June 2026: Key feature delivery and reliability improvements for Apache Spark. Implemented deterministic key resolution for dropDuplicates and dropDuplicatesWithinWatermark, improving reproducibility across engines and Scala versions while preserving checkpoint compatibility. This work reduces nondeterministic behavior in both streaming and batch workloads and aligns with SPARK-57489. Highlights include a shared analyzer rule-driven approach and a concrete commit enabling deterministic keys.

May 2026: Delivered streamlined stateful streaming enhancements in Spark, including removal of development-loop gating for the new state format in stream-stream join, introduction of statefulness analysis on LogicalPlan, and a fix to widen nullability of stateful operator outputs and state schemas.

Month: 2026-04 Key features delivered: - RangeScan API introduced in StateStore (RocksDB) enabling efficient range-based reads with inclusive start and exclusive end keys, plus support for rangeScanWithMultiValues. This lays the foundation for faster range queries and reduces scanned keys in large state stores. - Applied RangeScan API to TransformWithState Timer/TTL to narrow scans, reducing tombstones and improving timer/TTL eviction efficiency in RocksDB-backed state. - Integrated RangeScan API into stream-stream joins (format version 4) to scope scans to exact time windows, speeding eviction and matching work. - Stream-Stream Join Improvements: added support for non-outer joins in Update mode (Inner/LeftSemi) and introduced eviction-aware optimization to skip secondary index writes when evictions are not possible, reducing write overhead. - Stream-Stream Join Error Handling Improvement: replaced cryptic NPEs with more informative error messages and guidance in logs, accelerating debugging for users and developers. Major bugs fixed: - Timer Scanning Bug Fix in TransformWithState: removed the exclusive lower bound from timer scans to prevent losing timers across batches, restoring correctness for timer-driven workloads. - Accurate Progress Reporting in Spark Streaming: fixed progress counters for no-batch triggers to avoid confusion and flaky tests, improving test reliability and operator observability. Overall impact and accomplishments: - Increased reliability and performance of long-running streaming workloads by enabling precise range scans, reducing tombstone scanning, and cutting unnecessary writes. - Improved quality and maintainability of streaming code paths through clearer error messages and robust progress reporting, enabling faster triage and more predictable test outcomes. - Demonstrated deep proficiency with Spark Streaming internals, RocksDB-based StateStore optimizations, and safe refactoring patterns in live systems. Technologies/skills demonstrated: - Apache Spark (Streaming/SS), RocksDB StateStore, range scan primitives, Update mode semantics, non-outer join modes, eviction optimizations, UT enhancements, and plumbing for range-based data access.

March 2026 monthly summary focusing on performance improvements, correctness fixes, and feature maturation across Spark's streaming and RocksDB state store integration. The period delivered several targeted enhancements that reduce run-time, lower resource usage, and broaden data-format support, while strengthening test reliability.

February 2026 monthly summary for the Apache Spark contributions focused on streaming state management, performance optimization, and cross-language API enhancements. Key work spans RocksDB-backed state stores, Python DS streaming API integration, and state format engineering to support scalable stream processing. Key features delivered: - RocksDB Default Merge Operator Delimiter: changed to empty string to enable blind merges, improving streaming write throughput. Preserved backward compatibility via new SQL config spark.sql.streaming.stateStore.rocksdb.mergeOperatorVersion (default 2 for new queries; 1 for existing checkpoints). Commit: b2601be716598f8cc5326382f46b4d0c855c5220. PR SPARK-55131. - Admission Control and Trigger.AvailableNow in Python DS Streaming Reader: brought Python data source streaming reader to parity with Scala DSv2 by adding Admission Control and Trigger.AvailableNow support, with interface changes and new UTs. Commits: cbcc7f8a7f73576e1584d2ac2855640310e062eb. PR SPARK-55304. - Timestamp Key Encoders for State Stores: introduced first-class timestamp encoders (TimestampAsPrefixKeyStateEncoder and TimestampAsPostfixKeyStateEncoder) to optimize serialization and support timestamp placement strategies. Commit: cc9913bc9839396fc6c6205bc891f0324fb5d480. PR SPARK-55129. - StateStore API: Iterator and PrefixScan for multi-value retrieval: extended API to support multi-values in column families, enabling more flexible and efficient state scans. Commit: d9e1ee44576285f07c4d1803eec411a764a48d8b. PR SPARK-55494. - New State Format for Stream-Stream Join: introduced a new state format with secondary indexing and event-time aware key encoding to reduce eviction overhead and enable efficient joins. Commit: fcf8636105c060b0cc05712d503d4f6fe5a5dff5. PR SPARK-55144. - Cross-cutting impact and testing: added UTs, refactoring for compatibility, and groundwork for follow-up features (e.g., Avro support and further RocksDB optimizations).

Re-enabled and stabilized Spark streaming tests in CI for connect compatibility, restoring coverage and reducing flaky failures. Verified CI robustness with a zstandard-based reproducer setup, ensuring reproducible outcomes across local and CI environments. This work closes SPARK-53266 and strengthens streaming reliability for downstream developers and users.

November 2025: Focused delivery in apache/spark with two high-impact items improving reliability and build hygiene.

In 2025-10, shipped four key capabilities in Apache Spark that enhance streaming workloads and optimize query execution, delivering tangible business value for large-scale data pipelines and real-time analytics. Highlights include enabling stateful streaming with a WATERMARK clause in SQL, enabling Adaptive Query Execution (AQE) for stateless streaming with dedicated configuration, and introducing restart-time shuffle partition tuning for stateless streaming to adapt to changing input volumes without losing progress. Additionally, a targeted SQL config to govern AQE behavior for stateless streaming was added to improve rollout safety and operator predictability. These changes reduce backfill pain, improve resource utilization, and accelerate time-to-insight for streaming workloads. Tech leadership and collaboration across SQL, streaming, and runtime planning teams were demonstrated through end-to-end testing and documentation updates.

2025-09 monthly summary: Stability and reliability improvements focused on Spark's stateful streaming path and Arrow-based data transfer. The work centers on simplifying and hardening ListState serialization to avoid known Arrow-related issues while preserving compatibility with existing proto paths.