linkedin/openhouse
Nov 2024 – Oct 2025
10 Months active
Languages Used
DockerfileGradleJavaPythonSQLScalaShellYAML
Technical Skills
Backend DevelopmentBuild ConfigurationBuild System Configuration (Gradle)Database ManagementDockerIceberg Integration
Levi Jiang
PROFILE
Levi Jiang
Jiang Jiang contributed to linkedin/openhouse by engineering robust backend features and reliability improvements across data platform services. Over ten months, Jiang delivered enhancements such as partition-aware data layout optimization, multi-engine job orchestration, and scalable catalog pagination, leveraging Java, Spark, and SQL. He addressed concurrency and observability challenges by refining job scheduling, implementing thread-safe OpenTelemetry metrics, and improving scheduler fault tolerance. Jiang’s work included schema evolution, asynchronous metadata processing, and persistent sort order management, all supported by targeted bug fixes and expanded test coverage. These efforts resulted in a more scalable, observable, and maintainable data infrastructure for large-scale analytics.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
23Total
Bugs
7
Commits
23
Features
12
Lines of code
7,749
Activity Months10
Work History
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for linkedin/openhouse focused on stabilizing and improving observability. Key deliverable: OpenTelemetry metrics fix for service name attribute and a refactor of OpenHouseOtelEmitter to correctly handle gauge builders with and without attributes, significantly improving metric reporting accuracy and robustness across environments. Related commit: 09602441e290dfc2baab507396289da3ee64bd48 ("Fix otel config bug (#383)"). Impact: more trustworthy dashboards, reduced false positives, and faster incident detection and resolution. Technologies/skills demonstrated: OpenTelemetry instrumentation, metric builder patterns, code refactoring, debugging complex config issues, and maintaining observability quality across services.
1 Commits
Oct 1, 2025October 2025 monthly summary for linkedin/openhouse focused on stabilizing and improving observability. Key deliverable: OpenTelemetry metrics fix for service name attribute and a refactor of OpenHouseOtelEmitter to correctly handle gauge builders with and without attributes, significantly improving metric reporting accuracy and robustness across environments. Related commit: 09602441e290dfc2baab507396289da3ee64bd48 ("Fix otel config bug (#383)"). Impact: more trustworthy dashboards, reduced false positives, and faster incident detection and resolution. Technologies/skills demonstrated: OpenTelemetry instrumentation, metric builder patterns, code refactoring, debugging complex config issues, and maintaining observability quality across services.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 | Repository: linkedin/openhouse Overview: Two key contributions in Sep 2025 focused on reliability of metrics instrumentation and enabling data-driven insight into sort performance. These efforts strengthen observability, concurrency safety, and data processing capabilities, delivering measurable business value in reliability and operational insight. Key features delivered and major bugs fixed: - OpenTelemetry Thread-Safe Metric Emission (bug): Fixed race conditions in metric reporting by synchronizing core OpenTelemetry emission methods to ensure thread-safe, reliable metric collection under concurrent loads. Commit 6fbdc4153d6304a0a47d4ca46211a79385f1b3e3 ("Avoid contention in otel emission (#372)"). - Spark Sort Stats: Compression Rate Collector (feature): Introduced a Spark job type to collect data compression rates after sorting, including sampling, rewriting with a sort strategy, calculating compression rate, and storing the result as a table property. Commit 19d4b0508451ac0895c75b624cb4a4e0c1b56cf1 ("Add spark app to collect data compression rate after sorting (#375)"). Overall impact and accomplishments: - Improved reliability and observability by ensuring thread-safe metric emission, reducing metric skew and potential loss of visibility under high concurrency. - Enabled data-driven optimization through post-sort compression-rate metrics, facilitating better decisions around data processing pipelines and storage efficiency. - Consolidated instrumentation and data collection patterns, contributing to repeatable workstreams for performance and reliability improvements. Technologies/skills demonstrated: - OpenTelemetry instrumentation and thread-safety practices - Spark-based data processing and metrics collection - Observability, data quality, and performance optimization - Code collaboration and change traceability through commit messages Business value: - More reliable metrics under concurrent workloads directly improves incident response, monitoring dashboards, and SLA adherence. - Post-sort compression-rate insights enable cost-aware data processing and storage planning.
