Hive crashing after an upgrade - issue with existing larger tables

2011-08-18 Thread Bejoy Ks
Hi Experts

        I was working on hive with larger volume data  with hive 0.7 . Recently 
my hive installation was upgraded to 0.7.1 . After the upgrade I'm having a lot 
of issues with queries that were already working fine with larger data. The 
queries that took seconds to return results is now taking hours, for most 
larger tables even the map reduce jobs are not getting triggered. Queries like 
Select * and describe are working fine since they don't involve any map reduce 
jobs. For the jobs that didn't even get triggered I got the following error 
from job tracker

Job initialization failed: java.io.IOException: Split metadata size exceeded 
1000. 
Aborting job job_201106061630_6993 at 
org.apache.hadoop.mapreduce.split.SplitMetaInfoReader.readSplitMetaInfo(SplitMetaInfoReader.java:48)
 
at org.apache.hadoop.mapred.JobInProgress.createSplits(JobInProgress.java:807) 
at org.apache.hadoop.mapred.JobInProgress.initTasks(JobInProgress.java:701) 
at org.apache.hadoop.mapred.JobTracker.initJob(JobTracker.java:4013) 
at 
org.apache.hadoop.mapred.EagerTaskInitializationListener$InitJob.run(EagerTaskInitializationListener.java:79)
 
at 
java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
 
at 
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) 
at java.lang.Thread.run(Thread.java:619) 


Looks like some metadata issue. My cluster is on CDH3-u0 . Has anyone faced 
similar issues before. Please share your thoughts what could be the probable 
cause of the error.

Thank You


Re: Hive crashing after an upgrade - issue with existing larger tables

2011-08-18 Thread bejoy_ks
A small correction to my previous post. The CDH version is CDH u1 not u0
Sorry for the confusion

Regards
Bejoy K S

-Original Message-
From: Bejoy Ks bejoy...@yahoo.com
Date: Thu, 18 Aug 2011 05:51:58 
To: hive user groupuser@hive.apache.org
Reply-To: user@hive.apache.org
Subject: Hive crashing after an upgrade - issue with existing larger tables

Hi Experts

        I was working on hive with larger volume data  with hive 0.7 . Recently 
my hive installation was upgraded to 0.7.1 . After the upgrade I'm having a lot 
of issues with queries that were already working fine with larger data. The 
queries that took seconds to return results is now taking hours, for most 
larger tables even the map reduce jobs are not getting triggered. Queries like 
Select * and describe are working fine since they don't involve any map reduce 
jobs. For the jobs that didn't even get triggered I got the following error 
from job tracker

Job initialization failed: java.io.IOException: Split metadata size exceeded 
1000. 
Aborting job job_201106061630_6993 at 
org.apache.hadoop.mapreduce.split.SplitMetaInfoReader.readSplitMetaInfo(SplitMetaInfoReader.java:48)
 
at org.apache.hadoop.mapred.JobInProgress.createSplits(JobInProgress.java:807) 
at org.apache.hadoop.mapred.JobInProgress.initTasks(JobInProgress.java:701) 
at org.apache.hadoop.mapred.JobTracker.initJob(JobTracker.java:4013) 
at 
org.apache.hadoop.mapred.EagerTaskInitializationListener$InitJob.run(EagerTaskInitializationListener.java:79)
 
at 
java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
 
at 
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) 
at java.lang.Thread.run(Thread.java:619) 


Looks like some metadata issue. My cluster is on CDH3-u0 . Has anyone faced 
similar issues before. Please share your thoughts what could be the probable 
cause of the error.

Thank You



Re: Hive crashing after an upgrade - issue with existing larger tables

2011-08-18 Thread Carl Steinbach
Hi,

The original CDH3U1 release of Hive contained a configuration bug which we
recently fixed in an update. You can get the update by refreshing your Hive
packages. Afterwards please verify that you are using the following Hive
package: hive-0.7.1+42.9

You can also fix the problem by modifying your hive-site.xml file to include
the following setting:

mapred.max.split.size=25600

Thanks.

Carl

On Thu, Aug 18, 2011 at 8:48 AM, bejoy...@yahoo.com wrote:

 A small correction to my previous post. The CDH version is CDH u1 not u0
 Sorry for the confusion

 Regards
 Bejoy K S
 --
 *From: * Bejoy Ks bejoy...@yahoo.com
 *Date: *Thu, 18 Aug 2011 05:51:58 -0700 (PDT)
 *To: *hive user groupuser@hive.apache.org
 *ReplyTo: * user@hive.apache.org
 *Subject: *Hive crashing after an upgrade - issue with existing larger
 tables

 Hi Experts
 I was working on hive with larger volume data  with hive 0.7 .
 Recently my hive installation was upgraded to 0.7.1 . After the upgrade I'm
 having a lot of issues with queries that were already working fine with
 larger data. The queries that took seconds to return results is now taking
 hours, for most larger tables even the map reduce jobs are not getting
 triggered. Queries like Select * and describe are working fine since they
 don't involve any map reduce jobs. For the jobs that didn't even get
 triggered I got the following error from job tracker

 Job initialization failed: java.io.IOException: Split metadata size
 exceeded 1000.
 Aborting job job_201106061630_6993 at
 org.apache.hadoop.mapreduce.split.SplitMetaInfoReader.readSplitMetaInfo(SplitMetaInfoReader.java:48)

 at
 org.apache.hadoop.mapred.JobInProgress.createSplits(JobInProgress.java:807)
 at org.apache.hadoop.mapred.JobInProgress.initTasks(JobInProgress.java:701)

 at org.apache.hadoop.mapred.JobTracker.initJob(JobTracker.java:4013)
 at
 org.apache.hadoop.mapred.EagerTaskInitializationListener$InitJob.run(EagerTaskInitializationListener.java:79)

 at
 java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)

 at
 java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908)

 at java.lang.Thread.run(Thread.java:619)


 Looks like some metadata issue. My cluster is on CDH3-u0 . Has anyone faced
 similar issues before. Please share your thoughts what could be the probable
 cause of the error.

 Thank You