April 2026 summary for risingwavelabs/risingwave: Delivered a configurable join cache eviction interval to improve streaming join performance; fixed ASOF join correctness across scenarios with extensive tests; added error-safe Iceberg CREATE SOURCE restrictions to prevent PK-related schema issues; improved documentation and error messaging; overall impact includes better performance tunability, stronger correctness guarantees, and a smoother user experience.

Concise monthly summary for 2026-03 focusing on business value and technical achievements for risingwavelabs/risingwave.

February 2026 monthly summary for risingwavelabs/risingwave highlighting feature delivery, bug fixes, and overall impact across streaming, optimizer/MV, and Iceberg work. Key actions and outcomes: - Streaming and backfill locality enhancements: improved locality backfill mechanism for aggregate functions, serverless backfill support for streaming queries, and refactored locality enforcement to streamline streaming rewrite, enabling more predictable latency and resource usage in streaming workloads. - MV-based query optimizer and materialized view enhancements: enhancements to optimizer and MV handling, including support for equivalent batch orders for eq-prefix scans, MV selection in batch queries, and MV pruning to remove unnecessary materialized views, improving plan quality and reducing query plan complexity. - Iceberg stability, PK handling, and maintenance: stability and maintenance improvements for Iceberg with composite primary key support for refreshable tables, cleanup and metadata handling during sink drops, dependency upgrades, and test reliability improvements. Overall impact and accomplishments: - Business value: better streaming performance and lower latency for backfill-heavy workloads; more efficient and accurate batch query execution through MV-enabled optimization; and safer ongoing maintenance of Iceberg-backed data lakes with improved test reliability. - Technical achievements: delivered end-to-end enhancements across streaming backfill, query optimization, MV handling, and Iceberg stability; implemented serverless capabilities and refactored enforcement logic; advanced MV pruning and batch-order support; upgraded dependencies and stabilized tests. Technologies/skills demonstrated: - Streaming systems: backfill locality, serverless backfill, and locality enforcement refactor - Query optimization: MV-based optimizations, batch order equivalence, MV selection, and pruning - Data lake maintenance: Iceberg PK handling, sink drops, and dependency management

January 2026 performance and reliability summary: focused on speeding up and hardening RisingWave's core query processing, strengthening streaming reliability, expanding Iceberg integration, improving execution plan explainability, and bolstering observability and maintainability. These efforts translate into faster, more accurate queries, more robust streaming pipelines, deeper debugging capabilities, and smoother operations.

Month: 2025-12 — Delivered a focused set of features and reliability fixes to improve performance, compatibility, and data correctness, while enhancing explainability and developer ergonomics. The work strengthens data plane stability and speeds up future integrations through modernized dependencies and SDK-driven changes.

November 2025 (repository: risingwavelabs/risingwave) delivered substantial improvements in data locality processing, Iceberg integration, and query optimization, translating to higher reliability, data correctness, and performance at scale. The work expanded core data ingestion backfill capabilities and strengthened Iceberg-based workflows with better upsert handling, credentials management, and robust scan strategies, while tuning engine behavior for efficiency. Key features delivered: - Locality Backfill Enhancements: license guard, locality-aware stream keys, progress/state tracking, stream deduplication, improved locality shard logic, and expanded tests, enabling more predictable and faster backfill in production. - Iceberg Connector & Engine Enhancements: upsert handling in sinks, AWS Glue/S3 credentials with ARNs and role assumption, snapshot-based scans, extended list intervals, error recovery during scans, and instrumentation for observability. - Iceberg Engine Tuning & Compaction: configurable compaction controls (max snapshots, small-file thresholds, deleted file counts) to optimize storage and processing workload. - Optimizer & Query Performance Enhancements: improved top-N index selection, deduplication in aggregation, and enhanced gap-fill plan robustness to accelerate workloads and improve plan stability. - System Query Single-Row Enforcement (bug fix): runtime single-row enforcement to maintain PostgreSQL compatibility for system queries. Overall impact: - Increased data reliability and correctness for backfill and Iceberg-based pipelines. - Improved performance and resource efficiency through optimized planning, compaction, and list-scanning logic. - Expanded test coverage and instrumentation for better maintainability and faster issue diagnosis. Technologies/skills demonstrated: - Iceberg integration (upsert semantics, snapshot-based scans, ARNs/role assumption) - AWS credentials management and IAM-based access patterns - Query optimization tactics (top-N indexing, dedup, gap-fill robustness) - Backfill workflows, shard logic, and stateful processing - Observability and test-driven improvements across data pipelines

