aminghadersohi commented on code in PR #43870:
URL: https://github.com/apache/superset/pull/43870#discussion_r4190076981
##########
superset/config.py:
##########
@@ -2462,15 +2480,23 @@ def engine_context_manager( # pylint:
disable=unused-argument
"lo_truncate",
"lo_truncate64",
"lo_unlink",
- # XML functions that can execute SQL
+ # XML functions that can execute SQL. These read tables with no FROM
+ # clause and no sub-query of their own, so a gate that reasons about
+ # table references alone cannot see them -- the whole family has to be
+ # named here. All four shapes, for each of database / schema / table /
+ # query.
"database_to_xml",
"database_to_xmlschema",
+ "database_to_xml_and_xmlschema",
+ "schema_to_xml",
+ "schema_to_xmlschema",
+ "schema_to_xml_and_xmlschema",
"query_to_xml",
"query_to_xmlschema",
- "table_to_xml",
- "table_to_xml_and_xmlschema",
"query_to_xml_and_xmlschema",
+ "table_to_xml",
"table_to_xmlschema",
+ "table_to_xml_and_xmlschema",
Review Comment:
`ts_stat('select …')` and `ts_rewrite(q, 'select …')` also run their text
argument as a query with no FROM or sub-query; `stored_expression_error` passes
`ts_stat('select 1') || :value` at this head.
```suggestion
"table_to_xml_and_xmlschema",
# Full-text search functions that run their text argument as a query.
"ts_rewrite",
"ts_stat",
```
##########
superset-frontend/src/components/Datasource/components/CollectionTable/index.tsx:
##########
@@ -326,6 +330,26 @@ export default function CRUDCollection({
}));
}, []);
+ // Read through a ref, and keyed on the nonce alone, because callers pass an
+ // inline arrow: an effect depending on the predicate's identity would run on
+ // every render and reopen a row the user had just collapsed, which is the
+ // opposite of the additive behaviour the prop documents.
+ const expandItemWhereRef = useRef(expandItemWhere);
+ expandItemWhereRef.current = expandItemWhere;
+
+ useEffect(() => {
+ const matches = expandItemWhereRef.current;
+ if (!matches || expandItemNonce === undefined) {
+ return;
+ }
+ const target = collectionArray.find(matches);
+ if (!target) {
+ return;
+ }
+ // Merged rather than replaced, so rows the user opened stay open.
+ setExpandedColumns(prev => ({ ...prev, [target.id]: true }));
+ }, [expandItemNonce, collectionArray]);
Review Comment:
With `collectionArray` in the deps this re-runs on every edit or sort, so a
revealed row the user collapsed reopens on their next change to any row,
despite the comment above saying it is keyed on the nonce alone.
```suggestion
const target = collectionArrayRef.current.find(matches);
if (!target) {
return;
}
// Merged rather than replaced, so rows the user opened stay open.
setExpandedColumns(prev => ({ ...prev, [target.id]: true }));
}, [expandItemNonce]);
```
##########
superset/connectors/sqla/partition_mapping.py:
##########
@@ -0,0 +1,2258 @@
+# 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 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; a collision only makes the count
+#: disagree, which fails closed.
+_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.
+ """
+ if not transform:
+ 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))
+ rendered = _compile_literal(clause, dialect)
+ selections.append(f"{rendered} AS v{index}")
+ return "SELECT " + ", ".join(selections) + from_suffix
+
+
+def probe_sql_is_evaluable(sql: str, engine: str, expected: int) -> bool:
+ """
+ Whether the compiled probe is still the query `build_probe_sql` intended.
+
+ Every write-side gate reads the transform with ``:value`` standing in for a
+ value. The escape this guards against only completes once a *real* value is
+ rendered in its place, so it is invisible until this point -- and the
values
+ are supplied by whoever is filtering a chart, not only by the owner who
+ wrote the transform.
+
+ Three things have to hold, and all three are about the probe still being
+ readable rather than about taste:
+
+ * One statement. A value that closes the expression and starts another
+ would otherwise run both.
