googleapis · chelsea-lin · Jun 13, 2025 · Jun 11, 2025 · Jun 11, 2025 · Jun 12, 2025
@@ -40,6 +40,7 @@
    json_extract_array,
    json_extract_string_array,
    json_query,
+    json_query_array,
    json_set,
    json_value,
    parse_json,
@@ -67,6 +68,7 @@
    "json_extract_array",
    "json_extract_string_array",
    "json_query",
+    "json_query_array",
    "json_set",
    "json_value",
    "parse_json",

@@ -133,6 +133,10 @@ def json_extract_array(
    `STRING` or `JSON` values. This function uses single quotes and brackets to
    escape invalid JSONPath characters in JSON keys.

+    .. deprecated:: 2.5.0
+        The ``json_extract_array`` is deprecated and will be removed in a future version.
+        Use ``json_query_array`` instead.
+
    **Examples:**

        >>> import bigframes.pandas as bpd
@@ -172,6 +176,11 @@ def json_extract_array(
    Returns:
        bigframes.series.Series: A new Series with the parsed arrays from the input.
    """
+    msg = (
+        "The `json_extract_array` is deprecated and will be removed in a future version. "
+        "Use `json_query_array` instead."
+    )
+    warnings.warn(bfe.format_message(msg), category=UserWarning)
    return input._apply_unary_op(ops.JSONExtractArray(json_path=json_path))


@@ -273,6 +282,56 @@ def json_query(
    return input._apply_unary_op(ops.JSONQuery(json_path=json_path))


+def json_query_array(
+    input: series.Series,
+    json_path: str = "$",
+) -> series.Series:
+    """Extracts a JSON array and converts it to a SQL array of JSON-formatted
+    `STRING` or `JSON` values. This function uses double quotes to escape invalid
+    JSONPath characters in JSON keys. For example: `"a.b"`.
+
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.bigquery as bbq
+        >>> bpd.options.display.progress_bar = None
+
+        >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]'])
+        >>> bbq.json_query_array(s)
+        0    ['1' '2' '3']
+        1        ['4' '5']
+        dtype: list<item: string>[pyarrow]
+
+        >>> s = bpd.Series([
+        ...   '{"fruits": [{"name": "apple"}, {"name": "cherry"}]}',
+        ...   '{"fruits": [{"name": "guava"}, {"name": "grapes"}]}'
+        ... ])
+        >>> bbq.json_query_array(s, "$.fruits")
+        0    ['{"name":"apple"}' '{"name":"cherry"}']
+        1    ['{"name":"guava"}' '{"name":"grapes"}']
+        dtype: list<item: string>[pyarrow]
+
+        >>> s = bpd.Series([
+        ...   '{"fruits": {"color": "red",   "names": ["apple","cherry"]}}',
+        ...   '{"fruits": {"color": "green", "names": ["guava", "grapes"]}}'
+        ... ])
+        >>> bbq.json_query_array(s, "$.fruits.names")
+        0    ['"apple"' '"cherry"']
+        1    ['"guava"' '"grapes"']
+        dtype: list<item: string>[pyarrow]
+
+    Args:
+        input (bigframes.series.Series):
+            The Series containing JSON data (as native JSON objects or JSON-formatted strings).
+        json_path (str):
+            The JSON path identifying the data that you want to obtain from the input.
+
+    Returns:
+        bigframes.series.Series: A new Series with the parsed arrays from the input.
+    """
+    return input._apply_unary_op(ops.JSONQueryArray(json_path=json_path))
+
+
 def json_value(
    input: series.Series,
    json_path: str,

@@ -1379,6 +1379,19 @@ def json_query(json_or_json_string, json_path: ibis_dtypes.str):  # type: ignore
    return json_query_op(json_or_json_string=x, json_path=op.json_path)


+@scalar_op_compiler.register_unary_op(ops.JSONQueryArray, pass_op=True)
+def json_query_array_op_impl(x: ibis_types.Value, op: ops.JSONQueryArray):
+    # Define a user-defined function whose returned type is dynamically matching the input.
+    def json_query_array(json_or_json_string, json_path: ibis_dtypes.str):  # type: ignore
+        """Extracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value."""
+        ...
+
+    return_type = x.type()
+    json_query_array.__annotations__["return"] = ibis_dtypes.Array[return_type]  # type: ignore
+    json_query_op = ibis_udf.scalar.builtin(json_query_array)
+    return json_query_op(json_or_json_string=x, json_path=op.json_path)
+
+
 @scalar_op_compiler.register_unary_op(ops.ParseJSON, pass_op=True)
 def parse_json_op_impl(x: ibis_types.Value, op: ops.ParseJSON):
    return parse_json(json_str=x)

