aminghadersohi commented on code in PR #43771:
URL: https://github.com/apache/superset/pull/43771#discussion_r3972014690
##########
superset/mcp_service/chart/query_result.py:
##########
@@ -15,82 +15,1521 @@
# specific language governing permissions and limitations
# under the License.
-"""Helpers for interpreting ChartDataCommand result envelopes."""
+"""Canonicalize and validate ``ChartDataCommand`` result envelopes."""
import math
+import time as system_time
+from bisect import bisect_right
from collections.abc import Mapping
+from dataclasses import dataclass
+from datetime import date, datetime, time, timedelta, timezone
+from decimal import Decimal
+from enum import Enum
from typing import Any
+from uuid import UUID
+from zoneinfo import ZoneInfo
+import numpy as np
+import pandas as pd
+import pytz
+from dateutil import tz as dateutil_tz, zoneinfo as dateutil_zoneinfo
+from pydantic import BaseModel
+from pydantic_core import to_json
+
+from superset.common.chart_data import ChartDataResultFormat
+from superset.common.db_query_status import QueryStatus
+from superset.constants import CACHE_DISABLED_TIMEOUT
from superset.mcp_service.chart.schemas import ChartError
+from superset.utils.core import (
+ ExtraFiltersReasonType,
+ ExtraFiltersTimeColumnType,
+ GenericDataType,
+)
FAILED_QUERY_STATUSES = frozenset(
{"error", "failed", "stopped", "timed_out", "cancelled", "canceled"}
)
+# These are aggregate envelope limits, not per-query allowances. In particular,
+# splitting a result across the maximum number of queries must not multiply the
+# permitted rows, nodes, or encoded bytes.
+MAX_QUERY_RESULTS = 32
+MAX_QUERY_RESULT_ROWS_PER_QUERY = 50_000
+MAX_QUERY_RESULT_ROWS = 100_000
+MAX_QUERY_RESULT_COLUMNS = 4_096
+MAX_QUERY_RESULT_VALUES = 2_500_000
+MAX_QUERY_RESULT_VALUE_BYTES = 16 * 1024 * 1024
+MAX_QUERY_RESULT_METADATA_BYTES = 1024 * 1024
+MAX_QUERY_RESULT_METADATA_ITEMS = 32_768
+MAX_RESULT_VALUE_ITEMS = 4_096
+MAX_RESULT_VALUE_DEPTH = 32
+MAX_RESULT_STRING_LENGTH = 65_536
+MAX_RESULT_KEY_LENGTH = 4_096
+MAX_RESULT_INTEGER_BITS = 4_096
+MAX_RESULT_INTEGER_DIGITS = 1_234
+MAX_RESULT_DECIMAL_DIGITS = 1_024
+MAX_RESULT_DECIMAL_MAGNITUDE = 4_096
+MAX_RESULT_DECIMAL_STORAGE = 2_048
+MAX_QUERY_RESULT_ROWCOUNT = 2**63 - 1
+MAX_QUERY_RESULT_CACHE_TIMEOUT = 2**31 - 1
+MAX_QUERY_RESULT_TIMESTAMP_LENGTH = 64
+
+_ERROR_KEYS = ("error", "errors", "error_message", "message", "detail")
+_MAX_ERROR_TEXT_BYTES = 2_000
+_TRUSTED_TIMEZONE_TYPES = (timezone, ZoneInfo)
+_SAFE_RESULT_ENUM_TYPES = frozenset(
+ {
+ ChartDataResultFormat,
+ QueryStatus,
+ ExtraFiltersReasonType,
+ ExtraFiltersTimeColumnType,
+ GenericDataType,
+ }
+)
+_RESULT_FORMAT_VALUES = frozenset(
+ object.__getattribute__(member, "_value_") for member in
ChartDataResultFormat
+)
+_COLTYPE_VALUES = frozenset(
+ object.__getattribute__(member, "_value_") for member in GenericDataType
+)
+_NUMPY_INTEGER_TYPES = frozenset(
+ type(value)
+ for value in (
+ np.int8(0),
+ np.int16(0),
+ np.int32(0),
+ np.int64(0),
+ np.uint8(0),
+ np.uint16(0),
+ np.uint32(0),
+ np.uint64(0),
+ )
+)
+_NUMPY_FLOAT_TYPES = frozenset(
+ type(value)
+ for value in (np.float16(0), np.float32(0), np.float64(0),
np.longdouble(0))
+)
+_PANDAS_NAT_TYPE = type(pd.NaT)
+_PANDAS_NA_TYPE = type(pd.NA)
+_PANDAS_PERIOD_TYPE = type(pd.Period("2000-01", freq="M"))
+_PANDAS_INTERVAL_TYPE = type(pd.Interval(0, 1))
+_DATEUTIL_FIXED_TIMEZONE_TYPES = frozenset(
+ {type(dateutil_tz.tzoffset(None, 0)), type(dateutil_tz.tzutc())}
+)
+_DATEUTIL_NAMED_TIMEZONE_TYPES = frozenset(
+ {dateutil_tz.tzfile, dateutil_zoneinfo.tzfile}
+)
+_DATEUTIL_TTINFO_TYPE = type(
+ object.__getattribute__(dateutil_tz.gettz("UTC"),
"__dict__")["_ttinfo_std"]
+)
+_DATEUTIL_LOCAL_TIMEZONE_TYPE = type(dateutil_tz.tzlocal())
+_PYTZ_FIXED_TIMEZONE_TYPES = frozenset({type(pytz.FixedOffset(1))})
+_MAX_DATEUTIL_TRANSITIONS = 4_096
+_MAX_DATEUTIL_TTINFOS = 256
+_MAX_DATEUTIL_TRANSITION_MAGNITUDE = 10**12
+
+
+@dataclass
+class _ResultBudget:
+ """Aggregate counters shared by every query and metadata value."""
+
+ rows: int = 0
+ values: int = 0
+ json_bytes: int = 0
+ metadata_items: int = 0
+ metadata_bytes: int = 0
+
+
+@dataclass(frozen=True)
+class _DateutilTimezoneState:
+ """Hook-free subset of a validated exact dateutil tzfile transition
table."""
-def _query_error_text(value: Any) -> str | None:
- """Convert a bounded query error payload into a useful message."""
- if value is None or value is False:
+ transitions: tuple[int, ...]
+ transition_offsets: tuple[int, ...]
+ standard_offset: int
+ before_offset: int | None
+
+
+def _invalid_result(message: str) -> ChartError:
+ return ChartError(
+ error=f"Chart query returned {message}.",
+ error_type="InvalidQueryResult",
+ )
+
+
+def _invalid_metadata(label: str) -> ChartError:
+ return ChartError(
+ error=f"{label} returned hostile or malformed metadata.",
+ error_type="InvalidQueryResult",
+ )
+
+
+def _safe_enum_value(value: Any, expected: frozenset[type[Any]]) -> Any | None:
+ """Read trusted enum storage without invoking public conversion hooks."""
+ if type(value) not in expected or type(value) not in
_SAFE_RESULT_ENUM_TYPES:
return None
- if isinstance(value, Mapping):
- for key in ("error", "error_message", "message", "detail"):
- if text := _query_error_text(value.get(key)):
- return text
+ return object.__getattribute__(value, "_value_")
+
+
+def _bounded_utf8_length(value: str, maximum: int) -> int | None:
+ """Return the exact UTF-8 size while bounding pre-encoding work."""
+ if str.__len__(value) > maximum:
return None
- if isinstance(value, (list, tuple)):
- parts = [text for item in value if (text := _query_error_text(item))]
- return "; ".join(parts[:3]) or None
- text = str(value)
- return text[:2000] if text else None
+ try:
+ encoded = str.encode(value, "utf-8", errors="strict")
+ except UnicodeEncodeError:
+ return None
+ size = bytes.__len__(encoded)
+ return size if size <= maximum else None
-def _failure_for_query_payload(
- payload: Mapping[str, Any], label: str
-) -> ChartError | None:
- """Extract one failure from a top-level or per-query payload."""
+def _json_string_size(value: str, maximum: int) -> int | None:
+ """Return compact UTF-8 JSON string size without serializing the value."""
+ raw_size = _bounded_utf8_length(value, maximum)
+ if raw_size is None:
+ return None
+ escaped_size = raw_size + 2
+ for character in value:
+ codepoint = ord(character)
+ if character in {'"', "\\", "\b", "\t", "\n", "\f", "\r"}:
+ escaped_size += 1
+ elif codepoint < 0x20:
+ escaped_size += 5
+ return escaped_size
+
+
+def _integer_json_size(value: int) -> int:
+ """Return exact decimal JSON size without rendering the bounded integer."""
+ magnitude = -value if value < 0 else value
+ if magnitude == 0:
+ digits = 1
+ else:
+ bits = int.bit_length(magnitude)
+ digits = ((bits - 1) * 30103) // 100000 + 1
+ if magnitude >= 10**digits:
+ digits += 1
+ return digits + (value < 0)
+
+
+def _container_json_syntax_size(item_count: int, *, mapping: bool) -> int:
+ """Return braces/brackets plus compact separators and mapping colons."""
+ if item_count == 0:
+ return 2
+ return 2 + item_count - 1 + (item_count if mapping else 0)
+
+
+def _normalized_scalar_json_size(value: Any) -> int:
+ """Return exact compact JSON size for a normalized scalar."""
+ value_type = type(value)
+ if value is None:
+ return 4
+ if value_type is bool:
+ return 4 if value else 5
+ if value_type is str:
+ size = _json_string_size(value, MAX_RESULT_STRING_LENGTH)
+ assert size is not None
+ return size
+ if value_type is int:
+ return _integer_json_size(value)
+ if value_type is float:
+ return len(float.__repr__(value))
+ if value_type is Decimal:
+ # Pydantic serializes Decimal values as JSON strings so their exact
+ # finite value survives the wire projection without binary rounding.
+ text = Decimal.__str__(value)
+ size = _json_string_size(text, MAX_RESULT_STRING_LENGTH)
+ assert size is not None
+ return size
+ raise AssertionError("result scalar was not normalized")
+
+
+def _pydantic_scalar_json_size(value: Any) -> int:
+ """Return the scalar size emitted by Pydantic's JSON serializer."""
+ if type(value) is float:
+ # pydantic-core uses the shortest exponent (``1e-7``), while Python's
+ # repr retains a leading zero (``1e-07``).
