Hi everyone...

Came across this question in Stackoverflow and wanted to try my hand in trying it. Unfortunately I have not been able to fix it...

https://stackoverflow.com/questions/78953930/create-and-load-sql-server-temp-table-table-or-table-fr...

Some of the observations for your consideration...

1. I was able to create the temp table both (`#table` and `##table`) within a pyodbc code block. It is available within that function but not accessible outside of it.

2. Using `prepareQuery` I am able to create the temp table in both read and write, but again it is only available within that read/write command i.e. `spark.read.format(jdbc)` and not outside of it.

3. Directly tried writing a dataframe using the `createTableOptions` and `createTableColumnTypes` options, but even that is not going anywhere.

Questions:

1. How do I create a SQL Server temp table (not databricks) and load it from a spark dataframe and have it accessible across the notebook.

2. What is the scope of a DB Connection in spark. Is it limited to just that one spark.read/spark.write command ? Is it just one connection per command or one connection per executor or core ?

3. Is there any way to ensure that the spark.write does not commit if there is a failure ? I have tried AutoCommit to False, but it is not working. I read somewhere that spark does indeed commit every n rows even in the case of a failure.

Useful Link:

https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html

I hope brighter minds than mine can help me with the issue. 

Thank you very much...