October 2025 performance summary for risingwavelabs/risingwave: The team delivered critical Iceberg integration improvements and streaming locality enhancements, strengthening observability, safety, and efficiency across cloud/OSS deployments. Key features were implemented to standardize Iceberg REST catalog User-Agent tagging, enforce locality in streaming and optimizer, robustly handle Iceberg schema evolution with pruning, and improve sink reliability with exactly-once semantics. In parallel, a data-integrity fix for distribution keys hardened the runtime against key value changes. These changes reduce operational risk, lower unnecessary churn, and improve end-to-end throughput for streaming pipelines and Iceberg-backed workloads. Technologies demonstrated include Iceberg integration, streaming locality, query planning robustness, and secure credential handling.

September 2025 (risingwavelabs/risingwave) focused on expanding testing coverage, strengthening reliability, and advancing the optimizer and security-related features. Delivered concrete business value through end-to-end validation, integration readiness, and deployment enhancements, while improving stability and security for production use.

August 2025 performance summary: Delivered several high-impact features and reliability improvements across core data plane and Iceberg-backed storage. Business value realized through improved query correctness and performance, streamlined maintenance, and enhanced observability. Key accomplishments include schema-qualified function binding in the binder with end-to-end tests, optimizer enhancement to inline scalar subqueries via PullUpCorrelatedProjectValueRule, Lakekeeper-backed Iceberg catalog integration with e2e tests, VACUUM FULL support for Iceberg (with VACUUM adjusted to expiration handling), and a new Iceberg snapshot metric plus Grafana panel for operational visibility. Notable fixes included temporal join prev epoch handling and Iceberg S3 path style access fix, along with dependency updates addressing parquet max stats. Technologies demonstrated: Rust/daemon components, optimizer refactoring, Iceberg catalogs, Lakekeeper integration, end-to-end testing, and observability tooling (Grafana).

July 2025: Focused on advancing Iceberg integration in RisingWave with improvements to compaction, catalog configuration, and sink stability. Deliverables include manual compaction capabilities for Iceberg engines and sinks, HTTP header support for Iceberg catalog requests, and Parquet row-group sizing adjustments to enhance throughput. Reliability and performance were tightened through asynchronous drop tasks to mitigate race conditions, faster end-to-end tests, and targeted fixes for plan generation and location handling. These changes collectively improve data-lake integration, reduce operational risk, and enable faster iteration.

June 2025 monthly summary for risingwavelabs/risingwave: Delivered targeted enhancements to query planning and optimization, added test observability for Iceberg end-to-end testing, and fixed critical reliability issues across Proto handling and the Iceberg metastore password workflow. These efforts improved runtime performance, testing efficiency, and system robustness for production workloads.

May 2025 highlights for risingwavelabs/risingwave focused on Iceberg integration, data maintenance, and observability. Delivered configurable Iceberg compaction management, partitioning and interval data type support, CDC readiness, and robust system metadata migrations, while strengthening data integrity and security checks. These efforts improve data reliability, query performance, and governance for Iceberg-backed workloads.

