hughhhh commented on code in PR #43870:
URL: https://github.com/apache/superset/pull/43870#discussion_r4319664119
##########
superset/commands/dataset/refresh.py:
##########
@@ -72,6 +72,22 @@ def run(self) -> Model:
ex.message,
)
+ # `fetch_metadata` writes the columns itself rather than going through
+ # `DatasetDAO.update`, so neither of the mapping's own repairs runs. It
+ # can drop the column `partition_column` names -- leaving a reference
+ # that fails `UpdateDatasetCommand`'s validation on every later edit,
+ # including a description-only PUT, which carries no columns payload
and
+ # so cannot reach the branch that forgives a stored-only dangle -- and
+ # it can *set* `main_dttm_col`, which moves the effective mapped column
+ # and can bring a parked transform live.
+ #
+ # No flush needed to read the columns: `fetch_metadata` reassigns the
+ # relationship, so the surviving set is already correct in memory.
+ DatasetDAO.clear_dangling_partition_mapping(
+ self._model, {column.column_name for column in self._model.columns}
+ )
+ DatasetDAO.clear_unmapped_partition_transforms(self._model)
Review Comment:
Fixed. `clear_unmapped_partition_transforms` runs before the metadata change
as well as after it, so the old mapping's real transform is no longer erased
while a transform parked on the newly mapped column survives.
##########
superset/commands/dataset/update.py:
##########
@@ -416,6 +423,146 @@ def _validate_expressions(
)
)
+ def _validate_partition_mapping(self, exceptions: list[ValidationError])
-> None:
+ """
+ Validate the dataset's partition filter mapping.
+
+ Only the blocking (Tier 1) issues become validation errors. Tier 2
+ issues -- an unparseable transform, a transform missing `:value` --
+ deliberately let the save through and leave the mapping inactive, per
+ the PRD, so a half-written transform doesn't cost the owner the rest of
+ their edits. They are surfaced by the editor, not by rejecting the PUT.
+
+ The transform is authored by a dataset owner, the same principal and
+ trust level as a calculated-column expression, so it also goes through
+ `validate_stored_expression` -- the parser gate that already governs
+ stored expressions. That gate only applies to a transform that parses:
+ it rejects an unparseable expression outright, which would turn a
Tier-2
+ issue into a blocking one and undo the paragraph above. Skipping it
+ there costs nothing -- an unparseable transform is never emitted into a
+ query -- and it is not a hole for templating, because Jinja in a
+ transform is already a Tier-1 blocking issue of its own.
+
+ Every path that *reads* a mapping is gated on the feature flag, so this
+ one is too: with the flag off nothing mirrors, and rejecting a save
over
+ a mapping that can never be consumed would be a validation error the
+ owner has no way to act on.
+ """
+ if not is_feature_enabled("PARTITION_FILTER_MAPPING"):
+ return
+
+ self._model = cast(SqlaTable, self._model)
+
+ columns = self._properties.get("columns")
+ column_names = (
+ {column["column_name"] for column in columns}
+ if columns is not None
+ else {column.column_name for column in self._model.columns}
+ )
+
+ partition_column = self._properties.get(
+ "partition_column", self._model.partition_column
+ )
+ partition_mapped_column = self._properties.get(
+ "partition_mapped_column", self._model.partition_mapped_column
+ )
+ main_dttm_col = self._properties.get("main_dttm_col",
self._model.main_dttm_col)
+ if not partition_column:
+ return
+
+ # A column payload drops every column it omits, and `update_columns`
+ # then runs `DatasetDAO.clear_dangling_partition_mapping` to drop a
+ # mapping whose columns went with them -- but that happens later,
during
+ # `run()`. Validate the state that cleanup leaves behind, or a metadata
+ # sync that legitimately removes the mapped column is rejected before
+ # the cleanup meant to handle it ever runs, which is exactly the
+ # orphaned case the cleanup exists for.
+ #
+ # Only a *stored* reference is forgiven. Asking in this very request to
+ # map onto a column the same request does not define is a mistake worth
+ # reporting, not something to quietly clean up.
+ if columns is not None:
Review Comment:
Fixed. `RefreshDatasetCommand` runs
`DatasetDAO.clear_dangling_partition_mapping` itself, so a reference the
refresh drops is repaired there rather than left for the next PUT to fail on.
##########
superset/connectors/sqla/models.py:
##########
@@ -1689,6 +1726,8 @@ class SqlaTable(
"normalize_columns",
"always_filter_main_dttm",
"folders",
+ "partition_column",
Review Comment:
Fixed. `DuplicateDatasetCommand` now copies `partition_value_transform` and
`partition_transform_is_monotonic` when it reconstructs the columns, so the
clone's mapping survives.
##########
superset/connectors/sqla/partition_mapping.py:
##########
@@ -0,0 +1,2338 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Partition filter mapping.
+
+Datasets on Hadoop-family engines are commonly partitioned on a *technical*
+column -- an epoch integer, a lowercased region key -- that no analyst would
+filter on. Unless a query carries a predicate on that column the engine scans
+every partition.
+
+A dataset owner names one partition column ``p``, one business column that
+filters are mirrored from, and a value transform ``T`` (a SQL expression
+containing a ``:value`` placeholder). Superset then appends an equivalent
+predicate on ``p`` to every query, so chart authors change nothing and queries
+prune.
+
+The load-bearing assumption
+---------------------------
+Everything here reasons about ``T(col) op T(v)``, but what is emitted is
+``p op T(v)`` -- a predicate on a *physically different column*. The step from
+one to the other is::
+
+ p = T(mapped_col) for every row in the table
+
+Superset cannot verify that; it is a property of whatever ETL populates the
+partition column. If that job lags, backfills with different logic, or writes
+the partition key in a different timezone, mirrored predicates silently drop
+real rows. The mapping is only as trustworthy as the pipeline behind it.
+
+Operator safety
+---------------
+A mirrored predicate ``P2`` may only be ``AND``-ed onto a query when the
+original predicate ``P1`` *implies* it:
+
+=========================================== =============================
+Original Safe when
+=========================================== =============================
+``col = v``, ``col IN (...)`` ``T`` is a function *and* the
+ engine compares ``col``
+ byte-exactly
+``col >=|>|<|<= v``, ``TEMPORAL_RANGE`` only if ``T`` is monotonic
+``col != v``, ``NOT IN``, ``LIKE``, ... never
+=========================================== =============================
+
+Monotonic here means non-decreasing, not strictly increasing: a bucketing
+transform such as a day key is monotonic and maps a whole day of timestamps
+onto one value. ``col < v`` then only implies ``T(col) <= T(v)``, so a strict
+bound is mirrored non-strictly -- see `mirror_operator`.
+
+Equality under the engine's own rules
+-------------------------------------
+"``T`` is a function" gives ``col = v`` implies ``T(col) = T(v)`` for *value*
+equality. The engine compares with *SQL* equality, and the two part company
+wherever that comparison is not byte-exact: under a case-insensitive collation
+a stored ``country`` of ``'us'`` satisfies a filter for ``'US'``, while the
+mirror ``region_key = hex('US')`` excludes the row and the chart loses it with
+nothing to indicate why. Trailing-space-insensitive ``CHAR`` comparison says
+the same thing about padding.
+
+So equality and ``IN`` are withdrawn for a *string* mapped column unless the
+engine spec declares ``binary_string_comparison`` -- `equality_mirrors_safely`.
+Numeric and temporal comparison is exact everywhere and is untouched, and range
+mirroring is untouched too: it already rests on the owner declaring ``T``
+order-preserving *with respect to the column's own order*, which is an
+assertion about the very semantics this is checking for.
+
+What the gate cannot see is a column declaring its own collation
+(``country COLLATE NOCASE``) on an otherwise byte-exact engine. Nothing in
+SQLAlchemy's reflection or the engine specs exposes that, so there it remains
+the owner's assumption, like ``p = T(mapped_col)`` itself.
+
+Resolution of the value the engine compares
+-------------------------------------------
+Every row of the table above assumes the engine compares the value the mirror
+was built from. It does not always. A ``DATE`` column compared against
+``'2026-07-06 10:08:11'`` is compared on the date part alone -- whether the
+engine writes the literal itself (``TO_DATE('2026-07-06', 'YYYY-MM-DD')`` on
+Postgres, ``DATE '2026-07-06'`` on Presto, Trino, BigQuery and some two dozen
+others) or applies an implicit cast to a bound string. The filter therefore
+keeps the whole of that day while ``p = T(2026-07-06 10:08:11)`` keeps one
+instant of it: narrower than the predicate it stands in for, which is the one
+thing a mirror may never be.
+
+What an engine throws away is detected from the column's type rather than
+enumerated per engine -- `ExploreMixin._engine_literal_resolution` -- and what
+to do about it is a property of the *operator*, not of the value:
+
+* a bound rounds outward, backward for a lower one and forward for an upper
one,
+ through `ExploreMixin._round_bounds_outward`. The mirror then reads one extra
+ bucket at the edge, and the original predicate is still in the query to
+ exclude the rows that bucket adds.
+* an equality, or one member of an ``IN`` list, has nowhere to widen to: these
+ predicates are ``AND``-ed onto the query, so "or the day either side" is not
+ something the mirror can express. They decline, which costs the query its
+ pruning and nothing else.
+
+So a ``DATE``-typed mapped column mirrors a range at day granularity, and does
+not mirror an equality whose value carries a time of day at all. Map a
+``TIMESTAMP`` column instead and every operator is available again.
+
+Negations are never safe because ``T`` need not be injective:
+``lower(:value)`` with ``country != 'US'`` mirrors to ``region_key != 'us'``,
+which wrongly excludes rows whose ``country`` is already lowercase ``'us'`` --
+rows the original filter *keeps*.
+
+Monotonicity is a property of the transform, not of the column's data type:
+``hour(:value)``, ``date_format(:value, 'dd')`` and ``dayofweek(:value)`` are
+all reasonable transforms on a ``TIMESTAMP`` column and none of them preserve
+ordering. It is therefore declared by the owner, not inferred.
+
+Time grains
+-----------
+A grained filter -- drill-to-detail, mostly -- compares the *truncated* column,
+``trunc(col) op v``, so the raw bounds it carries do not describe the rows it
+keeps. A row in the final partial bucket satisfies ``trunc(col) < until`` while
+``col < until`` excludes it.
