Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/14537#discussion_r80216989
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala ---
@@ -237,21 +237,24 @@ private[hive] class
HiveMetastoreCatalog(sparkSession: SparkSession) extends Log
new Path(metastoreRelation.catalogTable.storage.locationUri.get),
partitionSpec)
- val inferredSchema = if (fileType.equals("parquet")) {
- val inferredSchema =
- defaultSource.inferSchema(sparkSession, options,
fileCatalog.allFiles())
- inferredSchema.map { inferred =>
- ParquetFileFormat.mergeMetastoreParquetSchema(metastoreSchema,
inferred)
- }.getOrElse(metastoreSchema)
- } else {
- defaultSource.inferSchema(sparkSession, options,
fileCatalog.allFiles()).get
+ val schema = fileType match {
+ case "parquet" =>
+ val inferredSchema =
+ defaultSource.inferSchema(sparkSession, options,
fileCatalog.allFiles())
+ inferredSchema.map { inferred =>
+
ParquetFileFormat.mergeMetastoreParquetSchema(metastoreSchema, inferred)
+ }.getOrElse(metastoreSchema)
+ case "orc" =>
+ metastoreSchema
--- End diff --
I went through the code path again, seems we must infer the schema here.
In metastore, we store the table schema and partition columns.
`HadoopFsRelation` need a `dataSchema` which is the real schema of the data
files. Normally it's just the table schema exclude partition columns, however,
Spark SQL supports a special case: partition columns can also exist in data
files. (see the doc for `HadoopFsRelation.dataSchema`). This information is not
preserved in metastore, so we have to infer the data schema based on data files
here.
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