SQLite¶
Support for the SQLite database.
The following table summarizes current support levels for database release versions.
Support type |
Versions |
---|---|
3.21, 3.28+ |
|
3.12+ |
|
3.7.16+ |
DBAPI Support¶
The following dialect/DBAPI options are available. Please refer to individual DBAPI sections for connect information.
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.
Ensuring Text affinity¶
The DDL rendered for these types is the standard DATE
, TIME
and DATETIME
indicators. However, custom storage formats can also be
applied to these types. When the
storage format is detected as containing no alpha characters, the DDL for
these types is rendered as DATE_CHAR
, TIME_CHAR
, and DATETIME_CHAR
,
so that the column continues to have textual affinity.
See also
Type Affinity - in the SQLite documentation
SQLite Auto Incrementing Behavior¶
Background on SQLite’s autoincrement is at: https://sqlite.org/autoinc.html
Key concepts:
SQLite has an implicit “auto increment” feature that takes place for any non-composite primary-key column that is specifically created using “INTEGER PRIMARY KEY” for the type + primary key.
SQLite also has an explicit “AUTOINCREMENT” keyword, that is not equivalent to the implicit autoincrement feature; this keyword is not recommended for general use. SQLAlchemy does not render this keyword unless a special SQLite-specific directive is used (see below). However, it still requires that the column’s type is named “INTEGER”.
Using the AUTOINCREMENT Keyword¶
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)
Allowing autoincrement behavior SQLAlchemy types other than Integer/INTEGER¶
SQLite’s typing model is based on naming conventions. Among other things, this
means that any type name which contains the substring "INT"
will be
determined to be of “integer affinity”. A type named "BIGINT"
,
"SPECIAL_INT"
or even "XYZINTQPR"
, will be considered by SQLite to be
of “integer” affinity. However, the SQLite autoincrement feature, whether
implicitly or explicitly enabled, requires that the name of the column’s type
is exactly the string “INTEGER”. Therefore, if an application uses a type
like BigInteger
for a primary key, on SQLite this type will need to
be rendered as the name "INTEGER"
when emitting the initial CREATE
TABLE
statement in order for the autoincrement behavior to be available.
One approach to achieve this is to use Integer
on SQLite
only using TypeEngine.with_variant()
:
table = Table(
"my_table", metadata,
Column("id", BigInteger().with_variant(Integer, "sqlite"), primary_key=True)
)
Another is to use a subclass of BigInteger
that overrides its DDL
name to be INTEGER
when compiled against SQLite:
from sqlalchemy import BigInteger
from sqlalchemy.ext.compiler import compiles
class SLBigInteger(BigInteger):
pass
@compiles(SLBigInteger, 'sqlite')
def bi_c(element, compiler, **kw):
return "INTEGER"
@compiles(SLBigInteger)
def bi_c(element, compiler, **kw):
return compiler.visit_BIGINT(element, **kw)
table = Table(
"my_table", metadata,
Column("id", SLBigInteger(), primary_key=True)
)
Database Locking Behavior / Concurrency¶
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.
Transaction Isolation Level / Autocommit¶
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.
When using the pysqlite driver, the "AUTOCOMMIT"
isolation level is also
available, which will alter the pysqlite connection using the .isolation_level
attribute on the DBAPI connection and set it to None for the duration
of the setting.
New in version 1.3.16: added support for SQLite AUTOCOMMIT isolation level when using the pysqlite / sqlite3 SQLite driver.
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 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.
INSERT/UPDATE/DELETE…RETURNING¶
The SQLite dialect supports SQLite 3.35’s INSERT|UPDATE|DELETE..RETURNING
syntax. INSERT..RETURNING
may be used
automatically in some cases in order to fetch newly generated identifiers in
place of the traditional approach of using cursor.lastrowid
, however
cursor.lastrowid
is currently still preferred for simple single-statement
cases for its better performance.
To specify an explicit RETURNING
clause, use the
_UpdateBase.returning()
method on a per-statement basis:
# INSERT..RETURNING
result = connection.execute(
table.insert().
values(name='foo').
returning(table.c.col1, table.c.col2)
)
print(result.all())
# UPDATE..RETURNING
result = connection.execute(
table.update().
where(table.c.name=='foo').
values(name='bar').
returning(table.c.col1, table.c.col2)
)
print(result.all())
# DELETE..RETURNING
result = connection.execute(
table.delete().
where(table.c.name=='foo').
returning(table.c.col1, table.c.col2)
)
print(result.all())
New in version 2.0: Added support for SQLite RETURNING
SAVEPOINT Support¶
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.
Transactional DDL¶
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 transaction 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.
Foreign Key Support¶
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:
At least version 3.6.19 of SQLite must be in use
The SQLite library must be compiled without the SQLITE_OMIT_FOREIGN_KEY or SQLITE_OMIT_TRIGGER symbols enabled.
The
PRAGMA foreign_keys = ON
statement must be emitted on all connections before use – including the initial call toMetaData.create_all()
.
