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https://issues.apache.org/jira/browse/SPARK-38058?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17484281#comment-17484281
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Hyukjin Kwon commented on SPARK-38058:
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Is this an issue from Spark, Azure SQL Server or somewhere else? Doesn't look
like a problem specific to Spark. Would be great to post some evidance and logs
that it's an issue from Spark.
> Writing a spark dataframe to Azure Sql Server is causing duplicate records
> intermittently
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-38058
> URL: https://issues.apache.org/jira/browse/SPARK-38058
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Spark Core
> Affects Versions: 3.1.0
> Reporter: john
> Priority: Major
>
> We are using JDBC option to insert transformed data in a spark DataFrame to a
> table in Azure SQL Server. Below is the code snippet we are using for this
> insert. However, we noticed on few occasions that some records are being
> duplicated in the destination table. This is happening for large tables. e.g.
> if a DataFrame has 600K records, after inserting data into the table, we get
> around 620K records. we still want to understand why that's happening.
> {{DataToLoad.write.jdbc(url = jdbcUrl, table = targetTable, mode =
> "overwrite", properties = jdbcConnectionProperties)}}
>
> Only reason we could think of is that while inserts are happening in
> distributed fashion, if one of the executors fail in between, they are being
> re-tried and could be inserting duplicate records. This could be totally
> meaningless but just to see if that could be an issue.{{{}{}}}
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