# Example/Tour

This code demonstrates usage of APSW. It gives you a good overview of all the things that can be done. Also included is output so you can see what gets printed when you run the code.

There are also specific examples in the classes, functions, and attribute documentation.

    #!/usr/bin/env python3

    # This code uses Python's optional typing annotations.  You can
    # ignore them and do not need to use them.  If you do use them
    # then you must include this future annotations line first.
    from __future__ import annotations

    from typing import Optional, Iterator, Any

    import os
    import sys
    import time
    import datetime
    import apsw
    import apsw.ext
    import random
    import re
    from pathlib import Path

    # pretty formatting
    from pprint import pprint

## Checking APSW and SQLite versions

    # Where the extension module is on the filesystem
    print("      Using APSW file", apsw.__file__)

    # From the extension
    print("         APSW version", apsw.apsw_version())

    # From the sqlite header file at APSW compile time
    print("SQLite header version", apsw.SQLITE_VERSION_NUMBER)

    # The SQLite code running
    print("   SQLite lib version", apsw.sqlite_lib_version())

    # If True then SQLite is incorporated into the extension.
    # If False then a shared library is being used, or static linking
    print("   Using amalgamation", apsw.using_amalgamation)

          Using APSW file /space/apsw/apsw/__init__.cpython-313-x86_64-linux-gnu.so
             APSW version 3.50.2.0
    SQLite header version 3050002
       SQLite lib version 3.50.2
       Using amalgamation True

## Best Practice

Ensure SQLite usage prevents common mistakes, and gets best performance via apsw.bestpractice

    import apsw.bestpractice

    apsw.bestpractice.apply(apsw.bestpractice.recommended)

## Logging

It is a good idea to get SQLite’s logs as you will get more information about errors. Best practice also includes this. `apsw.ext.log_sqlite()` forwards SQLite’s log messages to the `logging` module.

    apsw.ext.log_sqlite()

    # You can also write to SQLite's log
    apsw.log(apsw.SQLITE_ERROR, "A message from Python")

## Opening the database

You open the database by using `Connection`

    # Default will create the database if it doesn't exist
    connection = apsw.Connection("dbfile")

    # Open existing read-only
    connection = apsw.Connection(
        "dbfile", flags=apsw.SQLITE_OPEN_READONLY
    )

    # Open existing read-write (exception if it doesn't exist)
    connection = apsw.Connection(
        "dbfile", flags=apsw.SQLITE_OPEN_READWRITE
    )

## Executing SQL

Use `Connection.execute()` to execute SQL

    connection.execute("create table point(x,y,z)")
    connection.execute("insert into point values(1, 2, 3)")
    # You can use multiple ; separated statements
    connection.execute(
        """
        insert into point values(4, 5, 6);
        create table log(timestamp, event);
        create table foo(a, b, c);
        create table important(secret, data);
    """
    )

    # read rows
    for row in connection.execute("select * from point"):
        print(row)

    (1, 2, 3)
    (4, 5, 6)

## Why you use bindings to provide values

It is tempting to compose strings with the values in them, but it is easy to mangle the query especially if values contain punctuation and unicode. It is known as SQL injection. Bindings are the correct way to supply values to queries.

    # a simple value
    event = "system started"
    # DO NOT DO THIS
    query = f"insert into log values(0, '{ event }')"
    print("query:", query)

    # BECAUSE ... a bad guy could provide a value like this
    event = "bad guy here') ; drop table important; -- comment"
    # which has effects like this
    query = f"insert into log values(0, '{ event }')"
    print("bad guy:", query)

    query: insert into log values(0, 'system started')
    bad guy: insert into log values(0, 'bad guy here') ; drop table important; -- comment')

## Bindings (sequence)

Bindings can be provided as a sequence such as with a tuple or list. Use ? to show where the values go.

    query = "insert into log values(?, ?)"
    data = (7, "transmission started")
    connection.execute(query, data)

    # You can also use numbers after the ? to select
    # values from the sequence.  Note that numbering
    # starts at 1
    query = "select ?1, ?3, ?2"
    data = ("alpha", "beta", "gamma")
    for row in connection.execute(query, data):
        print(row)

    ('alpha', 'gamma', 'beta')

## Bindings (dict)

You can also supply bindings with a dictionary. Use :NAME, @NAME, or $NAME, to provide the key name in the query. Names are case sensitive.

    query = "insert into point values(:x, @Y, $z)"
    data = {"x": 7, "Y": 8, "z": 9}
    connection.execute(query, data)

## Transactions

By default each statement is its own transaction. A transaction finishes by flushing data to storage and waiting for the operating system to confirm it is permanently there (ie will survive a power failure) which takes a while.

    # 3 separate transactions
    connection.execute("insert into point values(2, 2, 2)")
    connection.execute("insert into point values(3, 3, 3)")
    connection.execute("insert into point values(4, 4, 4)")

    # You can use BEGIN / COMMIT to manually make a transaction
    connection.execute("BEGIN")
    connection.execute("insert into point values(2, 2, 2)")
    connection.execute("insert into point values(3, 3, 3)")
    connection.execute("insert into point values(4, 4, 4)")
    connection.execute("COMMIT")

    # Or use `with` that does it automatically
    with connection:
        connection.execute("insert into point values(2, 2, 2)")
        connection.execute("insert into point values(3, 3, 3)")
        connection.execute("insert into point values(4, 4, 4)")

    # Nested transactions are supported
    with connection:
        connection.execute("insert into point values(2, 2, 2)")
        with connection:
            connection.execute("insert into point values(3, 3, 3)")
            connection.execute("insert into point values(4, 4, 4)")

## executemany

You can execute the same SQL against a sequence using `Connection.executemany()`

    data = (
        (1, 1, 1),
        (2, 2, 2),
        (3, 3, 3),
        (4, 4, 4),
        (5, 5, 5),
    )
    query = "insert into point values(?,?,?)"

    # we do it in a transaction
    with connection:
        # the query is run for each item in data
        connection.executemany(query, data)

## Pragmas

SQLite has a wide variety of pragmas to control the database configuration and library behaviour. See the Tips for maintaining your schema.

    # WAL mode is good for write performance
    connection.pragma("journal_mode", "wal")

    # Foreign keys are off by default, so turn them on
    connection.pragma("foreign_keys", True)

    # You can use this to see if any other connection (including other processes) has
    # changed the database
    connection.pragma("data_version")

    # Useful at startup to detect some database corruption
    check = connection.pragma("integrity_check")
    if check != "ok":
        print("Integrity check errors", check)

## Tracing execution

You can trace execution of SQL statements and their bindings. This involves code changes and is described in more detail here.

