DISTRIBUTED DATABASE DESIGN

Distributed Database Design Introduction – Alternative design strategies Distribution design issues Data fragmentation Data allocation

DISTRIBUTED DATABASE DESIGN The organization of distributed systems can be investigated along three orthogonal dimensions: 1. Level of sharing 2. Behavior of access patterns 3. Level of knowledge on access pattern behavior

DISTRIBUTED DATABASE DESIGN Level of sharing no sharing - each application and its data execute at one site, data sharing - all the programs are replicated at all the sites, but data files are not, data plus program sharing - both data and programs may be shared. Behavior of access patterns static - access patterns of user requests do not change over time, dynamic - access patterns of user requests change over time. Level of knowledge on access pattern behavior complete information - the access patterns can reasonably be predicted and do not deviate significantly from the predictions, partial information - there are deviation from the predictions.

ALTERNATIVE DESIGN STRATEGIES Two major strategies that have been identified for designing distributed databases are: the top-down approach the bottom-up approach

ALTERNATIVE DESIGN STRATEGIES TOP-DOWN DESIGN PROCESS

ALTERNATIVE DESIGN STRATEGIES TOP-DOWN DESIGN PROCESS view design - defining the interfaces for end users, conceptual design - is the process by which the enterprise is examined to determine entity types and relationships among these entities. One can possibly divide this process into to related activity groups: entity analysis - is concerned with determining the entities, their attributes, and the relationships among these entities, functional analysis - is concerned with determining the fundamental functions with which the modeled enterprise is involved. The results of these two steps need to be cross-referenced to get a better understanding of which functions deal with which entities.

ALTERNATIVE DESIGN STRATEGIES TOP-DOWN DESIGN PROCESS distributions design - design the local conceptual schemas by distributing the entities over the sites of the distributed system. The distribution design activity consists of two steps: fragmentation allocation physical design - is the process, which maps the local conceptual schemas to the physical storage devices available at the corresponding sites, observation and monitoring - the results is some form of feedback, which may result in backing up to one of the earlier steps in the design.

ALTERNATIVE DESIGN STRATEGIES BOTTOM-UP DESIGN PROCESS Top-down design is a suitable approach when a database system is being designed from scratch. If a number of databases already exist, and the design task involves integrating them into one database - the bottom-up approach is suitable for this type of environment. The starting point of bottom-up design is the individual local conceptual schemas. The process consists of integrating local schemas into the global conceptual schema.

Fragmentation Alternatives JNO JNAME BUDGET LOC J1 Instrumental 150,000 Montreal J2 Database Dev. 135,000 New York J3 CAD/CAM 250,000 New York J4 Maintenance 350,000 Paris J Horizontal Partitioning Vertical Partitioning J1 JNO BUDGET J1 150,000 J2 135,000 J3 250,000 J4 310,000 JNO JNAME BUDGET LOC J1 Instrumental 150,000 Montreal J2 Database Dev. 135,000 New York J2 JNO JNAME LOC J1 Instrumentation Montreal J2 Database Devl New York J3 CAD/CAM New York J4 Maintenance Paris JNO JNAME BUDGET LOC J3 CAD/CAM 150,000 Montreal J4 Maintenance. 310,000 Paris

DISTRIBUTION DESIGN ISSUES REASONS FOR FRAGMENTATION Why fragment at all? How should we fragment? How much should we fragment? Is there any way to test the correctness of decompositions? How should we allocate? What is the necessary information for fragmentation and allocation?

Why fragment at all? Reasons: Disadvantages: Interquery concurrency Intraquery concurrency Disadvantages: Vertical fragmentation may incur overhead. Attributes participating in a dependency may be allocated to different sites. Integrity checking is more costly.

