Spark difference between reduceByKey vs. groupByKey vs. aggregateByKey vs. combineByKey

groupByKey:

Syntax:

sparkContext.textFile("hdfs://")
                    .flatMap(line => line.split(" ") )
                    .map(word => (word,1))
                    .groupByKey()
                    .map((x,y) => (x,sum(y)))
            

groupByKey can cause out of disk problems as data is sent over the network and collected on the reduced workers.

reduceByKey:

Syntax:

sparkContext.textFile("hdfs://")
                    .flatMap(line => line.split(" "))
                    .map(word => (word,1))
                    .reduceByKey((x,y)=> (x+y))

Data are combined at each partition, with only one output for one key at each partition to send over the network. reduceByKey required combining all your values into another value with the exact same type.

aggregateByKey:

same as reduceByKey, which takes an initial value.

3 parameters as input

1. initial value
2. Combiner logic
3. sequence op logic

Example:

val keysWithValuesList = Array("foo=A", "foo=A", "foo=A", "foo=A", "foo=B", "bar=C", "bar=D", "bar=D")
    val data = sc.parallelize(keysWithValuesList)
    //Create key value pairs
    val kv = data.map(_.split("=")).map(v => (v(0), v(1))).cache()
    val initialCount = 0;
    val addToCounts = (n: Int, v: String) => n + 1
    val sumPartitionCounts = (p1: Int, p2: Int) => p1 + p2
    val countByKey = kv.aggregateByKey(initialCount)(addToCounts, sumPartitionCounts)

ouput:
Aggregate By Key sum Results
bar -> 3
foo -> 5

combineByKey:

3 parameters as input

1. Initial value: unlike aggregateByKey, need not pass constant always, we can pass a function that will return a new value.
2. merging function
3. combine function

Example:

val result = rdd.combineByKey(
                        (v) => (v,1),
                        ( (acc:(Int,Int),v) => acc._1 +v , acc._2 +1 ) ,
                        ( acc1:(Int,Int),acc2:(Int,Int) => (acc1._1+acc2._1) , (acc1._2+acc2._2)) 
                        ).map( { case (k,v) => (k,v._1/v._2.toDouble) })
        result.collect.foreach(println)

reduceByKey,aggregateByKey,combineByKey preferred over groupByKey

Reference:
Avoid groupByKey