.0 onwards. OrcInputFormat is has
>> strategies to decided when to read footers and when not to read footers
>> automatically. You can configure the strategy that you want based on the
>> workload. In case of many small files, footers will not be read and with
>> large files footers will be read for split pruning.
>>
>> The default strategy does it automatically (choosing between when to read
>> and when not to footers). It is configurable as well.
>>
>> >
>> > Thanks
>> > Prasanth
>> >
>> >> On Feb 25, 2016, at 7:08 AM, Patrick Duin <pa...@gmail.com> wrote:
>> >>
>> >> Hi,
>> >>
>> >> We've recently moved one of our datasets to ORC and we use Cascading
>> and Hive to read this data. We've had problems reading the data via
>> Cascading, because of the generation of splits.
>> >> We read in a large number of files (thousands) and they are about 1GB
>> each. We found that the split calculation took minutes on our cluster and
>> often didn't succeed at all (when our namenode was busy).
>> >> When digging through the code of the
>> 'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.class' we figured out that
>> if we make the files less then the ORC block size (256MB) the code would
>> avoid lots of namenode calls. We applied this solution and made our files
>> smaller and that solved the problem. Split calculation in our job went from
>> 10+ mins to a couple of seconds and always succeeds.
>> >> We feel it is counterintuitive as bigger files are usually better in
>> HDFS. We've also seen that doing a hive query on the data does not present
>> this problem. Internally Hive seem to take a completely different execution
>> path and is not using the OrcInputFormat but uses
>> 'org.apache.hadoop.hive.ql.io.CombineHiveInputFormat.class'.
>> >>
>> >> Can someone explain the reason for this difference or shed some light
>> on the behaviour we are seeing? Any help will be greatly appreciated. We
>> are using hive-0.14.0.
>> >>
>> >> Kind regards,
>> >> Patrick
>> >>
>> >> Here is the stack-trace that we would see when our Cascading job
>> failed to calculate the splits:
>> >> Caused by: java.lang.RuntimeException: serious problem
>> >> at
>> org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$Context.waitForTasks(OrcInputFormat.java:478)
>> >> at
>> org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:949)
>> >> at
>> org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:974)
>> >> at
>> com.hotels.corc.mapred.CorcInputFormat.getSplits(CorcInputFormat.java:201)
>> >> at
>> cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:200)
>> >> at
>> cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:142)
>> >> at
>> org.apache.hadoop.mapreduce.JobSubmitter.writeOldSplits(JobSubmitter.java:624)
>> >> at
>> org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:616)
>> >> at
>> org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:492)
>> >> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1296)
>> >> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1293)
>> >> 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:1628)
>> >> at org.apache.hadoop.mapreduce.Job.submit(Job.java:1293)
>> >> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:585)
>> >> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:580)
>> >> at java.security.AccessController.doPrivileged(Native Method)
>> >> at javax.security.auth.Subject.doAs(Subject.java:415)
>> >> at
>> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
>> >> at
>> org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:580)
>> >> at
>> org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:571)
>> >> at
>> cascading.flow.hadoop.planner.HadoopFlowStepJob.internalNonBlockingStart(HadoopFlowStepJob.java:106)
>> >> at
>> cascading.flow.planner.FlowStepJob.blockOnJob(FlowStepJob.java:265)
>> >> at
>> cascading.flow.planner.FlowStepJob.start(FlowStepJob.java:184)
>> >> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:146)
>> >> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:48)
>> >> ... 4 more
>> >> Caused by: java.lang.NullPointerException
>> >> at
>> org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
>> >
>>
>>
>
> ------------------------------
> DISCLAIMER: The information transmitted is intended only for the person or
> entity to which it is addressed and may contain confidential and/or
> privileged material. Any review, re-transmission, dissemination or other
> use of, or taking of any action in reliance upon this information by
> persons or entities other than the intended recipient is prohibited. If you
> received this in error, please contact the sender and destroy any copies of
> this document and any attachments.
