1 Lecture 7 Distributed Data Bases: Principles and Architectures

2 Distributed Data Base (DDB) – Definition A logically interrelated collection of shared data, physically distributed over a computer network. Implies data description at two levels: Global (the view of the whole); Local (where data is actually held).

3 DDBMS (Distributed DBMS) The software system that permits the management of the distributed data base and makes the distribution transparent to users: Transparent – users are unaware of the underlying local structure: Data requests do not specify distribution sites -but they may notice performance differences (e.g. if local data has to be moved to another site along a slow line).

4 Characteristics of a DDB Collection of logically related shared data. Data is split into a number of fragments (horizontal or vertical – restrict or project). Fragments may be replicated. Fragments / replicas are allocated to sites. Fragments are in effect views; Replicas are duplicates – only acceptable if redundancy is controlled.

5 Why distribute? Natural match of data with location Can have each division, department or office hold its own data with some degree of autonomy. Autonomy – to have control (self-determination, self-rule) Users can decide policies locally (devolved). Still need global DBA to ensure entire system works.

6 Why distribute? (cont.) More flexible operation: Improved availability -one node failure does not bring the whole system down. Improved reliability -replication ensures that copies of data are still available if a node fails. Improved performance -accessing most data locally reduces network overheads. Readily handle expansion -can add new nodes with local schema -followed by simple adjustments to global definition.

7 Problems with Distribution Complexity Global and local schema must be integrated. Design techniques involve more stages. Replications rigorously handled. Network needs to be robust. Costs More people effort needed to handle the complexity –although cheaper to buy power with smaller machines rather than larger ones.

8 Problems with Distribution (cont.) Security Many more potential access points for would- be violators. Integrity Need to ensure that combination of local and global constraints gives the required effect. Experience Fairly immature technology –not yet translated to standards.

9 Homogeneous and Heterogeneous DDBMS A homogeneous DDBMS uses the same database product at all sites. A heterogeneous DDBMS uses different data base products at various sites –may arise from corporate mergers.

10 Degrees of Heterogeneity  Same software, different hardware can be handled fairly easily. Oracle 9i : Oracle 8i – differences slight. Oracle 9i : SQL Server – same underlying relational model, different syntax in places. Oracle 9i : Objectivity – object-relational (SQL-1999) and ODMG respectively, different underlying model.

11 Interoperability Ability to work with each other. In a loosely coupled environment: Full details of each system not needed –BUT need to have interfaces for reliably exchanging messages without error or misunderstanding Solutions: Standardized specifications; Mediation. Differences in implementation may still lead to breakdowns in communication.

12 Simple Problem in Interoperability - 1 Two schemas in SQL-1999: AB author varchar2(50),author_surname varchar2(40), author, initials varchar2(10), title varchar2(300),title varchar2(200), keyword1 varchar2(30),keywd keywordarr; keyword2 varchar2(30); CREATE TYPE keywordarr AS VARRAY(8) OF varchar2(30); Note: homogeneous model – both SQL-1999 – but difficulties.

13 Different Standards e.g. Names: Person (surname, first_name, …) or Person (first_name, surname, …) or Person (name, …) First two may easily be made equivalent but convention in third needs to be understood. Note also possibilities of A.N.Other, AN Other, A N Other, etc.

14 Possible Solutions In schema B, define a function which amalgamates the two parts of author into one value. Will need to look manually at format of author in schema A. If format inconsistent, need some pre-processing. Other inconsistencies require decisions: Fixed two entries for keyword vs. array dimension 8. Different name for keyword attribute. Different size for title fields (presumably adopt higher). In a heterogeneous environment, we need also to relate schema constructions, e.g. is CLASS the same as TABLE?

15 Simple Problem in Interoperability - 2 Homogeneous Models The same information may be held as attribute name, relation name or a value in different data bases. e.g. library fines could be held in a dedicated relation Fine (amount, borrower_id) –or as an attribute of Loan (id, isbn, date_out, fine) –or as a value Charge (1.25, ‘fine’).

16 Architectures for Interoperability 1 1. Global schema integration Produces a single new schema (C) for the different information systems with schemas (A, B). A C B

17 Global Schema Integration Advantages Transparent to end users – appears as a single information system. Disadvantages Difficult to perform integration – needs human understanding. Local autonomy lost. Static – does not evolve automatically. Tightly-coupled.

