Table of Contents
What Main Benefit Do Combiners Provide for Mapper Output Shuffling?
Hadoop combiners aggregate mapper outputs locally to minimize network data transfer during shuffle, cutting bandwidth and speeding MapReduce jobs for operations like word count without altering final results.
Question
What is a key advantage of using combiners in MapReduce?
A. They determine reducer partitioning
B. They control HDFS replication
C. They minimize data transferred across the network
D. They replace reducers entirely
Answer
C. They minimize data transferred across the network
Explanation
A key advantage of using combiners in MapReduce is that they perform local aggregation on each mapper’s output before the shuffle phase, consolidating multiple identical key-value pairs (like several <“word”, 1> into <“word”, N>) directly on the map node to drastically reduce the volume of intermediate data shuffled across the network to reducers. This cuts bandwidth usage, I/O on spill files, and CPU load on reducers since fewer records need processing, speeding up jobs significantly for aggregative operations like counting or summing where the combiner logic matches the reducer. Combiners execute optionally after map() but before partitioning/sorting (not guaranteed per spill or replacing reducers/partitioners/HDFS settings), making network optimization their primary benefit in bandwidth-constrained clusters.