Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/5214#discussion_r27322315
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala ---
@@ -775,6 +777,32 @@ private[sql] object ParquetRelation2 extends Logging {
})
}
+ /**
+ * Returns the original schema from the Parquet file with any missing
nullable fields from the
+ * Hive Metastore schema merged in.
+ *
+ * When constructing a DataFrame from a collection of structured data,
the resulting object has
+ * a schema corresponding to the union of the fields present in each
element of the collection.
+ * Spark SQL simply assigns a null value to any field that isn't present
for a particular row.
+ * In some cases, it is possible that a given table partition stored as
a Parquet file doesn't
+ * contain a particular nullable field in its schema despite that field
being present in the
+ * table schema obtained from the Hive Metastore. This method returns a
schema representing the
+ * Parquet file schema along with any additional nullable fields from
the Metastore schema
+ * merged in.
+ */
+ private[parquet] def mergeMissingNullableFields(
+ metastoreSchema: StructType,
+ parquetSchema: StructType): StructType = {
+ val fieldMap = metastoreSchema.map(f => f.name.toLowerCase -> f).toMap
+ val missingFields = metastoreSchema
+ .map(_.name.toLowerCase)
+ .diff(parquetSchema.map(_.name.toLowerCase))
+ .map(fieldMap(_))
+ .filter(_.nullable)
+ StructType(parquetSchema ++ missingFields)
--- End diff --
When the metastore schema is available, we are actually converting a
metastore Parquet table into `ParquetRelation2`. Thus, the final reconciled
schema should have exactly the same fields as the metastore schema, and simply
drop any fields only appear in the Parquet data file.
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