Question
Consider the following schedule involving two transactions T1 and T2 operating on a dataset table
storing aggregate metrics:
(i) Compute the final value of X (assume initial X = 100).
(ii) Determine whether the schedule is serializable.
(iii) Identify the concurrency issue involved and explain its impact on analytics accuracy.
(c) Explain the Two-Phase Locking (2PL) protocol. Discuss how it ensures consistency in multi-user
analytical environments. Can deadlocks still occur? Explain with a suitable example.
Answer :
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Skip in 10
Assume the schedule is as follows: * T1: Read(X) → X = X + 20 → Write(X) * T2: Read(X) → X = X × 2 → Write(X) with an initial value of X = 100, and the operations are interleaved in the following order: * T1: Read(X = 100) * T2: Read(X = 100) * T1: X = 120; Write(X = 120) * T2: X = 200; Write(X = 200) The final value of X is 200. Although T1 updates X to 120, its update is later overwritten by T2, which performs its computation using the old value of X (100). Thus, the effect of T1 is lost, and the final value becomes 200 instead of reflecting both transactions. To determine whether the schedule is serializable, a precedence (serialization) graph can be constructed. Both transactions access the same data item X, and at least one operation in each conflicting pair is a write. Since T1 writes X after T2 has already read X, there is a conflict. Likewise, T2 writes X after T1 has written X, creating another conflict. These conflicting operations produce edges in both directions between T1 and T2, resulting in a cycle in the precedence graph. Because the graph contains a cycle, the schedule is not conflict serializable. It is also not equivalent to either possible serial execution. If executed serially: * T1 followed by
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