+ return len(to_json(value))
+ return _normalized_scalar_json_size(value)
+
+
+def _charge_json_bytes(
+ budget: _ResultBudget, size: int, *, metadata: bool = False
+) -> str | None:
+ budget.json_bytes += size
+ if budget.json_bytes > MAX_QUERY_RESULT_VALUE_BYTES:
+ return "too many aggregate JSON bytes"
+ if metadata:
+ budget.metadata_bytes += size
+ if budget.metadata_bytes > MAX_QUERY_RESULT_METADATA_BYTES:
+ return "too many aggregate metadata JSON bytes"
+ return None
+
+
+def _charge_value(budget: _ResultBudget, *, metadata: bool = False) -> str |
None:
+ budget.values += 1
+ if budget.values > MAX_QUERY_RESULT_VALUES:
+ return "too many aggregate values"
+ if metadata:
+ budget.metadata_items += 1
+ if budget.metadata_items > MAX_QUERY_RESULT_METADATA_ITEMS:
+ return "too many aggregate metadata values"
+ return None
+
+
+def _charge_text(
+ value: str,
+ budget: _ResultBudget,
+ *,
+ key: bool = False,
+ metadata: bool = False,
+) -> str | None:
+ maximum = (
+ MAX_RESULT_KEY_LENGTH
+ if key
+ else MAX_QUERY_RESULT_METADATA_BYTES
+ if metadata
+ else MAX_RESULT_STRING_LENGTH
+ )
+ size = _json_string_size(value, maximum)
+ if size is None:
+ return "an invalid or oversized object key" if key else "invalid text
data"
+ return _charge_json_bytes(budget, size, metadata=metadata)
+
+
+def _integer_failure(value: int) -> str | None:
+ bits = int.bit_length(value)
+ if bits > MAX_RESULT_INTEGER_BITS:
+ return "an oversized integer"
+ digits = 1 if bits == 0 else ((bits - 1) * 30103) // 100000 + 1
+ if digits > MAX_RESULT_INTEGER_DIGITS:
+ return "an oversized integer"
+ return None
+
+
+def _decimal_failure(value: Decimal) -> str | None:
+ if Decimal.__sizeof__(value) > MAX_RESULT_DECIMAL_STORAGE:
+ return "an oversized Decimal"
+ if not Decimal.is_finite(value):
+ return "a non-finite Decimal"
+ parts = Decimal.as_tuple(value)
+ if tuple.__len__(parts.digits) > MAX_RESULT_DECIMAL_DIGITS:
+ return "an oversized Decimal"
+ exponent = parts.exponent
+ if type(exponent) is not int or abs(exponent) >
MAX_RESULT_DECIMAL_MAGNITUDE:
+ return "an oversized Decimal"
+ return None
+
+
+def _type_mro(value_type: type[Any]) -> tuple[type[Any], ...]:
+ """Read a concrete type's MRO without consulting metaclass overrides."""
+ try:
+ mro = type.__getattribute__(value_type, "__mro__")
+ except (AttributeError, TypeError): # pragma: no cover - defensive
metaclass
+ return ()
+ return mro if type(mro) is tuple else ()
+
+
+def _timezone_name_without_hooks(tzinfo: Any) -> str | None: # noqa: C901
+ """Read common pytz/dateutil zone state without dispatching timezone
hooks."""
+ value_mro = _type_mro(type(tzinfo))
+ if any(base is pytz.tzinfo.BaseTzInfo for base in value_mro):
+ for base in value_mro:
+ try:
+ namespace = type.__getattribute__(base, "__dict__")
+ except (AttributeError, TypeError): # pragma: no cover
+ continue
+ zone = namespace.get("zone")
+ if type(zone) is str and _bounded_utf8_length(zone, 256) is not
None:
+ try:
+ canonical = pytz.timezone(zone)
+ except (KeyError, ValueError):
+ return None
+ # Generated pytz types are trusted; arbitrary subclasses that
+ # inherit their internal fields are not.
+ return zone if type(canonical) is type(tzinfo) else None
+
+ if type(tzinfo) not in _DATEUTIL_NAMED_TIMEZONE_TYPES:
+ return None
+
+ try:
+ namespace = object.__getattribute__(tzinfo, "__dict__")
+ except (AttributeError, TypeError):
+ return None
+ if type(namespace) is not dict:
+ return None
+ filename = dict.get(namespace, "_filename")
+ if type(filename) is not str or _bounded_utf8_length(filename, 4_096) is
None:
+ return None
+ marker = "/zoneinfo/"
+ if (offset := str.find(filename, marker)) >= 0:
+ name = str.__getitem__(filename, slice(offset + len(marker), None))
+ elif not str.startswith(filename, "/") and str.find(filename, "\\") < 0:
+ name = filename
+ else:
+ return None
+ parts = str.split(name, "/")
+ if not parts or any(part in {"", ".", ".."} for part in parts):
+ return None
+ return name if _bounded_utf8_length(name, 256) is not None else None
+
+
+def _object_namespace(value: Any) -> dict[str, Any] | None:
+ """Read exact instance storage without descriptor dispatch."""
+ try:
+ namespace = object.__getattribute__(value, "__dict__")
+ except (AttributeError, TypeError):
+ return None
+ return namespace if type(namespace) is dict else None
+
+
+def _dateutil_ttinfo_offset_without_hooks(value: Any) -> int | None:
+ """Validate one exact dateutil transition record and return its offset."""
+ if type(value) is not _DATEUTIL_TTINFO_TYPE:
+ return None
+ try:
+ offset = object.__getattribute__(value, "offset")
+ delta = object.__getattribute__(value, "delta")
+ isdst = object.__getattribute__(value, "isdst")
+ abbreviation = object.__getattribute__(value, "abbr")
+ is_standard = object.__getattribute__(value, "isstd")
+ is_gmt = object.__getattribute__(value, "isgmt")
+ dst_offset = object.__getattribute__(value, "dstoffset")
+ except (AttributeError, TypeError):
+ return None
+ if type(offset) is not int or not -86_400 < offset < 86_400:
+ return None
+ if type(delta) is not timedelta or delta != timedelta(seconds=offset):
+ return None
+ if type(isdst) is not int or isdst not in {0, 1}:
+ return None
+ if abbreviation is not None and (
+ type(abbreviation) is not str or _bounded_utf8_length(abbreviation,
256) is None
+ ):
+ return None
+ if type(is_standard) is not bool or type(is_gmt) is not bool:
+ return None
+ if type(dst_offset) is not timedelta:
+ return None
+ if not -timedelta(days=1) < dst_offset < timedelta(days=1):
+ return None
+ return offset
+
+
+def _dateutil_named_state_without_hooks( # noqa: C901
+ tzinfo: Any,
+) -> _DateutilTimezoneState | None:
+ """Validate bounded exact dateutil tzfile state without timezone hooks."""
+ if type(tzinfo) not in _DATEUTIL_NAMED_TIMEZONE_TYPES:
+ return None
+ if _timezone_name_without_hooks(tzinfo) is None:
+ return None
+ namespace = _object_namespace(tzinfo)
+ if namespace is None:
+ return None
+ transitions = dict.get(namespace, "_trans_list")
+ utc_transitions = dict.get(namespace, "_trans_list_utc")
+ transition_info = dict.get(namespace, "_trans_idx")
+ info_list = dict.get(namespace, "_ttinfo_list")
+ standard_info = dict.get(namespace, "_ttinfo_std")
+ before_info = dict.get(namespace, "_ttinfo_before")
+ first_info = dict.get(namespace, "_ttinfo_first")
+ if (
+ type(transitions) is not tuple
+ or type(utc_transitions) is not tuple
+ or type(transition_info) is not tuple
+ or type(info_list) is not list
+ or tuple.__len__(transitions) > _MAX_DATEUTIL_TRANSITIONS
+ or tuple.__len__(utc_transitions) != tuple.__len__(transitions)
+ or tuple.__len__(transition_info) != tuple.__len__(transitions)
+ or list.__len__(info_list) == 0
+ or list.__len__(info_list) > _MAX_DATEUTIL_TTINFOS
+ ):
+ return None
+
+ previous_transition: int | None = None
+ previous_utc_transition: int | None = None
+ for index in range(tuple.__len__(transitions)):
+ transition = tuple.__getitem__(transitions, index)
+ utc_transition = tuple.__getitem__(utc_transitions, index)
+ if (
+ type(transition) is not int
+ or type(utc_transition) is not int
+ or abs(transition) > _MAX_DATEUTIL_TRANSITION_MAGNITUDE
+ or abs(utc_transition) > _MAX_DATEUTIL_TRANSITION_MAGNITUDE
+ or (previous_transition is not None and transition <=
previous_transition)
+ or (
+ previous_utc_transition is not None
+ and utc_transition <= previous_utc_transition
+ )
+ ):
+ return None
+ previous_transition = transition
+ previous_utc_transition = utc_transition
+
+ known_info_ids: set[int] = set()
+ for index in range(list.__len__(info_list)):
+ info = list.__getitem__(info_list, index)
+ if _dateutil_ttinfo_offset_without_hooks(info) is None:
+ return None
+ known_info_ids.add(id(info))
+ for info in (standard_info, before_info, first_info):
+ if info is not None and id(info) not in known_info_ids:
+ return None
+ for index in range(tuple.__len__(transition_info)):
+ if id(tuple.__getitem__(transition_info, index)) not in known_info_ids:
+ return None
+ if not transitions:
+ if (
+ standard_info is not list.__getitem__(info_list, 0)
+ or first_info is not standard_info
+ or before_info is not None
+ ):
+ return None
+ else:
+ expected_standard = None
+ expected_dst = None
+ for index in range(tuple.__len__(transition_info) - 1, -1, -1):
+ info = tuple.__getitem__(transition_info, index)
+ is_dst = object.__getattribute__(info, "isdst")
+ if expected_standard is None and not is_dst:
+ expected_standard = info
+ elif expected_dst is None and is_dst:
+ expected_dst = info
+ if expected_standard is not None and expected_dst is not None:
+ break
+ if expected_standard is None:
+ expected_standard = expected_dst
+ expected_before = None
+ for index in range(list.__len__(info_list)):
+ info = list.__getitem__(info_list, index)
+ if not object.__getattribute__(info, "isdst"):
+ expected_before = info
+ break
+ if expected_before is None:
+ expected_before = list.__getitem__(info_list, 0)
+ if standard_info is not expected_standard or before_info is not
expected_before:
+ return None
+ standard_offset = _dateutil_ttinfo_offset_without_hooks(standard_info)
+ if standard_offset is None:
+ return None
+ before_offset = (
+ _dateutil_ttinfo_offset_without_hooks(before_info)
+ if before_info is not None
+ else None
+ )
+ transition_offsets: list[int] = []
+ previous_offset: int | None = None
+ previous_base_offset: int | None = None
+ previous_is_dst: int | None = None
+ previous_dst_offset = 0
+ for index in range(tuple.__len__(transition_info)):
+ info = tuple.__getitem__(transition_info, index)
+ if id(info) not in known_info_ids:
+ return None
+ offset = _dateutil_ttinfo_offset_without_hooks(info)
+ if offset is None:
+ return None
+ is_dst = object.__getattribute__(info, "isdst")
+ dst_offset_seconds = 0
+ if previous_is_dst is not None and is_dst:
+ if not previous_is_dst:
+ assert previous_offset is not None
+ dst_offset_seconds = offset - previous_offset
+ if not dst_offset_seconds and previous_dst_offset:
+ dst_offset_seconds = previous_dst_offset
+ previous_dst_offset = dst_offset_seconds
+ base_offset = offset - dst_offset_seconds
+ adjustment = base_offset
+ if (
+ previous_base_offset is not None
+ and base_offset != previous_base_offset
+ and is_dst != previous_is_dst
+ ):
+ adjustment = previous_base_offset
+ if (
+ tuple.__getitem__(transitions, index)
+ != tuple.__getitem__(utc_transitions, index) + adjustment
+ ):
+ return None
+ transition_offsets.append(offset)
+ previous_offset = offset
+ previous_base_offset = base_offset
+ previous_is_dst = is_dst
+ if transitions and before_offset is None:
+ return None
+ return _DateutilTimezoneState(
+ transitions=transitions,
+ transition_offsets=tuple(transition_offsets),
+ standard_offset=standard_offset,
+ before_offset=before_offset,
+ )
+
+
+def _dateutil_named_offset_without_hooks( # noqa: C901
+ value: datetime, tzinfo: Any
+) -> timezone | None:
+ """Preserve dateutil's source-selected wall offset from validated state."""
