Two-Phase Locking and Distributed 2PL Protocols

Lecture 32 details the Two-Phase Locking (2PL) protocol, proving why 2PL guarantees conflict serializability. The core rule of 2PL dictates that a transaction must acquire all necessary locks during a growing (expansion) phase before releasing any locks during a shrinking phase; once a lock is released, no further locks may be acquired. The lecture explores 2PL variants: Strict 2PL (retaining all exclusive locks until transaction commit/abort to eliminate cascading aborts) and Rigorous 2PL (holding all shared and exclusive locks until transaction termination). Students contrast distributed 2PL implementations: Centralized 2PL (a single site manages all global locks), Primary Copy 2PL (locks for replicated data are handled at designated primary copy sites), and Distributed 2PL (local lock managers at each site manage resident data locks). 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.