Distributed execution is running a database query in parallel across multiple servers or nodes. It allows scaling out query processing over clustered commodity infrastructure.
Databases designed for distributed execution split query stages across nodes holding subsets of partitioned data, coordinating parallelism for faster results.
Distributed execution works together with query execution, parallel execution within a node, and partitioning strategies to optimize performance and scalability across large datasets and clusters. Distributed query engines handle node communication, Failure recovery, and other aspects of coordinated execution across nodes.
In a distributed database, queries execute after optimization by:
Distributed execution frameworks manage coordination, data transfers, progress tracking, parallelism.
Distributing query execution harnesses resources of many commodity servers to create scale-out shared-nothing architectures cost effectively.
It provides flexibility to elastically grow compute for larger workloads. By dividing work across nodes, individual servers handle a fraction of load, improving performance.
Distributed execution helps for:
Some key design aspects for distributed execution include:
Some popular distributed databases utilizing clustered execution are:
Query execution is the process of carrying out the actual steps to retrieve results for a database query as per the generated execution plan.
Read more ->Parallel execution refers to techniques for speeding up database query processing by leveraging multiple CPUs, servers, or resources concurrently.
Read more ->Database partitioning refers to splitting large tables into smaller, independent pieces called partitions stored across different filegroups, drives or nodes.
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