# `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 a SQL interface compliant with the DB-API 2.0 specification described by PEP 249, and requires SQLite 3.7.15 or newer.

To use the module, you must first create a `Connection` object that represents the database. Here the data will be stored in the `example.db` file:

    import sqlite3
    con = sqlite3.connect('example.db')

You can also supply the special name `:memory:` to create a database in RAM.

Once you have a `Connection`, you can create a `Cursor` object and call its `execute()` method to perform SQL commands:

    cur = con.cursor()

    # Create table
    cur.execute('''CREATE TABLE stocks
                   (date text, trans text, symbol text, qty real, price real)''')

    # Insert a row of data
    cur.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)")

    # Save (commit) the changes
    con.commit()

    # We can also close the connection if we are done with it.
    # Just be sure any changes have been committed or they will be lost.
    con.close()

The data you’ve saved is persistent and is available in subsequent sessions:

    import sqlite3
    con = sqlite3.connect('example.db')
    cur = con.cursor()

To retrieve data after executing a SELECT statement, you can either treat the cursor as an iterator, call the cursor’s `fetchone()` method to retrieve a single matching row, or call `fetchall()` to get a list of the matching rows.

This example uses the iterator form:

    >>> for row in cur.execute('SELECT * FROM stocks ORDER BY price'):
            print(row)

    ('2006-01-05', 'BUY', 'RHAT', 100, 35.14)
    ('2006-03-28', 'BUY', 'IBM', 1000, 45.0)
    ('2006-04-06', 'SELL', 'IBM', 500, 53.0)
    ('2006-04-05', 'BUY', 'MSFT', 1000, 72.0)

Usually your SQL operations will need to use values from Python variables. You shouldn’t assemble your query using Python’s string operations because doing so is insecure; it makes your program vulnerable to an SQL injection attack (see the xkcd webcomic for a humorous example of what can go wrong):

    # Never do this -- insecure!
    symbol = 'RHAT'
    cur.execute("SELECT * FROM stocks WHERE symbol = '%s'" % symbol)

Instead, use the DB-API’s parameter substitution. Put a placeholder wherever you want to use a value, and then provide a tuple of values as the second argument to 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. For the named style, it can be either a sequence or `dict` instance. The length of the sequence must match the number of placeholders, or a `ProgrammingError` is raised. If a `dict` is given, it must contain keys for all named parameters. Any extra items are ignored. Here’s an example of both styles:

    import sqlite3

    con = sqlite3.connect(":memory:")
    cur = con.cursor()
    cur.execute("create table lang (name, first_appeared)")

    # This is the qmark style:
    cur.execute("insert into lang values (?, ?)", ("C", 1972))

    # The qmark style used with executemany():
    lang_list = [
        ("Fortran", 1957),
        ("Python", 1991),
        ("Go", 2009),
    ]
    cur.executemany("insert into lang values (?, ?)", lang_list)

    # And this is the named style:
    cur.execute("select * from lang where first_appeared=:year", {"year": 1972})
    print(cur.fetchall())

    con.close()

See also

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.

## Module functions and constants

`sqlite3.``version`

  The version number of this module, as a string. This is not the version of the SQLite library.

`sqlite3.``version_info`

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

`sqlite3.``sqlite_version`

  The version number of the run-time SQLite library, as a string.

`sqlite3.``sqlite_version_info`

  The version number of the run-time SQLite library, as a tuple of integers.

`sqlite3.``PARSE_DECLTYPES`

  This constant is meant to be used with the detect_types parameter of the `connect()` function.

  Setting it makes the `sqlite3` module parse the declared type for each column it returns. It will parse out the first word of the declared type, i. e. for “integer primary key”, it will parse out “integer”, or for “number(10)” it will parse out “number”. Then for that column, it will look into the converters dictionary and use the converter function registered for that type there.

`sqlite3.``PARSE_COLNAMES`

  This constant is meant to be used with the detect_types parameter of the `connect()` function.

