You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:33:51 UTC
[jira] [Resolved] (SPARK-16741) spark.speculation causes duplicate
rows in df.write.jdbc()
[ https://issues.apache.org/jira/browse/SPARK-16741?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-16741.
----------------------------------
Resolution: Incomplete
> spark.speculation causes duplicate rows in df.write.jdbc()
> ----------------------------------------------------------
>
> Key: SPARK-16741
> URL: https://issues.apache.org/jira/browse/SPARK-16741
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 1.6.2
> Environment: PySpark 1.6.2, Oracle Linux 6.5, Oracle 11.2
> Reporter: Zoltan Fedor
> Priority: Minor
> Labels: bulk-closed
>
> Since a fix added to Spark 1.6.2 we can write string data back into an Oracle database, so I went to try it out and found that rows showed up duplicated in the database table after they got inserted into our Oracle database.
> The code we use it very simple:
> df = sqlContext.sql("SELECT * FROM example_temp_table")
> df.write.jdbc("jdbc:oracle:thin:"+connection_script, "target_table")
> The data in the 'target_table' in the database has twice as many rows as the 'df' dataframe in SparkSQL.
> After some investigation it turns out that this is caused by our spark.speculation setting is being set to True.
> As soon as we turned this off, there were no more duplicates generated.
> This somewhat makes sense - spark.speculation causes the map jobs to run 2 copies - resulting in every row being inserted into our Oracle databases twice.
> Probably the df.jdbc.write() method does not consider a Spark context running in speculative mode, hence the inserts coming from the speculative map also get inserted - causing to have every record inserted twice.
> Likely that this bug is independent from the database type (we use Oracle) and whether PySpark is used or Scala or Java.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org