Distributed Databases Questions Long
Distributed query processing refers to the process of executing a query that involves data stored in multiple distributed databases. In a distributed database system, data is spread across multiple nodes or sites, and each site may have its own local database management system (DBMS). When a query is issued that requires data from multiple sites, distributed query processing comes into play.
The process of distributed query processing involves several steps:
1. Query Parsing: The query is initially parsed by the global query optimizer, which is responsible for generating an optimal query execution plan. The global query optimizer analyzes the query and determines the best way to execute it by considering factors such as data distribution, network bandwidth, and site capabilities.
2. Query Decomposition: Once the query is parsed, it is decomposed into subqueries that can be executed at individual sites. The global query optimizer breaks down the query into smaller parts, each of which can be executed independently at the respective sites.
3. Data Localization: In this step, the global query optimizer determines which data needs to be accessed from which sites. It identifies the relevant data and ensures that it is available at the appropriate sites for query execution. This may involve data replication or data movement across sites to ensure data availability.
4. Subquery Execution: The decomposed subqueries are sent to the respective sites for execution. Each site executes its assigned subquery using its local DBMS. The local query optimizer at each site generates a local query execution plan based on the available data and resources at that site.
5. Data Exchange and Integration: Once the subqueries are executed at individual sites, the intermediate results are exchanged and integrated to produce the final result. This involves transferring the relevant data between sites and performing any necessary operations, such as join or aggregation, to combine the results.
6. Result Consolidation: Finally, the global query optimizer consolidates the intermediate results received from different sites to produce the final result of the distributed query. This may involve additional operations, such as sorting or duplicate elimination, to ensure the correctness and consistency of the result.
Overall, distributed query processing aims to optimize the execution of queries that involve distributed data by leveraging the capabilities of individual sites and minimizing data transfer across the network. It involves query decomposition, data localization, subquery execution, data exchange, and result consolidation to efficiently process queries and provide accurate results from distributed databases.