apache/celeborn
Feb 2025 – Mar 2025
2 Months active
Languages Used
JavaPythonScala
Technical Skills
Big DataClient-Server CommunicationCode RefactoringData ProcessingDistributed SystemsPerformance Optimization
wangshengjie3
PROFILE
Wangshengjie3
Worked on the apache/celeborn repository to enhance shuffle performance and reliability for skewed data workloads in distributed systems. Developed adaptive skewed partition handling and chunk-offset local reading in Reduce Mode, leveraging Scala and Java to align with Spark’s Adaptive Query Execution. Introduced LocalPartitionReader support for chunk-offset reads, reducing tail latency and improving throughput. Further strengthened the shuffle path by enabling stage reruns for skew-partition reads, ensuring safe retries and proper rollback of dependent stages. Focused on fault tolerance, performance optimization, and robust integration testing, the work improved end-to-end reliability for large-scale data processing pipelines using Spark and Celeborn.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
3,321
Activity Months2
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Shared Repositories
80
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 (apache/celeborn): Delivered a feature to strengthen the Celeborn shuffle path by enabling stage reruns for skew-partition reads when using chunkOffsets optimization. This work addresses the limitation of retrying skew/shuffle reads, ensuring indeterminate or Celeborn-skewed shuffles are retried safely and that dependent stages can rollback correctly. The result is more reliable and efficient handling of skewed workloads, with traceability to CELEBORN-1856 and the associated commit for auditability.
1 Commits • 1 Features
Mar 1, 2025March 2025 (apache/celeborn): Delivered a feature to strengthen the Celeborn shuffle path by enabling stage reruns for skew-partition reads when using chunkOffsets optimization. This work addresses the limitation of retrying skew/shuffle reads, ensuring indeterminate or Celeborn-skewed shuffles are retried safely and that dependent stages can rollback correctly. The result is more reliable and efficient handling of skewed workloads, with traceability to CELEBORN-1856 and the associated commit for auditability.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for apache/celeborn: Delivered adaptive skewed partition handling and chunk-offset local reading in Reduce Mode to reduce timeouts and improve shuffle performance. Implemented LocalPartitionReader support to read partitions by chunk offsets when optimizeSkewedPartitionRead is enabled, aligning with Spark's Adaptive Query Execution and Celeborn client components. Added end-to-end tests and validated integration with Spark AED. Resulted in lower tail latency for skewed workloads and improved local read throughput across Celeborn deployments.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for apache/celeborn: Delivered adaptive skewed partition handling and chunk-offset local reading in Reduce Mode to reduce timeouts and improve shuffle performance. Implemented LocalPartitionReader support to read partitions by chunk offsets when optimizeSkewedPartitionRead is enabled, aligning with Spark's Adaptive Query Execution and Celeborn client components. Added end-to-end tests and validated integration with Spark AED. Resulted in lower tail latency for skewed workloads and improved local read throughput across Celeborn deployments.
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Quality Metrics
Correctness90.0%
Maintainability83.4%
Architecture86.6%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaPythonScala
Technical Skills
Big DataClient-Server CommunicationCode RefactoringData ProcessingDistributed SystemsFault TolerancePerformance OptimizationShuffle OptimizationShuffle ServiceSparkSystem DesignUnit Testing
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