+ state = _dateutil_named_state_without_hooks(tzinfo)
+ if state is None:
+ return None
+ epoch_ordinal = date.toordinal(date(1970, 1, 1))
+ wall_timestamp = (
+ (datetime.toordinal(value) - epoch_ordinal) * 86_400
+ + value.hour * 3_600
+ + value.minute * 60
+ + value.second
+ )
+ transitions = state.transitions
+ selected_offset: int | None
+ if not transitions:
+ selected_offset = state.standard_offset
+ else:
+ index = bisect_right(transitions, wall_timestamp) - 1
+
+ def offset_at(transition_index: int | None) -> int | None:
+ if transition_index is None or transition_index + 1 >=
len(transitions):
+ return state.standard_offset
+ if transition_index < 0:
+ return state.before_offset
+ return state.transition_offsets[transition_index]
+
+ if index > 0:
+ selected_offset = offset_at(index)
+ previous_offset = offset_at(index - 1)
+ if selected_offset is None or previous_offset is None:
+ return None
+ is_ambiguous = wall_timestamp < transitions[index] + (
+ previous_offset - selected_offset
+ )
+ if not value.fold and is_ambiguous:
+ index -= 1
+ selected_offset = offset_at(index)
+ if selected_offset is None:
+ return None
+ try:
+ return timezone(timedelta(seconds=selected_offset))
+ except (OverflowError, ValueError):
+ return None
+
+
+def _pytz_named_offset_without_hooks(tzinfo: Any) -> timezone | None:
+ """Return a localized pytz zone's stored offset without calling hooks."""
+ if _timezone_name_without_hooks(tzinfo) is None:
+ return None
+ namespace = _object_namespace(tzinfo)
+ offset = dict.get(namespace, "_utcoffset") if namespace is not None else
None
+ if offset is None:
+ # Static pytz zones store their fixed offset on the generated class.
+ class_namespace = type.__getattribute__(type(tzinfo), "__dict__")
+ offset = class_namespace.get("_utcoffset")
+ if type(offset) is not timedelta:
+ return None
+ try:
+ return timezone(offset)
+ except ValueError:
+ return None
+
+
+def _dateutil_local_offset_without_hooks(
+ value: datetime, tzinfo: Any
+) -> timezone | None:
+ """Select a dateutil local offset using builtin system-time data."""
+ if type(tzinfo) is not _DATEUTIL_LOCAL_TIMEZONE_TYPE:
+ return None
+ namespace = _object_namespace(tzinfo)
+ if namespace is None:
+ return None
+ standard_offset = dict.get(namespace, "_std_offset")
+ daylight_offset = dict.get(namespace, "_dst_offset")
+ has_daylight = dict.get(namespace, "_hasdst")
+ if (
+ type(standard_offset) is not timedelta
+ or type(daylight_offset) is not timedelta
+ or type(has_daylight) is not bool
+ ):
+ return None
+ selected_offset = standard_offset
+ if has_daylight:
+ epoch = datetime(1970, 1, 1)
+ naive = datetime(
+ value.year,
+ value.month,
+ value.day,
+ value.hour,
+ value.minute,
+ value.second,
+ value.microsecond,
+ )
+ timestamp = (naive - epoch).total_seconds()
+ try:
+ is_daylight = bool(
+ system_time.localtime(timestamp +
system_time.timezone).tm_isdst
+ )
+ daylight_saved = daylight_offset - standard_offset
+ previous_is_daylight = bool(
+ system_time.localtime(
+ timestamp
+ - timedelta.total_seconds(daylight_saved)
+ + system_time.timezone
+ ).tm_isdst
+ )
+ except (OverflowError, OSError, ValueError):
+ return None
+ if not is_daylight and is_daylight != previous_is_daylight:
+ is_daylight = not bool(value.fold)
+ selected_offset = daylight_offset if is_daylight else standard_offset
+ try:
+ return timezone(selected_offset)
+ except ValueError:
+ return None
+
+
+def _canonical_timezone(tzinfo: Any) -> timezone | ZoneInfo | None: # noqa:
C901
+ if any(type(tzinfo) is trusted for trusted in _TRUSTED_TIMEZONE_TYPES):
+ return tzinfo
+ if tzinfo is pytz.UTC:
+ return timezone.utc
+ if type(tzinfo) in _DATEUTIL_FIXED_TIMEZONE_TYPES:
+ try:
+ namespace = object.__getattribute__(tzinfo, "__dict__")
+ except (AttributeError, TypeError):
+ return timezone.utc if type(tzinfo) is type(dateutil_tz.tzutc())
else None
+ if type(namespace) is not dict:
+ return None
+ offset = dict.get(namespace, "_offset")
+ if type(offset) is not timedelta:
+ return timezone.utc if type(tzinfo) is type(dateutil_tz.tzutc())
else None
+ if abs(offset) >= timedelta(days=1):
+ return None
+ return timezone(offset)
+ if type(tzinfo) in _PYTZ_FIXED_TIMEZONE_TYPES:
+ try:
+ namespace = object.__getattribute__(tzinfo, "__dict__")
+ except (AttributeError, TypeError):
+ return None
+ if type(namespace) is not dict:
+ return None
+ minutes = dict.get(namespace, "_minutes")
+ if type(minutes) is not int or not -1_440 < minutes < 1_440:
+ return None
+ return timezone(timedelta(minutes=minutes))
+ return None
+
+
+def _timestamp_offset_without_hooks(value: pd.Timestamp) -> timezone | None:
+ """Recover a timestamp's stored wall-clock offset without timezone
hooks."""
+ multipliers = {"s": 1_000_000_000, "ms": 1_000_000, "us": 1_000, "ns": 1}
+ multiplier = multipliers.get(value.unit)
+ if multiplier is None:
+ return None
+ try:
+ instant_ns = int(value.asm8.view("i8")) * multiplier
+ epoch_ordinal = date.toordinal(date(1970, 1, 1))
+ wall_ns = (
+ (
+ (datetime.toordinal(value) - epoch_ordinal) * 86_400
+ + value.hour * 3600
+ + value.minute * 60
+ + value.second
+ )
+ * 1_000_000_000
+ + value.microsecond * 1000
+ + value.nanosecond
+ )
+ offset_ns = wall_ns - instant_ns
+ if offset_ns % 1000:
+ return None
+ return timezone(timedelta(microseconds=offset_ns // 1000))
+ except (OverflowError, TypeError, ValueError):
+ return None
+
+
+def _canonical_datetime(value: datetime) -> tuple[str | None, str | None]:
+ """Serialize an exact datetime through trusted timezone state only."""
+ tzinfo = value.tzinfo
+ canonical_value = value
+ if tzinfo is not None and not any(
+ type(tzinfo) is trusted for trusted in _TRUSTED_TIMEZONE_TYPES
+ ):
+ canonical_tz = (
+ _pytz_named_offset_without_hooks(tzinfo)
+ or _dateutil_local_offset_without_hooks(value, tzinfo)
+ or _dateutil_named_offset_without_hooks(value, tzinfo)
+ or _canonical_timezone(tzinfo)
+ )
+ if canonical_tz is None:
+ return None, "a datetime with an unsupported timezone"
+ canonical_value = datetime(
+ value.year,
+ value.month,
+ value.day,
+ value.hour,
+ value.minute,
+ value.second,
+ value.microsecond,
+ tzinfo=canonical_tz,
+ fold=value.fold,
+ )
+ try:
+ return datetime.isoformat(canonical_value), None
+ except (OverflowError, TypeError, ValueError):
+ return None, "an invalid datetime"
+
+
+def _canonical_time(value: time) -> tuple[str | None, str | None]:
+ """Serialize an exact time through trusted timezone state only."""
+ tzinfo = value.tzinfo
+ canonical_value = value
+ if tzinfo is not None and not any(
+ type(tzinfo) is trusted for trusted in _TRUSTED_TIMEZONE_TYPES
+ ):
+ if _dateutil_named_state_without_hooks(tzinfo) is not None:
+ canonical_value = time(
+ value.hour,
+ value.minute,
+ value.second,
+ value.microsecond,
+ fold=value.fold,
+ )
+ else:
+ canonical_tz = _canonical_timezone(tzinfo)
+ if canonical_tz is None and type(tzinfo) is
_DATEUTIL_LOCAL_TIMEZONE_TYPE:
+ namespace = _object_namespace(tzinfo)
+ if namespace is not None and dict.get(namespace, "_hasdst") is
False:
+ offset = dict.get(namespace, "_std_offset")
+ if type(offset) is timedelta:
+ try:
+ canonical_tz = timezone(offset)
+ except ValueError:
+ canonical_tz = None
+ if canonical_tz is None:
+ return None, "a time with an unsupported timezone"
+ canonical_value = time(
+ value.hour,
+ value.minute,
+ value.second,
+ value.microsecond,
+ tzinfo=canonical_tz,
+ fold=value.fold,
+ )
+ try:
+ return time.isoformat(canonical_value), None
+ except (OverflowError, TypeError, ValueError):
+ return None, "an invalid time"
+
+
+def _canonical_timestamp(value: pd.Timestamp) -> tuple[str | None, str | None]:
+ """Preserve a trusted timestamp's instant, offset, nanoseconds, and
fold."""
+ try:
+ tzinfo = value.tzinfo
+ if tzinfo is not None and not any(
+ type(tzinfo) is trusted for trusted in _TRUSTED_TIMEZONE_TYPES
+ ):
+ supported_timezone = (
+ _canonical_timezone(tzinfo) is not None
+ or _pytz_named_offset_without_hooks(tzinfo) is not None
+ or type(tzinfo) is _DATEUTIL_LOCAL_TIMEZONE_TYPE
+ or _dateutil_named_state_without_hooks(tzinfo) is not None
+ )
+ if not supported_timezone:
+ return None, "a timestamp with an unsupported timezone"
+ canonical_tz = _timestamp_offset_without_hooks(value)
+ if canonical_tz is None:
+ return None, "an invalid timestamp"
+ raw_value = value.asm8.view("i8")
+ value = pd.Timestamp(raw_value, unit=value.unit,
tz="UTC").tz_convert(
+ canonical_tz
+ )
+ return pd.Timestamp.isoformat(value), None
+ except (KeyError, OverflowError, TypeError, ValueError):
+ return None, "an invalid timestamp"
+
+
+def _normalize_scalar(value: Any) -> tuple[Any, str | None]: # noqa: C901
+ """Convert one exact trusted producer scalar to a JSON-safe scalar."""
+ value_type = type(value)
+ if value is None or value_type is bool or value_type is str:
+ return value, None
+ if value_type is int:
+ return value, _integer_failure(value)
+ if value_type is float:
+ if math.isnan(value):
+ return None, None
+ return (value, None) if math.isfinite(value) else (None, "a non-finite
number")
+ if value_type is Decimal:
+ return value, _decimal_failure(value)
+ if value_type is datetime:
+ return _canonical_datetime(value)
+ if value_type is time:
+ return _canonical_time(value)
+ if value_type is date:
+ return date.isoformat(value), None
+ if value_type is timedelta:
+ try:
+ return pd.Timedelta(value).isoformat(), None
+ except (OverflowError, TypeError, ValueError):
+ return None, "an invalid duration"
+ if value_type is UUID:
+ return UUID.__str__(value), None
+
+ if value_type is _PANDAS_NAT_TYPE or value_type is _PANDAS_NA_TYPE:
+ return None, None
+ if value_type is pd.Timestamp:
+ return _canonical_timestamp(value)
+ if value_type is pd.Timedelta:
+ if pd.isna(value):
+ return None, None
+ try:
+ return pd.Timedelta.isoformat(value), None
+ except (OverflowError, TypeError, ValueError):
+ return None, "an invalid pandas duration"
+ if value_type is _PANDAS_PERIOD_TYPE or value_type is
_PANDAS_INTERVAL_TYPE:
+ # These concrete immutable pandas extension scalars are trusted. Exact
+ # type checks deliberately exclude subclasses with conversion hooks.
