Return a sliding window count of elements in the stream.
Example:
import org.apache.spark._
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql
.{Row, SaveMode, SparkSession}
import org.apache.spark.sql.SQLContext
Logger.getLogger("org").setLevel(Level.ERROR)
val spark = SparkSession
.builder()
.config("spark.master", "local[2]")
.appName("streaming for book")
.getOrCreate()
spark.sparkContext.setCheckpointDir("/tmp/")
import spark.implicits._
val sc=spark.sparkContext
val ssc = new StreamingContext(sc, Seconds(1))
val messages1 = ssc
.textFileStream("/tmp/filestream1/")
val messages2 = ssc
.textFileStream("/tmp/filestream2/")
val messages3 = messages1.union(messages2)
val countMsg=messages3
.countByWindow(Seconds(10), Seconds(5))
countMsg.print()
ssc.start()
ssc.awaitTermination()
/*
input files:
send 2 files to /tmp/filestream1/ /tmp/filestream2/
cat xx.txt
a a b c d
d h i j k
cat yy.txt
a a b c d
d h i j k
cp xx.txt /tmp/filestream1;cp yy.txt /tmp/filestream2
output: it counts each line in file, including
last line which is empty, but count as 1 too
-------------------------------------------
Time: 1583610236000 ms
-------------------------------------------
6
-------------------------------------------
Time: 1583610241000 ms
-------------------------------------------
6
*/