dongjoon-hyun commented on a change in pull request #24043: [SPARK-11412][SQL]
Support merge schema for ORC
URL: https://github.com/apache/spark/pull/24043#discussion_r287911373
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -82,14 +83,95 @@ object OrcUtils extends Logging {
: Option[StructType] = {
val ignoreCorruptFiles = sparkSession.sessionState.conf.ignoreCorruptFiles
val conf = sparkSession.sessionState.newHadoopConf()
- // TODO: We need to support merge schema. Please see SPARK-11412.
files.toIterator.map(file => readSchema(file.getPath, conf,
ignoreCorruptFiles)).collectFirst {
case Some(schema) =>
logDebug(s"Reading schema from file $files, got Hive schema string:
$schema")
CatalystSqlParser.parseDataType(schema.toString).asInstanceOf[StructType]
}
}
+ /**
+ * Read single ORC file schema using native version of ORC
+ */
+ def singleFileSchemaReader(file: String, conf: Configuration,
ignoreCorruptFiles: Boolean)
+ : Option[StructType] = {
+ OrcUtils.readSchema(new Path(file), conf, ignoreCorruptFiles)
+ .map(s =>
CatalystSqlParser.parseDataType(s.toString).asInstanceOf[StructType])
+ }
+
+ /**
+ * Figures out a merged ORC schema with a distributed Spark job.
+ */
+ def mergeSchemasInParallel(
+ sparkSession: SparkSession,
+ files: Seq[FileStatus],
+ singleFileSchemaReader: (String, Configuration, Boolean) =>
Option[StructType])
+ : Option[StructType] = {
+ val serializedConf = new
SerializableConfiguration(sparkSession.sessionState.newHadoopConf())
+
+ val filePaths = files.map(_.getPath.toString)
+
+ // Set the number of partitions to prevent following schema reads from
generating many tasks
+ // in case of a small number of orc files.
+ val numParallelism = Math.min(Math.max(filePaths.size, 1),
+ sparkSession.sparkContext.defaultParallelism)
+
+ val ignoreCorruptFiles = sparkSession.sessionState.conf.ignoreCorruptFiles
+
+ // Issues a Spark job to read ORC schema in parallel.
+ val partiallyMergedSchemas =
+ sparkSession
+ .sparkContext
+ .parallelize(filePaths, numParallelism)
+ .mapPartitions { iterator =>
+ // Reads Orc schema in multi-threaded manner.
+ val partFiles = iterator.toSeq
+ val schemas = ThreadUtils.parmap(partFiles, "readingOrcSchemas", 8)
{ currentFile =>
+ singleFileSchemaReader(currentFile, serializedConf.value,
ignoreCorruptFiles)
+ }.flatten
+
+ if (schemas.isEmpty) {
+ Iterator.empty
+ } else {
+ var mergedSchema = schemas.head
+ schemas.tail.foreach { schema =>
+ try {
+ mergedSchema = mergedSchema.merge(schema)
+ } catch { case cause: SparkException =>
+ throw new SparkException(
+ s"Failed merging schema:\n${schema.treeString}", cause)
+ }
+ }
+ Iterator.single(mergedSchema)
+ }
+ }.collect()
+
+ if (partiallyMergedSchemas.isEmpty) {
+ None
+ } else {
+ var finalSchema = partiallyMergedSchemas.head
+ partiallyMergedSchemas.tail.foreach { schema =>
+ try {
+ finalSchema = finalSchema.merge(schema)
+ } catch { case cause: SparkException =>
+ throw new SparkException(
+ s"Failed merging schema:\n${schema.treeString}", cause)
+ }
+ }
+ Some(finalSchema)
+ }
+ }
+
+ def inferSchema(sparkSession: SparkSession, files: Seq[FileStatus], options:
Map[String, String])
+ : Option[StructType] = {
Review comment:
So, this function is used in `native` ORC readers (OrcFileFormat/OrcTable),
and `hive` OrcFileFormat has its own implementation of `inferSchema`, right?
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