xupefei/spark
Oct 2024 – Mar 2025
6 Months active
Languages Used
PythonScala
Technical Skills
PandasPythonStateful ProcessingStreaming Data ProcessingAPI integrationApache Spark
jingz-db
PROFILE
Jingz-db
Over six months, contributed to the xupefei/spark repository by building and enhancing stateful streaming capabilities in Apache Spark, focusing on the TransformWithStateInPandas API. Delivered features such as event-time timer support, initial state management, and schema evolution handling, enabling more robust and flexible streaming analytics. Used Python, Scala, and Pandas to implement timer infrastructure, state metadata versioning, and Spark Connect integration for both Python and Scala APIs. Prioritized reliability by stabilizing CI pipelines and addressing test flakiness, ensuring dependable deployments. The work improved stateful processing correctness, deployment flexibility, and maintainability for Spark streaming workloads in production environments.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
10Total
Bugs
2
Commits
10
Features
5
Lines of code
6,520
Activity Months6
Your Network
478 peopleSame Organization
@databricks.com299
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered Spark Connect support for TransformWithState and stabilized CI for Python tests, strengthening streaming capabilities and release reliability for xupefei/spark. Key outcomes include feature parity with Spark Connect and reduced CI noise, enabling faster, more trustworthy deployments.
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered Spark Connect support for TransformWithState and stabilized CI for Python tests, strengthening streaming capabilities and release reliability for xupefei/spark. Key outcomes include feature parity with Spark Connect and reduced CI noise, enabling faster, more trustworthy deployments.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 — Delivered Spark Connect support for TransformWithStateInPandas in Python, enabling stateful streaming transformations over Spark Connect and expanding Python API coverage. This work enhances deployment flexibility for Python streaming workloads and aligns with the Spark Connect roadmap. Technologies demonstrated include Python, Spark Connect, PySpark, and Pandas-based stateful processing.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 — Delivered Spark Connect support for TransformWithStateInPandas in Python, enabling stateful streaming transformations over Spark Connect and expanding Python API coverage. This work enhances deployment flexibility for Python streaming workloads and aligns with the Spark Connect roadmap. Technologies demonstrated include Python, Spark Connect, PySpark, and Pandas-based stateful processing.
January 2025
1 Commits
Jan 1, 2025In January 2025, the primary objective was to stabilize the TransformWithStateInPandas test suite in the xupefei/spark repository by addressing TTL expiration-related flakiness. This effort focused on improving CI reliability and reducing flaky test failures, rather than introducing new user-facing features. The changes were scoped to test stabilization and do not alter production behavior.
1 Commits
Jan 1, 2025In January 2025, the primary objective was to stabilize the TransformWithStateInPandas test suite in the xupefei/spark repository by addressing TTL expiration-related flakiness. This effort focused on improving CI reliability and reducing flaky test failures, rather than introducing new user-facing features. The changes were scoped to test stabilization and do not alter production behavior.
January 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 Monthly Summary for xupefei/spark. Delivered a forward-looking enhancement to the TransformWithStateInPandas operator by adding support for new versions of state metadata and state schemas, enabling handling of state metadata source and state data source readers and laying the groundwork for future schema evolution. The work is captured in commit c92021091502b15b6020e6e4cc9b148009450ba5 (SPARK-50578). No major bugs fixed this month; the focus was on feature delivery, code quality, and maintainability around state management. Overall impact: increases reliability and flexibility of stateful processing, reduces downstream maintenance risk during schema migrations, and improves forward compatibility for evolving data contracts. Technologies/skills demonstrated: Spark stateful processing, Python, versioned state metadata/schema handling, and traceable commits with SPARK-50578 reference.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 Monthly Summary for xupefei/spark. Delivered a forward-looking enhancement to the TransformWithStateInPandas operator by adding support for new versions of state metadata and state schemas, enabling handling of state metadata source and state data source readers and laying the groundwork for future schema evolution. The work is captured in commit c92021091502b15b6020e6e4cc9b148009450ba5 (SPARK-50578). No major bugs fixed this month; the focus was on feature delivery, code quality, and maintainability around state management. Overall impact: increases reliability and flexibility of stateful processing, reduces downstream maintenance risk during schema migrations, and improves forward compatibility for evolving data contracts. Technologies/skills demonstrated: Spark stateful processing, Python, versioned state metadata/schema handling, and traceable commits with SPARK-50578 reference.
November 2024
4 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for xupefei/spark. Key features delivered: Streaming State Management Enhancements across TransformWithStateInPandas and Spark, including initial state initialization, support for multiple initial state rows in Spark streaming, event-time output specification, and a new Timer API with enhanced Initial State API. Major bugs fixed: resolved issues around initial state handling, added handleInitialState support with the state data source reader, and enabled operator chaining in TransformWithStateInPandas to improve pipeline reliability. Overall impact: strengthened reliability and correctness of stateful streaming workloads, enabling time-aware analytics and simpler state initialization across pipelines. Technologies/skills demonstrated: Python, PySpark, Spark Structured Streaming, stateful APIs, Timer API, API integration, and cross-repo collaboration.
4 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for xupefei/spark. Key features delivered: Streaming State Management Enhancements across TransformWithStateInPandas and Spark, including initial state initialization, support for multiple initial state rows in Spark streaming, event-time output specification, and a new Timer API with enhanced Initial State API. Major bugs fixed: resolved issues around initial state handling, added handleInitialState support with the state data source reader, and enabled operator chaining in TransformWithStateInPandas to improve pipeline reliability. Overall impact: strengthened reliability and correctness of stateful streaming workloads, enabling time-aware analytics and simpler state initialization across pipelines. Technologies/skills demonstrated: Python, PySpark, Spark Structured Streaming, stateful APIs, Timer API, API integration, and cross-repo collaboration.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Implemented timer support for stateful processing in the TransformWithStateInPandas API, enabling event-time timers and timer management within Pandas-style transformations. Introduced core timer infrastructure (TimerValues, ExpiredTimerInfo) and extended handleInputRows to accommodate timer handling. This work aligns with SPARK-49513 and delivers a significant capability enhancement for time-based stateful processing, enabling more robust streaming analytics and complex event-time workflows. No separate bug fixes were recorded this month; the focus was on feature delivery. The changes improve reliability and performance of time-based stateful processing, delivering direct business value by enabling advanced analytics with a pandas-like API.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Implemented timer support for stateful processing in the TransformWithStateInPandas API, enabling event-time timers and timer management within Pandas-style transformations. Introduced core timer infrastructure (TimerValues, ExpiredTimerInfo) and extended handleInputRows to accommodate timer handling. This work aligns with SPARK-49513 and delivers a significant capability enhancement for time-based stateful processing, enabling more robust streaming analytics and complex event-time workflows. No separate bug fixes were recorded this month; the focus was on feature delivery. The changes improve reliability and performance of time-based stateful processing, delivering direct business value by enabling advanced analytics with a pandas-like API.
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Quality Metrics
Correctness90.0%
Maintainability82.0%
Architecture88.0%
Performance82.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonScala
Technical Skills
API integrationApache SparkCI/CDData ProcessingPandasPythonScalaSparkStateful ProcessingStreamingStreaming DataStreaming Data Processingdata engineeringsoftware testingstate management
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