BISQL – Laymen to SQL Developer # 41 – Distributed Databases #2 – Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design

Hi Folks,

This post is part of Series Database Management Systems

Currently running topic for this series is listed as below :

Series of Database Management Systems

>>Chapter 1 : DBMS [Database Management Systems]

>>Chapter 2 : Database Core Concepts and  Applications

>>Chapter 3 : Record Storage and Primary File Organization

>>Chapter 4 : Index Structures of Files

>>Chapter 5 : Entity-Relationship Model

>>Chapter 6 : Relational Algebra

>>Chapter 7 : SQL

>>Chapter 8 : Functional Dependencies and  Normalization For Relational Databases

>>Chapter 9 : Transaction Processing

>>Chapter 10 : Concurrency Control Techniques

>>Chapter 11 : Recovery Techniques

>>Chapter 12 : Distributed Databases<You are Here>

Continuing from my previous post on this series.

We are going to Cover the Following Points in this article

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

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

Data fragmentation: Techniques that are used to break up the database into logical units called fragments that may be assigned for storage at the various sites. In a DDBMS, decisions must be made regarding which site should be used to store which portions of the database. There are three types of fragmentation:

1. Horizontal fragmentation: A 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. For example, we may divide employee relation into three horizontal fragments with the following conditions: (DNO=10), (DNO=20) AND (DNO=30) – each fragment contains the Employee tuples working for a particular department.

2. Vertical fragmentations: It is a collection of only certain attributes of the relation. It divides a relation "vertically" by columns. For ex: we may want to fragment the employee relation into two vertical fragments. The first fragment includes personal information – Name, B date, Address and the Second includes work related information-SSN, Salary, Mgr no etc.

3. Mixed fragmentation: Mixing of horizontal and vertical fragmentation is called mixed fragmentation.

Data Replication and Allocation: Replication is useful in improving the availability of data. This replication of the whole database at every site in the distributed system is called fully replicated database. This can improve availability because the system can continue to operate as long as at least one site is up. It improves performance of retrieval for global queries, because the result of such a query can be obtained locally from any one site. The disadvantage is that it can slow down update operations, since update must be performed on every copy of the database to keep the copies consistent. Full replication makes the concurrency control and recovery techniques more expensive.

The other extreme from full replication is no replicating – that is, each fragment is stored at only one location, whereas in partial replication some fragments of the database may be replicated and others may not. Some people carry partially replicated databases with them on laptops.

Allocation: Each copy of a fragment must be assigned to a particular site in the distributed system. This process is called data distribution or allocation.

Type of Distributed DB Systems:

In DDB software is distributed over multiple sites connected by network. It is categorized as:

The first factor is the degree of homogeneity of the DDBMS software. If all servers (or individual local DDMSs) use identical software and all users use identical software, the DDBMS is called homogeneous; otherwise, it is called heterogeneous. At the other extreme is the federated DDBMS or multidatabase system. In such a system each server has an independent DBMS, own local users, local programmers and DBA. In heterogeneous FDBS one server may be RDBMS, another may be network DBMS, and the third one may be hierarchical DBMS etc. In such a way, it is necessary to have a canonical system language and language translators to translate canonical language to the language of each server.

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