Month: 2025-04 Overview: - Focused on Iceberg ecosystem integration, reliability, performance, and dependency optimization within risingwavelabs/risingwave. Delivered flexible catalog configurations, robust engine behavior, and leaner dependencies, enabling faster onboarding of Iceberg-based workloads and more stable cross-database operations. Key features delivered: - Iceberg Engine Enhancements and Catalog Integration: Introduced hosted SQL catalog support and Iceberg JDBC catalog views, enabling flexible catalog configurations and exposure of system tables. Representative commits include feat(iceberg): support hosted SQL catalog for iceberg engine (#21351) and feat(iceberg): support iceberg jdbc catalog views (#21400). - Iceberg Engine Reliability and Maintenance: Enforced sink decoupling for Iceberg engine table creation, optimized planning via scan API-based file enumeration, fixed partition name formatting in the Iceberg sink, and removed EMR serverless compaction feature to reduce maintenance burden. Representative commits include fix(iceberg): check sink decouple for iceberg engine (#21235), fix(iceberg): fix iceberg partition by name (#21590), chore(iceberg): remove emr compaction (#21641). - Iceberg Query Performance Optimizations: Improved query performance through more robust decimal predicate pushdown and optimized LIKE expressions for index usage. Representative commits include fix(iceberg): fix iceberg decimal predicate pushdown (#21470) and fix(optimizer): fix like rewrite rule (#21500). - Iceberg Dependency Cleanup: Cleaned up Iceberg-related dependencies by removing Parquet Avro and other Iceberg data libraries, reducing runtime footprint and maintenance surface. Representative commits include fix(iceberg): remove parquet avro lib (#21344), fix(iceberg): remove iceberg parquet lib (#21436), fix(iceberg): remove iceberg data lib (#21546). - Cross-database Binder Robustness: Ensured a specified database exists before binding and improved error propagation for cross-database schema operations. Representative commit: fix(binder): check database if exists for binder (#21571). Major bugs fixed: - Cross-database Binder Robustness: Ensured target database exists before binding and improved error propagation for cross-database schema operations, reducing misbindings and downstream errors. Overall impact and accomplishments: - Enhanced business value by enabling flexible Iceberg catalog configurations and stable access to system tables, facilitating broader data source onboarding. - Improved reliability and stability of the Iceberg engine through sink decoupling, partition-name correctness, and removal of legacy EMR compaction, lowering operational risk. - Increased performance of Iceberg queries via robust decimal predicate pushdown and improved LIKE handling, accelerating analytics workloads. - Reduced maintenance cost and footprint by cleaning up unnecessary Iceberg-related dependencies. - Strengthened cross-database operations with explicit existence checks and clearer error propagation, reducing failures in multi-database workflows. Technologies/skills demonstrated: - Iceberg engine integration and catalog management - SQL catalog design and exposure of system tables - Performance optimization (predicate pushdown, LIKE rewrite) - Dependency management and cleanup of unnecessary libraries - Cross-database binding robustness and error handling

March 2025 performance and delivery summary: Focused on Iceberg catalog enhancements, catalog governance, and reliability improvements across the RisingWave project. Delivered cross-cloud Iceberg config loading, namespace management, S3 SigV4-supported Iceberg tables, and ASOF join optimization, along with targeted fixes (Postgres dialect quoting, GCS catalog type enforcement) and maintenance (Pulsar reader deprecation, iceberg-rs upgrades).

February 2025 performance summary for risingwavelabs/risingwave focusing on delivering enterprise-ready Iceberg integration, improving data reliability, and enhancing compatibility across BI tools. Key capabilities were activated behind licensing, enabling Iceberg engine usage with data source/sink support and catalog parameter validation. JDBC sink robustness was strengthened with auto-commit control, proper transaction rollback on close, and a shortened query timeout to 60 seconds, reducing latency and preventing long-running failures. Iceberg table creation warnings were resolved and partition handling corrected by ensuring defaults for wildcard indexes and unique partition IDs with proper partition spec IDs. JNI reliability and error reporting were improved to provide clearer, actionable messages and better resource management. Char casting compatibility improvements were implemented by routing char casts to varchar, alongside refining Iceberg predicate pushdown to support correct operator and data type handling, with an integration test to validate end-to-end behavior.

Monthly summary for 2025-01 focusing on delivering cloud-enabled Iceberg capabilities and reliability improvements in risingwavelabs/risingwave. This month, we delivered cloud storage connectivity for Iceberg, automated data compaction via EMR Serverless, batch DQL support for Iceberg, and key fixes to improve correctness and observability. These efforts collectively enhance data ingestion flexibility, reduce maintenance costs, and improve query performance transparency for customers relying on Iceberg-backed workloads.

December 2024 focused on delivering robust Iceberg integration and configuration capabilities for RisingWave, enabling engine-level Iceberg table support, time travel, and nested data types with strong tests and validations. Implementations included JNI catalog refactor and a targeted bypass of Iceberg partition table optimization to address a sink-related issue. The work established a stable foundation for lakehouse workloads, improved data freshness, and increased analytics capabilities while maintaining reliability through tests and defaults.

2024-11 Monthly Summary for risingwavelabs/risingwave. Focused on delivering robust Iceberg integration, improved data access for epoch-indexed tables, and stability enhancements that reduce operational noise and enable automation. Key business-value outcomes include more secure remote catalog access, simpler configuration, and clearer error reporting plus actionable tests.

October 2024: Correct binding of correlated subqueries within CTEs in the optimizer, with regression tests added to prevent recurrence.