percona/percona-server-mongodb
May 2025 – Mar 2026
7 Months active
Languages Used
C++JavaScript
Technical Skills
Aggregation FrameworkBackend DevelopmentDatabase InternalsDatabase TestingPerformance TuningQuery Optimization
Carlos Alonso
PROFILE
Carlos Alonso
Worked on the percona/percona-server-mongodb repository, delivering features and fixes that improved query planning, aggregation, and test reliability. Leveraged C++ and JavaScript to modularize cost-based ranking, enhance multi-plan execution analytics, and optimize aggregation pipelines for sharded environments. Focused on explainability by refining Explain outputs and adding detailed tracking of rejected plans, while also addressing test flakiness through robust environment resets and targeted bug fixes. Improved memory management and type safety in query planner components, reducing plan churn and increasing maintainability. The work emphasized performance tuning, database optimization, and reliable testing, resulting in more transparent, efficient, and stable database operations.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
23Total
Bugs
6
Commits
23
Features
10
Lines of code
6,978
Activity Months7
Your Network
596 peopleShared Repositories
247
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026: Delivered targeted improvements to the MongoDB query planner in percona-server-mongodb focusing on explainability and robustness. Implemented a multi-plan stage to clarify backup plan involvement in Explain outputs and performed a type-safe casting refactor to harden index tag handling. These changes are implemented via commits 1354b8bf767d8add073a29026fbfe9969e8191fa and a6accae6f238ed4f1c5af5647d7b0587c11f312e, resulting in clearer explain plans, reduced risk of incorrect plan usage, and a more maintainable codebase. This work strengthens performance debugging, increases reliability of query planning, and demonstrates strong skills in planning analysis, type safety, and refactoring.
2 Commits • 1 Features
Mar 1, 2026March 2026: Delivered targeted improvements to the MongoDB query planner in percona-server-mongodb focusing on explainability and robustness. Implemented a multi-plan stage to clarify backup plan involvement in Explain outputs and performed a type-safe casting refactor to harden index tag handling. These changes are implemented via commits 1354b8bf767d8add073a29026fbfe9969e8191fa and a6accae6f238ed4f1c5af5647d7b0587c11f312e, resulting in clearer explain plans, reduced risk of incorrect plan usage, and a more maintainable codebase. This work strengthens performance debugging, increases reliability of query planning, and demonstrates strong skills in planning analysis, type safety, and refactoring.
March 2026
February 2026
2 Commits • 1 Features
Feb 1, 2026Month: 2026-02 — Performance and reliability improvements in the Percona Server for MongoDB, focused on aggregation pipeline optimization and plan handling in sharded environments. Delivered a set of enhancements to plan selection, coupled with targeted tests and memory management improvements that reduce plan churn and improve overall throughput.
February 2026
2 Commits • 1 Features
Feb 1, 2026Month: 2026-02 — Performance and reliability improvements in the Percona Server for MongoDB, focused on aggregation pipeline optimization and plan handling in sharded environments. Delivered a set of enhancements to plan selection, coupled with targeted tests and memory management improvements that reduce plan churn and improve overall throughput.
January 2026
10 Commits • 4 Features
Jan 1, 2026Monthly summary for 2026-01: Focused on transparency, reliability, and performance insights for query planning and aggregation workflows. Key features delivered: - Enhanced Explainability for Automatic CE Planning: detailed explanations and tracking of rejected plans to improve transparency of the query planner's decisions. - Multi-Plan Execution Analytics and State: metrics and state management for multi-plan executions; records statistics for winning plan trials. - Count Query Plan Explanation Enhancements: wraps winning and rejected plans in CountStages to improve count query explanations. - Aggregation Plan Explainability: Union With and Stale Mongos Handling: improves explainability for pipelines using $unionWith and addresses stale mongos nodes. - RouterStageSkip Zero-Skip Fix: allows skip value of zero to support aggregation queries with skip=0 when optimization is disabled. Major bugs fixed: - CBR Plans Population when CBR Cannot Decide: correctly mark as rejected when CBR cannot decide. - Aggregation Test Robustness: Stale Mongos Scenarios: optimized and stabilized tests in stale mongos contexts. - Aggregation Test Robustness: Mongos Context Logging: improved logging for optimization decisions to increase test resilience. - Aggregation Test Robustness: Other related stability improvements observed in test suite. Overall impact and accomplishments: Improved planning transparency, diagnosability, and reliability across core query planning and aggregation paths; measurable business value via better cost estimation, faster diagnosis, and more robust analytics. Technologies/skills demonstrated: instrumentation for query planning, cross-component analytics, state persistence across planner steps, robust test design, and handling of stale replica-set scenarios.
