Reducing data transfer with semijoins In distributed query processing, a semijoin strategy does which of the following to minimize communication cost?

Introduction / Context:
Network I/O is a major cost in distributed joins. A classic optimization is the semijoin, which uses projections on join attributes to filter remote relations before transferring full tuples, thereby cutting down data shipped across the network.

Given Data / Assumptions:

• Two relations R and S reside at different sites.
• We need R ⋈ S on some join attributes (e.g., R.k = S.k).
• Goal: minimize data transferred while preserving correctness.

Concept / Approach:

In a semijoin, the initiating site sends only the distinct join attribute values (π_k(R)) to the remote site holding S. The remote site filters S to S’ = σ_{k ∈ π_k(R)}(S) and returns only the matching rows (or sometimes just their keys). This preselection step avoids shipping irrelevant tuples from S that would not contribute to the final join.

Step-by-Step Solution:

Verification / Alternative check:

Semijoin-based query plans are widely cited in distributed optimization, especially when selectivity is high and join attribute domains are much smaller than full tuples.

Why Other Options Are Wrong:

• Sending all attributes (b/d) increases traffic unnecessarily.
• Sending join attributes but returning all rows (a) defeats the purpose.
• Broadcasting all data (e) is worst-case and rarely optimal.

Common Pitfalls:

• Using semijoins when selectivity is poor, yielding little reduction.
• Forgetting to deduplicate projected keys before shipping.

Final Answer:

Sends only the join attributes to a remote site and then returns only the required matching rows.