You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Herman van Hovell (JIRA)" <ji...@apache.org> on 2018/01/22 02:46:00 UTC
[jira] [Updated] (SPARK-23173) from_json can produce nulls for
fields which are marked as non-nullable
[ https://issues.apache.org/jira/browse/SPARK-23173?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Herman van Hovell updated SPARK-23173:
--------------------------------------
Description:
The {{from_json}} function uses a schema to convert a string into a Spark SQL struct. This schema can contain non-nullable fields. The underlying {{JsonToStructs}} expression does not check if a resulting struct respects the nullability of the schema. This leads to very weird problems in consuming expressions. In our case parquet writing would produce an illegal parquet file.
There are roughly solutions here:
# Assume that each field in schema passed to {{from_json}} is nullable, and ignore the nullability information set in the passed schema.
# Validate the object during runtime, and fail execution if the data is null where we are not expecting this.
I currently am slightly in favor of option 1, since this is the more performant option and a lot easier to do.
WDYT? cc [~rxin] [~marmbrus] [~hyukjin.kwon] [~brkyvz]]
was:
The {{from_json}} function uses a schema to convert a string into a Spark SQL struct. This schema can contain non-nullable fields. The underlying {{JsonToStructs}} expression does not check if a resulting struct respects the nullability of the schema. This leads to very weird problems in consuming expressions. In our case parquet writing would produce an illegal parquet file.
There are roughly solutions here:
# Assume that each field in schema passed to {{from_json}} is nullable, and ignore the nullability information set in the passed schema.
# Validate the object during runtime, and fail execution if the data is null where we are not expecting this.
I currently am slightly in favor of option 1, since this is the more performant option and a lot easier to do. WDYT? cc [~rxin] [~marmbrus] [~hyukjin.kwon] [~brkyvz]]
> from_json can produce nulls for fields which are marked as non-nullable
> -----------------------------------------------------------------------
>
> Key: SPARK-23173
> URL: https://issues.apache.org/jira/browse/SPARK-23173
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.1
> Reporter: Herman van Hovell
> Priority: Major
>
> The {{from_json}} function uses a schema to convert a string into a Spark SQL struct. This schema can contain non-nullable fields. The underlying {{JsonToStructs}} expression does not check if a resulting struct respects the nullability of the schema. This leads to very weird problems in consuming expressions. In our case parquet writing would produce an illegal parquet file.
> There are roughly solutions here:
> # Assume that each field in schema passed to {{from_json}} is nullable, and ignore the nullability information set in the passed schema.
> # Validate the object during runtime, and fail execution if the data is null where we are not expecting this.
> I currently am slightly in favor of option 1, since this is the more performant option and a lot easier to do.
> WDYT? cc [~rxin] [~marmbrus] [~hyukjin.kwon] [~brkyvz]]
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org