What is the best way to analyze an SQL query's execution plan?

1 Understand the basic elements

An SQL query's execution plan consists of several operators, which are the basic units of work that the database engine performs. Each operator has an input, an output, and a cost associated with it. The cost reflects the amount of resources, such as CPU time, memory, and disk I/O, that the operator consumes. The operators are connected by arrows, which indicate the flow of data from one operator to another. The execution plan also shows the estimated number of rows and the size of data that each operator processes.

3 Compare the actual and estimated values

One of the most important aspects of analyzing an SQL query's execution plan is to compare the actual and estimated values of the operators. The estimated values are based on the statistics that the database engine collects about the data distribution and cardinality. The actual values are based on the actual execution of the query. If there is a significant difference between the actual and estimated values, it means that the database engine has made a wrong assumption or has outdated statistics. This can lead to suboptimal choices of operators, join types, or indexes, and affect the performance of the query.

4 Identify the bottlenecks and expensive operators

Another key aspect of analyzing an SQL query's execution plan is to identify the bottlenecks and expensive operators that consume the most resources and take the longest time to execute. You can use the cost percentage, the execution time, or the number of logical reads to measure the impact of each operator on the query performance. You can also use tools such as SQL Server Management Studio or Oracle SQL Developer to highlight the most expensive operators in the execution plan. By identifying the bottlenecks and expensive operators, you can focus on optimizing the parts of the query that matter the most.

6 Test and measure the results

The final step of analyzing an SQL query's execution plan is to test and measure the results of your optimization efforts. You should run your query before and after applying any changes, and compare the execution plans, the execution times, and the resource consumption. You should also monitor the impact of your changes on the overall performance of the database system, such as the CPU utilization, the memory pressure, or the disk activity. You should use a consistent and representative data set and workload to test your queries, and avoid factors that can introduce variability, such as caching, concurrency, or network latency. By testing and measuring the results, you can verify the effectiveness of your optimization and ensure that you achieve your goals.

7 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?