>
Re: Running hive queries in different queue
Posted by Rajit Saha <rs...@lendingclub.com>.
Thanks a lot Sathi.
I also found in the Hive Execution Engine is MapReduce
set mapreduce.job.queuename=<queuename>; works
If the Hive Execution Engine is Tez
We need to do
set tez.queue.name=<queuename>;
Cheers
Rajit Saha
------------------------------------------------
Principal DevOps Engineer | BigData
LendingClub
------------------------------------------------
From: Sathi Chowdhury <sa...@lithium.com>>
Reply-To: "user@hive.apache.org<ma...@hive.apache.org>" <us...@hive.apache.org>>
Date: Friday, February 26, 2016 at 6:01 PM
To: "user@hive.apache.org<ma...@hive.apache.org>" <us...@hive.apache.org>>
Subject: Re: Running hive queries in different queue
I think in your hive script you can do
set mapreduce.job.queuename=<queuename>;
Thanks
Sathi
From: Rajit Saha
Reply-To: "user@hive.apache.org<ma...@hive.apache.org>"
Date: Friday, February 26, 2016 at 5:34 PM
To: "user@hive.apache.org<ma...@hive.apache.org>"
Subject: Running hive queries in different queue
Hi
I want to run hive query in a queue others than "default" queue from hive client command line . Can anybody please suggest a way to do it.
Regards
Rajit
On Feb 26, 2016, at 07:36, Patrick Duin <pa...@gmail.com>> wrote:
Hi Prasanth.
Thanks for the quick reply!
The logs don't show much more of the stacktrace I'm afraid:
java.lang.NullPointerException
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
The stacktrace isn't really the issue though. The NullPointer is a symptom caused by not being able to return any stripes, if you look at the line in the code it is because the 'stripes' field is null which should never happen. This, we think, is caused by failing namenode network traffic. We would have lots of IO warning in the logs saying block's cannot be found or e.g.:
16/02/01 13:20:34 WARN hdfs.BlockReaderFactory: I/O error constructing remote block reader.
java.io.IOException: java.lang.InterruptedException
at org.apache.hadoop.ipc.Client.call(Client.java:1448)
at org.apache.hadoop.ipc.Client.call(Client.java:1400)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:232)
at com.sun.proxy.$Proxy32.getServerDefaults(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getServerDefaults(ClientNamenodeProtocolTranslatorPB.java:268)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy33.getServerDefaults(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient.getServerDefaults(DFSClient.java:1007)
at org.apache.hadoop.hdfs.DFSClient.shouldEncryptData(DFSClient.java:2062)
at org.apache.hadoop.hdfs.DFSClient.newDataEncryptionKey(DFSClient.java:2068)
at org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.checkTrustAndSend(SaslDataTransferClient.java:208)
at org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.peerSend(SaslDataTransferClient.java:159)
at org.apache.hadoop.hdfs.net.TcpPeerServer.peerFromSocketAndKey(TcpPeerServer.java:90)
at org.apache.hadoop.hdfs.DFSClient.newConnectedPeer(DFSClient.java:3123)
at org.apache.hadoop.hdfs.BlockReaderFactory.nextTcpPeer(BlockReaderFactory.java:755)
at org.apache.hadoop.hdfs.BlockReaderFactory.getRemoteBlockReaderFromTcp(BlockReaderFactory.java:670)
at org.apache.hadoop.hdfs.BlockReaderFactory.build(BlockReaderFactory.java:337)
at org.apache.hadoop.hdfs.DFSInputStream.blockSeekTo(DFSInputStream.java:576)
at org.apache.hadoop.hdfs.DFSInputStream.readWithStrategy(DFSInputStream.java:800)
at org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:848)
at java.io.DataInputStream.readFully(DataInputStream.java:195)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.extractMetaInfoFromFooter(ReaderImpl.java:407)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:311)
at org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:228)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.populateAndCacheStripeDetails(OrcInputFormat.java:885)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:771)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.InterruptedException
at java.util.concurrent.FutureTask.awaitDone(FutureTask.java:400)
at java.util.concurrent.FutureTask.get(FutureTask.java:187)
at org.apache.hadoop.ipc.Client$Connection.sendRpcRequest(Client.java:1047)
at org.apache.hadoop.ipc.Client.call(Client.java:1442)
... 33 more
Our job doesn't always fail sometimes splits get calculated. We suspect when the namenode is too busy our job maybe hits some time-outs and the whole thing fails.
