问题
In a python script, I need to run a query on one datasource and insert each row from that query into a table on a different datasource. I'd normally do this with a single insert/select statement with a tsql linked server join but I don't have a linked server connection to this particular datasource.
I'm having trouble finding a simple pyodbc example of this. Here's how I'd do it but I'm guessing executing an insert statement inside a loop is pretty slow.
result = ds1Cursor.execute(selectSql)
for row in result:
insertSql = "insert into TableName (Col1, Col2, Col3) values (?, ?, ?)"
ds2Cursor.execute(insertSql, row[0], row[1], row[2])
ds2Cursor.commit()
Is there a better bulk way to insert records with pyodbc? Or is this a relatively efficient way to do this anyways. I'm using SqlServer 2012, and the latest pyodbc and python versions.
回答1:
The best way to handle this is to use the pyodbc function executemany
.
ds1Cursor.execute(selectSql)
result = ds1Cursor.fetchall()
ds2Cursor.executemany('INSERT INTO [TableName] (Col1, Col2, Col3) VALUES (?, ?, ?)', result)
ds2Cursor.commit()
回答2:
Here's a function that can do the bulk insert into SQL Server database.
import pyodbc
import contextlib
def bulk_insert(table_name, file_path):
string = "BULK INSERT {} FROM '{}' (WITH FORMAT = 'CSV');"
with contextlib.closing(pyodbc.connect("MYCONN")) as conn:
with contextlib.closing(conn.cursor()) as cursor:
cursor.execute(string.format(table_name, file_path))
conn.commit()
conn.close()
This definitely works.
UPDATE: I've noticed at the comments, as well as coding regularly, that pyodbc is better supported than pypyodbc.
来源:https://stackoverflow.com/questions/37008848/basic-pyodbc-bulk-insert