SQLAlchemy 0.6.1 Documentation

Version: 0.6.1 Last Updated: 07/25/2016 21:14:41
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Support for the SQLite database.

For information on connecting using a specific driver, see the documentation section regarding that driver.

Date and Time Types

SQLite does not have built-in DATE, TIME, or DATETIME types, and pysqlite does not provide out of the box functionality for translating values between Python datetime objects and a SQLite-supported format. SQLAlchemy’s own DateTime and related types provide date formatting and parsing functionality when SQlite is used. The implementation classes are DATETIME, DATE and TIME. These types represent dates and times as ISO formatted strings, which also nicely support ordering. There’s no reliance on typical “libc” internals for these functions so historical dates are fully supported.

Auto Incrementing Behavior

Background on SQLite’s autoincrement is at: http://sqlite.org/autoinc.html

Two things to note:

  • The AUTOINCREMENT keyword is not required for SQLite tables to generate primary key values automatically. AUTOINCREMENT only means that the algorithm used to generate ROWID values should be slightly different.
  • SQLite does not generate primary key (i.e. ROWID) values, even for one column, if the table has a composite (i.e. multi-column) primary key. This is regardless of the AUTOINCREMENT keyword being present or not.

To specifically render the AUTOINCREMENT keyword on the primary key column when rendering DDL, add the flag sqlite_autoincrement=True to the Table construct:

Table('sometable', metadata,
        Column('id', Integer, primary_key=True), 

Transaction Isolation Level

create_engine() accepts an isolation_level parameter which results in the command PRAGMA read_uncommitted <level> being invoked for every new connection. Valid values for this parameter are SERIALIZABLE and READ UNCOMMITTED corresponding to a value of 0 and 1, respectively.


Support for the SQLite database via pysqlite.

Note that pysqlite is the same driver as the sqlite3 module included with the Python distribution.


When using Python 2.5 and above, the built in sqlite3 driver is already installed and no additional installation is needed. Otherwise, the pysqlite2 driver needs to be present. This is the same driver as sqlite3, just with a different name.

The pysqlite2 driver will be loaded first, and if not found, sqlite3 is loaded. This allows an explicitly installed pysqlite driver to take precedence over the built in one. As with all dialects, a specific DBAPI module may be provided to create_engine() to control this explicitly:

from sqlite3 import dbapi2 as sqlite
e = create_engine('sqlite+pysqlite:///file.db', module=sqlite)

Full documentation on pysqlite is available at: http://www.initd.org/pub/software/pysqlite/doc/usage-guide.html

Connect Strings

The file specification for the SQLite database is taken as the “database” portion of the URL. Note that the format of a url is:


This means that the actual filename to be used starts with the characters to the right of the third slash. So connecting to a relative filepath looks like:

# relative path
e = create_engine('sqlite:///path/to/database.db')

An absolute path, which is denoted by starting with a slash, means you need four slashes:

# absolute path
e = create_engine('sqlite:////path/to/database.db')

To use a Windows path, regular drive specifications and backslashes can be used. Double backslashes are probably needed:

# absolute path on Windows
e = create_engine('sqlite:///C:\\path\\to\\database.db')

The sqlite :memory: identifier is the default if no filepath is present. Specify sqlite:// and nothing else:

# in-memory database
e = create_engine('sqlite://')

Compatibility with sqlite3 “native” date and datetime types

The pysqlite driver includes the sqlite3.PARSE_DECLTYPES and sqlite3.PARSE_COLNAMES options, which have the effect of any column or expression explicitly cast as “date” or “timestamp” will be converted to a Python date or datetime object. The date and datetime types provided with the pysqlite dialect are not currently compatible with these options, since they render the ISO date/datetime including microseconds, which pysqlite’s driver does not. Additionally, SQLAlchemy does not at this time automatically render the “cast” syntax required for the freestanding functions “current_timestamp” and “current_date” to return datetime/date types natively. Unfortunately, pysqlite does not provide the standard DBAPI types in cursor.description, leaving SQLAlchemy with no way to detect these types on the fly without expensive per-row type checks.

Usage of PARSE_DECLTYPES can be forced if one configures “native_datetime=True” on create_engine():

engine = create_engine('sqlite://', 
                connect_args={'detect_types': sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES},

With this flag enabled, the DATE and TIMESTAMP types (but note - not the DATETIME or TIME types...confused yet ?) will not perform any bind parameter or result processing. Execution of “func.current_date()” will return a string. “func.current_timestamp()” is registered as returning a DATETIME type in SQLAlchemy, so this function still receives SQLAlchemy-level result processing.

Threading Behavior

Pysqlite connections do not support being moved between threads, unless the check_same_thread Pysqlite flag is set to False. In addition, when using an in-memory SQLite database, the full database exists only within the scope of a single connection. It is reported that an in-memory database does not support being shared between threads regardless of the check_same_thread flag - which means that a multithreaded application cannot share data from a :memory: database across threads unless access to the connection is limited to a single worker thread which communicates through a queueing mechanism to concurrent threads.

To provide a default which accomodates SQLite’s default threading capabilities somewhat reasonably, the SQLite dialect will specify that the SingletonThreadPool be used by default. This pool maintains a single SQLite connection per thread that is held open up to a count of five concurrent threads. When more than five threads are used, a cleanup mechanism will dispose of excess unused connections.

Two optional pool implementations that may be appropriate for particular SQLite usage scenarios:

  • the sqlalchemy.pool.StaticPool might be appropriate for a multithreaded application using an in-memory database, assuming the threading issues inherent in pysqlite are somehow accomodated for. This pool holds persistently onto a single connection which is never closed, and is returned for all requests.
  • the sqlalchemy.pool.NullPool might be appropriate for an application that makes use of a file-based sqlite database. This pool disables any actual “pooling” behavior, and simply opens and closes real connections corresonding to the connect() and close() methods. SQLite can “connect” to a particular file with very high efficiency, so this option may actually perform better without the extra overhead of SingletonThreadPool. NullPool will of course render a :memory: connection useless since the database would be lost as soon as the connection is “returned” to the pool.


In contrast to SQLAlchemy’s active handling of date and time types for pysqlite, pysqlite’s default behavior regarding Unicode is that all strings are returned as Python unicode objects in all cases. So even if the Unicode type is not used, you will still always receive unicode data back from a result set. It is strongly recommended that you do use the Unicode type to represent strings, since it will raise a warning if a non-unicode Python string is passed from the user application. Mixing the usage of non-unicode objects with returned unicode objects can quickly create confusion, particularly when using the ORM as internal data is not always represented by an actual database result string.

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