+
+Which direction a grain rounds is not knowable from the duration alone, and the
+obvious guess is wrong: ``WEEK_ENDING_SATURDAY`` rounds *forward* on Hive and
+Presto, and Ocient's grains are ``ROUND``, i.e. to nearest. What every grain
+does satisfy is a bound on the displacement::
+
+ |trunc(ts) - ts| < width(grain)
+
+so widening *both* bounds by one bucket width is no narrower than the real
+predicate whichever way the grain rounds -- and it needs no monotonicity of
+``trunc`` itself, which is what rescues Hive's oddly-anchored ``P1W``. Width is
+a property of the grain, not of the engine, which is what makes this
+maintainable; see `grain_bucket_width`. A grain whose SQL an operator supplied
+has no known width, and does not mirror at all.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import logging
+import numbers
+import re
+from collections.abc import Sequence
+from dataclasses import dataclass
+from datetime import date, datetime
+from functools import lru_cache
+from typing import Any, cast, TYPE_CHECKING
+
+import numpy as np
+import pandas as pd
+import sqlalchemy as sa
+from dateutil.relativedelta import relativedelta
+from flask import current_app as app
+from flask_babel import lazy_gettext as _
+from sqlalchemy.engine.interfaces import Dialect
+from sqlalchemy.sql.elements import ColumnElement
+
+from superset.constants import LRU_CACHE_MAX_SIZE, TimeGrain
+from superset.exceptions import (
+ QueryClauseValidationException,
+ SupersetParseError,
+ SupersetSecurityException,
+)
+from superset.extensions import cache_manager, feature_flag_manager
+from superset.sql.parse import SQLScript, SQLStatement
+from superset.superset_typing import FilterValues
+from superset.utils import core as utils, json
+from superset.utils.core import FilterOperator
+
+if TYPE_CHECKING:
+ from superset.connectors.sqla.models import SqlaTable, TableColumn
+ from superset.db_engine_specs.base import BaseEngineSpec
+ from superset.models.core import Database
+
+logger = logging.getLogger(__name__)
+
+FEATURE_FLAG = "PARTITION_FILTER_MAPPING"
+
+#: Longest transform accepted anywhere. A transform is one SQL expression an
+#: owner types by hand, so this is generous; the point is that it is bounded.
+#: The bound matters because `is_transform_active` parses the stored value on
+#: each Explore load, and an unbounded string would make that parse the
+#: expensive part of rendering a chart.
+#:
+#: The typed column field, the import schema and the preview request each
+#: enforce it through a marshmallow `Length` validator, which is what produces
+#: the per-field message those three callers want. `stored_expression_error`
+#: enforces it too, for the paths that load no schema -- the deprecated
+#: `POST /datasource/save/` is one, and it writes straight through
+#: `update_from_object`.
+MAX_TRANSFORM_LENGTH = 1024
+
+#: Placeholder the owner writes in the transform, e.g.
``unix_timestamp(:value)``.
+#: Matched with word boundaries so ``:values`` is not mistaken for it.
+VALUE_PLACEHOLDER_RE = re.compile(r":value\b")
+
+#: Balanced Jinja blocks. The probe would render these in a different context
+#: at a different time from the chart query, so they are rejected at save time.
+JINJA_BLOCK_RE = re.compile(r"\{\{.*?\}\}|\{%.*?%\}|\{#.*?#\}", re.DOTALL)
+
+#: Substituted for ``:value`` before parsing -- sqlglot rejects a bare
``:value``
+#: on most dialects. Mirrors the ``_JINJA_BLOCK_RE`` -> ``NULL`` trick used by
+#: ``validate_stored_expression``.
+_PARSE_STANDIN = "NULL"
+
+#: Substituted for ``:value`` to find out *where* the placeholder landed, which
+#: `_PARSE_STANDIN` cannot answer: ``NULL`` is a keyword, so after substitution
+#: the parse tree no longer records that a placeholder was ever there. An
+#: identifier does leave a trace -- a column reference -- and one that is
+#: missing is a placeholder the engine will never evaluate.
+#:
+#: Named so it cannot plausibly collide with a real column, but a transform
that
+#: writes it anyway is refused rather than counted. A hand-written occurrence
+#: does not merely make the count disagree: it *supplies* the column reference
a
+#: non-executable placeholder failed to produce, and the two cancel exactly.
See
+#: `placeholder_is_executable`.
+_PLACEHOLDER_STANDIN = "superset_pfm_value_standin"
+
+#: Functions whose value depends on wall-clock time or randomness. The probe
+#: runs in a different session at a different moment from the chart query and
+#: its result is then cached, so any of these freezes a snapshot of probe time
+#: into the emitted predicate.
+NON_DETERMINISTIC_FUNCTIONS = {
+ "CURRENT_DATE",
+ "CURRENT_TIME",
+ "CURRENT_TIMESTAMP",
+ "NOW",
+ "RAND",
+ "RANDOM",
+ "UUID",
+}
+
+#: Functions that mean "now" only in their zero-argument form. On Hive and
+#: Impala ``unix_timestamp()`` is the current time while ``unix_timestamp(x)``
+#: -- the canonical transform for this feature -- is pure.
+NON_DETERMINISTIC_WHEN_NILADIC = {"UNIX_TIMESTAMP"}
+
+#: Safe for any function ``T``.
+MIRRORABLE_ALWAYS = {FilterOperator.EQUALS, FilterOperator.IN}
+
+#: Safe only when ``T`` preserves ordering.
+MIRRORABLE_IF_MONOTONIC = {
+ FilterOperator.GREATER_THAN,
+ FilterOperator.GREATER_THAN_OR_EQUALS,
+ FilterOperator.LESS_THAN,
+ FilterOperator.LESS_THAN_OR_EQUALS,
+ FilterOperator.TEMPORAL_RANGE,
+}
+
+
+#: Every operator that can be mirrored under *some* transform. The preview
+#: endpoint accepts these; whether a given one actually mirrors still depends
on
+#: the monotonicity declaration.
+MIRRORABLE_OPERATORS = MIRRORABLE_ALWAYS | MIRRORABLE_IF_MONOTONIC
+
+#: Operators the preview endpoint can construct. `TEMPORAL_RANGE` is mirrored
by
+#: the query path, but as *two* bounds that `_collect_partition_mirror_range`
+#: decomposes a time range into -- the preview request carries sample values,
not
+#: a since/until pair, so there is no range for it to build. The editor never
+#: asks for one either: `previewOperatorFor` sends `>=`, `=` or `IN`.
+PREVIEWABLE_OPERATORS = MIRRORABLE_OPERATORS - {FilterOperator.TEMPORAL_RANGE}
+
+
+#: A transform that preserves ordering need not be *strictly* increasing: a day
+#: key such as ``to_char(ts, 'YYYYMMDD')`` maps a whole day of timestamps onto
+#: one value. For such a ``T``, ``col < v`` only implies ``T(col) <= T(v)`` and
+#: ``col > v`` only implies ``T(col) >= T(v)`` -- the rows in the bucket at the
+#: boundary satisfy the non-strict form and not the strict one. A strict mirror
+#: therefore drops rows the filter keeps, which is the one thing a mirror may
+#: never do, so a strict bound is mirrored non-strictly.
+_NON_STRICT_EQUIVALENT = {
+ FilterOperator.GREATER_THAN: FilterOperator.GREATER_THAN_OR_EQUALS,
+ FilterOperator.LESS_THAN: FilterOperator.LESS_THAN_OR_EQUALS,
+}
+
+
+def mirror_operator(operator: FilterOperator) -> FilterOperator:
+ """
+ The operator a mirrored predicate uses to stand in for ``operator``.
+
+ Strict bounds widen; everything else passes through. Idempotent, so it is
+ safe wherever a request may already have been widened.
+
+ The widened bound reads one extra partition at the edge, and the original
+ predicate is still in the query to exclude the rows that partition adds --
+ the same trade as the grain widening and the ``NULL``-partition escape.
+ Declaring the transform strictly increasing would buy that partition back,
+ which is not worth a second property only the owner can vouch for.
+ """
+ return _NON_STRICT_EQUIVALENT.get(operator, operator)
+
+
+def mirrorable_operators(
+ is_monotonic: bool, *, equality_is_safe: bool = True
+) -> set[FilterOperator]:
+ """
+ The operators whose predicates may be mirrored onto the partition column.
+
+ :param is_monotonic: whether the owner declared the transform
+ order-preserving
+ :param equality_is_safe: whether the engine's ``=`` on the mapped column
+ compares the way ``T`` was written against -- see
+ `equality_mirrors_safely`
+
+ Both call sites -- the query path through `PartitionMapping.mirrors` and
the
+ Explore indicator through `partition_filter_mapping_summary` -- come
through
+ here, which is what keeps the glyph from promising pruning the SQL will not
+ do.
+ """
+ operators = MIRRORABLE_ALWAYS if equality_is_safe else set()
+ if is_monotonic:
+ operators = operators | MIRRORABLE_IF_MONOTONIC
+ return set(operators)
+
+
+def equality_mirrors_safely(
+ column: TableColumn | None,
+ db_engine_spec: type[BaseEngineSpec],
+) -> bool:
+ """
+ Whether ``col = v`` on this column implies ``T(col) = T(v)`` in the engine.
+
+ The operator matrix calls equality safe "for any function ``T``" because
+ ``T`` is a function, so ``col = v`` gives ``T(col) = T(v)``. That reasons
+ about *value* equality while the engine reasons about *SQL* equality, and
+ the two part company under any comparison that is not byte-exact: with a
+ case-insensitive collation a stored ``country`` of ``'us'`` satisfies a
+ filter for ``'US'``, but the mirror ``region_key = hex('US')`` excludes the
+ row and the chart loses it with no indication why.
Trailing-space-insensitive
+ ``CHAR`` comparison says the same thing about padding.
+
+ Only string columns are at risk; numeric and temporal comparison is exact
+ everywhere. And only equality: range mirroring already requires the owner
+ to declare ``T`` order-preserving *with respect to the column's own order*,
+ which is an assertion about the same comparison semantics this is checking
+ for -- so the declaration covers it where equality has nothing to cover it.
+
+ What this cannot see is a column that declares its own collation
+ (``country COLLATE NOCASE``) on an otherwise byte-exact engine. Neither
+ SQLAlchemy's reflection nor the engine specs expose it, so on such a column
+ the assumption stays where ``p = T(mapped_col)`` already is: with the
owner.
+ """
+ if column is None:
+ return True
+ try:
+ is_string = column.type_generic == utils.GenericDataType.STRING
+ except Exception: # pylint: disable=broad-except # noqa: BLE001
+ # An unresolvable type is treated as a string: the gate exists to stop
+ # a silent wrong answer, so it fails closed.
+ return False
+ if not is_string:
+ return True
+ return bool(db_engine_spec.binary_string_comparison)
+
+
+#: How wide one bucket of each built-in time grain is.
+#:
+#: Keyed on the ISO duration a filter carries in its ``grain``. Written out
+#: rather than parsed: four of the week grains are ISO *intervals* with an
+#: anchor (``P1W/1970-01-03T00:00:00Z``) that ``isodate.parse_duration``
+#: rejects outright, and ``PT0.5H`` / ``P0.25Y`` are fractional. A literal
+#: table is also the thing a reviewer can check a line at a time.
+#:
+#: ``relativedelta`` rather than ``timedelta`` so the calendar grains stay
+#: calendar arithmetic: a month is not 30 days.
