How a distributed query can be optimized?
Distributed query optimization refers to the process of producing a plan for the processing of a query to a distributed database system. The plan is called a query execution plan. The fragments, which can be redundant and replicated, are allocated to different database servers in the distributed system.
What are the main issues for distributed query optimization?
Distributed query optimization requires evaluation of a large number of query trees each of which produce the required results of a query….Distributed Query Optimization
- Optimal utilization of resources in the distributed system.
- Query trading.
- Reduction of solution space of the query.
What is cost based query optimization?
Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use.
What is meant by query optimization in distributed database management system?
Query optimization is a feature of many relational database management systems and other databases such as graph databases. The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans. A query is a request for information from a database.
What is very much required to process a query in a distributed database?
Query processing in a distributed system requires the transmission f data between computers in a network. The arrangement of data transmissions and local data processing is known as a distribution strategy for a query.
What are the factors that contribute to the cost of a query?
Some of the factors that the optimizer uses to determine the cost of each query plan are:
- The number of I/O requests that are associated with each file system access.
- The CPU work that is required to determine which rows meet the query predicate.
- The resources that are required to sort or group the data.
What is the difference between heuristic and cost based query optimization?
Cost-based optimization is expensive, even with dynamic programming. Heuristic optimization transforms the query-tree by using a set of rules that typically (but not in all cases) improves execution performance.
What are the steps in cost based query optimization?
The first step is to use ANALYZE TABLE COMPUTE STATISTICS SQL command to compute table statistics….
- Access cost to secondary storage-
- Memory usage cost-
- Storage cost-
- Computational cost-
- Communication cost-
What is query optimization with example?
Query optimization is the overall process of choosing the most efficient means of executing a SQL statement. SQL is a nonprocedural language, so the optimizer is free to merge, reorganize, and process in any order. The database optimizes each SQL statement based on statistics collected about the accessed data.
How are global query optimizations used in distributed databases?
In a distributed database, fragmentation results in relations being stored in separate sites, with some fragments possibly being replicated. This stage maps the distributed query on the global schema to separate queries on individual fragments using data distribution and replication information. Global Query Optimization.
What makes query processing difficult in a distributed system?
In a distributed system, several additional factors further complicate query processing. The first is the cost of transferring data over the net-work.
Who is the buyer of a distributed query?
In query trading algorithm for distributed database systems, the controlling/client site for a distributed query is called the buyer and the sites where the local queries execute are called sellers. The buyer formulates a number of alternatives for choosing sellers and for reconstructing the global results.
How does the global optimizer work in distributed systems?
If there is no replication, the global optimizer runs local queries at the sites where the fragments are stored. If there is replication, the global optimizer selects the site based upon communication cost, workload, and server speed.