Support for the SQLite database.
The following dialect/DBAPI options are available. Please refer to individual DBAPI sections for connect information.
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.
Background on SQLite’s autoincrement is at: http://sqlite.org/autoinc.html
Two things to note:
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)SQLite is not designed for a high level of write concurrency. The database itself, being a file, is locked completely during write operations within transactions, meaning exactly one “connection” (in reality a file handle) has exclusive access to the database during this period - all other “connections” will be blocked during this time.
The Python DBAPI specification also calls for a connection model that is always in a transaction; there is no connection.begin() method, only connection.commit() and connection.rollback(), upon which a new transaction is to be begun immediately. This may seem to imply that the SQLite driver would in theory allow only a single filehandle on a particular database file at any time; however, there are several factors both within SQlite itself as well as within the pysqlite driver which loosen this restriction significantly.
However, no matter what locking modes are used, SQLite will still always lock the database file once a transaction is started and DML (e.g. INSERT, UPDATE, DELETE) has at least been emitted, and this will block other transactions at least at the point that they also attempt to emit DML. By default, the length of time on this block is very short before it times out with an error.
This behavior becomes more critical when used in conjunction with the SQLAlchemy ORM. SQLAlchemy’s Session object by default runs within a transaction, and with its autoflush model, may emit DML preceding any SELECT statement. This may lead to a SQLite database that locks more quickly than is expected. The locking mode of SQLite and the pysqlite driver can be manipulated to some degree, however it should be noted that achieving a high degree of write-concurrency with SQLite is a losing battle.
For more information on SQLite’s lack of write concurrency by design, please see Situations Where Another RDBMS May Work Better - High Concurrency near the bottom of the page.
The following subsections introduce areas that are impacted by SQLite’s file-based architecture and additionally will usually require workarounds to work when using the pysqlite driver.
SQLite supports “transaction isolation” in a non-standard way, along two axes. One is that of the PRAGMA read_uncommitted instruction. This setting can essentially switch SQLite between its default mode of SERIALIZABLE isolation, and a “dirty read” isolation mode normally referred to as READ UNCOMMITTED.
SQLAlchemy ties into this PRAGMA statement using the create_engine.isolation_level parameter of create_engine(). Valid values for this parameter when used with SQLite are "SERIALIZABLE" and "READ UNCOMMITTED" corresponding to a value of 0 and 1, respectively. SQLite defaults to SERIALIZABLE, however its behavior is impacted by the pysqlite driver’s default behavior.
The other axis along which SQLite’s transactional locking is impacted is via the nature of the BEGIN statement used. The three varieties are “deferred”, “immediate”, and “exclusive”, as described at BEGIN TRANSACTION. A straight BEGIN statement uses the “deferred” mode, where the the database file is not locked until the first read or write operation, and read access remains open to other transactions until the first write operation. But again, it is critical to note that the pysqlite driver interferes with this behavior by not even emitting BEGIN until the first write operation.
Warning
SQLite’s transactional scope is impacted by unresolved issues in the pysqlite driver, which defers BEGIN statements to a greater degree than is often feasible. See the section Serializable isolation / Savepoints / Transactional DDL for techniques to work around this behavior.
SQLite supports SAVEPOINTs, which only function once a transaction is begun. SQLAlchemy’s SAVEPOINT support is available using the Connection.begin_nested() method at the Core level, and Session.begin_nested() at the ORM level. However, SAVEPOINTs won’t work at all with pysqlite unless workarounds are taken.
Warning
SQLite’s SAVEPOINT feature is impacted by unresolved issues in the pysqlite driver, which defers BEGIN statements to a greater degree than is often feasible. See the section Serializable isolation / Savepoints / Transactional DDL for techniques to work around this behavior.
The SQLite database supports transactional DDL as well. In this case, the pysqlite driver is not only failing to start transactions, it also is ending any existing transction when DDL is detected, so again, workarounds are required.
Warning
SQLite’s transactional DDL is impacted by unresolved issues in the pysqlite driver, which fails to emit BEGIN and additionally forces a COMMIT to cancel any transaction when DDL is encountered. See the section Serializable isolation / Savepoints / Transactional DDL for techniques to work around this behavior.