+ * No sub-query. A placeholder inside a string literal lets a value close
+ the quote and concatenate one; `stored_expression_error` refuses a
+ sub-query written openly, and this refuses the same thing assembled.
+ * Exactly ``expected`` projections, aliased ``v0`` upward. A comment
+ swallows the rest of its line including its own alias, and the engine
+ still answers with a column -- so the count alone does not notice.
+ Checking the aliases is what makes a missing one visible.
+
+ A ``FROM`` clause is allowed: ``select_without_from_suffix`` adds one on
the
+ engines that cannot select without a table.
+ """
+ try:
+ script = SQLScript(sql, engine)
+ except SupersetParseError:
+ return False
+ if len(script.statements) != 1:
+ return False
+ statement = script.statements[0]
+ if statement.has_subquery():
+ return False
+ return statement.get_select_aliases() == [f"v{index}" for index in
range(expected)]
+
+
+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:
+ """
+ `_probe` plus the advisory record of how it went.
+
+ One place rather than one per `return`, because every way out of `_probe`
+ is a verdict and the next reader of `known_mirror_verdict` cannot tell the
+ difference between "declined" and "never asked".
+ """
+ values = _probe(database, catalog, schema, transform, distinct,
errors=errors)
+ record_mirror_verdict(
+ database,
+ catalog,
+ schema,
+ transform,
+ mirrors=values is not None and not any(value is None for value in
values),
+ )
+ return values
+
+
+def _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,
+ )
+ if not probe_sql_is_evaluable(sql, database.backend, len(distinct)):
+ # Declining rather than raising, for the same reason every other
+ # failure here declines: the chart query is already correct without
+ # the mirror, and it is the only thing the caller is waiting on.
+ logger.warning(
+ "Refusing to run a partition transform probe whose compiled
SQL "
+ "is not a plain projection per input value; queries will not "
+ "prune"
+ )
+ return None
+ 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
+ column_count = frame.shape[1]
+ if column_count != 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",
+ column_count,
+ len(distinct),
+ )
+ return None
+ # Cell-wise rather than through `frame.iloc[0]`. A row is a Series, so
+ # it carries one dtype for every probe column: a row mixing an exact
+ # int64 with a float unifies to float64, and an integer partition key
+ # above 2^53 comes back rounded. `_to_python_scalar` converts
+ # faithfully but runs after the loss, so the mirror asks for a key the
+ # warehouse does not hold and drops the row the filter matched.
+ return [
+ _to_python_scalar(frame.iat[0, 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 _mirror_verdict_cache_key(
+ database: Database,
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+) -> str:
+ """
+ `_probe_cache_key` without the values.
+
+ Whether a transform can be evaluated at all is a property of the transform
+ and the connection, not of the filter that happened to be probing it, so
+ one chart's answer settles the question for the next one.
+ """
+ payload = json.dumps(
+ [
+ database.id,
+ database.backend,
+ database.sqlalchemy_uri,
+ database.extra,
+ str(database.changed_on),
+ catalog,
+ schema,
+ transform,
+ ],
+ default=repr,
+ )
+ digest = hashlib.md5(payload.encode("utf-8")).hexdigest() # noqa: S324
+ return f"partition_transform_verdict:{digest}"
+
+
+def record_mirror_verdict(
+ database: Database,
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+ *,
+ mirrors: bool,
+) -> None:
+ """
+ Remember whether this transform produced a mirror, for the UI to read.
+
+ Advisory only. Nothing in the query path consults it: a probe that failed
+ once is retried on the next chart exactly as it is today, because declining
+ to prune for a whole cache timeout over one transient engine blip is the
+ trade `evaluate_transform` deliberately refuses -- see the comment where it
+ declines to cache a failure.
+
+ What this is for is the *claim*. `is_transform_active` parses the transform
+ and no more, so a transform the database rejects -- a misspelled function
+ is the common case -- is "active", and the editor's banner and Explore's
+ pruning glyph promise a speed-up that never happens. A promise is exactly
+ the thing that may be withheld on one bad answer and restored on a good
+ one, which is why this is a separate entry from the probe's own results
+ rather than a relaxation of that rule.
+
+ Written by `_run_probe`, so the preview endpoint -- which shares the
+ evaluator -- settles the question before the owner has even saved.
+ """
+ timeout = app.config.get("PARTITION_TRANSFORM_PROBE_CACHE_TIMEOUT", 24 *
60 * 60)
+ try:
+ cache_manager.cache.set(
+ _mirror_verdict_cache_key(database, catalog, schema, transform),
+ mirrors,
+ timeout=timeout,
+ )
+ except Exception: # pylint: disable=broad-except
+ logger.warning("Could not cache partition transform verdict",
exc_info=True)
+
+
+def known_mirror_verdict(
+ database: Database,
+ catalog: str | None,
+ schema: str | None,
+ transform: str,
+) -> bool | None:
+ """
+ The last verdict `record_mirror_verdict` stored, or `None` if there is
none.
+
+ `None` is the honest answer on a cold cache and is deliberately not
`False`:
+ reporting a working mapping as broken until something probes it would
+ trade one wrong claim for another. The alternative -- probing from the
+ read side -- would put a warehouse round trip on every Explore and
+ dashboard load, and a failing transform is not cached at all, so it would
+ be a round trip every single time.
+ """
+ try:
+ verdict = cache_manager.cache.get(
+ _mirror_verdict_cache_key(database, catalog, schema, transform)
+ )
+ except Exception: # pylint: disable=broad-except # noqa: BLE001
+ return None
+ return verdict if isinstance(verdict, bool) else None
+
+
+def _compile_literal(element: Any, dialect: Dialect | None) -> str:
+ """
+ Compile with inline literals, undoing the paramstyle's percent doubling.
+
+ Both ``TextClause`` and literal compilation run the dialect's
+ ``post_process_text``, which doubles every ``%`` whenever the identifier
+ preparer asks it to -- true for MySQL and Postgres and for every spec
+ inheriting their paramstyle. That doubling exists so a later DBAPI
+ parameter-interpolation pass can undo it, and the probe has no such pass:
+ it executes with no bound parameters at all. So a transform of
+ ``date_format(:value, '%Y%m%d')`` reaches the warehouse asking for the
+ literal string ``%%Y%%m%%d``, the probe returns that instead of a date, and
+ the mirrored predicate drops every row the filter keeps.
+
+ The same normalization ``Database.compile_sqla_query`` applies for the same
+ reason. ``_double_percents`` is private and not guaranteed on a third-party
+ dialect, hence the ``getattr`` default.
+ """
+ compiled = str(
+ element.compile(dialect=dialect, compile_kwargs={"literal_binds":
True})
+ )
+ if dialect is not None and getattr(
+ dialect.identifier_preparer, "_double_percents", False
+ ):
+ compiled = compiled.replace("%%", "%")
+ return compiled
+
+
+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 *asked* to stand in for 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
+ -- including a self-mapping nobody asked for, where the partition column
+ merely happens to be the default datetime column.
+
+ 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,
+ )
+ )
+
+ if partition_mapped_column and partition_mapped_column == partition_column:
+ # Asked for explicitly, so report it as the mistake it is.
+ 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,
+ )
+ )
+ elif not partition_mapped_column and main_dttm_col == partition_column:
+ # Resolved onto itself only because the default datetime column happens
+ # to be the partition column -- a state metadata sync could produce
+ # without anyone asking for it. The mapping is inert either way, since
+ # `resolve_partition_mapping` bails out on it, so this reports and lets
+ # the write through: blocking here means a dataset already in the state
+ # can never be saved again, not even to change its description, and
+ # there is no field whose edit would clear the error first.