+ try:
+ normalized_text = str(value)
+ except (OverflowError, TypeError, ValueError):
+ return None, "an invalid pandas scalar"
+ if _bounded_utf8_length(normalized_text, MAX_RESULT_STRING_LENGTH) is
None:
+ return None, "an oversized pandas scalar"
+ return normalized_text, None
+ if value_type in _NUMPY_INTEGER_TYPES:
+ normalized = int(value)
+ return normalized, _integer_failure(normalized)
+ if value_type in _NUMPY_FLOAT_TYPES:
+ normalized_float = float(value)
+ if math.isnan(normalized_float):
+ return None, None
+ return (
+ (normalized_float, None)
+ if math.isfinite(normalized_float)
+ else (None, "a non-finite NumPy number")
+ )
+ if value_type is np.bool_:
+ return bool(value), None
+ if value_type is np.str_:
+ return str(value), None
+ if value_type is np.datetime64:
+ if np.isnat(value):
+ return None, None
+ try:
+ return _canonical_timestamp(pd.Timestamp(value))
+ except (OverflowError, TypeError, ValueError):
+ return None, "an invalid NumPy timestamp"
+ if value_type is np.timedelta64:
+ if np.isnat(value):
+ return None, None
+ try:
+ return pd.Timedelta(value).isoformat(), None
+ except (OverflowError, TypeError, ValueError):
+ return None, "an invalid NumPy duration"
+ return None, "an unsupported or subclassed value"
+
+
+def _normalize_value( # noqa: C901
+ value: Any,
+ budget: _ResultBudget,
+ *,
+ enum_types: frozenset[type[Any]] = frozenset(),
+ metadata: bool = False,
+) -> tuple[Any, str | None]:
+ """Iteratively normalize one bounded exact-container value tree."""
+ stack: list[
+ tuple[Any, list[Any] | dict[str, Any] | None, int | str | None, int,
bool]
+ ] = [(value, None, None, 0, False)]
+ active_containers: set[int] = set()
+ root = value
+
+ while stack:
+ item, parent, slot, depth, leaving = stack.pop()
+ if leaving:
+ active_containers.remove(id(item))
+ continue
+ if depth > MAX_RESULT_VALUE_DEPTH:
+ return None, "excessively nested data"
+ if reason := _charge_value(budget, metadata=metadata):
+ return None, reason
+
+ if type(item) is list:
+ identity = id(item)
+ if identity in active_containers:
+ return None, "cyclic containers"
+ active_containers.add(identity)
+ width = list.__len__(item)
+ if width > MAX_RESULT_VALUE_ITEMS:
+ return None, "an oversized array"
+ if reason := _charge_json_bytes(
+ budget,
+ _container_json_syntax_size(width, mapping=False),
+ metadata=metadata,
+ ):
+ return None, reason
+ stack.append((item, None, None, depth, True))
+ stack.extend(
+ (list.__getitem__(item, index), item, index, depth + 1, False)
+ for index in range(width - 1, -1, -1)
+ )
+ continue
+
+ if type(item) is dict:
+ identity = id(item)
+ if identity in active_containers:
+ return None, "cyclic containers"
+ active_containers.add(identity)
+ width = dict.__len__(item)
+ if width > MAX_RESULT_VALUE_ITEMS:
+ return None, "an oversized object"
+ if reason := _charge_json_bytes(
+ budget,
+ _container_json_syntax_size(width, mapping=True),
+ metadata=metadata,
+ ):
+ return None, reason
+ children: list[tuple[Any, dict[str, Any], str, int, bool]] = []
+ for key, child in dict.items(item):
+ if type(key) is not str:
+ return None, "a non-string object key"
+ if reason := _charge_text(key, budget, key=True,
metadata=metadata):
+ return None, reason
+ children.append((child, item, key, depth + 1, False))
+ stack.append((item, None, None, depth, True))
+ stack.extend(reversed(children))
+ continue
+
+ source_item = item
+ if type(item) in enum_types:
+ normalized = _safe_enum_value(item, enum_types)
+ if normalized is None:
+ return None, "an unsupported enum"
+ item = normalized
+ elif any(base is Enum for base in _type_mro(type(item))):
+ return None, "an enum outside its expected metadata slot"
+
+ normalized, reason = _normalize_scalar(item)
+ if reason is not None:
+ return None, reason
+ max_string_bytes = (
+ MAX_QUERY_RESULT_METADATA_BYTES if metadata else
MAX_RESULT_STRING_LENGTH
+ )
+ if type(normalized) is str:
+ scalar_size = _json_string_size(normalized, max_string_bytes)
+ if scalar_size is None:
+ return None, "invalid text data"
+ else:
+ scalar_size = _normalized_scalar_json_size(normalized)
+ if reason := _charge_json_bytes(
+ budget,
+ scalar_size,
+ metadata=metadata,
+ ):
+ return None, reason
+ if parent is None:
+ root = normalized
+ elif normalized is not source_item:
+ if type(parent) is list:
+ assert type(slot) is int
+ list.__setitem__(parent, slot, normalized)
+ else:
+ assert type(parent) is dict
+ assert type(slot) is str
+ dict.__setitem__(parent, slot, normalized)
+ return root, None
+
+
+def _normalize_metadata_value(
+ payload: dict[str, Any], key: str, budget: _ResultBudget
+) -> str | None:
+ enum_slots: dict[str, frozenset[type[Any]]] = {
+ "status": frozenset({QueryStatus}),
+ "result_format": frozenset({ChartDataResultFormat}),
+ "coltypes": frozenset({GenericDataType}),
+ "applied_filters": frozenset({ExtraFiltersTimeColumnType}),
+ "rejected_filters": frozenset(
+ {ExtraFiltersReasonType, ExtraFiltersTimeColumnType}
+ ),
+ }
+ value = dict.__getitem__(payload, key)
+ normalized, reason = _normalize_value(
+ value,
+ budget,
+ enum_types=enum_slots.get(key, frozenset()),
+ metadata=True,
+ )
+ if reason is None and normalized is not value:
+ dict.__setitem__(payload, key, normalized)
+ return reason
+
+
+def _error_text(value: Any) -> str | None:
+ """Extract a bounded error from an already validated primitive tree."""
+ stack: list[Any] = [value]
+ parts: list[str] = []
+ used = 0
+ while stack and len(parts) < 3 and used < _MAX_ERROR_TEXT_BYTES:
+ item = stack.pop()
+ if type(item) is dict:
+ stack.extend(
+ reversed(
+ [dict.__getitem__(item, key) for key in _ERROR_KEYS if key
in item]
+ )
+ )
+ continue
+ if type(item) is list:
+ stack.extend(
+ list.__getitem__(item, index)
+ for index in range(list.__len__(item) - 1, -1, -1)
+ )
+ continue
+ if item is None or item is False:
+ continue
+ if type(item) is str:
+ remaining = _MAX_ERROR_TEXT_BYTES - used - (2 if parts else 0)
+ encoded = str.encode(item, "utf-8")[:remaining]
+ text = bytes.decode(encoded, "utf-8", errors="ignore")
+ if text:
+ parts.append(text)
+ used += bytes.__len__(encoded) + (2 if len(parts) > 1 else 0)
+ return "; ".join(parts) or None
+
+
+def _failure_for_payload(payload: dict[str, Any], label: str) -> ChartError |
None:
for key in ("error", "errors", "error_message"):
- if message := _query_error_text(payload.get(key)):
+ if key in payload and (message :=
_error_text(dict.__getitem__(payload, key))):
return ChartError(
error=f"{label} failed: {message}", error_type="QueryError"
)
- raw_status = payload.get("status")
- status = str(getattr(raw_status, "value", raw_status) or "")
+ raw_status = dict.get(payload, "status")
+ status = raw_status if type(raw_status) is str else ""
normalized_status = status.strip().casefold().replace("-", "_").replace("
", "_")
if normalized_status in FAILED_QUERY_STATUSES:
message = (
- _query_error_text(payload.get("message"))
- or _query_error_text(payload.get("error_message"))
+ _error_text(dict.get(payload, "message"))
+ or _error_text(dict.get(payload, "error_message"))
or normalized_status
)
return ChartError(error=f"{label} failed: {message}",
error_type="QueryError")
- if payload.get("success") is False:
- message = _query_error_text(payload.get("message")) or "request failed"
+ if dict.get(payload, "success") is False:
+ message = _error_text(dict.get(payload, "message")) or "request failed"
return ChartError(error=f"{label} failed: {message}",
error_type="QueryError")
+ if raw_status is None and "data" not in payload and "queries" not in
payload:
+ if message := _error_text(dict.get(payload, "message")):
+ return ChartError(
+ error=f"{label} failed: {message}", error_type="QueryError"
+ )
+ return None
+
+
+def _metadata_shape_error( # noqa: C901
+ payload: dict[str, Any], label: str, budget: _ResultBudget
+) -> ChartError | None:
+ if "success" in payload and type(dict.__getitem__(payload, "success")) is
not bool:
+ return _invalid_metadata(label)
+ if "status" in payload and type(dict.__getitem__(payload, "status")) is
not str:
+ return _invalid_metadata(label)
if (
- raw_status is None
- and "data" not in payload
- and "queries" not in payload
- and (message := _query_error_text(payload.get("message")))
+ "result_format" in payload
+ and dict.__getitem__(payload, "result_format") not in
_RESULT_FORMAT_VALUES
):
- return ChartError(error=f"{label} failed: {message}",
error_type="QueryError")
+ return _invalid_metadata(label)
+ for count_key in ("rowcount", "sql_rowcount", "total_rows"):
+ if count_key in payload:
+ count = dict.__getitem__(payload, count_key)
+ if count is not None and not (
+ type(count) is int and 0 <= count <= MAX_QUERY_RESULT_ROWCOUNT
+ ):
+ return _invalid_metadata(label)
+ if "is_cached" in payload:
+ cached = dict.__getitem__(payload, "is_cached")
+ if cached is None:
+ if reason := _charge_json_bytes(
+ budget, 1, metadata=True
+ ): # ``false`` vs ``null``
+ return _invalid_result(reason)
+ dict.__setitem__(payload, "is_cached", False)
+ elif type(cached) is not bool:
+ return _invalid_metadata(label)
+ if "cache_timeout" in payload:
+ timeout = dict.__getitem__(payload, "cache_timeout")
+ if timeout is not None and not (
+ type(timeout) is int
+ and (
+ timeout == CACHE_DISABLED_TIMEOUT
+ or 0 <= timeout <= MAX_QUERY_RESULT_CACHE_TIMEOUT
+ )
+ ):
+ return _invalid_metadata(label)
+ if "cache_key" in payload:
+ cache_key = dict.__getitem__(payload, "cache_key")
+ if cache_key is not None and (type(cache_key) is not str or not
cache_key):
+ return _invalid_metadata(label)
+ for timestamp_key in ("cached_dttm", "cache_dttm", "queried_dttm"):
+ if timestamp_key not in payload:
+ continue
+ timestamp = dict.__getitem__(payload, timestamp_key)
+ if timestamp is None:
+ continue
+ if (
+ type(timestamp) is not str
+ or not timestamp
+ or len(timestamp) > MAX_QUERY_RESULT_TIMESTAMP_LENGTH
+ ):
+ return _invalid_metadata(label)
+ normalized = f"{timestamp[:-1]}+00:00" if timestamp.endswith("Z") else
timestamp
+ try:
+ parsed = datetime.fromisoformat(normalized)
+ except ValueError:
+ return _invalid_metadata(label)
+ if parsed.tzinfo is None or parsed.utcoffset() != timedelta(0):
Review Comment:
Addressed: naive cache timestamps are interpreted as UTC and canonicalized,
while non-UTC aware timestamps remain rejected. The cache metadata regression
now covers a naive `cache_dttm`; MyPy and Ruff pass.
