Distributed query processing is the procedure of answering queries (which means mainly read operations on large data sets) in a distributed environment where data is managed at multiple sites in a computer network. Query processing involves the transformation of a high-level query (e.g., formulated in SQL) into a query execution plan that can be efficiently executed by the underlying system.
The main challenges of distributed query processing are:
- How to decompose a query into subqueries that can be executed at different sites
- How to allocate the subqueries to the sites and schedule their execution
- How to transmit the intermediate and final results among the sites
- How to optimize the query execution plan to minimize the total cost (e.g., response time, network traffic, resource consumption)
The main benefits of distributed query processing are:
- Improved performance and scalability by exploiting parallelism and locality
- Increased availability and fault tolerance by replicating data across sites
- Enhanced flexibility and adaptability by allowing dynamic changes in the system configuration and workload
In this article, we will review some of the basic concepts and techniques of distributed query processing, such as:
- Distributed database architectures and models
- Distributed query languages and operators
- Distributed query optimization algorithms and strategies
- Distributed query execution methods and protocols