# `sqlite3` — DB-API 2.0 interface for SQLite databases

Source code: Lib/sqlite3/

SQLite is a C library that provides a lightweight disk-based database that doesn’t require a separate server process and allows accessing the database using a nonstandard variant of the SQL query language. Some applications can use SQLite for internal data storage. It’s also possible to prototype an application using SQLite and then port the code to a larger database such as PostgreSQL or Oracle.

The `sqlite3` module was written by Gerhard Häring. It provides an SQL interface compliant with the DB-API 2.0 specification described by PEP 249, and requires SQLite 3.7.15 or newer.

This document includes four main sections:

- Tutorial teaches how to use the `sqlite3` module.

- Reference describes the classes and functions this module defines.

- How-to guides details how to handle specific tasks.

- Wytłumaczenie provides in-depth background on transaction control.

Zobacz także

https://www.sqlite.org

  The SQLite web page; the documentation describes the syntax and the available data types for the supported SQL dialect.

https://www.w3schools.com/sql/

  Tutorial, reference and examples for learning SQL syntax.

PEP 249 - Database API Specification 2.0

  PEP written by Marc-André Lemburg.

## Tutorial

In this tutorial, you will create a database of Monty Python movies using basic `sqlite3` functionality. It assumes a fundamental understanding of database concepts, including cursors and transactions.

First, we need to create a new database and open a database connection to allow `sqlite3` to work with it. Call `sqlite3.connect()` to create a connection to the database `tutorial.db` in the current working directory, implicitly creating it if it does not exist:

    import sqlite3
    con = sqlite3.connect("tutorial.db")

The returned `Connection` object `con` represents the connection to the on-disk database.

In order to execute SQL statements and fetch results from SQL queries, we will need to use a database cursor. Call `con.cursor()` to create the `Cursor`:

    cur = con.cursor()

Now that we’ve got a database connection and a cursor, we can create a database table `movie` with columns for title, release year, and review score. For simplicity, we can just use column names in the table declaration – thanks to the flexible typing feature of SQLite, specifying the data types is optional. Execute the `CREATE TABLE` statement by calling `cur.execute(...)`:

    cur.execute("CREATE TABLE movie(title, year, score)")

We can verify that the new table has been created by querying the `sqlite_master` table built-in to SQLite, which should now contain an entry for the `movie` table definition (see The Schema Table for details). Execute that query by calling `cur.execute(...)`, assign the result to `res`, and call `res.fetchone()` to fetch the resulting row:

    >>> res = cur.execute("SELECT name FROM sqlite_master")
    >>> res.fetchone()
    ('movie',)

We can see that the table has been created, as the query returns a `tuple` containing the table’s name. If we query `sqlite_master` for a non-existent table `spam`, `res.fetchone()` will return `None`:

    >>> res = cur.execute("SELECT name FROM sqlite_master WHERE name='spam'")
    >>> res.fetchone() is None
    True

Now, add two rows of data supplied as SQL literals by executing an `INSERT` statement, once again by calling `cur.execute(...)`:

    cur.execute("""
        INSERT INTO movie VALUES
            ('Monty Python and the Holy Grail', 1975, 8.2),
            ('And Now for Something Completely Different', 1971, 7.5)
    """)

The `INSERT` statement implicitly opens a transaction, which needs to be committed before changes are saved in the database (see Transaction control for details). Call `con.commit()` on the connection object to commit the transaction:

    con.commit()

We can verify that the data was inserted correctly by executing a `SELECT` query. Use the now-familiar `cur.execute(...)` to assign the result to `res`, and call `res.fetchall()` to return all resulting rows:

    >>> res = cur.execute("SELECT score FROM movie")
    >>> res.fetchall()
    [(8.2,), (7.5,)]

The result is a `list` of two `tuple`s, one per row, each containing that row’s `score` value.

Now, insert three more rows by calling `cur.executemany(...)`:

    data = [
        ("Monty Python Live at the Hollywood Bowl", 1982, 7.9),
        ("Monty Python's The Meaning of Life", 1983, 7.5),
        ("Monty Python's Life of Brian", 1979, 8.0),
    ]
    cur.executemany("INSERT INTO movie VALUES(?, ?, ?)", data)
    con.commit()  # Remember to commit the transaction after executing INSERT.

Notice that `?` placeholders are used to bind `data` to the query. Always use placeholders instead of string formatting to bind Python values to SQL statements, to avoid SQL injection attacks (see How to use placeholders to bind values in SQL queries for more details).

We can verify that the new rows were inserted by executing a `SELECT` query, this time iterating over the results of the query:

    >>> for row in cur.execute("SELECT year, title FROM movie ORDER BY year"):
    ...     print(row)
    (1971, 'And Now for Something Completely Different')
    (1975, 'Monty Python and the Holy Grail')
    (1979, "Monty Python's Life of Brian")
    (1982, 'Monty Python Live at the Hollywood Bowl')
    (1983, "Monty Python's The Meaning of Life")

Each row is a two-item `tuple` of `(year, title)`, matching the columns selected in the query.

Finally, verify that the database has been written to disk by calling `con.close()` to close the existing connection, opening a new one, creating a new cursor, then querying the database:

    >>> con.close()
    >>> new_con = sqlite3.connect("tutorial.db")
    >>> new_cur = new_con.cursor()
    >>> res = new_cur.execute("SELECT title, year FROM movie ORDER BY score DESC")
    >>> title, year = res.fetchone()
    >>> print(f'The highest scoring Monty Python movie is {title!r}, released in {year}')
    The highest scoring Monty Python movie is 'Monty Python and the Holy Grail', released in 1975

You’ve now created an SQLite database using the `sqlite3` module, inserted data and retrieved values from it in multiple ways.

Zobacz także

- How-to guides for further reading:

  > - How to use placeholders to bind values in SQL queries

  > - How to adapt custom Python types to SQLite values

  > - How to convert SQLite values to custom Python types

  > - How to use the connection context manager

  > - How to create and use row factories

- Wytłumaczenie for in-depth background on transaction control.

## Reference

### Module functions

`sqlite3.``connect`(database, timeout=5.0, detect_types=0, isolation_level='DEFERRED', check_same_thread=True, factory=sqlite3.Connection, cached_statements=100, uri=False)

  Open a connection to an SQLite database.

