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Proof: Assuming 'HR', 'Engineering', and 'Sales' are the only departments present in the database, every row will evaluate to true for exactly one predicate. If a new department (e.g., 'Marketing') is introduced, completeness fails.
The original relation can be rebuilt using the union operator:
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Coordinator Participant │ │ │ ─── 1. PREPARE ─────────────────> │ │ │ [Enters READY State] │ <── 2. VOTE_COMMIT ────────────── │ [Log written to disk] │ │ X NETWORK CRASH │ │ │ (Blocked state: cannot │ │ unilaterally commit/abort)
During the execution of a Two-Phase Commit (2PC) protocol, a participant node votes VOTE_COMMIT in Phase 1. Before it can receive the final GLOBAL_COMMIT or GLOBAL_ABORT decision from the coordinator in Phase 2, the network link drops, isolating the participant.
Managing "lock" and "unlock" phases across multiple nodes. Solutions often deal with Global Deadlock Detection , where a cycle exists in the Wait-For-Graph across different sites. Coordinator Participant │ │ │ ─── 1
Every employee tuple satisfies at least one minterm predicate.
The system lacks and Fragmentation Transparency .
Efficiency in a distributed system depends on how data is divided. Exercises often ask for the best way to fragment a database based on access patterns. Managing "lock" and "unlock" phases across multiple nodes
Proof: Since the predicates are mutually exclusive (an employee cannot belong to two departments simultaneously in this schema), the intersection of any two fragments is empty ( ). The fragmentation is disjoint. 3. Distributed Query Optimization
Finding formal exercise solutions for the authoritative textbook Principles of Distributed Database Systems
Three fragments R1 (size 100 tuples), S2 (size 500 tuples), T3 (size 2000 tuples) at three different sites. Compute the cheapest join order for R1 ⨝ S2 ⨝ T3 . Assume transmission cost = 1 unit per tuple, and local join cost ignored.