gistfile1.txt

import org.elasticsearch.spark._

val options = Map("pushdown" -> "true", "es.nodes" -> "172.17.0.1", "es.port" -> "9200", "es.field.read.as.array.include" -> "posTimes,pos.coordinates,origDetections", "es.index.auto.create" -> "false")
val df = sqlContext.read.format("org.elasticsearch.spark.sql")
                        .options(options).load("tracks/track")

df.groupBy("kind").count().show()
//df.show()
//df.groupBy("review.user.screen_name").count().show()

//df.printSchema()
//sc.esRDD("reviews/scored_review")
df.registerTempTable("esTracks")                        
//val userCount = df.count()
//print(userCount)

                        

gistfile2.txt

%dep

z.load("/jars/elasticsearch-hadoop-2.1.2.jar")

gistfile3.txt

import org.elasticsearch.spark._

val options = Map("pushdown" -> "true", "es.nodes" -> "172.17.0.1", "es.port" -> "9200", "es.field.read.as.array.include" -> "", "es.index.auto.create" -> "false")
val df = sqlContext.read.format("org.elasticsearch.spark.sql")
                        .options(options).load("reviews/review")

//df.groupBy("kind").count().show()
//df.show()
//df.groupBy("review.user.screen_name").count().show()

//df.printSchema()
//sc.esRDD("reviews/scored_review")
df.registerTempTable("esReview")                        
//val userCount = df.count()
//print(userCount)

gistfile4.txt

import org.elasticsearch.spark._
import org.apache.spark.sql.Row

val options = Map("pushdown" -> "true", "es.nodes" -> "172.17.0.1", "es.port" -> "9200", "es.field.read.as.array.include" -> "posTimes,pos.coordinates,origDetections", "es.index.auto.create" -> "false")
val df = sqlContext.read.format("org.elasticsearch.spark.sql")
                        .options(options).load("tracks/track")

df.groupBy("kind").count().show()
val result = df.groupBy("kind").count().map {
    case Row(kind: String, count: Long) => {
        kind + "\t" + count
    }
}.collect()
print ("%table Kind\tcount\n" + result.mkString("\n"))