gkneighb commented on code in PR #41860: URL: https://github.com/apache/superset/pull/41860#discussion_r3578270540
########## superset/mcp_service/chart/plugins/histogram.py: ########## @@ -0,0 +1,197 @@ +# 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. + +"""Histogram chart type plugin.""" + +from __future__ import annotations + +import re +from collections.abc import Mapping +from typing import Any, ClassVar + +from superset.mcp_service.chart.chart_utils import ( + _summarize_filters, + map_histogram_config, +) +from superset.mcp_service.chart.plugin import BaseChartPlugin +from superset.mcp_service.chart.schemas import ColumnRef, HistogramChartConfig +from superset.mcp_service.chart.validation.dataset_validator import DatasetValidator +from superset.mcp_service.common.error_schemas import ChartGenerationError + + +class HistogramChartPlugin(BaseChartPlugin): + """Plugin for histogram chart type.""" + + chart_type = "histogram" + display_name = "Histogram" + native_viz_types: ClassVar[Mapping[str, str]] = { + "histogram_v2": "Histogram", + } + + def pre_validate( + self, + config: dict[str, Any], + ) -> ChartGenerationError | None: + if "column" not in config: + return ChartGenerationError( + error_type="missing_histogram_fields", + message="Histogram missing required field: 'column'", + details=( + "Histograms bin the values of a single numeric column " + "into frequency buckets" + ), + suggestions=[ + "Add 'column' field: {'name': 'numeric_column'}", + "Example: {'chart_type': 'histogram', " + "'column': {'name': 'trip_duration'}, 'bins': 10}", + ], + error_code="MISSING_HISTOGRAM_FIELDS", + ) + return None + + def extract_column_refs(self, config: Any) -> list[ColumnRef]: + if not isinstance(config, HistogramChartConfig): + return [] + refs: list[ColumnRef] = [config.column] + refs.extend(config.groupby or []) + if config.filters: + for f in config.filters: + refs.append(ColumnRef(name=f.column)) + return refs + + def to_form_data( + self, config: Any, dataset_id: int | str | None = None + ) -> dict[str, Any]: + return map_histogram_config(config) + + def generate_name(self, config: Any, dataset_name: str | None = None) -> str: + what = f"Distribution of {config.column.label or config.column.name}" + if config.groupby: + what += " by " + ", ".join(g.label or g.name for g in config.groupby) + context = _summarize_filters(config.filters) + return self._with_context(what, context) + + def post_map_validate( + self, + config: Any, + form_data: dict[str, Any], + dataset_id: int | str | None = None, + ) -> ChartGenerationError | None: + """Require a numeric binned column, mirroring the Explore UI. + + The frontend control panel restricts the histogram column to + ``GenericDataType.Numeric``; without this check an LLM picking a + text column passes pre-validation and only fails at query time. + """ + if not isinstance(config, HistogramChartConfig) or dataset_id is None: + return None + + dataset_context = DatasetValidator._get_dataset_context(dataset_id) + if dataset_context is None: + return None + + col_info = next( + ( + col + for col in dataset_context.available_columns + if col["name"].lower() == (config.column.name or "").lower() + ), + None, + ) + if col_info is None: + # Column existence is validated separately; don't double-report. + return None + + def _is_numeric(col: dict[str, Any]) -> bool: + if col.get("is_numeric", False): + return True + # Backends report many spellings (BIGINT, SMALLINT, REAL, NUMBER, + # DOUBLE PRECISION); match numeric tokens at word boundaries so + # INTERVAL/POINT (which merely contain "INT") stay non-numeric. + type_upper = str(col.get("type", "")).upper() + return bool( + re.search( + r"\b(?:TINY|SMALL|MEDIUM|BIG)?INT(?:EGER)?\b" + r"|\bFLOAT\b|\bDOUBLE\b|\bDECIMAL\b" + r"|\bNUMERIC\b|\bREAL\b|\bNUMBER\b", + type_upper, + ) + ) + + if _is_numeric(col_info): + return None + + numeric_columns = sorted( + col["name"] for col in dataset_context.available_columns if _is_numeric(col) + ) + return ChartGenerationError( + error_type="non_numeric_histogram_column", + message=( + f"Histograms bin numeric values; column " + f"'{config.column.name}' has type " + f"{col_info.get('type', 'UNKNOWN')}." + ), + details=( + "The Explore UI restricts the histogram column to numeric " + "columns; non-numeric columns fail or produce meaningless " + "bins at query time." + ), + suggestions=( + [f"Numeric columns in this dataset: {', '.join(numeric_columns)}"] + if numeric_columns + else ["Use get_dataset_info to inspect the dataset's columns"] + ) + + ["For category frequencies, use chart_type='xy' with kind='bar'"], + error_code="NON_NUMERIC_HISTOGRAM_COLUMN", + ) + + def resolve_viz_type(self, config: Any) -> str: + return "histogram_v2" Review Comment: Confirmed and fixed in the latest push — histogram_v2 added to the preview classification map, matching the dual-spelling treatment heatmap already has. -- This is an automated message from the Apache Git Service. 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