Our intuition has been the same as you suggest, bigger files is better. But we see a degradation in performance as soon as our files get bigger than the ORC block size. Keeping file size within ORC block size sounds silly but when looking at the code (OrcInputFormat) we think it cuts out a bunch of code that is causing us problems. The code we are trying to hit is: https://github.com/apache/hive/blob/release-0.14.0/ql/src/java/org/apache/hadoop/hive/ql/io/orc/OrcInputFormat.java#L656. Avoiding the scheduling.
In our case we are not using any SARG but we do use column projection.
Any idea why if we query the data via Hive we don't have this issue?
Let me know if you need more information. Thanks for the insights, much appreciated.
Kind regards,
Patrick
2016-02-25 22:20 GMT+01:00 Prasanth Jayachandran <pj...@hortonworks.com>>:
> On Feb 25, 2016, at 3:15 PM, Prasanth Jayachandran <pj...@hortonworks.com>> wrote:
>
> Hi Patrick
>
> Can you paste entire stacktrace? Looks like NPE happened during split generation but stack trace is incomplete to know what caused it.
>
> In Hive 0.14.0, the stripe size is changed to 64MB. The default block size for ORC files is 256MB. 4 stripes can fit a block. ORC does padding to avoid stripes straddling HDFS blocks. During split calculation, ORC footer which contains stripe level column statistics is read to perform split pruning based on predicate condition specified via SARG(Search Argument).
>
> For example: Assume column ‘state’ is sorted and the predicate condition is ‘state’=“CA"
> Stripe 1: min = AZ max = FL
> Stripe 2: min = GA max = MN
> Stripe 3: min = MS max = SC
> Stripe 4: min = SD max = WY
>
> In this case, only stripe 1 satisfies the above predicate condition. So only 1 split with stripe 1 will be created.
> So if there are huge number of small files, then footers from all files has to be read to do split pruning. If there are few number of large files then only few footers have to be read. Also the minimum splittable position is stripe boundary. So having fewer large files has the advantage of reading less data during split pruning.
>
> If you can send me the full stacktrace, I can tell what is causing the exception here. I will also let you know of any workaround/next hive version with the fix.
>
> In more recent hive versions, hive 1.2.0 onwards. OrcInputFormat is has strategies to decided when to read footers and when not to read footers automatically. You can configure the strategy that you want based on the workload. In case of many small files, footers will not be read and with large files footers will be read for split pruning.
The default strategy does it automatically (choosing between when to read and when not to footers). It is configurable as well.
>
> Thanks
> Prasanth
>
>> On Feb 25, 2016, at 7:08 AM, Patrick Duin <pa...@gmail.com>> wrote:
>>
>> Hi,
>>
>> We've recently moved one of our datasets to ORC and we use Cascading and Hive to read this data. We've had problems reading the data via Cascading, because of the generation of splits.
>> We read in a large number of files (thousands) and they are about 1GB each. We found that the split calculation took minutes on our cluster and often didn't succeed at all (when our namenode was busy).
>> When digging through the code of the 'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.class' we figured out that if we make the files less then the ORC block size (256MB) the code would avoid lots of namenode calls. We applied this solution and made our files smaller and that solved the problem. Split calculation in our job went from 10+ mins to a couple of seconds and always succeeds.
