
Worked extensively on Apache Spark and TrinoDB, focusing on backend development and data engineering to improve correctness, reliability, and observability in large-scale data systems. Delivered targeted bug fixes and feature enhancements, such as refining Spark’s metric reporting and schema evolution logic, and addressing concurrency issues in Spark SQL. In TrinoDB, improved progress reporting, numeric stability, and error handling in array functions. Employed Scala, Java, and SQL to implement robust solutions, expanding test coverage and ensuring accurate metrics. All work was contributed directly to the apache/spark and trinodb/trino repositories, emphasizing thorough testing and minimal user-facing changes for production stability.
June 2026: Across Apache Spark and TrinoDB, delivered targeted feature improvements and critical bug fixes that strengthen correctness, metrics reliability, and fault-tolerant behavior. Focused on enhanced test coverage for schema evolution, numeric stability, and robust progress reporting, with significant improvements in observability and test quality. Business value realized includes reduced production risk, more accurate metrics, and stronger SQL semantics across large-scale workloads.
June 2026: Across Apache Spark and TrinoDB, delivered targeted feature improvements and critical bug fixes that strengthen correctness, metrics reliability, and fault-tolerant behavior. Focused on enhanced test coverage for schema evolution, numeric stability, and robust progress reporting, with significant improvements in observability and test quality. Business value realized includes reduced production risk, more accurate metrics, and stronger SQL semantics across large-scale workloads.
Monthly work summary for 2026-05 focusing on Spark SQL stability, correctness, and reliability improvements. Delivered a targeted set of fixes that reduce deadlocks, fix query failures with advanced SQL constructs, and ensure robust schema evolution in MERGE operations. The work enhances concurrency, query correctness, and data transformation reliability in Spark SQL, delivering tangible business value for users running complex analytics workloads.
Monthly work summary for 2026-05 focusing on Spark SQL stability, correctness, and reliability improvements. Delivered a targeted set of fixes that reduce deadlocks, fix query failures with advanced SQL constructs, and ensure robust schema evolution in MERGE operations. The work enhances concurrency, query correctness, and data transformation reliability in Spark SQL, delivering tangible business value for users running complex analytics workloads.
Month: 2025-12 — Focused on metric correctness and observability in Spark. Delivered a targeted bug fix to METRIC_RESULT_SIZE so the metric reflects the actual result size after computation, rather than always zero. This improved resource accounting and monitoring accuracy without introducing user-facing changes. The work was verified locally and linked to SPARK-54533; related Jira issue #53243 closed.
Month: 2025-12 — Focused on metric correctness and observability in Spark. Delivered a targeted bug fix to METRIC_RESULT_SIZE so the metric reflects the actual result size after computation, rather than always zero. This improved resource accounting and monitoring accuracy without introducing user-facing changes. The work was verified locally and linked to SPARK-54533; related Jira issue #53243 closed.
Month: 2025-11 — Focused on improving correctness and reliability of Spark's status metrics in the UI. Delivered a targeted bug fix in apache/spark that corrects the argument order in the createMetrics call within LiveEntityHelpers.makeNegative, ensuring accurate negation of metrics across shuffled metric categories. The patch (commit a75815e7b7e79a01cae9b595842aae5328e809be) aligns with SPARK-54505, includes a new unit test, and was reviewed for correctness and minimal surface area. This change does not introduce user-facing changes but improves the integrity of metrics shown in the UI, reducing the risk of misinterpretation in dashboards and reports. Closes #53218 from jiwen624/makeNegative-arg-order. Authored-by: Eric Yang; Signed-off-by: Wenchen Fan.
Month: 2025-11 — Focused on improving correctness and reliability of Spark's status metrics in the UI. Delivered a targeted bug fix in apache/spark that corrects the argument order in the createMetrics call within LiveEntityHelpers.makeNegative, ensuring accurate negation of metrics across shuffled metric categories. The patch (commit a75815e7b7e79a01cae9b595842aae5328e809be) aligns with SPARK-54505, includes a new unit test, and was reviewed for correctness and minimal surface area. This change does not introduce user-facing changes but improves the integrity of metrics shown in the UI, reducing the risk of misinterpretation in dashboards and reports. Closes #53218 from jiwen624/makeNegative-arg-order. Authored-by: Eric Yang; Signed-off-by: Wenchen Fan.

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