+ issues.append(
+ MappingValidationIssue(
+ field="partition_mapped_column",
+ message=_(
+ "%(name)s is both the partition column and the default "
+ "datetime column, so the mapping points at itself and "
+ "stays inactive. Set a mapped column override, or change "
+ "the default datetime column.",
+ name=partition_column,
+ ),
+ blocking=False,
+ )
+ )
+
+ 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 is_bare_expression(transform, engine):
+ # Blocking, and deliberately placed in `validate_transform` rather than
+ # only at the probe: one issue here closes the PUT, the importer, the
+ # query-time gate through `is_transform_active`, the Explore
indicator's
+ # active verdict and the preview endpoint, all of which already consult
+ # this function.
+ return [
+ MappingValidationIssue(
+ field=field,
+ message=_(
+ "A value transform must be a single SQL expression. It "
+ "cannot carry a FROM, WHERE, GROUP BY or any other clause,
"
+ "and it cannot contain a sub-query."
+ ),
+ blocking=True,
+ )
+ ]
+
+ 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 not placeholder_is_executable(transform, engine):
+ # Blocking, and for the same reason the shape gate above is: this is
+ # the function the PUT, the importer, `is_transform_active`, the
+ # Explore indicator and the preview all consult, so one issue here
+ # closes every door at once. A placeholder inside a string literal is
+ # a sub-query the engine will run once a filter value closes the quote;
+ # one inside a comment silently eats the probe's own alias.
+ return [
+ MappingValidationIssue(
+ field=field,
+ message=_(
+ "The :value placeholder must appear where the engine will "
+ "evaluate it. It cannot sit inside a string literal or a "
+ "comment."
+ ),
+ blocking=True,
+ )
+ ]
+
+ 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."),
+ }
+
+ columns_by_name = {str(column.column_name): column for column in
datasource.columns}
+ if mapped_column not in columns_by_name:
+ return {
+ "valid": False,
+ "reason": "validation",
+ "error": _("%(name)s is not a column on this dataset.",
name=mapped_column),
+ }
+
+ # The bail-out `resolve_partition_mapping` makes at query time, and that
+ # `partition_filter_mapping_summary` repeats for the Explore indicator.
+ # Without it here, preview reports a valid emitted predicate for a mapping
+ # no chart will ever mirror -- the one answer a preview panel must not
give.
+ if has_active_advanced_data_type(columns_by_name[mapped_column]):
+ return {
+ "valid": False,
+ "reason": "validation",
+ "error": _(
+ "%(name)s has an advanced data type, so its filters are "
+ "translated into their own predicate shape and cannot be "
+ "mirrored onto the partition column.",
+ name=mapped_column,
+ ),
+ }
+
+ engine = datasource.database.backend
+ for issue in validate_partition_mapping(
+ column_names=set(columns_by_name),
+ 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(
+ partition_column=str(partition_column),
+ mapped_column=mapped_column,
+ value_transform=cast(str, value_transform),
+ is_monotonic=is_monotonic,
+ equality_is_safe=equality_mirrors_safely(
+ columns_by_name[mapped_column], datasource.database.db_engine_spec
+ ),
+ )
+ sample_input = _render_sample_input(
+ datasource, mapped_column, operator, sample_values
+ )
+
+ if operator not in PREVIEWABLE_OPERATORS:
+ # Checked before `mirrors`, which lets `TEMPORAL_RANGE` through when
the
+ # transform is declared monotonic -- and the single sample value would
+ # then reach `handle_comparison_filter`, which has no case for a range
+ # and raises. The request schema rejects it too; this keeps the
function
+ # honest for any other caller.
+ return {
+ "valid": False,
+ "reason": "operator",
+ "sample_input": sample_input,
+ "error": _(
+ "A %(operator)s filter is mirrored as a pair of bounds, which "
+ "a preview of a single value cannot describe.",
+ operator=operator.value,
+ ),
+ }
+
+ if not mapping.mirrors(operator):
+ return {
+ "valid": False,
+ "reason": "operator",
+ "sample_input": sample_input,
+ "error": _(
+ "A %(operator)s filter is only mirrored when the transform "
+ "preserves ordering, which this one is not declared to do.",
+ operator=operator.value,
+ ),
+ }
+
+ value = _probe_input(
+ datasource, columns_by_name[mapped_column], operator, sample_values
+ )
+ errors: list[str] = []
+ type_errors: list[str] = []
+ predicates = build_mirrored_predicates(
+ datasource,
+ mapping,
+ [(operator, value)],
+ errors=errors,
+ type_errors=type_errors,
+ )
+ if type_errors:
+ # Its own reason, because "the database would not evaluate this" and
+ # "it evaluated and answered with the wrong kind of thing" are
+ # different problems with different fixes, and the owner is the only
+ # one who can tell which transform they meant to write.