>> We feel it is counterintuitive as bigger files are usually better in HDFS. We've also seen that doing a hive query on the data does not present this problem. Internally Hive seem to take a completely different execution path and is not using the OrcInputFormat but uses 'org.apache.hadoop.hive.ql.io.CombineHiveInputFormat.class'.
>>
>> Can someone explain the reason for this difference or shed some light on the behaviour we are seeing? Any help will be greatly appreciated. We are using hive-0.14.0.
>>
>> Kind regards,
>> Patrick
>>
>> Here is the stack-trace that we would see when our Cascading job failed to calculate the splits:
>> Caused by: java.lang.RuntimeException: serious problem
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$Context.waitForTasks(OrcInputFormat.java:478)
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:949)
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:974)
>> at com.hotels.corc.mapred.CorcInputFormat.getSplits(CorcInputFormat.java:201)
>> at cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:200)
>> at cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:142)
>> at org.apache.hadoop.mapreduce.JobSubmitter.writeOldSplits(JobSubmitter.java:624)
>> at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:616)
>> at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:492)
>> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1296)
>> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1293)
>> 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:1628)
>> at org.apache.hadoop.mapreduce.Job.submit(Job.java:1293)
>> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:585)
>> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:580)
>> at java.security.AccessController.doPrivileged(Native Method)
>> at javax.security.auth.Subject.doAs(Subject.java:415)
>> at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
>> at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:580)
>> at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:571)
>> at cascading.flow.hadoop.planner.HadoopFlowStepJob.internalNonBlockingStart(HadoopFlowStepJob.java:106)
>> at cascading.flow.planner.FlowStepJob.blockOnJob(FlowStepJob.java:265)
>> at cascading.flow.planner.FlowStepJob.start(FlowStepJob.java:184)
>> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:146)
>> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:48)
>> ... 4 more
>> Caused by: java.lang.NullPointerException
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
>
________________________________
DISCLAIMER: The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review, re-transmission, dissemination or other use of, or taking of any action in reliance upon this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender and destroy any copies of this document and any attachments.
Re: Running hive queries in different queue
Posted by Sathi Chowdhury <sa...@lithium.com>.
I think in your hive script you can do
set mapreduce.job.queuename=<queuename>;
Thanks
Sathi
From: Rajit Saha
Reply-To: "user@hive.apache.org<ma...@hive.apache.org>"
Date: Friday, February 26, 2016 at 5:34 PM
To: "user@hive.apache.org<ma...@hive.apache.org>"
Subject: Running hive queries in different queue
Hi
I want to run hive query in a queue others than "default" queue from hive client command line . Can anybody please suggest a way to do it.
Regards
Rajit
On Feb 26, 2016, at 07:36, Patrick Duin <pa...@gmail.com>> wrote:
Hi Prasanth.
Thanks for the quick reply!
The logs don't show much more of the stacktrace I'm afraid:
java.lang.NullPointerException
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
The stacktrace isn't really the issue though. The NullPointer is a symptom caused by not being able to return any stripes, if you look at the line in the code it is because the 'stripes' field is null which should never happen. This, we think, is caused by failing namenode network traffic. We would have lots of IO warning in the logs saying block's cannot be found or e.g.:
16/02/01 13:20:34 WARN hdfs.BlockReaderFactory: I/O error constructing remote block reader.
