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Chapter: Fundamentals of Database Systems - Additional Database Topics: Security and Distribution - Distributed Databases

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Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design

1. Data Fragmentation 2. Data Replication and Allocation 3. Example of Fragmentation, Allocation, and Replication

Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design

 

In this section we discuss techniques that are used to break up the database into logical units, called fragments, which may be assigned for storage at the various sites. We also discuss the use of data replication, which permits certain data to be stored in more than one site, and the process of allocating fragments—or replicas of fragments—for storage at the various sites. These techniques are used during the process of distributed database design. The information concerning data fragmentation, allocation, and replication is stored in a global directory that is accessed by the DDBS applications as needed.

 

1. Data Fragmentation

 

In a DDB, decisions must be made regarding which site should be used to store which portions of the database. For now, we will assume that there is no replication; that is, each relation—or portion of a relation—is stored at one site only. We discuss replication and its effects later in this section. We also use the terminology of relational databases, but similar concepts apply to other data models. We assume that we are starting with a relational database schema and must decide on how to dis-tribute the relations over the various sites. To illustrate our discussion, we use the relational database schema in Figure 3.5.

 

Before we decide on how to distribute the data, we must determine the logical units of the database that are to be distributed. The simplest logical units are the relations themselves; that is, each whole relation is to be stored at a particular site. In our example, we must decide on a site to store each of the relations EMPLOYEE,

 

DEPARTMENT, PROJECT, WORKS_ON, and DEPENDENT in Figure 3.5. In many cases, however, a relation can be divided into smaller logical units for distribution. For example, consider the company database shown in Figure 3.6, and assume there are three computer sites—one for each department in the company.

 

We may want to store the database information relating to each department at the computer site for that department. A technique called horizontal fragmentation can be used to partition each relation by department.

Horizontal Fragmentation. A horizontal fragment of a relation is a subset of the tuples in that relation. The tuples that belong to the horizontal fragment are specified by a condition on one or more attributes of the relation. Often, only a sin-gle attribute is involved. For example, we may define three horizontal fragments on the EMPLOYEE relation in Figure 3.6 with the following conditions: (Dno = 5), (Dno = 4), and (Dno = 1)—each fragment contains the EMPLOYEE tuples working for a particular department. Similarly, we may define three horizontal fragments for the PROJECT relation, with the conditions (Dnum = 5), (Dnum = 4), and (Dnum = 1)—each fragment contains the PROJECT tuples controlled by a particular department. Horizontal fragmentation divides a relation horizontally by grouping rows to create subsets of tuples, where each subset has a certain logical meaning. These fragments can then be assigned to different sites in the distributed system. Derived horizontal fragmentation applies the partitioning of a primary relation (DEPARTMENT in our example) to other secondary relations (EMPLOYEE and PROJECT in our example), which are related to the primary via a foreign key. This way, related data between the primary and the secondary relations gets fragmented in the same way.

 

Vertical Fragmentation. Each site may not need all the attributes of a relation, which would indicate the need for a different type of fragmentation. Vertical fragmentation divides a relation “vertically” by columns. A vertical fragment of a relation keeps only certain attributes of the relation. For example, we may want to fragment the EMPLOYEE relation into two vertical fragments. The first fragment includes personal information—Name, Bdate, Address, and Sex—and the second includes work-related information—Ssn, Salary, Super_ssn, and Dno. This vertical fragmentation is not quite proper, because if the two fragments are stored separately, we cannot put the original employee tuples back together, since there is no common attribute between the two fragments. It is necessary to include the primary key or some candidate key attribute in every vertical fragment so that the full relation can be reconstructed from the fragments. Hence, we must add the Ssn attribute to the personal information fragment.

 

Notice that each horizontal fragment on a relation R can be specified in the relational algebra by a σCi(R) operation. A set of horizontal fragments whose conditions C1, C2, ..., Cn include all the tuples in R—that is, every tuple in R satisfies (C1 OR COR ... OR Cn)—is called a complete horizontal fragmentation of R. In many cases a complete horizontal fragmentation is also disjoint; that is, no tuple in R satisfies (Ci AND Cj) for any i j. Our two earlier examples of horizontal fragmentation for the EMPLOYEE and PROJECT relations were both complete and disjoint. To recon-struct the relation R from a complete horizontal fragmentation, we need to apply the UNION operation to the fragments.