+GRAIN_BUCKET_WIDTHS: dict[str, relativedelta] = {
+ TimeGrain.SECOND: relativedelta(seconds=1),
+ TimeGrain.FIVE_SECONDS: relativedelta(seconds=5),
+ TimeGrain.THIRTY_SECONDS: relativedelta(seconds=30),
+ TimeGrain.MINUTE: relativedelta(minutes=1),
+ TimeGrain.FIVE_MINUTES: relativedelta(minutes=5),
+ TimeGrain.TEN_MINUTES: relativedelta(minutes=10),
+ TimeGrain.FIFTEEN_MINUTES: relativedelta(minutes=15),
+ TimeGrain.THIRTY_MINUTES: relativedelta(minutes=30),
+ TimeGrain.HALF_HOUR: relativedelta(minutes=30),
+ TimeGrain.HOUR: relativedelta(hours=1),
+ TimeGrain.SIX_HOURS: relativedelta(hours=6),
+ TimeGrain.DAY: relativedelta(days=1),
+ TimeGrain.WEEK: relativedelta(days=7),
+ TimeGrain.WEEK_STARTING_SUNDAY: relativedelta(days=7),
+ TimeGrain.WEEK_STARTING_MONDAY: relativedelta(days=7),
+ TimeGrain.WEEK_ENDING_SATURDAY: relativedelta(days=7),
+ TimeGrain.WEEK_ENDING_SUNDAY: relativedelta(days=7),
+ TimeGrain.MONTH: relativedelta(months=1),
+ TimeGrain.QUARTER: relativedelta(months=3),
+ TimeGrain.QUARTER_YEAR: relativedelta(months=3),
+ TimeGrain.YEAR: relativedelta(years=1),
+}
+
+
+def grain_bucket_width(grain: str | None, engine: str) -> relativedelta | None:
+ """
+ How far a grain's truncation can move a timestamp, or ``None`` if unknown.
+
+ A grained filter compares the *truncated* column, so the raw bounds it
+ carries do not describe the rows it keeps. Widening both bounds by one
+ bucket recovers a predicate that is no narrower than the real one -- see
+ `_collect_partition_mirror_range` for the argument. That only works for a
+ grain whose bucket width Superset knows, which excludes anything an
+ operator supplied.
+
+ :param grain: the ISO duration from the filter's ``grain``
+ :param engine: the engine spec's ``engine``, to check per-engine overrides
+ """
+ if not grain:
+ return None
+
+ # An operator-declared grain carries whatever duration string they typed,
+ # and `TIME_GRAIN_ADDON_EXPRESSIONS` can redefine a *built-in* grain's SQL
+ # per engine -- `P1D` could be anything at all. Neither has a width we can
+ # claim to know, so both fall back to not mirroring.
+ if grain in app.config["TIME_GRAIN_ADDONS"]:
+ return None
+ if grain in app.config["TIME_GRAIN_ADDON_EXPRESSIONS"].get(engine, {}):
+ return None
+
+ return GRAIN_BUCKET_WIDTHS.get(grain)
+
+
+@dataclass(frozen=True)
+class PartitionMapping:
+ """A resolved, usable partition filter mapping."""
+
+ partition_column: str
+ mapped_column: str
+ value_transform: str
+ is_monotonic: bool
+ #: Whether the engine compares the mapped column the way ``T`` was written
+ #: against. Resolved once where the column and the engine are both in hand,
+ #: rather than re-derived at each `mirrors` call.
+ equality_is_safe: bool = True
+
+ def mirrors(self, operator: FilterOperator) -> bool:
+ return operator in mirrorable_operators(
+ self.is_monotonic, equality_is_safe=self.equality_is_safe
+ )
+
+
+def contains_value_placeholder(transform: str | None) -> bool:
+ """Whether the transform contains the ``:value`` placeholder."""
+ return bool(transform) and VALUE_PLACEHOLDER_RE.search(transform or "") is
not None
+
+
+def contains_jinja(transform: str | None) -> bool:
+ """Whether the transform contains a balanced Jinja block."""
+ return bool(transform) and JINJA_BLOCK_RE.search(transform or "") is not
None
+
+
+def parse_skeleton(transform: str, standin: str = _PARSE_STANDIN) -> str:
+ """
+ The transform with ``:value`` substituted out, ready for a SQL parser.
+
+ ``sanitize_clause`` / sqlglot choke on a bare ``:value`` on most dialects,
+ so the placeholder is swapped for a benign literal first -- the same trick
+ ``validate_stored_expression`` uses for Jinja blocks.
+
+ :param standin: what to substitute. `placeholder_is_executable` passes
+ `_PLACEHOLDER_STANDIN` to find out where the placeholder landed.
+ """
+ # Substituted through a function so `standin` is taken literally. As a
+ # replacement *string* `re.sub` would read backslashes in it as escapes,
+ # and `build_probe_sql` passes rendered SQL here: a filter value containing
+ # `\n` would probe a newline instead of the two characters the predicate
+ # compares, and one containing `\1` would raise outright.
+ return VALUE_PLACEHOLDER_RE.sub(lambda _: standin, transform)
+
+
+#: Prefix the transform is wrapped in before parsing. Its length is subtracted
+#: from any reported column so positions refer to what the owner actually
typed.
+_SELECT_PREFIX = "SELECT "
+
+
+def _parse_skeleton(
+ transform: str, engine: str, standin: str = _PARSE_STANDIN
+) -> SQLStatement | None:
+ """
+ Parse ``SELECT <transform>`` with the placeholder substituted out.
+
+ Returns ``None`` when the transform does not parse.
+ """
+ try:
+ return SQLStatement(
+ f"{_SELECT_PREFIX}{parse_skeleton(transform, standin)}", engine
+ )
+ except SupersetParseError:
+ return None
+
+
+def parse_error_detail(transform: str, engine: str) -> str | None:
+ """
+ Where the parser gave up on the transform.
+
+ Returns ``None`` when it parses, or when the parser offered no position.
+ Note that sqlglot parses unknown functions happily -- a misspelled function
+ name is not a parse error, it is an engine error, and surfaces only when
the
+ transform is evaluated.
+
+ The parser's own ``highlight`` is deliberately dropped: it would name the
+ ``NULL`` we substituted for ``:value``, which is not a token the owner
typed.
+ """
+ try:
+ SQLStatement(f"{_SELECT_PREFIX}{parse_skeleton(transform)}", engine)
+ except SupersetParseError as ex:
+ column = (ex.error.extra or {}).get("column")
+ if not isinstance(column, int):
+ return None
+ return str(
+ _(
+ "syntax error at position %(position)d.",
+ position=_position_in_transform(transform, column),
+ )
+ )
+ return None
+
+
+def _position_in_transform(transform: str, parsed_column: int) -> int:
+ """
+ Map a column in the parsed skeleton back to the transform as typed.
+
+ Two substitutions stand between them: the ``SELECT`` prefix, and every
+ ``:value`` that became a shorter ``NULL``. Without unwinding both, a
+ reported position drifts left by two characters per placeholder ahead of it
+ -- which is worst exactly where transforms usually break, at the end.
+ """
+ position = max(parsed_column - len(_SELECT_PREFIX), 0)
+ shift = len(":value") - len(_PARSE_STANDIN)
+ preceding = sum(
+ 1
+ for index, match in enumerate(VALUE_PLACEHOLDER_RE.finditer(transform))
+ if match.start() - index * shift < position
+ )
+ return position + preceding * shift
+
+
+def placeholder_is_executable(transform: str | None, engine: str) -> bool:
+ """
+ Whether every ``:value`` sits where the engine will evaluate it.
+
+ `contains_value_placeholder` is a regex over raw text, and `parse_skeleton`
+ substitutes before parsing, so neither notices a placeholder that is not in
+ an executable position. Two shapes reach the probe that way, and both are
+ worse than the inactive mapping a malformed transform earns:
+
+ ``':value'`` -- the placeholder inside a string literal. `build_probe_sql`
+ binds it anyway, because SQLAlchemy's own ``text()`` scan is no more
+ literal-aware than the regex, and the dialect then renders the bind
+ *including its own quotes* in the middle of the owner's quotes. A value of
+ ``" || (SELECT secret FROM vault LIMIT 1) || "`` closes the literal and
+ compiles to a working sub-query -- the very read primitive
+ `stored_expression_error` refuses when it is written openly.
+
+ ``1 -- :value`` -- the placeholder inside a comment. The probe's selections
+ are joined on one line, so the comment swallows its own ``AS v0`` alias and
+ everything after it; a single-value probe still returns one column, the
+ constant is accepted as the transform's result, and the mirror then emits
+ ``partition_col = <constant>`` for every filter value. That is wrong rows,
+ not lost pruning.
+
+ Answered structurally: substitute an identifier rather than a keyword and
+ count the column references it produced. A literal holds no column
+ reference and a comment is not parsed at all, so either case comes back
+ short of the number of placeholders the owner wrote.
+
+ Which is why a transform naming the stand-in itself is refused outright.
The
+ count is the whole gate, and a hand-written occurrence pays the difference:
+ ``concat(superset_pfm_value_standin, ':value')`` has one placeholder,
inside
+ a literal that contributes nothing, and one bare reference the owner wrote
--
+ so the counts match and the gate passes a placeholder the engine will never
+ evaluate. The probe then renders the bound value inside the owner's quotes,
+ where a value closing the literal can alias a column of any table the
+ connection can read into the stand-in's name and have the probe hand it
back
+ through the preview. There is no legitimate transform naming this
identifier,
+ so refusing it costs nothing and makes the count mean what it claims.
+ """
+ if not transform:
+ return False
+ if _PLACEHOLDER_STANDIN in transform.lower():
+ # Lowercased on both sides because `count_bare_column_references` is
+ # case-insensitive too: a dialect may normalize an unquoted identifier,
+ # so `SUPERSET_PFM_VALUE_STANDIN` would otherwise balance the count
+ # while slipping past a case-sensitive check here.
+ return False
+ expected = len(VALUE_PLACEHOLDER_RE.findall(transform))
+ if not expected:
+ return False
+ statement = _parse_skeleton(transform, engine, _PLACEHOLDER_STANDIN)
+ if statement is None:
+ return False
+ return statement.count_bare_column_references(_PLACEHOLDER_STANDIN) ==
expected
+
+
+def is_parseable(transform: str | None, engine: str) -> bool:
+ """
+ Whether the transform parses as a single select expression.
+
+ "Single" is the load-bearing word. `SELECT lower(:value), 'x'` parses just
+ as happily as `SELECT lower(:value)`, but it returns two columns per input
+ value, and the probe reads one column per input -- so an `IN` filter would
+ get a predicate built from the wrong halves of the wrong rows. Rejecting
the
+ list here is what lets the probe trust its own column count.
+ """
+ if not transform or not transform.strip():
+ return False
+ statement = _parse_skeleton(transform, engine)
+ return statement is not None and statement.count_select_expressions() == 1
+
+
+def is_bare_expression(transform: str | None, engine: str) -> bool:
+ """
+ Whether the transform is a scalar expression and nothing more.
+
+ `is_parseable` is not enough, because it counts only the projection.
+ ``secret || :value FROM vault`` holds exactly one select expression, so it
+ parses as "a single expression" -- and `build_probe_sql` then emits
+ ``SELECT secret || 'us' AS v0``, where its own alias is read as a table
+ alias on the FROM clause the transform smuggled in. The probe reads a
+ column the owner was never granted and hands it back through the predicate
+ the preview panel renders.
+
+ A transform is a scalar function of ``:value`` by definition -- the whole
+ feature rests on ``partition_col = T(mapped_col)``, which only type-checks
+ for scalar ``T`` -- so there is no legitimate transform with a clause of
+ its own, and none with a sub-query either.