java.io.IOException: java.lang.InterruptedException
at org.apache.hadoop.ipc.Client.call(Client.java:1448)
at org.apache.hadoop.ipc.Client.call(Client.java:1400)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:232)
at com.sun.proxy.$Proxy32.getServerDefaults(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getServerDefaults(ClientNamenodeProtocolTranslatorPB.java:268)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy33.getServerDefaults(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient.getServerDefaults(DFSClient.java:1007)
at org.apache.hadoop.hdfs.DFSClient.shouldEncryptData(DFSClient.java:2062)
at org.apache.hadoop.hdfs.DFSClient.newDataEncryptionKey(DFSClient.java:2068)
at org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.checkTrustAndSend(SaslDataTransferClient.java:208)
at org.apache.hadoop.hdfs.protocol.datatransfer.sasl.SaslDataTransferClient.peerSend(SaslDataTransferClient.java:159)
at org.apache.hadoop.hdfs.net.TcpPeerServer.peerFromSocketAndKey(TcpPeerServer.java:90)
at org.apache.hadoop.hdfs.DFSClient.newConnectedPeer(DFSClient.java:3123)
at org.apache.hadoop.hdfs.BlockReaderFactory.nextTcpPeer(BlockReaderFactory.java:755)
at org.apache.hadoop.hdfs.BlockReaderFactory.getRemoteBlockReaderFromTcp(BlockReaderFactory.java:670)
at org.apache.hadoop.hdfs.BlockReaderFactory.build(BlockReaderFactory.java:337)
at org.apache.hadoop.hdfs.DFSInputStream.blockSeekTo(DFSInputStream.java:576)
at org.apache.hadoop.hdfs.DFSInputStream.readWithStrategy(DFSInputStream.java:800)
at org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:848)
at java.io.DataInputStream.readFully(DataInputStream.java:195)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.extractMetaInfoFromFooter(ReaderImpl.java:407)
at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:311)
at org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:228)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.populateAndCacheStripeDetails(OrcInputFormat.java:885)
at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:771)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.InterruptedException
at java.util.concurrent.FutureTask.awaitDone(FutureTask.java:400)
at java.util.concurrent.FutureTask.get(FutureTask.java:187)
at org.apache.hadoop.ipc.Client$Connection.sendRpcRequest(Client.java:1047)
at org.apache.hadoop.ipc.Client.call(Client.java:1442)
... 33 more
Our job doesn't always fail sometimes splits get calculated. We suspect when the namenode is too busy our job maybe hits some time-outs and the whole thing fails.
Our intuition has been the same as you suggest, bigger files is better. But we see a degradation in performance as soon as our files get bigger than the ORC block size. Keeping file size within ORC block size sounds silly but when looking at the code (OrcInputFormat) we think it cuts out a bunch of code that is causing us problems. The code we are trying to hit is: https://github.com/apache/hive/blob/release-0.14.0/ql/src/java/org/apache/hadoop/hive/ql/io/orc/OrcInputFormat.java#L656. Avoiding the scheduling.
In our case we are not using any SARG but we do use column projection.
Any idea why if we query the data via Hive we don't have this issue?
Let me know if you need more information. Thanks for the insights, much appreciated.
Kind regards,
Patrick
2016-02-25 22:20 GMT+01:00 Prasanth Jayachandran <pj...@hortonworks.com>>:
> On Feb 25, 2016, at 3:15 PM, Prasanth Jayachandran <pj...@hortonworks.com>> wrote:
>
> Hi Patrick
>
> Can you paste entire stacktrace? Looks like NPE happened during split generation but stack trace is incomplete to know what caused it.
>
> In Hive 0.14.0, the stripe size is changed to 64MB. The default block size for ORC files is 256MB. 4 stripes can fit a block. ORC does padding to avoid stripes straddling HDFS blocks. During split calculation, ORC footer which contains stripe level column statistics is read to perform split pruning based on predicate condition specified via SARG(Search Argument).
>
> For example: Assume column ‘state’ is sorted and the predicate condition is ‘state’=“CA"
> Stripe 1: min = AZ max = FL
> Stripe 2: min = GA max = MN
> Stripe 3: min = MS max = SC
> Stripe 4: min = SD max = WY
>
> In this case, only stripe 1 satisfies the above predicate condition. So only 1 split with stripe 1 will be created.