  Setting this makes the SQLite interface parse the column name for each column it returns. It will look for a string formed [mytype] in there, and then decide that ‘mytype’ is the type of the column. It will try to find an entry of ‘mytype’ in the converters dictionary and then use the converter function found there to return the value. The column name found in `Cursor.description` does not include the type, i. e. if you use something like `'as "Expiration date [datetime]"'` in your SQL, then we will parse out everything until the first `'['` for the column name and strip the preceeding space: the column name would simply be “Expiration date”.

`sqlite3.``connect`(database[, timeout, detect_types, isolation_level, check_same_thread, factory, cached_statements, uri])

  Opens a connection to the SQLite database file database. By default returns a `Connection` object, unless a custom factory is given.

  database is a path-like object giving the pathname (absolute or relative to the current working directory) of the database file to be opened. You can use `":memory:"` to open a database connection to a database that resides in RAM instead of on disk.

  When a database is accessed by multiple connections, and one of the processes modifies the database, the SQLite database is locked until that transaction is committed. The timeout parameter specifies how long the connection should wait for the lock to go away until raising an exception. The default for the timeout parameter is 5.0 (five seconds).

  For the isolation_level parameter, please see the `isolation_level` property of `Connection` objects.

  SQLite natively supports only the types TEXT, INTEGER, REAL, BLOB and NULL. If you want to use other types you must add support for them yourself. The detect_types parameter and the using custom converters registered with the module-level `register_converter()` function allow you to easily do that.

  detect_types defaults to 0 (i. e. off, no type detection), you can set it to any combination of `PARSE_DECLTYPES` and `PARSE_COLNAMES` to turn type detection on. Due to SQLite behaviour, types can’t be detected for generated fields (for example `max(data)`), even when detect_types parameter is set. In such case, the returned type is `str`.

  By default, check_same_thread is `True` and only the creating thread may use the connection. If set `False`, the returned connection may be shared across multiple threads. When using multiple threads with the same connection writing operations should be serialized by the user to avoid data corruption.

  By default, the `sqlite3` module uses its `Connection` class for the connect call. You can, however, subclass the `Connection` class and make `connect()` use your class instead by providing your class for the factory parameter.

  Consult the section SQLite and Python types of this manual for details.

  The `sqlite3` module internally uses a statement cache to avoid SQL parsing overhead. If you want to explicitly set the number of statements that are cached for the connection, you can set the cached_statements parameter. The currently implemented default is to cache 100 statements.

  If uri is true, database is interpreted as a URI. This allows you to specify options. For example, to open a database in read-only mode you can use:

      db = sqlite3.connect('file:path/to/database?mode=ro', uri=True)

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

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

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

  Changed in version 3.4: Added the uri parameter.

  Changed in version 3.7: database can now also be a path-like object, not only a string.

  Changed in version 3.10: Added the `sqlite3.connect/handle` auditing event.

`sqlite3.``register_converter`(typename, callable)

  Registers a callable to convert a bytestring from the database into a custom Python type. The callable will be invoked for all database values that are of the type typename. Confer the parameter detect_types of the `connect()` function for how the type detection works. Note that typename and the name of the type in your query are matched in case-insensitive manner.

`sqlite3.``register_adapter`(type, callable)

  Registers a callable to convert the custom Python type type into one of SQLite’s supported types. The callable callable accepts as single parameter the Python value, and must return a value of the following types: int, float, str or bytes.

`sqlite3.``complete_statement`(sql)

  Returns `True` if the string sql contains one or more complete SQL statements terminated by semicolons. It does not verify that the SQL is syntactically correct, only that there are no unclosed string literals and the statement is terminated by a semicolon.

  This can be used to build a shell for SQLite, as in the following example:

      # A minimal SQLite shell for experiments

      import sqlite3

      con = sqlite3.connect(":memory:")
      con.isolation_level = None
      cur = con.cursor()

      buffer = ""

      print("Enter your SQL commands to execute in sqlite3.")
      print("Enter a blank line to exit.")

      while True:
          line = input()
          if line == "":
              break
          buffer += line
          if sqlite3.complete_statement(buffer):
              try:
                  buffer = buffer.strip()
                  cur.execute(buffer)

                  if buffer.lstrip().upper().startswith("SELECT"):
                      print(cur.fetchall())
              except sqlite3.Error as e:
                  print("An error occurred:", e.args[0])
              buffer = ""

      con.close()

`sqlite3.``enable_callback_tracebacks`(flag)

  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.