 

A vertical fragment on a relation R can be specified by a πLi(R) operation in the relational algebra. A set of vertical fragments whose projection lists L1, L2, ..., Ln include all the attributes in R but share only the primary key attribute of R is called a complete vertical fragmentation of R. In this case the projection lists satisfy the fol-lowing two conditions:

 

        L1 L2 ... Ln = ATTRS(R).

        Li Lj = PK(R) for any i j, where ATTRS(R) is the set of attributes of R and PK(R) is the primary key of R.

 

To reconstruct the relation R from a complete vertical fragmentation, we apply the OUTER UNION operation to the vertical fragments (assuming no horizontal fragmentation is used). Notice that we could also apply a FULL OUTER JOIN operation and get the same result for a complete vertical fragmentation, even when some horizontal fragmentation may also have been applied. The two vertical fragments of the EMPLOYEE relation with projection lists L1 = {Ssn, Name, Bdate, Address, Sex} and L2 = {Ssn, Salary, Super_ssn, Dno} constitute a complete vertical fragmentation of EMPLOYEE.

 

Two horizontal fragments that are neither complete nor disjoint are those defined on the EMPLOYEE relation in Figure 3.5 by the conditions (Salary > 50000) and (Dno = 4); they may not include all EMPLOYEE tuples, and they may include common tuples. Two vertical fragments that are not complete are those defined by the attribute lists L1 = {Name, Address} and L2 = {Ssn, Name, Salary}; these lists violate both conditions of a complete vertical fragmentation.

 

Mixed (Hybrid) Fragmentation. We can intermix the two types of fragmentation, yielding a mixed fragmentation. For example, we may combine the horizon-tal and vertical fragmentations of the EMPLOYEE relation given earlier into a mixed fragmentation that includes six fragments. In this case, the original relation can be reconstructed by applying UNION and OUTER UNION (or OUTER JOIN) operations in the appropriate order. In general, a fragment of a relation R can be specified by a SELECT-PROJECT combination of operations πL(σC(R)). If

 

            = TRUE (that is, all tuples are selected) and L ATTRS(R), we get a vertical fragment, and if C TRUE and L = ATTRS(R), we get a horizontal fragment. Finally, if   L          ≠ TRUE and L ≠ ATTRS(R), we get a mixed fragment. Notice that a relation can itself be considered a fragment with C = TRUE and L = ATTRS(R). In the following discussion, the term fragment is used to refer to a relation or to any of the preced-ing types of fragments.

 

A fragmentation schema of a database is a definition of a set of fragments that includes all attributes and tuples in the database and satisfies the condition that the whole database can be reconstructed from the fragments by applying some sequence of OUTER UNION (or OUTER JOIN) and UNION operations. It is also sometimes useful—although not necessary—to have all the fragments be disjoint except for the repetition of primary keys among vertical (or mixed) fragments. In the latter case, all replication and distribution of fragments is clearly specified at a subsequent stage, separately from fragmentation.

 

An allocation schema describes the allocation of fragments to sites of the DDBS; hence, it is a mapping that specifies for each fragment the site(s) at which it is stored. If a fragment is stored at more than one site, it is said to be replicated. We discuss data replication and allocation next.

 

2. Data Replication and Allocation

 

Replication is useful in improving the availability of data. The most extreme case is replication of the whole database at every site in the distributed system, thus creating a fully replicated distributed database. This can improve availability remarkably because the system can continue to operate as long as at least one site is up. It also improves performance of retrieval for global queries because the results of such queries can be obtained locally from any one site; hence, a retrieval query can be processed at the local site where it is submitted, if that site includes a server module. The disadvantage of full replication is that it can slow down update operations drastically, since a single logical update must be performed on every copy of the database to keep the copies consistent. This is especially true if many copies of the database exist. Full replication makes the concurrency control and recovery techniques more expensive than they would be if there was no replication, as we will see in Section 25.7.

 

The other extreme from full replication involves having no replication—that is, each fragment is stored at exactly one site. In this case, all fragments must be dis-joint, except for the repetition of primary keys among vertical (or mixed) fragments. This is also called nonredundant allocation.

 

Between these two extremes, we have a wide spectrum of partial replication of the data—that is, some fragments of the database may be replicated whereas others may not. The number of copies of each fragment can range from one up to the total number of sites in the distributed system. A special case of partial replication is occurring heavily in applications where mobile workers—such as sales forces, financial plan-ners, and claims adjustors—carry partially replicated databases with them on laptops and PDAs and synchronize them periodically with the server database.7 A descrip-tion of the replication of fragments is sometimes called a replication schema.