DISTRIBUTION DESIGN ISSUES REASONS FOR FRAGMENTATION The important issue is the appropriate unit of distribution. For a number of reasons it is only natural to consider subsets of relations as distribution units. If the applications that have views defined on a given relation reside at different sites, two alternatives can be followed, with the entire relation being the unit of distribution. The relation is not replicated and is stored at only one site, or it is replicated at all or some of the sites where the applications reside. The fragmentation of relations typically results in the parallel execution of a single query by dividing it into a set of sub queries that operate on fragments. Thus, fragmentation typically increases the level of concurrency and therefore the system throughput.

DISTRIBUTION DESIGN ISSUES REASONS FOR FRAGMENTATION There are also the disadvantages of fragmentation: if the application have conflicting requirements which prevent decomposition of the relation into mutually exclusive fragments, those applications whose views are defined on more than one fragment may suffer performance degradation, the second problem is related to semantic data control, specifically to integrity checking.

Degree of Fragmentation Application views are usually subsets of relations. Hence, it is only natural to consider subsets of relations as distribution units. The appropriate degree of fragmentation is dependent on the applications.

DISTRIBUTION DESIGN ISSUES FRAGMENTATION ALTERNATIVES The are clearly two alternatives: horizontal fragmentation vertical fragmentation The fragmentation may, of course, be nested. If the nestings are of different types, one gets hybrid fragmentation.

DISTRIBUTION DESIGN ISSUES DEGREE OF FRAGMENTATION The extent to which the database should be fragmented is an important decision that affects the performance of query execution. The degree of fragmentation goes from one extreme, that is, not to fragment at all, to the other extreme, to fragment to the level of individual tuples (in the case of horizontal fragmentation) or to the level of individual attributes (in the case of vertical fragmentation).

DISTRIBUTION DESIGN ISSUES CORRECTNESS RULES OF FRAGMENTATION Completeness (ensure no loss of fragments) If a relation instance R is decomposed into fragments R1,R2, ..., Rn, each data item that can be found in R can also be found in one or more of Ri’s. This property is also important in fragmentation since it ensures that the data in a global relation is mapped into fragments without any loss. (lossless decomposition property) Reconstruction (functional dependency preserved) If a relation R is decomposed into fragments R1,R2, ..., Rn, it should be possible to define a relational operator  such that: R = Ri,  RiFR The reconstruct ability of the relation from its fragments ensures that constraints defined on the data in the form of dependencies are preserved.

DISTRIBUTION DESIGN ISSUES CORRECTNESS RULES OF FRAGMENTATION Disjointness If a relation R is horizontally decomposed into fragments R1,R2, ..., Rn and data item di is in Rj, it is not in any other fragment Rk (k  j). This criterion ensures that the horizontal fragments are disjoint. If relation R is vertically decomposed, its primary key attributes are typically repeated in all its fragments. Therefore, in case of vertical partitioning, disjointness is defined only on the nonprimary key attributes of a relation.

DISTRIBUTION DESIGN ISSUES ALLOCATION ALTERNATIVES The reasons for replication are reliability and efficiency of read-only queries. Read-only queries that access the same data items can be executed in parallel since copies exist on multiple sites. The execution of update queries cause trouble since the system has to ensure that all the copies of the data are updated properly. The decisions regarding replication is a trade-off which depends on the ratio of the read-only queries to the update queries.

DISTRIBUTION DESIGN ISSUES ALLOCATION ALTERNATIVES A nonreplicated database (commonly called a partitioned database) contains fragments that are allocated to sites, and there is only one copy of any fragment on the network. In case of replication, either the database exists in its entirety at each site (fully replicated database), or fragments are distributed to the sites in such a way that copies of a fragment may reside in multiple sites (partially replicated database).

DISTRIBUTION DESIGN ISSUES ALLOCATION ALTERNATIVES

DISTRIBUTION DESIGN ISSUES INFORMATION REQUIREMENTS The information needed for distribution design can be divided into four categories: database information, application information, communication network information, computer system information.

