Slow or failed SQL queries can cause significant lags and delays in database response time. While a delay of a few milliseconds might not seem like much for individual processes, they can easily compound when spread across the organization and lead to bad customer experiences.
SQL tuning makes code more efficient, which is an integral part of optimizing database query performance. However, manual performance optimization can be a drain on the time and resources of your database administrators. It’s also manual and error-prone, requiring admins with the skills and expertise to locate the underperforming queries and correct the code. It requires knowledge of SQL statements and waits types, blocked queries, and a deep understanding of how indexes work.
One common question is about how to measure query performance in SQL Server – you can start by investigating your queries and improving their performance by considering the following steps:
While tuning a database, ensure that you look at tables (not views) and then record the rowcount. Next, you can filter the rowcount by evaluating the JOIN and WHERE clauses. Make sure the tables are as selective as possible while working with smaller datasets. Then, examine the query columns for SELECT * and scalar functions for more information than the query requires. Make the index searchable and eliminate the need to run a full database scan.
Examine the existing keys, constraints, and indexes to prevent duplication and overlap. Please note the primary definition and find out whether it’s clustered. Make sure statistics are on before running the execution plan. Once you execute the plan, measure the logical reads and record the results. Fewer logical I/Os means that the query is running fast. Modify the query to focus on the most expensive operations. Then rerun it and compare the findings. Adjust the indexes to include a covering or filtering index if needed.
The following SQL query and database optimization tips can help you improve query performance:
One easy way to reduce the workload of each query on your database is to set a limit on the information that each query can retrieve. Many developers will use the SELECT * statement early to recover the complete information in a database table. In large tables containing several fields and rows, using SELECT * words can drain your database resources by retrieving vast amounts of unnecessary data. Admins can create queries to retrieve the necessary information using SELECT statements with precisely defined fields.
While the SELECT DISTINCT statement helps remove duplicates from a query, it uses tremendous processing power. You can select more fields and generate unique results without affecting the query performance.
The top objective of SQL tuning is to achieve greater efficiency, and in the SQL Order of Operations, WHERE clauses will be executed before HAVING statements. It limits the number of records retrieved from your SQL database, making filters more efficient. However, HAVING statements should be used for filtering aggregated fields.
Wildcards are useful for broad searches, which also runs the risk of inefficiency. Using a leading wildcard with an ending wildcard is inefficient, as it queries the database for all records and text-match the two wildcards. For example, while searching for customer names, %Mit% would retrieve both “Mitch” and “Smith,” whereas Son% would only return “Mitch.”
A LIMIT statement before running a query allows DBAs to quickly survey the results to determine whether the question needs further refinement. Queries potentially returning many results are difficult, but using a LIMIT statement streamlines the process of testing query adjustments.
Some SQL developers prefer using WHERE clauses to make joins, which runs the risk of creating a Cartesian Join. Cartesian Joins or CROSS JOINs generate all possible combinations of the variables listed in the SQL statement. It can cause issues with large databases because all possible combinations from large tables could generate billions of results. Using the INNER JOIN clause addresses this problem.
Some SQL developers prefer using WHERE clauses to make joins, which runs the risk of creating a Cartesian Join. Cartesian Joins or CROSS JOINs generate all possible combinations of the variables listed in the SQL statement. It can cause issues with large databases because all possible combinations from large tables could generate billions of results. Using the INNER JOIN clause addresses this problem.
Manual SQL query tuning is a complex, time-consuming process, which is why using automated SQL query tuning tools enables database administrators to maximize their server and database performance. SQL query tuning tools are essential for optimal performance, freeing DBAs and IT support staff to focus on more mission-critical initiatives. The best database tools perform three key tasks:
SQL tuning applications monitor database performance and provide user interfaces with graphic representations of query performance and wait times. This can help admins identify resource bottlenecks with incredible speed and accuracy. With a historical log of KPIs, these monitoring tools are invaluable in ensuring that all current SQL queries work as intended.