## Connection Objects

class `sqlite3.``Connection`

  A SQLite database connection has the following attributes and methods:

  `isolation_level`

    Get or set the current default isolation level. `None` for autocommit mode or one of “DEFERRED”, “IMMEDIATE” or “EXCLUSIVE”. See section Controlling Transactions for a more detailed explanation.

  `in_transaction`

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

    New in version 3.2.

  `cursor`(factory=Cursor)

    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`()

    This method commits the current transaction. If you don’t call this method, anything you did since the last call to `commit()` is not visible from other database connections. If you wonder why you don’t see the data you’ve written to the database, please check you didn’t forget to call this method.

  `rollback`()

    This method rolls back any changes to the database since the last call to `commit()`.

  `close`()

    This closes the database connection. Note that this does not automatically call `commit()`. If you just close your database connection without calling `commit()` first, your changes will be lost!

  `execute`(sql[, parameters])

    This is a nonstandard shortcut that creates a cursor object by calling the `cursor()` method, calls the cursor’s `execute()` method with the parameters given, and returns the cursor.

  `executemany`(sql[, parameters])

    This is a nonstandard shortcut that creates a cursor object by calling the `cursor()` method, calls the cursor’s `executemany()` method with the parameters given, and returns the cursor.

  `executescript`(sql_script)

    This is a nonstandard shortcut that creates a cursor object by calling the `cursor()` method, calls the cursor’s `executescript()` method with the given sql_script, and returns the cursor.

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

    Creates a user-defined function that you can later use from within SQL statements under the function name name. num_params is the number of parameters the function accepts (if num_params is -1, the function may take any number of arguments), and func is a Python callable that is called as the SQL function. If deterministic is true, the created function is marked as deterministic, which allows SQLite to perform additional optimizations. This flag is supported by SQLite 3.8.3 or higher, `NotSupportedError` will be raised if used with older versions.

    The function can return any of the types supported by SQLite: bytes, str, int, float and `None`.

    Changed in version 3.8: The deterministic parameter was added.

    Example:

        import sqlite3
        import hashlib

        def md5sum(t):
            return hashlib.md5(t).hexdigest()

        con = sqlite3.connect(":memory:")
        con.create_function("md5", 1, md5sum)
        cur = con.cursor()
        cur.execute("select md5(?)", (b"foo",))
        print(cur.fetchone()[0])

        con.close()

  `create_aggregate`(name, num_params, aggregate_class)

    Creates a user-defined aggregate function.

    The aggregate class must implement a `step` method, which accepts the number of parameters num_params (if num_params is -1, the function may take any number of arguments), and a `finalize` method which will return the final result of the aggregate.

    The `finalize` method can return any of the types supported by SQLite: bytes, str, int, float and `None`.

    Example:

        import sqlite3

        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.cursor()
        cur.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)

    Creates a collation with the specified name and callable. The callable will be passed two string arguments. It should return -1 if the first is ordered lower than the second, 0 if they are ordered equal and 1 if the first is ordered higher than the second. Note that this controls sorting (ORDER BY in SQL) so your comparisons don’t affect other SQL operations.

    Note that the callable will get its parameters as Python bytestrings, which will normally be encoded in UTF-8.

    The following example shows a custom collation that sorts “the wrong way”:

        import sqlite3

        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.cursor()
        cur.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()

    To remove a collation, call `create_collation` with `None` as callable:

        con.create_collation("reverse", None)

  `interrupt`()

    You can call this method from a different thread to abort any queries that might be executing on the connection. The query will then abort and the caller will get an exception.

  `set_authorizer`(authorizer_callback)

    This routine registers a callback. The callback is invoked for each attempt to access a column of a table in the database. The callback should return `SQLITE_OK` if access is allowed, `SQLITE_DENY` if the entire SQL statement should be aborted with an error and `SQLITE_IGNORE` if the column should be treated as a NULL value. These constants are available in the `sqlite3` module.