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()
Warning
When SQLite foreign keys are enabled, it is not possible to emit CREATE or DROP statements for tables that contain mutually-dependent foreign key constraints; to emit the DDL for these tables requires that ALTER TABLE be used to create or drop these constraints separately, for which SQLite has no support.
See also
SQLite Foreign Key Support - on the SQLite web site.
Events - SQLAlchemy event API.
- Creating/Dropping Foreign Key Constraints via ALTER - more information on SQLAlchemy’s facilities for handling
mutually-dependent foreign key constraints.
ON CONFLICT support for constraints¶
See also
This section describes the DDL version of “ON CONFLICT” for SQLite, which occurs within a CREATE TABLE statement. For “ON CONFLICT” as applied to an INSERT statement, see INSERT…ON CONFLICT (Upsert).
SQLite supports a non-standard DDL clause known as ON CONFLICT which can be applied
to primary key, unique, check, and not null constraints. In DDL, it is
rendered either within the “CONSTRAINT” clause or within the column definition
itself depending on the location of the target constraint. To render this
clause within DDL, the extension parameter sqlite_on_conflict
can be
specified with a string conflict resolution algorithm within the
PrimaryKeyConstraint
, UniqueConstraint
,
CheckConstraint
objects. Within the Column
object,
there
are individual parameters sqlite_on_conflict_not_null
,
sqlite_on_conflict_primary_key
, sqlite_on_conflict_unique
which each
correspond to the three types of relevant constraint types that can be
indicated from a Column
object.
See also
ON CONFLICT - in the SQLite documentation
New in version 1.3.
The sqlite_on_conflict
parameters accept a string argument which is just
the resolution name to be chosen, which on SQLite can be one of ROLLBACK,
ABORT, FAIL, IGNORE, and REPLACE. For example, to add a UNIQUE constraint
that specifies the IGNORE algorithm:
some_table = Table(
'some_table', metadata,
Column('id', Integer, primary_key=True),
Column('data', Integer),
UniqueConstraint('id', 'data', sqlite_on_conflict='IGNORE')
)
The above renders CREATE TABLE DDL as:
CREATE TABLE some_table (
id INTEGER NOT NULL,
data INTEGER,
PRIMARY KEY (id),
UNIQUE (id, data) ON CONFLICT IGNORE
)
When using the Column.unique
flag to add a UNIQUE constraint
to a single column, the sqlite_on_conflict_unique
parameter can
be added to the Column
as well, which will be added to the
UNIQUE constraint in the DDL:
some_table = Table(
'some_table', metadata,
Column('id', Integer, primary_key=True),
Column('data', Integer, unique=True,
sqlite_on_conflict_unique='IGNORE')
)
rendering:
CREATE TABLE some_table (
id INTEGER NOT NULL,
data INTEGER,
PRIMARY KEY (id),
UNIQUE (data) ON CONFLICT IGNORE
)
To apply the FAIL algorithm for a NOT NULL constraint,
sqlite_on_conflict_not_null
is used:
some_table = Table(
'some_table', metadata,
Column('id', Integer, primary_key=True),
Column('data', Integer, nullable=False,
sqlite_on_conflict_not_null='FAIL')
)
this renders the column inline ON CONFLICT phrase:
CREATE TABLE some_table (
id INTEGER NOT NULL,
data INTEGER NOT NULL ON CONFLICT FAIL,
PRIMARY KEY (id)
)
Similarly, for an inline primary key, use sqlite_on_conflict_primary_key
:
some_table = Table(
'some_table', metadata,
Column('id', Integer, primary_key=True,
sqlite_on_conflict_primary_key='FAIL')
)
SQLAlchemy renders the PRIMARY KEY constraint separately, so the conflict resolution algorithm is applied to the constraint itself:
CREATE TABLE some_table (
id INTEGER NOT NULL,
PRIMARY KEY (id) ON CONFLICT FAIL
)
INSERT…ON CONFLICT (Upsert)¶
See also
This section describes the DML version of “ON CONFLICT” for SQLite, which occurs within an INSERT statement. For “ON CONFLICT” as applied to a CREATE TABLE statement, see ON CONFLICT support for constraints.
From version 3.24.0 onwards, SQLite supports “upserts” (update or insert)
of rows into a table via the ON CONFLICT
clause of the INSERT
statement. A candidate row will only be inserted if that row does not violate
any unique or primary key constraints. In the case of a unique constraint violation, a
secondary action can occur which can be either “DO UPDATE”, indicating that
the data in the target row should be updated, or “DO NOTHING”, which indicates
to silently skip this row.
Conflicts are determined using columns that are part of existing unique constraints and indexes. These constraints are identified by stating the columns and conditions that comprise the indexes.