##########
superset/mcp_service/chart/tool/get_chart_data.py:
##########
@@ -1115,56 +1224,22 @@ async def _query_from_form_data( # noqa: C901
error_type="NoData",
)
- columns = []
- for col_name in raw_columns:
- sample_values = [
- row.get(col_name) for row in data[:3] if row.get(col_name) is
not None
- ]
- data_type = "string"
- if sample_values and all(
- isinstance(v, (int, float)) for v in sample_values
- ):
- data_type = "numeric"
- columns.append(
- DataColumn(
- name=col_name,
- display_name=col_name.replace("_", " ").title(),
- data_type=data_type,
- sample_values=sample_values[:3],
- null_count=sum(1 for row in data if row.get(col_name) is
None),
- unique_count=len({str(row.get(col_name)) for row in data}),
- )
- )
+ columns = _build_data_columns(
+ data, raw_columns, query_result.get("coltypes", [])
+ )
cache_status = get_cache_status_from_result(
query_result, force_refresh=request.force_refresh
)
chart_name = form_data.get("slice_name", "Unsaved chart")
- if request.format in {"csv", "excel"}:
- from superset.models.slice import Slice
-
- # A transient chart supplies export metadata without saving
anything.
- chart = Slice(id=0, slice_name=chart_name, viz_type=viz_type)
- export = (
- _export_data_as_csv
- if request.format == "csv"
- else _export_data_as_excel
- )
- return export(
- chart,
- data[: request.limit] if request.limit else data,
- raw_columns,
- cache_status,
- PerformanceMetadata(query_duration_ms=0,
cache_status="fresh_query"),
- )
summary = (
f"Unsaved chart ({viz_type}). "
f"Contains {len(data)} rows across {len(raw_columns)} columns."
)
await ctx.report_progress(4, 4, "Building response")
- return ChartData(
+ response = ChartData(
Review Comment:
Addressed: the unsaved form-data path now branches to the existing CSV/Excel
exporters before constructing JSON ChartData. The unsaved Decimal matrix now
includes CSV and verifies the export payload; MyPy and Ruff pass.
##########
superset/mcp_service/app.py:
##########
@@ -395,8 +395,8 @@ def get_default_instructions(
- chart_type="table": Data table for detailed views
- chart_type="table", viz_type="ag-grid-table": Interactive AG Grid table
- chart_type="pie": Pie chart for proportional data (set donut=True for donut)
-- chart_type="gauge": Gauge/dial for one numeric metric, optionally grouped
- into up to 10 dials (native viz_type is "gauge_chart")
+- chart_type="sunburst": Hierarchical part-to-whole chart (hierarchy + metric
Review Comment:
Addressed: Gauge remains listed alongside Sunburst in the MCP capability
catalog; its existing registry/display-name behavior is preserved.
Gauge/Sunburst shared tests remain in the exact-head unit suite.
##########
superset/mcp_service/chart/chart_utils.py:
##########
@@ -1249,6 +1336,659 @@ def map_gauge_config(config: GaugeChartConfig) ->
Dict[str, Any]:
return form_data
+def map_sunburst_config(config: SunburstChartConfig) -> Dict[str, Any]:
+ """Map typed Sunburst config to the ECharts ``sunburst_v2`` form_data.
+
+ The frontend control panel stores hierarchy levels under ``columns`` and
+ metrics under singular ``metric`` / ``secondary_metric`` keys. Its
+ buildQuery adds primary-metric descending ordering when ``sort_by_metric``
+ is enabled; server-side query builders mirror that transform separately.
+ """
+ form_data: Dict[str, Any] = {
+ "viz_type": "sunburst_v2",
+ "columns": [dimension.name for dimension in config.hierarchy],
+ "metric": create_metric_object(config.metric),
+ "sort_by_metric": config.sort_by_metric,
+ "row_limit": config.row_limit,
+ "show_labels": config.show_labels,
+ "show_labels_threshold": config.show_labels_threshold,
+ "show_total": config.show_total,
+ "show_null_values": config.show_null_values,
+ "label_type": config.label_type,
+ "number_format": config.number_format,
+ "date_format": config.date_format,
+ }
+ if config.secondary_metric is not None:
+ form_data["secondary_metric"] =
create_metric_object(config.secondary_metric)
+ if config.color_scheme is not None:
+ form_data["color_scheme"] = config.color_scheme
+ if config.linear_color_scheme is not None:
+ form_data["linear_color_scheme"] = config.linear_color_scheme
+ if config.time_range is not None:
+ form_data["time_range"] = config.time_range
+ if config.temporal_column is not None:
+ form_data["granularity_sqla"] = config.temporal_column
+ if config.time_grain is not None:
+ form_data["time_grain_sqla"] = config.time_grain
+
+ _copy_sunburst_native_envelope(form_data, config)
+
+ add_currency_format(form_data, config.currency_format)
+ _add_adhoc_filters(form_data, config.filters)
+ return form_data
+
+
+# Sunburst fields with explicit omission/clear semantics. Mapper defaults must
+# not overwrite same-viz state when the typed field was omitted, while explicit
+# clears must also beat the shared preservation registry on cross-viz updates.
+# Required query roles (hierarchy and metric) are deliberately absent: a full
+# replacement always updates them.
+_SUNBURST_UPDATE_FIELD_KEYS: dict[str, str] = {
+ "time_range": "time_range",
+ "time_grain": "time_grain_sqla",
+ "temporal_column": "granularity_sqla",
+ "sort_by_metric": "sort_by_metric",
+ "row_limit": "row_limit",
+ "color_scheme": "color_scheme",
+ "linear_color_scheme": "linear_color_scheme",
+ "show_labels": "show_labels",
+ "show_labels_threshold": "show_labels_threshold",
+ "show_total": "show_total",
+ "show_null_values": "show_null_values",
+ "label_type": "label_type",
+ "number_format": "number_format",
+ "date_format": "date_format",
+ "currency_format": "currency_format",
+ "extra_form_data": "extra_form_data",
+ "url_params": "url_params",
+ "standardized_form_data": "standardizedFormData",
+}
+
+
+# Presentation controls emitted sparsely by chart mappers need three-way update
+# semantics: omitted preserves saved native state, an explicit value replaces
+# it, and explicit ``None``/``False`` clears a truthy saved value when the
mapper
+# has no canonical false/null representation. Query roles are intentionally
+# absent: a replacement config always owns those through the plugin contract.
+# Paths below also cover nested axis/legend models so an omitted nested
property
+# is not mistaken for an explicit clear of the whole control.
+_MODELED_UPDATE_CONTROL_PATHS: dict[str, dict[str, tuple[tuple[str, ...],
...]]] = {
+ "PieChartConfig": {
+ "color_scheme": (("color_scheme",),),
+ "show_labels": (("show_labels",),),
+ "show_legend": (("show_legend",),),
+ "legendOrientation": (("legend_orientation",),),
+ "label_type": (("label_type",),),
+ "number_format": (("number_format",),),
+ "date_format": (("date_format",),),
+ "sort_by_metric": (("sort_by_metric",),),
+ "row_limit": (("row_limit",),),
+ "donut": (("donut",),),
+ "show_total": (("show_total",),),
+ "labels_outside": (("labels_outside",),),
+ "outerRadius": (("outer_radius",),),
+ "innerRadius": (("inner_radius",),),
+ "currency_format": (("currency_format",),),
+ },
+ "TableChartConfig": {
+ "row_limit": (("row_limit",),),
+ "color_scheme": (("color_scheme",),),
+ "column_config": (("column_config",),),
+ },
+ "XYChartConfig": {
+ "row_limit": (("row_limit",),),
+ "series_limit": (("series_limit",),),
+ "stack": (("stacked",),),
+ "orientation": (("orientation",),),
+ "x_axis_title": (("x_axis", "title"),),
+ "x_axis_format": (("x_axis", "format"),),
+ "y_axis_title": (("y_axis", "title"),),
+ "y_axis_format": (("y_axis", "format"),),
+ "y_axis_scale": (("y_axis", "scale"),),
+ "show_legend": (("legend", "show"),),
+ "legendOrientation": (("legend", "position"), ("legend_orientation",)),
+ "x_axis_time_format": (("x_axis_time_format",),),
+ "show_value": (("show_value",),),
+ "currency_format": (("currency_format",),),
+ "color_scheme": (("color_scheme",),),
+ },
+ "HistogramChartConfig": {
+ "bins": (("bins",),),
+ "normalize": (("normalize",),),
+ "cumulative": (("cumulative",),),
+ "row_limit": (("row_limit",),),
+ },
+ "BoxPlotChartConfig": {
+ "whiskerOptions": (
+ ("whisker_type",),
+ ("percentile_low",),
+ ("percentile_high",),
+ ),
+ "row_limit": (("row_limit",),),
+ "number_format": (("number_format",),),
+ "date_format": (("date_format",),),
+ },
+ "WaterfallChartConfig": {
+ "show_total": (("show_total",),),
+ "show_legend": (("show_legend",),),
+ "increase_label": (("increase_label",),),
+ "decrease_label": (("decrease_label",),),
+ "total_label": (("total_label",),),
+ "x_axis_time_format": (("x_axis_time_format",),),
+ "y_axis_format": (("y_axis_format",),),
+ "currency_format": (("currency_format",),),
+ "row_limit": (("row_limit",),),
+ },
+ "BigNumberChartConfig": {
+ "subheader": (("subheader",),),
+ "y_axis_format": (("y_axis_format",),),
+ "time_format": (("time_format",),),
+ "currency_format": (("currency_format",),),
+ "color_scheme": (("color_scheme",),),
+ "start_y_axis_at_zero": (("start_y_axis_at_zero",),),
+ "compare_lag": (("compare_lag",),),
+ "aggregation": (("aggregation",),),
+ },
+ "HandlebarsChartConfig": {
+ "row_limit": (("row_limit",),),
+ "order_desc": (("order_desc",),),
+ "styleTemplate": (("style_template",),),
+ },
+ "PivotTableChartConfig": {
+ "aggregateFunction": (("aggregate_function",),),
+ "rowTotals": (("show_row_totals",),),
+ "colTotals": (("show_column_totals",),),
+ "transposePivot": (("transpose",),),
+ "combineMetric": (("combine_metric",),),
+ "valueFormat": (("value_format",),),
+ "date_format": (("date_format",),),
+ "currency_format": (("currency_format",),),
+ "row_limit": (("row_limit",),),
+ },
+ "InteractivePivotChartConfig": {
+ "order_desc": (("sort_descending",),),
+ "row_limit": (("row_limit",),),
+ "rowGroupCounts": (("show_row_group_counts",),),
+ "rowTotals": (("show_row_totals",),),
+ "colTotals": (("show_column_totals",),),
+ "colSubTotals": (("show_column_subtotals",),),
+ "valueFormat": (("value_format",),),
+ "date_format": (("date_format",),),
+ "currency_format": (("currency_format",),),
+ "colOrder": (("column_sort",),),
+ "allow_render_html": (("allow_render_html",),),
+ "expand_pivot_groups": (("expand_pivot_groups",),),
+ "time_compare": (("comparison_period",),),
+ "comparison_type": (("comparison_type",),),
+ },
+ "MixedTimeseriesChartConfig": {
+ "seriesType": (("primary_kind",),),
+ "area": (("primary_kind",),),
+ "seriesTypeB": (("secondary_kind",),),
+ "areaB": (("secondary_kind",),),
+ "show_legend": (("show_legend",),),
+ "legendOrientation": (("legend_orientation",),),
+ "show_value": (("show_value",),),
+ "color_scheme": (("color_scheme",),),
+ "currency_format": (("currency_format",),),
+ "currency_format_secondary": (("currency_format_secondary",),),
+ "xAxisTitle": (("x_axis", "title"),),
+ "x_axis_time_format": (("x_axis", "format"),),
+ "yAxisTitle": (("y_axis", "title"),),
+ "y_axis_format": (("y_axis", "format"),),
+ "logAxis": (("y_axis", "scale"),),
+ "yAxisTitleSecondary": (("y_axis_secondary", "title"),),
+ "y_axis_format_secondary": (("y_axis_secondary", "format"),),
+ "logAxisSecondary": (("y_axis_secondary", "scale"),),
+ "row_limit": (("row_limit",),),
+ },
+}
+
+
+def _model_path_was_set(config: Any, path: tuple[str, ...]) -> bool:
+ """Return whether every component of a Pydantic model path was supplied."""
