
Marko Sisovic enhanced the apache/spark repository by delivering an API update for the PostgreSQL JDBC Connector, introducing a beforeFetch method in JDBCDialect that leverages JDBCOptions for option handling. This change modernized the connector’s configuration approach, enabling case-insensitive fetchsize processing and ensuring autocommit is consistently applied, regardless of option casing. Marko implemented the update in Scala, focusing on database management and robust software development practices. Comprehensive tests were added in PostgresDialectSuite to validate the new behavior. The work improved reliability for Postgres integrations, maintained backward compatibility, and set a clear path for future API migration within Spark’s SQL ecosystem.
2025-12 Monthly Summary for apache/spark development: Delivered a focused API enhancement for the PostgreSQL JDBC Connector that aligns with Spark’s data source option semantics and improves reliability of fetch behavior. Key improvements: - PostgreSQL JDBC Connector: Added beforeFetch API in JDBCDialect to accept JDBCOptions, enabling case-insensitive handling of fetchsize and ensuring autocommit is applied correctly irrespective of the fetchsize option's casing. - API modernization: Transitioned toward using JDBCOptions instead of Map[String, String] for options, with deprecation of the old API planned for Spark 4.2.0 per project guidance. - Testing and quality: Added new tests under PostgresDialectSuite to validate case-insensitive option handling and autocommit behavior. Commit and work trace: - Core changes committed in 4c306ff5162ca738caad047b2a7740023cc43c57 as part of SPARK-54581, which documents the case-insensitivity changes and the new beforeFetch API. - PR closes #53308 (Marko Sisovic) with authorship and sign-off by the Spark team. Impact and business value: - Reliability: Ensures correct autocommit behavior and fetchsize handling across PostgreSQL dialects, reducing edge-case failures. - Developer experience: Simplifies option handling via JDBCOptions and aligns behavior with Spark docs, reducing confusion and integration friction for dialect implementers. - Maintains backward compatibility paths while setting clear modernization trajectory. Technologies/skills demonstrated: - Spark SQL JDBC data source extension (JDBCDialect, PostgresDialect) - API design and migration planning (JDBCOptions API, deprecation strategy) - JVM/Scala testing practices (PostgresDialectSuite) and CI readiness Overall: 1 meaningful feature delivery with production-quality tests, enabling more robust Postgres integrations and a clearer upgrade path for downstream users.
2025-12 Monthly Summary for apache/spark development: Delivered a focused API enhancement for the PostgreSQL JDBC Connector that aligns with Spark’s data source option semantics and improves reliability of fetch behavior. Key improvements: - PostgreSQL JDBC Connector: Added beforeFetch API in JDBCDialect to accept JDBCOptions, enabling case-insensitive handling of fetchsize and ensuring autocommit is applied correctly irrespective of the fetchsize option's casing. - API modernization: Transitioned toward using JDBCOptions instead of Map[String, String] for options, with deprecation of the old API planned for Spark 4.2.0 per project guidance. - Testing and quality: Added new tests under PostgresDialectSuite to validate case-insensitive option handling and autocommit behavior. Commit and work trace: - Core changes committed in 4c306ff5162ca738caad047b2a7740023cc43c57 as part of SPARK-54581, which documents the case-insensitivity changes and the new beforeFetch API. - PR closes #53308 (Marko Sisovic) with authorship and sign-off by the Spark team. Impact and business value: - Reliability: Ensures correct autocommit behavior and fetchsize handling across PostgreSQL dialects, reducing edge-case failures. - Developer experience: Simplifies option handling via JDBCOptions and aligns behavior with Spark docs, reducing confusion and integration friction for dialect implementers. - Maintains backward compatibility paths while setting clear modernization trajectory. Technologies/skills demonstrated: - Spark SQL JDBC data source extension (JDBCDialect, PostgresDialect) - API design and migration planning (JDBCOptions API, deprecation strategy) - JVM/Scala testing practices (PostgresDialectSuite) and CI readiness Overall: 1 meaningful feature delivery with production-quality tests, enabling more robust Postgres integrations and a clearer upgrade path for downstream users.

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