SQLAlchemy provides ON CONFLICT
support via the SQLite-specific
insert()
function, which provides
the generative methods Insert.on_conflict_do_update()
and Insert.on_conflict_do_nothing()
:
>>> from sqlalchemy.dialects.sqlite import insert
>>> insert_stmt = insert(my_table).values(
... id='some_existing_id',
... data='inserted value')
>>> do_update_stmt = insert_stmt.on_conflict_do_update(
... index_elements=['id'],
... set_=dict(data='updated value')
... )
>>> print(do_update_stmt)
{printsql}INSERT INTO my_table (id, data) VALUES (?, ?)
ON CONFLICT (id) DO UPDATE SET data = ?{stop}
>>> do_nothing_stmt = insert_stmt.on_conflict_do_nothing(
... index_elements=['id']
... )
>>> print(do_nothing_stmt)
{printsql}INSERT INTO my_table (id, data) VALUES (?, ?)
ON CONFLICT (id) DO NOTHING
New in version 1.4.
See also
Upsert - in the SQLite documentation.
Specifying the Target¶
Both methods supply the “target” of the conflict using column inference:
The
Insert.on_conflict_do_update.index_elements
argument specifies a sequence containing string column names,Column
objects, and/or SQL expression elements, which would identify a unique index or unique constraint.When using
Insert.on_conflict_do_update.index_elements
to infer an index, a partial index can be inferred by also specifying theInsert.on_conflict_do_update.index_where
parameter:>>> stmt = insert(my_table).values(user_email='a@b.com', data='inserted data') >>> do_update_stmt = stmt.on_conflict_do_update( ... index_elements=[my_table.c.user_email], ... index_where=my_table.c.user_email.like('%@gmail.com'), ... set_=dict(data=stmt.excluded.data) ... ) >>> print(do_update_stmt) {printsql}INSERT INTO my_table (data, user_email) VALUES (?, ?) ON CONFLICT (user_email) WHERE user_email LIKE '%@gmail.com' DO UPDATE SET data = excluded.data
The SET Clause¶
ON CONFLICT...DO UPDATE
is used to perform an update of the already
existing row, using any combination of new values as well as values
from the proposed insertion. These values are specified using the
Insert.on_conflict_do_update.set_
parameter. This
parameter accepts a dictionary which consists of direct values
for UPDATE:
>>> stmt = insert(my_table).values(id='some_id', data='inserted value')
>>> do_update_stmt = stmt.on_conflict_do_update(
... index_elements=['id'],
... set_=dict(data='updated value')
... )
>>> print(do_update_stmt)
{printsql}INSERT INTO my_table (id, data) VALUES (?, ?)
ON CONFLICT (id) DO UPDATE SET data = ?
Warning
The Insert.on_conflict_do_update()
method does not take
into account Python-side default UPDATE values or generation functions,
e.g. those specified using Column.onupdate
. These
values will not be exercised for an ON CONFLICT style of UPDATE, unless
they are manually specified in the
Insert.on_conflict_do_update.set_
dictionary.
Updating using the Excluded INSERT Values¶
In order to refer to the proposed insertion row, the special alias
Insert.excluded
is available as an attribute on
the Insert
object; this object creates an “excluded.” prefix
on a column, that informs the DO UPDATE to update the row with the value that
would have been inserted had the constraint not failed:
>>> stmt = insert(my_table).values(
... id='some_id',
... data='inserted value',
... author='jlh'
... )
>>> do_update_stmt = stmt.on_conflict_do_update(
... index_elements=['id'],
... set_=dict(data='updated value', author=stmt.excluded.author)
... )
>>> print(do_update_stmt)
{printsql}INSERT INTO my_table (id, data, author) VALUES (?, ?, ?)
ON CONFLICT (id) DO UPDATE SET data = ?, author = excluded.author
Additional WHERE Criteria¶
The Insert.on_conflict_do_update()
method also accepts
a WHERE clause using the Insert.on_conflict_do_update.where
parameter, which will limit those rows which receive an UPDATE:
>>> stmt = insert(my_table).values(
... id='some_id',
... data='inserted value',
... author='jlh'
... )
>>> on_update_stmt = stmt.on_conflict_do_update(
... index_elements=['id'],
... set_=dict(data='updated value', author=stmt.excluded.author),
... where=(my_table.c.status == 2)
... )
>>> print(on_update_stmt)
{printsql}INSERT INTO my_table (id, data, author) VALUES (?, ?, ?)
ON CONFLICT (id) DO UPDATE SET data = ?, author = excluded.author
WHERE my_table.status = ?