September 2025
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 | Repository: linkedin/openhouse Overview: Two key contributions in Sep 2025 focused on reliability of metrics instrumentation and enabling data-driven insight into sort performance. These efforts strengthen observability, concurrency safety, and data processing capabilities, delivering measurable business value in reliability and operational insight. Key features delivered and major bugs fixed: - OpenTelemetry Thread-Safe Metric Emission (bug): Fixed race conditions in metric reporting by synchronizing core OpenTelemetry emission methods to ensure thread-safe, reliable metric collection under concurrent loads. Commit 6fbdc4153d6304a0a47d4ca46211a79385f1b3e3 ("Avoid contention in otel emission (#372)"). - Spark Sort Stats: Compression Rate Collector (feature): Introduced a Spark job type to collect data compression rates after sorting, including sampling, rewriting with a sort strategy, calculating compression rate, and storing the result as a table property. Commit 19d4b0508451ac0895c75b624cb4a4e0c1b56cf1 ("Add spark app to collect data compression rate after sorting (#375)"). Overall impact and accomplishments: - Improved reliability and observability by ensuring thread-safe metric emission, reducing metric skew and potential loss of visibility under high concurrency. - Enabled data-driven optimization through post-sort compression-rate metrics, facilitating better decisions around data processing pipelines and storage efficiency. - Consolidated instrumentation and data collection patterns, contributing to repeatable workstreams for performance and reliability improvements. Technologies/skills demonstrated: - OpenTelemetry instrumentation and thread-safety practices - Spark-based data processing and metrics collection - Observability, data quality, and performance optimization - Code collaboration and change traceability through commit messages Business value: - More reliable metrics under concurrent workloads directly improves incident response, monitoring dashboards, and SLA adherence. - Post-sort compression-rate insights enable cost-aware data processing and storage planning.
August 2025
4 Commits • 3 Features
Aug 1, 2025August 2025 highlights for linkedin/openhouse: Delivered pagination for OpenHouse Catalog (searchTables) across services and OpenHouseInternalCatalog to enable scalable listing of large datasets; introduced OpenTelemetry multi-destination metrics reporting via OtelEmitter and AppsOtelEmitter to centralize telemetry; fixed telemetry propagation bug ensuring otelEmitter is passed to status polling tasks for accurate metrics; added OpenHouse Table Sort Order Management with persistent sort-orders, including repository refactor for serialization/deserialization and API/tests updates. These changes improve data retrieval scalability, observability, and user experience, while strengthening monitoring and reliability.
4 Commits • 3 Features
Aug 1, 2025August 2025 highlights for linkedin/openhouse: Delivered pagination for OpenHouse Catalog (searchTables) across services and OpenHouseInternalCatalog to enable scalable listing of large datasets; introduced OpenTelemetry multi-destination metrics reporting via OtelEmitter and AppsOtelEmitter to centralize telemetry; fixed telemetry propagation bug ensuring otelEmitter is passed to status polling tasks for accurate metrics; added OpenHouse Table Sort Order Management with persistent sort-orders, including repository refactor for serialization/deserialization and API/tests updates. These changes improve data retrieval scalability, observability, and user experience, while strengthening monitoring and reliability.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 20252025-07 monthly summary for linkedin/openhouse focusing on delivering performance improvements and validating the DLO workflow. The month's primary feature delivered was Data Layout Optimization: Parallel Metadata Fetching for the DLO-exec app, designed to speed up the Data Layout Optimization step by parallelizing table metadata fetch with asynchronous operations, thereby reducing overall latency and improving run-time performance.
July 2025
1 Commits • 1 Features
Jul 1, 20252025-07 monthly summary for linkedin/openhouse focusing on delivering performance improvements and validating the DLO workflow. The month's primary feature delivered was Data Layout Optimization: Parallel Metadata Fetching for the DLO-exec app, designed to speed up the Data Layout Optimization step by parallelizing table metadata fetch with asynchronous operations, thereby reducing overall latency and improving run-time performance.
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 – linkedin/openhouse: Implemented core reliability enhancements to the Job Scheduler, resulting in more predictable job processing and reduced operational risk. Key changes include separate timeouts for queued and running tasks, a fix for scheduler hang with improved logging, and orphan task handling in the polling loop, all supported by expanded test coverage. These improvements deliver tangible business value by preventing indefinite executions, freeing resources, and improving observability.
3 Commits • 1 Features
Jun 1, 2025June 2025 – linkedin/openhouse: Implemented core reliability enhancements to the Job Scheduler, resulting in more predictable job processing and reduced operational risk. Key changes include separate timeouts for queued and running tasks, a fix for scheduler hang with improved logging, and orphan task handling in the polling loop, all supported by expanded test coverage. These improvements deliver tangible business value by preventing indefinite executions, freeing resources, and improving observability.
June 2025
May 2025
2 Commits
May 1, 2025May 2025 highlights for linkedin/openhouse focusing on data correctness and scheduler reliability. Delivered two key bug fixes with measurable impact on data display and system stability, accompanied by targeted improvements in concurrency handling.
May 2025
2 Commits
May 1, 2025May 2025 highlights for linkedin/openhouse focusing on data correctness and scheduler reliability. Delivered two key bug fixes with measurable impact on data display and system stability, accompanied by targeted improvements in concurrency handling.