There are simpler convenient mechanisms for individual statement tracing, summarising a block of code, and SQLite’s interface which is used by them.

    def my_tracer(
        cursor: apsw.Cursor,
        statement: str,
        bindings: Optional[apsw.Bindings],
    ) -> bool:
        "Called just before executing each statement"
        print("SQL:", statement.strip())
        print("Bindings:", bindings)
        return True  # if you return False then execution is aborted

    # you can trace a single cursor
    cursor = connection.cursor()
    cursor.exec_trace = my_tracer
    cursor.execute(
        """
            drop table if exists bar;
            create table bar(x,y,z);
            select * from point where x=?;
            """,
        (3,),
    )

    # if set on a connection then all cursors are traced
    connection.exec_trace = my_tracer
    # and clearing it
    connection.exec_trace = None

    SQL: drop table if exists bar;
    Bindings: ()
    SQL: create table bar(x,y,z);
    Bindings: ()
    SQL: select * from point where x=?;
    Bindings: (3,)

## Tracing returned rows

You can trace returned rows, including modifying what is returned or skipping it completely. See more about tracing.

    def row_tracer(
        cursor: apsw.Cursor, row: apsw.SQLiteValues
    ) -> apsw.SQLiteValues:
        """Called with each row of results before they are handed off.  You can return None to
        cause the row to be skipped or a different set of values to return"""
        print("Row:", row)
        return row

    # you can trace a single cursor
    cursor = connection.cursor()
    cursor.row_trace = row_tracer
    for row in cursor.execute("select x,y from point where x>4"):
        pass

    # if set on a connection then all cursors are traced
    connection.row_trace = row_tracer
    # and clearing it
    connection.row_trace = None

    Row: (7, 8)
    Row: (5, 5)

## Defining scalar functions

Scalar functions take one or more values and return one value. They are registered by calling `Connection.create_scalar_function()`.

    def ilove7(*args: apsw.SQLiteValue) -> int:
        "A scalar function"
        print(f"ilove7 got { args } but I love 7")
        return 7

    connection.create_scalar_function("seven", ilove7)

    for row in connection.execute(
        "select seven(x,y) from point where x>4"
    ):
        print("row", row)

    ilove7 got (7, 8) but I love 7
    row (7,)
    ilove7 got (5, 5) but I love 7
    row (7,)

## Defining aggregate functions

Aggregate functions are called multiple times with matching rows, and then provide a final value. An example is calculating an average. They are registered by calling `Connection.create_aggregate_function()`.

    class longest:
        # Find which value when represented as a string is
        # the longest

        def __init__(self) -> None:
            self.longest = ""

        def step(self, *args: apsw.SQLiteValue) -> None:
            # Called with each matching row
            for arg in args:
                if len(str(arg)) > len(self.longest):
                    self.longest = str(arg)

        def final(self) -> str:
            # Called at the very end
            return self.longest

    connection.create_aggregate_function("longest", longest)
    print(connection.execute("select longest(event) from log").get)

    transmission started

## Defining window functions

Window functions input values come from a “window” around a row of interest. Four methods are called as the window moves to add, remove, get the current value, and finalize.

An example is calculating an average of values in the window to compare to the row. They are registered by calling `Connection.create_window_function()`.

This is the Python equivalent to the C based example in the SQLite documentation

    class SumInt:
        def __init__(self):
            self.v = 0

        def step(self, arg):
            print("step", arg)
            self.v += arg

        def inverse(self, arg):
            print("inverse", arg)
            self.v -= arg

        def final(self):
            print("final", self.v)
            return self.v

        def value(self):
            print("value", self.v)
            return self.v

    connection.create_window_function("sumint", SumInt)

    for row in connection.execute(
        """
            CREATE TABLE t3(x, y);
            INSERT INTO t3 VALUES('a', 4),
                                 ('b', 5),
                                 ('c', 3),
                                 ('d', 8),
                                 ('e', 1);
            -- Use the window function
            SELECT x, sumint(y) OVER (
            ORDER BY x ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING
            ) AS sum_y
            FROM t3 ORDER BY x;
        """
    ):
        print("ROW", row)

    step 4
    step 5
    value 9
    ROW ('a', 9)
    step 3
    value 12
    ROW ('b', 12)
    inverse 4
    step 8
    value 16
    ROW ('c', 16)
    inverse 5
    step 1
    value 12
    ROW ('d', 12)
    inverse 3
    value 9
    ROW ('e', 9)
    final 9

## Defining collations (sorting)

How you sort can depend on the languages or values involved. You register a collation by calling `Connection.create_collation()`.

    # This example sorting mechanisms understands some text followed by a
    # number and ensures the number portion gets sorted correctly

    connection.execute("create table names(name)")
    connection.executemany(
        "insert into names values(?)",
        (
            ("file1",),
            ("file7",),
            ("file17",),
            ("file20",),
            ("file3",),
        ),
    )

    print("Standard sorting")
    for row in connection.execute("select * from names order by name"):
        print(row)

    def str_num_collate(
        s1: apsw.SQLiteValue, s2: apsw.SQLiteValue
    ) -> int:
        # return -1 if s1<s2, +1 if s1>s2 else 0 for equal

        def parts(s: str) -> list:
            "Converts str into list of alternating str and int parts"
            return [
                int(v) if v.isdigit() else v
                for v in re.split(r"(\d+)", s)
            ]

        ps1 = parts(str(s1))
        ps2 = parts(str(s2))

        # compare
        if ps1 < ps2:
            return -1
        if ps1 > ps2:
            return 1
        return 0

    connection.create_collation("strnum", str_num_collate)

    print("\nUsing strnum")
    for row in connection.execute(
        "select * from names order by name collate strnum"
    ):
        print(row)

    Standard sorting
    ('file1',)
    ('file17',)
    ('file20',)
    ('file3',)
    ('file7',)

    Using strnum
    ('file1',)
    ('file3',)
    ('file7',)
    ('file17',)
    ('file20',)

## Accessing results by column name

You can access results by column name using `dataclasses`. APSW provides `apsw.ext.DataClassRowFactory` for names.

    import apsw.ext

    connection.execute(
        """
        create table books(id, title, author, year);
        insert into books values(7, 'Animal Farm', 'George Orwell', 1945);
        insert into books values(37, 'The Picture of Dorian Gray', 'Oscar Wilde', 1890);
        """
    )

    # Normally you use column numbers
    for row in connection.execute(
        "select title, id, year from books where author=?",
        ("Oscar Wilde",),
    ):
        # this is very fragile
        print("title", row[0])
        print("id", row[1])
        print("year", row[2])

    # Turn on dataclasses - frozen makes them read-only
    connection.row_trace = apsw.ext.DataClassRowFactory(
        dataclass_kwargs={"frozen": True}
    )

    print("\nNow with dataclasses\n")

    # Same query - note using AS to set column name
    for row in connection.execute(
        """SELECT title,
               id AS book_id,
               year AS book_year
               FROM books WHERE author = ?""",
        ("Oscar Wilde",),
    ):
        print("title", row.title)
        print("id", row.book_id)
        print("year", row.book_year)

    # clear
    connection.row_trace = None

    title The Picture of Dorian Gray
    id 37
    year 1890

    Now with dataclasses

    title The Picture of Dorian Gray
    id 37
    year 1890

## Type conversion into/out of database

You can use `apsw.ext.TypesConverterCursorFactory` to do conversion, both for types you define and for other types.

    import apsw.ext

    registrar = apsw.ext.TypesConverterCursorFactory()
    connection.cursor_factory = registrar

    # A type we define - deriving from SQLiteTypeAdapter automatically registers conversion
    # to a SQLite value
    class Point(apsw.ext.SQLiteTypeAdapter):
        def __init__(self, x, y):
            self.x = x
            self.y = y

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

        def __eq__(self, other: object) -> bool:
            return (
                isinstance(other, type(self))
                and self.x == other.x
                and self.y == other.y
            )

        def to_sqlite_value(self) -> str:
            # called to convert Point into something SQLite supports
            return f"{ self.x };{ self.y }"

        # This converter will be registered
        @classmethod
        def convert_from_sqlite(cls, value: str) -> Point:
            return cls(*(float(part) for part in value.split(";")))

    # Existing types
    def complex_to_sqlite_value(c: complex) -> str:
        return f"{ c.real }+{ c.imag }"

    def datetime_to_sqlite_value(dt: datetime.datetime) -> float:
        # Represent as floating point UTC value no matter
        # what timezone is used. Also consider other
        # formats like ISO8601.
        return dt.timestamp()

