Great Thanks a lot Costin.
Are people supposed to deploy the Spark workers on the same ES cluster? I
guess it would make sense for data to remain local and avoid network
transfers altogether?
Thanks a lot,
Mohamed.
On Monday, December 8, 2014 10:19:12 AM UTC-5, Costin Leau wrote:
>
> Hi,
>
> First off I recommend using the native integration (aka the Java/Scala
> APIs) instead of MapReduce. The latter works but
> the former is better performing and more flexible.
>
> ES works in a similar fashion to the HDFS store - the data doesn't go
> through the master rather, each task has its own
> partition on works on its own set of data. Behind the scenes we map each
> worker to an index shard (if there aren't
> enough workers, then some will work across multiple shards).
>
>
> On 12/8/14 4:59 PM, Mohamed Lrhazi wrote:
> > am trying to understand how spark and ES work... could someone please
> help me answer this question..
> >
> > val conf = new Configuration()
> > conf.set("es.resource", "radio/artists")
> > conf.set("es.query", "?q=me*")
> > val esRDD = sc.newHadoopRDD(conf, classOf[EsInputFormat[Text,
> MapWritable]],
> > classOf[Text], classOf[MapWritable]))
> > val docCount = esRDD.count();
> >
> >
> > When and where is data being transferred from ES? is it all collected on
> the Spark master node, then partitioned and
> > sent to the worker nodes? or is each worker node talking to ES to
> somehow get a partition of the data?
> >
> > How does this effectively work?
> >
> > Thanks a lot,
> > Mohamed.
> >
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