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https://issues.apache.org/jira/browse/SPARK-10659?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14905856#comment-14905856
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Cheng Lian edited comment on SPARK-10659 at 9/24/15 5:51 AM:
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This behavior had once been a hacky way to workaround interoperability with
Hive (fields in Hive schemata are always nullable). I think we can remove this
one now. One potential design space problem need to be fixed is that, when
persisting a DataFrame as a table in Parquet format into Hive metastore, what
should we do if the schema has non-nullable fields. Basically two choices:
# Persist the table in Spark SQL data source specific format, which is Hive
incompatible, this preserves Parquet schema
# Turn the schema into nullable form and save it in Hive compatible format
I'd go for 1.
was (Author: lian cheng):
This behavior had once been a hacky way to workaround interoperability with
Hive (fields in Hive schemata are always nullable). I think we can remove this
one now. One potential design space problem need to be fixed is that, when
persisting a DataFrame as a table in Parquet format into Hive metastore, what
should we do if the schema has non-nullable fields. Basically two choices:
# Persist the table in Spark SQL data source specific format, which is Hive
incompatible, this preserves Parquet schema
# Turn the schema into nullable form and save it in Hive compatible format
I'd go for 1.
> DataFrames and SparkSQL saveAsParquetFile does not preserve REQUIRED (not
> nullable) flag in schema
> --------------------------------------------------------------------------------------------------
>
> Key: SPARK-10659
> URL: https://issues.apache.org/jira/browse/SPARK-10659
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.3.0, 1.3.1, 1.4.0, 1.4.1, 1.5.0
> Reporter: Vladimir Picka
>
> DataFrames currently automatically promotes all Parquet schema fields to
> optional when they are written to an empty directory. The problem remains in
> v1.5.0.
> The culprit is this code:
> {code}
> val relation = if (doInsertion) {
> // This is a hack. We always set
> nullable/containsNull/valueContainsNull to true
> // for the schema of a parquet data.
> val df =
> sqlContext.createDataFrame(
> data.queryExecution.toRdd,
> data.schema.asNullable)
> val createdRelation =
> createRelation(sqlContext, parameters,
> df.schema).asInstanceOf[ParquetRelation2]
> createdRelation.insert(df, overwrite = mode == SaveMode.Overwrite)
> createdRelation
> }
> {code}
> which was implemented as part of this PR:
> https://github.com/apache/spark/commit/1b490e91fd6b5d06d9caeb50e597639ccfc0bc3b
> This very unexpected behaviour for some use cases when files are read from
> one place and written to another like small file packing - it ends up with
> incompatible files because required can't be promoted to optional normally.
> It is essence of a schema that it enforces "required" invariant on data. It
> should be supposed that it is intended.
> I believe that a better approach is to have default behaviour to keep schema
> as is and provide f.e. a builder method or option to allow forcing to
> optional.
> Right now we have to overwrite private API so that our files are rewritten as
> is with all its perils.
> Vladimir
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