Github user rajeshbalamohan commented on a diff in the pull request:
https://github.com/apache/spark/pull/11978#discussion_r57537799
--- Diff: core/src/main/scala/org/apache/spark/SparkContext.scala ---
@@ -979,6 +979,7 @@ class SparkContext(config: SparkConf) extends Logging
with ExecutorAllocationCli
// A Hadoop configuration can be about 10 KB, which is pretty big, so
broadcast it.
val confBroadcast = broadcast(new
SerializableConfiguration(hadoopConfiguration))
val setInputPathsFunc = (jobConf: JobConf) =>
FileInputFormat.setInputPaths(jobConf, path)
+ clean(setInputPathsFunc)
--- End diff --
Thanks @srowen. Yes, for invocations via sc.textFile. Adding additional
method like following and passing initLocalJobConfFuncOpt to it can help avoid
closure cleaning in this scenario. However, this would call for changes in all
other places where sc.textFile is invoked. Intension was to allow user to make
use of HadoopRDD directly (if needed) without having to incur the cost of
closure cleaning (e.g in sql modules). Hence did not make those additional
changes.
```
def newTextFile(
path: String,
initLocalJobConfFuncOpt: Option[JobConf => Unit],
minPartitions: Int = defaultMinPartitions): RDD[String] = withScope {
assertNotStopped()
hadoopFile(path, classOf[TextInputFormat], initLocalJobConfFuncOpt,
classOf[LongWritable], classOf[Text],
minPartitions).map(pair => pair._2.toString).setName(path)
}
def hadoopFile[K, V](
path: String,
inputFormatClass: Class[_ <: InputFormat[K, V]],
initLocalJobConfFuncOpt: Option[JobConf => Unit],
keyClass: Class[K],
valueClass: Class[V],
minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope {
assertNotStopped()
// A Hadoop configuration can be about 10 KB, which is pretty big, so
broadcast it.
val confBroadcast = broadcast(new
SerializableConfiguration(hadoopConfiguration))
new HadoopRDD(
this,
confBroadcast,
initLocalJobConfFuncOpt,
inputFormatClass,
keyClass,
valueClass,
minPartitions).setName(path)
}
e.g
sc.newTextFile(tmpFilePath, Some(setInputPathsFunc), 4).count()
```
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