[jira] [Updated] (HIVE-8457) MapOperator initialization when multiple Spark threads is enabled. [Spark Branch]

["Chao (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HIVE-8457?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Chao updated HIVE-8457:
-----------------------
    Description: 
Currently, on the Spark branch, each thread it is bound with a thread-local IOContext, which gets initialized when we generates an input {{HadoopRDD}}, and later used in {{MapOperator}}, {{FilterOperator}}, etc.

And, given the introduction of HIVE-8118, we may have multiple downstream RDDs that share the same input {{HadoopRDD}}, and we would like to have the {{HadoopRDD}} to be cached, to avoid scanning the same table multiple times. A typical case would be like the following:

{noformat}
     inputRDD     inputRDD
        |            |
       MT_11        MT_12
        |            |
       RT_1         RT_2
{noformat}

Here, {{MT_11}} and {{MT_12}} are {{MapTran}} from a splitted {{MapWork}},
and {{RT_1}} and {{RT_2}} are two {{ReduceTran}}. Note that, this example is simplified, as we may also have {{ShuffleTran}} between {{MapTran}} and {{ReduceTran}}.

When multiple Spark threads are running, {{MT_11} may be executed first, and it will ask for an iterator from the {{HadoopRDD}} will trigger the creation of the iterator, which in turn triggers the initialization of the {{IOContext}} associated with that particular thread.

Now, before {{MT_12}} starts executing, it will also ask for an iterator from the
{{HadoopRDD}}, and since the RDD is already cached, instead of creating a new iterator, it will just fetch it from the cached result. However, the problem is, this will skip the initialization of the IOContext associated with this particular thread. When {{MT_12}} starts executing, it will first initialize the {{MapOperator}}, but since the {{IOContext}} is not initialized, this will fail miserably. 

  was:
Currently, on the Spark branch, each thread it is bound with a thread-local IOContext, which gets initialized when we generates a input {{HadoopRDD}}, and later used in {{MapOperator}}, {{FilterOperator}}, etc.

And, given the introduction of HIVE-8118, we may have multiple downstream RDDs that share the same input {{HadoopRDD}}, and we would like to have the {{HadoopRDD}} to be cached, to avoid scanning the same table multiple times. A typical case would be like the following:

{noformat}
     inputRDD     inputRDD
        |            |
       MT_11        MT_12
        |            |
       RT_1         RT_2
{noformat}

Here, {{MT_11}} and {{MT_12}} are {{MapTran}}s from a splitted {{MapWork}},
and {{RT_1}} and {{RT_2}} are two {{ReduceTran}}s. Note that, this example is simplified, as we may also have {{ShuffleTran}} between {{MapTran}} and {{ReduceTran}}.

When multiple Spark threads are running, {{MT_11} may be executed first, and it will ask for an iterator from the {{HadoopRDD}} will trigger the creation of the iterator, which in turn triggers the initialization of the {{IOContext}} associated with that particular thread.

Now, before {{MT_12}} starts executing, it will also ask for an iterator from the
{{HadoopRDD}}, and since the RDD is already cached, instead of creating a new iterator, it will just fetch it from the cached result. However, the problem is, this will skip the initialization of the IOContext associated with this particular thread. When {{MT_12}} starts executing, it will first initialize the {{MapOperator}}, but since the {{IOContext}} is not initialized, this will fail miserably. 


> MapOperator initialization when multiple Spark threads is enabled. [Spark Branch]
> ---------------------------------------------------------------------------------
>
>                 Key: HIVE-8457
>                 URL: https://issues.apache.org/jira/browse/HIVE-8457
>             Project: Hive
>          Issue Type: Bug
>          Components: Spark
>            Reporter: Chao
>
> Currently, on the Spark branch, each thread it is bound with a thread-local IOContext, which gets initialized when we generates an input {{HadoopRDD}}, and later used in {{MapOperator}}, {{FilterOperator}}, etc.
> And, given the introduction of HIVE-8118, we may have multiple downstream RDDs that share the same input {{HadoopRDD}}, and we would like to have the {{HadoopRDD}} to be cached, to avoid scanning the same table multiple times. A typical case would be like the following:
> {noformat}
>      inputRDD     inputRDD
>         |            |
>        MT_11        MT_12
>         |            |
>        RT_1         RT_2
> {noformat}
> Here, {{MT_11}} and {{MT_12}} are {{MapTran}} from a splitted {{MapWork}},
> and {{RT_1}} and {{RT_2}} are two {{ReduceTran}}. Note that, this example is simplified, as we may also have {{ShuffleTran}} between {{MapTran}} and {{ReduceTran}}.
> When multiple Spark threads are running, {{MT_11} may be executed first, and it will ask for an iterator from the {{HadoopRDD}} will trigger the creation of the iterator, which in turn triggers the initialization of the {{IOContext}} associated with that particular thread.
> Now, before {{MT_12}} starts executing, it will also ask for an iterator from the
> {{HadoopRDD}}, and since the RDD is already cached, instead of creating a new iterator, it will just fetch it from the cached result. However, the problem is, this will skip the initialization of the IOContext associated with this particular thread. When {{MT_12}} starts executing, it will first initialize the {{MapOperator}}, but since the {{IOContext}} is not initialized, this will fail miserably. 



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