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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> Costin 
>

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