We almost never overlook SQL Server speed improvement in the fast-paced field of database management. Now that data volumes are increasing and user expectations are increasing, looking for good and effective SQL server performance tuning techniques is a necessity. This is how we’ve seen, firsthand, how a well-tuned database can increase productivity, improve user experience, and drive business success. Next, we’ll explore several strategies for improving SQL Server performance to confront these challenges. In this post, I will cover configuration best practices, database design principles that speed up your queries, and query optimization techniques.

In addition, we will go through ongoing performance monitoring methods such as using SQL Server Management Studio and dynamic management views. This article will end by giving you a complete toolkit to handle performance issues and to make sure that your SQL Server is running at the peak of performance.

Best Practice for SQL Server Configuration

Fine-tuning SQL Server configuration settings have also been found to make a tremendous impact on performance. There are some main points that we should know to optimize SQL Server instances for better speed and efficiency and we will explore some of them. Max Degree of Parallelism (MAXDOP)The Maximum Degree of Parallelism (MAXDOP), is one of the most crucial settings we need to consider. In this case, a configuration is set that defines how many processors can SQL Server employ for the parallel execution of plans. By default, processor affinity is disabled by SQL Server, meaning that it might use all of them, which isn’t always ideal for performance.

We find that setting MAXDOP based on NUMA nodes and processors per node gives better results. We set MAXDOP at or less than the number of logical processors for servers with just one NUMA node and less than eight logical processors. On servers with multiple NUMA nodes, we want to keep MAXDOP close to or below the number of logical processors per NUMA node, capped at 16. On an aside, we can also override the server-level MAXDOP at the database level, query level, and workload group. It gives us a bit of flexibility to fine-tune parallelism for certain scenarios.

Cost Threshold for Parallelism

The Cost Threshold for Parallelism is another critical setting.

This value specifies when SQL Server decides to perform parallel plan execution. As often is the case, if you set the default setting of 5 it is probably too low for modern systems and can create unnecessary parallelism for simple queries. However, Raising this value can make a big difference in performance, especially in OLTP workloads. Typically we’ll begin with a quantity of 30–50, and adjust further depending on the characteristics of our particular workload. This approach guarantees that serial execution will be for all simpler queries so that the overall system performance is given a boost, while complex queries will use parallelism.

Lock Pages in Memory

Sometimes controversial, but somewhat useful option to Lock Pages in Memory. When selected, SQL Server can keep these pages in physical memory and does not send them to disk. In an environment where memory pressure is important, this setting is especially useful. Locking the pages in memory allows us to avoid the performance setback of having to go to memory often to swap out pages. But using this option cautiously is mandatory, as using it affects other processes running on the same server.

You don’t have to do anything else; to enable Lock Pages in Memory, we have to give the SQL Server service account the “Lock Pages in Memory” user right using the Windows Group Policy tool. This is a straightforward process but needs us to look at our overall system resources and workload patterns to ensure we have made the best decision. Application of these SQL Server configuration best practices has led to marked improvements in query performance and overall machine metabolism. But keep in mind that each environment is different. To do this we always suggest thorough testing and monitoring as we adjust these settings to match our particular performance goals or workload characteristics.

Performance database design

As we discovered, a well-designed database is key to achieving the best possible SQL Server performance. In this section, we take a look at some useful ways to improve the speed and efficiency of your database design.

Table Partitioning

The idea of table partitioning is a powerful and easy-to-use technique based on two main purposes: to speed up the queries and to manage large datasets efficiently. Heightened data retrieval and maintenance operations are made possible by dividing a table into smaller, more manageable pieces.

A common but false assumption is that only partitioning results in faster query execution. But in this case, we learned that a table scan still is a table scan whether the table is partitioned or not. Improved maintenance and data management are where the real benefits of partitioning come from.

We’ve found that partitioning is particularly useful for:

1. Piecemeal restores: We can partition data into yearly or quarterly ReadOnly filegroups, making it easier to restore specific portions of data when needed.
2. Maintenance improvements: Partitioning allows us to perform filegroup-level backups, reducing the frequency of backups for read-only data.
3. Index and statistics maintenance: Data that isn’t changing doesn’t require regular maintenance, saving us time and resources.

As a rule of thumb, we remember that “You partition for maintenance, not performance”.

Indexing Strategies

Proper indexing is crucial for SQL Server performance tuning. We’ve learned that the right indexes can significantly improve query speed and reduce resource consumption.

When designing our indexing strategy, we consider the following:

1. Find commonly used queries: We examine the query patterns in our application to ascertain the most frequently used queries. Based on these findings, we build indexes that prioritize and improve these important queries.
2. Consider column cardinality: Columns with high cardinality (a large number of unique values) are generally better candidates for indexing than those with low cardinality.
3. Use covering indexes: If a query can be satisfied using only the columns in an index, it’s considered a ‘covered’ query. Creating covering indexes for often-used queries can result in significant speed increases.
4. Index foreign keys: This is a best practice we always follow, as it can significantly enhance join performance.

