apache/spark
May 2026 – May 2026
1 Month active
Languages Used
Scala
Technical Skills
Data EngineeringSQLScalaSpark
Chen Wang
PROFILE
Chen Wang
Worked on the apache/spark repository to deliver end-to-end support for metric views in v2 catalogs, enabling third-party catalogs to manage metric view lifecycles with full metadata fidelity. Developed a first-class METRIC_VIEW table type and integrated it with the v2 catalog surface, ensuring CREATE VIEW operations with metrics were routed through the updated path while maintaining compatibility with v1 behavior. Introduced a typed dependency model to capture multi-level namespace lineage and extended view metadata for improved governance. Enhanced error handling, standardized view-type processing, and improved observability through enriched DESCRIBE and SHOW outputs using Scala, Spark, and SQL.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,997
Activity Months1
Your Network
733 peopleSame Organization
@databricks.com332
Work History
May 2026
1 Commits • 1 Features
May 1, 2026In May 2026, delivered end-to-end support for metric views across v2 catalogs in Apache Spark, enabling third-party catalogs to own the lifecycle of metric views with full metadata fidelity. Implemented a first-class METRIC_VIEW table type, integrated with the v2 surface (ViewCatalog/TableViewCatalog), and routed CREATE VIEW ... WITH METRICS through the v2 path while preserving v1 behavior. Introduced a typed dependency model (Dependency, TableDependency, FunctionDependency, DependencyList) to capture multi-level namespace lineage, and extended ViewInfo with viewDependencies for accurate DESCRIBE and governance. Standardized view-type handling across v1/v2 with new helpers (CatalogTable.isViewLike) and uniform error classes (UNSUPPORTED_SHOW_CREATE_TABLE.ON_METRIC_VIEW, INVALID_METRIC_VIEW_YAML). Hardened drop/rename semantics and HMS round-tripping for metric views, and added multi-level namespace support for metric view targets. Enhanced observability via DESCRIBE/SHOW outputs and consistent properties (metric_view.*) across catalog kinds.
1 Commits • 1 Features
May 1, 2026In May 2026, delivered end-to-end support for metric views across v2 catalogs in Apache Spark, enabling third-party catalogs to own the lifecycle of metric views with full metadata fidelity. Implemented a first-class METRIC_VIEW table type, integrated with the v2 surface (ViewCatalog/TableViewCatalog), and routed CREATE VIEW ... WITH METRICS through the v2 path while preserving v1 behavior. Introduced a typed dependency model (Dependency, TableDependency, FunctionDependency, DependencyList) to capture multi-level namespace lineage, and extended ViewInfo with viewDependencies for accurate DESCRIBE and governance. Standardized view-type handling across v1/v2 with new helpers (CatalogTable.isViewLike) and uniform error classes (UNSUPPORTED_SHOW_CREATE_TABLE.ON_METRIC_VIEW, INVALID_METRIC_VIEW_YAML). Hardened drop/rename semantics and HMS round-tripping for metric views, and added multi-level namespace support for metric view targets. Enhanced observability via DESCRIBE/SHOW outputs and consistent properties (metric_view.*) across catalog kinds.
May 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Scala
Technical Skills
Data EngineeringSQLScalaSpark
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline