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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