With a cost-effective manner in a Distributed Database

With the launch of high speed communication networks,
significant research is devoted to developing highly efficient techniques for
processing complex queries in a cost-effective manner in a Distributed Database
Environment. “A Distributed Database is a
collection of logically interrelated database distributed over a computer
network so as to improve the performance, reliability, availability and
modularity of the distributed systems”. Query processing is much more
difficult in Distributed Environments than in Centralized Environments. Since
the data is geographically distributed onto multiple sites, the processing of
query involves transmission of data among different sites. The recovery of data
from different sites is known as Distributed Query Processing (DQP). The query
processor selects data from databases located at multiple sites in a network
and performs processing over multiple CPU’s to achieve a single query result
set.

The performance of a distributed query is critically
dependent upon the capacity of the query optimizer to originate efficient query
processing strategies 1. Query Optimization is one of the most important and
expensive stages in accomplishing distributed queries. The complexity of the
optimization process is determined by:

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·        
The
number of relations referenced

·        
Types
of initial query access methods

·        
The
set of rules involved for generating

·        
possible
query trees or query graphs.

 

Once the user entered the Query, it is transformed into
a standard relational algebra form, the optimizer searches for an optimal query
execution plan 2. The number of possible alternative query plans increases
exponentially with increase in the number of relations required for processing
the query. The query optimizer needs to discover the large search space for
generating optimal query plans. The query optimization problem in large-scale
distributed databases is NP-hard 5 6 in nature and difficult to solve as
exploring all the query plans in this large search space is not feasible. This
problem in Distributed Databases is a Combinatorial Optimization problem. The
Combinatorial Optimization problem has been addressed by various techniques
like simulated annealing, iterative improvement, two-phase optimization,
Deterministic, Greedy and Heuristic Algorithms to find an optimal solution by
taking the time and cost complexity of executing these queries into
consideration 3 4.

In this paper, an attempt has been made to study the
various Search Strategies that can be implemented to determine Optimal Query
Execution Plans in the processing of Distributed Queries. The remainder of this
study is as follows. Section 2 discusses the components of Distributed Query
Optimization. In Section 3, various Solution Algorithms that have been applied
by scientist for query optimization are discussed and finally section 4
concludes the

research paper and provides scope for future studies.

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