hughhhh commented on code in PR #42284: URL: https://github.com/apache/superset/pull/42284#discussion_r3657984541
########## superset/common/form_data_query_context.py: ########## @@ -0,0 +1,227 @@ +# 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. +""" +Synthesize a query context from a chart's saved form data (``params``). + +A chart's ``query_context`` is normally generated client-side by each viz +plugin's ``buildQuery`` and only persisted when the chart is (re-)saved in +Explore. Charts that predate that behavior keep their ``params`` (form data) but +carry no ``query_context``, so server-side consumers that need to run the query +(e.g. the dashboard Excel export) have nothing to execute. + +This module rebuilds a best-effort query context from the form data — columns, +metrics, filters (including free-form SQL and the time range), ordering and time +grain — mirroring the shared parts of the viz plugins' ``buildQuery``. It does +**not** reproduce plugin post-processing (pivot, contribution/percent +transforms, rolling/forecast) or multi-query fan-out, so callers must restrict it +to viz types whose data maps faithfully to a single plain query. +""" + +from __future__ import annotations + +from typing import Any + +from superset.utils import json + + +def adhoc_filters_to_query_filters( + adhoc_filters: list[dict[str, Any]], +) -> list[dict[str, Any]]: + """ + Convert ``SIMPLE`` adhoc filters into QueryObject filter clauses. + + Adhoc filters use ``{subject, operator, comparator}`` while a query object + expects ``{col, op, val}``. Only ``SIMPLE`` WHERE-clause filters are + convertible here; free-form ``SQL`` filters have no ``{col, op, val}`` + equivalent and are handled separately (see :func:`freeform_where_having`). + """ + result: list[dict[str, Any]] = [] + for flt in adhoc_filters or []: + if ( + flt.get("expressionType") == "SIMPLE" + and (flt.get("clause") or "WHERE").upper() == "WHERE" + ): + result.append( + { + "col": flt.get("subject"), + "op": flt.get("operator"), + "val": flt.get("comparator"), + } + ) + return result + + +def freeform_where_having(form_data: dict[str, Any]) -> dict[str, str]: + """ + Collect free-form SQL predicates into a query ``extras`` mapping. + + Mirrors ``processFilters`` on the frontend: ``SQL`` adhoc filters (and a + legacy top-level ``where``) join into ``extras.where`` / ``extras.having`` by + clause, so a chart restricted by a custom SQL predicate exports the same rows + it displays instead of the full, unrestricted result. + """ + where: list[str] = [] + having: list[str] = [] + if form_data.get("where"): + where.append(form_data["where"]) + for flt in form_data.get("adhoc_filters") or []: + if flt.get("expressionType") == "SQL" and flt.get("sqlExpression"): + clause = (flt.get("clause") or "WHERE").upper() + (having if clause == "HAVING" else where).append(flt["sqlExpression"]) + + extras: dict[str, str] = {} + if where: + extras["where"] = " AND ".join(f"({clause})" for clause in where) + if having: + extras["having"] = " AND ".join(f"({clause})" for clause in having) + return extras + + +def columns_from_form_data(form_data: dict[str, Any]) -> list[Any]: + """ + Derive the query's grouping/raw columns from form data. + + Handles raw-mode tables (``all_columns``/``columns``), an ``x_axis`` (string + or adhoc column), and ``groupby`` dimensions, de-duplicating while preserving + order. + """ + if form_data.get("query_mode") == "raw" and ( + form_data.get("all_columns") or form_data.get("columns") + ): + return list(form_data.get("all_columns") or form_data.get("columns") or []) + + groupby_columns: list[Any] = form_data.get("groupby") or [] + raw_columns: list[Any] = form_data.get("columns") or [] + # Prefer explicit raw columns only when they are actually present; a stale + # empty ``columns: []`` key must not shadow the group-by dimensions (which + # would silently drop the grouping and change the aggregation). + columns = raw_columns.copy() if raw_columns else groupby_columns.copy() + + x_axis = form_data.get("x_axis") + if isinstance(x_axis, str) and x_axis and x_axis not in columns: + columns.insert(0, x_axis) + elif isinstance(x_axis, dict): + col_name = x_axis.get("column_name") + if col_name and col_name not in columns: + columns.insert(0, col_name) + return columns + + +def orderby_from_form_data( + form_data: dict[str, Any], metrics: list[Any] +) -> list[list[Any]]: + """ + Derive ordering so a ``row_limit`` returns the chart's top-N, not an + arbitrary N. + + Raw-mode tables order by ``order_by_cols`` (stored as JSON ``[col, asc]`` + pairs). Aggregate charts order by the configured sort metric + (``timeseries_limit_metric``, or the first metric when ``sort_by_metric`` is + set), otherwise fall back to the first metric descending — matching the + table/pie ``buildQuery`` defaults. + """ + if order_by_cols := form_data.get("order_by_cols") or []: + parsed: list[list[Any]] = [] + for col in order_by_cols: + if isinstance(col, str): + try: + col = json.loads(col) + except (TypeError, ValueError): + continue + parsed.append(col) + return parsed + + if not metrics: + return [] + + order_desc = form_data.get("order_desc", True) + sort_metric = form_data.get("timeseries_limit_metric") or ( + metrics[0] if form_data.get("sort_by_metric") else None + ) + if sort_metric is not None: + return [[sort_metric, not order_desc]] + # No explicit sort metric: default to the first metric, descending. + return [[metrics[0], False]] + + +def build_query_context_from_form_data( + form_data: dict[str, Any], + datasource: dict[str, Any], + viz_type: str | None = None, +) -> dict[str, Any]: + """ + Build a query-context payload (the JSON shape ``ChartDataQueryContextSchema`` + loads) from a chart's form data and datasource reference. + + :param form_data: The chart's saved ``params`` parsed to a dict. + :param datasource: ``{"id": <int>, "type": "table"}`` datasource reference. + :param viz_type: The chart's viz type, used for viz-specific handling. + :returns: A single-query query-context dict. + """ + metrics = list(form_data.get("metrics") or []) + # Single-metric charts (e.g. Big Number) store ``metric`` rather than + # ``metrics``. + if not metrics and form_data.get("metric"): + metrics = [form_data["metric"]] + # Table percent metrics are computed as additional query metrics. + if viz_type == "table" and form_data.get("percent_metrics"): + metrics = [*metrics, *form_data["percent_metrics"]] Review Comment: Good call — excluded in 53350ae4c9. Since the rebuild doesn't apply the contribution/percent post-processing, carrying `percent_metrics` would export raw aggregates under a column the user expects to be a "% of total", i.e. silently-wrong values. Omitting them is the safer choice (consistent with the rest of this rebuild); `time_grain_sqla` is still carried. Test updated to assert they're excluded. ########## superset/common/form_data_query_context.py: ########## @@ -0,0 +1,227 @@ +# 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. +""" +Synthesize a query context from a chart's saved form data (``params``). + +A chart's ``query_context`` is normally generated client-side by each viz +plugin's ``buildQuery`` and only persisted when the chart is (re-)saved in +Explore. Charts that predate that behavior keep their ``params`` (form data) but +carry no ``query_context``, so server-side consumers that need to run the query +(e.g. the dashboard Excel export) have nothing to execute. + +This module rebuilds a best-effort query context from the form data — columns, +metrics, filters (including free-form SQL and the time range), ordering and time +grain — mirroring the shared parts of the viz plugins' ``buildQuery``. It does +**not** reproduce plugin post-processing (pivot, contribution/percent +transforms, rolling/forecast) or multi-query fan-out, so callers must restrict it +to viz types whose data maps faithfully to a single plain query. +""" + +from __future__ import annotations + +from typing import Any + +from superset.utils import json + + +def adhoc_filters_to_query_filters( + adhoc_filters: list[dict[str, Any]], +) -> list[dict[str, Any]]: + """ + Convert ``SIMPLE`` adhoc filters into QueryObject filter clauses. + + Adhoc filters use ``{subject, operator, comparator}`` while a query object + expects ``{col, op, val}``. Only ``SIMPLE`` WHERE-clause filters are + convertible here; free-form ``SQL`` filters have no ``{col, op, val}`` + equivalent and are handled separately (see :func:`freeform_where_having`). + """ + result: list[dict[str, Any]] = [] + for flt in adhoc_filters or []: + if ( + flt.get("expressionType") == "SIMPLE" + and (flt.get("clause") or "WHERE").upper() == "WHERE" + ): + result.append( + { + "col": flt.get("subject"), + "op": flt.get("operator"), + "val": flt.get("comparator"), + } + ) + return result + + +def freeform_where_having(form_data: dict[str, Any]) -> dict[str, str]: + """ + Collect free-form SQL predicates into a query ``extras`` mapping. + + Mirrors ``processFilters`` on the frontend: ``SQL`` adhoc filters (and a + legacy top-level ``where``) join into ``extras.where`` / ``extras.having`` by + clause, so a chart restricted by a custom SQL predicate exports the same rows + it displays instead of the full, unrestricted result. + """ + where: list[str] = [] + having: list[str] = [] + if form_data.get("where"): + where.append(form_data["where"]) + for flt in form_data.get("adhoc_filters") or []: + if flt.get("expressionType") == "SQL" and flt.get("sqlExpression"): + clause = (flt.get("clause") or "WHERE").upper() + (having if clause == "HAVING" else