[ https://issues.apache.org/jira/browse/SPARK-27943?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16856595#comment-16856595 ]
Apache Spark commented on SPARK-27943: -------------------------------------- User 'beliefer' has created a pull request for this issue: https://github.com/apache/spark/pull/24372 > Implement default constraint with Column for Hive table > ------------------------------------------------------- > > Key: SPARK-27943 > URL: https://issues.apache.org/jira/browse/SPARK-27943 > Project: Spark > Issue Type: New Feature > Components: SQL > Affects Versions: 2.3.0, 2.4.0 > Reporter: jiaan.geng > Priority: Major > > Default constraint with column is ANSI standard. > Hive 3.0+ has supported default constraint > ref:https://issues.apache.org/jira/browse/HIVE-18726 > But Spark SQL implement this feature not yet. > Hive is widely used in production environments and is the standard in the > field of big data in fact. But Hive exists many version used in production > and the feature between each version are different. > Spark SQL need to implement default constraint, but there are two points to > pay attention to in design: > One is Spark SQL should reduce coupling with Hive. > Another is default constraint could compatible with different versions of > Hive. > We want to save the metadata of default constraint into properties of Hive > table, and then we restore metadata from the properties after client gets > newest metadata. > The implement is the same as other metadata (e.g. > partition,bucket,statistics). > Because default constraint is part of column, so I think could reuse the > metadata of StructField. The default constraint will cached by metadata of > StructField. > This is a big work, wo I want to split this work into some sub tasks, as > follows: > -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org