  Parametry

    - database (path-like object) – The path to the database file to be opened. Pass `":memory:"` to open a connection to a database that is in RAM instead of on disk.

    - timeout (float) – How many seconds the connection should wait before raising an `OperationalError` when a table is locked. If another connection opens a transaction to modify a table, that table will be locked until the transaction is committed. Default five seconds.

    - detect_types (int) – Control whether and how data types not natively supported by SQLite are looked up to be converted to Python types, using the converters registered with `register_converter()`. Set it to any combination (using `|`, bitwise or) of `PARSE_DECLTYPES` and `PARSE_COLNAMES` to enable this. Column names takes precedence over declared types if both flags are set. Types cannot be detected for generated fields (for example `max(data)`), even when the detect_types parameter is set; `str` will be returned instead. By default (`0`), type detection is disabled.

    - isolation_level (str | None) – The `isolation_level` of the connection, controlling whether and how transactions are implicitly opened. Can be `"DEFERRED"` (default), `"EXCLUSIVE"` or `"IMMEDIATE"`; or `None` to disable opening transactions implicitly. See Transaction control for more.

    - check_same_thread (bool) – If `True` (default), `ProgrammingError` will be raised if the database connection is used by a thread other than the one that created it. If `False`, the connection may be accessed in multiple threads; write operations may need to be serialized by the user to avoid data corruption. See `threadsafety` for more information.

    - factory (Connection) – A custom subclass of `Connection` to create the connection with, if not the default `Connection` class.

    - cached_statements (int) – The number of statements that `sqlite3` should internally cache for this connection, to avoid parsing overhead. By default, 100 statements.

    - uri (bool) – If set to `True`, database is interpreted as a URI with a file path and an optional query string. The scheme part must be `"file:"`, and the path can be relative or absolute. The query string allows passing parameters to SQLite, enabling various How to work with SQLite URIs.

  Typ zwracany

    Connection

  Raises an auditing event `sqlite3.connect` with argument `database`.

  Raises an auditing event `sqlite3.connect/handle` with argument `connection_handle`.

  Nowe w wersji 3.4: The uri parameter.

  Zmienione w wersji 3.7: database can now also be a path-like object, not only a string.

  Nowe w wersji 3.10: The `sqlite3.connect/handle` auditing event.

`sqlite3.``complete_statement`(statement)

  Return `True` if the string statement appears to contain one or more complete SQL statements. No syntactic verification or parsing of any kind is performed, other than checking that there are no unclosed string literals and the statement is terminated by a semicolon.

  Na przykład:

      >>> sqlite3.complete_statement("SELECT foo FROM bar;")
      True
      >>> sqlite3.complete_statement("SELECT foo")
      False

  This function may be useful during command-line input to determine if the entered text seems to form a complete SQL statement, or if additional input is needed before calling `execute()`.

`sqlite3.``enable_callback_tracebacks`(flag, /)

  Enable or disable callback tracebacks. By default you will not get any tracebacks in user-defined functions, aggregates, converters, authorizer callbacks etc. If you want to debug them, you can call this function with flag set to `True`. Afterwards, you will get tracebacks from callbacks on `sys.stderr`. Use `False` to disable the feature again.

`sqlite3.``register_adapter`(type, adapter, /)

  Register an adapter callable to adapt the Python type type into an SQLite type. The adapter is called with a Python object of type type as its sole argument, and must return a value of a type that SQLite natively understands.

`sqlite3.``register_converter`(typename, converter, /)

  Register the converter callable to convert SQLite objects of type typename into a Python object of a specific type. The converter is invoked for all SQLite values of type typename; it is passed a `bytes` object and should return an object of the desired Python type. Consult the parameter detect_types of `connect()` for information regarding how type detection works.

  Note: typename and the name of the type in your query are matched case-insensitively.

### Module constants

`sqlite3.``PARSE_COLNAMES`

  Pass this flag value to the detect_types parameter of `connect()` to look up a converter function by using the type name, parsed from the query column name, as the converter dictionary key. The type name must be wrapped in square brackets (`[]`).

      SELECT p as "p [point]" FROM test;  ! will look up converter "point"

  This flag may be combined with `PARSE_DECLTYPES` using the `|` (bitwise or) operator.

`sqlite3.``PARSE_DECLTYPES`

  Pass this flag value to the detect_types parameter of `connect()` to look up a converter function using the declared types for each column. The types are declared when the database table is created. `sqlite3` will look up a converter function using the first word of the declared type as the converter dictionary key. For example:

      CREATE TABLE test(
         i integer primary key,  ! will look up a converter named "integer"
         p point,                ! will look up a converter named "point"
         n number(10)            ! will look up a converter named "number"
       )

  This flag may be combined with `PARSE_COLNAMES` using the `|` (bitwise or) operator.

`sqlite3.``SQLITE_OK`
`sqlite3.``SQLITE_DENY`
`sqlite3.``SQLITE_IGNORE`

  Flags that should be returned by the authorizer_callback callable passed to `Connection.set_authorizer()`, to indicate whether:

  - Access is allowed (`SQLITE_OK`),

  - The SQL statement should be aborted with an error (`SQLITE_DENY`)

  - The column should be treated as a `NULL` value (`SQLITE_IGNORE`)

`sqlite3.``apilevel`

  String constant stating the supported DB-API level. Required by the DB-API. Hard-coded to `"2.0"`.

`sqlite3.``paramstyle`

  String constant stating the type of parameter marker formatting expected by the `sqlite3` module. Required by the DB-API. Hard-coded to `"qmark"`.

  Informacja

  The `named` DB-API parameter style is also supported.

`sqlite3.``sqlite_version`

  Version number of the runtime SQLite library as a `string`.

`sqlite3.``sqlite_version_info`

  Version number of the runtime SQLite library as a `tuple` of `integers`.

`sqlite3.``threadsafety`

  Integer constant required by the DB-API, stating the level of thread safety the `sqlite3` module supports. Currently hard-coded to `1`, meaning „Threads may share the module, but not connections.” However, this may not always be true. You can check the underlying SQLite library’s compile-time threaded mode using the following query:

      import sqlite3
      con = sqlite3.connect(":memory:")
      con.execute("""
          select * from pragma_compile_options
          where compile_options like 'THREADSAFE=%'
      """).fetchall()

  Note that the SQLITE_THREADSAFE levels do not match the DB-API 2.0 `threadsafety` levels.