    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`(handler, n)

    This routine registers a callback. The callback is 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 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)

    Registers trace_callback to be called for each SQL statement that is actually executed by the SQLite backend.

    The only argument passed to the callback is the statement (as string) 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 Python module and the execution of triggers defined in the current database.

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

    New in version 3.3.

  `enable_load_extension`(enabled)

    This routine allows/disallows the SQLite engine to load SQLite extensions from shared libraries. 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.

    Loadable extensions are disabled by default. See 1.

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

    New in version 3.2.

    Changed in version 3.10: Added the `sqlite3.enable_load_extension` auditing event.

        import sqlite3

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

        # enable extension loading
        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)

    This routine loads a SQLite extension from a shared library. You have to enable extension loading with `enable_load_extension()` before you can use this routine.

    Loadable extensions are disabled by default. See 1.

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

    New in version 3.2.

    Changed in version 3.10: Added the `sqlite3.load_extension` auditing event.

  `row_factory`

    You can change this attribute to a callable that accepts the cursor and the original row as a tuple and will return the real result row. This way, you can implement more advanced ways of returning results, such as returning an object that can also access columns by name.

    Example:

        import sqlite3

        def dict_factory(cursor, row):
            d = {}
            for idx, col in enumerate(cursor.description):
                d[col[0]] = row[idx]
            return d

        con = sqlite3.connect(":memory:")
        con.row_factory = dict_factory
        cur = con.cursor()
        cur.execute("select 1 as a")
        print(cur.fetchone()["a"])

        con.close()

    If returning a tuple doesn’t suffice and you want name-based access to columns, you should consider setting `row_factory` to the highly-optimized `sqlite3.Row` type. `Row` provides both index-based and case-insensitive name-based access to columns with almost no memory overhead. It will probably be better than your own custom dictionary-based approach or even a db_row based solution.

  `text_factory`

    Using this attribute you can control what objects are returned for the `TEXT` data type. By default, this attribute is set to `str` and the `sqlite3` module will return Unicode objects for `TEXT`. If you want to return bytestrings instead, you can set it to `bytes`.

    You can also set it to any other callable that accepts a single bytestring parameter and returns the resulting object.

    See the following example code for illustration:

        import sqlite3

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

        AUSTRIA = "\xd6sterreich"

        # by default, rows are returned as Unicode
        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`

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

  `iterdump`()

    Returns an iterator to dump the database in an SQL text format. Useful when saving an in-memory database for later restoration. This function provides the same capabilities as the `.dump` command in the sqlite3 shell.

    Example:

        # Convert file existing_db.db to SQL dump file dump.sql
        import sqlite3

        con = sqlite3.connect('existing_db.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=0.25)

    This method makes a backup of a SQLite database even while it’s being accessed by other clients, or concurrently by the same connection. The copy will be written into the mandatory argument target, that must be another `Connection` instance.

    By default, or when pages is either `0` or a negative integer, the entire database is copied in a single step; otherwise the method performs a loop copying up to pages pages at a time.

    If progress is specified, it must either be `None` or a callable object that will be executed at each iteration with three integer arguments, respectively the status of the last iteration, the remaining number of pages still to be copied and the total number of pages.

    The name argument specifies the database name that will be copied: it must be a string containing either `"main"`, the default, to indicate the main database, `"temp"` to indicate the temporary database or the name specified after the `AS` keyword in an `ATTACH DATABASE` statement for an attached database.

    The sleep argument specifies the number of seconds to sleep by between successive attempts to backup remaining pages, can be specified either as an integer or a floating point value.

    Example 1, copy an existing database into another:

        import sqlite3

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

        con = sqlite3.connect('existing_db.db')
        bck = sqlite3.connect('backup.db')
        with bck:
            con.backup(bck, pages=1, progress=progress)
        bck.close()
        con.close()

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

        import sqlite3

        source = sqlite3.connect('existing_db.db')
        dest = sqlite3.connect(':memory:')
        source.backup(dest)

    New in version 3.7.

## Cursor Objects

class `sqlite3.``Cursor`

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

  `execute`(sql[, parameters])

    Executes an SQL statement. Values may be bound to the statement using placeholders.

