Github user xuanyuanking commented on a diff in the pull request:
https://github.com/apache/spark/pull/17702#discussion_r156256317
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -668,4 +672,31 @@ object DataSource extends Logging {
}
globPath
}
+
+ /**
+ * Return all paths represented by the wildcard string.
+ * Follow [[InMemoryFileIndex]].bulkListLeafFile and reuse the conf.
+ */
+ private def getGlobbedPaths(
+ sparkSession: SparkSession,
+ fs: FileSystem,
+ hadoopConf: SerializableConfiguration,
+ qualified: Path): Seq[Path] = {
+ val paths = SparkHadoopUtil.get.expandGlobPath(fs, qualified)
+ if (paths.size <=
sparkSession.sessionState.conf.parallelPartitionDiscoveryThreshold) {
+ SparkHadoopUtil.get.globPathIfNecessary(fs, qualified)
+ } else {
+ val parallelPartitionDiscoveryParallelism =
+
sparkSession.sessionState.conf.parallelPartitionDiscoveryParallelism
+ val numParallelism = Math.min(paths.size,
parallelPartitionDiscoveryParallelism)
+ val expanded = sparkSession.sparkContext
--- End diff --
@vanzin Thanks for you reply.
```
Why do this using a Spark job, instead of just a local thread pool?
```
As the DFS generally deployed together with NodeManagers for better data
locality, while using client mode and driver in different region with cluster,
using a Spark job will resolve the problem of cross region interaction in our
scenario.
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