February 2026 monthly summary for StarRocks/starrocks focusing on business value and technical achievements. Delivered a targeted feature to optimize external lake table queries by limiting the number of partitions scanned, resulting in improved performance and resource management for lake-based workloads. No major bugs fixed this month. Key outcomes include: implementation of a partition scan limiter for Lake Tables External queries, performance impact in reduced scan scope and faster query execution, and strong alignment with performance and scalability goals for external table workloads.

November 2025 monthly summary for pinterest/starrocks focusing on Paimon Table Query Enhancements. The month delivered a targeted feature to query Paimon-backed tables using branch, tag, version, and timestamp, significantly increasing data retrieval flexibility and enabling version-aware analytics for downstream pipelines and BI workloads.

Month: 2025-10. This period centered on stabilizing statistics collection for Paimon tables within crossoverJie/starrocks. The primary delivery was a targeted bug fix to ensure accurate statistics reporting rather than adding new features. The change reduces noise in analytics and improves data-driven decision making for customers relying on Paimon table statistics.

August 2025 overall: Delivered two major enhancements for data integrity and runtime observability across the crossoverJie/starrocks project, with clear business value in safer data management and improved query monitoring.

May 2025: Delivered real-time query execution progress visibility for crossoverJie/starrocks. Added ExecProgress to QueryStatisticsInfo and a new API endpoint, surfacing live progress in /current_queries to improve observability, SLA adherence, and workload management. This work is tied to commit 22721e77be202fd4fccb7b5da6ec808956dcbafa (Enhancement: add query progress into /current_queries command result (#58777)).

April 2025 monthly summary for crossoverJie/starrocks focusing on delivering observable business value through enhanced data-tuning capabilities, improved query visibility, and increased cluster reliability. The work highlights progress in data insight tooling, query execution transparency, and robustness in replica selection, aligning with performance and reliability objectives for data-heavy workloads.

Month: 2024-12 Overview: This month focused on delivering a stability-oriented feature for large-scale OLAP workloads in the crossoverJie/starrocks repository, with clear business value in predictable performance and safer resource usage. Key features delivered: - OLAP Partition Scan Limit Session Variable: Introduced scan_olap_partition_num_limit to cap the maximum number of partitions scanned in internal OLAP tables. Default is 0 (no limit) and can be configured per session to prevent heavy scans on large datasets. This enhances stability and predictability of queries operating on large tables. Major bugs fixed: - No critical bug fixes were reported in this period. The primary focus was on a stability feature to mitigate risk during large-scale scans. Overall impact and accomplishments: - Improved reliability for large-scale OLAP workloads by preventing excessive partition scans, reducing risk of stress on cluster resources and improving query stability during peak load. - The change is low-risk, backward-compatible, and easily configurable, aligning with business goals of predictable performance and safer resource usage. - Documented and merged as part of upstream efforts (PR #53747) with a focused commit (a0a25b4707d0c7ff4dfe22caa19c758063dedaaf). Technologies/skills demonstrated: - Configuration-driven feature implementation (session-scoped variable) and internal OLAP table handling. - Change management and code contribution workflow (commit referenced, PR integration). - Collaboration with cross-team and upstream project alignment for stability enhancements.