@@ -109,6 +109,7 @@
    JSONExtractArray,
    JSONExtractStringArray,
    JSONQuery,
+    JSONQueryArray,
    JSONSet,
    JSONValue,
    ParseJSON,
@@ -359,6 +360,7 @@
    "JSONExtractArray",
    "JSONExtractStringArray",
    "JSONQuery",
+    "JSONQueryArray",
    "JSONSet",
    "JSONValue",
    "ParseJSON",

@@ -37,6 +37,23 @@ def output_type(self, *input_types):
        return input_type


+@dataclasses.dataclass(frozen=True)
+class JSONQueryArray(base_ops.UnaryOp):
+    name: typing.ClassVar[str] = "json_query_array"
+    json_path: str
+
+    def output_type(self, *input_types):
+        input_type = input_types[0]
+        if not dtypes.is_json_like(input_type):
+            raise TypeError(
+                "Input type must be a valid JSON object or JSON-formatted string type."
+                + f" Received type: {input_type}"
+            )
+        return pd.ArrowDtype(
+            pa.list_(dtypes.bigframes_dtype_to_arrow_dtype(input_type))
+        )
+
+
 @dataclasses.dataclass(frozen=True)
 class JSONExtractArray(base_ops.UnaryOp):
    name: typing.ClassVar[str] = "json_extract_array"

@@ -128,7 +128,8 @@ def test_json_extract_array_from_json():
        ['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4", "5"]}', "{}"],
        dtype=dtypes.JSON_DTYPE,
    )
-    actual = bbq.json_extract_array(s, "$.a")
+    with pytest.warns(UserWarning, match="The `json_extract_array` is deprecated"):
+        actual = bbq.json_extract_array(s, "$.a")

    # This code provides a workaround for issue https://github.com/apache/arrow/issues/45262,
    # which currently prevents constructing a series using the pa.list_(db_types.JSONArrrowType())
@@ -241,6 +242,66 @@ def test_json_query_w_invalid_series_type():
        bbq.json_query(s, "$.a")


+def test_json_query_array_from_json():
+    s = bpd.Series(
+        ['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4", "5"]}', "{}"],
+        dtype=dtypes.JSON_DTYPE,
+    )
+    actual = bbq.json_query_array(s, "$.a")
+
+    # This code provides a workaround for issue https://github.com/apache/arrow/issues/45262,
+    # which currently prevents constructing a series using the pa.list_(db_types.JSONArrrowType())
+    sql = """
+        SELECT 0 AS id, [JSON '"ab"', JSON '"2"', JSON '"3 xy"'] AS data,
+        UNION ALL
+        SELECT 1, [],
+        UNION ALL
+        SELECT 2, [JSON '"4"', JSON '"5"'],
+        UNION ALL
+        SELECT 3, null,
+    """
+    df = bpd.read_gbq(sql).set_index("id").sort_index()
+    expected = df["data"]
+    expected.index.name = None
+    expected.name = None
+
+    pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas())
+
+
+def test_json_query_array_from_json_strings():
+    s = bpd.Series(
+        ['{"a": ["ab", "2", "3 xy"]}', '{"a": []}', '{"a": ["4","5"]}', "{}"],
+        dtype=pd.StringDtype(storage="pyarrow"),
+    )
+    actual = bbq.json_query_array(s, "$.a")
+    expected = bpd.Series(
+        [['"ab"', '"2"', '"3 xy"'], [], ['"4"', '"5"'], None],
+        dtype=pd.ArrowDtype(pa.list_(pa.string())),
+    )
+
+    pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas())
+
+
+def test_json_query_array_from_json_array_strings():
+    s = bpd.Series(
+        ["[1, 2, 3]", "[]", "[4,5]"],
+        dtype=pd.StringDtype(storage="pyarrow"),
+    )
+    actual = bbq.json_query_array(s)
+    expected = bpd.Series(
+        [["1", "2", "3"], [], ["4", "5"]],
+        dtype=pd.ArrowDtype(pa.list_(pa.string())),
+    )
+
+    pd.testing.assert_series_equal(actual.to_pandas(), expected.to_pandas())
+
+
+def test_json_query_array_w_invalid_series_type():
+    s = bpd.Series([1, 2])
+    with pytest.raises(TypeError):
+        bbq.json_query_array(s)
+
+
 def test_json_value_from_json():
    s = bpd.Series(
        ['{"a": {"b": [1, 2]}}', '{"a": {"c": 1}}', '{"a": {"b": 0}}'],