`sqlite3.``version`

  Version number of this module as a `string`. This is not the version of the SQLite library.

`sqlite3.``version_info`

  Version number of this module as a `tuple` of `integers`. This is not the version of the SQLite library.

### Connection objects

class `sqlite3.``Connection`

  Each open SQLite database is represented by a `Connection` object, which is created using `sqlite3.connect()`. Their main purpose is creating `Cursor` objects, and Transaction control.

  Zobacz także

  - How to use connection shortcut methods

  - How to use the connection context manager

  An SQLite database connection has the following attributes and methods:

  `cursor`(factory=Cursor)

    Create and return a `Cursor` object. The cursor method accepts a single optional parameter factory. If supplied, this must be a callable returning an instance of `Cursor` or its subclasses.

  `commit`()

    Commit any pending transaction to the database. If there is no open transaction, this method is a no-op.

  `rollback`()

    Roll back to the start of any pending transaction. If there is no open transaction, this method is a no-op.

  `close`()

    Close the database connection. Any pending transaction is not committed implicitly; make sure to `commit()` before closing to avoid losing pending changes.

  `execute`(sql, parameters=(), /)

    Create a new `Cursor` object and call `execute()` on it with the given sql and parameters. Return the new cursor object.

  `executemany`(sql, parameters, /)

    Create a new `Cursor` object and call `executemany()` on it with the given sql and parameters. Return the new cursor object.

  `executescript`(sql_script, /)

    Create a new `Cursor` object and call `executescript()` on it with the given sql_script. Return the new cursor object.

  `create_function`(name, narg, func, *, deterministic=False)

    Create or remove a user-defined SQL function.

    Parametry

      - name (str) – The name of the SQL function.

      - narg (int) – The number of arguments the SQL function can accept. If `-1`, it may take any number of arguments.

      - func (callback | None) – A callable that is called when the SQL function is invoked. The callable must return a type natively supported by SQLite. Set to `None` to remove an existing SQL function.

      - deterministic (bool) – If `True`, the created SQL function is marked as deterministic, which allows SQLite to perform additional optimizations.

    Wyrzuca

      NotSupportedError – If deterministic is used with SQLite versions older than 3.8.3.

    Nowe w wersji 3.8: The deterministic parameter.

    Example:

        >>> import hashlib
        >>> def md5sum(t):
        ...     return hashlib.md5(t).hexdigest()
        >>> con = sqlite3.connect(":memory:")
        >>> con.create_function("md5", 1, md5sum)
        >>> for row in con.execute("SELECT md5(?)", (b"foo",)):
        ...     print(row)
        ('acbd18db4cc2f85cedef654fccc4a4d8',)

  `create_aggregate`(name, /, n_arg, aggregate_class)

    Create or remove a user-defined SQL aggregate function.

    Parametry

      - name (str) – The name of the SQL aggregate function.

      - n_arg (int) – The number of arguments the SQL aggregate function can accept. If `-1`, it may take any number of arguments.

      - aggregate_class (class | None) –

        A class must implement the following methods:

        - `step()`: Add a row to the aggregate.

        - `finalize()`: Return the final result of the aggregate as a type natively supported by SQLite.

        The number of arguments that the `step()` method must accept is controlled by n_arg.

        Set to `None` to remove an existing SQL aggregate function.

    Example:

        class MySum:
            def __init__(self):
                self.count = 0

            def step(self, value):
                self.count += value

            def finalize(self):
                return self.count

        con = sqlite3.connect(":memory:")
        con.create_aggregate("mysum", 1, MySum)
        cur = con.execute("CREATE TABLE test(i)")
        cur.execute("INSERT INTO test(i) VALUES(1)")
        cur.execute("INSERT INTO test(i) VALUES(2)")
        cur.execute("SELECT mysum(i) FROM test")
        print(cur.fetchone()[0])

        con.close()

  `create_collation`(name, callable)

    Create a collation named name using the collating function callable. callable is passed two `string` arguments, and it should return an `integer`:

    - `1` if the first is ordered higher than the second

    - `-1` if the first is ordered lower than the second

    - `0` if they are ordered equal

    The following example shows a reverse sorting collation:

        def collate_reverse(string1, string2):
            if string1 == string2:
                return 0
            elif string1 < string2:
                return 1
            else:
                return -1

        con = sqlite3.connect(":memory:")
        con.create_collation("reverse", collate_reverse)

        cur = con.execute("CREATE TABLE test(x)")
        cur.executemany("INSERT INTO test(x) VALUES(?)", [("a",), ("b",)])
        cur.execute("SELECT x FROM test ORDER BY x COLLATE reverse")
        for row in cur:
            print(row)
        con.close()

    Remove a collation function by setting callable to `None`.

  `interrupt`()

    Call this method from a different thread to abort any queries that might be executing on the connection. Aborted queries will raise an `OperationalError`.

  `set_authorizer`(authorizer_callback)

    Register callable authorizer_callback to be invoked for each attempt to access a column of a table in the database. The callback should return one of `SQLITE_OK`, `SQLITE_DENY`, or `SQLITE_IGNORE` to signal how access to the column should be handled by the underlying SQLite library.

    The first argument to the callback signifies what kind of operation is to be authorized. The second and third argument will be arguments or `None` depending on the first argument. The 4th argument is the name of the database („main”, „temp”, etc.) if applicable. The 5th argument is the name of the inner-most trigger or view that is responsible for the access attempt or `None` if this access attempt is directly from input SQL code.

    Please consult the SQLite documentation about the possible values for the first argument and the meaning of the second and third argument depending on the first one. All necessary constants are available in the `sqlite3` module.

  `set_progress_handler`(progress_handler, n)

    Register callable progress_handler to be invoked for every n instructions of the SQLite virtual machine. This is useful if you want to get called from SQLite during long-running operations, for example to update a GUI.

    If you want to clear any previously installed progress handler, call the method with `None` for progress_handler.

    Returning a non-zero value from the handler function will terminate the currently executing query and cause it to raise an `OperationalError` exception.

  `set_trace_callback`(trace_callback)

    Register callable trace_callback to be invoked for each SQL statement that is actually executed by the SQLite backend.

