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https://issues.apache.org/jira/browse/SPARK-26915?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-26915:
------------------------------------
Assignee: Apache Spark
> File source should write without schema inference and validation in
> DataFrameWriter.save()
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-26915
> URL: https://issues.apache.org/jira/browse/SPARK-26915
> Project: Spark
> Issue Type: Task
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Gengliang Wang
> Assignee: Apache Spark
> Priority: Major
>
> Spark supports writing to file data sources without getting and validation
> with the table schema.
> For example,
> ```
> spark.range(10).write.orc(path)
> val newDF = spark.range(20).map(id => (id.toDouble,
> id.toString)).toDF("double", "string")
> newDF.write.mode("overwrite").orc(path)
> ```
> 1. There is no need to get/infer the schema from the table/path
> 2. The schema of `newDF` can be different with the original table schema.
> However, from https://github.com/apache/spark/pull/23606/files#r255319992 we
> can see that the feature above is missing in data source V2. Currently, data
> source V2 always validates the output query with the table schema. Even after
> the catalog support of DS V2 is implemented, I think it is hard to support
> both behaviors with the current API/framework.
> This PR proposes to process file sources as a special case in
> `DataFrameWriter.save()`. So that we can keep the original behavior for this
> DataFrame API.
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