Hi Spark devs, I'd like to call a vote on the SPIP*: DSV2 Enhanced Partition Stats Filtering.*
*Summary:* The DataSource V2 (DSV2) framework does not currently provide full data-skipping capabilities comparable to Spark-native sources, primarily due to limitations in Catalyst expression evaluation. This SPIP bridges that gap to achieve partition-skipping parity. To support this, Spark will push new *PartitionPredicate* objects that encapsulate Catalyst partition filter expressions and the evaluation logic, allowing data sources to skip irrelevant partitions effectively. *Relevant Links:* - - *SPIP Doc:* https://docs.google.com/document/d/17vcw411PxSRLWoK-BiLI56UiNdokLWtovF8JZUlDTOo/edit?usp=sharing - *Discuss Thread:* https://lists.apache.org/thread/p2cwngj9bmtcbmyplds833s9lwts8bwc - *JIRA:* SPARK-55596 <https://issues.apache.org/jira/browse/SPARK-55596> - *POC PR:* PR 54459 <https://github.com/apache/spark/pull/54459> *The vote will be open for at least 72 hours. *Please vote: [ ] +1: Accept the proposal as an official SPIP [ ] +0 [ ] -1: I don't think this is a good idea because ... Thanks, Gengliang Wang
