For weeks, her team's distributed transaction system had been plagued by phantom reads and lost updates. Every time they thought they had the concurrency control figured out, a new anomaly would ripple through the nodes like a digital seismic wave.
: Implementation of ACID properties (Atomicity, Consistency, Isolation, Durability) across multiple sites.
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Exercises often ask you to choose the optimal join order minimising communication cost (e.g., from Exercise Sheet #8). For weeks, her team's distributed transaction system had
Consider a distributed database system with three sites: A, B, and C. Each site has a copy of a relation R. The relation R has the following tuples:
Site A has the following fragment of R:
|R′|=10,000×0.20=2,000 tuplesthe absolute value of cap R prime end-absolute-value equals 10 comma 000 cross 0.20 equals 2 comma 000 tuples Ship the reduced relation R′cap R prime from Site 1 to Site 2 to complete the final join ( This public link is valid for 7 days
Given TS(T1)=10, TS(T2)=20. At site X, data item D has write_TS=5 , read_TS=5 . T2 issues write(D) . T1 issues write(D) later. Apply basic timestamp ordering (TO) rules.
Basic TO rule for write_TS:
: Managing simultaneous data access using distributed locking or timestamp ordering. Can’t copy the link right now
Exercises in this area (e.g., from Chapter 7 of the 3rd edition) ask you to transform a global query into a reduced query on fragments.
What are the main advantages of a distributed database system?
A classic exercise solution to reduce communication cost. Instead of sending an entire Table A to Table B’s site for a join, the system sends only the joining column of A. Table B filters its rows against this column and sends back only the matching records. This drastically reduces the volume of data crossing the network. Concurrency Control and Consistency