left
fun <K, V, W> <Error class: unknown class><<Error class: unknown class><K, V>>.leftOuterJoin(other: <Error class: unknown class><<Error class: unknown class><K, W>>, numPartitions: Int = dstream().ssc().sc().defaultParallelism()): <Error class: unknown class><<Error class: unknown class><K, <Error class: unknown class><V, <Error class: unknown class><W>>>>
Content copied to clipboard
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.
fun <K, V, W> <Error class: unknown class><<Error class: unknown class><K, V>>.leftOuterJoin(other: <Error class: unknown class><<Error class: unknown class><K, W>>, partitioner: <Error class: unknown class>): <Error class: unknown class><<Error class: unknown class><K, <Error class: unknown class><V, <Error class: unknown class><W>>>>
Content copied to clipboard
Return a new DStream by applying 'left outer join' between RDDs of this DStream and other DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.