Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/15951#discussion_r89010923
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -291,22 +360,24 @@ case class DataSource(
throw new AnalysisException(s"Path does not exist: $qualified")
}
// Sufficient to check head of the globPath seq for non-glob
scenario
- if (checkFilesExist && !fs.exists(globPath.head)) {
+ // Don't need to check once again if files exist in streaming
mode
+ if (!isStreaming && !fs.exists(globPath.head)) {
throw new AnalysisException(s"Path does not exist:
${globPath.head}")
}
globPath
}.toArray
- // If they gave a schema, then we try and figure out the types of
the partition columns
- // from that schema.
- val partitionSchema = userSpecifiedSchema.map { schema =>
- StructType(
- partitionColumns.map { c =>
- // TODO: Case sensitivity.
- schema
- .find(_.name.toLowerCase() == c.toLowerCase())
- .getOrElse(throw new AnalysisException(s"Invalid
partition column '$c'"))
- })
+ val (schema, inferredPartitionColumns) =
getOrInferFileFormatSchema(format)
+ val dataSchema = if (isStreaming) {
+ schema
+ } else {
+ StructType(schema.dropRight(inferredPartitionColumns.length))
--- End diff --
nvm. with justPartitioning param, its more confusing as it is right now. so
I like this suggestion
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