JingsongLi opened a new issue, #735: URL: https://github.com/apache/incubator-paimon/issues/735
### Search before asking - [X] I searched in the [issues](https://github.com/apache/incubator-paimon/issues) and found nothing similar. ### Motivation In the world of data warehouse, it is very common to use one or more columns from a complex type such as a map, or to put many subfields into it. These operations can greatly affect query performance because: 1. These operations are very wasteful IO. For example, if we have a field type of Map, which contains dozens of subfields, we need to read the entire column when reading this column. And Spark will traverse the entire map to get the value of the target key. 2. Cannot take advantage of vectorized reads when reading nested type columns. 3. Filter pushdown cannot be used when reading nested columns. It is necessary to introduce the materialized column feature in Flink Table Store, which transparently solves the above problems of arbitrary columnar storage (not just Parquet). ### Solution _No response_ ### Anything else? _No response_ ### Are you willing to submit a PR? - [ ] I'm willing to submit a PR! -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@paimon.apache.org.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