Skipping Rows with DO NOTHING¶
ON CONFLICT
may be used to skip inserting a row entirely
if any conflict with a unique constraint occurs; below this is illustrated
using the Insert.on_conflict_do_nothing()
method:
>>> stmt = insert(my_table).values(id='some_id', data='inserted value')
>>> stmt = stmt.on_conflict_do_nothing(index_elements=['id'])
>>> print(stmt)
{printsql}INSERT INTO my_table (id, data) VALUES (?, ?) ON CONFLICT (id) DO NOTHING
If DO NOTHING
is used without specifying any columns or constraint,
it has the effect of skipping the INSERT for any unique violation which
occurs:
>>> stmt = insert(my_table).values(id='some_id', data='inserted value')
>>> stmt = stmt.on_conflict_do_nothing()
>>> print(stmt)
{printsql}INSERT INTO my_table (id, data) VALUES (?, ?) ON CONFLICT DO NOTHING
Type Reflection¶
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 https://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:
INTEGER
is returned if the type name includes the stringINT
TEXT
is returned if the type name includes the stringCHAR
,CLOB
orTEXT
NullType
is returned if the type name includes the stringBLOB
REAL
is returned if the type name includes the stringREAL
,FLOA
orDOUB
.Otherwise, the
NUMERIC
type is used.
Partial Indexes¶
A partial index, e.g. one which uses a WHERE clause, can be specified
with the DDL system using the argument sqlite_where
:
tbl = Table('testtbl', m, Column('data', Integer))
idx = Index('test_idx1', tbl.c.data,
sqlite_where=and_(tbl.c.data > 5, tbl.c.data < 10))
The index will be rendered at create time as:
CREATE INDEX test_idx1 ON testtbl (data)
WHERE data > 5 AND data < 10
Dotted Column Names¶
Using table or column names that explicitly have periods in them is not recommended. While this is generally a bad idea for relational databases in general, as the dot is a syntactically significant character, the SQLite driver up until version 3.10.0 of SQLite has a bug which requires that SQLAlchemy filter out these dots in result sets.
The bug, entirely outside of SQLAlchemy, can be illustrated thusly:
import sqlite3
assert sqlite3.sqlite_version_info < (3, 10, 0), "bug is fixed in this version"
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
cursor.execute("create table x (a integer, b integer)")
cursor.execute("insert into x (a, b) values (1, 1)")
cursor.execute("insert into x (a, b) values (2, 2)")
cursor.execute("select x.a, x.b from x")
assert [c[0] for c in cursor.description] == ['a', 'b']
cursor.execute('''
select x.a, x.b from x where a=1
union
select x.a, x.b from x where a=2
''')
assert [c[0] for c in cursor.description] == ['a', 'b'], \
[c[0] for c in cursor.description]
The second assertion fails:
Traceback (most recent call last):
File "test.py", line 19, in <module>
[c[0] for c in cursor.description]
AssertionError: ['x.a', 'x.b']
Where above, the driver incorrectly reports the names of the columns including the name of the table, which is entirely inconsistent vs. when the UNION is not present.
SQLAlchemy relies upon column names being predictable in how they match to the original statement, so the SQLAlchemy dialect has no choice but to filter these out:
from sqlalchemy import create_engine
eng = create_engine("sqlite://")
conn = eng.connect()
conn.exec_driver_sql("create table x (a integer, b integer)")
conn.exec_driver_sql("insert into x (a, b) values (1, 1)")
conn.exec_driver_sql("insert into x (a, b) values (2, 2)")
result = conn.exec_driver_sql("select x.a, x.b from x")
assert result.keys() == ["a", "b"]
result = conn.exec_driver_sql('''
select x.a, x.b from x where a=1
union
select x.a, x.b from x where a=2
''')
assert result.keys() == ["a", "b"]
Note that above, even though SQLAlchemy filters out the dots, both names are still addressable:
>>> row = result.first()
>>> row["a"]
1
>>> row["x.a"]
1
>>> row["b"]
1
>>> row["x.b"]
1
Therefore, the workaround applied by SQLAlchemy only impacts
CursorResult.keys()
and Row.keys()
in the public API. In
the very specific case where an application is forced to use column names that
contain dots, and the functionality of CursorResult.keys()
and
Row.keys()
is required to return these dotted names unmodified,
the sqlite_raw_colnames
execution option may be provided, either on a
per-Connection
basis:
result = conn.execution_options(sqlite_raw_colnames=True).exec_driver_sql('''
select x.a, x.b from x where a=1
union
select x.a, x.b from x where a=2
''')
assert result.keys() == ["x.a", "x.b"]
or on a per-Engine
basis:
engine = create_engine("sqlite://", execution_options={"sqlite_raw_colnames": True})
When using the per-Engine
execution option, note that
Core and ORM queries that use UNION may not function properly.
SQLite-specific table options¶
One option for CREATE TABLE is supported directly by the SQLite
dialect in conjunction with the Table
construct:
WITHOUT ROWID
:Table("some_table", metadata, ..., sqlite_with_rowid=False)
See also
Reflecting internal schema tables¶
Reflection methods that return lists of tables will omit so-called
“SQLite internal schema object” names, which are referred towards by SQLite
as any object name that is prefixed with sqlite_
. An example of
such an object is the sqlite_sequence
table that’s generated when
the AUTOINCREMENT
column parameter is used. In order to return
these objects, the parameter sqlite_include_internal=True
may be
passed to methods such as MetaData.reflect()
or
Inspector.get_table_names()
.