    # ... require manual registration
    registrar.register_adapter(complex, complex_to_sqlite_value)
    registrar.register_adapter(
        datetime.datetime, datetime_to_sqlite_value
    )

    # conversion from a SQLite value requires registration
    registrar.register_converter("POINT", Point.convert_from_sqlite)

    # ... and for stdlib types
    def sqlite_to_complex(v: str) -> complex:
        return complex(*(float(part) for part in v.split("+")))

    def sqlite_to_datetime(v: float) -> datetime.datetime:
        # Keep the UTC values coming back from the database
        # as UTC
        return datetime.datetime.fromtimestamp(v, datetime.timezone.utc)

    registrar.register_converter("COMPLEX", sqlite_to_complex)
    registrar.register_converter("TIMESTAMP", sqlite_to_datetime)

    # note that the type names are case sensitive and must match the
    # registration
    connection.execute(
        "create table conversion(p POINT, c COMPLEX, t TIMESTAMP)"
    )

    # convert going into database
    test_data = (Point(5.2, 7.6), 3 + 4j, datetime.datetime.now())
    connection.execute(
        "insert into conversion values(?, ?, ?)", test_data
    )
    print("inserted", test_data)

    # and coming back out
    print("querying data")
    for row in connection.execute("select * from conversion"):
        for i, value in enumerate(row):
            print(f"column {i} = { value !r}")

    # clear registrar
    connection.cursor_factory = apsw.Cursor

    inserted (Point(5.2, 7.6), (3+4j), datetime.datetime(2025, 6, 29, 5, 23, 21, 544116))
    querying data
    column 0 = Point(5.2, 7.6)
    column 1 = (3+4j)
    column 2 = datetime.datetime(2025, 6, 29, 12, 23, 21, 544116, tzinfo=datetime.timezone.utc)

## Runtime Python objects

While only 5 types can be stored, you can pass any Python objects to and from your functions at runtime.

    # Python set which isn't a supported SQLite type
    # containing items like a complex number and stdout which
    # definitely aren't SQLite compatible
    py_value = {1, 2, "three", 4 + 5j, sys.stdout}

    # Trying to pass it as a value gives TypeError
    try:
        print(connection.execute("select ?", (py_value,)).get)
    except TypeError as exc:
        print(exc)

    # Now wrap it and it works
    print(
        "select ?",
        connection.execute("select ?", (apsw.pyobject(py_value),)).get,
    )

    # It is still null at the SQL level
    print(
        "select typeof(?)",
        connection.execute(
            "select typeof(?)", (apsw.pyobject(py_value),)
        ).get,
    )

    # Lets make a set which SQLite knows nothing about
    def make_set(*args):
        print(f"make_set got {args!r}")
        # this will return a set, so we also need to mark it
        return apsw.pyobject(set(args))

    connection.create_scalar_function("make_set", make_set)

    print(
        "select make_set(?, ?, ?)",
        connection.execute(
            "select make_set(?, ?, ?)",
            (
                # these aren't SQLite types
                apsw.pyobject(3 + 4j),
                apsw.pyobject(sys.stdin),
                # but a string is
                "hello",
            ),
        ).get,
    )

    Bad binding argument type supplied - argument #1: type set
    select ? {1, 2, <_io.StringIO object at 0x743ab96b1e40>, 'three', (4+5j)}
    select typeof(?) null
    make_set got ((3+4j), <_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>, 'hello')
    select make_set(?, ?, ?) {'hello', <_io.TextIOWrapper name='<stdin>' mode='r' encoding='utf-8'>, (3+4j)}

## Query limiting

`apsw.ext.query_limit()` limits rows and time in a block across all the queries within the block

    import apsw.ext

    # Use this to make many (virtual) rows
    apsw.ext.make_virtual_module(
        connection, "generate_series", apsw.ext.generate_series
    )

    rows = []

    with apsw.ext.query_limit(connection, row_limit=20):
        # 11 rows will come from this
        for (number,) in connection.execute(
            "select * from generate_series(0, 10)"
        ):
            rows.append(number)
        # next query would be 1,000 but we will hit
        # the limit
        for (number,) in connection.execute(
            "select * from generate_series(0, 999)"
        ):
            rows.append(number)

    # lets see what we got
    print(f"{len(rows)=}")

    # We can also time limit
    start = time.monotonic()
    with apsw.ext.query_limit(connection, timeout=0.2):
        for (number,) in connection.execute(
            "select * from generate_series(0, 1000000000)"
        ):
            pass

    print(f"After {time.monotonic() - start:.3f} seconds, we hit {number=}")

    # We used the default "no exception" exception.  Lets have an explicit exception.
    # with both row and time limits ...
    try:
        with apsw.ext.query_limit(
            connection,
            row_limit=1000,
            timeout=1000,
            row_exception=IndexError,
            timeout_exception=TimeoutError,
        ):
            for (number,) in connection.execute(
                "select * from generate_series(0, 1000000000)"
            ):
                pass
    except Exception as exc:
        print(f"{exc=}")

    len(rows)=20
    After 0.200 seconds, we hit number=326796
    exc=IndexError('query row limit hit')

## Query details

`apsw.ext.query_info()` can provide a lot of information about a query (without running it)

    import apsw.ext

    # test tables
    connection.execute(
        """
        create table customers(
            id INTEGER PRIMARY KEY,
            name CHAR,
            address CHAR);
        create table orders(
            id INTEGER PRIMARY KEY,
            customer_id INTEGER,
            item MY_OWN_TYPE);
        create index cust_addr on customers(address);
    """
    )

    query = """
        SELECT * FROM orders
        JOIN customers ON orders.customer_id=customers.id
        WHERE address = ?;
        SELECT 7;"""

    # ask for all information available
    qd = apsw.ext.query_info(
        connection,
        query,
        actions=True,  # which tables/views etc and how they are accessed
        explain=True,  # shows low level VDBE
        explain_query_plan=True,  # how SQLite solves the query
    )

    print("query", qd.query)
    print("\nbindings_count", qd.bindings_count)
    print("\nbindings_names", qd.bindings_names)
    print("\nexpanded_sql", qd.expanded_sql)
    print("\nfirst_query", qd.first_query)
    print("\nquery_remaining", qd.query_remaining)
    print("\nis_explain", qd.is_explain)
    print("\nis_readonly", qd.is_readonly)
    print("\ndescription")
    pprint(qd.description)
    if hasattr(qd, "description_full"):
        print("\ndescription_full")
        pprint(qd.description_full)

    print("\nquery_plan")
    pprint(qd.query_plan)
    print("\nFirst 5 actions")
    pprint(qd.actions[:5])
    print("\nFirst 5 explain")
    pprint(qd.explain[:5])

    query
        SELECT * FROM orders
        JOIN customers ON orders.customer_id=customers.id
        WHERE address = ?;
        SELECT 7;

    bindings_count 1

    bindings_names (None,)

    expanded_sql None

    first_query
        SELECT * FROM orders
        JOIN customers ON orders.customer_id=customers.id
        WHERE address = ?;

    query_remaining SELECT 7;

    is_explain 0

    is_readonly True

    description
    (('id', 'INTEGER'),
     ('customer_id', 'INTEGER'),
     ('item', 'MY_OWN_TYPE'),
     ('id', 'INTEGER'),
     ('name', 'CHAR'),
     ('address', 'CHAR'))

    description_full
    (('id', 'INTEGER', 'main', 'orders', 'id'),
     ('customer_id', 'INTEGER', 'main', 'orders', 'customer_id'),
     ('item', 'MY_OWN_TYPE', 'main', 'orders', 'item'),
     ('id', 'INTEGER', 'main', 'customers', 'id'),
     ('name', 'CHAR', 'main', 'customers', 'name'),
     ('address', 'CHAR', 'main', 'customers', 'address'))

    query_plan
    QueryPlan(detail='QUERY PLAN',
              sub=[QueryPlan(detail='SCAN orders', sub=None),
                   QueryPlan(detail='SEARCH customers USING INTEGER PRIMARY KEY '
                                    '(rowid=?)',
                             sub=None)])

