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https://issues.apache.org/jira/browse/SPARK-32013?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Noritaka Sekiyama updated SPARK-32013:
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Description:
For ETL workload, there is a common requirement to perform SQL statement
before/after reading/writing over JDBC.
Here's examples;
- Create a view with specific conditions
- Delete/Update some records
- Truncate a table (it is already possible in `truncate` option)
- Execute stored procedure
Currently `query` options is available to specify SQL statement against JDBC
datasource when loading data as DataFrame.
https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html
However, this query is only for reading data, and it does not support the
common examples listed above.
If Spark can support executing SQL statement against JDBC datasources
before/after reading/writing over JDBC, it can cover a lot of common use-cases.
Note: Databricks' old Redshift connector has similar option like `preactions`
and `postactions`.
was:
For ETL workload, there is a common requirement to perform SQL statement
before/after reading/writing over JDBC.
Here's examples;
- Create a view with specific conditions
- Delete/Update some records
- Truncate a table (it is already possible in `truncate` option)
- Execute stored procedure
Currently `query` options is available to specify SQL statement against JDBC
datasource when loading data as DataFrame.
https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html
However, this query is only for reading data, and it does not support the
common examples listed above.
If Spark can support executing SQL statement against JDBC datasources
before/after reading/writing over JDBC, it can cover a lot of common use-cases.
> Support query execution before/after reading/writing over JDBC
> --------------------------------------------------------------
>
> Key: SPARK-32013
> URL: https://issues.apache.org/jira/browse/SPARK-32013
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Noritaka Sekiyama
> Priority: Major
>
> For ETL workload, there is a common requirement to perform SQL statement
> before/after reading/writing over JDBC.
> Here's examples;
> - Create a view with specific conditions
> - Delete/Update some records
> - Truncate a table (it is already possible in `truncate` option)
> - Execute stored procedure
> Currently `query` options is available to specify SQL statement against JDBC
> datasource when loading data as DataFrame.
> https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html
> However, this query is only for reading data, and it does not support the
> common examples listed above.
> If Spark can support executing SQL statement against JDBC datasources
> before/after reading/writing over JDBC, it can cover a lot of common
> use-cases.
> Note: Databricks' old Redshift connector has similar option like `preactions`
> and `postactions`.
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