codeant-ai-for-open-source[bot] commented on code in PR #41469:
URL: https://github.com/apache/superset/pull/41469#discussion_r3484723159
##########
superset-frontend/plugins/plugin-chart-table/src/buildQuery.ts:
##########
@@ -227,7 +227,10 @@ export const buildQuery: BuildQuery<TableChartFormData> = (
formData?.result_type === 'results');
if (isDownloadQuery) {
- moreProps.row_limit = Number(formDataCopy.row_limit) || 0;
+ moreProps.row_limit =
+ formDataCopy.row_limit != null
+ ? Number(formDataCopy.row_limit)
+ : undefined;
Review Comment:
**Suggestion:** The new conversion only checks for null/undefined before
calling `Number`, so non-numeric string values (for example legacy or
URL-provided `row_limit`) become `NaN` here. That bypasses the normal
`buildQueryObject` sanitization and injects an invalid `row_limit` into
download queries, which can serialize differently from the display query and
still miss cache (and may produce incorrect backend handling). Normalize
invalid numeric values to `undefined` instead of passing through `NaN`. [cache]
<details>
<summary><b>Severity Level:</b> Major ⚠️</summary>
```mdx
❌ Table chart CSV/XLSX exports may still miss cache.
⚠️ Malformed row_limit values yield inconsistent export query objects.
⚠️ Edge-case exports may ignore intended row limits.
```
</details>
<details>
<summary><b>Steps of Reproduction ✅ </b></summary>
```mdx
1. In a unit test, import `buildQuery` from
`superset-frontend/plugins/plugin-chart-table/src/buildQuery.ts` (function
defined around
line 57) and call it with a `TableChartFormData` object where
`result_format: 'csv'`,
`result_type: 'results'`, and `row_limit: 'not-a-number'` (a non-numeric
string), plus any
valid datasource/metrics.
2. During `buildQuery` execution, the callback passed to `buildQueryContext`
computes
`isDownloadQuery` (lines ~75–78) which evaluates to `true` for
`result_format: 'csv'`,
then enters the `if (isDownloadQuery)` block at line 229.
3. Inside that block (lines 230–233) the code sets `moreProps.row_limit =
formDataCopy.row_limit != null ? Number(formDataCopy.row_limit) :
undefined;`. With
`row_limit: 'not-a-number'`, `formDataCopy.row_limit != null` is `true`,
`Number('not-a-number')` produces `NaN`, so the resulting query object in
`buildQueryContext(...).queries[0]` has `row_limit` set to `NaN`.
4. The same chart’s on-screen (display) query, built with identical form
data but
`result_format: 'json'` / non-download settings, does not pass through this
`isDownloadQuery` block and instead uses the sanitized `row_limit` from the
underlying
`buildQueryContext`/`QueryObject` creation, so its `row_limit` is a valid
integer or
`undefined`, not `NaN`. Because the backend cache keys for SQLA datasets are
derived by
passing the query object into `get_sqla_query` with `SQLA_QUERY_KEYS`
including
`"row_limit"` (see `superset/connectors/sqla/models.py:2131–2137` and
`superset/models/helpers.py:197–202` where `"row_limit"` is listed), the
export query
containing `row_limit: NaN` (serialized to `null`) produces a different
cache key than the
display query’s `row_limit` (valid int or omitted), so CSV/XLSX exports for
such malformed
`row_limit` values will bypass the populated cache and re-execute the query.
Normalizing
invalid numeric inputs to `undefined` here would keep export and display
query shapes
aligned and restore cache reuse.
```
</details>
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<details>
<summary><b>Prompt for AI Agent 🤖 </b></summary>
```mdx
This is a comment left during a code review.
**Path:** superset-frontend/plugins/plugin-chart-table/src/buildQuery.ts
**Line:** 230:233
**Comment:**
*Cache: The new conversion only checks for null/undefined before
calling `Number`, so non-numeric string values (for example legacy or
URL-provided `row_limit`) become `NaN` here. That bypasses the normal
`buildQueryObject` sanitization and injects an invalid `row_limit` into
download queries, which can serialize differently from the display query and
still miss cache (and may produce incorrect backend handling). Normalize
invalid numeric values to `undefined` instead of passing through `NaN`.
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>
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