+ """
+ if not transform or not transform.strip():
+ return False
+ statement = _parse_skeleton(transform, engine)
+ return (
+ statement is not None
+ and statement.is_bare_select_expression()
+ and not statement.has_subquery()
+ )
+
+
+def is_unfinished(transform: str | None, engine: str) -> bool:
+ """
+ Whether the transform is not SQL yet, as opposed to the wrong SQL.
+
+ The distinction matters because the two deserve opposite treatment. A
+ half-typed ``unix_timestamp(:value`` is what a text input produces on the
+ way to something valid: a PUT stores it and reports the mapping inactive,
+ so discarding it would mean an export could not round-trip the dataset it
+ came from. ``unix_timestamp(:value); DROP TABLE t`` is not on the way to
+ anything, and has to be refused wherever it is offered.
+
+ Both fail `is_parseable`, and both fail to parse as a single *statement* --
+ so neither of those tells them apart. Parsing as a *script* does: the
+ multi-statement form is two valid statements, while unfinished text is no
+ statement at all.
+ """
+ if not transform or not transform.strip():
+ return False
+ try:
+ SQLScript(f"{_SELECT_PREFIX}{parse_skeleton(transform)}", engine)
+ except SupersetParseError:
+ return True
+ return False
+
+
+def find_non_deterministic_functions(transform: str, engine: str) -> set[str]:
+ """
+ Names of non-deterministic functions the transform calls.
+
+ ``UNIX_TIMESTAMP`` is only reported in its zero-argument form, which means
+ "now" on Hive and Impala; the one-argument form is the canonical temporal
+ transform and stays allowed.
+ """
+ statement = _parse_skeleton(transform, engine)
+ if statement is None:
+ return set()
+
+ found = {
+ name
+ for name in NON_DETERMINISTIC_FUNCTIONS
+ if statement.check_functions_present({name})
+ }
+ return found | _find_niladic_calls(statement)
+
+
+def _find_niladic_calls(statement: SQLStatement) -> set[str]:
+ """
+ Names from ``NON_DETERMINISTIC_WHEN_NILADIC`` called with no arguments.
+
+ Note some dialects resolve the zero-argument form themselves -- Hive parses
+ ``unix_timestamp()`` straight to ``CURRENT_TIMESTAMP`` -- in which case the
+ name-based check above has already caught it. This is the backstop for the
+ dialects that do not.
+ """
+ return NON_DETERMINISTIC_WHEN_NILADIC & statement.get_niladic_functions()
+
+
+def resolve_partition_mapping(datasource: SqlaTable) -> PartitionMapping |
None:
+ """
+ Resolve the dataset's mapping, or ``None`` when nothing may be mirrored.
+
+ Every bail-out here is defensive as well as functional: save-time
validation
+ rejects most of these, but rows predating the validation can still violate
+ the invariants, and a column sync can invalidate a mapping that was fine
+ when it was written.
+
+ The transform gate is `is_transform_active`, the same function the Explore
+ indicator reads, which is `validate_transform` with the messages discarded.
+ Anything narrower here would be a second, weaker statement of the same
rule:
+ a transform calling `now()` that reached storage without passing
+ `UpdateDatasetCommand` -- through import, or through a bundle written by
hand
+ -- would be reported inactive by the editor and still mirrored by this
+ function, freezing a snapshot of probe time into the predicate with nothing
+ on screen to say so.
+ """
+ if not feature_flag_manager.is_feature_enabled(FEATURE_FLAG):
+ return None
+
+ partition_column = getattr(datasource, "partition_column", None)
+ if not partition_column:
+ return None
+
+ columns_by_name = {column.column_name: column for column in
datasource.columns}
+ if partition_column not in columns_by_name:
+ # The partition column was dropped by a column sync or at the source.
+ return None
+
+ mapped_column_name = (
+ getattr(datasource, "partition_mapped_column", None) or
datasource.main_dttm_col
+ )
+ if not mapped_column_name or mapped_column_name not in columns_by_name:
+ return None
+
+ if mapped_column_name == partition_column:
+ # Self-mapping: the mirrored predicate would duplicate the original.
+ return None
+
+ mapped_column = columns_by_name[mapped_column_name]
+ transform = getattr(mapped_column, "partition_value_transform", None)
+ if not is_transform_active(transform, datasource.database.backend):
+ return None
+
+ if has_active_advanced_data_type(mapped_column):
+ # `translate_filter` builds its own predicate shape from *translated*
+ # values, so the `(operator, value)` pair the operator matrix reasons
+ # about does not exist and mirroring would apply the wrong values.
+ return None
+
+ return PartitionMapping(
+ partition_column=str(partition_column),
+ mapped_column=str(mapped_column_name),
+ value_transform=cast(str, transform),
+ is_monotonic=bool(
+ getattr(mapped_column, "partition_transform_is_monotonic", False)
+ ),
+ equality_is_safe=equality_mirrors_safely(
+ mapped_column, datasource.database.db_engine_spec
+ ),
+ )
+
+
+def has_active_advanced_data_type(column: TableColumn) -> bool:
+ """
+ Whether the column's advanced data type is configured and switched on.
+
+ Such a column is never mirrored: ``translate_filter`` builds its own
+ predicate shape from translated values, so the ``(operator, value)`` pair
+ the operator matrix reasons about does not exist.
+ """
+ advanced_data_type = getattr(column, "advanced_data_type", None)
+ if not advanced_data_type:
+ return False
+ if not
feature_flag_manager.is_feature_enabled("ENABLE_ADVANCED_DATA_TYPES"):
+ return False
+ return advanced_data_type in app.config.get("ADVANCED_DATA_TYPES", {})
+
+
+def _denylist_engine_key(database: "Database") -> str:
+ """
+ The name ``DISALLOWED_SQL_*`` is keyed by for this database.
+
+ The engine spec's own ``engine`` first, because that is what every other
+ denylist gate uses (`_raise_for_disallowed_sql`, `sql_lab`) and what an
+ operator writing the config reads in the documentation; a spec covering
+ several SQLAlchemy backends through ``engine_aliases`` reports the one
+ canonical name under which its entry is written.
+
+ Falling back to the URL's backend, because resolving the spec loads the
+ SQLAlchemy dialect entrypoint, which imports the driver package. On a
+ deployment missing an optional driver that raises rather than merely being
+ absent, asking for the spec here would turn a dataset import into a hard
+ failure -- and for every engine without aliases the two names are the same
+ string anyway.
+ """
+ try:
+ return database.db_engine_spec.engine
+ except Exception: # pylint: disable=broad-except # noqa: BLE001
+ return database.backend
+
+
+def stored_expression_error(
+ database: "Database",
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+) -> str | None:
+ """
+ Why this transform may not be stored or run, if there is a reason.
+
+ Stricter than the policy a general stored expression goes through, because
+ a value transform is a narrower thing: `build_probe_sql` binds only
+ `:value` and splices the rest of the transform in as SQL text, which the
+ engine then executes, so an ungated transform is arbitrary SQL. A dataset
+ editor without SQL Lab could store `(SELECT secret FROM protected_table
+ LIMIT 1) || :value`, or `secret || :value FROM protected_table`, and read
+ the answer back out of the emitted predicate the preview panel renders.
+
+ So the transform must be a bare scalar expression: no clause of its own and
+ no sub-query, whatever `ALLOW_ADHOC_SUBQUERY` says. That is not a
+ restriction the feature pays for -- it rests on
+ ``partition_col = T(mapped_col)``, which only type-checks for scalar ``T``
+ -- and it is what makes the table denylist and the RLS rewrite moot here,
+ since neither has a table reference left to govern.
+
+ Returns the engine-agnostic reason as a string rather than raising,
+ because its four callers disagree about what to do with it: the preview
+ reports it at the field, a PUT and the legacy datasource save refuse the
+ write, the importer drops the transform and keeps the dataset, and the
+ probe simply declines to run. Raising would make three of those four
+ write a `try` around a question.
+
+ Imported inside the function rather than at module scope:
+ `connectors.sqla.models` imports this module, so the dependency only runs
+ one way at import time.
+ """
+ from superset.connectors.sqla.models import ( # pylint:
disable=import-outside-toplevel,cyclic-import
+ validate_stored_expression,
+ )
+
+ # Ahead of the parse, both because it is the cheaper question and because
+ # the parse is the cost the bound exists to contain.
+ if len(transform) > MAX_TRANSFORM_LENGTH:
+ return str(
+ _(
+ "A partition value transform cannot be longer than "
+ "%(limit)d characters.",
+ limit=MAX_TRANSFORM_LENGTH,
+ )
+ )
+
+ statement = _parse_skeleton(transform, database.backend)
+ if statement is None:
+ return str(
+ _("A partition value transform must parse as a single SQL
expression.")
+ )
+
+ # The shape gate again, even though `validate_transform` blocks the same
+ # thing at save time. This is the last door before the transform becomes
SQL
+ # an engine runs, and the only door a row written by an earlier release --
+ # or by an importer that ran with the feature flag off -- still passes
+ # through.
+ if not statement.is_bare_select_expression():
+ return str(
+ _(
+ "A partition value transform must be a single SQL expression, "
+ "with no FROM, WHERE or other clause."
+ )
+ )
+
+ # Unconditionally, not under `ALLOW_ADHOC_SUBQUERY`. A transform is a
scalar
+ # function of `:value`, so a sub-query in one has no legitimate use, and it
+ # is a read primitive whose answer the preview panel renders back to its
+ # caller. Refusing it is also what lets this function skip the table
+ # denylist and the row-level-security rewrite: with no FROM clause and no
+ # sub-query there is no table reference for either to govern.
+ if statement.has_subquery():
+ return str(_("A partition value transform cannot contain a
sub-query."))
+
+ # The shape gates above read the transform with `:value` replaced by the
+ # `NULL` keyword, which leaves no trace of where the placeholder was -- so
+ # none of them can see a placeholder that is not in an executable position.
+ # Checked here as well as in `validate_transform` because this is the door
+ # a row written by an earlier release still passes through, and because
+ # the escape only completes once a filter value is bound.
+ #
+ # Guarded on the placeholder being present at all: a transform without one
+ # is inert rather than refused (`validate_transform` reports it as Tier 2),
+ # and refusing it here would make this gate stricter than the PUT's.
+ if contains_value_placeholder(transform) and not placeholder_is_executable(
+ transform, database.backend
+ ):
+ return str(
+ _(
+ "A partition value transform must use the :value placeholder "
+ "where the engine will evaluate it, not inside a string "
+ "literal or a comment."
+ )
+ )
+
+ denied = app.config["DISALLOWED_SQL_FUNCTIONS"].get(
+ _denylist_engine_key(database), set()
+ )
+ if denied and (found := statement.get_disallowed_functions(denied)):
+ return str(
+ _(
+ "A partition value transform cannot call %(functions)s.",
+ functions=", ".join(sorted(found)),
+ )
+ )
+
+ # Kept as the tail call rather than replaced: this is where
`sanitize_clause`
+ # runs. Its own sub-query branch returns rewritten, RLS-injected SQL that
+ # this function has no way to propagate -- but that branch is unreachable
+ # from here now, because a transform containing a sub-query was refused
+ # above, so discarding the return value is correct rather than a leak.
