PairDStreamFunctions.mapWithState
与 updateState
类似,mapWithState
可用于根据即将到来的数据创建有状态 DStream。它需要 StateSpec
:
import org.apache.spark.streaming._
object StatefulStats {
val state = StateSpec.function(
(key: String, current: Option[Double], state: State[StatCounter]) => {
(current, state.getOption) match {
case (Some(x), Some(cnt)) => state.update(cnt.merge(x))
case (Some(x), None) => state.update(StatCounter(x))
case (None, None) => state.update(StatCounter())
case _ =>
}
(key, state.get)
}
)
}
它取得关键 key
,当前 value
和累积 State
并返回新状态。把这一切放在一起:
import org.apache.spark._
import org.apache.spark.streaming.dstream.DStream
import scala.collection.mutable.Queue
import org.apache.spark.util.StatCounter
object MapStateByKeyApp {
def main(args: Array[String]) {
val sc = new SparkContext("local", "mapWithState", new SparkConf())
val ssc = new StreamingContext(sc, Seconds(10))
ssc.checkpoint("/tmp/chk")
val queue = Queue(
sc.parallelize(Seq(("foo", 5.0), ("bar", 1.0))),
sc.parallelize(Seq(("foo", 1.0), ("foo", 99.0))),
sc.parallelize(Seq(("bar", 22.0), ("foo", 1.0))),
sc.emptyRDD[(String, Double)],
sc.parallelize(Seq(("foo", 1.0), ("bar", 1.0)))
)
val inputStream: DStream[(String, Double)] = ssc.queueStream(queue)
inputStream.mapWithState(StatefulStats.state).print()
ssc.start()
ssc.awaitTermination()
ssc.stop()
}
}
最后预期输出:
(bar,(count: 1, mean: 1.000000, stdev: 0.000000, max: 1.000000, min: 1.000000))
Time: 1469923290000 ms(foo,(count: 3, mean: 35.000000, stdev: 45.284287, max: 99.000000, min: 1.000000))
(foo,(count: 3, mean: 35.000000, stdev: 45.284287, max: 99.000000, min: 1.000000))
Time: 1469923300000 ms(bar,(count: 2, mean: 11.500000, stdev: 10.500000, max: 22.000000, min: 1.000000))
(foo,(count: 4, mean: 26.500000, stdev: 41.889736, max: 99.000000, min: 1.000000))
Time: 1469923310000 msTime: 1469923320000 ms(foo,(count: 5, mean: 21.400000, stdev: 38.830916, max: 99.000000, min: 1.000000))
(bar,(count: 3, mean: 8.000000, stdev: 9.899495, max: 22.000000, min: 1.000000))