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 functionality. He implemented a Spark factorial function in C++ using a lookup table for efficient computation of 0–20 factorials, returning bigint results and handling out-of-range inputs with NULLs. In addition, Arnav added LeftSemiProjectJoin support to the nested loop join algorithm, updating output schema and match flag logic for correctness. He also introduced boolean-to-timestamp casting in Spark SQL, refining core expression logic and cast hooks. His work demonstrated depth in backend development, SQL optimization, and database internals.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
379
Activity Months2
Your Network
145 peopleShared Repositories
101
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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