October 2025 monthly work summary focusing on key accomplishments across Doris repos. Delivered performance improvements, memory efficiency enhancements, and a new condition cache feature, along with thorough documentation to enable adoption. These changes improve query latency, reduce memory pressure, and enhance cache hit rates for repeated filter evaluations.

September 2025 monthly summary for Jibing-Li/incubator-doris: Delivered benchmarking tooling, pipeline performance optimizations, and data access improvements. Implemented Coffee-Bench benchmarking tooling to measure performance with a 17-query suite, consolidated materialization into a single operator with a hyper scheduler and blocking RPC for improved execution flow, and introduced batch row ID lookups with segment caching to reduce overhead. No explicit bug fixes were recorded in this period; the focus was on measurable performance gains, reliability, and maintenance of performance validation capabilities.

Performance-focused monthly summary for 2025-08. In Jibing-Li/incubator-doris, delivered a targeted bug fix for non_nullable with non-nullable columns, added a memory-efficient VExpr refactor to reduce memory footprint and improve initialization clarity, and updated regression tests to validate the new behavior. These changes enhance correctness, stability, and runtime efficiency, aligning with business goals of reliable query execution and lower memory pressure in large-scale deployments.

July 2025 performance and stability review: Across Doris repositories, delivered key stability, performance, observability, and build-target improvements, along with documentation enhancements. Highlights include stabilizing the segment cache path for topN queries, speeding up LIKE queries via dictionary encoding, aligning timeouts to per-query durations, adding ARM_MARCH-based build customization, and introducing a STDDEV alias in documentation.

June 2025 monthly summary focusing on stability, reliability, and observability improvements across core Doris components. Highlights include fixes to TopN and materialization pipeline robustness, concurrency safety, and safe dependency handling; enhanced observability for TopN/data retrieval; and a documentation correction for storage size values in the Doris website. These changes reduce runtime errors, improve data correctness, and enable safer concurrent workloads, delivering business value through more predictable performance and easier troubleshooting.

May 2025 focused on improving user guidance for query tuning and boosting runtime performance in the Doris stack. Delivered a documentation overhaul for the Doris website and implemented targeted performance optimizations in the execution engine and cloud mode TopN path. The work enhances business value by reducing support overhead and delivering faster query responses for cloud deployments.

April 2025 monthly work summary for Jibing-Li/incubator-doris focused on delivering high-impact architectural improvements, stabilizing data handling, and aligning FE/BE behavior to reduce operational risk. Key changes enhanced throughput, memory efficiency, and cast reliability while standardizing date/time semantics across components.

March 2025 monthly summary for Jibing-Li/incubator-doris: Delivered targeted architectural refinements and a critical correctness fix that together improve performance, reliability, and analytics accuracy for business-critical queries.

February 2025 monthly summary for Jibing-Li/incubator-doris: delivered key correctness improvements across shuffling, top-N aggregation, query caching, and merging-exchange materialization, plus internal backend refactors to boost performance and readability. Regression tests added for critical correctness paths. These changes collectively improved data correctness, stability, and reliability in multi-backend deployments, with measurable impact on throughput and error rates in production workloads.

January 2025 performance summary for Jibing-Li/incubator-doris. Delivered targeted internal refactors and performance improvements, along with critical correctness fixes, to increase maintainability, reliability, and query efficiency while preserving external behavior. Highlights include partition sort cleanup, date validation performance enhancements, and scheduling robustness, plus fixes to percentile aggregates and group array intersection to prevent crashes and incorrect results.

December 2024 monthly summary: Focused on delivering performance improvements for the Doris query engine and maintaining a clean, well-documented codebase across two repositories. Key work included delivery of performance enhancements and code cleanup in the core project, plus user-facing documentation to help customers optimize query execution. Key features delivered: - incubator-doris: Query Performance Enhancements — concurrency locking refinements in the Fragment Manager and memory-efficient handling of compound predicates; replaced std::unordered_map with phmap to improve latency and memory usage. Commits: 829b4b79d178ad878fbc20f4057b77583ef26af7; 0c97e0470f20a85f27d9d63673f1f3b44a82f164. - incubator-doris: Code Cleanup and Refactor — removed obsolete components (OldCounts, TransformerToStringTwoArgument) and unused util/type_traits.h include to simplify maintenance. Commit: 2b2051209dc7bd445a13805f007b4b965b8f7a88. - apache/doris-website: Documentation for Query Acceleration and Parallelism Tuning — comprehensive user-facing docs covering BITMAP precise deduplication, HLL deduplication, and guidance on parallelism tuning and runtime filter wait times. Commits: fddc63ad30e2aec5d13cfad4e9d6d8958defb020; 47f3aca6e33ce368c18685a4eb3c88a2342cbf37. Major bugs fixed: None reported this month. The focus was on performance enhancements, code maintainability, and documentation to reduce onboarding time and improve user guidance. Overall impact and accomplishments: - Improved query performance and memory efficiency in core query processing, contributing to faster analytics and lower resource usage. - Cleaner codebase with removed legacy paths, reducing maintenance burden and risk of regressions. - Clear, actionable documentation enabling users to leverage query acceleration features and optimize parallelism. Technologies and skills demonstrated: - C++ performance engineering (Fragment Manager, memory-predicate handling; phmap integration). - Code refactoring and maintenance discipline. - Technical writing and user documentation for performance features and tuning.

Month: 2024-11 | Repository: Jibing-Li/incubator-doris. Focused on performance-oriented refactors for the Vectorized Query Engine and interface simplification to improve maintainability and throughput. Key features delivered: 1) Vectorized Query Engine Data Type Handling Refactor: removed the unless API and simplified size calculations for data types (notably nullable types) by directly using get_size_of_value_in_memory(); internal optimization to streamline data representations. Commit: ea6cd589696f548db16d6ee4a18375cbc46e7252. 2) IColumn Interface Cleanup: removed several virtual methods (is_bitmap, is_hll, is_numeric, is_column_decimal) to simplify the interface and improve code clarity; Commit: 46575e59bac2c39088ecd925d1d20097d86667a3. Major bugs fixed: None reported in this period. Overall impact and accomplishments: improved internal data type representations and memory size handling, which can translate to measurable performance gains in vectorized query execution; simplified interface reduces maintenance burden and improves code readability for ongoing vectorization work. Technologies/skills demonstrated: C++, performance-oriented refactoring, memory management, interface design, and vectorized engine optimization.