    First 5 actions
    [QueryAction(action=21,
                 action_name='SQLITE_SELECT',
                 column_name=None,
                 database_name=None,
                 file_name=None,
                 function_name=None,
                 module_name=None,
                 operation=None,
                 pragma_name=None,
                 pragma_value=None,
                 table_name=None,
                 trigger_name=None,
                 trigger_or_view=None,
                 view_name=None),
     QueryAction(action=20,
                 action_name='SQLITE_READ',
                 column_name='id',
                 database_name='main',
                 file_name=None,
                 function_name=None,
                 module_name=None,
                 operation=None,
                 pragma_name=None,
                 pragma_value=None,
                 table_name='orders',
                 trigger_name=None,
                 trigger_or_view=None,
                 view_name=None),
     QueryAction(action=20,
                 action_name='SQLITE_READ',
                 column_name='customer_id',
                 database_name='main',
                 file_name=None,
                 function_name=None,
                 module_name=None,
                 operation=None,
                 pragma_name=None,
                 pragma_value=None,
                 table_name='orders',
                 trigger_name=None,
                 trigger_or_view=None,
                 view_name=None),
     QueryAction(action=20,
                 action_name='SQLITE_READ',
                 column_name='item',
                 database_name='main',
                 file_name=None,
                 function_name=None,
                 module_name=None,
                 operation=None,
                 pragma_name=None,
                 pragma_value=None,
                 table_name='orders',
                 trigger_name=None,
                 trigger_or_view=None,
                 view_name=None),
     QueryAction(action=20,
                 action_name='SQLITE_READ',
                 column_name='id',
                 database_name='main',
                 file_name=None,
                 function_name=None,
                 module_name=None,
                 operation=None,
                 pragma_name=None,
                 pragma_value=None,
                 table_name='customers',
                 trigger_name=None,
                 trigger_or_view=None,
                 view_name=None)]

    First 5 explain
    [VDBEInstruction(addr=0,
                     opcode='Init',
                     comment=None,
                     p1=0,
                     p2=17,
                     p3=0,
                     p4=None,
                     p5=0),
     VDBEInstruction(addr=1,
                     opcode='OpenRead',
                     comment=None,
                     p1=0,
                     p2=12,
                     p3=0,
                     p4='3',
                     p5=0),
     VDBEInstruction(addr=2,
                     opcode='OpenRead',
                     comment=None,
                     p1=1,
                     p2=11,
                     p3=0,
                     p4='3',
                     p5=0),
     VDBEInstruction(addr=3,
                     opcode='Rewind',
                     comment=None,
                     p1=0,
                     p2=16,
                     p3=0,
                     p4=None,
                     p5=0),
     VDBEInstruction(addr=4,
                     opcode='Column',
                     comment=None,
                     p1=0,
                     p2=1,
                     p3=1,
                     p4=None,
                     p5=0)]

## Blob I/O

BLOBS (binary large objects) are supported by SQLite. Note that you cannot change the size of one, but you can allocate one filled with zeroes, and then later open it and read / write the contents similar to a file, without having the entire blob in memory. Use `Connection.blob_open()` to open a blob.

    connection.execute("create table blobby(x,y)")
    # Add a blob we will fill in later
    connection.execute("insert into blobby values(1, zeroblob(10000))")
    # Or as a binding
    connection.execute(
        "insert into blobby values(2, ?)", (apsw.zeroblob(20000),)
    )
    # Open a blob for writing.  We need to know the rowid
    rowid = connection.execute("select ROWID from blobby where x=1").get
    blob = connection.blob_open("main", "blobby", "y", rowid, True)
    blob.write(b"hello world")
    blob.seek(2000)
    blob.read(24)
    # seek relative to the end
    blob.seek(-32, 2)
    blob.write(b"hello world, again")
    blob.close()

## Backup an open database

You can backup a database that is open. The pages are copied in batches of your choosing and allow continued use of the source database.

    # We will copy a disk database into this memory database
    destination = apsw.Connection(":memory:")

    # Copy into destination
    with destination.backup("main", connection, "main") as backup:
        # The source database can change while doing the backup
        # and the backup will still pick up those changes
        while not backup.done:
            backup.step(7)  # copy up to 7 pages each time
            # monitor progress
            print(backup.remaining, backup.page_count)

    15 22
    8 22
    1 22
    0 22

## Authorizer (control what SQL can do)

You can allow, deny, or ignore what SQL does. Use `Connection.authorizer` to set an authorizer.

    def auth(
        operation: int,
        p1: Optional[str],
        p2: Optional[str],
        db_name: Optional[str],
        trigger_or_view: Optional[str],
    ) -> int:
        """Called when each operation is prepared.  We can return SQLITE_OK, SQLITE_DENY or
        SQLITE_IGNORE"""
        # find the operation name
        print(
            apsw.mapping_authorizer_function[operation],
            p1,
            p2,
            db_name,
            trigger_or_view,
        )
        if (
            operation == apsw.SQLITE_CREATE_TABLE
            and p1
            and p1.startswith("private")
        ):
            return apsw.SQLITE_DENY  # not allowed to create tables whose names start with private

        return apsw.SQLITE_OK  # always allow

    connection.authorizer = auth
    connection.execute("insert into names values('foo')")
    connection.execute("select name from names limit 1")
    try:
        connection.execute("create table private_stuff(secret)")
        print("Created secret table!")
    except Exception as e:
        print(e)

    # Clear authorizer
    connection.authorizer = None

    SQLITE_INSERT names None main None
    SQLITE_SELECT None None None None
    SQLITE_READ names name main None
    SQLITE_INSERT sqlite_master None main None
    SQLITE_CREATE_TABLE private_stuff None main None
    not authorized

## Progress handler

Some operations (eg joins, sorting) can take many operations to complete. Register a progress handler callback with `Connection.set_progress_handler()` which lets you provide feedback and allows cancelling.

    # create a table with random numbers
    with connection:
        connection.execute("create table numbers(x)")
        connection.executemany(
            "insert into numbers values(?)",
            ((random.randint(0, 9999999999),) for _ in range(100)),
        )

    def progress_handler() -> bool:
        print("progress handler called")
        return False  # returning True aborts

    # register handler every 50 vdbe instructions
    connection.set_progress_handler(progress_handler, 50)

    # Sorting the numbers to find the biggest
    for max_num in connection.execute("select max(x) from numbers"):
        print(max_num)

    # Clear handler
    connection.set_progress_handler(None)

    progress handler called
    progress handler called
    progress handler called
    progress handler called
    progress handler called
    progress handler called
    progress handler called
    progress handler called
    (9811449831,)

## File Control

We can get/set low level information using the `Connection.file_control()` interface. In this example we get the data version. There is a pragma but it doesn’t change for commits on the same connection.

