Member since
03-23-2015
1288
Posts
114
Kudos Received
98
Solutions
My Accepted Solutions
| Title | Views | Posted |
|---|---|---|
| 5201 | 06-11-2020 02:45 PM | |
| 4684 | 04-21-2020 03:38 PM | |
| 3655 | 02-27-2020 05:51 PM | |
| 3661 | 01-23-2020 03:41 AM | |
| 23445 | 01-14-2020 07:14 PM |
08-11-2019
04:42 PM
Hi Vinod, You should NOT need to restart services on weekly or monthly basis, unless you have scheduled maintenance work like making configurations changes. CDH services are expected to continue their services without the need to restart, unless you face any issues like memory pressure, crashing etc, which will require further investigation. Regarding YARN aggregation log not cleared properly, please refer to below post and KB: https://community.cloudera.com/t5/Support-Questions/Yarn-Aggregate-Log-Retention-Setting/m-p/81382 https://my.cloudera.com/knowledge/YARN-logs-under-tmp-logs-user-name-logs-not-cleared-properly?id=75330 Cheers Eric
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08-09-2019
02:38 AM
Thanks a lot. I removed the create hive table statement.The job is successfully executed now.
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08-04-2019
08:54 PM
1 Kudo
Thanks you. Yes, It is not problem of UDF. I have 2 HiveServer2 server host. I have been register udf only 1 server host. Maybe. It is reason.
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07-29-2019
05:12 PM
I filed https://issues.apache.org/jira/browse/IMPALA-8807 to fix the docs.
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07-25-2019
03:28 AM
Got it working on my cluster. Thanks Lars and Eric. Cheers, Anand
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07-18-2019
01:23 AM
Hi Eric, I think the "C6 Compatibility" can be excluded because the driver for Windows works in conjunction with the same HDP cluster. Probably a problem with the implementation of the driver for Linux or RedHat. I will not further investigate and will mark this post as solved. Regards, Michael.
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07-15-2019
03:35 AM
Hi, The purpose of compression is to save space, not speed up query time. Compression actually adds overhead to decompress the data before data can be read, so I would expect the query against compressed data will be slightly slower than uncompressed. So what you see is totally normal to me. Cheers Eric
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07-15-2019
03:12 AM
1 Kudo
Hi, I do not think there is any different. Spark lazily executes statements, so you second 2 jobs version will behave the same way as the first single job, in my opinion. Cheers Eric
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