Why would I get a memory error with fast_executemany on a tiny df?

Question

I was looking for ways to speed up pushing a dataframe to sql server and stumbled upon an approach here. This approach blew me away in terms of speed. Using normal to_sql took almost 2 hours and this script was done in 12.54 seconds to push a 100k row X 100 column df.

So after testing the code below with a sample df, I attempted to use a df that had many different datatypes (int, string, floats, Booleans). However, I was sad to see a memory error. So I started reducing the size of my df to to see what the limitations were. I noticed that if my df had any strings then I wasn't able to load to sql server. I am having trouble isolating the issue further. The script below is taken from the question in the link, however, I added a tiny df with strings. Any suggestions on how to rectify this issue would be great!

import pandas as pd
import numpy as np
import time
from sqlalchemy import create_engine, event
from urllib.parse import quote_plus
import pyodbc

conn =  "DRIVER={SQL Server};SERVER=SERVER_IP;DATABASE=DB_NAME;UID=USER_ID;PWD=PWD"
quoted = quote_plus(conn)
new_con = 'mssql+pyodbc:///?odbc_connect={}'.format(quoted)
engine = create_engine(new_con)


@event.listens_for(engine, 'before_cursor_execute')
def receive_before_cursor_execute(conn, cursor, statement, params, context, executemany):
    print("FUNC call")
    if executemany:
        cursor.fast_executemany = True


table_name = 'fast_executemany_test'
df1 = pd.DataFrame({'col1':['tyrefdg','ertyreg','efdgfdg'],
                   'col2':['tydfggfdgrefdg','erdfgfdgfdgfdgtyreg','edfgfdgdfgdffdgfdg']
                   })



s = time.time()
df1.to_sql(table_name, engine, if_exists = 'replace', chunksize = None)
print(time.time() - s)

Answer 1

I was able to reproduce your issue using pyodbc 4.0.23. The MemoryError was related to your use of the ancient

DRIVER={SQL Server}

Further testing using

DRIVER=ODBC Driver 11 for SQL Server

also failed, with

Function sequence error (0) (SQLParamData)

which was related to an existing pyodbc issue on GitHub. I posted my findings here.

That issue is still under investigation. In the meantime you might be able to proceed by

• using a newer ODBC driver like DRIVER=ODBC Driver 13 for SQL Server, and
• running pip install pyodbc==4.0.22 to use an earlier version of pyodbc.