How can you speed up query execution with parallelism?

1 What is parallelism?

Parallelism is the ability of a database system to divide a query into smaller units of work and distribute them among multiple threads, processes, or servers. By doing this, the system can use more resources and reduce the overall execution time of the query. Parallelism can be applied at different levels, such as the query level, the operator level, or the data level, depending on the architecture and configuration of the database system.

2 How does parallelism work?

The query optimizer is responsible for deciding whether to use parallelism or not, depending on factors such as the complexity of the query, the size of the data, the available resources, and the system settings. If parallelism is used, it determines how many parallel workers to assign to the query and how to split the query into subtasks. The parallel workers can be threads within a process or processes within a server or across multiple servers. The query coordinator distributes the subtasks to these workers and coordinates their execution. The parallel workers perform their subtasks independently and send the results back to the coordinator. Finally, it combines all of these results and returns the final output to the user or application.

5 How can you use parallelism?

Parallelism is a powerful technique that can help you speed up query execution and improve database performance. To make the most of this technique, you must first check the system settings and parameters that control parallelism, such as the degree of parallelism, the maximum number of parallel workers, the parallel threshold, and the parallel hint. Next, analyze your queries to identify which ones can benefit from parallelism, such as those involving large tables, complex expressions, or multiple joins. You can then apply parallelism to your queries using hints, clauses, or functions that indicate the desired degree of parallelism or the specific operations or data that should be parallelized. Finally, test and monitor your queries and system to verify the results and effects of parallelism. Keep in mind that while parallelism is beneficial in many cases, it is not a one-size-fits-all solution and requires careful planning, analysis, and testing.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?