Table of Contents
Why Combiners Need Associative Commutative Properties in Hadoop?
Combiner functions require associative and commutative math to guarantee partial local aggregations match full reducer results regardless of data grouping order in MapReduce jobs.
Question
Why must combiner functions be associative and commutative?
A. To avoid using the shuffle phase
B. To eliminate input splits
C. To increase reducer count
D. To guarantee consistent aggregation regardless of order
Answer
D. To guarantee consistent aggregation regardless of order
Explanation
Combiner functions must be associative and commutative because they perform partial local aggregations on mapper outputs in an order-agnostic manner—data may be grouped differently across map tasks, spills, or network transfers—requiring that sum(a, b, c) equals sum(sum(a, b), c) regardless of parentheses (associative) and sum(a, b) equals sum(b, a) (commutative) to match the final reducer result exactly. Without these properties, operations like averages (needing total sum/count pairs) or unique lists fail since partial combiner results cannot reliably merge, while safe examples like sum, max, min work identically in any partitioning. This ensures combiner optimization produces mathematically equivalent outputs to reducer-only processing, avoiding incorrect analytics despite reduced network traffic.