Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/10861#discussion_r50627071
--- Diff: core/src/main/scala/org/apache/spark/SparkContext.scala ---
@@ -967,6 +967,29 @@ class SparkContext(config: SparkConf) extends Logging
with ExecutorAllocationCli
new HadoopRDD(this, conf, inputFormatClass, keyClass, valueClass,
minPartitions)
}
+ /**
+ * Get an RDD for a Hadoop-readable dataset from the Hadoop JobConf.
+ *
+ * @param broadcastedConf A general Hadoop Configuration, or a subclass
of it.
+ * @param initLocalJobConfFuncOpt Optional closure used to initialize
any JobConf
+ * that HadoopRDD creates.
+ * @param inputFormatClass Class of the InputFormat
+ * @param keyClass Class of the keys
+ * @param valueClass Class of the values
+ * @param minPartitions Minimum number of Hadoop Splits to generate.
+ */
+ def hadoopRDD[K, V](
+ broadcastedConf: Broadcast[SerializableConfiguration],
--- End diff --
The idea here is to let users share the broadcast of the conf across
multiple `hadoopRDD` calls (e.g. when unioning many HadoopRDDs together)? If
so, this issue has come up a number of times in the past and may be worth a
holistic design review because I think there are some hacks in Spark SQL to
address this problem there and it would be nice to have a unified solution for
this.
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