IBM/velox
Apr 2025 – May 2025
2 Months active
Languages Used
C++RST
Technical Skills
Backend DevelopmentC++DocumentationData EngineeringDatabase InternalsJoin Algorithms
arnavb
PROFILE
Arnavb
Over a two-month period, contributed three backend features to the IBM/velox repository, focusing on Spark SQL interoperability and mathematical function support. Developed a Spark factorial function in C++ for Velox, using a lookup table to efficiently compute factorials for integers 0–20 and returning bigints or NULL for out-of-range values. Enhanced join processing by adding LeftSemiProjectJoin support to the nested loop join implementation, updating output schema and match flag logic. Further improved SQL compatibility by implementing boolean-to-timestamp casting in Spark SQL pipelines. Work emphasized backend development, data engineering, and SQL query optimization, with thorough documentation and integration throughout.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
379
Activity Months2
Your Network
162 peopleShared Repositories
117
Work History
May 2025
2 Commits • 2 Features
May 1, 2025May 2025 monthly summary for IBM/velox. Focused on delivering two high-impact features that improve correctness and Spark SQL interoperability under the Velox engine, with clear business value for analytics workloads.
2 Commits • 2 Features
May 1, 2025May 2025 monthly summary for IBM/velox. Focused on delivering two high-impact features that improve correctness and Spark SQL interoperability under the Velox engine, with clear business value for analytics workloads.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a new Spark factorial function in the Velox library, enabling factorial computations in Spark via Velox. Implemented a 0–20 factorial using a lookup table; results are returned as bigints and NULL for out-of-range inputs. This enhances Spark math workloads, improves end-user capabilities, and integrates cleanly with existing Velox functionality.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a new Spark factorial function in the Velox library, enabling factorial computations in Spark via Velox. Implemented a 0–20 factorial using a lookup table; results are returned as bigints and NULL for out-of-range inputs. This enhances Spark math workloads, improves end-user capabilities, and integrates cleanly with existing Velox functionality.
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture93.4%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++RST
Technical Skills
Backend DevelopmentC++Data EngineeringDatabase InternalsDocumentationJoin AlgorithmsSQLSQL Query Optimization
Repositories Contributed To
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