IBM/velox
Apr 2025 – May 2025
2 Months active
Languages Used
C++RST
Technical Skills
Backend DevelopmentC++DocumentationData EngineeringDatabase InternalsJoin Algorithms
arnavb
PROFILE
Arnavb
Arnav Bhattacharjee contributed to the IBM/velox repository by developing three backend features over two months, focusing on Spark SQL interoperability and mathematical function support. He implemented a Spark factorial function in C++ using a lookup table for integers 0–20, returning bigint results and handling out-of-range inputs with NULL, which expanded Spark’s math capabilities through Velox. In subsequent work, Arnav added LeftSemiProjectJoin support to the nested loop join, updating output schema and match flag logic, and introduced boolean-to-timestamp casting for Spark SQL. His work demonstrated depth in backend development, data engineering, and SQL query optimization, with thorough documentation 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
111 people
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
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