+ return {
+ "valid": False,
+ "reason": "type",
+ "sample_input": sample_input,
+ "error": type_errors[0],
+ }
+ if not predicates:
+ return {
+ "valid": False,
+ "reason": "engine",
+ "sample_input": sample_input,
+ "error": (
+ _(
+ "The transform could not be evaluated against the "
+ "database: %(reason)s",
+ reason=errors[0],
+ )
+ if errors
+ else _("The transform could not be evaluated against the
database.")
+ ),
+ }
+
+ return {
+ "valid": True,
+ "sample_input": sample_input,
+ "emitted_predicate": _render_predicate(datasource, predicates[0]),
+ }
+
+
+def _probe_input(
+ datasource: SqlaTable,
+ column: "TableColumn",
+ operator: FilterOperator,
+ sample_values: list[str],
+) -> Any:
+ """
+ The sample values as the query path would bind them.
+
+ The request carries samples as strings, and the probe is keyed on exactly
+ what it is handed, so passing them through raw asks the engine a different
+ question from the one a chart asks. A sample of ``2025`` on a numeric
+ column has to arrive as the integer the chart compares: under a transform
+ of ``typeof(:value)`` the raw string previewed ``'text'`` where the chart
+ emitted ``'integer'``. And because `_probe_cache_key` keys on each value's
+ ``repr``, the two also missed each other's cache entirely -- so the "a
+ previewed transform warms the chart path for free" claim in
+ `preview_partition_mapping`'s docstring did not actually hold.
+
+ Coerced by `filter_values_handler`, the same function the query path uses,
+ rather than by a second implementation of the same rules.
+
+ Ranges are not previewable (see `PREVIEWABLE_OPERATORS`), so the bound
+ conversion `_collect_partition_mirror_range` applies -- a ``datetime`` into
+ the column's stored representation -- has no counterpart here.
+ """
+ is_list = operator == FilterOperator.IN
+ column_spec =
datasource.db_engine_spec.get_column_spec(native_type=column.type)
+ handled = datasource.filter_values_handler(
+ # `list[str]` is not a `list[FilterValue]` to mypy, invariantly, even
+ # though every `str` is a `FilterValue`.
+ values=cast(FilterValues, sample_values if is_list else
sample_values[0]),
+ operator=operator,
+ target_generic_type=(
+ column_spec.generic_type if column_spec else
utils.GenericDataType.STRING
+ ),
+ target_native_type=column.type,
+ is_list_target=is_list,
+ db_engine_spec=datasource.db_engine_spec,
+ db_extra=datasource.db_extra,
+ )
+
+ def bindable(value: Any) -> Any:
+ # An epoch-milliseconds sample on a temporal column comes back as
+ # engine SQL rather than a value, and SQL cannot be the value of a bind
+ # parameter -- carried as text so the probe substitutes it, the same
way
+ # an array literal is.
+ if isinstance(value, ColumnElement):
+ return raw_probe_value(datasource.database, value)
+ return value
+
+ if is_list and isinstance(handled, (list, tuple)):
+ return [bindable(entry) for entry in handled]
+ return bindable(handled)
+
+
+def _render_sample_input(
+ datasource: SqlaTable,
+ mapped_column: str,
+ operator: FilterOperator,
+ values: list[str],
+) -> str:
+ """
+ The filter being previewed, written the way an owner would read it.
+
+ Display-only -- it is never executed -- but it is still read as SQL, so the
+ column is quoted and the values are rendered by the dialect rather than
+ concatenated raw. A column named ``order date`` would otherwise come out as
+ two bare words.
