February 2026 (apache/doris) - Key accomplishments and business value: - Bug fixed: Character Column Aggregation Correctness Bug. Fixed min/max handling for character columns, trimmed trailing padding zeros for accurate aggregations, and updated data types for cache management to boost performance and correctness in query execution. Commit: 6873adb44a74780d5c1fc29163a0b3870422c168. - Feature delivered: Colocate Execution Parallelism Enhancement. Reworked parallelism calculation from bucket count to tablet count for better resource utilization; introduced a session variable colocate_max_parallel_num to control maximum parallelism and cap calculated parallelism at system limits to improve performance and stability. Commit: 90ffecd7fb97a8bb335ad98428425ae600d2af5e. - Overall impact: Improved accuracy and reliability of character-based aggregations, leading to correct results in edge cases. Enhanced query performance and stability for colocate workloads through smarter parallelism and safer resource utilization. - Technologies/skills demonstrated: SQL query optimization, parallel execution tuning, cache management, data type handling, feature flag/session variable usage, and PR-driven debugging.

January 2026 highlights for apache/doris focused on faster, more reliable queries, improved data processing throughput, and stronger stability during schema evolution. Key features delivered include end-to-end query performance and data scanning optimizations, enhanced block-level data transfer, and richer runtime observability for materialization. Major stability improvements address data-type correctness, zone-map handling, and schema-change safety, reducing risk of outages and incorrect results. Key features delivered: - Query performance optimizations and data scanning improvements (top-N streaming, batch block fetching with output-order integrity, and improved compound predicate handling). - Block-level data transfer enhancements (scanner block merging before projection, origin/padding block management, EOS handling improvements, and block swap for efficient data transfer). - Backend materialization monitoring enhancement (tracking max rows per backend and exposing a runtime metric for skew/debugging). Major bugs fixed: - Condition cache digest correctness across data types (datetime precision issues affecting optimization). - Zone map parsing integrity for not-null zones (not-null propagation when zone map not initialized). - Schema-change stability: avoid coredumps due to row store mismatch and enforce error codes for mismatches. Overall impact and accomplishments: - Substantial reduction in query latency and improved throughput due to execution and scanning optimizations, with practical improvements illustrated by commit-level changes. - Improved observability and诊 debugging capabilities for materialization workloads, enabling faster identification of data-skew and bottlenecks. - Increased system resilience against data-type edge cases, zone-map edge cases, and schema-evolution scenarios, resulting in fewer failures and faster recovery. Technologies/skills demonstrated: - Performance optimization patterns (streaming, batch processing, and predicate pushdown). - Low-level data processing optimizations (block management, EOS sequencing, batch sizing). - Observability and profiling (materialization metrics and runtime counters). - Data correctness across type casting, zone maps, and schema-change error handling.

December 2025 performance and reliability enhancements across Doris query execution, shuffle, and materialization pathways. Delivered key features including: condition cache enablement with digest-based caching for filter predicates, set-based digest computation speedups via a pdqsort replacement, and correctness improvements to digest calculation. Introduced a locality-prioritized random shuffle to boost data processing throughput and fixed a materialization operator bug by switching to unordered_map with robust backend ID handling. These changes reduce query latency on IN predicates, improve cacheability, and increase stability in query execution.

Monthly summary for 2025-11 focused on reliability, performance, and scalable query results in apache/doris. Delivered key features and fixes that improve correctness of condition caches, optimize aggregate processing with nullable columns, stabilize index-scanning performance, and streamline execution data handling. These changes reduce query latency, increase throughput for large analytics workloads, and improve consistency of results across complex predicates and projections. Technologies/skills demonstrated include C++/BE vectorized execution, digest/hash semantics, nullability handling, pushdown optimizations, and code refactors simplifying time-tracking and resource measurement.

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.