+ current = config
+ for field_name in path:
+ if field_name not in getattr(current, "model_fields_set", set()):
+ return False
+ current = getattr(current, field_name, None)
+ if current is None:
+ # An explicit null parent clears all of its mapped descendants.
+ return True
+ return True
+
+
+def _apply_modeled_update_semantics(
+ existing_form_data: Mapping[str, Any],
+ new_form_data: Dict[str, Any],
+ config: Any,
+) -> set[str]:
+ """Preserve truly omitted modeled controls and return explicit clears."""
+ explicit_clears: set[str] = set()
+ controls = _MODELED_UPDATE_CONTROL_PATHS.get(type(config).__name__, {})
+ for form_key, paths in controls.items():
+ if any(_model_path_was_set(config, path) for path in paths):
+ if form_key not in new_form_data:
+ explicit_clears.add(form_key)
+ continue
+ if form_key in existing_form_data:
+ new_form_data[form_key] = existing_form_data[form_key]
+ else:
+ new_form_data.pop(form_key, None)
+ return explicit_clears
+
+
+_TEMPORAL_FORM_DATA_KEYS = frozenset(
+ {
+ "granularity",
+ "granularity_sqla",
+ "since",
+ "time_grain",
+ "time_grain_sqla",
+ "time_range",
+ "until",
+ }
+)
+
+
+def _is_temporal_filter(filter_: Any) -> bool:
+ """Return whether a native, adhoc, or legacy filter carries a time
range."""
+ return isinstance(filter_, dict) and (
+ filter_.get("operator") == FilterOperator.TEMPORAL_RANGE.value
+ or filter_.get("op") == FilterOperator.TEMPORAL_RANGE.value
+ or filter_.get("col") in {"__time_col", "__time_grain", "__time_range"}
+ )
+
+
+def _without_temporal_filters(value: Any) -> Any:
+ """Copy a filter list without temporal predicates, preserving other
shapes."""
+ if not isinstance(value, list):
+ return value
+ return [filter_ for filter_ in value if not _is_temporal_filter(filter_)]
+
+
+def _scrub_temporal_form_data(form_data: Mapping[str, Any]) -> Dict[str, Any]:
+ """Remove every source capable of reconstructing explicitly cleared time
state."""
+ scrubbed = dict(form_data)
+ for key in _TEMPORAL_FORM_DATA_KEYS:
+ scrubbed.pop(key, None)
+ scrubbed.pop(MCP_DASHBOARD_TIME_FILTER_SUBJECT, None)
+
+ for key in ("adhoc_filters", "extra_filters", "filters"):
+ if key in scrubbed:
+ scrubbed[key] = _without_temporal_filters(scrubbed[key])
+
+ extra_form_data = scrubbed.get("extra_form_data")
+ if isinstance(extra_form_data, dict):
+ cleaned_extra = dict(extra_form_data)
+ for key in _TEMPORAL_FORM_DATA_KEYS:
+ cleaned_extra.pop(key, None)
+ for key in ("adhoc_filters", "extra_filters", "filters"):
+ if key in cleaned_extra:
+ cleaned_extra[key] =
_without_temporal_filters(cleaned_extra[key])
+ scrubbed["extra_form_data"] = cleaned_extra
+ elif extra_form_data is None:
+ scrubbed.pop("extra_form_data", None)
+ return scrubbed
+
+
+# One bounded registry owns state that may survive a form-data replacement.
+# Query roles and plugin-specific controls are deliberately absent. This keeps
+# cross-viz transitions preview/save-safe without chart-by-chart allowlists
that
+# can drift as new plugins are registered.
+FORM_DATA_UPDATE_PRESERVE_KEYS: dict[str, frozenset[str]] = {
+ "envelope": frozenset(
+ {
+ "dashboardId",
+ "dashboards",
+ "datasource",
+ "extra_form_data",
+ "slice_id",
+ "slice_name",
+ "standardizedFormData",
+ "url_params",
+ }
+ ),
+ "presentation": frozenset(
+ {
+ "color_scheme",
+ "currency_format",
+ "date_format",
+ "legendOrientation",
+ "linear_color_scheme",
+ "number_format",
+ "show_legend",
+ }
+ ),
+ "filters": frozenset({"adhoc_filters", "extra_filters", "filters"}),
+ "time": frozenset(
+ {
+ "granularity_sqla",
+ "since",
+ "time_grain_sqla",
+ "time_range",
+ "until",
+ }
+ ),
+}
+_FORM_DATA_UPDATE_PRESERVE_KEYS = frozenset().union(
+ *FORM_DATA_UPDATE_PRESERVE_KEYS.values()
+)
+
+
+def _merge_preserved_adhoc_filters(
+ existing_form_data: Mapping[str, Any],
+ new_form_data: Mapping[str, Any],
+ *,
+ drop_existing_temporal: bool,
+) -> list[Any] | None:
+ """Merge omitted structured filters while removing stale time bindings."""
+ previous = existing_form_data.get("adhoc_filters")
+ generated = new_form_data.get("adhoc_filters")
+ if not isinstance(previous, list):
+ return list(generated) if isinstance(generated, list) else None
+
+ previous_binding =
existing_form_data.get(MCP_DASHBOARD_TIME_FILTER_SUBJECT)
+ new_binding = new_form_data.get(MCP_DASHBOARD_TIME_FILTER_SUBJECT)
+ merged: list[Any] = []
+ for filter_ in previous:
+ is_temporal = (
+ isinstance(filter_, dict)
+ and filter_.get("operator") == FilterOperator.TEMPORAL_RANGE.value
+ )
+ stale_generated_binding = (
+ is_temporal
+ and previous_binding
+ and previous_binding != new_binding
+ and filter_.get("subject") == previous_binding
+ and filter_.get("comparator") == NO_TIME_RANGE
+ )
+ if (drop_existing_temporal and is_temporal) or stale_generated_binding:
+ continue
+ merged.append(filter_)
+
+ for filter_ in generated if isinstance(generated, list) else []:
+ if isinstance(filter_, dict):
+ same_filter = any(
+ isinstance(previous_filter, dict)
+ and previous_filter.get("clause") == filter_.get("clause")
+ and previous_filter.get("expressionType")
+ == filter_.get("expressionType")
+ and previous_filter.get("subject") == filter_.get("subject")
+ and previous_filter.get("operator") == filter_.get("operator")
+ for previous_filter in merged
+ )
+ if same_filter:
+ continue
+ elif filter_ in merged:
+ continue
+ merged.append(filter_)
+ return merged
+
+
+def preserve_previous_adhoc_filters(
+ new_form_data: Dict[str, Any], previous_form_data: Mapping[str, Any]
+) -> None:
+ """Compatibility entry point backed by the shared filter merge."""
+ filters = _merge_preserved_adhoc_filters(
+ previous_form_data,
+ new_form_data,
+ drop_existing_temporal=False,
+ )
+ if filters is not None:
+ new_form_data["adhoc_filters"] = filters
+
+
+def _merge_allowlisted_form_data(
+ existing_form_data: Mapping[str, Any],
+ new_form_data: Mapping[str, Any],
+) -> Dict[str, Any]:
+ """Start from mapped target state and add only registry-approved
omissions."""
+ merged = dict(new_form_data)
+ for key in _FORM_DATA_UPDATE_PRESERVE_KEYS:
+ if key not in merged and key in existing_form_data:
+ merged[key] = existing_form_data[key]
+ return merged
+
+
+def merge_form_data_for_update( # noqa: C901
+ existing_form_data: Dict[str, Any],
+ new_form_data: Dict[str, Any],
+ config: Any,
+ *,
+ dataset_rebind: bool = False,
+) -> Dict[str, Any]:
+ """Merge mapped updates without leaking query roles across visualizations.
+
+ Same-viz updates retain native controls outside the simplified MCP schema
by
+ starting from saved form data. Cross-viz updates remain bounded by the
+ shared preservation registry. Explicit clears are applied last.
+ """
+ if dataset_rebind:
+ existing_form_data = scrub_dataset_bound_form_data(existing_form_data)
+
+ same_viz = existing_form_data.get("viz_type") ==
new_form_data.get("viz_type")
+ explicit_control_clears = (
+ _apply_modeled_update_semantics(existing_form_data, new_form_data,
config)
Review Comment:
Addressed: `GaugeChartConfig` is included in modeled update controls, so
omitted presentation fields preserve saved values instead of mapper defaults
replacing them. `test_partial_gauge_update_preserves_omitted_controls` covers
`show_progress=False` and `split_number=5`.
##########
superset/mcp_service/chart/chart_utils.py:
##########
@@ -1249,6 +1336,659 @@ def map_gauge_config(config: GaugeChartConfig) ->
Dict[str, Any]:
return form_data
+def map_sunburst_config(config: SunburstChartConfig) -> Dict[str, Any]:
+ """Map typed Sunburst config to the ECharts ``sunburst_v2`` form_data.
+
+ The frontend control panel stores hierarchy levels under ``columns`` and
+ metrics under singular ``metric`` / ``secondary_metric`` keys. Its
+ buildQuery adds primary-metric descending ordering when ``sort_by_metric``
+ is enabled; server-side query builders mirror that transform separately.
+ """
+ form_data: Dict[str, Any] = {
+ "viz_type": "sunburst_v2",
+ "columns": [dimension.name for dimension in config.hierarchy],
+ "metric": create_metric_object(config.metric),
+ "sort_by_metric": config.sort_by_metric,
+ "row_limit": config.row_limit,
+ "show_labels": config.show_labels,
+ "show_labels_threshold": config.show_labels_threshold,
+ "show_total": config.show_total,
+ "show_null_values": config.show_null_values,
+ "label_type": config.label_type,
+ "number_format": config.number_format,
+ "date_format": config.date_format,
+ }
+ if config.secondary_metric is not None:
+ form_data["secondary_metric"] =
create_metric_object(config.secondary_metric)
+ if config.color_scheme is not None:
+ form_data["color_scheme"] = config.color_scheme
+ if config.linear_color_scheme is not None:
+ form_data["linear_color_scheme"] = config.linear_color_scheme
+ if config.time_range is not None:
+ form_data["time_range"] = config.time_range
+ if config.temporal_column is not None:
+ form_data["granularity_sqla"] = config.temporal_column
+ if config.time_grain is not None:
+ form_data["time_grain_sqla"] = config.time_grain
+
+ _copy_sunburst_native_envelope(form_data, config)
+
+ add_currency_format(form_data, config.currency_format)
+ _add_adhoc_filters(form_data, config.filters)
+ return form_data
+
+
+# Sunburst fields with explicit omission/clear semantics. Mapper defaults must
+# not overwrite same-viz state when the typed field was omitted, while explicit
+# clears must also beat the shared preservation registry on cross-viz updates.