    # We use ctypes to provide the correct C level data types and pointers
    import ctypes

    def get_data_version(db):
        # unsigned 32 bit integer
        data_version = ctypes.c_uint32(0)
        ok = db.file_control(
            "main",  # or an attached database name
            apsw.SQLITE_FCNTL_DATA_VERSION,  # code
            ctypes.addressof(data_version),
        )  # pass C level pointer
        assert ok, "SQLITE_FCNTL_DATA_VERSION was not understood!"
        return data_version.value

    # Show starting values
    print(
        "fcntl",
        get_data_version(connection),
        "pragma",
        connection.pragma("data_version"),
    )

    # See the fcntl value versus pragma value
    for sql in (
        "create table fcntl_example(x)",
        "begin ; insert into fcntl_example values(3)",
        # we can see the version doesn't change inside a transaction
        "insert into fcntl_example values(4)",
        "commit",
        "pragma user_version=1234",
    ):
        print(sql)
        connection.execute(sql)
        print(
            "fcntl",
            get_data_version(connection),
            "pragma",
            connection.pragma("data_version"),
        )

    fcntl 40 pragma 2
    create table fcntl_example(x)
    fcntl 41 pragma 2
    begin ; insert into fcntl_example values(3)
    fcntl 41 pragma 2
    insert into fcntl_example values(4)
    fcntl 41 pragma 2
    commit
    fcntl 42 pragma 2
    pragma user_version=1234
    fcntl 43 pragma 2

## Commit hook

A commit hook can allow or veto commits. Register a commit hook with `Connection.set_commit_hook()`.

    def my_commit_hook() -> bool:
        print("in commit hook")
        hour = time.localtime()[3]
        if hour >= 8 and hour < 18:
            print("commits okay at this time")
            return False  # let commit go ahead
        print("no commits out of hours")
        return True  # abort commits outside of 8am through 6pm

    connection.set_commit_hook(my_commit_hook)
    try:
        with connection:
            connection.execute(
                """create table example(x,y,z);
                               insert into example values (3,4,5)"""
            )
    except apsw.ConstraintError:
        print("commit was not allowed")

    connection.set_commit_hook(None)

    in commit hook
    no commits out of hours
    commit was not allowed

## Update hook

Update hooks let you know that data has been added, changed, or removed. For example you could use this to discard cached information. Register a hook using `Connection.set_update_hook()`.

    def my_update_hook(
        type: int, db_name: str, table_name: str, rowid: int
    ) -> None:
        op: str = apsw.mapping_authorizer_function[type]
        print(
            f"Updated: { op } db { db_name }, table { table_name }, rowid { rowid }"
        )

    connection.set_update_hook(my_update_hook)
    connection.execute("insert into names values(?)", ("file93",))
    connection.execute(
        "update names set name=? where name=?", ("file94", "file93")
    )
    connection.execute("delete from names where name=?", ("file94",))

    # Clear the hook
    connection.set_update_hook(None)

    Updated: SQLITE_INSERT db main, table names, rowid 7
    Updated: SQLITE_UPDATE db main, table names, rowid 7
    Updated: SQLITE_DELETE db main, table names, rowid 7

## Virtual tables

Virtual tables let you provide data on demand as a SQLite table so you can use SQL queries against that data. Writing your own virtual table requires understanding how to return less than all the data via the BestIndex method.

You can export a Python function as a virtual table in 3 lines of code using `apsw.ext.make_virtual_module()`, being able to provide both positional and keyword arguments.

For the first example you’ll find `apsw.ext.generate_series()` useful instead.

    # Yield a row at a time
    def table_range(start=1, stop=100, step=1):
        for i in range(start, stop + 1, step):
            yield (i,)

    # set column names
    table_range.columns = ("value",)
    # set how to access what table_range returns
    table_range.column_access = apsw.ext.VTColumnAccess.By_Index

    # register it
    apsw.ext.make_virtual_module(connection, "range", table_range)

    # see it work.  we can provide both positional and keyword
    # arguments
    query = "SELECT * FROM range(90) WHERE step=2"
    print(apsw.ext.format_query_table(connection, query))

    # the parameters are hidden columns so '*' doesn't select them
    # but you can ask
    query = "SELECT *, start, stop, step FROM range(89) WHERE step=3"
    print(apsw.ext.format_query_table(connection, query))

    # Expose the unicode database.
    import unicodedata

    # A more complex example exporting unicodedata module

    # The methods we will call on each codepoint
    unicode_methods = (
        "name",
        "decimal",
        "digit",
        "numeric",
        "category",
        "combining",
        "bidirectional",
        "east_asian_width",
        "mirrored",
        "decomposition",
    )

    # the function we will turn into a virtual table returning
    # each row as a dict
    def unicode_data(start=0, stop=sys.maxunicode):
        # some methods raise ValueError on some codepoints
        def call(meth: str, c: str):
            try:
                return getattr(unicodedata, meth)(c)
            except ValueError:
                return None

        for c in range(start, stop + 1):
            yield {k: call(k, chr(c)) for k in unicode_methods}

    # setup column names and access
    unicode_data.columns = unicode_methods
    unicode_data.column_access = apsw.ext.VTColumnAccess.By_Name

    # register
    apsw.ext.make_virtual_module(connection, "unicode_data", unicode_data)

    # how many codepoints are in each category?
    query = """
        SELECT count(*), category FROM unicode_data
           WHERE stop = 0xffff  -- BMP only
           GROUP BY category
           ORDER BY category
           LIMIT 10"""
    print(apsw.ext.format_query_table(connection, query))

    # A more complex example - given a list of directories return information
    # about the files within them recursively
    def get_files_info(
        directories: str,
        sep: str = os.pathsep,
        *,
        ignore_symlinks: bool = True,
    ) -> Iterator[dict[str, Any]]:
        for root in directories.split(sep):
            with os.scandir(root) as sd:
                for entry in sd:
                    if entry.is_symlink() and ignore_symlinks:
                        continue
                    if entry.is_dir():
                        yield from get_files_info(
                            os.path.join(root, entry.name),
                            ignore_symlinks=ignore_symlinks,
                        )
                    elif entry.is_file():
                        s = entry.stat()
                        yield {
                            "directory": root,
                            "name": entry.name,
                            "extension": os.path.splitext(entry.name)[1],
                            **{
                                k: getattr(s, k)
                                for k in get_files_info.stat_columns
                            },
                        }

    # which stat columns do we want?
    get_files_info.stat_columns = tuple(
        n for n in dir(os.stat(".")) if n.startswith("st_")
    )
    # setup columns and access by providing an example of the first entry returned
    (
        get_files_info.columns,
        get_files_info.column_access,
    ) = apsw.ext.get_column_names(next(get_files_info(".")))

    apsw.ext.make_virtual_module(connection, "files_info", get_files_info)

    # all the sys.path directories
    bindings = (
        os.pathsep.join(
            p
            for p in sys.path
            if os.path.isdir(p)
            #  except our current one
            and not os.path.samefile(p, ".")
        ),
    )

    # Find the 3 biggest files that aren't libraries
    query = """SELECT st_size, directory, name
                FROM files_info(?)
                WHERE extension NOT IN ('.a', '.so')
                ORDER BY st_size DESC
                LIMIT 3"""
    print(apsw.ext.format_query_table(connection, query, bindings))

    # Find the 3 oldest Python files
    query = """SELECT DATE(st_ctime, 'auto') AS date, directory, name
                FROM files_info(?)
                WHERE extension='.py'
                ORDER BY st_size DESC
                LIMIT 3"""
    print(apsw.ext.format_query_table(connection, query, bindings))

    # find space used by filename extension
    query = """SELECT extension, SUM(st_size) as total_size
                FROM files_info(?)
                GROUP BY extension
                ORDER BY extension"""
    print(apsw.ext.format_query_table(connection, query, bindings))

