SQL Server execution plans are an essential tool for database administrators and developers to understand the performance of SQL queries. An execution plan is a road map that shows how SQL Server executes a query, detailing how the data is retrieved from tables, the indexes used, and the operations performed.

Execution plans are useful because they allow database administrators and developers to identify performance bottlenecks and optimize queries to improve overall database performance. By looking at the execution plan, one can see which operations are consuming the most resources and identify the most expensive parts of a query.

Anyone who works with SQL Server databases, including database administrators, developers, and performance engineers, should be familiar with execution plans. They are particularly useful for troubleshooting slow queries, where the execution plan can reveal the cause of poor performance.

For example, an execution plan might show that a query is using a table scan instead of an index seek, leading to poor performance. With this knowledge, the query can be optimized by creating an appropriate index, significantly improving performance. In this article, we will discuss how execution plans are generated, how to interpret them, and how to use them to optimize query performance.

Generating an Execution Plan

SQL Server can generate two types of execution plans: estimated and actual. The estimated execution plan is generated before the query is executed, and it is based on statistics about the tables and indexes involved in the query. The actual execution plan is generated while the query is executing, and it includes real-time performance statistics.

Interpreting an Execution Plan

An execution plan can contain several operators, such as Index Scan, Index Seek, Nested Loops, Merge Join, and Sort. Each operator represents a step in the query execution process.

Index Scan: Scans the entire index looking for the requested data.

Index Seek: Seeks directly into the index looking for the requested data.

Nested Loops: Performs a join by looping through each row in one table and matching it with rows in another table.

Merge Join: Sorts both input sets and then merges them based on a specified join condition.

Sort: Sorts data based on one or more columns.

The execution plan also shows the cost for each operator, which is an estimate of the resources required to execute the operator. The cost is calculated based on factors such as the number of rows, data size, and the number of operations required. The cost of the various operators in a SQL Server execution plan can be interpreted as an estimate of the amount of work or resources required to execute a particular query. Understanding the cost of each operator can be helpful in identifying potential performance bottlenecks in a query and optimizing its execution plan.

Below are some common operators in SQL Server execution plans, as well as their cost and how to interpret the result:

Index scan – An index scan reads all the rows in an index, which can be expensive if the index is large or if there are many non-selective rows. The cost of an index scan is proportional to the number of rows in the index. A high cost for an index scan may indicate that the index is not being used effectively or that the query is not selective enough.

Index Seek – An index seek uses an index to look for specific rows in a table, which can be more efficient than an index scan if the query is selective enough. The cost of an index seek is proportional to the number of rows retrieved. High costs for an index seek may indicate that the query is not selective enough or that the index is not being used effectively.

Nested loops – A nested loop joins two tables by looping through one table for each row in the other table. The cost of a nested loop is proportional to the number of rows in the outer table times the number of matching rows in the inner table. A high cost for a nested loop may indicate that the tables being joined are not optimized for the join or that the query is not selective enough.

Merge Join – A merge join connects two sorted inputs by comparing each row in each input. The cost of a merge join is proportional to the number of rows in both inputs. A high merge join cost may indicate that the inputs are not sorted or that the query is not selective enough.

Sort – A sort operator sorts the output of a query based on a specific column or set of columns. The cost of a sort is proportional to the number of rows to sort times the logarithm of the number of rows to sort. The high cost of a sort may indicate that the query is not optimized for sorting or that the data to be sorted is too large.

When interpreting the cost of each operator, it’s important to consider the overall cost of the query and how each operator contributes to the total cost. For example, a single high-cost operator may not be a problem if the overall cost of the query is low. Similarly, a low-cost operator may be problematic if it’s used many times in the execution plan.

Optimizing an Execution Plan

An execution plan can help you identify performance issues, such as missing indexes or inefficient joins. One of the most important factors affecting query performance is indexes. Indexes can significantly improve query performance by allowing SQL Server to quickly find the data you need. When examining the execution plan, look for index seks, which are much faster than index scans. An index seek indicates that the query is using an index to find the requested data, while an index scan indicates that the entire index is being scanned to find the data.

Example: Consider the following query:

SELECT * FROM orders WHERE customer_id = 12345

If an execution plan displays an index seek for the customer_id column, it means that the query uses the index efficiently to find the data it needs. On the other hand, if the execution plan shows an Index Scan on the customer_id column, it means SQL Server is searching the entire index to find the data, which can be much slower.

Changing an index scan to an index seek can often result in improved query performance because it allows SQL Server to retrieve only the rows needed for the query instead of searching the entire index. An index seek is when SQL Server looks up specific rows in a table, whereas in an index scan, SQL Server reads all the rows in the index.

To turn an index scan into an index seek, you can modify the query or add an index to the table. A common method is to make the query more selective by adding additional conditions to the WHERE clause. For example, consider the following query:

SELECT * FROM orders WHERE customer_id = 12345

If the orders table has a large number of rows and an index on the customer_id column, SQL Server may choose to do an index scan to retrieve all the orders for the customer with ID 12345. To change this to an index seek, you can add additional conditions to the WHERE clause to make the query more selective, like this:

SELECT * FROM orders WHERE customer_id = 12345 AND order_date > ‘2022-01-01’

By adding the condition on the order_date column, the query becomes more selective and SQL Server can use the index to perform an index seek instead of an index scan.

Alternatively, you can add a covering index to the table that includes all the columns needed for the query. A covering index is an index that includes all the columns needed for a query, so SQL Server can retrieve all the data it needs directly from the index without having to look up additional data from the table. This can greatly improve query performance, especially for large tables.

In summary, to change an index scan to an index seek, you should modify the query to make it more selective or add a covering index to the table that includes all the columns needed for the query. This can lead to improved query performance and faster data retrieval from the table.

Another factor to consider when examining an execution plan is table scans vs. seeks. These are two methods used by SQL Server to retrieve data from tables. A table scan is when SQL Server reads every row in a table to find the data it needs, while a table seek is when SQL Server uses an index to look up specific rows in a table.

Table scans are slow and resource-intensive because SQL Server has to read every row in the table, even if it doesn’t need all the data. This can lead to slow query performance, especially for large tables. On the other hand, table seeks are much faster because SQL Server can use the index to quickly locate the data it needs without reading every row in the table.

To enhance a query to use a table seek, you can add an index to the table for the column being scanned. This allows SQL Server to use the index to find the data it needs instead of scanning the entire table.

For example, consider the following query:

SELECT * FROM orders WHERE order_date > ‘2022-01-01’

If the Orders table contains a large number of rows, SQL Server may choose to do a table scan to find all orders that match the condition. To improve performance, you can add an index for the order_date column as follows:

CREATE INDEX idx_order_date ON orders (order_date);

If the index is present, SQL Server can use it to perform a table seek, rather than a table scan, to find the orders that match the condition. This can significantly improve query performance.

Conclusion:

In this article, we discussed the importance of execution plans in Microsoft SQL Server and how they can be used to optimize query performance. We also showed how execution plans are generated, how to interpret them, and how to optimize queries based on the generated plan. Remember: by examining the execution plan, you can identify performance issues and make informed decisions to optimize your database queries.