New in version 2.0: Added the sqlite_include_internal=True
parameter.
Previously, these tables were not ignored by SQLAlchemy reflection
methods.
Note
The sqlite_include_internal
parameter does not refer to the
“system” tables that are present in schemas such as sqlite_master
.
See also
SQLite Internal Schema Objects - in the SQLite documentation.
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,
JSON,
SMALLINT,
TEXT,
TIME,
TIMESTAMP,
VARCHAR,
)
Object Name | Description |
---|---|
Represent a Python date object in SQLite using a string. |
|
Represent a Python datetime object in SQLite using a string. |
|
SQLite JSON type. |
|
Represent a Python time object in SQLite using a string. |
- class sqlalchemy.dialects.sqlite.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:%(minute)02d:%(second)02d.%(microsecond)06d"
e.g.:
2021-03-15 12:05:57.105542
The incoming storage format is by default parsed using the Python
datetime.fromisoformat()
function.Changed in version 2.0:
datetime.fromisoformat()
is used for default datetime string parsing.The storage format can be customized to some degree using the
storage_format
andregexp
parameters, such as:import re from sqlalchemy.dialects.sqlite import DATETIME dt = DATETIME(storage_format="%(year)04d/%(month)02d/%(day)02d " "%(hour)02d:%(minute)02d:%(second)02d", regexp=r"(\d+)/(\d+)/(\d+) (\d+)-(\d+)-(\d+)" )
- Parameters:
storage_format – format string which will be applied to the dict with keys year, month, day, hour, minute, second, and microsecond.
regexp – regular expression which will be applied to incoming result rows, replacing the use of
datetime.fromisoformat()
to parse incoming strings. If the regexp contains named groups, the resulting match dict is applied to the Python datetime() constructor as keyword arguments. Otherwise, if positional groups are used, the datetime() constructor is called with positional arguments via*map(int, match_obj.groups(0))
.
Class signature
class
sqlalchemy.dialects.sqlite.DATETIME
(sqlalchemy.dialects.sqlite.base._DateTimeMixin
,sqlalchemy.types.DateTime
)
- class sqlalchemy.dialects.sqlite.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.:
2011-03-15
The incoming storage format is by default parsed using the Python
date.fromisoformat()
function.Changed in version 2.0:
date.fromisoformat()
is used for default date string parsing.The storage format can be customized to some degree using the
storage_format
andregexp
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:
storage_format – format string which will be applied to the dict with keys year, month, and day.
regexp – regular expression which will be applied to incoming result rows, replacing the use of
date.fromisoformat()
to parse incoming strings. If the regexp contains named groups, the resulting match dict is applied to the Python date() constructor as keyword arguments. Otherwise, if positional groups are used, the date() constructor is called with positional arguments via*map(int, match_obj.groups(0))
.
Class signature
class
sqlalchemy.dialects.sqlite.DATE
(sqlalchemy.dialects.sqlite.base._DateTimeMixin
,sqlalchemy.types.Date
)
- class sqlalchemy.dialects.sqlite.JSON¶
SQLite JSON type.
SQLite supports JSON as of version 3.9 through its JSON1 extension. Note that JSON1 is a loadable extension and as such may not be available, or may require run-time loading.
JSON
is used automatically whenever the baseJSON
datatype is used against a SQLite backend.See also
JSON
- main documentation for the generic cross-platform JSON datatype.The
JSON
type supports persistence of JSON values as well as the core index operations provided byJSON
datatype, by adapting the operations to render theJSON_EXTRACT
function wrapped in theJSON_QUOTE
function at the database level. Extracted values are quoted in order to ensure that the results are always JSON string values.New in version 1.3.
Members
Class signature
class
sqlalchemy.dialects.sqlite.JSON
(sqlalchemy.types.JSON
)-
method
sqlalchemy.dialects.sqlite.JSON.
__init__(none_as_null: bool = False)¶ inherited from the
sqlalchemy.types.JSON.__init__
method ofJSON
Construct a
JSON
type.- Parameters:
none_as_null=False –
if True, persist the value
None
as a SQL NULL value, not the JSON encoding ofnull
. Note that when this flag is False, thenull()
construct can still be used to persist a NULL value, which may be passed directly as a parameter value that is specially interpreted by theJSON
type as SQL NULL:from sqlalchemy import null conn.execute(table.insert(), {"data": null()})
Note
JSON.none_as_null
does not apply to the values passed toColumn.default
andColumn.server_default
; a value ofNone
passed for these parameters means “no default present”.Additionally, when used in SQL comparison expressions, the Python value
None
continues to refer to SQL null, and not JSON NULL. TheJSON.none_as_null
flag refers explicitly to the persistence of the value within an INSERT or UPDATE statement. TheJSON.NULL
value should be used for SQL expressions that wish to compare to JSON null.See also
-
method
- class sqlalchemy.dialects.sqlite.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 incoming storage format is by default parsed using the Python
time.fromisoformat()
function.Changed in version 2.0:
time.fromisoformat()
is used for default time string parsing.The storage format can be customized to some degree using the
storage_format
andregexp
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:
storage_format – format string which will be applied to the dict with keys hour, minute, second, and microsecond.
regexp – regular expression which will be applied to incoming result rows, replacing the use of
datetime.fromisoformat()
to parse incoming strings. If the regexp contains named groups, the resulting match dict is applied to the Python time() constructor as keyword arguments. Otherwise, if positional groups are used, the time() constructor is called with positional arguments via*map(int, match_obj.groups(0))
.
