# JSON Example/Tour

This example shows using JSON with SQLite, and additional functionality provided by APSW to make it easier to use.

    #!/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 Any

    import base64
    import contextvars
    import datetime
    import decimal
    from types import MappingProxyType

    from pprint import pprint

    import apsw
    import apsw.ext

    # A database to work on
    connection = apsw.Connection("")

    # And the table
    connection.execute(
        """CREATE TABLE items(
            name,
            shape,
            extra
        );
    """
    )

    # This method is used to show queries and results
    def query(sql, bindings=None):
        print(f"\n\n{sql}")
        result = connection.execute(sql, bindings).fetchall()
        if len(result):
            pprint(result[0] if len(result) == 1 else result)

## Quick start

Described here in the JSON background and detail.

If you do not use blobs and just want it to look like SQLite has JSON as a native data type, then do the following.

    def convert_binding(
        cursor: apsw.Cursor, num: int, value: Any
    ) -> bytes:
        # called to convert unknown types - we convert to JSONB
        return apsw.jsonb_encode(value)

    def convert_jsonb(
        cursor: apsw.Cursor, column: int, value: bytes
    ) -> Any:
        # Called when a blob is valid JSONB.  If you don't use blobs then
        # this is all that is needed.  If you do use blobs then you can
        # examine information from the cursor/query to decide, shown
        # further down.
        return apsw.jsonb_decode(value)

    # install the callbacks
    connection.convert_binding = convert_binding
    connection.convert_jsonb = convert_jsonb

    example_data = {
        "origin": "Spain",
        "diameter": 7.5,
        "sugar": 23,
        "tags": ["citrus", "sweet", "juice"],
    }

    # Note how we can INSERT the data
    connection.execute(
        "INSERT INTO items VALUES(?, ?, ?)",
        ("orange", "round", example_data),
    )

    # And get it back
    query("SELECT shape, extra FROM items WHERE name='orange'")

    # If you want the data returned to be read only then use this.
    # Note how the list becomes a tuple and dict becomes MappingProxyType
    # which doesn't allow writes.

    def convert_jsonb_readonly(
        cursor: apsw.Cursor, column: int, value: bytes
    ) -> Any:
        return apsw.jsonb_decode(
            value, array_hook=tuple, object_hook=MappingProxyType
        )

    connection.convert_jsonb = convert_jsonb_readonly

    query("SELECT shape, extra FROM items WHERE name='orange'")

    SELECT shape, extra FROM items WHERE name='orange'
    ('round',
     {'diameter': 7.5,
      'origin': 'Spain',
      'sugar': 23,
      'tags': ['citrus', 'sweet', 'juice']})

    SELECT shape, extra FROM items WHERE name='orange'
    ('round',
     mappingproxy({'diameter': 7.5,
                   'origin': 'Spain',
                   'sugar': 23,
                   'tags': ('citrus', 'sweet', 'juice')}))

## SQLite JSON functions

SQLite has over 30 functions for dealing with JSON. Here are some of the most useful.

    # -> extracts a subcomponent in JSON text format.  Note how origin
    # includes the double quotes in the response.  $ is used to indicate
    # top level.
    query("SELECT extra -> '$.origin' FROM items")

    # ->> extracts as a SQLite value so origin in a plain string.
    query("SELECT extra ->> '$.origin' FROM items")

    # Lets get the first tag
    query("SELECT extra ->> '$.tags[0]' FROM items")

    # Iterate over each tag - you would typically use this with a JOIN
    query(
        "SELECT name, shape, value FROM items, json_each(items.extra, '$.tags')"
    )

    SELECT extra -> '$.origin' FROM items
    ('"Spain"',)

    SELECT extra ->> '$.origin' FROM items
    ('Spain',)

    SELECT extra ->> '$.tags[0]' FROM items
    ('citrus',)

    SELECT name, shape, value FROM items, json_each(items.extra, '$.tags')
    [('orange', 'round', 'citrus'),
     ('orange', 'round', 'sweet'),
     ('orange', 'round', 'juice')]

## Customising conversion

The `convert_binding()` and `convert_jsonb()` functions are provided with the `Cursor` as the first parameter, and a second parameter with the binding number or column being returned. You can use `Cursor.bindings_names` and `Cursor.description` for more details about the value being converted. You can also use `Cursor.connection` to get back to the connection and your own data structures. `contextvars` can be used to provide more.

