to_sql function takes forever to insert in oracle database

I am using pandas to do some analysis on a excel file, and once that analysis is complete, I want to insert the resultant dataframe into a database. The size of this dataframe is around 300,000 rows and 27 columns. I am using pd.to_sql method to insert dataframe in the database. When I use a MySQL database, insertion in the database takes place around 60-90 seconds. However when I try to insert the same dataframe using the same function in an oracle database, the process takes around 2-3 hours to complete.

Relevant code can be found below:

data_frame.to_sql(name='RSA_DATA',  con=get_engine(), if_exists='append',
                          index=False, chunksize=config.CHUNK_SIZE)

I tried using different chunk_sizes (from 50 to 3000), but the difference in time was only of the order of 10 minutes. Any solution to the above problem ?

[BenjaminHabert]

As mentioned by @wuhaochen I have also ran into this problem. For me the issue was that oracle was creating columns of CLOB data type for all the string columns of the pandas dataframe. I sped-up the code by explicitly setting the schema dtype parameter of to_sql() and using VARCHAR dtypes for string columns.

I think this should be the default behavior of to_sql as creating CLOB is counter-intuitive.

[iron0012]

to_sql() is still practically broken when working with Oracle without using the workaround recommended above.