10 Commits • 4 Features
Jan 1, 2026Monthly summary for 2026-01: Focused on transparency, reliability, and performance insights for query planning and aggregation workflows. Key features delivered: - Enhanced Explainability for Automatic CE Planning: detailed explanations and tracking of rejected plans to improve transparency of the query planner's decisions. - Multi-Plan Execution Analytics and State: metrics and state management for multi-plan executions; records statistics for winning plan trials. - Count Query Plan Explanation Enhancements: wraps winning and rejected plans in CountStages to improve count query explanations. - Aggregation Plan Explainability: Union With and Stale Mongos Handling: improves explainability for pipelines using $unionWith and addresses stale mongos nodes. - RouterStageSkip Zero-Skip Fix: allows skip value of zero to support aggregation queries with skip=0 when optimization is disabled. Major bugs fixed: - CBR Plans Population when CBR Cannot Decide: correctly mark as rejected when CBR cannot decide. - Aggregation Test Robustness: Stale Mongos Scenarios: optimized and stabilized tests in stale mongos contexts. - Aggregation Test Robustness: Mongos Context Logging: improved logging for optimization decisions to increase test resilience. - Aggregation Test Robustness: Other related stability improvements observed in test suite. Overall impact and accomplishments: Improved planning transparency, diagnosability, and reliability across core query planning and aggregation paths; measurable business value via better cost estimation, faster diagnosis, and more robust analytics. Technologies/skills demonstrated: instrumentation for query planning, cross-component analytics, state persistence across planner steps, robust test design, and handling of stale replica-set scenarios.
January 2026
December 2025
5 Commits • 2 Features
Dec 1, 2025Month 2025-12 — Percona Server MongoDB: concise monthly summary focused on delivered features and technical impact. Key features delivered: - Enhanced Plan Ranking System with Modular Cost-Based Ranking (CBR) and Trial Evaluation: modularizes CBR into its own strategy, adds multiplanner support, and trial-based evaluation to improve query planning efficiency and accuracy. Commits traceable to: be87e3cf199809009118db1b13700c68e540d58e, 64557aa6e256d1954ae7076b92251d43bc152e94, 8b7abc82148a1b3bee6d462738702f307530b6b5, a59d2aaddef5ce69983179953a79b13070c7a295, with related planning improvements to multiplanner integration. - Plan Ranking Strategy for Empty Result Queries: introduces a dedicated plan ranking strategy for queries that return no results to improve planner efficiency and accuracy for empty results (CBRForNOMPResults). Commit: 37766037bb77426b6d4b9ae4034f9c4099dcf6b4. Major bugs fixed (stability and correctness improvements): - Stabilized multiplanner integration into the plan ranker and improved handling of empty-result planning paths. - Expanded test coverage for empty-result scenarios (CBRForNOMPResults) to prevent regressions. Overall impact and accomplishments: - Significantly improved query planning efficiency and accuracy through modular CBR and multiplanner-based evaluation. - Enhanced robustness for empty-result queries, reducing misrankings and unexpected plan churn. - Clearer separation of concerns with modular CBR, enabling faster future iterations and targeted optimizations. Technologies and skills demonstrated: - Modular architecture design for Cost-Based Ranking (CBR) - Cost-Based Ranking, multiplanner, and trial-based evaluation concepts - Plan ranking strategies for empty results - Refactoring, test-driven development, and end-to-end planning flow integration - Git-based collaboration and traceability across multiple commits Business value: - Lower query latency and more consistent plan quality, especially for complex queries and empty-result cases - Reduced resource consumption during plan evaluation and execution through smarter ranking decisions - Stronger foundation for future performance improvements and feature expansions in the planner and ranking subsystem.