    The only argument passed to the callback is the statement (as `str`) that is being executed. The return value of the callback is ignored. Note that the backend does not only run statements passed to the `Cursor.execute()` methods. Other sources include the transaction management of the `sqlite3` module and the execution of triggers defined in the current database.

    Passing `None` as trace_callback will disable the trace callback.

    Informacja

    Exceptions raised in the trace callback are not propagated. As a development and debugging aid, use `enable_callback_tracebacks()` to enable printing tracebacks from exceptions raised in the trace callback.

    Nowe w wersji 3.3.

  `enable_load_extension`(enabled, /)

    Enable the SQLite engine to load SQLite extensions from shared libraries if enabled is `True`; else, disallow loading SQLite extensions. SQLite extensions can define new functions, aggregates or whole new virtual table implementations. One well-known extension is the fulltext-search extension distributed with SQLite.

    Informacja

    The `sqlite3` module is not built with loadable extension support by default, because some platforms (notably macOS) have SQLite libraries which are compiled without this feature. To get loadable extension support, you must pass the `--enable-loadable-sqlite-extensions` option to configure.

    Raises an auditing event `sqlite3.enable_load_extension` with arguments `connection`, `enabled`.

    Nowe w wersji 3.2.

    Zmienione w wersji 3.10: Added the `sqlite3.enable_load_extension` auditing event.

        con.enable_load_extension(True)

        # Load the fulltext search extension
        con.execute("select load_extension('./fts3.so')")

        # alternatively you can load the extension using an API call:
        # con.load_extension("./fts3.so")

        # disable extension loading again
        con.enable_load_extension(False)

        # example from SQLite wiki
        con.execute("CREATE VIRTUAL TABLE recipe USING fts3(name, ingredients)")
        con.executescript("""
            INSERT INTO recipe (name, ingredients) VALUES('broccoli stew', 'broccoli peppers cheese tomatoes');
            INSERT INTO recipe (name, ingredients) VALUES('pumpkin stew', 'pumpkin onions garlic celery');
            INSERT INTO recipe (name, ingredients) VALUES('broccoli pie', 'broccoli cheese onions flour');
            INSERT INTO recipe (name, ingredients) VALUES('pumpkin pie', 'pumpkin sugar flour butter');
            """)
        for row in con.execute("SELECT rowid, name, ingredients FROM recipe WHERE name MATCH 'pie'"):
            print(row)

        con.close()

  `load_extension`(path, /)

    Load an SQLite extension from a shared library located at path. Enable extension loading with `enable_load_extension()` before calling this method.

    Raises an auditing event `sqlite3.load_extension` with arguments `connection`, `path`.

    Nowe w wersji 3.2.

    Zmienione w wersji 3.10: Added the `sqlite3.load_extension` auditing event.

  `iterdump`()

    Return an iterator to dump the database as SQL source code. Useful when saving an in-memory database for later restoration. Similar to the `.dump` command in the sqlite3 shell.

    Example:

        # Convert file example.db to SQL dump file dump.sql
        con = sqlite3.connect('example.db')
        with open('dump.sql', 'w') as f:
            for line in con.iterdump():
                f.write('%s\n' % line)
        con.close()

  `backup`(target, *, pages=- 1, progress=None, name='main', sleep=0.250)

    Create a backup of an SQLite database.

    Works even if the database is being accessed by other clients or concurrently by the same connection.

    Parametry

      - target (Connection) – The database connection to save the backup to.

      - pages (int) – The number of pages to copy at a time. If equal to or less than `0`, the entire database is copied in a single step. Defaults to `-1`.

      - progress (callback | None) – If set to a callable, it is invoked with three integer arguments for every backup iteration: the status of the last iteration, the remaining number of pages still to be copied, and the total number of pages. Defaults to `None`.

      - name (str) – The name of the database to back up. Either `"main"` (the default) for the main database, `"temp"` for the temporary database, or the name of a custom database as attached using the `ATTACH DATABASE` SQL statement.

      - sleep (float) – The number of seconds to sleep between successive attempts to back up remaining pages.

    Example 1, copy an existing database into another:

        def progress(status, remaining, total):
            print(f'Copied {total-remaining} of {total} pages...')

        src = sqlite3.connect('example.db')
        dst = sqlite3.connect('backup.db')
        with dst:
            src.backup(dst, pages=1, progress=progress)
        dst.close()
        src.close()

    Example 2, copy an existing database into a transient copy:

        src = sqlite3.connect('example.db')
        dst = sqlite3.connect(':memory:')
        src.backup(dst)

    Nowe w wersji 3.7.

  `in_transaction`

    This read-only attribute corresponds to the low-level SQLite autocommit mode.

    `True` if a transaction is active (there are uncommitted changes), `False` otherwise.

    Nowe w wersji 3.2.

  `isolation_level`

    This attribute controls the transaction handling performed by `sqlite3`. If set to `None`, transactions are never implicitly opened. If set to one of `"DEFERRED"`, `"IMMEDIATE"`, or `"EXCLUSIVE"`, corresponding to the underlying SQLite transaction behaviour, implicit transaction management is performed.

    If not overridden by the isolation_level parameter of `connect()`, the default is `""`, which is an alias for `"DEFERRED"`.

  `row_factory`

    The initial `row_factory` for `Cursor` objects created from this connection. Assigning to this attribute does not affect the `row_factory` of existing cursors belonging to this connection, only new ones. Is `None` by default, meaning each row is returned as a `tuple`.

    See How to create and use row factories for more details.

  `text_factory`

    A callable that accepts a `bytes` parameter and returns a text representation of it. The callable is invoked for SQLite values with the `TEXT` data type. By default, this attribute is set to `str`. If you want to return `bytes` instead, set text_factory to `bytes`.

    Example:

        con = sqlite3.connect(":memory:")
        cur = con.cursor()

        AUSTRIA = "Österreich"

        # by default, rows are returned as str
        cur.execute("SELECT ?", (AUSTRIA,))
        row = cur.fetchone()
        assert row[0] == AUSTRIA

        # but we can make sqlite3 always return bytestrings ...
        con.text_factory = bytes
        cur.execute("SELECT ?", (AUSTRIA,))
        row = cur.fetchone()
        assert type(row[0]) is bytes
        # the bytestrings will be encoded in UTF-8, unless you stored garbage in the
        # database ...
        assert row[0] == AUSTRIA.encode("utf-8")

        # we can also implement a custom text_factory ...
        # here we implement one that appends "foo" to all strings
        con.text_factory = lambda x: x.decode("utf-8") + "foo"
        cur.execute("SELECT ?", ("bar",))
        row = cur.fetchone()
        assert row[0] == "barfoo"

        con.close()

  `total_changes`

    Return the total number of database rows that have been modified, inserted, or deleted since the database connection was opened.