    # unregister a virtual table by passing None
    connection.create_module("files_info", None)

    ┌───────┐
    │ value │
    │ 90    │
    │ 92    │
    │ 94    │
    │ 96    │
    │ 98    │
    │ 100   │
    └───────┘

    ┌───────┬───────┬──────┬──────┐
    │ value │ start │ stop │ step │
    │ 89    │ 89    │ 100  │ 3    │
    │ 92    │ 89    │ 100  │ 3    │
    │ 95    │ 89    │ 100  │ 3    │
    │ 98    │ 89    │ 100  │ 3    │
    └───────┴───────┴──────┴──────┘

    ┌──────────┬──────────┐
    │ count(*) │ category │
    │ 65       │ Cc       │
    │ 43       │ Cf       │
    │ 1449     │ Cn       │
    │ 6400     │ Co       │
    │ 2048     │ Cs       │
    │ 1445     │ Ll       │
    │ 236      │ Lm       │
    │ 46126    │ Lo       │
    │ 31       │ Lt       │
    │ 1127     │ Lu       │
    └──────────┴──────────┘

    ┌─────────┬──────────────────────────────────────────┬─────────────────────────┐
    │ st_size │                directory                 │          name           │
    ├─────────┼──────────────────────────────────────────┼─────────────────────────┤
    │ 527465  │ /usr/lib/python3.13/pydoc_data/          │ topics.cpython-313.pyc  │
    │         │ __pycache__                              │                         │
    ├─────────┼──────────────────────────────────────────┼─────────────────────────┤
    │ 526924  │ /usr/lib/python3.13/pydoc_data           │ topics.py               │
    ├─────────┼──────────────────────────────────────────┼─────────────────────────┤
    │ 256658  │ /usr/lib/python3.13/tkinter/__pycache__  │ __init__.cpython-313.p- │
    │         │                                          │ yc                      │
    └─────────┴──────────────────────────────────────────┴─────────────────────────┘

    ┌────────────┬────────────────────────────────┬───────────────┐
    │    date    │           directory            │     name      │
    │ 2025-06-20 │ /usr/lib/python3.13/pydoc_data │ topics.py     │
    │ 2025-06-20 │ /usr/lib/python3.13            │ _pydecimal.py │
    │ 2025-06-20 │ /usr/lib/python3.13/tkinter    │ __init__.py   │
    └────────────┴────────────────────────────────┴───────────────┘

    ┌────────────┬────────────┐
    │ extension  │ total_size │
    │            │ 483963     │
    │ .a         │ 96521266   │
    │ .allowlist │ 56         │
    │ .bootstrap │ 1908       │
    │ .c         │ 11288      │
    │ .cfg       │ 341        │
    │ .csh       │ 937        │
    │ .css       │ 1325       │
    │ .fish      │ 2208       │
    │ .in        │ 3502       │
    │ .ini       │ 1670       │
    │ .json      │ 2162       │
    │ .local     │ 2128       │
    │ .o         │ 15512      │
    │ .ps1       │ 9031       │
    │ .py        │ 11176608   │
    │ .pyc       │ 11792276   │
    │ .rst       │ 9561       │
    │ .sh        │ 2729       │
    │ .so        │ 29503792   │
    │ .sql       │ 9486       │
    │ .stdlib    │ 13486      │
    │ .supp      │ 70         │
    │ .txt       │ 13809      │
    └────────────┴────────────┘

## VFS - Virtual File System

VFS lets you control how SQLite accesses storage. APSW makes it easy to “inherit” from an existing VFS and monitor or alter data as it flows through.

`URI` are shown as a way to receive parameters when opening/creating a database file, and `pragmas` for receiving parameters once a database is open.

    # This example VFS obfuscates the database file contents by xor all
    # bytes with 0xa5.

    def obfuscate(data: bytes):
        return bytes([x ^ 0xA5 for x in data])

    # Inheriting from a base of "" means the default vfs
    class ObfuscatedVFS(apsw.VFS):
        def __init__(self, vfsname="obfuscated", basevfs=""):
            self.vfs_name = vfsname
            self.base_vfs = basevfs
            super().__init__(self.vfs_name, self.base_vfs)

        # We want to return our own file implementation, but also
        # want it to inherit
        def xOpen(self, name, flags):
            in_flags = []
            for k, v in apsw.mapping_open_flags.items():
                if isinstance(k, int) and flags[0] & k:
                    in_flags.append(v)
            print("xOpen flags", " | ".join(in_flags))

            if isinstance(name, apsw.URIFilename):
                print("   uri filename", name.filename())
                # We can look at uri parameters
                print("   fast is", name.uri_parameter("fast"))
                print("   level is", name.uri_int("level", 3))
                print("   warp is", name.uri_boolean("warp", False))
                print(
                    "   notpresent is", name.uri_parameter("notpresent")
                )
                # all of them
                print("   all uris", name.parameters)
            else:
                print("   filename", name)
            return ObfuscatedVFSFile(self.base_vfs, name, flags)

    # The file implementation where we override xRead and xWrite to call our
    # encryption routine
    class ObfuscatedVFSFile(apsw.VFSFile):
        def __init__(self, inheritfromvfsname, filename, flags):
            super().__init__(inheritfromvfsname, filename, flags)

        def xRead(self, amount, offset):
            return obfuscate(super().xRead(amount, offset))

        def xWrite(self, data, offset):
            super().xWrite(obfuscate(data), offset)

        def xFileControl(self, op: int, ptr: int) -> bool:
            if op != apsw.SQLITE_FCNTL_PRAGMA:
                return super().xFileControl(op, ptr)
            # implement our own pragma
            p = apsw.VFSFcntlPragma(ptr)
            print(f"pragma received { p.name } = { p.value }")
            # what do we understand?
            if p.name == "my_custom_pragma":
                p.result = "orange"
                return True
            # We did not understand
            return False

    # To register the VFS we just instantiate it
    obfuvfs = ObfuscatedVFS()

    # Lets see what vfs are now available?
    print("VFS available", apsw.vfs_names())

    # Make an obfuscated db, passing in some URI parameters
    # default open flags
    open_flags = apsw.SQLITE_OPEN_READWRITE | apsw.SQLITE_OPEN_CREATE
    # add in using URI parameters
    open_flags |= apsw.SQLITE_OPEN_URI

    # uri parameters are after the ? separated by &
    obfudb = apsw.Connection(
        "file:myobfudb?fast=speed&level=7&warp=on&another=true",
        flags=open_flags,
        vfs=obfuvfs.vfs_name,
    )

    # Check it works
    obfudb.execute("create table foo(x,y); insert into foo values(1,2)")

    # Check it really is obfuscated on disk
    print("What is on disk", repr(Path("myobfudb").read_bytes()[:20]))

    # And unobfuscating it
    print(
        "Unobfuscated disk",
        repr(obfuscate(Path("myobfudb").read_bytes()[:20])),
    )

