linkedin/openhouse
Feb 2025 – Mar 2026
8 Months active
Languages Used
DockerfileShellJavaSQLGradle
Technical Skills
Configuration ManagementDockerHadoopData EngineeringIcebergJava
Stas Pak
PROFILE
Stas Pak
Worked on the linkedin/openhouse repository, delivering features and fixes that improved data processing reliability, system performance, and operational safety. Addressed environment stability by managing Hadoop versions in Docker, optimized compaction scheduling with data-driven heuristics, and enhanced Spark heartbeat mechanisms for better cluster liveness. Implemented parallel orphan file deletion using Java and concurrent programming, and introduced maintenance controls to safeguard data integrity during migrations. Refactored data layout strategy selection and improved stats collection reliability by simplifying asynchronous flows. Upgraded Iceberg for efficient data rewrites and refined HDFS storage metrics. Demonstrated expertise in Java, Spark, and backend development across complex distributed systems.
Overall Statistics
Feature vs Bugs
44%Features
Repository Contributions
9Total
Bugs
5
Commits
9
Features
4
Lines of code
820
Activity Months8
Your Network
127 peopleSame Organization
@linkedin.com100
Allen AverbukhMember
Xin(Adam) ChenMember
Ahmet Alp BalkanMember
Alex HopmannMember
Alan Z.Member
Aman GuptaMember
Amaan MarfatiaMember
AMAN PANDEYMember
Abhishek NathMember
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026: Delivered two core improvements for linkedin/openhouse that enhance reliability, performance, and reporting accuracy. Key actions: - Iceberg data rewrite optimization: Upgraded Iceberg to 1.5.2.8 to fix data rewrite task grouping to use data file length, improving processing efficiency. Added an integration test to validate the corrected behavior. (Commit: e429010c78bce2c0051e6855cf634b340bee31ec) - HDFS storage statistics accuracy: Fixed exclusion of hidden directories (starting with '.') from storage statistics collection to avoid inflating totalDirectorySizeInBytes and numObjectsInDirectory. Changes implemented in populateStorageStats. Validation included soak/testing in staging/production. (Commit: bb7b5dcc93c286bcf0ef40f1ef876eb3b1a64b9a) Impact: - Performance: More efficient data rewrites through length-based grouping, reducing compute time and resource usage for large datasets. - Accuracy: Storage metrics now reflect real data, enabling better capacity planning and reporting. - Quality: Added tests for the Iceberg change and thorough production-like testing for stats collection; improved overall reliability. Technologies/skills demonstrated: - Iceberg 1.5.2.8 upgrade, data rewrite optimization, integration testing - HDFS storage statistics logic and edge-case handling - Production-like testing (staging/soak) and PR-driven collaboration
2 Commits • 1 Features
Mar 1, 2026March 2026: Delivered two core improvements for linkedin/openhouse that enhance reliability, performance, and reporting accuracy. Key actions: - Iceberg data rewrite optimization: Upgraded Iceberg to 1.5.2.8 to fix data rewrite task grouping to use data file length, improving processing efficiency. Added an integration test to validate the corrected behavior. (Commit: e429010c78bce2c0051e6855cf634b340bee31ec) - HDFS storage statistics accuracy: Fixed exclusion of hidden directories (starting with '.') from storage statistics collection to avoid inflating totalDirectorySizeInBytes and numObjectsInDirectory. Changes implemented in populateStorageStats. Validation included soak/testing in staging/production. (Commit: bb7b5dcc93c286bcf0ef40f1ef876eb3b1a64b9a) Impact: - Performance: More efficient data rewrites through length-based grouping, reducing compute time and resource usage for large datasets. - Accuracy: Storage metrics now reflect real data, enabling better capacity planning and reporting. - Quality: Added tests for the Iceberg change and thorough production-like testing for stats collection; improved overall reliability. Technologies/skills demonstrated: - Iceberg 1.5.2.8 upgrade, data rewrite optimization, integration testing - HDFS storage statistics logic and edge-case handling - Production-like testing (staging/soak) and PR-driven collaboration
March 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 performance highlights for linkedin/openhouse: Delivered the Parallel Deletion of Orphan Files feature, introducing a new delete arg and an executor service to run deletions in parallel. This enables concurrent processing for large datasets, significantly improving cleanup throughput. Internal API changes were implemented to support parallel deletions, and existing tests were updated accordingly. The work is tracked under commit 0f038c57b00d8619b4a131b4ccae7976e0784095, with validation focused on performance and stability.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 performance highlights for linkedin/openhouse: Delivered the Parallel Deletion of Orphan Files feature, introducing a new delete arg and an executor service to run deletions in parallel. This enables concurrent processing for large datasets, significantly improving cleanup throughput. Internal API changes were implemented to support parallel deletions, and existing tests were updated accordingly. The work is tracked under commit 0f038c57b00d8619b4a131b4ccae7976e0784095, with validation focused on performance and stability.
December 2025
1 Commits
Dec 1, 2025December 2025 – OpenHouse (linkedin/openhouse): Improve stats collection reliability and validate end-to-end execution in local environments to support stable data quality and operational resilience. Overview: Focused on stabilizing the stats collection workflow for the u_openhouse table by moving from asynchronous to synchronous execution, reducing class-loader related failures and runtime complexity. Completed targeted manual validation in a local Docker setup to ensure end-to-end reliability without breaking existing APIs.
1 Commits
Dec 1, 2025December 2025 – OpenHouse (linkedin/openhouse): Improve stats collection reliability and validate end-to-end execution in local environments to support stable data quality and operational resilience. Overview: Focused on stabilizing the stats collection workflow for the u_openhouse table by moving from asynchronous to synchronous execution, reducing class-loader related failures and runtime complexity. Completed targeted manual validation in a local Docker setup to ensure end-to-end reliability without breaking existing APIs.
