Month: 2026-06 performance summary for apache/doris. 1) Key features delivered - MaxCompute integration improvements: removed BE JNI bridge by introducing MaxComputeFeClient; FE directly handles MaxCompute writes via thrift; centralized max block config with max_compute_write_max_count (default 20000 to preserve existing behavior); enhanced catalog creation validation and error handling. 2) Major bugs fixed - Iceberg/Parquet reliability: fixes for synthesized _row_id handling, Iceberg field IDs for position delete files, and name_mapping; added regression tests for hidden row-id column to ensure stability. - Iceberg MOR default enforcement: reject DORIS Iceberg COW DML; ensure default create mode is MOR. - Hive LZO test setup bug: ensure necessary database and table structures exist before Hive LZO tests to prevent failures. 3) Overall impact and accomplishments - Increased reliability and correctness for MaxCompute integration and Iceberg/Parquet paths; reduced risk of catalog misconfigurations and data path errors; improved regression coverage and test stability; smoother onboarding for MaxCompute users and more robust analytics workloads. 4) Technologies/skills demonstrated - Java/FE architecture and Thrift-based cross-service communication; removal of JNI bridge; MaxCompute connector design and config centralization; Iceberg/Parquet data-path fixes; regression testing and test infrastructure improvements; overall reliability engineering.

May 2026 (apache/doris): Focused on stability and reliability for test and Iceberg workflows. Achieved deterministic test runs by disabling the condition cache in test_orc_lazy_mat_profile and stabilized Iceberg-related tests by enforcing explicit DROP statements before table creation. These changes reduce flaky CI runs, ensure a clean test state, and support safer, faster iteration on Iceberg features.

In April 2026, focused on correctness, resilience, and scalable growth for apache/doris. Delivered three high-impact contributions spanning JDBC catalog correctness, block management standardization, and test stabilization, with measurable business value in query accuracy, resource predictability, and release confidence.

For 2026-03, delivered significant Iceberg integration improvements, stabilized data read/write paths across schema changes, added Iceberg v3 support with lineage and deletion vectors, improved ORC/Parquet reader/writer stability, and upgraded Spark to 4.0. These changes reduce data correctness risk, enable more robust analytics on Iceberg tables, and modernize the runtime stack across the Doris deployment.

February 2026 monthly summary for apache/doris development focusing on reader robustness, error reporting, and query planning visibility. Delivered targeted Parquet/ORC reader improvements and enhanced EXPLAIN VERBOSE metrics to improve reliability, debugging efficiency, and performance tuning visibility for data-intensive workloads.

January 2026 (2026-01) monthly summary for apache/doris: Delivered core data processing improvements and stability enhancements across JNI usage, Iceberg ingestion, Parquet reading, and test infrastructure. Focused on increasing data correctness, throughput, and release confidence through targeted feature work, bug fixes, and reliability improvements.

2025-12 monthly summary for apache/doris: Key stability and performance improvements across Parquet/Hudi data paths, with two critical bug fixes and a major Parquet reader enhancement deployed to production. The changes reduce runtime errors, increase data processing throughput, and improve query accuracy for complex Parquet workloads.

Concise monthly overview for 2025-11: Delivered performance-focused features and fixes in Doris, targeting Parquet query performance and JNI scanner efficiency. Key outcomes include enhanced Parquet predicate pushdown with min-max filtering and dynamic runtime filters, deferral of filter application until row group reader instantiation, and JNI Scanners timing optimizations reducing CPU usage during data append operations. These changes improve query latency, lower resource usage, and provide better profiling visibility for ongoing optimizations.

October 2025 monthly summary for apache/doris: Delivered stability improvements and architectural enhancements that reduce outages and improve data workflows. Implemented HDFS Reader stability fix to prevent core dumps during profile collection and added MaxCompute namespace/schema support to enable the new project-schema-table hierarchy with backward compatibility.

September 2025 performance summary focusing on delivering business value through robust data access, performance optimizations, and improved reliability. The month emphasized accelerating data workloads (especially external tables), expanding test coverage, and ensuring international users have seamless access to MaxCompute. Key outcomes include increased reliability of exports, faster query paths for TVFs, and reduced operational risk through stabilized tests and clearer telemetry.

Monthly summary for 2025-08: Delivered performance and reliability improvements in Doris. Implemented TopN runtime filter pushdown for Parquet/ORC and refined row-group filtering to support OR conditions and IN_FILTER pushdown, accelerating analytical queries. Fixed JSON import bug to properly convert boolean values to integers, restoring data correctness for boolean columns loaded from JSON. These changes enhance BI analytics performance and data ingestion reliability, showcasing expertise in performance optimization, data formats, and code refactoring.

