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[ANNOUNCE] Cloudera Enterprise 5.7 and Hive on Spark WHERE clause error

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Hi Team,

 

I installed cloudera manager cdh5.7 and configured spark. I loaded data into hive and trying to retrieve the data by following methods like

 

hive> set hive.execution.engine=spark;

 

select empno from pavan_solr_h where empno=7369.0;

 

Query ID = root_20160414144040_2aa7ca5e-fde9-459a-baf6-3b79ae1e419f
Total jobs = 1
Launching Job 1 out of 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Spark Job = cba63e09-b583-466d-8468-121d94deb634
Running with YARN Application = application_1460612046473_0004
Kill Command = /opt/cloudera/parcels/CDH-5.7.0-1.cdh5.7.0.p0.45/lib/hadoop/bin/yarn application -kill application_1460612046473_0004

Query Hive on Spark job[0] stages:
0

Status: Running (Hive on Spark job[0])
Job Progress Format
CurrentTime StageId_StageAttemptId: SucceededTasksCount(+RunningTasksCount-FailedTasksCount)/TotalTasksCount [StageCost]
2016-04-14 14:43:05,632 Stage-0_0: 0/1
2016-04-14 14:43:08,646 Stage-0_0: 0/1
2016-04-14 14:43:11,659 Stage-0_0: 0/1
2016-04-14 14:43:14,672 Stage-0_0: 0/1
2016-04-14 14:43:17,684 Stage-0_0: 0/1
2016-04-14 14:43:20,697 Stage-0_0: 0/1
2016-04-14 14:43:23,710 Stage-0_0: 0/1
2016-04-14 14:43:26,722 Stage-0_0: 0/1
2016-04-14 14:43:29,734 Stage-0_0: 0/1
2016-04-14 14:43:32,746 Stage-0_0: 0/1
2016-04-14 14:43:35,759 Stage-0_0: 0/1
2016-04-14 14:43:38,771 Stage-0_0: 0/1
2016-04-14 14:43:41,787 Stage-0_0: 0/1
2016-04-14 14:43:44,805 Stage-0_0: 0/1
2016-04-14 14:43:47,817 Stage-0_0: 0/1
2016-04-14 14:43:50,829 Stage-0_0: 0/1
2016-04-14 14:43:53,841 Stage-0_0: 0/1
2016-04-14 14:43:56,856 Stage-0_0: 0/1
2016-04-14 14:43:59,870 Stage-0_0: 0/1
2016-04-14 14:44:02,882 Stage-0_0: 0/1
2016-04-14 14:44:05,894 Stage-0_0: 0/1
2016-04-14 14:44:08,907 Stage-0_0: 0/1
2016-04-14 14:44:11,918 Stage-0_0: 0/1
2016-04-14 14:44:14,931 Stage-0_0: 0/1
2016-04-14 14:44:17,942 Stage-0_0: 0/1
2016-04-14 14:44:20,954 Stage-0_0: 0/1
2016-04-14 14:44:23,966 Stage-0_0: 0/1
2016-04-14 14:44:26,977 Stage-0_0: 0/1
2016-04-14 14:44:29,989 Stage-0_0: 0/1
2016-04-14 14:44:33,002 Stage-0_0: 0/1
2016-04-14 14:44:36,014 Stage-0_0: 0/1
2016-04-14 14:44:39,026 Stage-0_0: 0/1
2016-04-14 14:44:42,037 Stage-0_0: 0/1
2016-04-14 14:44:45,050 Stage-0_0: 0/1
2016-04-14 14:44:48,062 Stage-0_0: 0/1
2016-04-14 14:44:51,074 Stage-0_0: 0/1
2016-04-14 14:44:54,086 Stage-0_0: 0/1
2016-04-14 14:44:57,098 Stage-0_0: 0/1
2016-04-14 14:45:00,110 Stage-0_0: 0/1
2016-04-14 14:45:03,123 Stage-0_0: 0/1
2016-04-14 14:45:06,134 Stage-0_0: 0/1
2016-04-14 14:45:09,145 Stage-0_0: 0/1
2016-04-14 14:45:12,157 Stage-0_0: 0/1
2016-04-14 14:45:15,169 Stage-0_0: 0/1
2016-04-14 14:45:18,181 Stage-0_0: 0/1
2016-04-14 14:45:21,193 Stage-0_0: 0/1
2016-04-14 14:45:24,205 Stage-0_0: 0/1
2016-04-14 14:45:27,217 Stage-0_0: 0/1
2016-04-14 14:45:30,231 Stage-0_0: 0/1
Interrupting... Be patient, this might take some time.
Press Ctrl+C again to kill JVM
2016-04-14 14:45:33,243 Stage-0_0: 0/1
2016-04-14 14:45:36,255 Stage-0_0: 0/1
2016-04-14 14:45:39,267 Stage-0_0: 0/1
2016-04-14 14:45:42,281 Stage-0_0: 0/1
2016-04-14 14:45:45,293 Stage-0_0: 0/1
Exiting the JVM

 

I am not able to work with where clause in hive on spark.

The same command i can execute in cdh5.6 or less version, able to execute. Could you please let me know the issue. Is this a bug in current release. 

1 ACCEPTED SOLUTION

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Cloudera Employee

Hive CLI is deprecated. The recommended way of accessing Hive is via the beeline interface through HiveServer2. Can you give that a shot and see how it goes?

View solution in original post

2 REPLIES 2

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Cloudera Employee

Hive CLI is deprecated. The recommended way of accessing Hive is via the beeline interface through HiveServer2. Can you give that a shot and see how it goes?

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Hi Jeff,

 

Tried with beeline as you suggested and we overcome the issue. Thank you.