October 2025 monthly summary for apache/spark focusing on TransformWithState reliability and state data handling. Business value delivered through improved reliability, correctness, and test coverage for stateful streaming work. Key features delivered: - TransformWithState API reliability and test coverage: Added Python tests for TransformWithState APIs to validate output schemas with nested structs and ensure correct handling of composite/nested outputs; groundwork for preventing data shape regressions. Commit reference 0702d58074c55f571f79420c024d8d558170ea22. - State message handling robustness: Fixed a bug causing partial reads of large proto-like messages in the TransformWithState In PySpark StateServer by using readFully to reliably read the full message. Commit reference 3f663bf583135295dcaba9e03fe9a722eb55665b. Major bugs fixed: - Partial read bug for large proto messages in TransformWithStateStateServer: switched to readFully DataInputStream to guarantee complete message reads, preventing incomplete state updates. Overall impact and accomplishments: - Increased reliability and correctness of stateful transforms, reducing runtime errors and data inconsistencies for large state values. - Enhanced test coverage for nested output schemas, enabling safer refactors and future schema evolution. - Maintained software stability with no user-facing changes while boosting robustness and confidence in stateful workloads. Technologies/skills demonstrated: - Java IO: readFully usage for robust message reading. - PySpark and Python test automation: cross-language validation of TransformWithState APIs. - End-to-end testing practices: sbt packaging and Python test runners integration for comprehensive validation.