apache/gravitino
Dec 2025 – Dec 2025
1 Month active
Languages Used
Java
Technical Skills
JavaSparkbackend development
yangyuxia
PROFILE
Yangyuxia
Worked on the apache/gravitino repository to address stale data issues in Spark-Hive workflows by delivering a targeted bug fix. Focused on backend development using Java and Spark, the solution involved overriding the BaseCatalog.invalidateTable() method to ensure the fileStatusCache is cleared whenever a table is modified via a Hive client. This approach enabled Spark sessions, including those using the spark-connecto plugin, to accurately reflect the latest data and reduced the risk of querying outdated information. Added comprehensive integration tests to validate end-to-end cache invalidation and prevent regressions, demonstrating a collaborative and detail-oriented approach to software reliability.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
5
Activity Months1
Your Network
169 peopleWork History
December 2025
1 Commits
Dec 1, 2025December 2025: Delivered a critical bug fix to ensure data freshness in Spark-Hive workflows. Implemented a cache-invalidations improvement by overriding BaseCatalog.invalidateTable() to clear the table's fileStatusCache when modified via a Hive client, enabling accurate querying of the latest data. Added IT tests to validate end-to-end cache invalidation and prevent regressions. PRs: #9110 (#9111); co-authored by yangyx. Impact: more reliable, timely visibility of data changes for Spark sessions (including spark-connecto plugin users), reducing stale-data scenarios and improving user trust. Technologies demonstrated: Spark caching, Hive integration, BaseCatalog customization, end-to-end testing, and collaborative software delivery.
1 Commits
Dec 1, 2025December 2025: Delivered a critical bug fix to ensure data freshness in Spark-Hive workflows. Implemented a cache-invalidations improvement by overriding BaseCatalog.invalidateTable() to clear the table's fileStatusCache when modified via a Hive client, enabling accurate querying of the latest data. Added IT tests to validate end-to-end cache invalidation and prevent regressions. PRs: #9110 (#9111); co-authored by yangyx. Impact: more reliable, timely visibility of data changes for Spark sessions (including spark-connecto plugin users), reducing stale-data scenarios and improving user trust. Technologies demonstrated: Spark caching, Hive integration, BaseCatalog customization, end-to-end testing, and collaborative software delivery.
December 2025
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
JavaSparkbackend development
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