18 Architectures for Interoperability 2 2. Federated Data Base Systems Less tightly coupled schema than in 1. Each service specifies sharable objects through an export schema. Common data model. Internal command language. Decentralised control (local autonomy). 5-level architecture for federated system. e.g. Objectivity as Federated OODBMS

19 FDBMS Terminology IS – Internal Schema defining layout on disk of a conceptual schema CS – Conceptual Schema defining logical data base (e.g. relational – tables, attributes, domains). ES – External Schema defining views on conceptual schema.

20 Federated DBMS – 5-level Architecture Local CS Local IS DB Global CS Global ES Local ES DB

21 Federated Data Base: Loosely coupled Created by users. A E, B E are export schemas. V is a view. A, B are base schemas, retaining autonomy over those parts not exported. A B V AEAE BEBE

22 Federated Data Bases: Tightly coupled Created by administrators. Global schema integration on all export schemas. More formal than loosely-coupled. Much effort to resolve semantic inconsistencies.

23 Federated Data Base Systems – General Advantages Local autonomy preserved. Not all data needs to be integrated. Provide meta-data structures for views (external and export schema, data dictionary).

24 Federated Database Systems - Disadvantages by Approach Loosely coupled Duplication by different users in building views. Updating data defined in views can be difficult. Tightly coupled Similar to global schema integration: Complex, difficult to make changes dynamically. Much effort needed to resolve semantic inconsistencies.

25 Multi-Data-Base Language Approach No attempt at schema integration. All sites maintain complete autonomy. The various schemas can be heterogeneous, inconsistent w.r.t. services provided, and duplicate information in different ways. Language (e.g. MSQL) is used to integrate data bases at run time. Relational model used as Common Data Model.

26 Multi-Data-Base Language Approach A, B are schemas. MSQL is the run-time language. A B MSQL

27 Multi-Data-Base Language Approach – Advantages No preparatory work to understand semantics of schema. Dynamic – access latest versions. Very skilled users can succeed in reaching their goals. Interesting work on multi-data-base dependencies.

28 An Example Multi-Data-Base Language MSQL (Multidatabase SQL) Biased towards the relational model. Illustrates problems. Consider 2 data bases: Each on publications of a computing society; and query: “What is the name, address, title for each publication of an author appearing in both of the society’s data bases?”

29 MSQL Schema Schema 1 (for AIIA Database): Contacts (PersonID, Name, , …) Conference (Name, Type, …) Attendees (ID, Conf_ID, Speaker, …) Publ_Papers (P_ID, Title, Author_ID, …) Schema 2 (for IFIP Database): Member_Socs (Soc_Name, …) Conf (Conf_ID, …) Publ_Papers (P_Ref, Title, Conf_Ref, …) Authors (Name, , Paper_ID, …) Underlined attributes are primary keys. Attributes in italics are foreign keys.

30 MSQL for Query USE AIIA, IFIP SELECT Name, , Title FROM Authors, IFIP.Publ_Papers IFIP_Paper, Contacts, AIIA.Publ_papers AIIA_Paper WHERE Authors.Name = Contacts.Name AND Contacts.Person_ID = AIIA_Paper.Author_ID AND Authors.Paper_ID = IFIP_Paper.P_Ref; The USE clause declares the multi-data-bases which are used as qualifiers in the FROM clause to distinguish tables with the same name (thereafter distinguished by aliasing). Retrieves Name, address and Title from both data bases.

31 Potential Problems with MSQL Are names and domains of corresponding attributes the same? Can use LET command to create equivalences of names, but this does not solve domain incompatibility. What if one schema is not relational? The E-R model is often used as a neutral schema for translation and comparison of heterogeneous features.

32 Multi-data-base Language – Disadvantages in General Distribution is not transparent. Users must resolve inconsistencies themselves. Common language may restrict scope of heterogeneity (relational bias). Local autonomous systems may change their schema freely (so existing queries fail).

33 Comparison of Approaches By coupling: How tightly is the interoperable system connected to its underlying systems? By adaptability: How freely can the interoperable system evolve in line with the underlying schema? By transparency: How much understanding of the interoperable system do end-users need to have?

34 Comparison of Approaches Approach Coupling AdaptabilityTransparency Global Schema TightLowHigh Integration Federated MediumMediumMedium Data Bases Multi-data-base LooseHighLow Languages

35 Summary Trend: From Global Schema Integration –through Federated Data Bases –to Multi-data-base Language. Towards looser coupling, higher adaptability, and lower transparency.

36 Further Reading Management of Heterogeneous and Autonomous Database Systems Elmagarmid, Ahmed Rusinkiewicz, Marek Sheth, Amit Morgan Kaufmann (1999).