    # Custom pragma
    print(
        "pragma returned", obfudb.pragma("my_custom_pragma", "my value")
    )

    # Tidy up
    obfudb.close()
    os.remove("myobfudb")

    VFS available ['unix', 'obfuscated', 'memdb', 'unix-excl', 'unix-dotfile', 'unix-none']
    xOpen flags SQLITE_OPEN_CREATE | SQLITE_OPEN_MAIN_DB | SQLITE_OPEN_READWRITE | SQLITE_OPEN_URI
       uri filename /space/apsw/myobfudb
       fast is speed
       level is 7
       warp is True
       notpresent is None
       all uris ('fast', 'level', 'warp', 'another')
    pragma received journal_mode = wal
    xOpen flags SQLITE_OPEN_CREATE | SQLITE_OPEN_MAIN_JOURNAL | SQLITE_OPEN_READWRITE
       filename /space/apsw/myobfudb-journal
    pragma received foreign_keys = ON
    pragma received optimize = 65538
    xOpen flags SQLITE_OPEN_CREATE | SQLITE_OPEN_READWRITE | SQLITE_OPEN_WAL
       filename /space/apsw/myobfudb-wal
    pragma received recursive_triggers = ON
    What is on disk b'\xf6\xf4\xe9\xcc\xd1\xc0\x85\xc3\xca\xd7\xc8\xc4\xd1\x85\x96\xa5\xb5\xa5\xa7\xa7'
    Unobfuscated disk b'SQLite format 3\x00\x10\x00\x02\x02'
    pragma received my_custom_pragma = my value
    pragma returned orange

## Limits

SQLite lets you see and update various limits via `Connection.limit()`

    # Print some limits
    for limit in ("LENGTH", "COLUMN", "ATTACHED"):
        name = "SQLITE_LIMIT_" + limit
        max_name = "SQLITE_MAX_" + limit  # compile time limit
        orig = connection.limit(getattr(apsw, name))
        print(name, orig)
        # To get the maximum, set to 0x7fffffff and then read value back
        connection.limit(getattr(apsw, name), 0x7FFFFFFF)
        max = connection.limit(getattr(apsw, name))
        print(max_name, " ", max)

    # Set limit for size of a string
    connection.execute("create table testlimit(s)")
    connection.execute(
        "insert into testlimit values(?)", ("x" * 1024,)
    )  # 1024 char string
    connection.limit(apsw.SQLITE_LIMIT_LENGTH, 1023)  # limit is now 1023
    try:
        connection.execute(
            "insert into testlimit values(?)", ("y" * 1024,)
        )
        print("string exceeding limit was inserted")
    except apsw.TooBigError:
        print("Caught toobig exception")

    # reset back to largest value
    connection.limit(apsw.SQLITE_LIMIT_LENGTH, 0x7FFFFFFF)

    SQLITE_LIMIT_LENGTH 1000000000
    SQLITE_MAX_LENGTH   1000000000
    SQLITE_LIMIT_COLUMN 2000
    SQLITE_MAX_COLUMN   2000
    SQLITE_LIMIT_ATTACHED 125
    SQLITE_MAX_ATTACHED   125
    Caught toobig exception

## Shell

APSW includes a shell like the one in SQLite, and is also extensible from Python.

    import apsw.shell

    # Here we use the shell to do a csv export and then dump part of the
    # database

    # Export to a StringIO
    import io

    output = io.StringIO()
    shell = apsw.shell.Shell(stdout=output, db=connection)

    # How to execute a dot command
    shell.process_command(".mode csv")
    shell.process_command(".headers on")

    # How to execute SQL
    shell.process_sql(
        """
        create table csvtest(column1, column2 INTEGER);
        create index faster on csvtest(column1);
        insert into csvtest values(3, 4);
        insert into csvtest values('a b', NULL);
    """
    )

    # Or let the shell figure out SQL vs dot command
    shell.process_complete_line("select * from csvtest")

    # see the result
    print(output.getvalue())

    # reset output
    output.seek(0)

    # make a dump of the same table
    shell.process_command(".dump csvtest%")

    # see the result
    print("\nDump output\n")
    print(output.getvalue())

    column1,column2
    3,4
    a b,

    Dump output

    -- SQLite dump (by APSW 3.50.2.0)
    -- SQLite version 3.50.2
    -- Date: Sun Jun 29 05:23:22 2025
    -- Tables like: csvtest%
    -- Database: /space/apsw/dbfile
    -- User: rogerb @ clamps

    -- The values of various per-database settings
    PRAGMA page_size=4096;
    -- PRAGMA encoding='UTF-8';
    -- PRAGMA auto_vacuum=NONE;
    -- PRAGMA max_page_count=4294967294;

    BEGIN TRANSACTION;

    -- Table  csvtest
    DROP TABLE IF EXISTS csvtest;
    CREATE TABLE csvtest(column1, column2 INTEGER);
    INSERT INTO csvtest VALUES(3,4);
    INSERT INTO csvtest VALUES('a b',NULL);
    -- Triggers and indices on  csvtest
    CREATE INDEX faster on csvtest(column1);

    -- Database header
    pragma user_version=1234;

    COMMIT TRANSACTION;

## Statistics

SQLite provides statistics by `status()`. Use `Connection.status()` for per connection statistics.

    current_usage, max_usage = apsw.status(apsw.SQLITE_STATUS_MEMORY_USED)
    print(f"SQLite memory usage { current_usage } max { max_usage }")
    schema_used, _ = connection.status(apsw.SQLITE_DBSTATUS_SCHEMA_USED)
    print(
        f"{ schema_used } bytes used to store schema for this connection"
    )

    SQLite memory usage 517920 max 2494984
    5048 bytes used to store schema for this connection

## Tracing

This shows using `Connection.trace_v2()`

    # From https://www.sqlite.org/lang_with.html
    # Outlandish Recursive Query Examples

    query = """WITH RECURSIVE
                xaxis(x) AS (VALUES(-2.0) UNION ALL SELECT x+0.05 FROM xaxis WHERE x<1.2),
                yaxis(y) AS (VALUES(-1.0) UNION ALL SELECT y+0.1 FROM yaxis WHERE y<1.0),
                m(iter, cx, cy, x, y) AS (
                    SELECT 0, x, y, 0.0, 0.0 FROM xaxis, yaxis
                    UNION ALL
                    SELECT iter+1, cx, cy, x*x-y*y + cx, 2.0*x*y + cy FROM m
                    WHERE (x*x + y*y) < 4.0 AND iter<28
                ),
                m2(iter, cx, cy) AS (
                    SELECT max(iter), cx, cy FROM m GROUP BY cx, cy
                ),
                a(t) AS (
                    SELECT group_concat( substr(' .+*#', 1+min(iter/7,4), 1), '')
                    FROM m2 GROUP BY cy
                )
                SELECT group_concat(rtrim(t),x'0a') FROM a;"""

    def trace_hook(trace: dict) -> None:
        # check the sql and connection are as expected and remove from trace
        # so we don't print them
        assert (
            trace.pop("sql", query) == query
            and trace.pop("connection") is connection
        )
        print("code is ", apsw.mapping_trace_codes[trace["code"]])
        pprint(trace)

    connection.trace_v2(
        apsw.SQLITE_TRACE_STMT
        | apsw.SQLITE_TRACE_PROFILE
        | apsw.SQLITE_TRACE_ROW,
        trace_hook,
    )

    # We will get one each of the trace events
    for _ in connection.execute(query):
        pass

    # Turn off tracing
    connection.trace_v2(0, None)

    code is  SQLITE_TRACE_STMT
    {'code': 1, 'id': 232476536, 'total_changes': 146, 'trigger': False}
    code is  SQLITE_TRACE_ROW
    {'code': 4, 'id': 232476536}
    code is  SQLITE_TRACE_PROFILE
    {'code': 2,
     'id': 232476536,
     'nanoseconds': 17000000,
     'stmt_status': {'SQLITE_STMTSTATUS_AUTOINDEX': 0,
                     'SQLITE_STMTSTATUS_FILTER_HIT': 0,
                     'SQLITE_STMTSTATUS_FILTER_MISS': 0,
                     'SQLITE_STMTSTATUS_FULLSCAN_STEP': 1365,
                     'SQLITE_STMTSTATUS_MEMUSED': 15784,
                     'SQLITE_STMTSTATUS_REPREPARE': 0,
                     'SQLITE_STMTSTATUS_RUN': 1,
                     'SQLITE_STMTSTATUS_SORT': 2,
                     'SQLITE_STMTSTATUS_VM_STEP': 1015351},
     'total_changes': 146}

## System and SQLite resource usage in a block

Use `apsw.ext.ShowResourceUsage()` to see what resources a block of code does. We use the same query from above.

