EnxDev commented on code in PR #42284: URL: https://github.com/apache/superset/pull/42284#discussion_r3675772750
########## superset/common/form_data_query_context.py: ########## @@ -0,0 +1,273 @@ +# 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]], + where_only: bool = False, +) -> 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}``; free-form ``SQL`` filters have no ``{col, op, + val}`` equivalent and are handled separately (see + :func:`freeform_where_having`). + + By default all ``SIMPLE`` filters are converted (the behavior the MCP + compile/preview path relies on). Pass ``where_only=True`` to convert only + ``WHERE``-clause filters, matching the frontend's ``processFilters`` — the + dashboard export uses this so it applies the same rows the chart shows and + does not additionally filter on ``SIMPLE`` ``HAVING`` clauses. + """ + result: list[dict[str, Any]] = [] + for flt in adhoc_filters or []: + if flt.get("expressionType") != "SIMPLE": + continue + if where_only and (flt.get("clause") or "WHERE").upper() != "WHERE": + continue + 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 is_raw_query_mode(form_data: dict[str, Any]) -> bool: + """ + Whether the chart runs in raw (non-aggregated) mode, mirroring the frontend's + ``getQueryMode``: an explicit ``query_mode`` wins, otherwise the presence of + ``all_columns`` implies raw mode. + """ + if mode := form_data.get("query_mode"): + return mode == "raw" + return bool(form_data.get("all_columns")) + + +def orderby_from_form_data( + form_data: dict[str, Any], metrics: list[Any], viz_type: str | None = None +) -> 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) Review Comment: `orderby_from_form_data` checks `order_by_cols` in every mode, but this is a raw-mode-only control. Since its value isn't reset when switching to aggregate mode, a stale `order_by_cols` can cause the export to use a different ordering from the chart, potentially returning a different top-N. Could we only use `order_by_cols` when `is_raw_query_mode(form_data)` is true? It would also be good to add a regression test for an aggregate table with a stale `order_by_cols` value. -- This is an automated message from the Apache Git Service. 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