### Cursor objects

> A `Cursor` object represents a database cursor which is used to execute SQL statements, and manage the context of a fetch operation. Cursors are created using `Connection.cursor()`, or by using any of the connection shortcut methods.

> Cursor objects are iterators, meaning that if you `execute()` a `SELECT` query, you can simply iterate over the cursor to fetch the resulting rows:

>     for row in cur.execute("SELECT t FROM data"):
>         print(row)

class `sqlite3.``Cursor`

  A `Cursor` instance has the following attributes and methods.

  `execute`(sql, parameters=(), /)

    Execute SQL a single SQL statement, optionally binding Python values using placeholders.

    Parametry

      - sql (str) – A single SQL statement.

      - parameters (`dict` | sequence) – Python values to bind to placeholders in sql. A `dict` if named placeholders are used. A sequence if unnamed placeholders are used. See How to use placeholders to bind values in SQL queries.

    Wyrzuca

      Warning – If sql contains more than one SQL statement.

    If `isolation_level` is not `None`, sql is an `INSERT`, `UPDATE`, `DELETE`, or `REPLACE` statement, and there is no open transaction, a transaction is implicitly opened before executing sql.

    Use `executescript()` to execute multiple SQL statements.

  `executemany`(sql, parameters, /)

    For every item in parameters, repeatedly execute the parameterized SQL statement sql.

    Uses the same implicit transaction handling as `execute()`.

    Parametry

      - sql (str) – A single SQL DML statement.

      - parameters (iterable) – An iterable of parameters to bind with the placeholders in sql. See How to use placeholders to bind values in SQL queries.

    Wyrzuca

      - ProgrammingError – If sql is not a DML statment.

      - Warning – If sql contains more than one SQL statement.

    Example:

        rows = [
            ("row1",),
            ("row2",),
        ]
        # cur is an sqlite3.Cursor object
        cur.executemany("INSERT INTO data VALUES(?)", rows)

  `executescript`(sql_script, /)

    Execute the SQL statements in sql_script. If there is a pending transaction, an implicit `COMMIT` statement is executed first. No other implicit transaction control is performed; any transaction control must be added to sql_script.

    sql_script must be a `string`.

    Example:

        # cur is an sqlite3.Cursor object
        cur.executescript("""
            BEGIN;
            CREATE TABLE person(firstname, lastname, age);
            CREATE TABLE book(title, author, published);
            CREATE TABLE publisher(name, address);
            COMMIT;
        """)

  `fetchone`()

    If `row_factory` is `None`, return the next row query result set as a `tuple`. Else, pass it to the row factory and return its result. Return `None` if no more data is available.

  `fetchmany`(size=cursor.arraysize)

    Return the next set of rows of a query result as a `list`. Return an empty list if no more rows are available.

    The number of rows to fetch per call is specified by the size parameter. If size is not given, `arraysize` determines the number of rows to be fetched. If fewer than size rows are available, as many rows as are available are returned.

    Note there are performance considerations involved with the size parameter. For optimal performance, it is usually best to use the arraysize attribute. If the size parameter is used, then it is best for it to retain the same value from one `fetchmany()` call to the next.

  `fetchall`()

    Return all (remaining) rows of a query result as a `list`. Return an empty list if no rows are available. Note that the `arraysize` attribute can affect the performance of this operation.

  `close`()

    Close the cursor now (rather than whenever `__del__` is called).

    The cursor will be unusable from this point forward; a `ProgrammingError` exception will be raised if any operation is attempted with the cursor.

  `setinputsizes`(sizes, /)

    Required by the DB-API. Does nothing in `sqlite3`.

  `setoutputsize`(size, column=None, /)

    Required by the DB-API. Does nothing in `sqlite3`.

  `arraysize`

    Read/write attribute that controls the number of rows returned by `fetchmany()`. The default value is 1 which means a single row would be fetched per call.

  `connection`

    Read-only attribute that provides the SQLite database `Connection` belonging to the cursor. A `Cursor` object created by calling `con.cursor()` will have a `connection` attribute that refers to con:

        >>> con = sqlite3.connect(":memory:")
        >>> cur = con.cursor()
        >>> cur.connection == con
        True

  `description`

    Read-only attribute that provides the column names of the last query. To remain compatible with the Python DB API, it returns a 7-tuple for each column where the last six items of each tuple are `None`.

    It is set for `SELECT` statements without any matching rows as well.

  `lastrowid`

    Read-only attribute that provides the row id of the last inserted row. It is only updated after successful `INSERT` or `REPLACE` statements using the `execute()` method. For other statements, after `executemany()` or `executescript()`, or if the insertion failed, the value of `lastrowid` is left unchanged. The initial value of `lastrowid` is `None`.

    Informacja

    Inserts into `WITHOUT ROWID` tables are not recorded.

    Zmienione w wersji 3.6: Added support for the `REPLACE` statement.

  `rowcount`

    Read-only attribute that provides the number of modified rows for `INSERT`, `UPDATE`, `DELETE`, and `REPLACE` statements; is `-1` for other statements, including CTE queries. It is only updated by the `execute()` and `executemany()` methods.

  `row_factory`

    Control how a row fetched from this `Cursor` is represented. If `None`, a row is represented as a `tuple`. Can be set to the included `sqlite3.Row`; or a callable that accepts two arguments, a `Cursor` object and the `tuple` of row values, and returns a custom object representing an SQLite row.

    Defaults to what `Connection.row_factory` was set to when the `Cursor` was created. Assigning to this attribute does not affect `Connection.row_factory` of the parent connection.

    See How to create and use row factories for more details.

### Row objects

class `sqlite3.``Row`

  A `Row` instance serves as a highly optimized `row_factory` for `Connection` objects. It supports iteration, equality testing, `len()`, and mapping access by column name and index.

