Features beyond the DB API

Introduction

The following features are beyond the requirements of DB API 2.0. They are intended to provide a very Python-like, convenient programming experience, but you should not use them if your code needs to be portable between DB API modules. (Though I hope future DB API specifications will adopt some of these features.)

fetchval

The fetchval() convenience method returns the first column of the first row if there are results, otherwise it returns None.

count = cursor.execute('select count(*) from users').fetchval()

fast_executemany

(New in version 4.0.19.) Simply adding

# crsr is a pyodbc.Cursor object
crsr.fast_executemany = True

can boost the performance of executemany operations by greatly reducing the number of round-trips to the server.

Notes:

• This feature is "off" by default, and is currently only recommended for applications that use Microsoft's ODBC Driver for SQL Server.
• The parameter values are held in memory, so very large numbers of records (tens of millions or more) may cause memory issues.
• Writing fractional seconds of datetime.time values is supported, unlike normal pyodbc behavior
• See this tip regarding fast_executemany and temporary tables.
• For information on using fast_executemany with SQLAlchemy (and pandas) see the Stack Overflow question here.

Access Values By Name

The DB API specifies that results must be tuple-like, so columns are normally accessed by indexing into the sequence (e.g. row[0]) and pyodbc supports this. However, columns can also be accessed by name:

cursor.execute("select album_id, photo_id from photos where user_id=1")
row = cursor.fetchone()
print(row.album_id, row.photo_id)
print(row[0], row[1])  # same as above, but less readable

This makes the code easier to maintain when modifying SQL, more readable, and allows rows to be used where a custom class might otherwise be used. All rows from a single execute share the same dictionary of column names, so using Row objects to hold a large result set may also use less memory than creating a object for each row.

The SQL "as" keyword allows the name of a column in the result set to be specified. This is useful if a column name has spaces or if there is no name:

cursor.execute("select count(*) as photo_count from photos where user_id < 100")
row = cursor.fetchone()
print(row.photo_count)

Rows Values Can Be Replaced

Though SQL is very powerful, values sometimes need to be modified before they can be used. Rows allow their values to be replaced, which makes them even more convenient ad-hoc data structures.

# Replace the 'start_date' datetime in each row with one that has a time zone.
rows = cursor.fetchall()
for row in rows:
    row.start_date = row.start_date.astimezone(tz)

Note that columns cannot be added to rows; only values for existing columns can be modified.

Cursors are Iterable

The DB API makes this an optional feature. Each iteration returns a row object.

cursor.execute("select album_id, photo_id from photos where user_id=1")
for row in cursor:
    print(row.album_id, row.photo_id)

Cursor.execute() Returns the Cursor

The DB API specification does not specify the return value of Cursor.execute(). Previous versions of pyodbc (2.0.x) returned different values, but the 2.1+ versions always return the Cursor itself.

This allows for compact code such as:

for row in cursor.execute("select album_id, photo_id from photos where user_id=1"):
    print(row.album_id, row.photo_id)

row  = cursor.execute("select * from tmp").fetchone()
rows = cursor.execute("select * from tmp").fetchall()

count = cursor.execute("update photos set processed=1 where user_id=1").rowcount
count = cursor.execute("delete from photos where user_id=1").rowcount

Passing Parameters

As specified in the DB API, Cursor.execute() accepts an optional sequence of parameters:

cursor.execute("select a from tbl where b=? and c=?", (x, y))

However, this seems complicated for something as simple as passing parameters, so pyodbc also accepts the parameters directly. Note in this example that x & y are not in a tuple:

cursor.execute("select a from tbl where b=? and c=?", x, y)

Autocommit Mode

The DB API specifies that connections require a manual commit and pyodbc complies with this. However, connections also support autocommit, using the autocommit keyword of the connection function or the autocommit attribute of the Connection object:

cnxn = pyodbc.connect(cstring, autocommit=True)

or

cnxn.autocommit = True
cnxn.autocommit = False

Lowercase

Setting pyodbc.lowercase=True will cause all column names in rows to be lowercased. Some people find this easier to work with, particularly if a database has a mix of naming conventions. If your database is case-sensitive, however, it can cause some confusion.

Connection Pooling

ODBC connection pooling is turned on by default. It can be turned off by setting pyodbc.pooling=False before any connections are made.

Query Timeouts

The Connection.timeout attribute can be set to a number of seconds after which a query should raise an error. The value is in seconds and will cause an OperationalError to be raised with SQLSTATE HYT00 or HYT01. By default the timeout value is 0 which disables the timeout.

Miscellaneous ODBC Functions

Most of the ODBC catalog functions are available as methods on Cursor objects. The results are presented as SELECT results in rows that are fetched normally. The Cursor page documents these, but it may be helpful to refer to Microsoft's ODBC documentation for more details.

For example:

cnxn   = pyodbc.connect(...)
cursor = cnxn.cursor()
for row in cursor.tables():
    print(row.table_name)

ODBC Function	Method	Description
SQLTables	Cursor.tables	Returns a list of table, catalog, or schema names, and table types.
SQLColumns	Cursor.columns	Returns a list of column names in specified tables.
SQLStatistics	Cursor.statistics	Returns a list of statistics about a single table and the indexes associated with the table.
SQLSpecialColumns	Cursor.rowIdColumns	Returns a list of columns that uniquely identify a row.
SQLSpecialColumns	Cursor.rowVerColumns	Returns a list of columns that are automatically updated when any value in the row is updated.
SQLPrimaryKeys	Cursor.primaryKeys	Returns a list of column names that make up the primary key for a table.
SQLForeignKeys	Cursor.foreignKeys	Returns a list of column names that are foreign keys in the specified table (columns in the specified table that refer to primary keys in other tables) or foreign keys in other tables that refer to the primary key in the specified table.
SQLProcedures	Cursor.procedures	Returns information about the procedures in the data source.
SQLProcedures	Cursor.getTypeInfo	Returns a information about the specified data type or all data types supported by the driver.