Distributed Database Design and Distribution Issues

Lecture 11 introduces the distributed database design lifecycle, addressing the fundamental engineering questions of data partitioning and allocation across networked sites. The curriculum contrasts top-down design methodologies—appropriate for newly engineered homogeneous distributed systems starting from global conceptual requirements—with bottom-up integration methodologies used when aggregating pre-existing, heterogeneous database systems into a unified federation. Students evaluate the core distribution design issues: what logical fragments should be created, where should fragments be physically located, and should fragments be replicated across multiple sites. The lecture discusses quantitative information inputs required for distribution design, including database volume statistics, application access frequencies, site transaction processing capabilities, and communication link bandwidths. 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. Parallel data placement techniques, including round-robin, hash partitioning, and range partitioning, illustrate workload balancing across processing nodes. Parallel join algorithms evaluate parallel nested-loop, associative, and parallel hash join executions in high-throughput query environments. Distributed Object-Oriented Database Management Systems (OODBMS) explore object identity, encapsulation, complex object hierarchies, and pointer swizzling. Client-server and page-server architectures in distributed OODBMS are analyzed for cache coherence, client caching, and transaction synchronization. Multidatabase and federated database systems illustrate how schema integration and mediation resolve semantic heterogeneity across legacy data stores.