  Two `Row` objects compare equal if they have identical column names and values.

  See How to create and use row factories for more details.

  `keys`()

    Return a `list` of column names as `strings`. Immediately after a query, it is the first member of each tuple in `Cursor.description`.

  Zmienione w wersji 3.5: Added support of slicing.

### PrepareProtocol objects

class `sqlite3.``PrepareProtocol`

  The PrepareProtocol type’s single purpose is to act as a PEP 246 style adaption protocol for objects that can adapt themselves to native SQLite types.

### Wyjątki

The exception hierarchy is defined by the DB-API 2.0 (PEP 249).

exception `sqlite3.``Warning`

  This exception is raised by `sqlite3` if an SQL query is not a `string`, or if multiple statements are passed to `execute()` or `executemany()`. `Warning` is a subclass of `Exception`.

exception `sqlite3.``Error`

  The base class of the other exceptions in this module. Use this to catch all errors with one single `except` statement. `Error` is a subclass of `Exception`.

exception `sqlite3.``InterfaceError`

  This exception is raised by `sqlite3` for fetch across rollback, or if `sqlite3` is unable to bind parameters. `InterfaceError` is a subclass of `Error`.

exception `sqlite3.``DatabaseError`

  Exception raised for errors that are related to the database. This serves as the base exception for several types of database errors. It is only raised implicitly through the specialised subclasses. `DatabaseError` is a subclass of `Error`.

exception `sqlite3.``DataError`

  Exception raised for errors caused by problems with the processed data, like numeric values out of range, and strings which are too long. `DataError` is a subclass of `DatabaseError`.

exception `sqlite3.``OperationalError`

  Exception raised for errors that are related to the database’s operation, and not necessarily under the control of the programmer. For example, the database path is not found, or a transaction could not be processed. `OperationalError` is a subclass of `DatabaseError`.

exception `sqlite3.``IntegrityError`

  Exception raised when the relational integrity of the database is affected, e.g. a foreign key check fails. It is a subclass of `DatabaseError`.

exception `sqlite3.``InternalError`

  Exception raised when SQLite encounters an internal error. If this is raised, it may indicate that there is a problem with the runtime SQLite library. `InternalError` is a subclass of `DatabaseError`.

exception `sqlite3.``ProgrammingError`

  Exception raised for `sqlite3` API programming errors, for example trying to operate on a closed `Connection`, or trying to execute non-DML statements with `executemany()`. `ProgrammingError` is a subclass of `DatabaseError`.

exception `sqlite3.``NotSupportedError`

  Exception raised in case a method or database API is not supported by the underlying SQLite library. For example, setting deterministic to `True` in `create_function()`, if the underlying SQLite library does not support deterministic functions. `NotSupportedError` is a subclass of `DatabaseError`.

### SQLite and Python types

SQLite natively supports the following types: `NULL`, `INTEGER`, `REAL`, `TEXT`, `BLOB`.

The following Python types can thus be sent to SQLite without any problem:

Python type | SQLite type
--- | ---
`None` - z ang. - `Żaden` | `NULL`
`int` | `INTEGER`
`float` | `REAL`
`str` | `TEXT`
`bytes` | `BLOB`

This is how SQLite types are converted to Python types by default:

SQLite type | Python type
--- | ---
`NULL` | `None` - z ang. - `Żaden`
`INTEGER` | `int`
`REAL` | `float`
`TEXT` | depends on `text_factory`, `str` by default
`BLOB` | `bytes`

The type system of the `sqlite3` module is extensible in two ways: you can store additional Python types in an SQLite database via object adapters, and you can let the `sqlite3` module convert SQLite types to Python types via converters.

### Default adapters and converters

There are default adapters for the date and datetime types in the datetime module. They will be sent as ISO dates/ISO timestamps to SQLite.

The default converters are registered under the name „date” for `datetime.date` and under the name „timestamp” for `datetime.datetime`.

This way, you can use date/timestamps from Python without any additional fiddling in most cases. The format of the adapters is also compatible with the experimental SQLite date/time functions.

The following example demonstrates this.

    import sqlite3
    import datetime

    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)
    cur = con.cursor()
    cur.execute("create table test(d date, ts timestamp)")

    today = datetime.date.today()
    now = datetime.datetime.now()

    cur.execute("insert into test(d, ts) values (?, ?)", (today, now))
    cur.execute("select d, ts from test")
    row = cur.fetchone()
    print(today, "=>", row[0], type(row[0]))
    print(now, "=>", row[1], type(row[1]))

    cur.execute('select current_date as "d [date]", current_timestamp as "ts [timestamp]"')
    row = cur.fetchone()
    print("current_date", row[0], type(row[0]))
    print("current_timestamp", row[1], type(row[1]))

    con.close()

If a timestamp stored in SQLite has a fractional part longer than 6 numbers, its value will be truncated to microsecond precision by the timestamp converter.

Informacja

The default „timestamp” converter ignores UTC offsets in the database and always returns a naive `datetime.datetime` object. To preserve UTC offsets in timestamps, either leave converters disabled, or register an offset-aware converter with `register_converter()`.

## How-to guides

### How to use placeholders to bind values in SQL queries

SQL operations usually need to use values from Python variables. However, beware of using Python’s string operations to assemble queries, as they are vulnerable to SQL injection attacks. For example, an attacker can simply close the single quote and inject `OR TRUE` to select all rows:

    >>> # Never do this -- insecure!
    >>> symbol = input()
    ' OR TRUE; --
    >>> sql = "SELECT * FROM stocks WHERE symbol = '%s'" % symbol
    >>> print(sql)
    SELECT * FROM stocks WHERE symbol = '' OR TRUE; --'
    >>> cur.execute(sql)

Instead, use the DB-API’s parameter substitution. To insert a variable into a query string, use a placeholder in the string, and substitute the actual values into the query by providing them as a `tuple` of values to the second argument of the cursor’s `execute()` method.

