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Posted to issues@spark.apache.org by "Danny Guinther (JIRA)" <ji...@apache.org> on 2019/04/25 19:01:00 UTC
[jira] [Commented] (SPARK-19335) Spark should support doing an
efficient DataFrame Upsert via JDBC
[ https://issues.apache.org/jira/browse/SPARK-19335?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16826378#comment-16826378 ]
Danny Guinther commented on SPARK-19335:
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Any update on this?
Also, please forgive this dumb question, but I'm shocked that there's not more demand for this feature which makes me wonder if I have major misconceptions about Spark and its intended use. How do users survive without this functionality? I take it that the destination SQL database should have flexible up-time requirements allowing for drastic changes? The overwrite save mode is the only thing that offers anything like an UPDATE, but totally dropping/truncating the destination table seems inconceivable for many production environments. What am I missing?
> Spark should support doing an efficient DataFrame Upsert via JDBC
> -----------------------------------------------------------------
>
> Key: SPARK-19335
> URL: https://issues.apache.org/jira/browse/SPARK-19335
> Project: Spark
> Issue Type: Improvement
> Reporter: Ilya Ganelin
> Priority: Minor
>
> Doing a database update, as opposed to an insert is useful, particularly when working with streaming applications which may require revisions to previously stored data.
> Spark DataFrames/DataSets do not currently support an Update feature via the JDBC Writer allowing only Overwrite or Append.
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