Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/7238#discussion_r35408700
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala ---
@@ -345,6 +348,28 @@ private[sql] class ParquetRelation2(
// Schema of the whole table, including partition columns.
var schema: StructType = _
+ def filterDataStatusesWithoutSummaries(
+ leaves: Seq[FileStatus],
+ dataStatuses: Seq[FileStatus]): Seq[FileStatus] = {
+
+ // Get the partitions that have summary files
+ val typeInference =
sqlContext.conf.partitionColumnTypeInferenceEnabled()
+ val summariesPaths = metadataStatuses.map(_.getPath.getParent()) ++
+ commonMetadataStatuses.map(_.getPath.getParent())
+ val summariesPartitions =
PartitioningUtils.parsePartitions(summariesPaths,
+ PartitioningUtils.DEFAULT_PARTITION_NAME,
typeInference).partitions.toSet
+
+ dataStatuses.filterNot { d =>
+ val part =
PartitioningUtils.parsePartitions(Seq(d.getPath.getParent()),
+ PartitioningUtils.DEFAULT_PARTITION_NAME, typeInference)
--- End diff --
Parsing partitions over a single path is dangerous. For example, consider a
partition column whose values are hex number strings:
```
/base/p=01
/base/p=0a
```
When dealing with the first path only, the column type is inferred to be
`IntegerType`. But when you take both paths into consideration, the column type
turns to `StringType` instead.
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