An SQL statement may use one of two kinds of placeholders: question marks (qmark style) or named placeholders (named style). For the qmark style, parameters must be a sequence whose length must match the number of placeholders, or a `ProgrammingError` is raised. For the named style, parameters should be an instance of a `dict` (or a subclass), which must contain keys for all named parameters; any extra items are ignored. Here’s an example of both styles:

    con = sqlite3.connect(":memory:")
    cur = con.execute("CREATE TABLE lang(name, first_appeared)")

    # This is the named style used with executemany():
    data = (
        {"name": "C", "year": 1972},
        {"name": "Fortran", "year": 1957},
        {"name": "Python", "year": 1991},
        {"name": "Go", "year": 2009},
    )
    cur.executemany("INSERT INTO lang VALUES(:name, :year)", data)

    # This is the qmark style used in a SELECT query:
    params = (1972,)
    cur.execute("SELECT * FROM lang WHERE first_appeared = ?", params)
    print(cur.fetchall())

Informacja

PEP 249 numeric placeholders are not supported. If used, they will be interpreted as named placeholders.

### How to adapt custom Python types to SQLite values

SQLite supports only a limited set of data types natively. To store custom Python types in SQLite databases, adapt them to one of the Python types SQLite natively understands.

There are two ways to adapt Python objects to SQLite types: letting your object adapt itself, or using an adapter callable. The latter will take precedence above the former. For a library that exports a custom type, it may make sense to enable that type to adapt itself. As an application developer, it may make more sense to take direct control by registering custom adapter functions.

#### How to write adaptable objects

Suppose we have a `Point` class that represents a pair of coordinates, `x` and `y`, in a Cartesian coordinate system. The coordinate pair will be stored as a text string in the database, using a semicolon to separate the coordinates. This can be implemented by adding a `__conform__(self, protocol)` method which returns the adapted value. The object passed to protocol will be of type `PrepareProtocol`.

    class Point:
        def __init__(self, x, y):
            self.x, self.y = x, y

        def __conform__(self, protocol):
            if protocol is sqlite3.PrepareProtocol:
                return f"{self.x};{self.y}"

    con = sqlite3.connect(":memory:")
    cur = con.cursor()

    cur.execute("SELECT ?", (Point(4.0, -3.2),))
    print(cur.fetchone()[0])

#### How to register adapter callables

The other possibility is to create a function that converts the Python object to an SQLite-compatible type. This function can then be registered using `register_adapter()`.

    class Point:
        def __init__(self, x, y):
            self.x, self.y = x, y

    def adapt_point(point):
        return f"{point.x};{point.y}"

    sqlite3.register_adapter(Point, adapt_point)

    con = sqlite3.connect(":memory:")
    cur = con.cursor()

    cur.execute("SELECT ?", (Point(1.0, 2.5),))
    print(cur.fetchone()[0])

### How to convert SQLite values to custom Python types

Writing an adapter lets you convert from custom Python types to SQLite values. To be able to convert from SQLite values to custom Python types, we use converters.

Let’s go back to the `Point` class. We stored the x and y coordinates separated via semicolons as strings in SQLite.

First, we’ll define a converter function that accepts the string as a parameter and constructs a `Point` object from it.

Informacja

Converter functions are always passed a `bytes` object, no matter the underlying SQLite data type.

    def convert_point(s):
        x, y = map(float, s.split(b";"))
        return Point(x, y)

We now need to tell `sqlite3` when it should convert a given SQLite value. This is done when connecting to a database, using the detect_types parameter of `connect()`. There are three options:

- Implicit: set detect_types to `PARSE_DECLTYPES`

- Explicit: set detect_types to `PARSE_COLNAMES`

- Both: set detect_types to `sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES`. Column names take precedence over declared types.

The following example illustrates the implicit and explicit approaches:

    class Point:
        def __init__(self, x, y):
            self.x, self.y = x, y

        def __repr__(self):
            return f"Point({self.x}, {self.y})"

    def adapt_point(point):
        return f"{point.x};{point.y}"

    def convert_point(s):
        x, y = list(map(float, s.split(b";")))
        return Point(x, y)

    # Register the adapter and converter
    sqlite3.register_adapter(Point, adapt_point)
    sqlite3.register_converter("point", convert_point)

    # 1) Parse using declared types
    p = Point(4.0, -3.2)
    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES)
    cur = con.execute("CREATE TABLE test(p point)")

    cur.execute("INSERT INTO test(p) VALUES(?)", (p,))
    cur.execute("SELECT p FROM test")
    print("with declared types:", cur.fetchone()[0])
    cur.close()
    con.close()

    # 2) Parse using column names
    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLNAMES)
    cur = con.execute("CREATE TABLE test(p)")

    cur.execute("INSERT INTO test(p) VALUES(?)", (p,))
    cur.execute('SELECT p AS "p [point]" FROM test')
    print("with column names:", cur.fetchone()[0])

### Adapter and converter recipes

This section shows recipes for common adapters and converters.

    import datetime
    import sqlite3

    def adapt_date_iso(val):
        """Adapt datetime.date to ISO 8601 date."""
        return val.isoformat()

    def adapt_datetime_iso(val):
        """Adapt datetime.datetime to timezone-naive ISO 8601 date."""
        return val.isoformat()

    def adapt_datetime_epoch(val):
        """Adapt datetime.datetime to Unix timestamp."""
        return int(val.timestamp())

    sqlite3.register_adapter(datetime.date, adapt_date_iso)
    sqlite3.register_adapter(datetime.datetime, adapt_datetime_iso)
    sqlite3.register_adapter(datetime.datetime, adapt_datetime_epoch)

    def convert_date(val):
        """Convert ISO 8601 date to datetime.date object."""
        return datetime.date.fromisoformat(val.decode())

    def convert_datetime(val):
        """Convert ISO 8601 datetime to datetime.datetime object."""
        return datetime.datetime.fromisoformat(val.decode())

    def convert_timestamp(val):
        """Convert Unix epoch timestamp to datetime.datetime object."""
        return datetime.datetime.fromtimestamp(int(val))

    sqlite3.register_converter("date", convert_date)
    sqlite3.register_converter("datetime", convert_datetime)
    sqlite3.register_converter("timestamp", convert_timestamp)

### How to use connection shortcut methods

Using the `execute()`, `executemany()`, and `executescript()` methods of the `Connection` class, your code can be written more concisely because you don’t have to create the (often superfluous) `Cursor` objects explicitly. Instead, the `Cursor` objects are created implicitly and these shortcut methods return the cursor objects. This way, you can execute a `SELECT` statement and iterate over it directly using only a single call on the `Connection` object.