> So if there are huge number of small files, then footers from all files has to be read to do split pruning. If there are few number of large files then only few footers have to be read. Also the minimum splittable position is stripe boundary. So having fewer large files has the advantage of reading less data during split pruning.
>
> If you can send me the full stacktrace, I can tell what is causing the exception here. I will also let you know of any workaround/next hive version with the fix.
>
> In more recent hive versions, hive 1.2.0 onwards. OrcInputFormat is has strategies to decided when to read footers and when not to read footers automatically. You can configure the strategy that you want based on the workload. In case of many small files, footers will not be read and with large files footers will be read for split pruning.
The default strategy does it automatically (choosing between when to read and when not to footers). It is configurable as well.
>
> Thanks
> Prasanth
>
>> On Feb 25, 2016, at 7:08 AM, Patrick Duin <pa...@gmail.com>> wrote:
>>
>> Hi,
>>
>> We've recently moved one of our datasets to ORC and we use Cascading and Hive to read this data. We've had problems reading the data via Cascading, because of the generation of splits.
>> We read in a large number of files (thousands) and they are about 1GB each. We found that the split calculation took minutes on our cluster and often didn't succeed at all (when our namenode was busy).
>> When digging through the code of the 'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.class' we figured out that if we make the files less then the ORC block size (256MB) the code would avoid lots of namenode calls. We applied this solution and made our files smaller and that solved the problem. Split calculation in our job went from 10+ mins to a couple of seconds and always succeeds.
>> We feel it is counterintuitive as bigger files are usually better in HDFS. We've also seen that doing a hive query on the data does not present this problem. Internally Hive seem to take a completely different execution path and is not using the OrcInputFormat but uses 'org.apache.hadoop.hive.ql.io.CombineHiveInputFormat.class'.
>>
>> Can someone explain the reason for this difference or shed some light on the behaviour we are seeing? Any help will be greatly appreciated. We are using hive-0.14.0.
>>
>> Kind regards,
>> Patrick
>>
>> Here is the stack-trace that we would see when our Cascading job failed to calculate the splits:
>> Caused by: java.lang.RuntimeException: serious problem
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$Context.waitForTasks(OrcInputFormat.java:478)
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:949)
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.getSplits(OrcInputFormat.java:974)
>> at com.hotels.corc.mapred.CorcInputFormat.getSplits(CorcInputFormat.java:201)
>> at cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:200)
>> at cascading.tap.hadoop.io.MultiInputFormat.getSplits(MultiInputFormat.java:142)
>> at org.apache.hadoop.mapreduce.JobSubmitter.writeOldSplits(JobSubmitter.java:624)
>> at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:616)
>> at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:492)
>> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1296)
>> at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1293)
>> 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:1628)
>> at org.apache.hadoop.mapreduce.Job.submit(Job.java:1293)
>> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:585)
>> at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:580)
>> at java.security.AccessController.doPrivileged(Native Method)
>> at javax.security.auth.Subject.doAs(Subject.java:415)
>> at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
>> at org.apache.hadoop.mapred.JobClient.submitJobInternal(JobClient.java:580)
>> at org.apache.hadoop.mapred.JobClient.submitJob(JobClient.java:571)
>> at cascading.flow.hadoop.planner.HadoopFlowStepJob.internalNonBlockingStart(HadoopFlowStepJob.java:106)
>> at cascading.flow.planner.FlowStepJob.blockOnJob(FlowStepJob.java:265)
>> at cascading.flow.planner.FlowStepJob.start(FlowStepJob.java:184)
>> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:146)
>> at cascading.flow.planner.FlowStepJob.call(FlowStepJob.java:48)
>> ... 4 more
>> Caused by: java.lang.NullPointerException
>> at org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$SplitGenerator.run(OrcInputFormat.java:809)
>
________________________________
DISCLAIMER: The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review, re-transmission, dissemination or other use of, or taking of any action in reliance upon this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender and destroy any copies of this document and any attachments.