June 2026 monthly highlights: Implemented ANSI-mode aware RemainderFunction in Velox, aligning division-by-zero semantics with ANSI SQL; added tests, updated docs, and ensured performance remains unaffected. This delivers SQL-standard conformance and reduces surprising NULL results in division-by-zero scenarios.

March 2026 performance summary: Delivered key reliability, performance, and data-quality improvements across Velox and DataFusion (apache/datafusion-comet). Highlights include ANSI mode decimal overflow-checked arithmetic for Spark-compatible decimal arithmetic, a targeted code-quality initiative to reduce compile-time overhead, and the introduction of a Spark SQL Luhn check expression for data validation. Key outcomes across repos: - Velox: Implemented ANSI mode decimal overflow-checked arithmetic with checked_add, checked_subtract, and checked_multiply for decimal types to throw on overflow, enabling deterministic and correct decimal arithmetic in Spark ANSI mode. Included comprehensive tests covering all input type combinations, edge cases, precision caps, and boundary overflow. Commits: 7bc1be16af..., a186b5b297... - Velox: Code quality improvement adopting include-what-you-use; moved folly/Hash.h include from Type.h to Type.cpp to reduce unnecessary preprocessed lines, yielding notable compile-time savings. Commit: 5edfdda3c6... - DataFusion DataFusion-Comet: Spark SQL Luhn Check Expression added to validate credit-card-like data directly in SQL queries; registered SparkLuhnCheck UDF and wired up StaticInvoke for Spark 3.5+. Overall impact and accomplishments: - Strengthened numerical correctness and reliability for ANSI-mode decimal arithmetic in customer workloads, reducing risk of silent overflows and enabling compliant Spark ANSI-mode usage. - Improved developer productivity and CI efficiency through meaningful code hygiene improvements that reduce build times across the Velox codebase. - Expanded data quality capabilities in Spark queries, enabling early validation and fraud-detection-style checks within SQL, with minimal user-side changes. Technologies and skills demonstrated: - C++ engineering in large-scale data systems (Velox, DataFusion), with emphasis on correctness, test coverage, and maintainability. - Include-What-You-Use (IWYU) principles and compile-time optimization. - Spark integration work including StaticInvoke and UDF exposure for DataFusion-Spark interoperability, and Spark 3.5+ compatibility. - Cross-repo collaboration and end-to-end testing through CI.