"sqlite3" --- DB-API 2.0 interface for SQLite databases
*******************************************************

**Source code:** Lib/sqlite3/


Introduction
============

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.

* Explanation provides in-depth background on transaction control.


Tutorial
========

To use the module, start by creating 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')

The special path name ":memory:" can be provided to create a temporary
database in RAM.

Once a "Connection" has been established, 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 saved data is persistent: it can be reloaded in a subsequent
session even after restarting the Python interpreter:

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

At this point, our database only contains one row:

   >>> res = cur.execute('SELECT count(rowid) FROM stocks')
   >>> print(res.fetchone())
   (1,)

The result is a one-item "tuple": one row, with one column. Now, let
us insert three more rows of data, using "executemany()":

   >>> data = [
   ...    ('2006-03-28', 'BUY', 'IBM', 1000, 45.0),
   ...    ('2006-04-05', 'BUY', 'MSFT', 1000, 72.0),
   ...    ('2006-04-06', 'SELL', 'IBM', 500, 53.0),
   ... ]
   >>> cur.executemany('INSERT INTO stocks VALUES(?, ?, ?, ?, ?)', data)

Then, retrieve the data by iterating over the result of a "SELECT"
statement:

   >>> 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)

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 (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. 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*. 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.


Reference
=========


Module functions and constants
------------------------------

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"".

   Note:

     The "sqlite3" module supports both "qmark" and "numeric" DB-API
     parameter styles, because that is what the underlying SQLite
     library supports. However, the DB-API does not allow multiple
     values for the "paramstyle" attribute.

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.

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.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.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.connect(database, timeout=5.0, detect_types=0, isolation_level='DEFERRED', check_same_thread=True, factory=sqlite3.Connection, cached_statements=128, uri=False)

   Open a connection to an SQLite database.

   Parameters:
      * **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 exception, if the database is
        locked by another connection. If another connection opens a
        transaction to modify the database, it will be locked until
        that 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), only
        the creating thread may use the connection. If "False", the
        connection may be shared across multiple threads; if so, write
        operations should be serialized by the user to avoid data
        corruption.

      * **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 (Uniform Resource Identifier) 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
        Working with SQLite URIs.

   Return type:
      Connection

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

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

   New in version 3.4: The *uri* parameter.

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

   New in version 3.10: The "sqlite3.connect/handle" auditing event.

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.

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.complete_statement(statement)

   Returns "True" if the string *statement* 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, /)

   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.


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.

   See also:

     * Using connection shortcut methods

     * Using the connection as a context manager

   An SQLite database connection has the following attributes and
   methods:

   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"".

   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.

      New in version 3.2.

   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.

      Parameters:
         * **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.

      Raises:
         **NotSupportedError** -- If *deterministic* is used with
         SQLite versions older than 3.8.3.

      New in version 3.8: The *deterministic* parameter.

      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, /, n_arg, aggregate_class)

      Create or remove a user-defined SQL aggregate function.

      Parameters:
         * **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:

         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)

      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:

         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()

      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 exception.

   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 "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(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.

      Note:

        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.

      New in version 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.

      Note:

        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".

      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, /)

      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".

      New in version 3.2.

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

   row_factory

      A callable that accepts two arguments, a "Cursor" object and the
      raw row results as a "tuple", and returns a custom object
      representing an SQLite row.

      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

      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:

         import sqlite3

         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.

   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 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=0.250)

      Create a backup of an SQLite database.

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

      Parameters:
         * **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
           statment.

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

      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
--------------

   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 * from data"):
          print(row)

class sqlite3.Cursor

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

   execute(sql, parameters=(), /)

      Execute SQL statement *sql*. Bind values to the statement using
      placeholders that map to the *sequence* or "dict" *parameters*.

      "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.

      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*.

   executemany(sql, parameters, /)

      Execute parameterized SQL statement *sql* against all parameter
      sequences or mappings found in the sequence *parameters*.  It is
      also possible to use an *iterator* yielding parameters instead
      of a sequence. Uses the same implicit transaction handling as
      "execute()".

      Example:

         data = [
             ("row1",),
             ("row2",),
         ]
         # cur is an sqlite3.Cursor object
         cur.executemany("insert into t values(?)", data)

   executescript(sql_script, /)

      Execute the SQL statements in *sql_script*. If there is a
      pending transaciton, 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()

      Fetch the next row of a query result set as a "tuple". Return
      "None" if no more data is available.

   fetchmany(size=cursor.arraysize)

      Fetch 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()

      Fetch 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".

   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 (Common Table
      Expression) queries. It is only updated by the "execute()" and
      "executemany()" methods.

   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".

      Note:

        Inserts into "WITHOUT ROWID" tables are not recorded.

      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

      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.

   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


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, and supports iteration, "repr()", equality testing,
   "len()", and *mapping* access by column name and index.

   Two row objects compare equal if have equal columns and equal
   members.

   keys()

      Return a "list" of column names as "strings". 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


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.


Exceptions
----------

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"                          | "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 an SQLite database via object
adapters, and you can let the "sqlite3" module convert SQLite types to
Python types via converters.


How-to guides
=============


Using adapters to store custom Python types in SQLite databases
---------------------------------------------------------------

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.


Letting your object adapt itself
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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".

   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 Python
object to an SQLite-compatible type. This function can then be
registered using "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()


Converting 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.

Note:

  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:

   import sqlite3

   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}".encode("utf-8")

   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])
   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.

Note:

  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()".


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)

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

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

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


Using 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.

   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
-----------------------------------------

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.

Note:

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

   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()


Working with SQLite URIs
------------------------

Some useful URI tricks include:

* Open a database in read-only mode:

     con = sqlite3.connect("file:template.db?mode=ro", uri=True)

* 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)

* Create a shared named in-memory database:

     con1 = sqlite3.connect("file:mem1?mode=memory&cache=shared", uri=True)
     con2 = sqlite3.connect("file:mem1?mode=memory&cache=shared", uri=True)
     con1.execute("create table t(t)")
     con1.execute("insert into t values(28)")
     con1.commit()
     rows = con2.execute("select * from t").fetchall()

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


Explanation
===========


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. 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".

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