+# Required query roles (hierarchy and metric) are deliberately absent: a full
+# replacement always updates them.
+_SUNBURST_UPDATE_FIELD_KEYS: dict[str, str] = {
+ "time_range": "time_range",
+ "time_grain": "time_grain_sqla",
+ "temporal_column": "granularity_sqla",
+ "sort_by_metric": "sort_by_metric",
+ "row_limit": "row_limit",
+ "color_scheme": "color_scheme",
+ "linear_color_scheme": "linear_color_scheme",
+ "show_labels": "show_labels",
+ "show_labels_threshold": "show_labels_threshold",
+ "show_total": "show_total",
+ "show_null_values": "show_null_values",
+ "label_type": "label_type",
+ "number_format": "number_format",
+ "date_format": "date_format",
+ "currency_format": "currency_format",
+ "extra_form_data": "extra_form_data",
+ "url_params": "url_params",
+ "standardized_form_data": "standardizedFormData",
+}
+
+
+# Presentation controls emitted sparsely by chart mappers need three-way update
+# semantics: omitted preserves saved native state, an explicit value replaces
+# it, and explicit ``None``/``False`` clears a truthy saved value when the
mapper
+# has no canonical false/null representation. Query roles are intentionally
+# absent: a replacement config always owns those through the plugin contract.
+# Paths below also cover nested axis/legend models so an omitted nested
property
+# is not mistaken for an explicit clear of the whole control.
+_MODELED_UPDATE_CONTROL_PATHS: dict[str, dict[str, tuple[tuple[str, ...],
...]]] = {
+ "PieChartConfig": {
+ "color_scheme": (("color_scheme",),),
+ "show_labels": (("show_labels",),),
+ "show_legend": (("show_legend",),),
+ "legendOrientation": (("legend_orientation",),),
+ "label_type": (("label_type",),),
+ "number_format": (("number_format",),),
+ "date_format": (("date_format",),),
+ "sort_by_metric": (("sort_by_metric",),),
+ "row_limit": (("row_limit",),),
+ "donut": (("donut",),),
+ "show_total": (("show_total",),),
+ "labels_outside": (("labels_outside",),),
+ "outerRadius": (("outer_radius",),),
+ "innerRadius": (("inner_radius",),),
+ "currency_format": (("currency_format",),),
+ },
+ "TableChartConfig": {
+ "row_limit": (("row_limit",),),
+ "color_scheme": (("color_scheme",),),
+ "column_config": (("column_config",),),
+ },
+ "XYChartConfig": {
+ "row_limit": (("row_limit",),),
+ "series_limit": (("series_limit",),),
+ "stack": (("stacked",),),
+ "orientation": (("orientation",),),
+ "x_axis_title": (("x_axis", "title"),),
+ "x_axis_format": (("x_axis", "format"),),
+ "y_axis_title": (("y_axis", "title"),),
+ "y_axis_format": (("y_axis", "format"),),
+ "y_axis_scale": (("y_axis", "scale"),),
+ "show_legend": (("legend", "show"),),
+ "legendOrientation": (("legend", "position"), ("legend_orientation",)),
+ "x_axis_time_format": (("x_axis_time_format",),),
+ "show_value": (("show_value",),),
+ "currency_format": (("currency_format",),),
+ "color_scheme": (("color_scheme",),),
+ },
+ "HistogramChartConfig": {
+ "bins": (("bins",),),
+ "normalize": (("normalize",),),
+ "cumulative": (("cumulative",),),
+ "row_limit": (("row_limit",),),
+ },
+ "BoxPlotChartConfig": {
+ "whiskerOptions": (
+ ("whisker_type",),
+ ("percentile_low",),
+ ("percentile_high",),
+ ),
+ "row_limit": (("row_limit",),),
+ "number_format": (("number_format",),),
+ "date_format": (("date_format",),),
+ },
+ "WaterfallChartConfig": {
+ "show_total": (("show_total",),),
+ "show_legend": (("show_legend",),),
+ "increase_label": (("increase_label",),),
+ "decrease_label": (("decrease_label",),),
+ "total_label": (("total_label",),),
+ "x_axis_time_format": (("x_axis_time_format",),),
+ "y_axis_format": (("y_axis_format",),),
+ "currency_format": (("currency_format",),),
+ "row_limit": (("row_limit",),),
+ },
+ "BigNumberChartConfig": {
+ "subheader": (("subheader",),),
+ "y_axis_format": (("y_axis_format",),),
+ "time_format": (("time_format",),),
+ "currency_format": (("currency_format",),),
+ "color_scheme": (("color_scheme",),),
+ "start_y_axis_at_zero": (("start_y_axis_at_zero",),),
+ "compare_lag": (("compare_lag",),),
+ "aggregation": (("aggregation",),),
+ },
+ "HandlebarsChartConfig": {
+ "row_limit": (("row_limit",),),
+ "order_desc": (("order_desc",),),
+ "styleTemplate": (("style_template",),),
+ },
+ "PivotTableChartConfig": {
+ "aggregateFunction": (("aggregate_function",),),
+ "rowTotals": (("show_row_totals",),),
+ "colTotals": (("show_column_totals",),),
+ "transposePivot": (("transpose",),),
+ "combineMetric": (("combine_metric",),),
+ "valueFormat": (("value_format",),),
+ "date_format": (("date_format",),),
+ "currency_format": (("currency_format",),),
+ "row_limit": (("row_limit",),),
+ },
+ "InteractivePivotChartConfig": {
+ "order_desc": (("sort_descending",),),
+ "row_limit": (("row_limit",),),
+ "rowGroupCounts": (("show_row_group_counts",),),
+ "rowTotals": (("show_row_totals",),),
+ "colTotals": (("show_column_totals",),),
+ "colSubTotals": (("show_column_subtotals",),),
+ "valueFormat": (("value_format",),),
+ "date_format": (("date_format",),),
+ "currency_format": (("currency_format",),),
+ "colOrder": (("column_sort",),),
+ "allow_render_html": (("allow_render_html",),),
+ "expand_pivot_groups": (("expand_pivot_groups",),),
+ "time_compare": (("comparison_period",),),
+ "comparison_type": (("comparison_type",),),
+ },
+ "MixedTimeseriesChartConfig": {
+ "seriesType": (("primary_kind",),),
+ "area": (("primary_kind",),),
+ "seriesTypeB": (("secondary_kind",),),
+ "areaB": (("secondary_kind",),),
+ "show_legend": (("show_legend",),),
+ "legendOrientation": (("legend_orientation",),),
+ "show_value": (("show_value",),),
+ "color_scheme": (("color_scheme",),),
+ "currency_format": (("currency_format",),),
+ "currency_format_secondary": (("currency_format_secondary",),),
+ "xAxisTitle": (("x_axis", "title"),),
+ "x_axis_time_format": (("x_axis", "format"),),
+ "yAxisTitle": (("y_axis", "title"),),
+ "y_axis_format": (("y_axis", "format"),),
+ "logAxis": (("y_axis", "scale"),),
+ "yAxisTitleSecondary": (("y_axis_secondary", "title"),),
+ "y_axis_format_secondary": (("y_axis_secondary", "format"),),
+ "logAxisSecondary": (("y_axis_secondary", "scale"),),
+ "row_limit": (("row_limit",),),
+ },
+}
+
+
+def _model_path_was_set(config: Any, path: tuple[str, ...]) -> bool:
+ """Return whether every component of a Pydantic model path was supplied."""
+ current = config
+ for field_name in path:
+ if field_name not in getattr(current, "model_fields_set", set()):
+ return False
+ current = getattr(current, field_name, None)
+ if current is None:
+ # An explicit null parent clears all of its mapped descendants.
+ return True
+ return True
+
+
+def _apply_modeled_update_semantics(
+ existing_form_data: Mapping[str, Any],
+ new_form_data: Dict[str, Any],
+ config: Any,
+) -> set[str]:
+ """Preserve truly omitted modeled controls and return explicit clears."""
+ explicit_clears: set[str] = set()
+ controls = _MODELED_UPDATE_CONTROL_PATHS.get(type(config).__name__, {})
+ for form_key, paths in controls.items():
+ if any(_model_path_was_set(config, path) for path in paths):
+ if form_key not in new_form_data:
+ explicit_clears.add(form_key)
+ continue
+ if form_key in existing_form_data:
+ new_form_data[form_key] = existing_form_data[form_key]
+ else:
+ new_form_data.pop(form_key, None)
+ return explicit_clears
+
+
+_TEMPORAL_FORM_DATA_KEYS = frozenset(
+ {
+ "granularity",
+ "granularity_sqla",
+ "since",
+ "time_grain",
+ "time_grain_sqla",
+ "time_range",
+ "until",
+ }
+)
+
+
+def _is_temporal_filter(filter_: Any) -> bool:
+ """Return whether a native, adhoc, or legacy filter carries a time
range."""
+ return isinstance(filter_, dict) and (
+ filter_.get("operator") == FilterOperator.TEMPORAL_RANGE.value
+ or filter_.get("op") == FilterOperator.TEMPORAL_RANGE.value
+ or filter_.get("col") in {"__time_col", "__time_grain", "__time_range"}
+ )
+
+
+def _without_temporal_filters(value: Any) -> Any:
+ """Copy a filter list without temporal predicates, preserving other
shapes."""
+ if not isinstance(value, list):
+ return value
+ return [filter_ for filter_ in value if not _is_temporal_filter(filter_)]
+
+
+def _scrub_temporal_form_data(form_data: Mapping[str, Any]) -> Dict[str, Any]:
+ """Remove every source capable of reconstructing explicitly cleared time
state."""
+ scrubbed = dict(form_data)
+ for key in _TEMPORAL_FORM_DATA_KEYS:
+ scrubbed.pop(key, None)
+ scrubbed.pop(MCP_DASHBOARD_TIME_FILTER_SUBJECT, None)
+
+ for key in ("adhoc_filters", "extra_filters", "filters"):
+ if key in scrubbed:
+ scrubbed[key] = _without_temporal_filters(scrubbed[key])
+
+ extra_form_data = scrubbed.get("extra_form_data")
+ if isinstance(extra_form_data, dict):
+ cleaned_extra = dict(extra_form_data)
+ for key in _TEMPORAL_FORM_DATA_KEYS:
+ cleaned_extra.pop(key, None)
+ for key in ("adhoc_filters", "extra_filters", "filters"):
+ if key in cleaned_extra:
+ cleaned_extra[key] =
_without_temporal_filters(cleaned_extra[key])
+ scrubbed["extra_form_data"] = cleaned_extra
+ elif extra_form_data is None:
+ scrubbed.pop("extra_form_data", None)
+ return scrubbed
+
+
+# One bounded registry owns state that may survive a form-data replacement.
+# Query roles and plugin-specific controls are deliberately absent. This keeps
+# cross-viz transitions preview/save-safe without chart-by-chart allowlists
that
+# can drift as new plugins are registered.
