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- 1. Distributed Data Amanpartap Singh Pall Assistant Professor School of IT 1
- 3. Homogeneous Distributed Databases • In a homogeneous distributed database • All sites have identical software • Are aware of each other and agree to cooperate in processing user requests. • Each site surrenders part of its autonomy in terms of right to change schemas or software • Appears to user as a single system • In a heterogeneous distributed database • Different sites may use different schemas and software • Difference in schema is a major problem for query processing • Difference in software is a major problem for transaction processing • Sites may not be aware of each other and may provide only limited facilities for cooperation in transaction processing
- 4. DB architectures (1) Shared memory 4 P P P ... M
- 5. DB architectures (2) Shared disk 5 ... ... P M P P M M
- 6. DB architectures (3) Shared nothing 6 P M P M P M ...
- 7. DB architectures (4) Hybrid example – Hierarchical or Clustered 7 M P P P ... M P P P ...
- 8. Issues for selecting architecture • Reliability • Scalability • Geographic distribution of data • Performance • Cost 8
- 9. • Typically, parallel DBs: • Fast interconnect • Homogeneous software • High performance is goal • Transparency is goal 9
- 10. • Typically, distributed DBs: • Geographically distributed • Data sharing is goal (may run into heterogeneity, autonomy) • Disconnected operation possible 10
- 11. Distributed Database Challenges • Distributed Database Design • Deciding what data goes where • Depends on data access patterns of major applications • Two subproblems: • Fragmentation: partition tables into fragments • Allocation: allocate fragments to nodes 11
- 12. Distributed Data Storage • Assume relational data model • Replication • System maintains multiple copies of data, stored in different sites, for faster retrieval and fault tolerance. • Fragmentation • Relation is partitioned into several fragments stored in distinct sites • Replication and fragmentation can be combined • Relation is partitioned into several fragments: system maintains several identical replicas of each such fragment.
- 13. Data Replication • A relation or fragment of a relation is replicated if it is stored redundantly in two or more sites. • Full replication of a relation is the case where the relation is stored at all sites. • Fully redundant databases are those in which every site contains a copy of the entire database.
- 14. Data Replication (Cont.) • Advantages of Replication • Availability: failure of site containing relation r does not result in unavailability of r is replicas exist. • Parallelism: queries on r may be processed by several nodes in parallel. • Reduced data transfer: relation r is available locally at each site containing a replica of r. • Disadvantages of Replication • Increased cost of updates: each replica of relation r must be updated. • Increased complexity of concurrency control: concurrent updates to distinct replicas may lead to inconsistent data unless special concurrency control mechanisms are implemented. • One solution: choose one copy as primary copy and apply concurrency control operations on primary copy
- 15. Data Fragmentation • Division of relation r into fragments r1, r2, …, rn which contain sufficient information to reconstruct relation r. • Horizontal fragmentation: each tuple of r is assigned to one or more fragments • Vertical fragmentation: the schema for relation r is split into several smaller schemas • All schemas must contain a common candidate key (or superkey) to ensure lossless join property. • A special attribute, the tuple-id attribute may be added to each schema to serve as a candidate key. • Example : relation account with following schema • Account = (branch_name, account_number, balance )
- 16. Horizontal Fragmentation of account Relation branch_name account_number balance Hillside Hillside Hillside A-305 A-226 A-155 500 336 62 account1 = branch_name=“Hillside” (account ) branch_name account_number balance Valleyview Valleyview Valleyview Valleyview A-177 A-402 A-408 A-639 205 10000 1123 750 account2 = branch_name=“Valleyview” (account )
- 17. Vertical Fragmentation of employee_info Relation branch_name customer_name tuple_id Hillside Hillside Valleyview Valleyview Hillside Valleyview Valleyview Lowman Camp Camp Kahn Kahn Kahn Green deposit1 = branch_name, customer_name, tuple_id (employee_info ) 1 2 3 4 5 6 7 account_number balance tuple_id 500 336 205 10000 62 1123 750 1 2 3 4 5 6 7 A-305 A-226 A-177 A-402 A-155 A-408 A-639 deposit2 = account_number, balance, tuple_id (employee_info )
- 18. Advantages of Fragmentation • Horizontal: • allows parallel processing on fragments of a relation • allows a relation to be split so that tuples are located where they are most frequently accessed • Vertical: • allows tuples to be split so that each part of the tuple is stored where it is most frequently accessed • tuple-id attribute allows efficient joining of vertical fragments • allows parallel processing on a relation • Vertical and horizontal fragmentation can be mixed. • Fragments may be successively fragmented to an arbitrary depth.
- 19. Data Transparency •Data transparency: Degree to which system user may remain unaware of the details of how and where the data items are stored in a distributed system •Consider transparency issues in relation to: • Fragmentation transparency • Replication transparency • Location transparency
- 20. Naming of Data Items - Criteria 1. Every data item must have a system-wide unique name. 2. It should be possible to find the location of data items efficiently. 3. It should be possible to change the location of data items transparently. 4. Each site should be able to create new data items autonomously.
- 21. Centralized Scheme - Name Server • Structure: • name server assigns all names • each site maintains a record of local data items • sites ask name server to locate non-local data items • Advantages: • satisfies naming criteria 1-3 • Disadvantages: • does not satisfy naming criterion 4 • name server is a potential performance bottleneck • name server is a single point of failure
- 22. Use of Aliases • Alternative to centralized scheme: each site prefixes its own site identifier to any name that it generates i.e., site 17.account. • Fulfills having a unique identifier, and avoids problems associated with central control. • However, fails to achieve network transparency.
- 23. What is a Transaction? • A set of steps completed by a DBMS to accomplish a single user task. • Must be either entirely completed or aborted • No intermediate states are acceptable
- 24. Distributed Transactions • Transaction may access data at several sites. • Each site has a local transaction manager responsible for: • Maintaining a log for recovery purposes • Participating in coordinating the concurrent execution of the transactions executing at that site. • Each site has a transaction coordinator, which is responsible for: • Starting the execution of transactions that originate at the site. • Distributing subtransactions at appropriate sites for execution. • Coordinating the termination of each transaction that originates at the site, which may result in the transaction being committed at all sites or aborted at all sites.
- 26. System Failure Modes • Failures unique to distributed systems: • Failure of a site. • Loss of massages • Handled by network transmission control protocols such as TCP-IP • Failure of a communication link • Handled by network protocols, by routing messages via alternative links • Network partition • A network is said to be partitioned when it has been split into two or more subsystems that lack any connection between them • Note: a subsystem may consist of a single node • Network partitioning and site failures are generally indistinguishable.