    `execute()` will only execute a single SQL statement. If you try to execute more than one statement with it, it will raise a `Warning`. Use `executescript()` if you want to execute multiple SQL statements with one call.

  `executemany`(sql, seq_of_parameters)

    Executes a parameterized SQL command against all parameter sequences or mappings found in the sequence seq_of_parameters. The `sqlite3` module also allows using an iterator yielding parameters instead of a sequence.

        import sqlite3

        class IterChars:
            def __init__(self):
                self.count = ord('a')

            def __iter__(self):
                return self

            def __next__(self):
                if self.count > ord('z'):
                    raise StopIteration
                self.count += 1
                return (chr(self.count - 1),) # this is a 1-tuple

        con = sqlite3.connect(":memory:")
        cur = con.cursor()
        cur.execute("create table characters(c)")

        theIter = IterChars()
        cur.executemany("insert into characters(c) values (?)", theIter)

        cur.execute("select c from characters")
        print(cur.fetchall())

        con.close()

    Here’s a shorter example using a generator:

        import sqlite3
        import string

        def char_generator():
            for c in string.ascii_lowercase:
                yield (c,)

        con = sqlite3.connect(":memory:")
        cur = con.cursor()
        cur.execute("create table characters(c)")

        cur.executemany("insert into characters(c) values (?)", char_generator())

        cur.execute("select c from characters")
        print(cur.fetchall())

        con.close()

  `executescript`(sql_script)

    This is a nonstandard convenience method for executing multiple SQL statements at once. It issues a `COMMIT` statement first, then executes the SQL script it gets as a parameter. This method disregards `isolation_level`; any transation control must be added to sql_script.

    sql_script can be an instance of `str`.

    Example:

        import sqlite3

        con = sqlite3.connect(":memory:")
        cur = con.cursor()
        cur.executescript("""
            create table person(
                firstname,
                lastname,
                age
            );

            create table book(
                title,
                author,
                published
            );

            insert into book(title, author, published)
            values (
                'Dirk Gently''s Holistic Detective Agency',
                'Douglas Adams',
                1987
            );
            """)
        con.close()

  `fetchone`()

    Fetches the next row of a query result set, returning a single sequence, or `None` when no more data is available.

  `fetchmany`(size=cursor.arraysize)

    Fetches the next set of rows of a query result, returning a list. An empty list is returned when no more rows are available.

    The number of rows to fetch per call is specified by the size parameter. If it is not given, the cursor’s arraysize determines the number of rows to be fetched. The method should try to fetch as many rows as indicated by the size parameter. If this is not possible due to the specified number of rows not being available, fewer rows may be 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`()

    Fetches all (remaining) rows of a query result, returning a list. Note that the cursor’s arraysize attribute can affect the performance of this operation. An empty list is returned when no rows are available.

  `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.

  `rowcount`

    Although the `Cursor` class of the `sqlite3` module implements this attribute, the database engine’s own support for the determination of “rows affected”/”rows selected” is quirky.

    For `executemany()` statements, the number of modifications are summed up into `rowcount`.

    As required by the Python DB API Spec, the `rowcount` attribute “is -1 in case no `executeXX()` has been performed on the cursor or the rowcount of the last operation is not determinable by the interface”. This includes `SELECT` statements because we cannot determine the number of rows a query produced until all rows were fetched.

  `lastrowid`

    This read-only attribute provides the rowid of the last modified row. It is only set if you issued an `INSERT` or a `REPLACE` statement using the `execute()` method. For operations other than `INSERT` or `REPLACE` or when `executemany()` is called, `lastrowid` is set to `None`.

    If the `INSERT` or `REPLACE` statement failed to insert the previous successful rowid is returned.

    Changed in version 3.6: Added support for the `REPLACE` statement.

  `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.

  `description`

    This read-only attribute 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.

  `connection`

    This read-only attribute provides the SQLite database `Connection` used by the `Cursor` object. 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

## Row Objects

class `sqlite3.``Row`

  A `Row` instance serves as a highly optimized `row_factory` for `Connection` objects. It tries to mimic a tuple in most of its features.

  It supports mapping access by column name and index, iteration, representation, equality testing and `len()`.