Class signature
class
sqlalchemy.dialects.sqlite.TIME
(sqlalchemy.dialects.sqlite.base._DateTimeMixin
,sqlalchemy.types.Time
)
SQLite DML Constructs¶
Object Name | Description |
---|---|
insert(table) |
Construct a sqlite-specific variant |
SQLite-specific implementation of INSERT. |
- function sqlalchemy.dialects.sqlite.insert(table)¶
Construct a sqlite-specific variant
Insert
construct.The
sqlalchemy.dialects.sqlite.insert()
function creates asqlalchemy.dialects.sqlite.Insert
. This class is based on the dialect-agnosticInsert
construct which may be constructed using theinsert()
function in SQLAlchemy Core.The
Insert
construct includes additional methodsInsert.on_conflict_do_update()
,Insert.on_conflict_do_nothing()
.
- class sqlalchemy.dialects.sqlite.Insert¶
SQLite-specific implementation of INSERT.
Adds methods for SQLite-specific syntaxes such as ON CONFLICT.
The
Insert
object is created using thesqlalchemy.dialects.sqlite.insert()
function.New in version 1.4.
See also
Class signature
class
sqlalchemy.dialects.sqlite.Insert
(sqlalchemy.sql.expression.Insert
)-
attribute
sqlalchemy.dialects.sqlite.Insert.
excluded¶ Provide the
excluded
namespace for an ON CONFLICT statementSQLite’s ON CONFLICT clause allows reference to the row that would be inserted, known as
excluded
. This attribute provides all columns in this row to be referenceable.Tip
The
Insert.excluded
attribute is an instance ofColumnCollection
, which provides an interface the same as that of theTable.c
collection described at Accessing Tables and Columns. With this collection, ordinary names are accessible like attributes (e.g.stmt.excluded.some_column
), but special names and dictionary method names should be accessed using indexed access, such asstmt.excluded["column name"]
orstmt.excluded["values"]
. See the docstring forColumnCollection
for further examples.
-
attribute
sqlalchemy.dialects.sqlite.Insert.
inherit_cache: Optional[bool] = False¶ Indicate if this
HasCacheKey
instance should make use of the cache key generation scheme used by its immediate superclass.The attribute defaults to
None
, which indicates that a construct has not yet taken into account whether or not its appropriate for it to participate in caching; this is functionally equivalent to setting the value toFalse
, except that a warning is also emitted.This flag can be set to
True
on a particular class, if the SQL that corresponds to the object does not change based on attributes which are local to this class, and not its superclass.See also
Enabling Caching Support for Custom Constructs - General guideslines for setting the
HasCacheKey.inherit_cache
attribute for third-party or user defined SQL constructs.
-
method
sqlalchemy.dialects.sqlite.Insert.
on_conflict_do_nothing(index_elements=None, index_where=None) Self ¶ Specifies a DO NOTHING action for ON CONFLICT clause.
- Parameters:
index_elements – A sequence consisting of string column names,
Column
objects, or other column expression objects that will be used to infer a target index or unique constraint.index_where – Additional WHERE criterion that can be used to infer a conditional target index.
-
method
sqlalchemy.dialects.sqlite.Insert.
on_conflict_do_update(index_elements=None, index_where=None, set_=None, where=None) Self ¶ Specifies a DO UPDATE SET action for ON CONFLICT clause.
- Parameters:
index_elements – A sequence consisting of string column names,
Column
objects, or other column expression objects that will be used to infer a target index or unique constraint.index_where – Additional WHERE criterion that can be used to infer a conditional target index.
set_ –
A dictionary or other mapping object where the keys are either names of columns in the target table, or
Column
objects or other ORM-mapped columns matching that of the target table, and expressions or literals as values, specifying theSET
actions to take.New in version 1.4: The
Insert.on_conflict_do_update.set_
parameter supportsColumn
objects from the targetTable
as keys.Warning
This dictionary does not take into account Python-specified default UPDATE values or generation functions, e.g. those specified using
Column.onupdate
. These values will not be exercised for an ON CONFLICT style of UPDATE, unless they are manually specified in theInsert.on_conflict_do_update.set_
dictionary.where – Optional argument. If present, can be a literal SQL string or an acceptable expression for a
WHERE
clause that restricts the rows affected byDO UPDATE SET
. Rows not meeting theWHERE
condition will not be updated (effectively aDO NOTHING
for those rows).
