Client-Server and Peer-to-Peer DDBMS Architectures

Lecture 10 analyzes operational implementations of distributed database architectures, contrasting client-server systems with peer-to-peer configurations. The lecture explores two-tier client-server architectures, distinguishing thin-client models (where clients handle only presentation and servers execute all business logic and SQL processing via query shipping) from fat-client models (data shipping, where raw data pages are transmitted to clients for local caching and query processing). Three-tier architectures are introduced, inserting an intermediate application server layer to decouple business logic from database operations, improving security, connection pooling, and scalability. Finally, peer-to-peer distributed architectures are examined, where every participating node possesses identical functional capabilities and equal coordination authority. Formal transaction management frameworks analyze ACID properties in distributed environments, ensuring database integrity despite concurrent site failures. Serializability theory and conflict equivalence criteria provide mathematical foundations for proving the correctness of interleaved transaction schedules. Locking-based concurrency control protocols, including Two-Phase Locking, Distributed 2PL, and primary copy 2PL, are scrutinized for lock contention trade-offs. Distributed deadlock detection and resolution algorithms examine wait-for graphs, probe messages, and priority-based prevention strategies. Timestamp-ordering concurrency control mechanisms illustrate how physical and logical timestamps enforce serial ordering without deadlock vulnerability. Multi-version and conservative timestamp ordering algorithms are analyzed for read-heavy workloads and long-duration distributed transactions. Distributed query processing lifecycles trace query decomposition, algebraic normalization, semantic analysis, and elimination of redundant operations. Data localization algorithms transform global algebraic operator trees into localized fragment queries using relational reduction rules. Heuristic query optimization techniques illustrate how selection and projection operations are pushed down close to local physical fragments. Cost-based query optimization models analyze CPU overhead, disk page access costs, and communication network transmission delays. Distributed join optimization strategies contrast broadcast joins, partitioned hash joins, and semi-join reducers like the SDD-1 algorithm. Distributed reliability architectures examine failure classifications, including transaction aborts, site crashes, and network partitioning anomalies. Atomic commitment protocols, notably Two-Phase Commit (2PC) and Three-Phase Commit (3PC), are demonstrated with formal state transition diagrams. Parallel database system architectures, spanning Shared-Memory, Shared-Disk, and Shared-Nothing topologies, are contrasted for speedup and scaleup performance.