where).append(flt["sqlExpression"]) + + extras: dict[str, str] = {} + if where: + extras["where"] = " AND ".join(f"({clause})" for clause in where) + if having: + extras["having"] = " AND ".join(f"({clause})" for clause in having) + return extras + + +def columns_from_form_data(form_data: dict[str, Any]) -> list[Any]: + """ + Derive the query's grouping/raw columns from form data. + + Handles raw-mode tables (``all_columns``/``columns``), an ``x_axis`` (string + or adhoc column), and ``groupby`` dimensions, de-duplicating while preserving + order. + """ + if form_data.get("query_mode") == "raw" and ( + form_data.get("all_columns") or form_data.get("columns") + ): + return list(form_data.get("all_columns") or form_data.get("columns") or []) + + groupby_columns: list[Any] = form_data.get("groupby") or [] + raw_columns: list[Any] = form_data.get("columns") or [] + # Prefer explicit raw columns only when they are actually present; a stale + # empty ``columns: []`` key must not shadow the group-by dimensions (which + # would silently drop the grouping and change the aggregation). + columns = raw_columns.copy() if raw_columns else groupby_columns.copy() + + x_axis = form_data.get("x_axis") + if isinstance(x_axis, str) and x_axis and x_axis not in columns: + columns.insert(0, x_axis) + elif isinstance(x_axis, dict): + col_name = x_axis.get("column_name") + if col_name and col_name not in columns: + columns.insert(0, col_name) + return columns + + +def orderby_from_form_data( + form_data: dict[str, Any], metrics: list[Any] +) -> list[list[Any]]: + """ + Derive ordering so a ``row_limit`` returns the chart's top-N, not an + arbitrary N. + + Raw-mode tables order by ``order_by_cols`` (stored as JSON ``[col, asc]`` + pairs). Aggregate charts order by the configured sort metric + (``timeseries_limit_metric``, or the first metric when ``sort_by_metric`` is + set), otherwise fall back to the first metric descending — matching the + table/pie ``buildQuery`` defaults. + """ + if order_by_cols := form_data.get("order_by_cols") or []: + parsed: list[list[Any]] = [] + for col in order_by_cols: + if isinstance(col, str): + try: + col = json.loads(col) + except (TypeError, ValueError): + continue + parsed.append(col) + return parsed + + if not metrics: + return [] + + order_desc = form_data.get("order_desc", True) + sort_metric = form_data.get("timeseries_limit_metric") or ( + metrics[0] if form_data.get("sort_by_metric") else None + ) + if sort_metric is not None: + return [[sort_metric, not order_desc]] + # No explicit sort metric: default to the first metric, descending. + return [[metrics[0], False]] + + +def build_query_context_from_form_data( + form_data: dict[str, Any], + datasource: dict[str, Any], + viz_type: str | None = None, +) -> dict[str, Any]: + """ + Build a query-context payload (the JSON shape ``ChartDataQueryContextSchema`` + loads) from a chart's form data and datasource reference. + + :param form_data: The chart's saved ``params`` parsed to a dict. + :param datasource: ``{"id": <int>, "type": "table"}`` datasource reference. + :param viz_type: The chart's viz type, used for viz-specific handling. + :returns: A single-query query-context dict. + """ + metrics = list(form_data.get("metrics") or []) + # Single-metric charts (e.g. Big Number) store ``metric`` rather than + # ``metrics``. + if not metrics and form_data.get("metric"): + metrics = [form_data["metric"]] + # Table percent metrics are computed as additional query metrics. + if viz_type == "table" and form_data.get("percent_metrics"): + metrics = [*metrics, *form_data["percent_metrics"]] + + columns = columns_from_form_data(form_data) + # Only a Big Number *with a trendline* (viz_type ``big_number``) groups by its + # time column; ``big_number_total`` is a single aggregate and must not be + # grouped, or it would return one row per timestamp instead of a total. + if not columns and viz_type == "big_number" and form_data.get("granularity_sqla"): + columns = [form_data["granularity_sqla"]] + + # SIMPLE adhoc filters (+ legacy top-level ``filters``) become query filters; + # free-form SQL predicates go into ``extras``. + filters = adhoc_filters_to_query_filters(form_data.get("adhoc_filters", [])) + for flt in form_data.get("filters") or []: + if isinstance(flt, dict) and flt.get("col") is not None: + filters.append(flt) + + extras = freeform_where_having(form_data) + if form_data.get("time_grain_sqla"): + extras["time_grain_sqla"] = form_data["time_grain_sqla"] + + time_range = form_data.get("time_range") or "No filter" Review Comment: Fixed in 53350ae4c9 — when `time_range` is absent the rebuild now falls back to `since`/`until` (`"{since} : {until}"`) before defaulting to "No filter", so older charts export their configured range. 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