+ try:
+ validate_stored_expression(database, catalog, schema,
parse_skeleton(transform))
+ except SupersetSecurityException as ex:
+ return str(ex.error.message)
+ except QueryClauseValidationException as ex:
+ return str(ex.message)
+ return None
+
+
+@dataclass(frozen=True)
+class RawProbeValue:
+ """
+ A probe value that is already SQL text rather than a bindable scalar.
+
+ Almost every filter value is a Python scalar, which `build_probe_sql` binds
+ and lets the dialect render. An array column's value is not: the predicate
+ it mirrors compares an engine array literal -- ``array('a', 'b')`` on
+ ClickHouse -- which `BaseEngineSpec.array_literal` returns as a SQLAlchemy
+ expression, and an expression cannot be the value of a bind parameter.
+
+ Frozen, and holding text rather than the expression, for two reasons
+ besides immutability: `_probe_cache_key` keys on ``repr`` of each value,
and
+ an expression's ``repr`` carries its memory address -- so caching the probe
+ would key every call differently. `_hashable` needs it hashable too.
+
+ Build one with `raw_probe_value`, which renders the expression through the
+ same literal compilation the bound path uses.
+ """
+
+ sql: str
+
+
+def raw_probe_value(database: "Database", element: Any) -> RawProbeValue:
+ """
+ Freeze an engine-built expression into probe-ready SQL text.
+
+ The text is produced entirely by SQLAlchemy and the engine spec from values
+ that have already been coerced, so no caller-supplied text reaches the
probe
+ uninspected -- string elements are escaped by the dialect's own literal
+ processor. `probe_sql_is_evaluable` then re-reads the finished query, which
+ is what bounds the splice.
+
+ :param database: the database whose dialect renders the literal
+ :param element: a SQLAlchemy expression, e.g. from ``array_literal``
+ :return: the rendered expression, ready to stand in for ``:value``
+ """
+ return RawProbeValue(_compile_literal(element, _dialect_for(database)))
+
+
+def normalize_mixed_numbers(values: Sequence[Any]) -> list[Any]:
+ """
+ One numeric type across ``values``, so a bind cannot be inferred wrongly.
+
+ SQLAlchemy infers an ``IN`` bind's type from the first element it is given.
+ A list mixing an int with a float therefore binds as integer and truncates
+ every later float: ``[33, 29.02]`` emits ``IN (33, 29)`` and drops the row
+ matching ``29.02``.
+
+ Applied on both sides of the probe, which is why it lives here rather than
+ inline at either: the filter values collected for mirroring have to match
+ the ones the real predicate binds (see #33206), and the probe *results*
+ bound against the partition column have the same problem one step later.
+
+ Leaves the list alone when nothing in it is a float, so an exact integer
+ key above 2^53 is not rounded for no reason.
+
+ :param values: the values about to be bound
+ :return: the values, with every number widened to ``float`` if any was
+ """
+ if not any(isinstance(value, float) for value in values):
+ return list(values)
+ return [
+ float(value) if isinstance(value, (int, float)) else value for value
in values
+ ]
+
+
+def build_probe_sql(
+ transform: str,
+ values: list[Any],
+ dialect: Dialect | None = None,
+ from_suffix: str = "",
+) -> str:
+ """
+ Compile a single ``SELECT`` that evaluates the transform at every value.
+
+ Values are attacker-controlled (a Gamma user picks filter values), so they
+ are bound as parameters and rendered by the dialect's own literal processor
+ rather than interpolated into the SQL text.
+
+ ``from_suffix`` comes from the engine spec's
``select_without_from_suffix``.
+ A ``SELECT`` with no ``FROM`` is not universal SQL: Oracle and Db2 need a
+ one-row table to select from, and without it every probe on those engines
+ raises -- which `_run_probe` swallows, so the only symptom is a correctly
+ configured mapping that silently never prunes.
+
+ Note this deliberately does *not* go through ``BaseEngineSpec``'s text
+ helper, which escapes ``:`` on every engine but Athena and would destroy
the
+ ``:value`` placeholder before it can be bound.
+
+ Callers must have run `stored_expression_error` first. The ``AS v{index}``
+ alias and ``from_suffix`` are only safe on a transform with no clause of
its
+ own; on one carrying its own FROM they attach to that instead.
+ """
+ selections = []
+ for index, value in enumerate(values):
+ if isinstance(value, RawProbeValue):
+ # Already SQL, so substituted rather than bound -- and deliberately
+ # not run through `_compile_literal`, which has nothing to compile
+ # and would undouble percent signs the owner typed on purpose.
+ rendered = parse_skeleton(transform, value.sql)
+ else:
+ clause = sa.text(transform).bindparams(sa.bindparam("value",
value=value))
Review Comment:
Fixed. The question is answered by attempting the bind itself —
`sa.text(transform).bindparams(sa.bindparam("value", value=None))` in a try —
rather than by pattern-matching `::` or reading `text()`'s private parameter
map, so it cannot disagree with what `build_probe_sql` is about to do on any
dialect.
##########
superset/connectors/sqla/partition_mapping.py:
##########
@@ -0,0 +1,1314 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Partition filter mapping.
+
+Datasets on Hadoop-family engines are commonly partitioned on a *technical*
+column -- an epoch integer, a lowercased region key -- that no analyst would
+filter on. Unless a query carries a predicate on that column the engine scans
+every partition.
+
+A dataset owner names one partition column ``p``, one business column that
+filters are mirrored from, and a value transform ``T`` (a SQL expression
+containing a ``:value`` placeholder). Superset then appends an equivalent
+predicate on ``p`` to every query, so chart authors change nothing and queries
+prune.
+
+The load-bearing assumption
+---------------------------
+Everything here reasons about ``T(col) op T(v)``, but what is emitted is
+``p op T(v)`` -- a predicate on a *physically different column*. The step from
+one to the other is::
+
+ p = T(mapped_col) for every row in the table
+
+Superset cannot verify that; it is a property of whatever ETL populates the
+partition column. If that job lags, backfills with different logic, or writes
+the partition key in a different timezone, mirrored predicates silently drop
+real rows. The mapping is only as trustworthy as the pipeline behind it.
+
+Operator safety
+---------------
+A mirrored predicate ``P2`` may only be ``AND``-ed onto a query when the
+original predicate ``P1`` *implies* it:
+
+=========================================== =============================
+Original Safe when
+=========================================== =============================
+``col = v``, ``col IN (...)`` always -- ``T`` is a function
+``col >=|>|<|<= v``, ``TEMPORAL_RANGE`` only if ``T`` is monotonic
+``col != v``, ``NOT IN``, ``LIKE``, ... never
+=========================================== =============================
+
+Negations are never safe because ``T`` need not be injective:
+``lower(:value)`` with ``country != 'US'`` mirrors to ``region_key != 'us'``,
+which wrongly excludes rows whose ``country`` is already lowercase ``'us'`` --
+rows the original filter *keeps*.
+
+Monotonicity is a property of the transform, not of the column's data type:
+``hour(:value)``, ``date_format(:value, 'dd')`` and ``dayofweek(:value)`` are
+all reasonable transforms on a ``TIMESTAMP`` column and none of them preserve
+ordering. It is therefore declared by the owner, not inferred.
+
+Time grains
+-----------
+A grained filter -- drill-to-detail, mostly -- compares the *truncated* column,
+``trunc(col) op v``, so the raw bounds it carries do not describe the rows it
+keeps. A row in the final partial bucket satisfies ``trunc(col) < until`` while
+``col < until`` excludes it.
+
+Which direction a grain rounds is not knowable from the duration alone, and the
+obvious guess is wrong: ``WEEK_ENDING_SATURDAY`` rounds *forward* on Hive and
+Presto, and Ocient's grains are ``ROUND``, i.e. to nearest. What every grain
+does satisfy is a bound on the displacement::
+
+ |trunc(ts) - ts| < width(grain)
+
+so widening *both* bounds by one bucket width is no narrower than the real
+predicate whichever way the grain rounds -- and it needs no monotonicity of
+``trunc`` itself, which is what rescues Hive's oddly-anchored ``P1W``. Width is
+a property of the grain, not of the engine, which is what makes this
+maintainable; see `grain_bucket_width`. A grain whose SQL an operator supplied
+has no known width, and does not mirror at all.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import logging
+import re
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import Any, cast, TYPE_CHECKING
+
+import numpy as np
+import pandas as pd
+import sqlalchemy as sa
+from dateutil.relativedelta import relativedelta
+from flask import current_app as app
+from flask_babel import lazy_gettext as _
+from sqlalchemy.engine.interfaces import Dialect
+from sqlalchemy.sql.elements import ColumnElement
+
+from superset.constants import LRU_CACHE_MAX_SIZE, TimeGrain
+from superset.exceptions import (
+ QueryClauseValidationException,
+ SupersetParseError,
+ SupersetSecurityException,
+)
+from superset.extensions import cache_manager, feature_flag_manager
+from superset.sql.parse import SQLStatement
+from superset.utils import json
+from superset.utils.core import FilterOperator
+
+if TYPE_CHECKING:
+ from superset.connectors.sqla.models import SqlaTable, TableColumn
+ from superset.models.core import Database
+
+logger = logging.getLogger(__name__)
+
+FEATURE_FLAG = "PARTITION_FILTER_MAPPING"
+
+#: Longest transform accepted anywhere. A transform is one SQL expression an
+#: owner types by hand, so this is generous; the point is that it is bounded.
+#: Every entry point enforces it -- the typed column field, the import schema
+#: and the preview request -- because `is_transform_active` parses the stored
+#: value on each Explore load, and an unbounded string would make that parse
+#: the expensive part of rendering a chart.
+MAX_TRANSFORM_LENGTH = 1024
+
+#: Placeholder the owner writes in the transform, e.g.
``unix_timestamp(:value)``.
+#: Matched with word boundaries so ``:values`` is not mistaken for it.
+VALUE_PLACEHOLDER_RE = re.compile(r":value\b")
+
+#: Balanced Jinja blocks. The probe would render these in a different context
+#: at a different time from the chart query, so they are rejected at save time.
+JINJA_BLOCK_RE = re.compile(r"\{\{.*?\}\}|\{%.*?%\}|\{#.*?#\}", re.DOTALL)
+
+#: Substituted for ``:value`` before parsing -- sqlglot rejects a bare
``:value``
+#: on most dialects. Mirrors the ``_JINJA_BLOCK_RE`` -> ``NULL`` trick used by
+#: ``validate_stored_expression``.
+_PARSE_STANDIN = "NULL"
+
+#: Functions whose value depends on wall-clock time or randomness. The probe
+#: runs in a different session at a different moment from the chart query and
+#: its result is then cached, so any of these freezes a snapshot of probe time
+#: into the emitted predicate.
+NON_DETERMINISTIC_FUNCTIONS = {
+ "CURRENT_DATE",
+ "CURRENT_TIME",
+ "CURRENT_TIMESTAMP",
+ "NOW",
+ "RAND",
+ "RANDOM",
+ "UUID",
+}
+
+#: Functions that mean "now" only in their zero-argument form. On Hive and
+#: Impala ``unix_timestamp()`` is the current time while ``unix_timestamp(x)``
+#: -- the canonical transform for this feature -- is pure.
