apache/spark
Oct 2025 – Apr 2026
2 Months active
Languages Used
PythonScala
Technical Skills
Data ProcessingPythonScalaStreamingTestingPerformance Optimization
Jason Teoh
PROFILE
Jason Teoh
Worked on the apache/spark repository to enhance stateful streaming reliability and performance in PySpark. Delivered new Python tests for the TransformWithState API, validating nested output schemas and improving correctness for complex stateful workloads. Addressed a partial read issue in state message handling by updating Java IO logic to ensure complete data retrieval, reducing data inconsistencies. Later, optimized stateful processor serialization by eliminating unnecessary Row, list, and dictionary constructions, directly passing normalized tuples to internal schema methods. These changes, implemented in Python and Scala, improved throughput and reduced CPU usage, all while maintaining stable APIs and comprehensive end-to-end test coverage.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
619
Activity Months2
Your Network
401 peopleWork History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for the apache/spark repository focused on optimizing PySpark stateful processor serialization and improving overall performance. The work delivered is surgical and non-breaking for users, aimed at reducing per-record serialization overhead and improving streaming throughput while maintaining existing APIs.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for the apache/spark repository focused on optimizing PySpark stateful processor serialization and improving overall performance. The work delivered is surgical and non-breaking for users, aimed at reducing per-record serialization overhead and improving streaming throughput while maintaining existing APIs.
April 2026
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for apache/spark focusing on TransformWithState reliability and state data handling. Business value delivered through improved reliability, correctness, and test coverage for stateful streaming work. Key features delivered: - TransformWithState API reliability and test coverage: Added Python tests for TransformWithState APIs to validate output schemas with nested structs and ensure correct handling of composite/nested outputs; groundwork for preventing data shape regressions. Commit reference 0702d58074c55f571f79420c024d8d558170ea22. - State message handling robustness: Fixed a bug causing partial reads of large proto-like messages in the TransformWithState In PySpark StateServer by using readFully to reliably read the full message. Commit reference 3f663bf583135295dcaba9e03fe9a722eb55665b. Major bugs fixed: - Partial read bug for large proto messages in TransformWithStateStateServer: switched to readFully DataInputStream to guarantee complete message reads, preventing incomplete state updates. Overall impact and accomplishments: - Increased reliability and correctness of stateful transforms, reducing runtime errors and data inconsistencies for large state values. - Enhanced test coverage for nested output schemas, enabling safer refactors and future schema evolution. - Maintained software stability with no user-facing changes while boosting robustness and confidence in stateful workloads. Technologies/skills demonstrated: - Java IO: readFully usage for robust message reading. - PySpark and Python test automation: cross-language validation of TransformWithState APIs. - End-to-end testing practices: sbt packaging and Python test runners integration for comprehensive validation.
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for apache/spark focusing on TransformWithState reliability and state data handling. Business value delivered through improved reliability, correctness, and test coverage for stateful streaming work. Key features delivered: - TransformWithState API reliability and test coverage: Added Python tests for TransformWithState APIs to validate output schemas with nested structs and ensure correct handling of composite/nested outputs; groundwork for preventing data shape regressions. Commit reference 0702d58074c55f571f79420c024d8d558170ea22. - State message handling robustness: Fixed a bug causing partial reads of large proto-like messages in the TransformWithState In PySpark StateServer by using readFully to reliably read the full message. Commit reference 3f663bf583135295dcaba9e03fe9a722eb55665b. Major bugs fixed: - Partial read bug for large proto messages in TransformWithStateStateServer: switched to readFully DataInputStream to guarantee complete message reads, preventing incomplete state updates. Overall impact and accomplishments: - Increased reliability and correctness of stateful transforms, reducing runtime errors and data inconsistencies for large state values. - Enhanced test coverage for nested output schemas, enabling safer refactors and future schema evolution. - Maintained software stability with no user-facing changes while boosting robustness and confidence in stateful workloads. Technologies/skills demonstrated: - Java IO: readFully usage for robust message reading. - PySpark and Python test automation: cross-language validation of TransformWithState APIs. - End-to-end testing practices: sbt packaging and Python test runners integration for comprehensive validation.
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture93.4%
Performance86.6%
AI Usage80.0%
Skills & Technologies
Programming Languages
PythonScala
Technical Skills
Data ProcessingPerformance OptimizationPythonScalaStreamingTesting
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