In a distributed database system over cloud environment, the relations required by a query plan may be stored at multiple sites. The goal of a query optimizer is to provide an optimal Query Execution Plan (QEP) by comparing alternative query plans. Paper focuses on a design method which fits each algorithm to the environment it is best suited for. Taking advantage of strengths and eliminating weaknesses is the goal in implementing an algorithm. A survey of the proposed methods reveals their pros and cons. It will also focus on some of the major issues associated with parallel databases and how well these algorithms address them. It will look at various cost models, search algorithms and methods of generating query execution plans (QEPs), resource allocation techniques. Paper will discuss some of the ways in which queries can be optimized for parallel execution. In this paper, we focus on several techniques for query optimization in shared-nothing parallel database systems. These solutions deal with various issues associated with such database systems. For the parallel databases to be effective and efficient, various optimizing solutions need to be implemented. These implementations involve database processing and querying over parallel systems. Parallel database systems are being used nowadays in a wide variety of systems, right from database applications to decision support systems.
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