December 2025
5 Commits • 2 Features
Dec 1, 2025Month 2025-12 — Percona Server MongoDB: concise monthly summary focused on delivered features and technical impact. Key features delivered: - Enhanced Plan Ranking System with Modular Cost-Based Ranking (CBR) and Trial Evaluation: modularizes CBR into its own strategy, adds multiplanner support, and trial-based evaluation to improve query planning efficiency and accuracy. Commits traceable to: be87e3cf199809009118db1b13700c68e540d58e, 64557aa6e256d1954ae7076b92251d43bc152e94, 8b7abc82148a1b3bee6d462738702f307530b6b5, a59d2aaddef5ce69983179953a79b13070c7a295, with related planning improvements to multiplanner integration. - Plan Ranking Strategy for Empty Result Queries: introduces a dedicated plan ranking strategy for queries that return no results to improve planner efficiency and accuracy for empty results (CBRForNOMPResults). Commit: 37766037bb77426b6d4b9ae4034f9c4099dcf6b4. Major bugs fixed (stability and correctness improvements): - Stabilized multiplanner integration into the plan ranker and improved handling of empty-result planning paths. - Expanded test coverage for empty-result scenarios (CBRForNOMPResults) to prevent regressions. Overall impact and accomplishments: - Significantly improved query planning efficiency and accuracy through modular CBR and multiplanner-based evaluation. - Enhanced robustness for empty-result queries, reducing misrankings and unexpected plan churn. - Clearer separation of concerns with modular CBR, enabling faster future iterations and targeted optimizations. Technologies and skills demonstrated: - Modular architecture design for Cost-Based Ranking (CBR) - Cost-Based Ranking, multiplanner, and trial-based evaluation concepts - Plan ranking strategies for empty results - Refactoring, test-driven development, and end-to-end planning flow integration - Git-based collaboration and traceability across multiple commits Business value: - Lower query latency and more consistent plan quality, especially for complex queries and empty-result cases - Reduced resource consumption during plan evaluation and execution through smarter ranking decisions - Stronger foundation for future performance improvements and feature expansions in the planner and ranking subsystem.
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for percona/percona-server-mongodb focused on improving test reliability and CI stability through a bug fix that resets the query engine configuration after tests, ensuring test isolation and reproducible results. This work reduces flaky tests, accelerates debugging, and strengthens release readiness.
1 Commits
Oct 1, 2025October 2025 monthly summary for percona/percona-server-mongodb focused on improving test reliability and CI stability through a bug fix that resets the query engine configuration after tests, ensuring test isolation and reproducible results. This work reduces flaky tests, accelerates debugging, and strengthens release readiness.
October 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Focused feature delivery on performance-oriented improvements in the percona-server-mongodb project. The primary accomplishment is an optimization in the query planner that pushes down eligible filter predicates into IXSCAN, reducing the need for the FETCH stage when querying with indexed sorts.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Focused feature delivery on performance-oriented improvements in the percona-server-mongodb project. The primary accomplishment is an optimization in the query planner that pushes down eligible filter predicates into IXSCAN, reducing the need for the FETCH stage when querying with indexed sorts.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary — percona/percona-server-mongodb Focused on improving test reliability and query performance for stable, scalable operations in production workloads. Delivered targeted fixes and optimization that reduce false positives in test suites and enable faster query pruning, contributing to higher confidence in release readiness and better runtime performance for common workloads.
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary — percona/percona-server-mongodb Focused on improving test reliability and query performance for stable, scalable operations in production workloads. Delivered targeted fixes and optimization that reduce false positives in test suites and enable faster query pruning, contributing to higher confidence in release readiness and better runtime performance for common workloads.
May 2025
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Quality Metrics
Correctness87.8%
Maintainability83.4%
Architecture84.4%
Performance82.6%
AI Usage25.2%
Skills & Technologies
Programming Languages
C++JavaScript
Technical Skills
Aggregation FrameworkBackend DevelopmentC++C++ developmentC++ programmingDatabase InternalsDatabase OptimizationDatabase TestingIndex ManagementJavaScriptJavaScript programmingJavaScript testingMongoDBPerformance TuningQuery Optimization
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