Solution:
Replication ensures high availability but requires sophisticated protocols to keep data consistent.
Lock managers are distributed across sites. Locks are requested at the site where the data item resides. Distributed Deadlock Detection
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T1’s write is rejected (rolled back/restarted) because a later transaction already wrote D.
Wait-for graph edges: T1 at Site1 waits for T2 at Site2. T2 at Site2 waits for T3 at Site1. T3 at Site1 waits for T1 at Site1.
Understanding the role of the Coordinator and Participants during the "Voting" and "Decision" phases. T3 at Site1 waits for T1 at Site1
Both fragments contain ProjID (the join key). The global relation is reconstructed as V1 ⨝ V2 .
+---------------+ | Fragment 2 | | (Orders) | +---------------+ | | v +---------------+ +---------------+ | Site B | | Site D | | (Replica 1) | | (Replica 2) | +---------------+ +---------------+
The original relation must be recoverable using the Union ( ) operator: Salary) with two sites:
Concurrency control is essential for maintaining data integrity. One of the most common exercises involves analyzing schedules and applying locking mechanisms to prevent anomalies.
Distributed deadlocks occur when a wait-for cycle spans multiple sites.
The CAP theorem is one of the most important concepts in distributed systems, stating that a distributed data store can only provide two of three guarantees simultaneously:
Show how a local WFG can fail to see a deadlock that a Global WFG (GWFG) identifies. Solution Methodology: Construct local WFGs for Site A and Site B. Introduce External Nodes ( Pextcap P sub ext end-sub
Relation EMPLOYEE(EID, Name, Dept, Salary) with two sites:
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