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for linkedin/openhouse focusing on business value and technical achievements. Key accomplishments: - Achieved flexible, scalable job execution by introducing multi-engine and multi-coordinator support, with per-job engineType metadata to enable default engine selection and future engine-specific optimizations. - Refined data model to support new execution architecture: added engineType column to the job_row table, enabling clearer engine routing and analytics. - Simplified partition strategy management by deprecating and removing Data Layout Optimization (DLO) partition strategies from table properties, replacing scope saves with deletes, and updating tests accordingly to reduce configuration complexity and maintenance. Impact: - Enables parallel job execution across multiple coordinators and engines, improving throughput and fault isolation. - Improves flexibility for engine-specific optimizations and experimentation with minimal workflow changes. - Reduces technical debt and configuration surface area by removing obsolete DLO strategies, with streamlined tests and clearer data models. Technologies/skills demonstrated: - Backend service extension for multi-coordinator/multi-engine orchestration - Database schema evolution (MySQL) with engineType column - Deprecation and test adjustment workflows; test-driven migration of partition strategies - Clear mapping of commits to features for traceability and accountability
4 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for linkedin/openhouse focusing on business value and technical achievements. Key accomplishments: - Achieved flexible, scalable job execution by introducing multi-engine and multi-coordinator support, with per-job engineType metadata to enable default engine selection and future engine-specific optimizations. - Refined data model to support new execution architecture: added engineType column to the job_row table, enabling clearer engine routing and analytics. - Simplified partition strategy management by deprecating and removing Data Layout Optimization (DLO) partition strategies from table properties, replacing scope saves with deletes, and updating tests accordingly to reduce configuration complexity and maintenance. Impact: - Enables parallel job execution across multiple coordinators and engines, improving throughput and fault isolation. - Improves flexibility for engine-specific optimizations and experimentation with minimal workflow changes. - Reduces technical debt and configuration surface area by removing obsolete DLO strategies, with streamlined tests and clearer data models. Technologies/skills demonstrated: - Backend service extension for multi-coordinator/multi-engine orchestration - Database schema evolution (MySQL) with engineType column - Deprecation and test adjustment workflows; test-driven migration of partition strategies - Clear mapping of commits to features for traceability and accountability
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for linkedin/openhouse highlights the delivery and impact of the DLO Persistence Strategy: Partition-Aware Generation and Schema Alignment. This work introduces an isPartitioned flag and a partition-aware persistence strategy to generate persistence logic only for partitioned tables. It also includes a schema alignment fix that moves isPartitioned to the end of SQL statements to ensure correct column order after schema updates. The changes were implemented via two commits: 54c1179099f8a9df08557550bf1fa96b49d14dc8 (Refactor persistence strategy for dlo generation) and 40463e5d1abaa023472ca84f9b78757d5cb3782f (Fix bug in new column of dlo table).
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for linkedin/openhouse highlights the delivery and impact of the DLO Persistence Strategy: Partition-Aware Generation and Schema Alignment. This work introduces an isPartitioned flag and a partition-aware persistence strategy to generate persistence logic only for partitioned tables. It also includes a schema alignment fix that moves isPartitioned to the end of SQL statements to ensure correct column order after schema updates. The changes were implemented via two commits: 54c1179099f8a9df08557550bf1fa96b49d14dc8 (Refactor persistence strategy for dlo generation) and 40463e5d1abaa023472ca84f9b78757d5cb3782f (Fix bug in new column of dlo table).
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 — Developer contributed key data layout optimization enhancements for linkedin/openhouse, enabling generation and persistence of optimization strategies at both table and partition levels. This included refactoring strategy generation to distinguish table-level vs partition-level strategies and persisting them in separate DLO tables. Updated job configurations and data source implementations to support partition-specific strategy generation, laying groundwork for finer-grained optimization and future analytics.
1 Commits • 1 Features
Feb 1, 2025February 2025 — Developer contributed key data layout optimization enhancements for linkedin/openhouse, enabling generation and persistence of optimization strategies at both table and partition levels. This included refactoring strategy generation to distinguish table-level vs partition-level strategies and persisting them in separate DLO tables. Updated job configurations and data source implementations to support partition-specific strategy generation, laying groundwork for finer-grained optimization and future analytics.
February 2025
November 2024
3 Commits • 2 Features
Nov 1, 2024Month: 2024-11 • LinkedIn Openhouse – Highlights of delivered features, major bug fixes, and impact across the data platform. Focused on auditability, test stability, and runtime readiness to enable broader data workloads with Openhouse. Key investments this month include schema-level auditing support, CI stabilization, and runtime integration to support iceberg-based analytics and Spark workloads.
November 2024
3 Commits • 2 Features
Nov 1, 2024Month: 2024-11 • LinkedIn Openhouse – Highlights of delivered features, major bug fixes, and impact across the data platform. Focused on auditability, test stability, and runtime readiness to enable broader data workloads with Openhouse. Key investments this month include schema-level auditing support, CI stabilization, and runtime integration to support iceberg-based analytics and Spark workloads.
Activity
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Quality Metrics
Correctness87.8%
Maintainability85.2%
Architecture84.4%
Performance79.2%
AI Usage21.8%
Skills & Technologies
Programming Languages
DockerfileGradleGroovyJSONJavaKotlinPythonSQLScalaShell
Technical Skills
API DesignAPI DevelopmentAsynchronous ProgrammingBackend DevelopmentBig DataBug FixingBuild ConfigurationBuild System Configuration (Gradle)Compression AnalysisConcurrencyConfiguration ManagementData EngineeringData ManagementDatabase ManagementDebugging
Repositories Contributed To
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