Aleksander Eskilson created SPARK-17939: -------------------------------------------
Summary: Spark-SQL Nullability: Optimizations vs. Enforcement Clarification Key: SPARK-17939 URL: https://issues.apache.org/jira/browse/SPARK-17939 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 2.0.0 Reporter: Aleksander Eskilson Priority: Critical The notion of Nullability of of StructFields in DataFrames and Datasets creates some confusion. As has been pointed out previously [1], Nullability is a hint to the Catalyst optimizer, and is not meant to be a type-level enforcement. Allowing null fields can also help the reader successfully parse certain types of more loosely-typed data, like JSON and CSV, where null values are common, rather than just failing. There's already been some movement to clarify the meaning of Nullable in the API, but also some requests for a (perhaps completely separate) type-level implementation of Nullable that can act as an enforcement contract. This bug is logged here to discuss and clarify this issue. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org