+NON_DETERMINISTIC_WHEN_NILADIC = {"UNIX_TIMESTAMP"}
+
+#: Safe for any function ``T``.
+MIRRORABLE_ALWAYS = {FilterOperator.EQUALS, FilterOperator.IN}
+
+#: Safe only when ``T`` preserves ordering.
+MIRRORABLE_IF_MONOTONIC = {
+ FilterOperator.GREATER_THAN,
+ FilterOperator.GREATER_THAN_OR_EQUALS,
+ FilterOperator.LESS_THAN,
+ FilterOperator.LESS_THAN_OR_EQUALS,
+ FilterOperator.TEMPORAL_RANGE,
+}
+
+
+#: Every operator that can be mirrored under *some* transform. The preview
+#: endpoint accepts these; whether a given one actually mirrors still depends
on
+#: the monotonicity declaration.
+MIRRORABLE_OPERATORS = MIRRORABLE_ALWAYS | MIRRORABLE_IF_MONOTONIC
+
+#: Operators the preview endpoint can construct. `TEMPORAL_RANGE` is mirrored
by
+#: the query path, but as *two* bounds that `_collect_partition_mirror_range`
+#: decomposes a time range into -- the preview request carries sample values,
not
+#: a since/until pair, so there is no range for it to build. The editor never
+#: asks for one either: `previewOperatorFor` sends `>=`, `=` or `IN`.
+PREVIEWABLE_OPERATORS = MIRRORABLE_OPERATORS - {FilterOperator.TEMPORAL_RANGE}
+
+
+def mirrorable_operators(is_monotonic: bool) -> set[FilterOperator]:
+ """
+ The operators whose predicates may be mirrored onto the partition column.
+
+ :param is_monotonic: whether the owner declared the transform
+ order-preserving
+ """
+ if is_monotonic:
+ return MIRRORABLE_ALWAYS | MIRRORABLE_IF_MONOTONIC
+ return set(MIRRORABLE_ALWAYS)
+
+
+#: How wide one bucket of each built-in time grain is.
+#:
+#: Keyed on the ISO duration a filter carries in its ``grain``. Written out
+#: rather than parsed: four of the week grains are ISO *intervals* with an
+#: anchor (``P1W/1970-01-03T00:00:00Z``) that ``isodate.parse_duration``
+#: rejects outright, and ``PT0.5H`` / ``P0.25Y`` are fractional. A literal
+#: table is also the thing a reviewer can check a line at a time.
+#:
+#: ``relativedelta`` rather than ``timedelta`` so the calendar grains stay
+#: calendar arithmetic: a month is not 30 days.
+GRAIN_BUCKET_WIDTHS: dict[str, relativedelta] = {
+ TimeGrain.SECOND: relativedelta(seconds=1),
+ TimeGrain.FIVE_SECONDS: relativedelta(seconds=5),
+ TimeGrain.THIRTY_SECONDS: relativedelta(seconds=30),
+ TimeGrain.MINUTE: relativedelta(minutes=1),
+ TimeGrain.FIVE_MINUTES: relativedelta(minutes=5),
+ TimeGrain.TEN_MINUTES: relativedelta(minutes=10),
+ TimeGrain.FIFTEEN_MINUTES: relativedelta(minutes=15),
+ TimeGrain.THIRTY_MINUTES: relativedelta(minutes=30),
+ TimeGrain.HALF_HOUR: relativedelta(minutes=30),
+ TimeGrain.HOUR: relativedelta(hours=1),
+ TimeGrain.SIX_HOURS: relativedelta(hours=6),
+ TimeGrain.DAY: relativedelta(days=1),
+ TimeGrain.WEEK: relativedelta(days=7),
+ TimeGrain.WEEK_STARTING_SUNDAY: relativedelta(days=7),
+ TimeGrain.WEEK_STARTING_MONDAY: relativedelta(days=7),
+ TimeGrain.WEEK_ENDING_SATURDAY: relativedelta(days=7),
+ TimeGrain.WEEK_ENDING_SUNDAY: relativedelta(days=7),
+ TimeGrain.MONTH: relativedelta(months=1),
+ TimeGrain.QUARTER: relativedelta(months=3),
+ TimeGrain.QUARTER_YEAR: relativedelta(months=3),
+ TimeGrain.YEAR: relativedelta(years=1),
+}
+
+
+def grain_bucket_width(grain: str | None, engine: str) -> relativedelta | None:
+ """
+ How far a grain's truncation can move a timestamp, or ``None`` if unknown.
+
+ A grained filter compares the *truncated* column, so the raw bounds it
+ carries do not describe the rows it keeps. Widening both bounds by one
+ bucket recovers a predicate that is no narrower than the real one -- see
+ `_collect_partition_mirror_range` for the argument. That only works for a
+ grain whose bucket width Superset knows, which excludes anything an
+ operator supplied.
+
+ :param grain: the ISO duration from the filter's ``grain``
+ :param engine: the engine spec's ``engine``, to check per-engine overrides
+ """
+ if not grain:
+ return None
+
+ # An operator-declared grain carries whatever duration string they typed,
+ # and `TIME_GRAIN_ADDON_EXPRESSIONS` can redefine a *built-in* grain's SQL
+ # per engine -- `P1D` could be anything at all. Neither has a width we can
+ # claim to know, so both fall back to not mirroring.
+ if grain in app.config["TIME_GRAIN_ADDONS"]:
+ return None
+ if grain in app.config["TIME_GRAIN_ADDON_EXPRESSIONS"].get(engine, {}):
+ return None
+
+ return GRAIN_BUCKET_WIDTHS.get(grain)
+
+
+@dataclass(frozen=True)
+class PartitionMapping:
+ """A resolved, usable partition filter mapping."""
+
+ partition_column: str
+ mapped_column: str
+ value_transform: str
+ is_monotonic: bool
+
+ def mirrors(self, operator: FilterOperator) -> bool:
+ return operator in mirrorable_operators(self.is_monotonic)
+
+
+def contains_value_placeholder(transform: str | None) -> bool:
+ """Whether the transform contains the ``:value`` placeholder."""
+ return bool(transform) and VALUE_PLACEHOLDER_RE.search(transform or "") is
not None
+
+
+def contains_jinja(transform: str | None) -> bool:
+ """Whether the transform contains a balanced Jinja block."""
+ return bool(transform) and JINJA_BLOCK_RE.search(transform or "") is not
None
+
+
+def parse_skeleton(transform: str) -> str:
+ """
+ The transform with ``:value`` substituted out, ready for a SQL parser.
+
+ ``sanitize_clause`` / sqlglot choke on a bare ``:value`` on most dialects,
+ so the placeholder is swapped for a benign literal first -- the same trick
+ ``validate_stored_expression`` uses for Jinja blocks.
+ """
+ return VALUE_PLACEHOLDER_RE.sub(_PARSE_STANDIN, transform)
+
+
+#: Prefix the transform is wrapped in before parsing. Its length is subtracted
+#: from any reported column so positions refer to what the owner actually
typed.
+_SELECT_PREFIX = "SELECT "
+
+
+def _parse_skeleton(transform: str, engine: str) -> SQLStatement | None:
+ """
+ Parse ``SELECT <transform>`` with the placeholder substituted out.
+
+ Returns ``None`` when the transform does not parse.
+ """
+ try:
+ return SQLStatement(f"{_SELECT_PREFIX}{parse_skeleton(transform)}",
engine)
+ except SupersetParseError:
+ return None
+
+
+def parse_error_detail(transform: str, engine: str) -> str | None:
+ """
+ Where the parser gave up on the transform.
+
+ Returns ``None`` when it parses, or when the parser offered no position.
+ Note that sqlglot parses unknown functions happily -- a misspelled function
+ name is not a parse error, it is an engine error, and surfaces only when
the
+ transform is evaluated.
+
+ The parser's own ``highlight`` is deliberately dropped: it would name the
+ ``NULL`` we substituted for ``:value``, which is not a token the owner
typed.
+ """
+ try:
+ SQLStatement(f"{_SELECT_PREFIX}{parse_skeleton(transform)}", engine)
+ except SupersetParseError as ex:
+ column = (ex.error.extra or {}).get("column")
+ if not isinstance(column, int):
+ return None
+ return str(
+ _(
+ "syntax error at position %(position)d.",
+ position=_position_in_transform(transform, column),
+ )
+ )
+ return None
+
+
+def _position_in_transform(transform: str, parsed_column: int) -> int:
+ """
+ Map a column in the parsed skeleton back to the transform as typed.
+
+ Two substitutions stand between them: the ``SELECT`` prefix, and every
+ ``:value`` that became a shorter ``NULL``. Without unwinding both, a
+ reported position drifts left by two characters per placeholder ahead of it
+ -- which is worst exactly where transforms usually break, at the end.
+ """
+ position = max(parsed_column - len(_SELECT_PREFIX), 0)
+ shift = len(":value") - len(_PARSE_STANDIN)
+ preceding = sum(
+ 1
+ for index, match in enumerate(VALUE_PLACEHOLDER_RE.finditer(transform))
+ if match.start() - index * shift < position
+ )
+ return position + preceding * shift
+
+
+def is_parseable(transform: str | None, engine: str) -> bool:
+ """
+ Whether the transform parses as a single select expression.
+
+ "Single" is the load-bearing word. `SELECT lower(:value), 'x'` parses just
+ as happily as `SELECT lower(:value)`, but it returns two columns per input
+ value, and the probe reads one column per input -- so an `IN` filter would
+ get a predicate built from the wrong halves of the wrong rows. Rejecting
the
+ list here is what lets the probe trust its own column count.
+ """
+ if not transform or not transform.strip():
+ return False
+ statement = _parse_skeleton(transform, engine)
+ return statement is not None and statement.count_select_expressions() == 1
+
+
+def find_non_deterministic_functions(transform: str, engine: str) -> set[str]:
+ """
+ Names of non-deterministic functions the transform calls.
+
+ ``UNIX_TIMESTAMP`` is only reported in its zero-argument form, which means
+ "now" on Hive and Impala; the one-argument form is the canonical temporal
+ transform and stays allowed.
+ """
+ statement = _parse_skeleton(transform, engine)
+ if statement is None:
+ return set()
+
+ found = {
+ name
+ for name in NON_DETERMINISTIC_FUNCTIONS
+ if statement.check_functions_present({name})
+ }
+ return found | _find_niladic_calls(statement)
+
+
+def _find_niladic_calls(statement: SQLStatement) -> set[str]:
+ """
+ Names from ``NON_DETERMINISTIC_WHEN_NILADIC`` called with no arguments.
+
+ Note some dialects resolve the zero-argument form themselves -- Hive parses
+ ``unix_timestamp()`` straight to ``CURRENT_TIMESTAMP`` -- in which case the
+ name-based check above has already caught it. This is the backstop for the
+ dialects that do not.
