How to speed up bulk insert to MS SQL Server using pyodbc

As noted in a comment to another answer, the T-SQL BULK INSERT command will only work if the file to be imported is on the same machine as the SQL Server instance or is in an SMB/CIFS network location that the SQL Server instance can read. Thus it may not be applicable in the case where the source file is on a remote client.

pyodbc 4.0.19 added a Cursor#fast_executemany feature which may be helpful in that case. fast_executemany is "off" by default, and the following test code ...

cnxn = pyodbc.connect(conn_str, autocommit=True)
crsr = cnxn.cursor()
crsr.execute("TRUNCATE TABLE fast_executemany_test")

sql = "INSERT INTO fast_executemany_test (txtcol) VALUES (?)"
params = [(f'txt{i:06d}',) for i in range(1000)]
t0 = time.time()
crsr.executemany(sql, params)
print(f'{time.time() - t0:.1f} seconds')

... took approximately 22 seconds to execute on my test machine. Simply adding crsr.fast_executemany = True ...

cnxn = pyodbc.connect(conn_str, autocommit=True)
crsr = cnxn.cursor()
crsr.execute("TRUNCATE TABLE fast_executemany_test")

crsr.fast_executemany = True  # new in pyodbc 4.0.19

sql = "INSERT INTO fast_executemany_test (txtcol) VALUES (?)"
params = [(f'txt{i:06d}',) for i in range(1000)]
t0 = time.time()
crsr.executemany(sql, params)
print(f'{time.time() - t0:.1f} seconds')

... reduced the execution time to just over 1 second.

Update - July 2021: bcpyaz is a wrapper for Microsoft's bcp utility.

Update - April 2019: As noted in the comment from @SimonLang, BULK INSERT under SQL Server 2017 and later apparently does support text qualifiers in CSV files (ref: here).

BULK INSERT will almost certainly be much faster than reading the source file row-by-row and doing a regular INSERT for each row. However, both BULK INSERT and BCP have a significant limitation regarding CSV files in that they cannot handle text qualifiers (ref: here). That is, if your CSV file does not have qualified text strings in it ...

1,Gord Thompson,2015-04-15
2,Bob Loblaw,2015-04-07

... then you can BULK INSERT it, but if it contains text qualifiers (because some text values contains commas) ...

1,"Thompson, Gord",2015-04-15
2,"Loblaw, Bob",2015-04-07

... then BULK INSERT cannot handle it. Still, it might be faster overall to pre-process such a CSV file into a pipe-delimited file ...

1|Thompson, Gord|2015-04-15
2|Loblaw, Bob|2015-04-07

... or a tab-delimited file (where → represents the tab character) ...

1→Thompson, Gord→2015-04-15
2→Loblaw, Bob→2015-04-07

... and then BULK INSERT that file. For the latter (tab-delimited) file the BULK INSERT code would look something like this:

import pypyodbc
conn_str = "DSN=myDb_SQLEXPRESS;"
cnxn = pypyodbc.connect(conn_str)
crsr = cnxn.cursor()
sql = """
BULK INSERT myDb.dbo.SpikeData123
FROM 'C:\\__tmp\\biTest.txt' WITH (
    FIELDTERMINATOR='\\t',
    ROWTERMINATOR='\\n'
    );
"""
crsr.execute(sql)
cnxn.commit()
crsr.close()
cnxn.close()

Note: As mentioned in a comment, executing a BULK INSERT statement is only applicable if the SQL Server instance can directly read the source file. For cases where the source file is on a remote client, see this answer.