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https://issues.apache.org/jira/browse/HIVE-20512?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16678684#comment-16678684
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Bharathkrishna Guruvayoor Murali commented on HIVE-20512:
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AwaitTermination causes delay by blocking the main thread. Since we actually
don't care about whether loggerThread is executed during close, avoiding usage
of awaitTermination by canceling the scheduledFuture after shutDown as
suggested by Sahil.
Attaching HIVE-20512.8.patch with this change to see if the tests pass.
> Improve record and memory usage logging in SparkRecordHandler
> -------------------------------------------------------------
>
> Key: HIVE-20512
> URL: https://issues.apache.org/jira/browse/HIVE-20512
> Project: Hive
> Issue Type: Sub-task
> Components: Spark
> Reporter: Sahil Takiar
> Assignee: Bharathkrishna Guruvayoor Murali
> Priority: Major
> Attachments: HIVE-20512.1.patch, HIVE-20512.2.patch,
> HIVE-20512.3.patch, HIVE-20512.4.patch, HIVE-20512.5.patch,
> HIVE-20512.6.patch, HIVE-20512.7.patch, HIVE-20512.8.patch
>
>
> We currently log memory usage and # of records processed in Spark tasks, but
> we should improve the methodology for how frequently we log this info.
> Currently we use the following code:
> {code:java}
> private long getNextLogThreshold(long currentThreshold) {
> // A very simple counter to keep track of number of rows processed by the
> // reducer. It dumps
> // every 1 million times, and quickly before that
> if (currentThreshold >= 1000000) {
> return currentThreshold + 1000000;
> }
> return 10 * currentThreshold;
> }
> {code}
> The issue is that after a while, the increase by 10x factor means that you
> have to process a huge # of records before this gets triggered.
> A better approach would be to log this info at a given interval. This would
> help in debugging tasks that are seemingly hung.
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