Thank you very much.

I understand the performance implications and that Spark will download it 
before modifying. 
The JDBC database is just extremely small, it’s the BI/aggregated layer.

What’s interesting is that here it says I can use JDBC

https://spark.apache.org/docs/3.3.1/sql-ref-syntax-dml-insert-overwrite-directory.html

But when I try to I get an error that the underlying datastore should be file 
based, I guess a documentation mistake.

Thank you one more time.

> On 2 Feb 2023, at 23:11, Mich Talebzadeh <mich.talebza...@gmail.com> wrote:
> 
> Please bear in mind that insert/update delete operations are DML, whereas 
> CREATE/DROP TABLE are DDL operations that are best performed in the native 
> database which I presume is a transactional.
> 
> Can you CREATE TABLE before (any insert of data) using the native JDBC 
> database syntax?
> 
> Alternatively you may be able to do so in Python or SCALA but I don't know 
> the way in pure SQL.
> 
> if your JDBC database is Hive you can do so easily
> 
> HTH
> 
> 
> 
>    view my Linkedin profile 
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
> 
>  https://en.everybodywiki.com/Mich_Talebzadeh
> 
>  
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>  
> 
> 
> On Thu, 2 Feb 2023 at 17:26, Harut Martirosyan <harut.martiros...@gmail.com 
> <mailto:harut.martiros...@gmail.com>> wrote:
>> Generally, the problem is that I don’t find a way to automatically create a 
>> JDBC table in the JDBC database when I want to insert data into it using 
>> Spark SQL only, not DataFrames API.
>> 
>>> On 2 Feb 2023, at 21:22, Harut Martirosyan <harut.martiros...@gmail.com 
>>> <mailto:harut.martiros...@gmail.com>> wrote:
>>> 
>>> Hi, thanks for the reply.
>>> 
>>> Let’s imagine we have a parquet based table called parquet_table, now I 
>>> want to insert it into a new JDBC table, all using pure SQL.
>>> 
>>> If the JDBC table already exists, it’s easy, we do CREATE TABLE USING JDBC 
>>> and then we do INSERT INTO that table.
>>> 
>>> If the table doesn’t exist, is there a way to create it using Spark SQL 
>>> only? I don’t want to use DataFrames API, I know that I can use .write() 
>>> for that, but I want to keep it in pure SQL, since that is more 
>>> comprehendible for data analysts.
>>> 
>>>> On 2 Feb 2023, at 02:08, Mich Talebzadeh <mich.talebza...@gmail.com 
>>>> <mailto:mich.talebza...@gmail.com>> wrote:
>>>> 
>>>> Hi,
>>>> 
>>>> It is not very clear your statement below:
>>>> 
>>>> ".. If the table existed, I would create a table using JDBC in spark SQL 
>>>> and then insert into it, but I can't create a table if it doesn't exist in 
>>>> JDBC database..."
>>>> 
>>>> If the table exists in your JDBC database, why do you need to create it?
>>>> 
>>>> How do you verify if it exists? Can you share the code and the doc link?
>>>> 
>>>> HTH
>>>> 
>>>> 
>>>> 
>>>>    view my Linkedin profile 
>>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>> 
>>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>> 
>>>>  
>>>> Disclaimer: Use it at your own risk. Any and all responsibility for any 
>>>> loss, damage or destruction of data or any other property which may arise 
>>>> from relying on this email's technical content is explicitly disclaimed. 
>>>> The author will in no case be liable for any monetary damages arising from 
>>>> such loss, damage or destruction.
>>>>  
>>>> 
>>>> 
>>>> On Wed, 1 Feb 2023 at 19:33, Harut Martirosyan 
>>>> <harut.martiros...@gmail.com <mailto:harut.martiros...@gmail.com>> wrote:
>>>>> I have a resultset (defined in SQL), and I want to insert it into my JDBC 
>>>>> database using only SQL, not dataframes API.
>>>>> 
>>>>> If the table existed, I would create a table using JDBC in spark SQL and 
>>>>> then insert into it, but I can't create a table if it doesn't exist in 
>>>>> JDBC database.
>>>>> 
>>>>> How to do that using pure SQL (no python/scala/java)?
>>>>> 
>>>>> I am trying to use INSERT OVERWRITE DIRECTORY with JDBC file format 
>>>>> (according to the documentation) but as expected this functionality is 
>>>>> available only for File-based storage systems.
>>>>> 
>>>>> -- 
>>>>> RGRDZ Harut
>>> 
>> 

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