http://pymssql.org/en/latest/pymssql_examples.html

Example scripts using pymssql module.

Basic features (strict DB-API compliance)

from os import getenv
import pymssql

server = getenv("PYMSSQL_TEST_SERVER")
user = getenv("PYMSSQL_TEST_USERNAME")
password = getenv("PYMSSQL_TEST_PASSWORD")

conn = pymssql.connect(server, user, password, "tempdb")
cursor = conn.cursor()
cursor.execute("""
IF OBJECT_ID('persons', 'U') IS NOT NULL
    DROP TABLE persons
CREATE TABLE persons (
    id INT NOT NULL,
    name VARCHAR(100),
    salesrep VARCHAR(100),
    PRIMARY KEY(id)
)
""")
cursor.executemany(
    "INSERT INTO persons VALUES (%d, %s, %s)",
    [(1, 'John Smith', 'John Doe'),
     (2, 'Jane Doe', 'Joe Dog'),
     (3, 'Mike T.', 'Sarah H.')])
# you must call commit() to persist your data if you don't set autocommit to True
conn.commit()

cursor.execute('SELECT * FROM persons WHERE salesrep=%s', 'John Doe')
row = cursor.fetchone()
while row:
    print("ID=%d, Name=%s" % (row[0], row[1]))
    row = cursor.fetchone()

conn.close()

Connecting using Windows Authentication

When connecting using Windows Authentication, this is how to combine the database’s hostname and instance name, and the Active Directory/Windows Domain name and the username. This example uses raw strings (r'...') for the strings that contain a backslash.

conn = pymssql.connect(
    host=r'dbhostname\myinstance',
    user=r'companydomain\username',
    password=PASSWORD,
    database='DatabaseOfInterest'
)

Iterating through results

You can also use iterators instead of while loop.

conn = pymssql.connect(server, user, password, "tempdb")
cursor = conn.cursor()
cursor.execute('SELECT * FROM persons WHERE salesrep=%s', 'John Doe')

for row in cursor:
    print('row = %r' % (row,))

conn.close()

Note

Iterators are a pymssql extension to the DB-API.

Important note about Cursors

A connection can have only one cursor with an active query at any time. If you have used other Python DBAPI databases, this can lead to surprising results:

c1 = conn.cursor()
c1.execute('SELECT * FROM persons')

c2 = conn.cursor()
c2.execute('SELECT * FROM persons WHERE salesrep=%s', 'John Doe')

print( "all persons" )
print( c1.fetchall() )  # shows result from c2 query!

print( "John Doe" )
print( c2.fetchall() )  # shows no results at all!

In this example, the result printed after "all persons" will be the result of the second query (the list where salesrep='John Doe') and the result printed after “John Doe” will be empty. This happens because the underlying TDS protocol does not have client side cursors. The protocol requires that the client flush the results from the first query before it can begin another query.

(Of course, this is a contrived example, intended to demonstrate the failure mode. Actual use cases that follow this pattern are usually much more complicated.)

Here are two reasonable workarounds to this:

• Create a second connection. Each connection can have a query in progress, so multiple connections can execute multiple conccurent queries.

• use the fetchall() method of the cursor to recover all the results before beginning another query:

  c1.execute('SELECT ...')
  c1_list = c1.fetchall()
  
  c2.execute('SELECT ...')
  c2_list = c2.fetchall()
  
  # use c1_list and c2_list here instead of fetching individually from
  # c1 and c2
  

Rows as dictionaries

Rows can be fetched as dictionaries instead of tuples. This allows for accessing columns by name instead of index. Note the as_dict argument.

conn = pymssql.connect(server, user, password, "tempdb")
cursor = conn.cursor(as_dict=True)

cursor.execute('SELECT * FROM persons WHERE salesrep=%s', 'John Doe')
for row in cursor:
    print("ID=%d, Name=%s" % (row['id'], row['name']))

conn.close()

Note

The as_dict parameter to cursor() is a pymssql extension to the DB-API.

Using the with statement (context managers)

You can use Python’s with statement with connections and cursors. This frees you from having to explicitly close cursors and connections.

with pymssql.connect(server, user, password, "tempdb") as conn:
    with conn.cursor(as_dict=True) as cursor:
        cursor.execute('SELECT * FROM persons WHERE salesrep=%s', 'John Doe')
        for row in cursor:
            print("ID=%d, Name=%s" % (row['id'], row['name']))

Note

The context manager personality of connections and cursor is a pymssql extension to the DB-API.

Calling stored procedures

As of pymssql 2.0.0 stored procedures can be called using the rpc interface of db-lib.

with pymssql.connect(server, user, password, "tempdb") as conn:
    with conn.cursor(as_dict=True) as cursor:
        cursor.execute("""
        CREATE PROCEDURE FindPerson
            @name VARCHAR(100)
        AS BEGIN
            SELECT * FROM persons WHERE name = @name
        END
        """)
        cursor.callproc('FindPerson', ('Jane Doe',))
        for row in cursor:
            print("ID=%d, Name=%s" % (row['id'], row['name']))

Using pymssql with cooperative multi-tasking systems

New in version 2.1.0.

You can use the pymssql.set_wait_callback() function to install a callback function you should write yourself.

This callback can yield to another greenlet, coroutine, etc. For example, for gevent, you could use itsgevent.socket.wait_read() function:

import gevent.socket
import pymssql

def wait_callback(read_fileno):
    gevent.socket.wait_read(read_fileno)

pymssql.set_wait_callback(wait_callback)

The above is useful if you’re say, running a Gunicorn server with the gevent worker. With this callback in place, when you send a query to SQL server and are waiting for a response, you can yield to other greenlets and process other requests. This is super useful when you have high concurrency and/or slow database queries and lets you use less Gunicorn worker processes and still handle high concurrency.

Note

set_wait_callback() is a pymssql extension to the DB-API 2.0.