July 2025 performance summary for apache/doris. Focused on delivering external table data access enhancements, JNI safety, and build/deploy reliability to improve data ingestion, stability, and cross-environment compatibility. Key features delivered: - Doris External Table Readers: Schema Evolution and Type Conversions. Introduced TableSchemaChangeHelper to enable reading Hudi, Paimon, and Iceberg tables after schema changes, and implemented DATETIMEV2 to numeric conversions for Hive tables in Parquet/ORC formats. (Commits: b66c78cbbe014a7a6251971b1c71fd79f0134765; 2d48f1a229292b3e59358409637f2dd7a14aa75b) Major bugs fixed: - JNI Safety and Exception Handling Improvements: Added comprehensive exception checking and returning Status objects; enhances runtime error detection via -Xcheck:jni. (Commit: 192e9ae3731df41a11df6f406c23d0d5b3dadb1d) - Docker Pipeline Stability and Paimon Version Upgrade: Fixed pipeline instability by upgrading Paimon and uploading JARs to object storage to ensure reliable Maven repository access across environments. (Commit: 5f5aa50fb6b62349795e05e2dd7988eff4526b0e) Overall impact and accomplishments: - Enabled cross-format schema evolution for external tables, improving data freshness and correctness when reading Hudi/Paimon/Iceberg sources; ensured Hive compatibility for DATETIMEV2 in Parquet/ORC. - Reduced runtime JNI errors and improved error visibility, leading to more reliable native interactions. - Stabilized builds and deployments across environments by ensuring artifact availability and consistent Paimon versions, reducing deployment risk. Technologies/skills demonstrated: - C++ JNI safety, Java/C++ interop, error handling patterns, Parquet/ORC data formats, and cross-format schema evolution. - Data ingestion and governance with external tables, and robust CI/CD via Dockerized pipelines and Maven artifacts.

June 2025 monthly summary for apache/doris focusing on stabilizing external table workflows and ensuring accurate schema handling for Hudi-backed data. Delivered critical fixes that boost reliability in distributed deployments, improve data integrity, and reduce query-time anomalies across multi-backend configurations.

Concise monthly summary for 2025-05 focusing on delivering a high-impact feature for Top-N query performance, strong test coverage, and measurable improvements in memory usage and execution speed. Highlights business value delivered to the apache/doris project and the technical work completed this month.

April 2025 monthly summary for apache/doris. Focused on delivering robust JSON ingestion through Hive JsonSerDe, stabilizing Parquet/ORC readers, and enhancing deserialization safety. The work improves data ingestion reliability, reduces runtime crashes, and broadens test coverage for core data reading paths.

March 2025 performed targeted reliability and interoperability work for the apache/doris repository, focusing on data ingestion robustness, cross-format schema evolution, and MaxCompute integration. Key outcomes include Parquet ingestion robustness improvements, expanded MaxCompute timestamp support with safe handling, and unified top-level schema changes across Iceberg, Paimon, and Hudi. These efforts reduce ingestion failures, broaden external data source compatibility, and streamline schema migrations for analytics pipelines.

February 2025 highlights for apache/doris: Delivered a targeted feature to standardize cross-format type conversions during schema changes (ORC/Parquet), and fixed three critical reliability issues impacting data correctness and query reliability: HMS events stability with meta cache disabled, Parquet complex type cross-page null-map accuracy, and MaxCompute partition column ordering. These changes improve data correctness, partition pruning reliability, and cross-format compatibility, reducing post-change errors and enhancing operational stability for users relying on ORC/Parquet schemas and MaxCompute external tables.

Month: 2025-01 – This period delivered reliability, data correctness, and Hive compatibility improvements for apache/doris. Key features delivered include HTTP API resilience in Kubernetes (followers now directly request the master for API calls, reducing client-to-master failure risk), Hive 4 transactional tables support with ACID optimizations (read support for Hive 4 transactional tables, insert_only read fixes, and full-ACID query optimizations), and MetaCache invalidation correctness improvements. Major bugs fixed encompassed metaCache stale data issues, Hive translation instability cases, hive catalog follower event delivery, and edge-case fixes for full-ACID queries (e.g., select count(*)). Overall impact: increased stability and reliability in Kubernetes deployments, expanded Hive transactional workload support, and stronger metadata correctness, leading to better operational efficiency and data integrity. Technologies/skills demonstrated: Kubernetes API routing resilience, Hive 4/ACID, metadata caching (metaCache), and regression testing.

December 2024: Delivered targeted performance, reliability, and data-source compatibility enhancements for apache/doris, focusing on faster MaxCompute reads, safer handling of non-UTF-8 data, and more robust multi-backend query operations. Completed several partition pruning hardening for Hudi/Iceberg and improved startup and test stability.

Monthly summary for 2024-11 (apache/doris): delivered key stability and data-access improvements including a memory-leak fix in JVM metrics monitoring and enhanced Hive JSON reader for JSON tables. These changes reduce memory pressure, improve reliability, and broaden data access via Hive catalogs. Technologies used include JNI/JVM metrics, Java, JSON parsing, and Hive catalog integration. Business value includes improved production stability, faster data loading, and better compatibility with Hive-backed JSON datasets.