DISTRIBUTION DESIGN ISSUES FRAGMENTATION Horizontal fragmentation partitions a relation along its tuples Two versions of horizontal fragmentation Primary horizontal fragmentation of relation is performed using predicates that are defined on that relation Derived fragmentation is the partitioning of relation that results from predicates being defined on another relation

DISTRIBUTION DESIGN ISSUES FRAGMENTATION Vertical fragmentation partitions a relation into a set of smaller relations so that many of users aplications will run on only one fragment Vertical fragmentation is inherently more complicated than horizontal partitioning

DISTRIBUTION DESIGN ISSUES ALLOCATION Allocation problem there are set of fragments F= { F1, F2, ... , Fn } and network consisiting of sites S = { S1, S2, ... , Sm } on wich sets aplications Q= { q1, q2, ... , qq } is running The allocation problem involves finding the “optimal” distribution of F to S

DISTRIBUTION DESIGN ISSUES ALLOCATION One of important issues that need to be discussed is the definition of optimality The optimality can be defined with respects of two measures [ Dowdy and Foster, 1982 ] Minimal cost. The cost consists of the cost of storing each Fi at the site Sj, the cost of quering Fi at Sj, the cost of updating Fi, at all sites it is stored, and cost of data comunication. The allocation problem,then, attempts to find an alocations scheme that minimizes cost function.

DISTRIBUTION DESIGN ISSUES ALLOCATION Perfomance. The allocation strategy is designed to maintain a performance mertic. Two well-known are to minimize the response time and to maximize the system throughput at each site

A Fragment is a subset of a relation that is stored on a different site from another subset of the same relation.

1- Why

For large organizations, increased pressure of users Localization of Data and Local Autonomy Increased time/NW cost of centralized access Increased chances of failure Increased failure damage Distribution of data allows fast access of data due to localization Parallel execution of queries

2- How

Unit of Fragmentation Entire table is not a suitable unit

Fragmentation Alternatives

1- Vertical; Different subsets of attributes are stored at different places, like, Table EMP(eId, eName, eDept, eQual, eSal) Interests of the local and head offices may result following vertical partitions of this table: EMP1(eId, eName, eDept) EMP2(eId, eQual, eSal)

2- Horizontal Fragmentation: based on the localization of data rows of a table are split on multiple sites, like the data in . CLIENT(cAC#, cName, cAdr, cBal) table is placed in different databases based on their location, like from Lahore, Pindi, Karachi, Peshawar, Quetta

3- Degree of Fragmentation Between no to the extreme level that could be to the individual tuple or column level; a compromised decision

4- Correctness Rules for Fragmentation

If a relation R is fragmented into R1, R2, …, Rn, then Completeness: each of the data item (a tuple or a attribute) that can be in R can also be in one or more Ri ∀ x ∈ R, ∃ Ri such that x ∈ Ri

Reconstruction: it should be possible to define a relational operator such that the original relation can be reconstructed R = g(R1, R2, …, Rn)

Disjointness: if data item x is in Rj, it is not in any other fragment ∀ x ∈ Ri,  ∃ Rj such that x ∈ Rj, i ≠ j

5- Allocation Strategy: Partitioned, fully or partially replicated; depends mainly on requirements.

6:- Information Requirements: Different; discussed in each case individually

Horizontal Fragmentation

Partitions a table along its tuples is performed based on some Predicate/ Condition

Primary: Predicate defined on the same relation Derived: Predicate defined on another relation

Primary Horizontal Fragmentation(PHF)

Information Requirements

Database Information: We may need to consult the conceptual DB design. Apart from tables, we need relationships, cardinality and the owner and member tables

title, sal eNo, Name, title jNo, jName, budget, loc eNo, jNo, resp, dur PAY EMP ASIGN PROJ owner = PAY member = EMP

Application Requirement

1- Major simple predicates used in the user queries.

pj: Ai θ Value, where θ ∈{=, <, ≠≤, >, ≥} and Value ∈ Di Given a relation R(A1, A2, …, An), where Ai is attribute with domain Di, then a simple predicate pj has the form. pj: Ai θ Value, where θ ∈{=, <, ≠≤, >, ≥} and Value ∈ Di lnName = “Housing”, lnAmount > 200,000