sqlalchemy.dialects.sqlite.pysqlite

Driver

The sqlite3 Python DBAPI is standard on all modern Python versions; for cPython and Pypy, no additional installation is necessary.

Connect Strings

The file specification for the SQLite database is taken as the "database" portion of the URL. Note that the format of a SQLAlchemy 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://')

e = create_engine("sqlite:///file:path/to/database?mode=ro&uri=true")

e = create_engine(
    "sqlite:///file:path/to/database?"
    "check_same_thread=true&timeout=10&mode=ro&nolock=1&uri=true"
)

sqlite3.connect(
    "file:path/to/database?mode=ro&nolock=1",
    check_same_thread=True, timeout=10, uri=True
)

Regular Expression Support

Support for the _sql.ColumnOperators.regexp_match operator is provided using Python's re.search function. SQLite itself does not include a working regular expression operator; instead, it includes a non-implemented placeholder operator REGEXP that calls a user-defined function that must be provided.

SQLAlchemy's implementation makes use of the pysqlite create_function hook as follows:

def regexp(a, b):
    return re.search(a, b) is not None

sqlite_connection.create_function(
    "regexp", 2, regexp,
)

There is currently no support for regular expression flags as a separate argument, as these are not supported by SQLite's REGEXP operator, however these may be included inline within the regular expression string. See Python regular expressions for details.

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 originally 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.

Pysqlite does include a now-undocumented flag known as check_same_thread which will disable this check, however note that pysqlite connections are still not safe to use in concurrently in multiple threads. In particular, any statement execution calls would need to be externally mutexed, as Pysqlite does not provide for thread-safe propagation of error messages among other things. So while even :memory: databases can be shared among threads in modern SQLite, Pysqlite doesn't provide enough thread-safety to make this usage worth it.

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.

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

# 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)

Unicode

The pysqlite driver only returns Python unicode objects in result sets, never plain strings, and accommodates unicode objects within bound parameter values in all cases. Regardless of the SQLAlchemy string type in use, string-based result values will by Python unicode in Python 2. The .Unicode type should still be used to indicate those columns that require unicode, however, so that non-unicode values passed inadvertently will emit a warning. Pysqlite will emit an error if a non-unicode string is passed containing non-ASCII characters.

Dealing with Mixed String / Binary Columns in Python 3

The SQLite database is weakly typed, and as such it is possible when using binary values, which in Python 3 are represented as b'some string', that a particular SQLite database can have data values within different rows where some of them will be returned as a b'' value by the Pysqlite driver, and others will be returned as Python strings, e.g. '' values. This situation is not known to occur if the SQLAlchemy .LargeBinary datatype is used consistently, however if a particular SQLite database has data that was inserted using the Pysqlite driver directly, or when using the SQLAlchemy .String type which was later changed to .LargeBinary, the table will not be consistently readable because SQLAlchemy's .LargeBinary datatype does not handle strings so it has no way of "encoding" a value that is in string format.

To deal with a SQLite table that has mixed string / binary data in the same column, use a custom type that will check each row individually:

# note this is Python 3 only

from sqlalchemy import String
from sqlalchemy import TypeDecorator

class MixedBinary(TypeDecorator):
    impl = String
    cache_ok = True

    def process_result_value(self, value, dialect):
        if isinstance(value, str):
            value = bytes(value, 'utf-8')
        elif value is not None:
            value = bytes(value)

        return value

Then use the above MixedBinary datatype in the place where .LargeBinary would normally be used.

Serializable isolation / Savepoints / Transactional DDL

In the section :ref:`sqlite_concurrency`, we refer to the pysqlite driver's assortment of issues that prevent several features of SQLite from working correctly. The pysqlite DBAPI driver has several long-standing bugs which impact the correctness of its transactional behavior. In its default mode of operation, SQLite features such as SERIALIZABLE isolation, transactional DDL, and SAVEPOINT support are non-functional, and in order to use these features, workarounds must be taken.

The issue is essentially that the driver attempts to second-guess the user's intent, failing to start transactions and sometimes ending them prematurely, in an effort to minimize the SQLite databases's file locking behavior, even though SQLite itself uses "shared" locks for read-only activities.

SQLAlchemy chooses to not alter this behavior by default, as it is the long-expected behavior of the pysqlite driver; if and when the pysqlite driver attempts to repair these issues, that will be more of a driver towards defaults for SQLAlchemy.

The good news is that with a few events, we can implement transactional support fully, by disabling pysqlite's feature entirely and emitting BEGIN ourselves. This is achieved using two event listeners:

from sqlalchemy import create_engine, event

engine = create_engine("sqlite:///myfile.db")

@event.listens_for(engine, "connect")
def do_connect(dbapi_connection, connection_record):
    # disable pysqlite's emitting of the BEGIN statement entirely.
    # also stops it from emitting COMMIT before any DDL.
    dbapi_connection.isolation_level = None

@event.listens_for(engine, "begin")
def do_begin(conn):
    # emit our own BEGIN
    conn.exec_driver_sql("BEGIN")

Above, we intercept a new pysqlite connection and disable any transactional integration. Then, at the point at which SQLAlchemy knows that transaction scope is to begin, we emit "BEGIN" ourselves.

When we take control of "BEGIN", we can also control directly SQLite's locking modes, introduced at BEGIN TRANSACTION, by adding the desired locking mode to our "BEGIN":

@event.listens_for(engine, "begin")
def do_begin(conn):
    conn.exec_driver_sql("BEGIN EXCLUSIVE")