 

Each fragment—or each copy of a fragment—must be assigned to a particular site in the distributed system. This process is called data distribution (or data allocation). The choice of sites and the degree of replication depend on the performance and availability goals of the system and on the types and frequencies of transactions submitted at each site. For example, if high availability is required, transactions can be submitted at any site, and most transactions are retrieval only, a fully replicated database is a good choice. However, if certain transactions that access particular parts of the database are mostly submitted at a particular site, the corresponding set of fragments can be allocated at that site only. Data that is accessed at multiple sites can be replicated at those sites. If many updates are performed, it may be useful to limit replication. Finding an optimal or even a good solution to distributed data allocation is a complex optimization problem.


3. Example of Fragmentation, Allocation, and Replication

 

We now consider an example of fragmenting and distributing the company data-base in Figures 3.5 and 3.6. Suppose that the company has three computer sites— one for each current department. Sites 2 and 3 are for departments 5 and 4, respectively. At each of these sites, we expect frequent access to the EMPLOYEE and PROJECT information for the employees who work in that department and the proj-ects controlled by that department. Further, we assume that these sites mainly access the Name, Ssn, Salary, and Super_ssn attributes of EMPLOYEE. Site 1 is used by company headquarters and accesses all employee and project information regularly, in addition to keeping track of DEPENDENT information for insurance purposes.

 

According to these requirements, the whole database in Figure 3.6 can be stored at site 1. To determine the fragments to be replicated at sites 2 and 3, first we can horizontally fragment DEPARTMENT by its key Dnumber. Then we apply derived fragmentation to the EMPLOYEE, PROJECT, and DEPT_LOCATIONS relations based on their foreign keys for department number—called Dno, Dnum, and Dnumber, respec-tively, in Figure 3.5. We can vertically fragment the resulting EMPLOYEE fragments to include only the attributes {Name, Ssn, Salary, Super_ssn, Dno}. Figure 25.8 shows the mixed fragments EMPD_5 and EMPD_4, which include the EMPLOYEE tuples satisfying the conditions Dno = 5 and Dno = 4, respectively. The horizontal fragments of PROJECT, DEPARTMENT, and DEPT_LOCATIONS are similarly fragmented by department number. All these fragments—stored at sites 2 and 3—are replicated because they are also stored at headquarters—site 1.

 

We must now fragment the WORKS_ON relation and decide which fragments of WORKS_ON to store at sites 2 and 3. We are confronted with the problem that no attribute of WORKS_ON directly indicates the department to which each tuple belongs. In fact, each tuple in WORKS_ON relates an employee e to a project P. We could fragment WORKS_ON based on the department D in which e works or based on the department D that controls P. Fragmentation becomes easy if we have a constraint stating that D = D for all WORKS_ON tuples—that is, if employees can work only on projects controlled by the department they work for. However, there is no such constraint in our database in Figure 3.6. For example, the WORKS_ON tuple <333445555, 10, 10.0> relates an employee who works for department 5 with a project controlled by department 4. In this case, we could fragment WORKS_ON based on the department in which the employee works (which is expressed by the condition C) and then fragment further based on the department that controls the projects that employee is working on, as shown in Figure 25.9.

 

In Figure 25.9, the union of fragments G1, G2, and G3 gives all WORKS_ON tuples for employees who work for department 5. Similarly, the union of fragments G4, G5, and G6 gives all WORKS_ON tuples for employees who work for department 4. On the other hand, the union of fragments G1, G4, and G7 gives all WORKS_ON tuples for projects controlled by department 5. The condition for each of the fragments G1 through G9 is shown in Figure 25.9 The relations that represent M:N relationships, such as WORKS_ON, often have several possible logical fragmentations. In our distribution in Figure 25.8, we choose to include all fragments that can be joined to






either an EMPLOYEE tuple or a PROJECT tuple at sites 2 and 3. Hence, we place the union of fragments G1, G2, G3, G4, and G7 at site 2 and the union of fragments G4, G5, G6, G2, and G8 at site 3. Notice that fragments G2 and G4 are replicated at both sites. This allocation strategy permits the join between the local EMPLOYEE or PROJECT fragments at site 2 or site 3 and the local WORKS_ON fragment to be per-formed completely locally. This clearly demonstrates how complex the problem of database fragmentation and allocation is for large databases. The Selected Bibliography at the end of this chapter discusses some of the work done in this area.


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