`jsonb_encode()` and `jsonb_decode()` have parameters like the `json` module functions for controlling how non-JSON Python types can be converted to JSON compatible ones, and how JSON values are converted back to Python objects.

This section shows all of these in action at once!

    volume: contextvars.ContextVar[str] = contextvars.ContextVar(
        "volume", default="quiet"
    )

    def py_to_json(value):
        # Used by jsonb_encode to convert types that aren't JSON compatible
        if isinstance(value, bytes):
            # base64 encode binary data.  Note it returns bytes so we have to
            # convert to text.
            return base64.b64encode(value).decode("ascii")

        if isinstance(value, datetime.datetime):
            # ISO8601
            return value.isoformat()

        if isinstance(value, decimal.Decimal):
            # Create JSONB bytes directly.  Tag 5 is FLOAT.  str of a Decimal
            # gives a full precision string of the value
            return apsw.ext.make_jsonb(5, str(value))

        if isinstance(value, complex):
            # The above are all single values.  For objects with multiple
            # fields we return a dict with their members and a key to
            # detect this
            return {
                # I made up this key as unlikely to be used in other dicts
                "$py$type": "complex",
                # fields from complex
                "real": value.real,
                "imag": value.imag,
            }

        raise TypeError(f"Can't convert {value!r}")

    def convert_binding(cursor: apsw.Cursor, num: int, value: Any):
        # note that binding numbers start at 1
        print(f"\nconvert_binding callback {num=} {value=!r:.20}...")
        print(f"{cursor.bindings_count=}")
        print(f"{cursor.bindings_names=}")
        print(f"contextvar {volume.get()=}")

        return apsw.jsonb_encode(value, default=py_to_json)

    connection.convert_binding = convert_binding

    # The above deals with conversion to JSONB, now deal with
    # conversion from JSONB

    def object_hook(value: dict):
        # We will use this to convert back to a Python type
        match value.get("$py$type"):
            case None:
                # Doesn't have this key, so return as is
                return value
            case "complex":
                return complex(value["real"], value["imag"])
            case _:
                raise ValueError("Unknown $py$type")

    def convert_jsonb(cursor: apsw.Cursor, num: int, value: bytes):
        print(f"\nconvert_jsonb callback {num=} {value=!r:.20}...")
        # the description will contain the column names, declared types and
        # more if using description_full
        print(f"columns are {[col[0] for col in cursor.description]}")
        print(f"contextvar {volume.get()=}\n")

        # We want decimal to handle float conversion because it has more
        # precision
        return apsw.jsonb_decode(
            value, object_hook=object_hook, parse_float=decimal.Decimal
        )

    connection.convert_jsonb = convert_jsonb

    example_data = {
        "binary": b"\x01\x73\x94\x65",
        "date stamp": datetime.datetime.now(),
        "decimal": decimal.Decimal(
            "0.7843262344923523492342352344523423423423"
        ),
        "complex": 3 + 4j,
    }

    # Use the contextvar
    with volume.set("loud"):
        query(
            "SELECT $name AS scope, $data AS hello",
            {
                "name": "test",
                "data": example_data,
            },
        )

    SELECT $name AS scope, $data AS hello

    convert_binding callback num=2 value={'binary': b'\x01s\x...
    cursor.bindings_count=2
    cursor.bindings_names=('name', 'data')
    contextvar volume.get()='loud'

    convert_jsonb callback num=1 value=b'\xec\x00\x00\x00\x...
    columns are ['scope', 'hello']
    contextvar volume.get()='loud'

    ('test',
     {'binary': 'AXOUZQ==',
      'complex': (3+4j),
      'date stamp': '2026-03-13T05:50:31.426130',
      'decimal': Decimal('0.7843262344923523492342352344523423423423')})

## Cleanup

No cleanup is needed. Converters are automatically cleared when connections and cursors are no longer used.