apache/spark
Oct 2024 – Sep 2025
4 Months active
Languages Used
PythonScalaSQL
Technical Skills
API DevelopmentApache SparkData ProcessingData ValidationError HandlingPython
Uros Bojanic
PROFILE
Uros Bojanic
Uros Bojanic contributed to the apache/spark repository by engineering robust enhancements for time and string data handling in Spark SQL. Over four months, Uros developed cross-language APIs for UTF-8 validation and advanced time operations, including parsing, casting, and manipulation utilities that improved reliability and consistency across Scala, Python, and PySpark. He implemented features such as time_diff, time_trunc, and try_make_timestamp, enabling more expressive and accurate time-based analytics. Uros also addressed error diagnostics and collation-aware hashing, strengthening data quality and debugging. His work demonstrated depth in API development, data processing, and backend engineering using Python, Scala, and SQL.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
28Total
Bugs
2
Commits
28
Features
10
Lines of code
11,468
Activity Months4
Your Network
524 people
Work History
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for the apache/spark repository focused on SQL time operations enhancements. Delivered two key capabilities that improve time-based analytics and query expressiveness: a Scala API time_diff function for computing differences between times in specified units, and a new try_make_timestamp SQL function to construct timestamps from date and time inputs with optional timezone. These changes enhance time data type support and enable more robust, timezone-aware analytics in Spark SQL.
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for the apache/spark repository focused on SQL time operations enhancements. Delivered two key capabilities that improve time-based analytics and query expressiveness: a Scala API time_diff function for computing differences between times in specified units, and a new try_make_timestamp SQL function to construct timestamps from date and time inputs with optional timezone. These changes enhance time data type support and enable more robust, timezone-aware analytics in Spark SQL.
September 2025
August 2025
7 Commits • 4 Features
Aug 1, 2025August 2025 monthly summary for apache/spark (SQL/time module). Key features delivered include: Time_trunc function implemented in Scala API and PySpark to truncate timestamps to hour/minute/second/millisecond/microsecond; End-to-End SQL TIME literal tests covering 24-hour and 12-hour formats with valid and invalid cases; Collation-aware hashing improvements for Murmur3Hash and XxHash64 with a configuration toggle to revert to previous behavior; Timestamp creation from date/time fields and make_timestamp_ltz enhancements; All changes implemented to improve reliability, API coverage, and cross-language consistency. Major bugs fixed: time_diff invalid unit error message clarified to reference the function name. Overall impact: increased reliability and clarity for time-related operations, expanded API surface, safer hashing with collations, and improved test coverage enabling more robust data pipelines. Technologies/skills demonstrated: Scala API development, PySpark integration, SQL and end-to-end testing, cross-language API design, collation-aware hashing, and configurability.
August 2025
7 Commits • 4 Features
Aug 1, 2025August 2025 monthly summary for apache/spark (SQL/time module). Key features delivered include: Time_trunc function implemented in Scala API and PySpark to truncate timestamps to hour/minute/second/millisecond/microsecond; End-to-End SQL TIME literal tests covering 24-hour and 12-hour formats with valid and invalid cases; Collation-aware hashing improvements for Murmur3Hash and XxHash64 with a configuration toggle to revert to previous behavior; Timestamp creation from date/time fields and make_timestamp_ltz enhancements; All changes implemented to improve reliability, API coverage, and cross-language consistency. Major bugs fixed: time_diff invalid unit error message clarified to reference the function name. Overall impact: increased reliability and clarity for time-related operations, expanded API surface, safer hashing with collations, and improved test coverage enabling more robust data pipelines. Technologies/skills demonstrated: Scala API development, PySpark integration, SQL and end-to-end testing, cross-language API design, collation-aware hashing, and configurability.
July 2025
14 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary focused on time-related enhancements delivered for the Apache Spark project. The work targeted strengthening TIME handling across SQL, Scala, and PySpark to improve reliability, expressiveness, and cross-language consistency for time-based data processing. Key outcomes include a richer TIME type with parsing, casting, and extraction utilities; time and timestamp constructors; and time manipulation helpers, all designed to enable more robust ETL pipelines and richer time-based analytics. Summary of impact: - Increased capability and accuracy for time-based data operations, reducing ETL failures related to time parsing and conversions. - Cross-language API consistency (Scala and PySpark) lowering development friction and accelerating feature adoption across teams. - Foundations for advanced time-based analytics (intervals, time-based aggregation, and windowing) with reusable utilities across the stack.
14 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary focused on time-related enhancements delivered for the Apache Spark project. The work targeted strengthening TIME handling across SQL, Scala, and PySpark to improve reliability, expressiveness, and cross-language consistency for time-based data processing. Key outcomes include a richer TIME type with parsing, casting, and extraction utilities; time and timestamp constructors; and time manipulation helpers, all designed to enable more robust ETL pipelines and richer time-based analytics. Summary of impact: - Increased capability and accuracy for time-based data operations, reducing ETL failures related to time parsing and conversions. - Cross-language API consistency (Scala and PySpark) lowering development friction and accelerating feature adoption across teams. - Foundations for advanced time-based analytics (intervals, time-based aggregation, and windowing) with reusable utilities across the stack.
July 2025
October 2024
5 Commits • 2 Features
Oct 1, 2024Month 2024-10: Delivered core robustness improvements for Spark 4.0, focusing on UTF-8 handling, improved error diagnostics, and Spark SQL serialization. The work enhances data quality, accelerates debugging, and broadens expression capabilities across Scala and Python surfaces.
October 2024
5 Commits • 2 Features
Oct 1, 2024Month 2024-10: Delivered core robustness improvements for Spark 4.0, focusing on UTF-8 handling, improved error diagnostics, and Spark SQL serialization. The work enhances data quality, accelerates debugging, and broadens expression capabilities across Scala and Python surfaces.
Activity
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Quality Metrics
Correctness100.0%
Maintainability94.2%
Architecture96.4%
Performance94.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQLScala
Technical Skills
API DevelopmentAPI developmentApache SparkBig DataData AnalysisData EngineeringData ProcessingData ValidationDataFrame APIDate and Time HandlingError HandlingPySparkPythonSQLScala
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