-
attribute
Pysqlite¶
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.
DBAPI¶
Documentation and download information (if applicable) for pysqlite is available at: https://docs.python.org/library/sqlite3.html
Connecting¶
Connect String:
sqlite+pysqlite:///file_path
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://')
URI Connections¶
Modern versions of SQLite support an alternative system of connecting using a driver level URI, which has the advantage that additional driver-level arguments can be passed including options such as “read only”. The Python sqlite3 driver supports this mode under modern Python 3 versions. The SQLAlchemy pysqlite driver supports this mode of use by specifying “uri=true” in the URL query string. The SQLite-level “URI” is kept as the “database” portion of the SQLAlchemy url (that is, following a slash):
e = create_engine("sqlite:///file:path/to/database?mode=ro&uri=true")
Note
The “uri=true” parameter must appear in the query string
of the URL. It will not currently work as expected if it is only
present in the create_engine.connect_args
parameter dictionary.
The logic reconciles the simultaneous presence of SQLAlchemy’s query string and SQLite’s query string by separating out the parameters that belong to the Python sqlite3 driver vs. those that belong to the SQLite URI. This is achieved through the use of a fixed list of parameters known to be accepted by the Python side of the driver. For example, to include a URL that indicates the Python sqlite3 “timeout” and “check_same_thread” parameters, along with the SQLite “mode” and “nolock” parameters, they can all be passed together on the query string:
e = create_engine(
"sqlite:///file:path/to/database?"
"check_same_thread=true&timeout=10&mode=ro&nolock=1&uri=true"
)
Above, the pysqlite / sqlite3 DBAPI would be passed arguments as:
sqlite3.connect(
"file:path/to/database?mode=ro&nolock=1",
check_same_thread=True, timeout=10, uri=True
)
Regarding future parameters added to either the Python or native drivers. new
parameter names added to the SQLite URI scheme should be automatically
accommodated by this scheme. New parameter names added to the Python driver
side can be accommodated by specifying them in the
create_engine.connect_args
dictionary,
until dialect support is
added by SQLAlchemy. For the less likely case that the native SQLite driver
adds a new parameter name that overlaps with one of the existing, known Python
driver parameters (such as “timeout” perhaps), SQLAlchemy’s dialect would
require adjustment for the URL scheme to continue to support this.
As is always the case for all SQLAlchemy dialects, the entire “URL” process
can be bypassed in create_engine()
through the use of the
create_engine.creator
parameter which allows for a custom callable
that creates a Python sqlite3 driver level connection directly.
New in version 1.3.9.
See also
Uniform Resource Identifiers - in the SQLite documentation
Regular Expression Support¶
New in version 1.4.
Support for the 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.
See also
Python regular expressions: Documentation for Python’s regular expression syntax.
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¶
The sqlite3
DBAPI by default prohibits the use of a particular connection
in a thread which is not the one in which it was created. As SQLite has
matured, it’s behavior under multiple threads has improved, and even includes
options for memory only databases to be used in multiple threads.
The thread prohibition is known as “check same thread” and may be controlled
using the sqlite3
parameter check_same_thread
, which will disable or
enable this check. SQLAlchemy’s default behavior here is to set
check_same_thread
to False
automatically whenever a file-based database
is in use, to establish compatibility with the default pool class
QueuePool
.
The SQLAlchemy pysqlite
DBAPI establishes the connection pool differently
based on the kind of SQLite database that’s requested:
When a
:memory:
SQLite database is specified, the dialect by default will useSingletonThreadPool
. 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. Thecheck_same_thread
parameter defaults toTrue
.When a file-based database is specified, the dialect will use
QueuePool
as the source of connections. at the same time, thecheck_same_thread
flag is set to False by default unless overridden.Changed in version 2.0: SQLite file database engines now use
QueuePool
by default. Previously,NullPool
were used. TheNullPool
class may be used by specifying it via thecreate_engine.poolclass
parameter.
Disabling Connection Pooling for File Databases¶
Pooling may be disabled for a file based database by specifying the
NullPool
implementation for the poolclass()
parameter:
from sqlalchemy import NullPool
engine = create_engine("sqlite:///myfile.db", poolclass=NullPool)
It’s been observed that the NullPool
implementation incurs an
extremely small performance overhead for repeated checkouts due to the lack of
connection re-use implemented by QueuePool
. However, it still
may be beneficial to use this class if the application is experiencing
issues with files being locked.
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 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.
Dealing with Mixed String / Binary Columns¶
The SQLite database is weakly typed, and as such it is possible when using
binary values, which in Python 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:
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 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.exec_driver_sql("BEGIN")
Warning
When using the above recipe, it is advised to not use the
Connection.execution_options.isolation_level
setting on
Connection
and create_engine()
with the SQLite driver,
as this function necessarily will also alter the “.isolation_level” setting.