  If two `Row` objects have exactly the same columns and their members are equal, they compare equal.

  `keys`()

    This method returns a list of column names. Immediately after a query, it is the first member of each tuple in `Cursor.description`.

  Changed in version 3.5: Added support of slicing.

Let’s assume we initialize a table as in the example given above:

    con = sqlite3.connect(":memory:")
    cur = con.cursor()
    cur.execute('''create table stocks
    (date text, trans text, symbol text,
     qty real, price real)''')
    cur.execute("""insert into stocks
                values ('2006-01-05','BUY','RHAT',100,35.14)""")
    con.commit()
    cur.close()

Now we plug `Row` in:

    >>> con.row_factory = sqlite3.Row
    >>> cur = con.cursor()
    >>> cur.execute('select * from stocks')
    <sqlite3.Cursor object at 0x7f4e7dd8fa80>
    >>> r = cur.fetchone()
    >>> type(r)
    <class 'sqlite3.Row'>
    >>> tuple(r)
    ('2006-01-05', 'BUY', 'RHAT', 100.0, 35.14)
    >>> len(r)
    5
    >>> r[2]
    'RHAT'
    >>> r.keys()
    ['date', 'trans', 'symbol', 'qty', 'price']
    >>> r['qty']
    100.0
    >>> for member in r:
    ...     print(member)
    ...
    2006-01-05
    BUY
    RHAT
    100.0
    35.14

## Exceptions

exception `sqlite3.``Warning`

  A subclass of `Exception`.

exception `sqlite3.``Error`

  The base class of the other exceptions in this module. It is a subclass of `Exception`.

exception `sqlite3.``DatabaseError`

  Exception raised for errors that are related to the database.

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.``ProgrammingError`

  Exception raised for programming errors, e.g. table not found or already exists, syntax error in the SQL statement, wrong number of parameters specified, etc. It 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, e.g. an unexpected disconnect occurs, the data source name is not found, a transaction could not be processed, etc. It is a subclass of `DatabaseError`.

exception `sqlite3.``NotSupportedError`

  Exception raised in case a method or database API was used which is not supported by the database, e.g. calling the `rollback()` method on a connection that does not support transaction or has transactions turned off. It is a subclass of `DatabaseError`.

## SQLite and Python types

### Introduction

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` | `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`
`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 a SQLite database via object adaptation, and you can let the `sqlite3` module convert SQLite types to different Python types via converters.

### Using adapters to store additional Python types in SQLite databases

As described before, SQLite supports only a limited set of types natively. To use other Python types with SQLite, you must adapt them to one of the sqlite3 module’s supported types for SQLite: one of NoneType, int, float, str, bytes.

There are two ways to enable the `sqlite3` module to adapt a custom Python type to one of the supported ones.

#### Letting your object adapt itself

This is a good approach if you write the class yourself. Let’s suppose you have a class like this:

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

Now you want to store the point in a single SQLite column. First you’ll have to choose one of the supported types to be used for representing the point. Let’s just use str and separate the coordinates using a semicolon. Then you need to give your class a method `__conform__(self, protocol)` which must return the converted value. The parameter protocol will be `PrepareProtocol`.

    import sqlite3

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

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

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

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

    con.close()

#### Registering an adapter callable

The other possibility is to create a function that converts the type to the string representation and register the function with `register_adapter()`.

    import sqlite3

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

    def adapt_point(point):
        return "%f;%f" % (point.x, point.y)

    sqlite3.register_adapter(Point, adapt_point)

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

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

    con.close()

The `sqlite3` module has two default adapters for Python’s built-in `datetime.date` and `datetime.datetime` types. Now let’s suppose we want to store `datetime.datetime` objects not in ISO representation, but as a Unix timestamp.

    import sqlite3
    import datetime
    import time

    def adapt_datetime(ts):
        return time.mktime(ts.timetuple())

    sqlite3.register_adapter(datetime.datetime, adapt_datetime)

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

    now = datetime.datetime.now()
    cur.execute("select ?", (now,))
    print(cur.fetchone()[0])

    con.close()

### Converting SQLite values to custom Python types

Writing an adapter lets you send custom Python types to SQLite. But to make it really useful we need to make the Python to SQLite to Python roundtrip work.