+ """
+ return NON_DETERMINISTIC_WHEN_NILADIC & statement.get_niladic_functions()
+
+
+def resolve_partition_mapping(datasource: SqlaTable) -> PartitionMapping |
None:
+ """
+ Resolve the dataset's mapping, or ``None`` when nothing may be mirrored.
+
+ Every bail-out here is defensive as well as functional: save-time
validation
+ rejects most of these, but rows predating the validation can still violate
+ the invariants, and a column sync can invalidate a mapping that was fine
+ when it was written.
+
+ The transform gate is `is_transform_active`, the same function the Explore
+ indicator reads, which is `validate_transform` with the messages discarded.
+ Anything narrower here would be a second, weaker statement of the same
rule:
+ a transform calling `now()` that reached storage without passing
+ `UpdateDatasetCommand` -- through import, or through a bundle written by
hand
+ -- would be reported inactive by the editor and still mirrored by this
+ function, freezing a snapshot of probe time into the predicate with nothing
+ on screen to say so.
+ """
+ if not feature_flag_manager.is_feature_enabled(FEATURE_FLAG):
+ return None
+
+ partition_column = getattr(datasource, "partition_column", None)
+ if not partition_column:
+ return None
+
+ columns_by_name = {column.column_name: column for column in
datasource.columns}
+ if partition_column not in columns_by_name:
+ # The partition column was dropped by a column sync or at the source.
+ return None
+
+ mapped_column_name = (
+ getattr(datasource, "partition_mapped_column", None) or
datasource.main_dttm_col
+ )
+ if not mapped_column_name or mapped_column_name not in columns_by_name:
+ return None
+
+ if mapped_column_name == partition_column:
+ # Self-mapping: the mirrored predicate would duplicate the original.
+ return None
+
+ mapped_column = columns_by_name[mapped_column_name]
+ transform = getattr(mapped_column, "partition_value_transform", None)
+ if not is_transform_active(transform, datasource.database.backend):
+ return None
+
+ if has_active_advanced_data_type(mapped_column):
+ # `translate_filter` builds its own predicate shape from *translated*
+ # values, so the `(operator, value)` pair the operator matrix reasons
+ # about does not exist and mirroring would apply the wrong values.
+ return None
+
+ return PartitionMapping(
+ partition_column=str(partition_column),
+ mapped_column=str(mapped_column_name),
+ value_transform=cast(str, transform),
+ is_monotonic=bool(
+ getattr(mapped_column, "partition_transform_is_monotonic", False)
+ ),
+ )
+
+
+def has_active_advanced_data_type(column: TableColumn) -> bool:
+ """
+ Whether the column's advanced data type is configured and switched on.
+
+ Such a column is never mirrored: ``translate_filter`` builds its own
+ predicate shape from translated values, so the ``(operator, value)`` pair
+ the operator matrix reasons about does not exist.
+ """
+ advanced_data_type = getattr(column, "advanced_data_type", None)
+ if not advanced_data_type:
+ return False
+ if not
feature_flag_manager.is_feature_enabled("ENABLE_ADVANCED_DATA_TYPES"):
+ return False
+ return advanced_data_type in app.config.get("ADVANCED_DATA_TYPES", {})
+
+
+def stored_expression_error(
+ database: "Database",
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+) -> str | None:
+ """
+ Why this transform may not be stored or run, if there is a reason.
+
+ The same subquery, function-denylist and RLS policy every other stored
+ expression goes through, applied to a value transform. It matters more
+ here than the name suggests: `build_probe_sql` binds only `:value` and
+ splices the rest of the transform in as SQL text, which the engine then
+ executes, so an ungated transform is arbitrary SQL. A dataset editor
+ without SQL Lab could store `(SELECT secret FROM protected_table LIMIT 1)
+ || :value` and read the answer back out of the emitted predicate.
+
+ Returns the engine-agnostic reason as a string rather than raising,
+ because its four callers disagree about what to do with it: the preview
+ reports it at the field, a PUT and the legacy datasource save refuse the
+ write, the importer drops the transform and keeps the dataset, and the
+ probe simply declines to run. Raising would make three of those four
+ write a `try` around a question.
+
+ Imported inside the function rather than at module scope:
+ `connectors.sqla.models` imports this module, so the dependency only runs
+ one way at import time.
+ """
+ from superset.connectors.sqla.models import ( # pylint:
disable=import-outside-toplevel,cyclic-import
+ validate_stored_expression,
+ )
+
+ try:
+ validate_stored_expression(database, catalog, schema,
parse_skeleton(transform))
+ except SupersetSecurityException as ex:
+ return str(ex.error.message)
+ except QueryClauseValidationException as ex:
+ return str(ex.message)
+ return None
+
+
+def build_probe_sql(
+ transform: str,
+ values: list[Any],
+ dialect: Dialect | None = None,
+ from_suffix: str = "",
+) -> str:
+ """
+ Compile a single ``SELECT`` that evaluates the transform at every value.
+
+ Values are attacker-controlled (a Gamma user picks filter values), so they
+ are bound as parameters and rendered by the dialect's own literal processor
+ rather than interpolated into the SQL text.
+
+ ``from_suffix`` comes from the engine spec's
``select_without_from_suffix``.
+ A ``SELECT`` with no ``FROM`` is not universal SQL: Oracle and Db2 need a
+ one-row table to select from, and without it every probe on those engines
+ raises -- which `_run_probe` swallows, so the only symptom is a correctly
+ configured mapping that silently never prunes.
+
+ Note this deliberately does *not* go through ``BaseEngineSpec``'s text
+ helper, which escapes ``:`` on every engine but Athena and would destroy
the
+ ``:value`` placeholder before it can be bound.
+ """
+ selections = []
+ for index, value in enumerate(values):
+ clause = sa.text(transform).bindparams(sa.bindparam("value",
value=value))
+ compiled = clause.compile(
+ dialect=dialect,
+ compile_kwargs={"literal_binds": True},
+ )
+ selections.append(f"{compiled} AS v{index}")
+ return "SELECT " + ", ".join(selections) + from_suffix
+
+
+def evaluate_transform(
+ database: Database,
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+ values: list[Any],
+ *,
+ errors: list[str] | None = None,
+) -> list[Any] | None:
+ """
+ Evaluate ``transform`` against the engine once per distinct value.
+
+ Returns one result per input value, positionally aligned with ``values``,
or
+ ``None`` if anything at all goes wrong. Failing open costs pruning, never
+ correctness: the chart query still runs, it just scans more partitions.
+
+ The probe is pinned to the dataset's catalog and schema so session settings
+ match the chart query as closely as the connection pool allows. It still
+ runs in a *different* session, which is why transforms calling
+ session-dependent functions are rejected at save time.
+
+ :param errors: optional sink for the engine's own account of a failure. The
+ query path passes nothing and stays silent; the editor's preview passes
+ a list so it can tell the owner *why* the transform did not evaluate --
+ a misspelled function is the common case and sqlglot parses it happily.
+ """
+ if not values:
+ return None
+
+ # Dedupe so a 200-value `IN` list costs one column, not 200.
+ distinct: list[Any] = []
+ seen: set[Any] = set()
+ for value in values:
+ key = _hashable(value)
+ if key not in seen:
+ seen.add(key)
+ distinct.append(value)
+
+ cache_key = _probe_cache_key(database, catalog, schema, transform,
distinct)
+ cached = _cache_get(cache_key)
+ if cached is None:
+ cached = _run_probe(
+ database, catalog, schema, transform, distinct, errors=errors
+ )
+ if cached is None:
+ # Deliberately not cached: a transient engine blip would otherwise
+ # keep the dataset pruning-free for the whole cache timeout.
+ return None
+ _cache_set(cache_key, cached)
+
+ evaluated = dict(
+ zip((_hashable(value) for value in distinct), cached, strict=False)
+ )
+ return [evaluated[_hashable(value)] for value in values]
+
+
+def _run_probe(
+ database: Database,
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+ distinct: list[Any],
+ *,
+ errors: list[str] | None = None,
+) -> list[Any] | None:
+ # The last gate before the transform becomes SQL the engine runs, and the
+ # only one that covers a transform already in storage. The write-side
+ # checks can only speak for rows written after they existed; this speaks
+ # for every row, including ones a pre-fix release stored and ones a door
+ # that forgets to ask still lets in.
+ #
+ # Declining costs pruning, never correctness -- see `evaluate_transform`.
+ if reason := stored_expression_error(database, catalog, schema, transform):
+ logger.warning(
+ "Refusing to probe a partition transform that is not a storable "
+ "expression; queries will not prune: %s",
+ reason,
+ )
+ return None
+
+ try:
+ sql = build_probe_sql(
+ transform,
+ distinct,
+ _dialect_for(database),
+ database.db_engine_spec.select_without_from_suffix,
+ )
+ frame = database.get_df(sql=sql, catalog=catalog, schema=schema)
+ if frame is None or frame.empty:
+ logger.warning(
+ "Partition transform probe returned no rows; skipping
mirroring"
+ )
+ return None
+ row = frame.iloc[0]
+ if len(row) != len(distinct):
+ # The results cannot be aligned back to their inputs; skipping
+ # beats guessing which value produced which column. Too *many*
+ # columns is the dangerous direction and the reason this is `!=`
+ # rather than `<`: a transform whose select list holds two
+ # expressions returns 2N columns for N inputs, and reading the
+ # first N of them interleaves the expressions instead of selecting
+ # one per value -- a predicate built from the wrong values rather
+ # than one that is merely short. `is_parseable` rejects that
+ # transform before it is ever stored; this is the backstop for one
+ # that predates the check.
+ logger.warning(
+ "Partition transform probe returned %d values for %d inputs",
+ len(row),
+ len(distinct),
+ )
+ return None
+ return [_to_python_scalar(row.iloc[index]) for index in
range(len(distinct))]
+ except Exception as ex: # pylint: disable=broad-except
+ logger.warning(
+ "Partition transform probe failed; queries will not prune",
+ exc_info=True,
+ )
+ if errors is not None:
+ errors.append(str(ex))
+ return None
+
+
+def _to_python_scalar(value: Any) -> Any:
+ """
+ A probed value as a type SQLAlchemy can render and bind.
+
+ The probe reads its results out of a pandas frame, so a numeric column
+ arrives as a `numpy.int64` and a temporal one as a `pandas.Timestamp`.
+ SQLAlchemy has no literal renderer for either: `sa.literal(np.int64(...))`
+ infers `NullType` and raises `CompileError`, which is a 500 from the
preview
+ endpoint for the canonical epoch transform -- the most ordinary mapping
+ there is.
+
+ Pandas' missing-value sentinels are folded into `None` on the way through.
+ `NaT` is not a `Timestamp` and has no renderer either, and a transform that
+ returns nothing for an input it cannot convert is exactly the NULL case the
+ mirror already admits.
+ """
+ if value is None:
+ return None
+ try:
+ if pd.isna(value):
+ return None
+ except (TypeError, ValueError):
+ # Not something `isna` can judge (a list, a dict); pass it through and
+ # let the dialect decide.
+ pass
+ if isinstance(value, pd.Timestamp):
+ return value.to_pydatetime()
+ if isinstance(value, np.generic):
+ return value.item()
+ return value
+
+
+def _probe_cache_key(
+ database: Database,
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+ values: list[Any],
+) -> str:
+ """
+ Key on everything that can change the answer.
