apache/spark
Oct 2024 – Mar 2026
10 Months active
Languages Used
PythonScalaSQLJSONJavaProtoBuf
Technical Skills
API DevelopmentApache SparkData ProcessingData ValidationError HandlingPython
Uros Bojanic
PROFILE
Uros Bojanic
Uros Bojanic developed core geospatial and time-based analytics capabilities in the apache/spark repository, focusing on robust data type support, cross-language APIs, and reliable data processing. He introduced Geometry and Geography types with end-to-end Parquet I/O, Arrow serialization, and SRID-aware operations, enabling scalable geospatial analytics across Scala, Python, and Spark Connect. Uros enhanced SQL and DataFrame APIs for time parsing, manipulation, and timezone handling, and implemented safety gating for new features. His work emphasized maintainability through comprehensive testing, error handling, and modular code organization, leveraging technologies such as Scala, Python, and SQL to deliver production-ready, extensible solutions.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
89Total
Bugs
7
Commits
89
Features
25
Lines of code
31,029
Activity Months10
Your Network
589 peopleSame Organization
@databricks.com265
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary focusing on geospatial feature expansion in Spark. Delivered a major extension to the Spatial Reference System (SRS) registry by building it from PROJ 9.7.1 data, adding 10,000+ EPSG/ESRI entries. This significantly broadens CRS coverage for Geometry and Geography types across JVM and Python, enabling more accurate geo analytics and reducing ad-hoc CRS work. The change is non-breaking from a user perspective but delivers substantial capability improvements and interoperability. Validation was performed with self-contained scripts and auto-generated golden files, ensuring stable behavior across environments.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary focusing on geospatial feature expansion in Spark. Delivered a major extension to the Spatial Reference System (SRS) registry by building it from PROJ 9.7.1 data, adding 10,000+ EPSG/ESRI entries. This significantly broadens CRS coverage for Geometry and Geography types across JVM and Python, enabling more accurate geo analytics and reducing ad-hoc CRS work. The change is non-breaking from a user perspective but delivers substantial capability improvements and interoperability. Validation was performed with self-contained scripts and auto-generated golden files, ensuring stable behavior across environments.
March 2026
February 2026
10 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary focused on geospatial capabilities in Apache Spark. Delivered end-to-end Parquet I/O for geospatial data, strengthened correctness and error reporting for WKB parsing, enhanced SQL API and type handling for geospatial types, and improved text serialization/display for geospatial results. These changes enable reliable persistence, more robust analytics, and a smoother developer experience for geospatial workloads.
February 2026
10 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary focused on geospatial capabilities in Apache Spark. Delivered end-to-end Parquet I/O for geospatial data, strengthened correctness and error reporting for WKB parsing, enhanced SQL API and type handling for geospatial types, and improved text serialization/display for geospatial results. These changes enable reliable persistence, more robust analytics, and a smoother developer experience for geospatial workloads.
January 2026
6 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for apache/spark: Focused on usability enhancements, geo-data capabilities, and maintainability improvements. Key outcomes include enabling DROP TABLE to operate on VIEWS, advancing geospatial data support via Parquet schema conversion, standardizing geo handling across sources, and tightening test infrastructure and module boundaries to reduce maintenance overhead and risk.
6 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for apache/spark: Focused on usability enhancements, geo-data capabilities, and maintainability improvements. Key outcomes include enabling DROP TABLE to operate on VIEWS, advancing geospatial data support via Parquet schema conversion, standardizing geo handling across sources, and tightening test infrastructure and module boundaries to reduce maintenance overhead and risk.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Implemented safety gating for geospatial features in Spark Connect. Introduced a new SQL config (spark.sql.geospatial.enabled) to control geospatial capabilities and prevented the use of geo dataframes in Spark Connect until the feature is fully implemented. This follow-up to SPARK-54253 enforces the gating logic, reducing rollout risk and stabilizing user experience during iterative development. Added targeted unit tests to verify config effectiveness and feature boundary: GeographyConnectDataFrameSuite and GeometryConnectDataFrameSuite. The work aligns with broader platform stability goals and lays groundwork for a safe, scalable geospatial feature rollout.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Implemented safety gating for geospatial features in Spark Connect. Introduced a new SQL config (spark.sql.geospatial.enabled) to control geospatial capabilities and prevented the use of geo dataframes in Spark Connect until the feature is fully implemented. This follow-up to SPARK-54253 enforces the gating logic, reducing rollout risk and stabilizing user experience during iterative development. Added targeted unit tests to verify config effectiveness and feature boundary: GeographyConnectDataFrameSuite and GeometryConnectDataFrameSuite. The work aligns with broader platform stability goals and lays groundwork for a safe, scalable geospatial feature rollout.
November 2025
19 Commits • 3 Features
Nov 1, 2025November 2025 performance summary for the apache/spark geospatial initiative. Focused on delivering end-to-end geospatial data type support, cross-language encoders, SRID-aware operations, Spark Connect proto integration, and robust testing/parity. The work enables scalable, cross-language geospatial analytics across Scala/Java, Python, and Spark Connect, with controlled rollout via feature flag.
19 Commits • 3 Features
Nov 1, 2025November 2025 performance summary for the apache/spark geospatial initiative. Focused on delivering end-to-end geospatial data type support, cross-language encoders, SRID-aware operations, Spark Connect proto integration, and robust testing/parity. The work enables scalable, cross-language geospatial analytics across Scala/Java, Python, and Spark Connect, with controlled rollout via feature flag.
November 2025
October 2025
24 Commits • 5 Features
Oct 1, 2025October 2025 delivered major foundations for Spark's geospatial capability stack, spanning data types, APIs, ST function scaffolding, and cross-language support. Focused on delivering business value through tangible features, robust test coverage, and safer usage patterns. Highlights include cross-language type introductions for geospatial data, time-based geospatial function support, early ST expression/framework implementations, and centralized SR mapping for PySpark, as well as client-side representations and in-memory formats for faster prototyping and downstream integrations.
October 2025
24 Commits • 5 Features
Oct 1, 2025October 2025 delivered major foundations for Spark's geospatial capability stack, spanning data types, APIs, ST function scaffolding, and cross-language support. Focused on delivering business value through tangible features, robust test coverage, and safer usage patterns. Highlights include cross-language type introductions for geospatial data, time-based geospatial function support, early ST expression/framework implementations, and centralized SR mapping for PySpark, as well as client-side representations and in-memory formats for faster prototyping and downstream integrations.
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
Correctness99.4%
Maintainability88.8%
Architecture95.0%
Performance88.4%
AI Usage22.6%
Skills & Technologies
Programming Languages
JSONJavaProtoBufPythonSQLScala
Technical Skills
API DevelopmentAPI developmentApache SparkArrow serializationBackend DevelopmentBig DataCSV generationData AnalysisData EngineeringData ProcessingData SerializationData ValidationDataFrame APIDatabase ManagementDate and Time Handling
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline