Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/13775#discussion_r83764569
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileFormat.scala ---
@@ -131,31 +138,43 @@ class OrcFileFormat extends FileFormat with
DataSourceRegister with Serializable
val physicalSchema = maybePhysicalSchema.get
OrcRelation.setRequiredColumns(conf, physicalSchema,
requiredSchema)
- val orcRecordReader = {
- val job = Job.getInstance(conf)
- FileInputFormat.setInputPaths(job, file.filePath)
-
- val fileSplit = new FileSplit(
- new Path(new URI(file.filePath)), file.start, file.length,
Array.empty
- )
- // Custom OrcRecordReader is used to get
- // ObjectInspector during recordReader creation itself and can
- // avoid NameNode call in unwrapOrcStructs per file.
- // Specifically would be helpful for partitioned datasets.
- val orcReader = OrcFile.createReader(
- new Path(new URI(file.filePath)), OrcFile.readerOptions(conf))
- new SparkOrcNewRecordReader(orcReader, conf, fileSplit.getStart,
fileSplit.getLength)
+ val job = Job.getInstance(conf)
+ FileInputFormat.setInputPaths(job, file.filePath)
+
+ val fileSplit = new FileSplit(
+ new Path(new URI(file.filePath)), file.start, file.length,
Array.empty
+ )
+ // Custom OrcRecordReader is used to get
+ // ObjectInspector during recordReader creation itself and can
+ // avoid NameNode call in unwrapOrcStructs per file.
+ // Specifically would be helpful for partitioned datasets.
+ val orcReader = OrcFile.createReader(
+ new Path(new URI(file.filePath)), OrcFile.readerOptions(conf))
+
+ if (enableVectorizedReader) {
+ val conf = job.getConfiguration.asInstanceOf[JobConf]
--- End diff --
why can't you reuse the `conf` at line 129
(https://github.com/apache/spark/pull/13775/files#diff-01999ccbf13e95a0ea2d223f69d8ae23R129)
?
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