February 2026 monthly summary for the apache/doris contribution stream focused on stabilizing the test suite and improving CI reliability. Key changes were centered on the Hive writer tests and removing a test-side effect that could lead to flaky results, with upstream alignment via a cherry-pick.

January 2026 highlights: Delivered Hive staging directory management with a configurable hive_staging_dir catalog property and updated default staging behavior; added dynamic/progressive file split sizing for external table scans to optimize parallelism, with two modes (non-batch and batch) and backward-compatible user split-size override; integrated Parquet page-level caching into Doris's unified page cache to boost query performance. Fixed critical reliability bugs: a self-deadlock in the time-sharing task executor and correctness issues in predicate handling for nondeterministic functions to avoid spurious NULLs. Business impact: improved data ingestion reliability, faster large-scale scans, and lower query latency on Parquet workloads, with backward compatibility preserved and improved stability.

December 2025 monthly summary across Doris repos focused on performance improvements, stability fixes, and user-facing documentation. In apache/doris, delivered key features that optimize data processing pipelines and S3 client performance, and fixed a stability issue in Orc row reading. In apache/doris-website, published documentation for the new data cache warmup feature to accelerate user onboarding and query performance.

November 2025 monthly performance summary for apache/doris focusing on reliability, performance, and security improvements across core features. Delivered cross-cutting reliability fixes for the time-sharing task executor, introduced Parquet IO optimizations with Bloom filter-based pruning, implemented column pruning for complex data types, and strengthened warmup access control checks with accompanying tests. Highlights include measurable query performance gains, reduced IO, and improved security posture for internal and external workloads.

Month: 2025-10 - Summary: Delivered the Warm Up Select for External Tables Cache Preloading feature for Apache Doris. Implemented WARM UP SELECT SQL to preload file block caches into tables, enabling cache metrics collection via a blackhole sink and comprehensive test coverage. Feature gated by the enable_file_cache=true session variable to support controlled rollout. Commit reference included for traceability: d30170a74376031e8ef0f38fa1ebca8a0498df16. Overall impact: reduces cold-start latency for external table workloads by preloading caches, improves cache visibility, and strengthens test coverage for the external-tables cache path. Business value: faster query performance on external-table workloads, better cache analytics, and safer feature rollout across environments.

September 2025 monthly summary for apache/doris focused on strengthening correctness and performance in the vectorized execution path and Paimon reader. Key improvements include compile-time type safety checks for the vectorized engine, a refactor of size_t to int/uint32_t for compatibility with integer-based calculations, and the introduction of compile_check_begin and compile_check_end macros to enforce type-safety. In addition, the Paimon native reader was updated to correctly utilize late materialization pushdown, fixing predicate pushdown logic for Paimon format readers. These changes enhance query reliability, improve performance potential, and reduce risk in future vectorized optimizations.

July 2025 monthly summary for apache/doris focused on stability, reliability, and observability improvements. Delivered core backend enhancements, hardened test infrastructure for Iceberg and ORC, and expanded file-scanning observability to support data-driven optimization. These efforts contributed to reduced runtime failures, more reliable tests, and richer metrics for performance tuning.

June 2025 monthly work summary focusing on delivering performance improvements and reliability fixes for Apache Doris. Highlights include dynamic remote scan concurrency with adjusted external table scanner limits to optimize multi-catalog parallelism, and a critical ORC RleDecoderV2 readByte bug fix coupled with zlib decompression optimization via libdeflate, delivering gains in data integrity and decompression speed. These efforts reduce resource contention, improve query throughput, and strengthen data pipelines across distributed environments.

May 2025 performance and reliability focused month for apache/doris. Delivered ORC Reader correctness fixes and ORC/Parquet performance and observability enhancements, with new profiling metrics to aid troubleshooting. These changes improve query reliability for ORC inputs, speed up scans, and preserve late materialization benefits in multi-catalog scenarios.

April 2025 monthly summary focused on delivering robust data ingestion capabilities and stabilizing multi-catalog workloads. Key features delivered include ORC reading enhancements and targeted bug fixes that improve reliability, observability, and performance potential across ingestion paths.

March 2025 monthly delivery focused on expanding Doris data-read capabilities and strengthening reliability of Parquet workflows. Implemented ORC Merge IO facility by introducing OrcMergeRangeFileReader and adapting ORCFileInputStream to enable out-of-order reads and delayed materialization of complex ORC types; this unlocks more efficient query execution on large ORC datasets. This work is backed by commit aed3e84cd33121109925ff846f52185ba85acb8a (Feature: orc-reader). In parallel, intensified Parquet decoder robustness with unit tests covering boolean plain and RLE, dictionary, and byte array decoders, and applied fixes arising from these tests, including changes to error-handling return types and header/implementation refactoring; commits 6ce45c43e36eeb27573fc4df8a2ccf1cf2ab844e (Test/Fix: parquet-reader).

February 2025 focused on delivering a performance-oriented metadata caching feature for external tables in apache/doris, with clear business value through faster query planning and improved scalability.

January 2025 monthly summary for apache/doris: Focused on stability and performance in batch file scanning. The principal effort was addressing a scanner thread allocation bug in batch split mode for file scan operators. This fix ensures the maximum number of scanner threads is calculated correctly based on batch split mode, eliminating underutilization of compute resources and improving throughput. No new features shipped this month; main value delivered through a targeted performance and resource utilization improvement enabling more predictable query performance in larger deployments. This aligns with ongoing optimization efforts for multi-catalog scanning paths and batch processing.

December 2024: Delivered key Parquet reader enhancements and reader robustness improvements for Apache Doris. Implemented late materialization for complex Parquet types by refactoring ColumnSelectVector into a general FilterMap, enabling efficient handling of nested structures and filtering. Introduced caching of skipped batches to reduce unnecessary scans, improving performance on complex datasets. Fixed an excessive scanning issue within late materialization, further stabilizing read paths. Addressed runtime crashes related to column mutations by replacing mutate() with assume_mutable() across reader implementations, enabling safer handling of missing columns and column resizing. Overall impact: faster analytics on complex Parquet data, improved stability and maintainability, and stronger resilience to schema evolution.

Month: 2024-11 – Focused on stability and correctness in apache/doris. Delivered a critical bug fix for caching behavior when retrieving files by HMS partitions, addressing incorrect parameter transmission and ensuring the caching decision is applied correctly based on the number of partitions. This reduces data inconsistencies and improves performance under concurrent access. No new features were released this month; emphasis was on reliability and correctness with measurable business impact.