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")
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
User-Defined Functions¶
pysqlite supports a create_function() method that allows us to create our own user-defined functions (UDFs) in Python and use them directly in SQLite queries. These functions are registered with a specific DBAPI Connection.
SQLAlchemy uses connection pooling with file-based SQLite databases, so we need to ensure that the UDF is attached to the connection when it is created. That is accomplished with an event listener:
from sqlalchemy import create_engine
from sqlalchemy import event
from sqlalchemy import text
def udf():
return "udf-ok"
engine = create_engine("sqlite:///./db_file")
@event.listens_for(engine, "connect")
def connect(conn, rec):
conn.create_function("udf", 0, udf)
for i in range(5):
with engine.connect() as conn:
print(conn.scalar(text("SELECT UDF()")))
Aiosqlite¶
Support for the SQLite database via the aiosqlite driver.
DBAPI¶
Documentation and download information (if applicable) for aiosqlite is available at: https://pypi.org/project/aiosqlite/
Connecting¶
Connect String:
sqlite+aiosqlite:///file_path
The aiosqlite dialect provides support for the SQLAlchemy asyncio interface running on top of pysqlite.
aiosqlite is a wrapper around pysqlite that uses a background thread for each connection. It does not actually use non-blocking IO, as SQLite databases are not socket-based. However it does provide a working asyncio interface that’s useful for testing and prototyping purposes.
Using a special asyncio mediation layer, the aiosqlite dialect is usable as the backend for the SQLAlchemy asyncio extension package.
This dialect should normally be used only with the
create_async_engine()
engine creation function:
from sqlalchemy.ext.asyncio import create_async_engine
engine = create_async_engine("sqlite+aiosqlite:///filename")
The URL passes through all arguments to the pysqlite
driver, so all
connection arguments are the same as they are for that of Pysqlite.
User-Defined Functions¶
aiosqlite extends pysqlite to support async, so we can create our own user-defined functions (UDFs) in Python and use them directly in SQLite queries as described here: User-Defined Functions.
Pysqlcipher¶
Support for the SQLite database via the pysqlcipher driver.
Dialect for support of DBAPIs that make use of the SQLCipher backend.
Connecting¶
Connect String:
sqlite+pysqlcipher://:passphrase@/file_path[?kdf_iter=<iter>]
Driver¶
Current dialect selection logic is:
If the
create_engine.module
parameter supplies a DBAPI module, that module is used.Otherwise for Python 3, choose https://pypi.org/project/sqlcipher3/
If not available, fall back to https://pypi.org/project/pysqlcipher3/
For Python 2, https://pypi.org/project/pysqlcipher/ is used.
Warning
The pysqlcipher3
and pysqlcipher
DBAPI drivers are no
longer maintained; the sqlcipher3
driver as of this writing appears
to be current. For future compatibility, any pysqlcipher-compatible DBAPI
may be used as follows:
import sqlcipher_compatible_driver
from sqlalchemy import create_engine
e = create_engine(
"sqlite+pysqlcipher://:password@/dbname.db",
module=sqlcipher_compatible_driver
)
These drivers make use of the SQLCipher engine. This system essentially introduces new PRAGMA commands to SQLite which allows the setting of a passphrase and other encryption parameters, allowing the database file to be encrypted.
Connect Strings¶
The format of the connect string is in every way the same as that
of the pysqlite
driver, except that the
“password” field is now accepted, which should contain a passphrase:
e = create_engine('sqlite+pysqlcipher://:testing@/foo.db')
For an absolute file path, two leading slashes should be used for the database name:
e = create_engine('sqlite+pysqlcipher://:testing@//path/to/foo.db')
A selection of additional encryption-related pragmas supported by SQLCipher
as documented at https://www.zetetic.net/sqlcipher/sqlcipher-api/ can be passed
in the query string, and will result in that PRAGMA being called for each
new connection. Currently, cipher
, kdf_iter
cipher_page_size
and cipher_use_hmac
are supported:
e = create_engine('sqlite+pysqlcipher://:testing@/foo.db?cipher=aes-256-cfb&kdf_iter=64000')
Warning
Previous versions of sqlalchemy did not take into consideration the encryption-related pragmas passed in the url string, that were silently ignored. This may cause errors when opening files saved by a previous sqlalchemy version if the encryption options do not match.
Pooling Behavior¶
The driver makes a change to the default pool behavior of pysqlite
as described in Threading/Pooling Behavior. The pysqlcipher driver
has been observed to be significantly slower on connection than the
pysqlite driver, most likely due to the encryption overhead, so the
dialect here defaults to using the SingletonThreadPool
implementation,
instead of the NullPool
pool used by pysqlite. As always, the pool
implementation is entirely configurable using the
create_engine.poolclass
parameter; the
StaticPool
may
be more feasible for single-threaded use, or NullPool
may be used
to prevent unencrypted connections from being held open for long periods of
time, at the expense of slower startup time for new connections.