Enter 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.

Note

Converter functions always get called with a `bytes` object, no matter under which data type you sent the value to SQLite.

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

Now you need to make the `sqlite3` module know that what you select from the database is actually a point. There are two ways of doing this:

- Implicitly via the declared type

- Explicitly via the column name

Both ways are described in section Module functions and constants, in the entries for the constants `PARSE_DECLTYPES` and `PARSE_COLNAMES`.

The following example illustrates both approaches.

    import sqlite3

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

        def __repr__(self):
            return "(%f;%f)" % (self.x, self.y)

    def adapt_point(point):
        return ("%f;%f" % (point.x, point.y)).encode('ascii')

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

    # Register the adapter
    sqlite3.register_adapter(Point, adapt_point)

    # Register the converter
    sqlite3.register_converter("point", convert_point)

    p = Point(4.0, -3.2)

    #########################
    # 1) Using declared types
    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES)
    cur = con.cursor()
    cur.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()

    #######################
    # 1) Using column names
    con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLNAMES)
    cur = con.cursor()
    cur.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])
    cur.close()
    con.close()

### 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.

## Controlling Transactions

The underlying `sqlite3` library operates in `autocommit` mode by default, but the Python `sqlite3` module by default does not.

`autocommit` mode means that statements that modify the database take effect immediately. A `BEGIN` or `SAVEPOINT` statement disables `autocommit` mode, and a `COMMIT`, a `ROLLBACK`, or a `RELEASE` that ends the outermost transaction, turns `autocommit` mode back on.

The Python `sqlite3` module by default issues a `BEGIN` statement implicitly before a Data Modification Language (DML) statement (i.e. `INSERT`/`UPDATE`/`DELETE`/`REPLACE`).

You can control which kind of `BEGIN` statements `sqlite3` implicitly executes via the isolation_level parameter to the `connect()` call, or via the `isolation_level` property of connections. If you specify no isolation_level, a plain `BEGIN` is used, which is equivalent to specifying `DEFERRED`. Other possible values are `IMMEDIATE` and `EXCLUSIVE`.

You can disable the `sqlite3` module’s implicit transaction management by setting `isolation_level` to `None`. This will leave the underlying `sqlite3` library operating in `autocommit` mode. You can then completely control the transaction state by explicitly issuing `BEGIN`, `ROLLBACK`, `SAVEPOINT`, and `RELEASE` statements in your code.

Note that `executescript()` disregards `isolation_level`; any transaction control must be added explicitly.

Changed in version 3.6: `sqlite3` used to implicitly commit an open transaction before DDL statements. This is no longer the case.

## Using `sqlite3` efficiently

### Using shortcut methods

Using the nonstandard `execute()`, `executemany()` and `executescript()` methods of the `Connection` object, 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.

    import sqlite3

    langs = [
        ("C++", 1985),
        ("Objective-C", 1984),
    ]

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

    # Create the table
    con.execute("create table lang(name, first_appeared)")

    # Fill the table
    con.executemany("insert into lang(name, first_appeared) values (?, ?)", langs)

    # 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,
    # so the connection object should be closed manually
    con.close()

### Accessing columns by name instead of by index

One useful feature of the `sqlite3` module is the built-in `sqlite3.Row` class designed to be used as a row factory.

Rows wrapped with this class can be accessed both by index (like tuples) and case-insensitively by name:

    import sqlite3

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

    cur = con.cursor()
    cur.execute("select 'John' as name, 42 as age")
    for row in cur:
        assert row[0] == row["name"]
        assert row["name"] == row["nAmE"]
        assert row[1] == row["age"]
        assert row[1] == row["AgE"]

    con.close()

### Using the connection as a context manager

Connection objects can be used as context managers that automatically commit or rollback transactions. In the event of an exception, the transaction is rolled back; otherwise, the transaction is committed:

    import sqlite3

    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()

Footnotes

1(1,2)

  The sqlite3 module is not built with loadable extension support by default, because some platforms (notably Mac OS X) 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.