December 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered a maintenance enablement feature for tables within linkedin/openhouse, enabling temporary disabling of TMS writes during migrations or maintenance windows. Implemented a new TableMetadata.isMaintenanceJobDisabled flag and integrated enforcement in TableOperationTask, reducing risk during schema changes and production maintenance. This work improves data integrity, reduces downtime during migrations, and sets the foundation for safer maintenance workflows and smoother deployment cycles.
August 2025
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered a maintenance enablement feature for tables within linkedin/openhouse, enabling temporary disabling of TMS writes during migrations or maintenance windows. Implemented a new TableMetadata.isMaintenanceJobDisabled flag and integrated enforcement in TableOperationTask, reducing risk during schema changes and production maintenance. This work improves data integrity, reduces downtime during migrations, and sets the foundation for safer maintenance workflows and smoother deployment cycles.
May 2025
1 Commits
May 1, 2025May 2025 performance summary for linkedin/openhouse focused on data layout optimization. Delivered a critical bug fix in the Data Layout Strategy Selection and refactored the strategy filtering logic to ensure the most beneficial data layout strategies are selected. This reduces suboptimal layout choices and improves data processing efficiency, contributing to overall system performance and reliability. The work aligns with ongoing data optimization goals and lowers the risk of performance regressions in future releases.
1 Commits
May 1, 2025May 2025 performance summary for linkedin/openhouse focused on data layout optimization. Delivered a critical bug fix in the Data Layout Strategy Selection and refactored the strategy filtering logic to ensure the most beneficial data layout strategies are selected. This reduces suboptimal layout choices and improves data processing efficiency, contributing to overall system performance and reliability. The work aligns with ongoing data optimization goals and lowers the risk of performance regressions in future releases.
May 2025
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for linkedin/openhouse: Delivered reliability improvements for the Spark-based heartbeat mechanism by enabling periodic heartbeats every 5 minutes, replacing the previous one-shot behavior. This change improves liveness detection and fault tolerance across the cluster, reducing the risk of stale health signals. Implemented automated tests to verify heartbeat timing and resilience. Commit 8798209f3921001a1fd709f3e7d7b464fdd12d44 (Bug fix in Spark app heartbeats #309).
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for linkedin/openhouse: Delivered reliability improvements for the Spark-based heartbeat mechanism by enabling periodic heartbeats every 5 minutes, replacing the previous one-shot behavior. This change improves liveness detection and fault tolerance across the cluster, reducing the risk of stale health signals. Implemented automated tests to verify heartbeat timing and resilience. Commit 8798209f3921001a1fd709f3e7d7b464fdd12d44 (Bug fix in Spark app heartbeats #309).
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly performance summary for linkedin/openhouse. Delivered a feature enhancement to the compaction scheduler by introducing a heuristic to include unpartitioned tables in autocompaction based on activity (unpartitioned tables not updated in the last 7 days) informed by production data analysis. Implemented a new 'file count reduction discount' trait to govern this heuristic and enable future exploration for partitioned tables. The work is captured under the Compaction Scheduler Enhancement: Unpartitioned Table Heuristic with the commit: Extend autocomp to unpartitioned tables (#300) (eddcaffac403ba9b39b783c23a06e1775fec8074). Business value includes better resource utilization, reduced I/O for stale data, and groundwork for scaling compaction strategies across table types. No explicit bug fixes are documented for this month in the provided data. Technologies demonstrated include data-driven heuristics, trait-based configuration, and incremental schedule extensions to existing autocomp logic.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly performance summary for linkedin/openhouse. Delivered a feature enhancement to the compaction scheduler by introducing a heuristic to include unpartitioned tables in autocompaction based on activity (unpartitioned tables not updated in the last 7 days) informed by production data analysis. Implemented a new 'file count reduction discount' trait to govern this heuristic and enable future exploration for partitioned tables. The work is captured under the Compaction Scheduler Enhancement: Unpartitioned Table Heuristic with the commit: Extend autocomp to unpartitioned tables (#300) (eddcaffac403ba9b39b783c23a06e1775fec8074). Business value includes better resource utilization, reduced I/O for stale data, and groundwork for scaling compaction strategies across table types. No explicit bug fixes are documented for this month in the provided data. Technologies demonstrated include data-driven heuristics, trait-based configuration, and incremental schedule extensions to existing autocomp logic.
March 2025
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary focusing on critical DLO environment stability in Docker for linkedin/openhouse. Downgraded Hadoop from 3.x to 2.8.0 to resolve IllegalAccessError and ensure DLO applications run reliably in containerized deployments. This fix reduces environment-related failures, accelerates testing, and improves release readiness in Docker-based workflows. Demonstrated strong Hadoop version management, Docker deployment skills, and debugging of Java classloader issues.
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary focusing on critical DLO environment stability in Docker for linkedin/openhouse. Downgraded Hadoop from 3.x to 2.8.0 to resolve IllegalAccessError and ensure DLO applications run reliably in containerized deployments. This fix reduces environment-related failures, accelerates testing, and improves release readiness in Docker-based workflows. Demonstrated strong Hadoop version management, Docker deployment skills, and debugging of Java classloader issues.
Activity
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Quality Metrics
Correctness90.0%
Maintainability82.2%
Architecture82.2%
Performance82.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfileGradleJavaSQLShell
Technical Skills
AlgorithmsBackend DevelopmentConfiguration ManagementData EngineeringData StructuresDockerHadoopIcebergJavaSparkSystem DesignTestingbackend developmentconcurrent programmingtesting
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