Only statistics that have changed are shown in the summary. There are 20 SQLite values tracked including caching, and 20 system values.

    with apsw.ext.ShowResourceUsage(
        sys.stdout, db=connection, scope="thread"
    ):
        # some SQLite work
        rows = connection.execute(query).get
        # and some non-SQLite work - the imports cause filesystem access
        import statistics, tokenize, uuid, fractions, pydoc, decimal

        # and take some wall clock time
        time.sleep(1.3)

                Total CPU consumption 0.021
                           Wall clock 1.326
               Block input operations 232
            Maximum resident set size 192
                 Page faults - no I/O 86
         Involuntary context switches 140
           Voluntary context switches 5
                  Time in system mode 0.002
                    Time in user mode 0.019
               SQLite full table scan 1,365
               SQLite sort operations 2
                 SQLite vm operations 1,015,351
          SQLite statements completed 1
    SQLite allocations lookaside full 17,272

## SQL statement tracing in a block

Use `apsw.ext.Trace()` to see SQL statements inside a block of code. This also shows behind the scenes SQL.

    # Use None instead of stdout and no information is printed or gathered
    with apsw.ext.Trace(sys.stdout, db=connection, vtable=True):
        # APSW does a savepoint behind the scenes to wrap the block
        with connection:
            # Some regular SQL
            connection.execute("create table multi(x)")
            # executemany runs the same statement repeatedly
            connection.executemany(
                "insert into multi values(?)", ((x,) for x in range(5))
            )
            # See how many rows were processed
            connection.execute("select * from multi limit 2").fetchall()
            # You can also see how many rows were changed
            connection.execute("delete from multi where x < 4")
            # To implement the table, SQLIte issues the pragma behind the
            # scenes.  Note how the output shows this statement starting,
            # the pragma, and then this statement ending.
            connection.execute(
                "select count(*) from pragma_function_list"
            )

    > SAVEPOINT "_apsw-0"
        Time: 0.000
    > create table multi(x)
        Time: 0.000
    > insert into multi values(?)
        Time: 0.000  Rows: 1  Changes: 1
    > insert into multi values(?)
        Time: 0.000  Changes: 1
    > insert into multi values(?)
        Time: 0.000  Changes: 1
    > insert into multi values(?)
        Time: 0.000  Changes: 1
    > insert into multi values(?)
        Time: 0.000  Changes: 1
    > select * from multi limit 2
        Time: 0.000  Rows: 2
    > delete from multi where x < 4
        Time: 0.000  Changes: 4
    > select count(*) from pragma_function_list
    V PRAGMA function_list
        Time: 0.000  Rows: 211  VmStep: 1,481  Mem: 70.8KB
    < select count(*) from pragma_function_list
        Time: 0.000  Rows: 1  VmStep: 433
    > RELEASE SAVEPOINT "_apsw-0"
        Time: 0.007

## Formatting query results table

`apsw.ext.format_query_table()` makes it easy to format the results of a query in an automatic adjusting table, colour, sanitizing strings, truncation etc.

    # Create a table with some dummy data
    connection.execute(
        """CREATE TABLE dummy(quantity, [spaces in name], last);
        INSERT INTO dummy VALUES(3, 'some regular text to make this row interesting', x'030709');
        INSERT INTO dummy VALUES(3.14, 'Tiếng Việt', null);
        INSERT INTO dummy VALUES('', ?, ' ');
    """,
        ("special \t\n\f\0 cha\\rs",),
    )

    query = "SELECT * FROM dummy"
    # default
    print(apsw.ext.format_query_table(connection, query))

    # no unicode boxes and maximum sanitize the text
    kwargs = {"use_unicode": False, "string_sanitize": 2}
    print(apsw.ext.format_query_table(connection, query, **kwargs))

    # lets have unicode boxes and make things narrow
    kwargs = {
        "use_unicode": True,
        "string_sanitize": 0,
        "text_width": 30,
    }
    print(apsw.ext.format_query_table(connection, query, **kwargs))

    # have the values in SQL syntax
    kwargs = {"quote": True}
    print(apsw.ext.format_query_table(connection, query, **kwargs))

    ┌──────────┬────────────────────────────────────────────────┬─────────────┐
    │ quantity │                 spaces in name                 │    last     │
    ├──────────┼────────────────────────────────────────────────┼─────────────┤
    │ 3        │ some regular text to make this row interesting │ [ 3 bytes ] │
    ├──────────┼────────────────────────────────────────────────┼─────────────┤
    │ 3.14     │ Tiếng Việt                                     │ (null)      │
    ├──────────┼────────────────────────────────────────────────┼─────────────┤
    │          │ special                                        │             │
    │          │ \0 cha\\rs                                     │             │
    └──────────┴────────────────────────────────────────────────┴─────────────┘

    +----------+------------------------------------------------+-------------+
    | quantity |                 spaces in name                 |    last     |
    +----------+------------------------------------------------+-------------+
    | 3        | some.regular.text.to.make.this.row.interesting | [ 3 bytes ] |
    +----------+------------------------------------------------+-------------+
    | 3.14     | Ti.ng.Vi.t                                     | (null)      |
    +----------+------------------------------------------------+-------------+
    |          | special..                                      | .           |
    |          | ...cha\rs                                      |             |
    +----------+------------------------------------------------+-------------+

    ┌─────┬────────────────┬─────┐
    │ qu- │ spaces in name │ la- │
    │ an- │                │ st  │
    │ ti- │                │     │
    │ ty  │                │     │
    ├─────┼────────────────┼─────┤
    │ 3   │ some regular   │ [ 3 │
    │     │ text to make   │ by- │
    │     │ this row       │ te- │
    │     │ interesting    │ s ] │
    ├─────┼────────────────┼─────┤
    │ 3.- │ Tiếng Việt     │ (n- │
    │ 14  │                │ ul- │
    │     │                │ l)  │
    ├─────┼────────────────┼─────┤
    │     │ special        │     │
    │     │ \0 cha\\rs     │     │
    └─────┴────────────────┴─────┘

    ┌──────────┬──────────────────────────────────────────────────┬───────────┐
    │ quantity │                  spaces in name                  │   last    │
    ├──────────┼──────────────────────────────────────────────────┼───────────┤
    │ 3        │ 'some regular text to make this row interesting' │ X'030709' │
    ├──────────┼──────────────────────────────────────────────────┼───────────┤
    │ 3.14     │ 'Tiếng Việt'                                     │ NULL      │
    ├──────────┼──────────────────────────────────────────────────┼───────────┤
    │ ''       │ 'special                                         │ ' '       │
    │          │ \0 cha\\rs'                                      │           │
    └──────────┴──────────────────────────────────────────────────┴───────────┘

## Cleanup

As a general rule you do not need to do any cleanup. Standard Python garbage collection will take of everything. Even if the process crashes with a connection in the middle of a transaction, the next time SQLite opens that database it will automatically rollback the incomplete transaction.

    # You can close connections manually
    connection.close()