    # Create and fill the table.
    con = sqlite3.connect(":memory:")
    con.execute("CREATE TABLE lang(name, first_appeared)")
    data = [
        ("C++", 1985),
        ("Objective-C", 1984),
    ]
    con.executemany("INSERT INTO lang(name, first_appeared) VALUES(?, ?)", data)

    # Print the table contents
    for row in con.execute("SELECT name, first_appeared FROM lang"):
        print(row)

    print("I just deleted", con.execute("DELETE FROM lang").rowcount, "rows")

    # close() is not a shortcut method and it's not called automatically;
    # the connection object should be closed manually
    con.close()

### How to use the connection context manager

A `Connection` object can be used as a context manager that automatically commits or rolls back open transactions when leaving the body of the context manager. If the body of the `with` statement finishes without exceptions, the transaction is committed. If this commit fails, or if the body of the `with` statement raises an uncaught exception, the transaction is rolled back.

If there is no open transaction upon leaving the body of the `with` statement, the context manager is a no-op.

Informacja

The context manager neither implicitly opens a new transaction nor closes the connection.

    con = sqlite3.connect(":memory:")
    con.execute("CREATE TABLE lang(id INTEGER PRIMARY KEY, name VARCHAR UNIQUE)")

    # Successful, con.commit() is called automatically afterwards
    with con:
        con.execute("INSERT INTO lang(name) VALUES(?)", ("Python",))

    # con.rollback() is called after the with block finishes with an exception,
    # the exception is still raised and must be caught
    try:
        with con:
            con.execute("INSERT INTO lang(name) VALUES(?)", ("Python",))
    except sqlite3.IntegrityError:
        print("couldn't add Python twice")

    # Connection object used as context manager only commits or rollbacks transactions,
    # so the connection object should be closed manually
    con.close()

### How to work with SQLite URIs

Some useful URI tricks include:

- Open a database in read-only mode:

    >>> con = sqlite3.connect("file:tutorial.db?mode=ro", uri=True)
    >>> con.execute("CREATE TABLE readonly(data)")
    Traceback (most recent call last):
    OperationalError: attempt to write a readonly database

- Do not implicitly create a new database file if it does not already exist; will raise `OperationalError` if unable to create a new file:

    >>> con = sqlite3.connect("file:nosuchdb.db?mode=rw", uri=True)
    Traceback (most recent call last):
    OperationalError: unable to open database file

- Create a shared named in-memory database:

    db = "file:mem1?mode=memory&cache=shared"
    con1 = sqlite3.connect(db, uri=True)
    con2 = sqlite3.connect(db, uri=True)
    with con1:
        con1.execute("CREATE TABLE shared(data)")
        con1.execute("INSERT INTO shared VALUES(28)")
    res = con2.execute("SELECT data FROM shared")
    assert res.fetchone() == (28,)

More information about this feature, including a list of parameters, can be found in the SQLite URI documentation.

### How to create and use row factories

By default, `sqlite3` represents each row as a `tuple`. If a `tuple` does not suit your needs, you can use the `sqlite3.Row` class or a custom `row_factory`.

While `row_factory` exists as an attribute both on the `Cursor` and the `Connection`, it is recommended to set `Connection.row_factory`, so all cursors created from the connection will use the same row factory.

`Row` provides indexed and case-insensitive named access to columns, with minimal memory overhead and performance impact over a `tuple`. To use `Row` as a row factory, assign it to the `row_factory` attribute:

    >>> con = sqlite3.connect(":memory:")
    >>> con.row_factory = sqlite3.Row

Queries now return `Row` objects:

    >>> res = con.execute("SELECT 'Earth' AS name, 6378 AS radius")
    >>> row = res.fetchone()
    >>> row.keys()
    ['name', 'radius']
    >>> row[0]         # Access by index.
    'Earth'
    >>> row["name"]    # Access by name.
    'Earth'
    >>> row["RADIUS"]  # Column names are case-insensitive.
    6378

You can create a custom `row_factory` that returns each row as a `dict`, with column names mapped to values:

    def dict_factory(cursor, row):
        fields = [column[0] for column in cursor.description]
        return {key: value for key, value in zip(fields, row)}

Using it, queries now return a `dict` instead of a `tuple`:

    >>> con = sqlite3.connect(":memory:")
    >>> con.row_factory = dict_factory
    >>> for row in con.execute("SELECT 1 AS a, 2 AS b"):
    ...     print(row)
    {'a': 1, 'b': 2}

The following row factory returns a named tuple:

    from collections import namedtuple

    def namedtuple_factory(cursor, row):
        fields = [column[0] for column in cursor.description]
        cls = namedtuple("Row", fields)
        return cls._make(row)

`namedtuple_factory()` can be used as follows:

    >>> con = sqlite3.connect(":memory:")
    >>> con.row_factory = namedtuple_factory
    >>> cur = con.execute("SELECT 1 AS a, 2 AS b")
    >>> row = cur.fetchone()
    >>> row
    Row(a=1, b=2)
    >>> row[0]  # Indexed access.
    1
    >>> row.b   # Attribute access.
    2

With some adjustments, the above recipe can be adapted to use a `dataclass`, or any other custom class, instead of a `namedtuple`.

## Wytłumaczenie

### Transaction control

The `sqlite3` module does not adhere to the transaction handling recommended by PEP 249.

If the connection attribute `isolation_level` is not `None`, new transactions are implicitly opened before `execute()` and `executemany()` executes `INSERT`, `UPDATE`, `DELETE`, or `REPLACE` statements; for other statements, no implicit transaction handling is performed. Use the `commit()` and `rollback()` methods to respectively commit and roll back pending transactions. You can choose the underlying SQLite transaction behaviour — that is, whether and what type of `BEGIN` statements `sqlite3` implicitly executes – via the `isolation_level` attribute.

If `isolation_level` is set to `None`, no transactions are implicitly opened at all. This leaves the underlying SQLite library in autocommit mode, but also allows the user to perform their own transaction handling using explicit SQL statements. The underlying SQLite library autocommit mode can be queried using the `in_transaction` attribute.

The `executescript()` method implicitly commits any pending transaction before execution of the given SQL script, regardless of the value of `isolation_level`.

Zmienione w wersji 3.6: `sqlite3` used to implicitly commit an open transaction before DDL statements. This is no longer the case.