+FORM_DATA_UPDATE_PRESERVE_KEYS: dict[str, frozenset[str]] = {
+ "envelope": frozenset(
+ {
+ "dashboardId",
+ "dashboards",
+ "datasource",
+ "extra_form_data",
+ "slice_id",
+ "slice_name",
+ "standardizedFormData",
+ "url_params",
+ }
+ ),
+ "presentation": frozenset(
+ {
+ "color_scheme",
+ "currency_format",
+ "date_format",
+ "legendOrientation",
+ "linear_color_scheme",
+ "number_format",
+ "show_legend",
+ }
+ ),
+ "filters": frozenset({"adhoc_filters", "extra_filters", "filters"}),
+ "time": frozenset(
+ {
+ "granularity_sqla",
+ "since",
+ "time_grain_sqla",
+ "time_range",
+ "until",
+ }
+ ),
+}
+_FORM_DATA_UPDATE_PRESERVE_KEYS = frozenset().union(
+ *FORM_DATA_UPDATE_PRESERVE_KEYS.values()
+)
+
+
+def _merge_preserved_adhoc_filters(
+ existing_form_data: Mapping[str, Any],
+ new_form_data: Mapping[str, Any],
+ *,
+ drop_existing_temporal: bool,
+) -> list[Any] | None:
+ """Merge omitted structured filters while removing stale time bindings."""
+ previous = existing_form_data.get("adhoc_filters")
+ generated = new_form_data.get("adhoc_filters")
+ if not isinstance(previous, list):
+ return list(generated) if isinstance(generated, list) else None
+
+ previous_binding =
existing_form_data.get(MCP_DASHBOARD_TIME_FILTER_SUBJECT)
+ new_binding = new_form_data.get(MCP_DASHBOARD_TIME_FILTER_SUBJECT)
+ merged: list[Any] = []
+ for filter_ in previous:
+ is_temporal = (
+ isinstance(filter_, dict)
+ and filter_.get("operator") == FilterOperator.TEMPORAL_RANGE.value
+ )
+ stale_generated_binding = (
+ is_temporal
+ and previous_binding
+ and previous_binding != new_binding
+ and filter_.get("subject") == previous_binding
+ and filter_.get("comparator") == NO_TIME_RANGE
+ )
+ if (drop_existing_temporal and is_temporal) or stale_generated_binding:
+ continue
+ merged.append(filter_)
+
+ for filter_ in generated if isinstance(generated, list) else []:
+ if isinstance(filter_, dict):
+ same_filter = any(
+ isinstance(previous_filter, dict)
+ and previous_filter.get("clause") == filter_.get("clause")
+ and previous_filter.get("expressionType")
+ == filter_.get("expressionType")
+ and previous_filter.get("subject") == filter_.get("subject")
+ and previous_filter.get("operator") == filter_.get("operator")
+ for previous_filter in merged
+ )
+ if same_filter:
+ continue
+ elif filter_ in merged:
+ continue
+ merged.append(filter_)
+ return merged
+
+
+def preserve_previous_adhoc_filters(
+ new_form_data: Dict[str, Any], previous_form_data: Mapping[str, Any]
+) -> None:
+ """Compatibility entry point backed by the shared filter merge."""
+ filters = _merge_preserved_adhoc_filters(
+ previous_form_data,
+ new_form_data,
+ drop_existing_temporal=False,
+ )
+ if filters is not None:
+ new_form_data["adhoc_filters"] = filters
+
+
+def _merge_allowlisted_form_data(
+ existing_form_data: Mapping[str, Any],
+ new_form_data: Mapping[str, Any],
+) -> Dict[str, Any]:
+ """Start from mapped target state and add only registry-approved
omissions."""
+ merged = dict(new_form_data)
+ for key in _FORM_DATA_UPDATE_PRESERVE_KEYS:
+ if key not in merged and key in existing_form_data:
+ merged[key] = existing_form_data[key]
+ return merged
+
+
+def merge_form_data_for_update( # noqa: C901
+ existing_form_data: Dict[str, Any],
+ new_form_data: Dict[str, Any],
+ config: Any,
+ *,
+ dataset_rebind: bool = False,
+) -> Dict[str, Any]:
+ """Merge mapped updates without leaking query roles across visualizations.
+
+ Same-viz updates retain native controls outside the simplified MCP schema
by
+ starting from saved form data. Cross-viz updates remain bounded by the
+ shared preservation registry. Explicit clears are applied last.
+ """
+ if dataset_rebind:
+ existing_form_data = scrub_dataset_bound_form_data(existing_form_data)
+
+ same_viz = existing_form_data.get("viz_type") ==
new_form_data.get("viz_type")
+ explicit_control_clears = (
+ _apply_modeled_update_semantics(existing_form_data, new_form_data,
config)
+ if same_viz
+ else set()
+ )
+ if same_viz:
+ from superset.mcp_service.chart.registry import (
+ query_role_keys_for_viz_type,
+ )
+
+ # Strip every target-owned query role first, then overlay the mapper's
+ # complete replacement. This removes mutually exclusive aliases (for
+ # example Pie ``metrics`` vs ``metric`` and raw vs aggregate table
+ # roles) without dropping unmodeled native presentation controls.
+ query_role_keys = query_role_keys_for_viz_type(
+ str(new_form_data.get("viz_type"))
+ )
+ merged = {
+ key: value
+ for key, value in existing_form_data.items()
+ if key not in query_role_keys
+ }
+ merged.update(new_form_data)
+ if new_form_data.get("viz_type") == "mixed_timeseries" and not
dataset_rebind:
+ from superset.common.form_data_query_context import (
+ MIXED_TIMESERIES_SECONDARY_QUERY_KEYS,
+ )
+
+ # Query B inherits unsuffixed controls only when the suffixed key
is
+ # absent. Preserve explicit native clears for controls the typed
+ # mapper did not replace, so []/None never turns into accidental
+ # inheritance from query A. Valid comparison state is also
retained;
+ # malformed/stale dataset roles remain fail-closed and are dropped.
+ for key in MIXED_TIMESERIES_SECONDARY_QUERY_KEYS:
+ if key in new_form_data or key not in existing_form_data:
+ continue
+ value = existing_form_data[key]
+ is_explicit_clear = value is None or value in ([], {}, "")
+ is_valid_comparison = key == "comparison_type_b" and value in {
+ "values",
+ "difference",
+ "percentage",
+ "ratio",
+ }
+ is_valid_list_state = key in {
+ "adhoc_filters_b",
+ "annotation_layers_b",
+ "time_compare_b",
+ } and isinstance(value, list)
+ if is_explicit_clear or is_valid_comparison or
is_valid_list_state:
+ merged[key] = value
+ else:
+ merged = _merge_allowlisted_form_data(existing_form_data,
new_form_data)
+
+ for key in explicit_control_clears:
+ merged.pop(key, None)
+
+ fields_set: set[str] = getattr(config, "model_fields_set", set())
+ if getattr(config, "filters", None) == []:
+ merged.pop("adhoc_filters", None)
+ elif getattr(config, "filters", None) is None:
+ filters = _merge_preserved_adhoc_filters(
+ existing_form_data,
+ new_form_data,
+ drop_existing_temporal=bool(
+ {"temporal_column", "time_grain", "time_range"} & fields_set
+ ),
+ )
+ if filters is not None:
+ merged["adhoc_filters"] = filters
+
+ if not isinstance(config, SunburstChartConfig):
+ return merged
+
+ temporal_fields = {"time_grain", "temporal_column"}
+ for field_name, form_key in _SUNBURST_UPDATE_FIELD_KEYS.items():
+ if field_name in temporal_fields:
+ continue
+ if field_name not in fields_set:
+ if same_viz:
+ if form_key in existing_form_data:
+ merged[form_key] = existing_form_data[form_key]
+ else:
+ merged.pop(form_key, None)
+ continue
+
+ value = getattr(config, field_name)
+ if value is None:
+ merged.pop(form_key, None)
+ if field_name == "time_range":
+ # The generic query-context mapper reconstructs time_range from
+ # these legacy keys. A clear must remove all three sources.
+ merged.pop("since", None)
+ merged.pop("until", None)
+
+ # A null temporal control is an atomic clear. Apply it after every merge so
+ # cached/native aliases and extra-form-data overrides cannot recreate time
+ # state in QueryContextFactory.
+ explicit_temporal_clear = any(
+ field_name in fields_set and getattr(config, field_name) is None
+ for field_name in ("temporal_column", "time_grain", "time_range")
+ )
+ if explicit_temporal_clear:
+ merged = _scrub_temporal_form_data(merged)
Review Comment:
Addressed: after an atomic temporal clear, an explicitly supplied non-null
`time_range` is reapplied. `test_explicit_range_survives_clearing_saved_grain`
covers setting `Last month` while clearing the saved grain.
##########
superset/mcp_service/chart/chart_helpers.py:
##########
@@ -446,12 +564,200 @@ def _resolve_deck_gl_metrics(
if value:
metrics.append(value)
elif isinstance(prf, str) and _is_metric_ref(prf):
- # Legacy deck_scatter: point_radius_fixed as a bare non-numeric metric
key
- logger.debug("Legacy point_radius_fixed string metric encountered:
%s", prf)
metrics.append(prf)
return metrics
+def _deck_query_adapter( # noqa: C901
+ form_data: dict[str, Any], query: dict[str, Any], viz_type: str
+) -> dict[str, Any]:
+ """Apply the native frontend builder for a single Deck.gl layer."""
+ base_columns = list(query.get("columns") or [])
+ base_metrics = list(query.get("metrics") or [])
+ filters = list(query.get("filters") or [])
+ tooltips = _deck_tooltip_columns(form_data.get("tooltip_contents"))
+
+ def add_null(column: str, *, value: Any = ...) -> None:
+ clause: dict[str, Any] = {"col": column, "op": "IS NOT NULL"}
+ if value is not ...:
+ clause["val"] = value
+ filters.append(clause)
+
+ if viz_type == "deck_geojson":
+ geometry = form_data.get("geojson")
+ if not isinstance(geometry, str) or not geometry:
+ raise ValueError("GeoJSON column is required for GeoJSON charts")
+ columns = _add_deck_columns(base_columns, [geometry] if geometry else
[])
+ cross_filter = form_data.get("cross_filter_column")
+ if cross_filter:
+ columns = _add_deck_columns(columns, [cross_filter])
+ columns = _add_deck_columns(columns, tooltips)
+ if form_data.get("filter_nulls", True) and isinstance(geometry, str):
+ add_null(geometry)
+ query.update(
+ columns=columns,
+ metrics=[],
+ groupby=[],
+ filters=filters,
+ is_timeseries=False,
+ )
+ return query
+
+ if viz_type == "deck_polygon":
+ line_column = form_data.get("line_column")
+ if not isinstance(line_column, str) or not line_column:
+ raise ValueError("Polygon column is required for Polygon charts")
+ columns = _add_deck_columns(base_columns, [line_column] if line_column
else [])
+ cross_filter = form_data.get("cross_filter_column")
+ if cross_filter:
+ columns = _add_deck_columns(columns, [cross_filter])
+ columns = _add_deck_columns(columns, tooltips)
+ metrics: list[Any] = []
+ if metric := form_data.get("metric"):
+ metrics.append(metric)
+ radius = form_data.get("point_radius_fixed")
+ if (
+ isinstance(radius, dict)
+ and radius.get("type") == "metric"
+ and radius.get("value") is not None
+ ):
+ metrics.append(radius["value"])
+ if form_data.get("filter_nulls", True) and isinstance(line_column,
str):
+ add_null(line_column)
+ if metric:
+ add_null(_deck_metric_label(metric))
+ query.update(
+ columns=columns,
+ metrics=metrics,
+ filters=filters,
+ is_timeseries=False,
+ )
+ return query
+
+ if viz_type == "deck_path":
+ line_column = form_data.get("line_column")
+ if not isinstance(line_column, str) or not line_column:
+ raise ValueError("Line column is required for Path charts")
+ columns = list(base_columns)
+ metrics = list(base_metrics)
Review Comment:
Addressed: Deck Path filters `base_metrics` through the fixed-vs-metric
discriminator and bases aggregate mode on the filtered metrics. The regression
now asserts fixed `size="100"` yields no metrics and retains `path_col` as a
raw column.
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