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Check cached dataset config against incoming config on cache hit. If they differ, remove the stale entry and reload from source. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
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Replace TTL-based dataset cache with AsyncLoadingCache that accepts a loader and validator per get() call. Concurrent loads for the same key are deduplicated by storing futures directly. Move cache logic from _get_dataset to get_dataset for cleaner separation. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
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Fedir-Yatsenko
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Mar 23, 2026
| dataset_config = self.parse_data_set_config(config) | ||
| return await self._dataset_cache.get( | ||
| key=str(entity_id), | ||
| loader=lambda: self._get_dataset(entity_id, title, config, auth_context), |
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This behavior is not correct. If allow_offline=True and any problems occur, get_dataset should return an OfflineDataset without caching it
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Applicable issues
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Description of changes
Problem: When a dataset is retrieved from the in-memory cache, the system doesn't check whether its configuration has changed since it was cached. If an admin updates a dataset's config (e.g., dimensions, URN, citation, include_attributes), the stale cached dataset continues to be served until TTL expiry.
Solution: On cache hit in
QuanthubSdmx21DataSourceHandler._get_dataset(), compare the cached dataset's config against the incoming config using Pydantic model equality. If they differ, the stale entry is removed and the dataset is reloaded from the SDMX source.Also improved logging in
_propagate_config_to_channel_datasetsto trace per-channel-dataset decisions (skipped, auto-updated, needs reindex).Checklist
Reviewenvironment.By submitting this pull request, I confirm that my contribution is made under the terms of the MIT license.