srowen commented on a change in pull request #25899: SPARK-29089
DataFrameReader bottleneck in DataSource#checkAndGlobPathIfNecessary when
reading large amount of S3 files
URL: https://github.com/apache/spark/pull/25899#discussion_r327164735
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
##########
@@ -734,30 +734,45 @@ object DataSource extends Logging {
* Checks and returns files in all the paths.
*/
private[sql] def checkAndGlobPathIfNecessary(
- paths: Seq[String],
+ pathStrings: Seq[String],
hadoopConf: Configuration,
checkEmptyGlobPath: Boolean,
checkFilesExist: Boolean): Seq[Path] = {
- val allGlobPath = paths.flatMap { path =>
- val hdfsPath = new Path(path)
- val fs = hdfsPath.getFileSystem(hadoopConf)
- val qualified = hdfsPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
- val globPath = SparkHadoopUtil.get.globPathIfNecessary(fs, qualified)
-
- if (checkEmptyGlobPath && globPath.isEmpty) {
- throw new AnalysisException(s"Path does not exist: $qualified")
+ val qualifiedPaths = pathStrings
+ .map{pathString =>
+ val path = new Path(pathString)
+ val fs = path.getFileSystem(hadoopConf)
+ path.makeQualified(fs.getUri, fs.getWorkingDirectory)
}
- // Sufficient to check head of the globPath seq for non-glob scenario
- // Don't need to check once again if files exist in streaming mode
- if (checkFilesExist && !fs.exists(globPath.head)) {
- throw new AnalysisException(s"Path does not exist: ${globPath.head}")
+ // Split the paths into glob and non glob paths, because we don't need to
do an existence check
+ // for globbed paths.
+ val globPaths = qualifiedPaths
+ .filter(path => SparkHadoopUtil.get.isGlobPath(path))
+ val nonGlobPaths = qualifiedPaths
+ .filter(path => !SparkHadoopUtil.get.isGlobPath(path))
+
+ val globbedPaths = globPaths.par.flatMap { globPath =>
Review comment:
Yeah good point, we want to try to use ThreadUtils to avoid reusing the
global thread pool
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