+
+ Note this cache is independent of the chart-data cache: it is keyed on the
+ transform and its inputs, so it is correct to share across every chart on
+ every dataset that happens to use the same transform.
+ """
+ payload = json.dumps(
+ [
+ database.id,
+ database.backend,
+ # The probe asks this connection to evaluate the transform, so the
+ # answer belongs to it. `id` outlives an edit to the URI or to
+ # `extra` (a session timezone, say), which would otherwise serve
+ # values computed against the old environment until the entry
+ # expires. `changed_on` covers the edits that do not show up here,
+ # such as a rotated password, without putting a secret in the key.
+ database.sqlalchemy_uri,
+ database.extra,
+ str(database.changed_on),
+ catalog,
+ schema,
+ transform,
+ [repr(value) for value in values],
+ ],
+ default=repr,
+ )
+ digest = hashlib.md5(payload.encode("utf-8")).hexdigest() # noqa: S324
+ return f"partition_transform_probe:{digest}"
+
+
+def _cache_get(key: str) -> list[Any] | None:
+ try:
+ return cache_manager.cache.get(key)
+ except Exception: # pylint: disable=broad-except # noqa: BLE001
+ return None
+
+
+def _cache_set(key: str, value: list[Any]) -> None:
+ timeout = app.config.get("PARTITION_TRANSFORM_PROBE_CACHE_TIMEOUT", 24 *
60 * 60)
+ try:
+ cache_manager.cache.set(key, value, timeout=timeout)
+ except Exception: # pylint: disable=broad-except
+ logger.warning("Could not cache partition transform probe",
exc_info=True)
+
+
+def _dialect_for(database: Database) -> Dialect | None:
+ """
+ The dialect used to render literals in the probe SQL.
+
+ Falls back to SQLAlchemy's default dialect if the database cannot produce
+ one -- the probe is best-effort and a rendering mismatch surfaces as a
+ failed probe, which fails open to no pruning.
+ """
+ try:
+ dialect = database.get_dialect()
+ except Exception: # pylint: disable=broad-except # noqa: BLE001
+ return None
+ return dialect if isinstance(dialect, Dialect) else None
+
+
+def _hashable(value: Any) -> Any:
+ """Values arrive from user filters and are not guaranteed hashable."""
+ try:
+ hash(value)
+ except TypeError:
+ return repr(value)
+ return value
+
+
+@dataclass(frozen=True)
+class MappingValidationIssue:
+ """
+ One problem found with a mapping at save time.
+
+ ``blocking`` issues reject the save (400). The rest save fine and leave the
+ mapping inactive -- the PRD is explicit that a mapping "stays inactive
until
+ it parses", so a half-written transform must not cost the owner the rest of
+ their edits.
+ """
+
+ field: str
+ message: str
+ blocking: bool
+
+
+def validate_partition_mapping( # pylint: disable=too-many-arguments
+ *,
+ column_names: set[str],
+ partition_column: str | None,
+ partition_mapped_column: str | None,
+ main_dttm_col: str | None,
+ transform: str | None,
+ engine: str,
+) -> list[MappingValidationIssue]:
+ """
+ Validate a dataset's partition mapping, in two tiers.
+
+ Tier 1 (``blocking=True``) is structural and safety: the columns have to
+ exist, a column cannot be mapped onto itself, and the transform cannot
carry
+ Jinja or call a non-deterministic function. Tier 2 (``blocking=False``) is
+ everything that merely leaves the mapping inactive.
+
+ The Tier-1 transform checks need a successful parse to inspect anything, so
+ an unparseable transform falls through to Tier 2. That leaves them
+ unreachable in exactly the case where it doesn't matter: an unparseable
+ transform is never executed.
+ """
+ if not partition_column:
+ return []
+
+ issues: list[MappingValidationIssue] = []
+
+ if partition_column not in column_names:
+ issues.append(
+ MappingValidationIssue(
+ field="partition_column",
+ message=_(
+ "Partition column %(name)s is not a column on this
dataset.",
+ name=partition_column,
+ ),
+ blocking=True,
+ )
+ )
+
+ if partition_mapped_column and partition_mapped_column not in column_names:
+ issues.append(
+ MappingValidationIssue(
+ field="partition_mapped_column",
+ message=_(
+ "Mapped column %(name)s is not a column on this dataset.",
+ name=partition_mapped_column,
+ ),
+ blocking=True,
+ )
+ )
+
+ effective_mapped_column = partition_mapped_column or main_dttm_col
+ if effective_mapped_column and effective_mapped_column == partition_column:
+ issues.append(
+ MappingValidationIssue(
+ field="partition_column",
+ message=_(
+ "The partition column cannot be mapped onto itself. "
+ "%(name)s is both the partition column and the mapped "
+ "column.",
+ name=partition_column,
+ ),
+ blocking=True,
+ )
+ )
+
+ issues.extend(validate_transform(transform, engine))
+ return issues
+
+
+def validate_transform(
+ transform: str | None,
+ engine: str,
+) -> list[MappingValidationIssue]:
+ """Validate the value transform on its own. See
`validate_partition_mapping`."""
+ field = "partition_value_transform"
+
+ if contains_jinja(transform):
+ return [
+ MappingValidationIssue(
+ field=field,
+ message=_(
+ "Jinja templating is not supported in a partition value "
+ "transform. The transform is evaluated in a different "
+ "context and at a different time from the chart query, so "
+ "a template would not render the same way."
+ ),
+ blocking=True,
+ )
+ ]
+
+ if not transform or not transform.strip():
+ return [
+ MappingValidationIssue(
+ field=field,
+ message=_(
+ "No value transform is set, so no filter will be mirrored "
+ "onto the partition column."
+ ),
+ blocking=False,
+ )
+ ]
+
+ if not is_parseable(transform, engine):
+ detail = parse_error_detail(cast(str, transform), engine)
+ return [
+ MappingValidationIssue(
+ field=field,
+ message=(
+ _(
+ "The value transform could not be parsed: %(detail)s "
+ "The mapping is saved but stays inactive until it
does.",
+ detail=detail,
+ )
+ if detail
+ else _(
+ "The value transform could not be parsed. The mapping "
+ "is saved but stays inactive until it does."
+ )
+ ),
+ blocking=False,
+ )
+ ]
+
+ if not contains_value_placeholder(transform):
+ return [
+ MappingValidationIssue(
+ field=field,
+ message=_(
+ "The value transform must contain the :value placeholder, "
+ "which stands for the filter value being mirrored."
+ ),
+ blocking=False,
+ )
+ ]
+
+ if functions := find_non_deterministic_functions(transform, engine):
+ return [
+ MappingValidationIssue(
+ field=field,
+ message=_(
+ "The value transform calls %(functions)s, whose result "
+ "depends on when and where it runs. The transform is "
+ "evaluated in a separate session and the result is cached,
"
+ "so the emitted predicate would freeze a snapshot of that "
+ "moment.",
+ functions=", ".join(sorted(functions)),
+ ),
+ blocking=True,
+ )
+ ]
+
+ return []
+
+
+@lru_cache(maxsize=LRU_CACHE_MAX_SIZE)
+def is_transform_active(transform: str | None, engine: str) -> bool:
+ """
+ Whether Superset will mirror filters through this transform.
+
+ `validate_transform` with the messages discarded, so the Explore indicator
+ cannot advertise a mapping the save path recorded as inactive. Anything
+ cheaper here would be a second, weaker statement of the same rule, free to
+ drift from the one the save path applies.
+
+ Blocking issues count too. Jinja and non-deterministic transforms are
+ rejected on PUT, but `CreateDatasetCommand` and import do not validate the
+ mapping, so one can still reach the database -- and it would never mirror a
+ filter either.
+
+ Memoized because `partition_filter_mapping_summary` is serialized on every
+ Explore and dashboard load and this parses the transform. The key is
+ everything the answer depends on: `validate_transform` reads no session, no
+ locale and no config beyond `SQL_MAX_PARSE_LENGTH`, which bounds the parser
+ rather than changing a verdict within that bound.
+ """
+ return not validate_transform(transform, engine)
+
+
+def preview_partition_mapping( # pylint: disable=too-many-return-statements
+ datasource: SqlaTable,
+ *,
+ mapped_column: str,
+ value_transform: str | None,
+ sample_values: list[str],
+ operator: FilterOperator = FilterOperator.EQUALS,
+ is_monotonic: bool = False,
+ partition_column: str | None = None,
+) -> dict[str, Any]:
+ """
+ Evaluate a candidate mapping and describe the predicate it would emit.
+
+ Shares the evaluator -- and therefore the probe cache -- with the query
+ path, so preview and runtime cannot drift and a previewed transform warms
+ the chart path for free. The predicate itself comes from
+ `build_mirrored_predicates`, the same builder the query path uses, so
+ operator shapes cannot drift either: what the panel shows is what a chart
+ would emit.
+
+ Validation runs first and the engine second: a half-typed transform is by
+ definition unparseable, so most of what a text input produces costs zero
+ queries.
+
+ Every part of the mapping is taken from the request rather than the stored
+ dataset. The editor previews while the owner is still editing -- a mapping
+ that has to be saved before it can be checked is not a preview.
+ """
+ partition_column = partition_column or datasource.partition_column
+ if not partition_column:
+ return {
+ "valid": False,
+ "reason": "unconfigured",
+ "error": _("No partition column is set."),
+ }
+
+ column_names = {str(column.column_name) for column in datasource.columns}
+ if mapped_column not in column_names:
+ return {
+ "valid": False,
+ "reason": "validation",
+ "error": _("%(name)s is not a column on this dataset.",
name=mapped_column),
+ }
+
+ engine = datasource.database.backend
+ for issue in validate_partition_mapping(
+ column_names=column_names,
+ partition_column=str(partition_column),
+ partition_mapped_column=mapped_column,
+ main_dttm_col=datasource.main_dttm_col,
+ transform=value_transform,
+ engine=engine,
+ ):
+ return {
+ "valid": False,
+ # The parse failure is the one an owner sees constantly, and it is
+ # the only one the mockup gives its own headline to.
+ "reason": (
+ "parse"
+ if issue.field == "partition_value_transform"
+ and not is_parseable(value_transform, engine)
+ else "validation"
+ ),
+ "error": str(issue.message),
+ }
+
+ # The policy every other stored expression goes through. The save path
+ # applies it in `UpdateDatasetCommand._validate_partition_mapping`, but
+ # preview evaluates a candidate transform that has not been saved, so
+ # without this the gate had a door around it. See
+ # `stored_expression_error` for what is being kept out and why.
+ if reason := stored_expression_error(
+ datasource.database,
+ datasource.catalog,
+ datasource.schema,
+ cast(str, value_transform),
+ ):
+ return {"valid": False, "reason": "validation", "error": reason}
+
+ mapping = PartitionMapping(
Review Comment:
Fixed. Preview now calls
`has_active_advanced_data_type(columns_by_name[mapped_column])` and refuses
before evaluating the transform, which is the same exclusion runtime resolution
applies. Covered by
`test_preview_refuses_a_mapped_column_with_an_advanced_data_type`.
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