# reduceByKey(func, \[numTasks])

### reduceByKey(func, \[numTasks])

When called on a DStream of (K, V) pairs, return a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Note: By default, this uses Spark's default number of parallel tasks (2 for local mode, and in cluster mode the number is determined by the config property spark.default.parallelism) to do the grouping. You can pass an optional numTasks argument to set a different number of tasks.

```
import org.apache.spark._
import org.apache.spark.SparkContext._
import org.apache.spark.sql.{Row, SaveMode, SparkSession}
import org.apache.spark.sql.SQLContext
import org.apache.log4j.{Level, Logger}
import org.apache.spark.streaming.{Seconds, StreamingContext}

Logger.getLogger("org").setLevel(Level.ERROR)   

val spark = SparkSession
          .builder()
          .config("spark.master", "local[2]")
          .appName("streaming for book")
          .getOrCreate()
import spark.implicits._
val sc=spark.sparkContext
val ssc = new StreamingContext(sc, Seconds(1))

val dataDirectory="/tmp/filestream/"
val lines=ssc.textFileStream(dataDirectory)

val keyValues = lines.flatMap(_.split(" ")).filter(_.nonEmpty).map(x=>(x,1))
val keyCount=keyValues.reduceByKey((x,y)=>(x+y))

keyCount.print()
ssc.start()
ssc.awaitTermination()

/*
input file:

 cat y.txt
a a b c d
d h i j k


Output:

-------------------------------------------
Time: 1583561350000 ms
-------------------------------------------
(d,2)
(b,1)
(h,1)
(j,1)
(a,2)
(i,1)
(k,1)
(c,1)

*/
```


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