# SQLAlchemy 0.7 Documentation

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Release: 0.7.3 | Release Date: October 16, 2011

# SQLite

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),
            sqlite_autoincrement=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.

## SQLite Data Types

As with all SQLAlchemy dialects, all UPPERCASE types that are known to be valid with SQLite are importable from the top level dialect, whether they originate from `sqlalchemy.types` or from the local dialect:

    from sqlalchemy.dialects.sqlite import \
                BLOB, BOOLEAN, CHAR, DATE, DATETIME, DECIMAL, FLOAT, \
                INTEGER, NUMERIC, SMALLINT, TEXT, TIME, TIMESTAMP, \
                VARCHAR

class `sqlalchemy.dialects.sqlite.``DATETIME`(storage_format=None, regexp=None, **kw)

  Represent a Python datetime object in SQLite using a string.

  The default string storage format is:

      "%04d-%02d-%02d %02d:%02d:%02d.%06d" % (value.year,
                              value.month, value.day,
                              value.hour, value.minute,
                              value.second, value.microsecond)

  e.g.:

      2011-03-15 12:05:57.10558

  The storage format can be customized to some degree using the `storage_format` and `regexp` parameters, such as:

      import re
      from sqlalchemy.dialects.sqlite import DATETIME

      dt = DATETIME(
              storage_format="%04d/%02d/%02d %02d-%02d-%02d-%06d",
              regexp=re.compile("(\d+)/(\d+)/(\d+) (\d+)-(\d+)-(\d+)(?:-(\d+))?")
          )

  Parameters:

  - storage_format – format string which will be appled to the tuple `(value.year, value.month, value.day, value.hour, value.minute, value.second, value.microsecond)`, given a Python datetime.datetime() object.
  - regexp – regular expression which will be applied to incoming result rows. The resulting match object is appled to the Python datetime() constructor via `*map(int, match_obj.groups(0))`.

class `sqlalchemy.dialects.sqlite.``DATE`(storage_format=None, regexp=None, **kw)

  Represent a Python date object in SQLite using a string.

  The default string storage format is:

      "%04d-%02d-%02d" % (value.year, value.month, value.day)

  e.g.:

      2011-03-15

  The storage format can be customized to some degree using the `storage_format` and `regexp` parameters, such as:

      import re
      from sqlalchemy.dialects.sqlite import DATE

      d = DATE(
              storage_format="%02d/%02d/%02d",
              regexp=re.compile("(\d+)/(\d+)/(\d+)")
          )

  Parameters:

  - storage_format – format string which will be appled to the tuple `(value.year, value.month, value.day)`, given a Python datetime.date() object.
  - regexp – regular expression which will be applied to incoming result rows. The resulting match object is appled to the Python date() constructor via `*map(int, match_obj.groups(0))`.

class `sqlalchemy.dialects.sqlite.``TIME`(storage_format=None, regexp=None, **kw)

  Represent a Python time object in SQLite using a string.

  The default string storage format is:

      "%02d:%02d:%02d.%06d" % (value.hour, value.minute,
                              value.second,
                               value.microsecond)

  e.g.:

      12:05:57.10558

  The storage format can be customized to some degree using the `storage_format` and `regexp` parameters, such as:

      import re
      from sqlalchemy.dialects.sqlite import TIME

      t = TIME(
              storage_format="%02d-%02d-%02d-%06d",
              regexp=re.compile("(\d+)-(\d+)-(\d+)-(?:-(\d+))?")
          )

  Parameters:

  - storage_format – format string which will be appled to the tuple `(value.hour, value.minute, value.second, value.microsecond)`, given a Python datetime.time() object.
  - regexp – regular expression which will be applied to incoming result rows. The resulting match object is appled to the Python time() constructor via `*map(int, match_obj.groups(0))`.

## Pysqlite

Support for the SQLite database via pysqlite.

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

### Driver

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:

    driver://user:pass@host/database

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.

Keeping in mind that pysqlite’s parsing option is not recommended, nor should be necessary, for use with SQLAlchemy, 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},
                    native_datetime=True
                    )

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/Pooling Behavior

Pysqlite’s default behavior is to prohibit the usage of a single connection in more than one thread. This is controlled by the `check_same_thread` Pysqlite flag. This default is intended to work with older versions of SQLite that did not support multithreaded operation under various circumstances. In particular, older SQLite versions did not allow a `:memory:` database to be used in multiple threads under any circumstances.

SQLAlchemy sets up pooling to work with Pysqlite’s default behavior:

- When a `:memory:` SQLite database is specified, the dialect by default will use `SingletonThreadPool`. This pool maintains a single connection per thread, so that all access to the engine within the current thread use the same `:memory:` database - other threads would access a different `:memory:` database.

- When a file-based database is specified, the dialect will use `NullPool` as the source of connections. This pool closes and discards connections which are returned to the pool immediately. SQLite file-based connections have extremely low overhead, so pooling is not necessary. The scheme also prevents a connection from being used again in a different thread and works best with SQLite’s coarse-grained file locking.

  Note

  The default selection of `NullPool` for SQLite file-based databases is new in SQLAlchemy 0.7. Previous versions select `SingletonThreadPool` by default for all SQLite databases.

Modern versions of SQLite no longer have the threading restrictions, and assuming the sqlite3/pysqlite library was built with SQLite’s default threading mode of “Serialized”, even `:memory:` databases can be shared among threads.

#### Using a Memory Database in Multiple Threads

To use a `:memory:` database in a multithreaded scenario, the same connection object must be shared among threads, since the database exists only within the scope of that connection. The `StaticPool` implementation will maintain a single connection globally, and the `check_same_thread` flag can be passed to Pysqlite as `False`:

    from sqlalchemy.pool import StaticPool
    engine = create_engine('sqlite://',
                        connect_args={'check_same_thread':False},
                        poolclass=StaticPool)

Note that using a `:memory:` database in multiple threads requires a recent version of SQLite.

#### Using Temporary Tables with SQLite

Due to the way SQLite deals with temporary tables, if you wish to use a temporary table in a file-based SQLite database across multiple checkouts from the connection pool, such as when using an ORM `Session` where the temporary table should continue to remain after `commit()` or `rollback()` is called, a pool which maintains a single connection must be used. Use `SingletonThreadPool` if the scope is only needed within the current thread, or `StaticPool` is scope is needed within multiple threads for this case:

    # maintain the same connection per thread
    from sqlalchemy.pool import SingletonThreadPool
    engine = create_engine('sqlite:///mydb.db',
                        poolclass=SingletonThreadPool)

    # maintain the same connection across all threads
    from sqlalchemy.pool import StaticPool
    engine = create_engine('sqlite:///mydb.db',
                        poolclass=StaticPool)

Note that `SingletonThreadPool` should be configured for the number of threads that are to be used; beyond that number, connections will be closed out in a non deterministic way.

### Unicode

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.