+ """
+ quoted = datasource.quote_identifier(mapped_column)
+ if operator == FilterOperator.IN:
+ rendered = ", ".join(
+ _render_literal(datasource.database, value) for value in values
+ )
+ return f"{quoted} IN ({rendered})"
+ return (
+ f"{quoted} {operator.value} {_render_literal(datasource.database,
values[0])}"
+ )
+
+
+def _render_predicate(datasource: SqlaTable, predicate: ColumnElement[Any]) ->
str:
+ """
+ Compile a mirrored predicate to the text a reader would see in View query.
+
+ Every value in it is a probed constant by this point, so ``literal_binds``
+ renders the same literals the chart query carries.
+ """
+ return _compile_literal(predicate,
_dialect_for(datasource.database)).replace(
+ "\n", " "
+ )
+
+
+def _render_literal(database: Database, value: Any) -> str:
+ """
+ Render a value the way it appears in the generated SQL.
+
+ Compiled by the dialect rather than formatted by hand. `str()` would render
+ a NULL as the Python repr `None` and leave a date unquoted, neither of
which
+ is SQL, and hand-rolled quote-doubling would only ever have been right for
+ strings.
+ """
+ return _compile_literal(sa.literal(value), _dialect_for(database))
+
+
+def build_mirrored_predicates(
+ datasource: SqlaTable,
+ mapping: PartitionMapping,
+ requests: list[tuple[FilterOperator, Any]],
+ *,
+ errors: list[str] | None = None,
+ type_errors: list[str] | None = None,
+) -> list[ColumnElement[Any]]:
+ """
+ Turn collected ``(operator, value)`` mirror requests into predicates.
+
+ ``requests`` is expected to be deduplicated by the caller. Every value is
+ resolved in a single probe round trip -- one per chart query at most --
then
+ emitted as a literal constant, so "View query" shows the reader an ordinary
+ ``WHERE`` clause rather than an inline expression.
+
+ A strict bound is emitted non-strictly; see `mirror_operator`. That happens
+ here, at the one point where a ``FilterOperator`` becomes SQL, rather than
+ at the collection sites in `ExploreMixin`: the preview endpoint calls this
+ directly, so this is what keeps the predicate the editor shows an owner and
+ the predicate the chart carries from disagreeing.
+
+ :param errors: optional sink; see `evaluate_transform`.
+ :param type_errors: optional sink for a probe result the partition column
+ cannot hold -- see `probed_value_type_error`. Separate from ``errors``
+ because the two say different things to an owner: that one is "the
+ database would not evaluate this", and this one is "it evaluated, and
+ answered with the wrong kind of thing".
+ """
+ if not requests:
+ return []
+
+ partition_column = next(
+ (
+ column
+ for column in datasource.columns
+ if column.column_name == mapping.partition_column
+ ),
+ None,
+ )
+ if partition_column is None:
+ return []
+
+ # Flatten every value that needs probing into one list, remembering how
many
+ # each request consumed so the results can be handed back out.
+ flat: list[Any] = []
+ spans: list[tuple[FilterOperator, int, int]] = []
+ for operator, value in requests:
+ values = list(value) if operator == FilterOperator.IN else [value]
+ spans.append((operator, len(flat), len(values)))
+ flat.extend(values)
+
+ evaluated = evaluate_transform(
+ datasource.database,
+ datasource.catalog,
+ datasource.schema,
+ mapping.value_transform,
+ flat,
+ errors=errors,
+ )
+ if evaluated is None:
+ return []
+
+ if not _bounds_are_ordered(evaluated, spans):
+ return []
+
+ # Declining here is the last chance to decline at all: once these
+ # predicates are in the statement, a value the partition column cannot
+ # hold fails the whole chart rather than its pruning.
