apache/spark
Jun 2025 – Jun 2026
4 Months active
Languages Used
SQLScalaJavaMarkdown
Technical Skills
Data AnalysisData EngineeringData ProcessingDatabase ManagementSQLScala
Max Gekk
PROFILE
Max Gekk
Worked on the apache/spark repository to deliver advanced time and timestamp handling, focusing on nanosecond-precision support and robust SQL type integration. Over four months, developed new APIs and extended the Types Framework to enable precise TIMESTAMP_NTZ and TIMESTAMP_LTZ variants, improved parsing and formatting, and ensured compatibility with external systems like Hive, ORC, and JDBC. Addressed correctness in time arithmetic, casting, and null handling, while enhancing documentation and ANSI compliance. Leveraged Scala, Java, and SQL to implement features, expand test coverage, and refine error handling, resulting in more reliable data pipelines and higher-fidelity analytics for Spark SQL users.
Overall Statistics
Feature vs Bugs
79%Features
Repository Contributions
61Total
Bugs
10
Commits
61
Features
38
Lines of code
19,705
Activity Months4
Your Network
442 peopleShared Repositories
442
Work History
June 2026
33 Commits • 25 Features
Jun 1, 2026June 2026 monthly summary for apache/spark focused on enabling and stabilizing nanosecond-precision timestamp support across Spark SQL. Delivered broad nanoscale capabilities across data types, parsing/formatting, and interoperability with external data sources, while strengthening test coverage and ANSI-mode behavior. The work lays a foundation for higher-precision analytics and consistent nanos handling across the stack.
33 Commits • 25 Features
Jun 1, 2026June 2026 monthly summary for apache/spark focused on enabling and stabilizing nanosecond-precision timestamp support across Spark SQL. Delivered broad nanoscale capabilities across data types, parsing/formatting, and interoperability with external data sources, while strengthening test coverage and ANSI-mode behavior. The work lays a foundation for higher-precision analytics and consistent nanos handling across the stack.
June 2026
May 2026
10 Commits • 1 Features
May 1, 2026May 2026 monthly summary for apache/spark: Focused on nanosecond timestamp support in Spark SQL, end-to-end nanos handling, and columnar processing readiness. Implemented new public API types TimestampNTZNanosType and TimestampLTZNanosType, enabling TIMESTAMP_NTZ(p) and TIMESTAMP_LTZ(p) nanosecond variants. Added nanosecond-precision conversion for java.time LocalDateTime and Instant, plus dataset roundtrip validation. Built UnsafeRow and columnar (ColumnVector) groundwork for nanos, and tightened the corresponding config spark.sql.timestampNanosTypes.enabled with comprehensive tests. Delivered across SPARK-56876, SPARK-56969, SPARK-56981, SPARK-57033, and SPARK-57100, setting the stage for future datasource/JDBC nanos support and SQL standard alignment.
May 2026
10 Commits • 1 Features
May 1, 2026May 2026 monthly summary for apache/spark: Focused on nanosecond timestamp support in Spark SQL, end-to-end nanos handling, and columnar processing readiness. Implemented new public API types TimestampNTZNanosType and TimestampLTZNanosType, enabling TIMESTAMP_NTZ(p) and TIMESTAMP_LTZ(p) nanosecond variants. Added nanosecond-precision conversion for java.time LocalDateTime and Instant, plus dataset roundtrip validation. Built UnsafeRow and columnar (ColumnVector) groundwork for nanos, and tightened the corresponding config spark.sql.timestampNanosTypes.enabled with comprehensive tests. Delivered across SPARK-56876, SPARK-56969, SPARK-56981, SPARK-57033, and SPARK-57100, setting the stage for future datasource/JDBC nanos support and SQL standard alignment.
July 2025
13 Commits • 8 Features
Jul 1, 2025July 2025 — Apache Spark: Improved SQL TIME handling, expanded Thrift server support, and strengthened correctness and documentation. Key features delivered include: Thrift server support for TIME in SQL; TIME arithmetic including TIME subtract; support for +/- ANSI day-time intervals with TIME; improved TIME literals formatting; updated TIME-related documentation and ANSI compliance pages. Major bugs fixed: current_time() behavior corrected in SQL; fixed casting of TIME(n) to TIME(m); ensured NULL results are returned for datetime +/- operations; avoided casting TIME/TIMESTAMP to DECIMAL via Double; follow-up refactor rename timeToMicros to makeTime. Overall impact: higher correctness and reliability of time-based operations, broader client compatibility, and clearer documentation, enabling safer production deployments and easier integration with external systems. Technologies demonstrated: Spark SQL internals, TIME data type, casting and arithmetic across TIME/TIMESTAMP, ANSI interval support, Thrift server integration, and documentation discipline.
13 Commits • 8 Features
Jul 1, 2025July 2025 — Apache Spark: Improved SQL TIME handling, expanded Thrift server support, and strengthened correctness and documentation. Key features delivered include: Thrift server support for TIME in SQL; TIME arithmetic including TIME subtract; support for +/- ANSI day-time intervals with TIME; improved TIME literals formatting; updated TIME-related documentation and ANSI compliance pages. Major bugs fixed: current_time() behavior corrected in SQL; fixed casting of TIME(n) to TIME(m); ensured NULL results are returned for datetime +/- operations; avoided casting TIME/TIMESTAMP to DECIMAL via Double; follow-up refactor rename timeToMicros to makeTime. Overall impact: higher correctness and reliability of time-based operations, broader client compatibility, and clearer documentation, enabling safer production deployments and easier integration with external systems. Technologies demonstrated: Spark SQL internals, TIME data type, casting and arithmetic across TIME/TIMESTAMP, ANSI interval support, Thrift server integration, and documentation discipline.
July 2025
June 2025
5 Commits • 4 Features
Jun 1, 2025June 2025 monthly summary focused on delivering high-value Spark SQL improvements, strengthening robustness, and improving developer experience through clearer APIs and safer null handling. The work tightened precision semantics around TIME/TIMESTAMP, clarified expression responsibilities, and extended TIMESTAMP construction capabilities, enabling more reliable data pipelines with minimal user-facing changes.
June 2025
5 Commits • 4 Features
Jun 1, 2025June 2025 monthly summary focused on delivering high-value Spark SQL improvements, strengthening robustness, and improving developer experience through clearer APIs and safer null handling. The work tightened precision semantics around TIME/TIMESTAMP, clarified expression responsibilities, and extended TIMESTAMP construction capabilities, enabling more reliable data pipelines with minimal user-facing changes.
Activity
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Quality Metrics
Correctness98.6%
Maintainability85.8%
Architecture94.2%
Performance85.6%
AI Usage60.6%
Skills & Technologies
Programming Languages
JavaMarkdownSQLScala
Technical Skills
Apache SparkAvroBackend DevelopmentBig DataData AnalysisData EngineeringData ParsingData ProcessingData SerializationData TypesDataFrame APIDatabase ManagementError HandlingJDBCJava
Repositories Contributed To
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