codeant-ai-for-open-source[bot] commented on code in PR #39922: URL: https://github.com/apache/superset/pull/39922#discussion_r3493337405
########## superset/mcp_service/chart/plugins/mixed_timeseries.py: ########## @@ -0,0 +1,173 @@ +# 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. + +"""Mixed timeseries chart type plugin.""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any, ClassVar + +from superset.mcp_service.chart.chart_utils import ( + _mixed_timeseries_what, + _summarize_filters, + map_mixed_timeseries_config, +) +from superset.mcp_service.chart.plugin import BaseChartPlugin +from superset.mcp_service.chart.schemas import ColumnRef, MixedTimeseriesChartConfig +from superset.mcp_service.chart.validation.dataset_validator import DatasetValidator +from superset.mcp_service.common.error_schemas import ChartGenerationError + + +class MixedTimeseriesChartPlugin(BaseChartPlugin): + """Plugin for mixed_timeseries chart type.""" + + chart_type = "mixed_timeseries" + display_name = "Mixed Timeseries" + native_viz_types: ClassVar[Mapping[str, str]] = { + "mixed_timeseries": "Mixed Timeseries Chart", + } + + def pre_validate( + self, + config: dict[str, Any], + ) -> ChartGenerationError | None: + missing_fields = [] + + if "x" not in config and "x_axis" not in config: + missing_fields.append("'x' (X-axis temporal column)") + if not config.get("y") and not config.get("metrics"): + missing_fields.append("'y' (primary Y-axis metrics)") + if not config.get("y_secondary") and not config.get("metrics_b"): + missing_fields.append("'y_secondary' (secondary Y-axis metrics)") + + if missing_fields: + return ChartGenerationError( + error_type="missing_mixed_timeseries_fields", + message=( + f"Mixed timeseries chart missing required fields: " + f"{', '.join(missing_fields)}" + ), + details=( + "Mixed timeseries charts require an x-axis, primary metrics, " + "and secondary metrics" + ), + suggestions=[ + "Add 'x' field: {'name': 'date_column'}", + "Add 'y' field: [{'name': 'revenue', 'aggregate': 'SUM'}]", + "Add 'y_secondary': [{'name': 'orders', 'aggregate': 'COUNT'}]", + "Optional: 'primary_kind' and 'secondary_kind' for chart types", + ], + error_code="MISSING_MIXED_TIMESERIES_FIELDS", + ) + + for field_name in ["y", "y_secondary"]: + if not isinstance(config.get(field_name, []), list): + return ChartGenerationError( + error_type=f"invalid_{field_name}_format", + message=f"'{field_name}' must be a list of metrics", + details=( + f"The '{field_name}' field must be an array of metric " + "specifications" + ), + suggestions=[ + f"Wrap in array: '{field_name}': " + "[{'name': 'col', 'aggregate': 'SUM'}]", + ], + error_code=f"INVALID_{field_name.upper()}_FORMAT", + ) Review Comment: **Suggestion:** The list-type validation only inspects `y`/`y_secondary` keys, but this plugin also accepts legacy aliases (`metrics`/`metrics_b`). If callers use aliases with wrong types, pre-validation misses them and the request falls through to a generic Pydantic error path instead of returning the intended structured validation error. Validate both canonical and alias keys consistently. [logic error] <details> <summary><b>Severity Level:</b> Major ⚠️</summary> ```mdx - ❌ generate_chart returns generic error for alias metric misuse. - ⚠️ Mixed_timeseries callers get inconsistent validation feedback. ``` </details> <details> <summary><b>Steps of Reproduction ✅ </b></summary> ```mdx 1. Call MCP tool `generate_chart` for a mixed timeseries chart with a config that uses alias metric keys but with the wrong type, for example `{"chart_type": "mixed_timeseries", "x": {"name": "order_date"}, "metrics": {"name": "revenue", "aggregate": "SUM"}, "metrics_b": [{"name": "orders", "aggregate": "COUNT"}]}` (tool entry point in `superset/mcp_service/chart/tool/generate_chart.py:225-279`). 2. The request flows into `ValidationPipeline.validate_request_with_warnings()` (`validation/pipeline.py:90-103`), which calls `SchemaValidator.validate_request()` (`validation/schema_validator.py:40-52`) and then `_pre_validate_chart_type()` (`schema_validator.py:146-196`). 3. `_pre_validate_chart_type()` retrieves `MixedTimeseriesChartPlugin` and calls its `pre_validate()` (`plugins/mixed_timeseries.py:45-56, 78-92). The required-field checks at lines 53-56 correctly treat `"metrics"` / `"metrics_b"` as satisfying the presence of primary and secondary metrics, but the list-type validation loop at lines 78-92 only inspects `"y"` and `"y_secondary"`. Because the config uses `"metrics"` and `"metrics_b"` and omits `"y"` / `"y_secondary"`, `config.get("y", [])` and `config.get("y_secondary", [])` both default to empty lists, so no `ChartGenerationError` is raised even though `"metrics"` is a dict, not a list. 4. Pydantic then parses `GenerateChartRequest` and `MixedTimeseriesChartConfig` (`schemas.py:57-80, 1180-1259). The `y` field is declared as `List[ColumnRef]` with `validation_alias=AliasChoices("y", "metrics")` (`schemas.py:71-77), so the dict at `"metrics"` causes a structural validation error (invalid list type). This error is converted by `_enhance_validation_error()` (`schema_validator.py:201-239`) into a generic `"validation_error"` response instead of the intended, structured `"invalid_y_format"` / `"invalid_y_secondary_format"` errors, demonstrating that alias metric fields currently bypass the plugin’s explicit list-type validation. ``` </details> [](https://app.codeant.ai/fix-in-ide?tool=cursor&prompt_id=49847f9a0dc74170af266c266356ef8b&service=github&base_url=https%3A%2F%2Fgithub.com&org=apache&repo=apache%2Fsuperset) [](https://app.codeant.ai/fix-in-ide?tool=vscode-claude&prompt_id=49847f9a0dc74170af266c266356ef8b&service=github&base_url=https%3A%2F%2Fgithub.com&org=apache&repo=apache%2Fsuperset) *(Use Cmd/Ctrl + Click for best experience)* <details> <summary><b>Prompt for AI Agent 🤖 </b></summary> ```mdx This is a comment left during a code review. **Path:** superset/mcp_service/chart/plugins/mixed_timeseries.py **Line:** 78:92 **Comment:** *Logic Error: The list-type validation only inspects `y`/`y_secondary` keys, but this plugin also accepts legacy aliases (`metrics`/`metrics_b`). If callers use aliases with wrong types, pre-validation misses them and the request falls through to a generic Pydantic error path instead of returning the intended structured validation error. Validate both canonical and alias keys consistently. Validate the correctness of the flagged issue. If correct, How can I resolve this? If you propose a fix, implement it and please make it concise. Once fix is implemented, also check other comments on the same PR, and ask user if the user wants to fix the rest of the comments as well. if said yes, then fetch all the comments validate the correctness and implement a minimal fix ``` </details> <a href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F39922&comment_hash=639d3f175abfaf49c448ced997740b9fcced6f64a77c7ee2f15e8fed2f0488bd&reaction=like'>👍</a> | <a href='https://app.codeant.ai/feedback?pr_url=https%3A%2F%2Fgithub.com%2Fapache%2Fsuperset%2Fpull%2F39922&comment_hash=639d3f175abfaf49c448ced997740b9fcced6f64a77c7ee2f15e8fed2f0488bd&reaction=dislike'>👎</a> -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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