+ if reason := probed_value_type_error(partition_column, evaluated):
+ logger.warning(
+ "Partition transform returned a value the partition column cannot "
+ "hold; queries will not prune: %s",
+ reason,
+ )
+ if type_errors is not None:
+ type_errors.append(reason)
+ record_mirror_verdict(
+ datasource.database,
+ datasource.catalog,
+ datasource.schema,
+ mapping.value_transform,
+ mirrors=False,
+ )
+ return []
+
+ sqla_col = datasource.convert_tbl_column_to_sqla_col(partition_column)
+ predicates = _predicates_from_probe(
+ sqla_col, datasource.db_engine_spec, evaluated, spans
+ )
+ if not predicates:
+ return []
+
+ # A comparison against a NULL partition value is NULL, so a row parked in a
+ # NULL partition is dropped by the mirror even when the real filter matches
+ # it -- Hive and Impala's default partition, or a transform that returns
+ # NULL for an input it cannot convert. The mirror only has to be no
+ # narrower than the filter it stands in for, so admitting NULL partitions
+ # keeps those rows. Engines still prune; they read one extra partition.
+ return [sa.or_(sa.and_(*predicates), sqla_col.is_(None))]
+
+
+def _predicates_from_probe(
+ sqla_col: ColumnElement[Any],
+ db_engine_spec: type[BaseEngineSpec],
+ evaluated: list[Any],
+ spans: list[tuple[FilterOperator, int, int]],
+) -> list[ColumnElement[Any]]:
+ """
+ One predicate on the partition column per surviving mirror request.
+
+ A request whose probe answered `None` for any of its values is skipped
+ rather than emitted: `col = NULL` is `NULL`, so it would drop every row the
+ filter keeps. A strict bound is emitted non-strictly; see
`mirror_operator`.
+ """
+ predicates: list[ColumnElement[Any]] = []
+ for operator, start, length in spans:
+ chunk = evaluated[start : start + length]
+ if any(value is None for value in chunk):
+ continue
+ if operator == FilterOperator.IN:
+ # The probe answers per value, so a transform over a mixed-number
+ # filter can answer with a mixed-number partition key -- and
+ # `in_` infers the bind type from the first element it is handed.
+ # Without this, probe results of `[33, 29.02]` emit
+ # `IN (33, 29)` and prune away the partition holding `29.02`.
+ predicates.append(sqla_col.in_(normalize_mixed_numbers(chunk)))
+ else:
+ predicates.append(
+ db_engine_spec.handle_comparison_filter(
+ sqla_col, mirror_operator(operator), chunk[0]
+ )
+ )
+ return predicates
+
+
+#: Operators whose value is the lower end of a range, and whose upper-end
twins.
+#:
+#: Shared with `ExploreMixin._mirror_probe_input`, which rounds a bound
*outward*
+#: where the engine resolves it more coarsely than the filter carries --
backward
+#: for a lower bound, forward for an upper one. Which end an operator names is
+#: the only thing separating the two directions in either caller, so the two
have
+#: to agree on it rather than each classify for itself.
+LOWER_BOUND_OPERATORS = {
+ FilterOperator.GREATER_THAN,
+ FilterOperator.GREATER_THAN_OR_EQUALS,
+}
+UPPER_BOUND_OPERATORS = {
+ FilterOperator.LESS_THAN,
+ FilterOperator.LESS_THAN_OR_EQUALS,
+}
+
+
+#: What Python type a probe result may have, per generic type of the partition
+#: column it is about to be compared against.
+#:
+#: Only the types a mismatch can actually break a query with are listed. A
+#: ``STRING`` partition column is deliberately absent: every engine accepts a
+#: number in a text comparison, and refusing one would break a working mapping
+#: for the sake of tidiness. An unresolvable column type is absent for the
+#: stronger reason that there is nothing to check against.
+_PROBE_RESULT_TYPES: dict[utils.GenericDataType, tuple[type, ...]] = {
+ utils.GenericDataType.NUMERIC: (int, float),
Review Comment:
A `Decimal` probe result is refused here even against a NUMERIC partition
column, so `extract(epoch from :value)` on Postgres 14+ (and decimal arithmetic
on MySQL, Snowflake, Oracle) stops mirroring and preview reports `type`. Add
`Decimal`; no suggestion because the import sits ~2,000 lines up.
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