At the DAG level configuration:

See PairRDDFunctions.scala#L503-L505

    val createCombiner = (v: V) => CompactBuffer(v)
    val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
    val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2

which eventually calls: PairRDDFunctions.combineByKeyWithClassTag

val aggregator = new Aggregator[K, V, C](
      self.context.clean(createCombiner),
      self.context.clean(mergeValue),
      self.context.clean(mergeCombiners))

which in turn, boils down to:

new ShuffledRDD[K, V, C](self, partitioner)
        .setSerializer(serializer)
        .setAggregator(aggregator)
        .setMapSideCombine(mapSideCombine)

Reduce side

On the reduce side, when the DAG is actually materialized:

ShuffledRDD#compute is the actual RDD implm for a shuffled RDD.

This in turn uses BlockStoreShuffleReader#read which has the following important points:

1. Read the shuffle blocks from mappers (network via Netty or local): ShuffleBlockFetcherIterator
2. Deserialize: recordIter

3. Aggregate the (K,V)s by key: aggregatedIter aggregator#combineValuesByKey

   val combiners = new ExternalAppendOnlyMap[K, V, C]
   (createCombiner, mergeValue, mergeCombiners)
   

   ExternalAppendOnlyMap will accumulate in memory the (K,agg(V)) till spill limit is hit, then spills (keep a reference to an on-disk partial sorted iterator)

4. The final resultIter is actually provided by ExternalSorter which wraps the aggregatedIter and does the final merge sort of partial sorted results. See ExternalSorter#merge
   For groupBy the aggregator.isDefined=false, only ordering.isDefined=true

References:

[1] ExternalAppendOnlyMap: https://github.com/JerryLead/SparkInternals/blob/master/markdown/english/4-shuffleDetails.md#externalappendonlymap

repartitionAndSortWithPartition is ligher(?)