March 2026 performance summary for the Apache Spark project highlights two high-impact deliverables focused on reliability, performance, and operational resilience. The team executed a targeted bug fix and a feature improvement that together reduce downtime and prevent state-store related issues in streaming workloads.

January 2026 – Apache Spark (apache/spark): Delivered end-to-end offline state management for Stateful Streaming, enabling offline repartitioning and future use cases. Implemented State Rewriter to read-transform-write state stores, integrated with the Repartition runner for complete offline repartitioning, added a PySpark API and checkpointing for offline workflows, and introduced restart-safety for unfinished repartitioning to ensure consistency. Expanded testing with new and updated suites to validate the offline repartitioning workflow. Business value includes increased reliability, scalability, reduced downtime for maintenance, and better stateful streaming resilience.

December 2025 monthly summary for apache/spark: Delivered partition key extraction for streaming stateful operators to align state-store partitioning with operator repartitioning, enabling consistent repartition during query execution and offline state repartition. The work reduces correctness risk and data skew in streaming jobs, with no user-visible changes. Implemented with dedicated tests per operator to validate the behavior; closes SPARK-54443.

November 2025 at apache/spark delivered reliability improvements for stateful workloads and streaming state, focusing on data integrity, recoverability, and observability. Implemented row-level checksum verification for HDFS/RocksDB state stores (configurable, with read verification frequency) and automatic snapshot repair to rebuild state from the last good snapshot while applying subsequent changelogs; introduced offline repartition API for streaming state with a repartition runner and a streamingCheckpointManager. Expanded test suites and metrics to validate these flows and monitor repair activity. Note: the row-level checksum feature was rolled back due to observed performance concerns in CI/test suites to preserve stability, while maintaining coverage for the feature. Technologies involved include Spark state store APIs, HDFS, RocksDB, offset logs, changelogs, and streaming checkpoint management, with emphasis on performance-aware configurability and observability.

Month: 2025-10 Overview: Strengthened state integrity for streaming workloads in Apache Spark by delivering end-to-end verification for state checkpoints and RocksDB snapshot artifacts, plus checksum-based verification for state store files. This work improves reliability, observability, and safety for checkpoint-based recovery, with backward-compatible configuration toggles for flexible rollout. 1. Key features delivered - State Checkpoint Integrity Verification: Unifies file integrity verification for state checkpoints and RocksDB snapshot artifacts; adds a configuration toggle verifyNonEmptyFilesInZip to enable/disable verification; ensures RocksDB snapshot zip files do not contain empty files (except RocksDB logs); supports verification for delta, snapshot, changelog, and zip files with backward compatibility. - State Store file integrity verification using checksum: Generates and uploads checksum files to verify state store files during read; introduces a Spark conf to enable/disable this (enabled by default); backward compatible and can be enabled/disabled on existing checkpoints; currently applied to delta, snapshot, changelog, and zip. 2. Major bugs fixed - SPARK-54072: Prevent uploading empty files in RocksDB snapshot zip; ensures only the RocksDB log file may be empty; added non-empty file check and verifiable toggle. - SPARK-51972: Introduced state store file integrity verification using checksum; enables checksum-based verification during read; configured via new Spark conf; fully backward compatible. 3. Overall impact and accomplishments - Significantly increases checkpoint durability and correctness, reducing risk of recovery failures due to corrupted or incomplete state artifacts. - Improves observability and verifiability of state artifacts, enabling safer restarts and easier debugging. - Backward-compatible design enables incremental rollout without breaking existing workloads; new tests validate behavior across delta, snapshot, changelog, and zip. 4. Technologies/skills demonstrated - Apache Spark, RocksDB, and checkpointing internals. - File integrity verification, checksum-based verification, and configurable toggles. - Test-driven improvements with new tests; emphasis on backward compatibility and safe rollout.

June 2025 — Apache Spark (apache/spark): Delivered a targeted bug fix to improve stability and reliability in changelog ingestion. Implemented a robust parsing path for v1 changelog files to avoid NumberFormatException when version numbers are invalid, preventing query failures and upstream instability.

April 2025 monthly summary focusing on reliability and data integrity improvements in Spark's RocksDB integration. Delivered a targeted bug fix for snapshot creation that prevents SST file size mismatch corruption, enhancing stability for snapshot-based queries and analytics workloads.

February 2025 monthly summary for xupefei/spark. Focused on reliability improvements in Kafka integration and error reporting. Delivered a critical bug fix improving Kafka offset reading error handling by classifying errors instead of using assertions, leading to clearer user messages and faster remediation. The work centers on SPARK-50985 and the KafkaTokenProvider error flow. Commit: 572f57a0a41f0d3a4096c82944bdcba556d2b102. Impact: improved production stability, better UX for users configuring Kafka offsets.

November 2024 monthly summary for xupefei/spark: Delivered a RocksDB File Mapping Reuse Bug Fix addressing ineffective file reuse during checkpoint creation and version advancement. The fix improves performance by ensuring RocksDB file mappings are reused correctly, reducing IO overhead and stabilizing checkpoint workflows. Implemented in commit a0b4205d92513f68cf1b71e7c9827387af350b2a (SPARK-50151). This work enhances data reliability, throughput, and sets the groundwork for further RocksDB hardening in the project.