You could check the code of KafkaRDD, the locality (host) is got from
Kafka's partition and set in KafkaRDD, this will a hint for Spark to
schedule task on the preferred location.
override def getPreferredLocations(thePart: Partition): Seq[String] = {
val part = thePart.asInstanceOf[KafkaRDDPartition]
// TODO is additional hostname resolution necessary here
Seq(part.host)
}
On Wed, Oct 14, 2015 at 5:38 PM, Gerard Maas <gerard.m...@gmail.com> wrote:
> In the receiver-based kafka streaming model, given that each receiver
> starts as a long-running task, one can rely in a certain degree of data
> locality based on the kafka partitioning: Data published on a given
> topic/partition will land on the same spark streaming receiving node until
> the receiver dies and needs to be restarted somewhere else.
>
> As I understand, the direct-kafka streaming model just computes offsets
> and relays the work to a KafkaRDD. How is the execution locality compared
> to the receiver-based approach?
>
> thanks, Gerard.
>
