Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/16137#discussion_r91855254
--- Diff: core/src/main/scala/org/apache/spark/SparkContext.scala ---
@@ -956,24 +976,24 @@ class SparkContext(config: SparkConf) extends Logging
{
}
/**
- * Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given
its InputFormat and other
- * necessary info (e.g. file name for a filesystem-based dataset, table
name for HyperTable),
- * using the older MapReduce API (`org.apache.hadoop.mapred`).
+ * Get an RDD for a Hadoop-readable dataset from a Hadoop `JobConf`
given its `InputFormat`
+ * and other necessary info (e.g. file name for a filesystem-based
dataset, table name
+ * for HyperTable), using the older MapReduce API
(`org.apache.hadoop.mapred`).
*
- * @param conf JobConf for setting up the dataset. Note: This will be
put into a Broadcast.
+ * @note Because Hadoop's `RecordReader` class re-uses the same Writable
object for each
+ * record, directly caching the returned RDD or directly passing it to
an aggregation
+ * or shuffle operation will create many references to the same object.
+ * If you plan to directly cache, sort, or aggregate Hadoop writable
objects, you
+ * should first copy them using a `map` function.
+ * @param conf `JobConf` for setting up the dataset. Note: This will be
put into a Broadcast.
* Therefore if you plan to reuse this conf to create
multiple RDDs, you need to make
* sure you won't modify the conf. A safe approach is always
creating a new conf for
* a new RDD.
* @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.
- *
--- End diff --
As before, don't move this please
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