SQLAlchemy 1.3 Documentation
Dialects
- PostgreSQL
- MySQL
- SQLite¶
- Support for the SQLite database.
- Date and Time Types
- SQLite Auto Incrementing Behavior
- Database Locking Behavior / Concurrency
- Transaction Isolation Level / Autocommit
- SAVEPOINT Support
- Transactional DDL
- Foreign Key Support
- ON CONFLICT support for constraints
- Type Reflection
- Partial Indexes
- Dotted Column Names
- SQLite-specific table options
- SQLite Data Types
- Pysqlite
- Pysqlcipher
- Oracle
- Microsoft SQL Server
- Firebird
- Sybase
Project Versions
- Previous: MySQL
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- On this page:
- SQLite
- Support for the SQLite database.
- Date and Time Types
- SQLite Auto Incrementing Behavior
- Database Locking Behavior / Concurrency
- Transaction Isolation Level / Autocommit
- SAVEPOINT Support
- Transactional DDL
- Foreign Key Support
- ON CONFLICT support for constraints
- Type Reflection
- Partial Indexes
- Dotted Column Names
- SQLite-specific table options
- SQLite Data Types
- Pysqlite
- Pysqlcipher
SQLite¶
Support for the SQLite database.
The following table summarizes current support levels for database release versions.
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: http://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.
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.
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¶
SQLite supports a non-standard 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
)
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 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:
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.
New in version 0.9.3: Support for SQLite type affinity rules when reflecting columns.
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
New in version 0.9.9.
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.
Changed in version 1.1: The following SQLite issue has been resolved as of version 3.10.0 of SQLite. SQLAlchemy as of 1.1 automatically disables its internal workarounds based on detection of this version.
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.execute("create table x (a integer, b integer)")
conn.execute("insert into x (a, b) values (1, 1)")
conn.execute("insert into x (a, b) values (2, 2)")
result = conn.execute("select x.a, x.b from x")
assert result.keys() == ["a", "b"]
result = conn.execute('''
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
ResultProxy.keys()
and RowProxy.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 ResultProxy.keys()
and
RowProxy.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).execute('''
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
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(*args, **kwargs)¶
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.:
2011-03-15 12:05:57.10558
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. 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(storage_format=None, regexp=None, **kw)¶
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 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. 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(none_as_null=False)¶
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.
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.
See also
Class signature
class
sqlalchemy.dialects.sqlite.JSON
(sqlalchemy.types.JSON
)
- class sqlalchemy.dialects.sqlite.TIME(*args, **kwargs)¶
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
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. 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
)
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: http://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 specifing “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
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 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.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.
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.
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
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.execute("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.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
Pysqlcipher¶
Support for the SQLite database via the pysqlcipher driver.
pysqlcipher
is a fork of the standard pysqlite
driver to make
use of the SQLCipher backend.
pysqlcipher3
is a fork of pysqlcipher
for Python 3. This dialect
will attempt to import it if pysqlcipher
is non-present.
New in version 1.1.4: - added fallback import for pysqlcipher3
New in version 0.9.9: - added pysqlcipher dialect
DBAPI¶
Documentation and download information (if applicable) for pysqlcipher is available at: https://pypi.python.org/pypi/pysqlcipher
Connecting¶
Connect String:
sqlite+pysqlcipher://:passphrase/file_path[?kdf_iter=<iter>]
Driver¶
The driver here is the pysqlcipher driver, which makes 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.
pysqlcipher3 is a fork of pysqlcipher with support for Python 3, the driver is the same.
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')
Additional encryption-related pragmas must be executed manually,
using the first_connect
pool event. A selection of the pragmas supported
by SQLCipher is documented at
https://www.zetetic.net/sqlcipher/sqlcipher-api/.
Warning
Previously the documentation wrongly stated that these pragma could be passed in the url string. This has never worked for the 1.3 series of sqlalchemy. The 1.4 series adds proper support for them when passed in the url string.
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.
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