package com.veera.spark.session1
import com.typesafe.config._
import org.apache.spark.SparkContext, org.apache.spark.SparkConf
import org.apache.hadoop.fs._
object WordCount {
def main(args: Array[String]) {
val appConf = ConfigFactory.load()
val conf = new SparkConf().
setAppName(“Word Count”).
setMaster(appConf.getConfig(args(2)).getString(“executionMode”))
for(c <- conf.getAll)
println(c._2)
val sc = new SparkContext(conf)
val inputPath = args(0)
val outputPath = args(1)
// We need to use
HDFS FileSystem
API to perform validations on input and output path
val fs = FileSystem.get(sc.hadoopConfiguration)
val inputPathExists = fs.exists(new Path(inputPath))
val outputPathExists = fs.exists(new Path(outputPath))
if(!inputPathExists) {
println(“Invalid input path”)
return
}
if(outputPathExists)
fs.delete(new Path(outputPath), true)
val file = sc.textFile(inputPath).
flatMap(line=> line.split(" ")).
map(word=> (word, 1)).
reduceByKey((acc, value) => acc + value)
// changing tuple to delimited text before saving the output
// We can also use map(rec => rec.
1 + “\t” + rec.2)
file.
map(rec => rec.productIterator.mkString(“\t”)).
saveAsTextFile(outputPath)
}
}