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From Maciej Bryński (JIRA) <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-18022) java.lang.NullPointerException instead of real exception when saving DF to MySQL
Date Fri, 21 Oct 2016 03:56:58 GMT

    [ https://issues.apache.org/jira/browse/SPARK-18022?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15593846#comment-15593846
] 

Maciej Bryński edited comment on SPARK-18022 at 10/21/16 3:56 AM:
------------------------------------------------------------------

Only improvement in error handling.
Because right now I'm getting only NPE and I have to guess whats the real reason of error.



was (Author: maver1ck):
Only improvement in error handling.
Because right now I'm getting only NPE and have to guess whats the real reason of error.


> java.lang.NullPointerException instead of real exception when saving DF to MySQL
> --------------------------------------------------------------------------------
>
>                 Key: SPARK-18022
>                 URL: https://issues.apache.org/jira/browse/SPARK-18022
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.1
>            Reporter: Maciej Bryński
>            Priority: Minor
>
> Hi,
> I have found following issue.
> When there is an exception while saving dataframe to MySQL I'm unable to get it.
> Instead of I'm getting following stacktrace.
> {code}
> 16/10/20 06:00:35 WARN TaskSetManager: Lost task 56.0 in stage 10.0 (TID 3753, dwh-hn28.adpilot.co):
java.lang.NullPointerException: Cannot suppress a null exception.
>         at java.lang.Throwable.addSuppressed(Throwable.java:1046)
>         at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.savePartition(JdbcUtils.scala:256)
>         at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:314)
>         at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:313)
>         at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:902)
>         at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:902)
>         at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
>         at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
>         at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
>         at org.apache.spark.scheduler.Task.run(Task.scala:86)
>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
>         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         at java.lang.Thread.run(Thread.java:745)
> {code}
> The real exception could be for example duplicate on primary key etc.
> With this it's very difficult to debugging apps.



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