SQLite supports FOREIGN KEY syntax when emitting CREATE statements for tables, however by default these constraints have no effect on the operation of the table.
Constraint checking on SQLite has three prerequisites:
SQLAlchemy allows for the PRAGMA statement to be emitted automatically for new connections through the usage of events:
from sqlalchemy.engine import Engine
from sqlalchemy import event
@event.listens_for(Engine, "connect")
def set_sqlite_pragma(dbapi_connection, connection_record):
cursor = dbapi_connection.cursor()
cursor.execute("PRAGMA foreign_keys=ON")
cursor.close()SQLite types are unlike those of most other database backends, in that the string name of the type usually does not correspond to a “type” in a one-to-one fashion. Instead, SQLite links per-column typing behavior to one of five so-called “type affinities” based on a string matching pattern for the type.
SQLAlchemy’s reflection process, when inspecting types, uses a simple lookup table to link the keywords returned to provided SQLAlchemy types. This lookup table is present within the SQLite dialect as it is for all other dialects. However, the SQLite dialect has a different “fallback” routine for when a particular type name is not located in the lookup map; it instead implements the SQLite “type affinity” scheme located at http://www.sqlite.org/datatype3.html section 2.1.
The provided typemap will make direct associations from an exact string name match for the following types:
BIGINT, BLOB, BOOLEAN, BOOLEAN, CHAR, DATE, DATETIME, FLOAT, DECIMAL, FLOAT, INTEGER, INTEGER, NUMERIC, REAL, SMALLINT, TEXT, TIME, TIMESTAMP, VARCHAR, NVARCHAR, NCHAR
When a type name does not match one of the above types, the “type affinity” lookup is used instead:
New in version 0.9.3: Support for SQLite type affinity rules when reflecting columns.
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, \
VARCHARBases: sqlalchemy.dialects.sqlite.base._DateTimeMixin, sqlalchemy.types.DateTime
Represent a Python datetime object in SQLite using a string.
The default string storage format is:
"%(year)04d-%(month)02d-%(day)02d %(hour)02d:%(min)02d:%(second)02d.%(microsecond)06d"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="%(year)04d/%(month)02d/%(day)02d %(hour)02d:%(min)02d:%(second)02d",
regexp=r"(\d+)/(\d+)/(\d+) (\d+)-(\d+)-(\d+)"
)| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.sqlite.base._DateTimeMixin, sqlalchemy.types.Date
Represent a Python date object in SQLite using a string.
The default string storage format is:
"%(year)04d-%(month)02d-%(day)02d"e.g.:
2011-03-15The 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="%(month)02d/%(day)02d/%(year)04d",
regexp=re.compile("(?P<month>\d+)/(?P<day>\d+)/(?P<year>\d+)")
)| Parameters: |
|
|---|
Bases: sqlalchemy.dialects.sqlite.base._DateTimeMixin, sqlalchemy.types.Time
Represent a Python time object in SQLite using a string.
The default string storage format is:
"%(hour)02d:%(minute)02d:%(second)02d.%(microsecond)06d"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="%(hour)02d-%(minute)02d-%(second)02d-%(microsecond)06d",
regexp=re.compile("(\d+)-(\d+)-(\d+)-(?:-(\d+))?")
)| Parameters: |
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Support for the SQLite database via the pysqlite driver.
Note that pysqlite is the same driver as the sqlite3 module included with the Python distribution.
Documentation and download information (if applicable) for pysqlite is available at: http://docs.python.org/library/sqlite3.html
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)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://')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.
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.
Changed in version 0.7: Default selection of NullPool for SQLite file-based databases. Previous versions select SingletonThreadPool by default for all SQLite databases.
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.
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 Session.commit() or Session.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.
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.
In the section Database Locking Behavior / 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.execute("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.execute("BEGIN EXCLUSIVE")See also
BEGIN TRANSACTION - on the SQLite site
sqlite3 SELECT does not BEGIN a transaction - on the Python bug tracker
sqlite3 module breaks transactions and potentially corrupts data - on the Python bug tracker