Principles Of Distributed Database Systems Exercise Solutions Jun 2026

Vertical fragments must be reconstructs using the Join ( ) operator over a common primary key. 2. Distributed Query Processing & Optimization

Low latency. Writes return success quickly after updating a minority of nodes. Background anti-entropy protocols (gossip protocols) sync the remaining nodes over time. Summary Checklist for Exam Preparation

If we ship the entire Department table to Node 1 to perform the join locally:

Primary horizontal fragmentation divides a relation based on predicates run against its own attributes. Derived horizontal fragmentation divides a relation based on predicates applied to a different relation.

Data isn't unnecessarily duplicated (unless specifically replicated for availability). Vertical fragments must be reconstructs using the Join

A transaction cannot acquire any new locks once it has released its very first lock. Quorum Consistency Condition:

: Defining horizontal and vertical fragments for a given schema.

Query optimization in a DDBMS minimizes total cost, which heavily weights communication cost over local processing CPU/IO cost. Semi-Join Optimization Semi-joins (

Distributed query processing aims to minimize communication costs by reducing the amount of data transferred between sites. Writes return success quickly after updating a minority

The or author you are currently following (e.g., Özsu & Valduriez).

[Coordinator] ───(1) Prepare───> [Participants] [Coordinator] <───(2) Votes───── [Participants] │ [CRASH] (Before issuing Global Commit) │ ▼ [Participants Left Hanging in Uncertainty Window] Why Participants Block

and send to Site 1: We transmit only the unique values of the join attribute from Site 2 to Site 1.

Solution Tip: Use . By combining all simple predicates from applications, you create non-overlapping fragments that satisfy the "completeness" and "disjointness" rules. 2. Distributed Query Processing Derived horizontal fragmentation divides a relation based on

: The authors maintain a dedicated solutions page at the University of Waterloo .

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One designated coordinator site manages all lock requests. Problems focus on bottleneck analysis and single-point-of-failure vulnerabilities.

| Topic Category | Key Concepts | Exercise Focus | | :--- | :--- | :--- | | | Shared-Nothing vs. Shared-Disk; Client-Server vs. Peer-to-Peer | Identify the best architecture for a given scenario (e.g., OLTP vs. Data Warehousing). | | Data Distribution | Horizontal/Vertical Fragmentation; Hash vs. Range Partitioning | Design fragmentation schemes to minimize cross-node queries. | | Distributed Transactions | 2PC, 3PC; Serializability; Concurrency Control | Trace the execution of a transaction log; identify deadlocks. | | Consistency Models | Strong, Eventual, Sequential, Causal Consistency; Quorum Protocols | Given an application (e.g., Twitter feed), choose the appropriate consistency model. |

Cost=Tuples of R×Tuple Size of RCost equals Tuples of cap R cross Tuple Size of cap R