We’ve also found that filtered indexes can be highly effective for specific use cases. These indexes only store relevant rows and require less storage space, making them more efficient than traditional non-clustered indexes for certain queries.

Statistics Management

Statistics play a critical role in query optimization. We’ve learned that the SQL Server query optimizer relies on statistics to make informed decisions about execution plans.

To ensure optimal performance, we follow these best practices:

1. Keep AUTO_UPDATE_STATISTICS enabled: This allows SQL Server to automatically update statistics when needed.
2. Monitor large tables: For tables with more than 100 million rows, we consider manually updating statistics on a defined schedule, as waiting for the automatic 20% threshold might lead to suboptimal execution plans.
3. Address data skew: In cases where we know data distribution in a column is skewed, we update statistics manually with a full sample or create filtered statistics to generate better-quality query plans.
4. Track modifications: We use sys.dm_db_stats_properties to accurately track the number of rows changed in a table and decide if we need to update statistics manually.

By implementing these database design strategies, we’ve seen significant improvements in our SQL Server performance. However, we always remember that every environment is unique, and it’s essential to test and monitor these changes to ensure they align with our specific performance goals and workload characteristics.

Query Optimization Techniques

We’ve found that execution plan analysis is a crucial aspect of SQL server performance tuning techniques. By examining the execution plan, we can identify bottlenecks and optimize our queries for better performance. The execution plan provides a visual representation of how SQL Server processes a query, showing us the sequence of operations and their associated costs.

When analyzing an execution plan, we focus on the most expensive operations, which are typically represented by thicker arrows or higher percentages. These areas often present the best opportunities for optimization. For instance, we might notice that a particular join operation is consuming a significant portion of the query’s resources. In such cases, we can explore alternative join methods or consider adding appropriate indexes to improve performance.

One powerful tool at our disposal is the use of query hints. While we generally advise caution when using hints, as they can override the query optimizer’s decisions, there are situations where they can be beneficial. For example, we might use the FORCE ORDER hint to ensure that tables are joined in a specific order when we know it will yield better results. However, we always stress that hints should be used sparingly and only after thorough testing, as they can sometimes lead to unexpected performance issues.

Another technique we employ is the use of plan guides. Plan guides allow us to influence query optimization without modifying the original query text. This can be particularly useful when dealing with third-party applications where we can’t directly alter the code. With plan guides, we can attach query hints or even specify a fixed query plan for a particular statement.

We’ve found that plan guides are especially helpful in scenarios where a small subset of queries in a large application is not performing as expected. By creating a plan guide, we can force the query optimizer to use a specific execution plan that we know performs well for that particular query.

It’s important to note that while these techniques can be powerful, they should be used judiciously. The SQL Server query optimizer is generally quite adept at choosing optimal execution plans. We always recommend thorough testing and monitoring when implementing any optimization technique to ensure it truly improves performance in our specific environment.

In our experience, combining these query optimization techniques with regular performance monitoring and the use of dynamic management views has led to significant improvements in SQL Server performance. By carefully analyzing execution plans, selectively applying query hints, and utilizing plan guides where appropriate, we’ve been able to fine-tune our queries and achieve optimal speed for our database operations.

Ongoing Performance Management

We’ve found that maintaining optimal SQL Server performance is an ongoing process that requires continuous monitoring and fine-tuning. To ensure our databases run at peak efficiency, we’ve implemented several automated techniques that have proven invaluable in our day-to-day operations.

Automated Index Maintenance

One of the key aspects of our SQL server performance tuning techniques is automated index maintenance. We’ve learned that regular index rebuilding and reorganizing can significantly improve query performance. To achieve this, we use SQL Server Agent jobs to schedule and execute index maintenance tasks. These jobs run during off-peak hours to minimize the impact on our production workload.

We’ve configured our maintenance plan to rebuild indexes when fragmentation exceeds 30% and reorganize them when fragmentation is between 5% and 30%. This approach has helped us maintain optimal index health without unnecessarily consuming resources. Additionally, we’ve found that using the SORT_IN_TEMPDB option during index rebuilds can improve performance by offloading sort operations to the TempDB.

Statistics Auto-Update

In order for the query optimizer to make wise choices, statistics must be kept current. We’ve enabled the AUTO_UPDATE_STATISTICS option on our databases to ensure that statistics are automatically updated when they become outdated. This has helped us maintain query performance without manual intervention.

However, we’ve also learned that in some cases, more frequent statistics updates can be beneficial. For our critical tables with rapidly changing data, we’ve implemented custom jobs to update statistics more frequently than the automatic updates. This approach has led to more consistent query performance, especially for our complex analytical queries.

Query Performance Insights

To proactively identify and address performance issues, we leverage Query Performance Insight, a powerful tool provided by SQL Server. This feature has been instrumental in helping us identify top resource-consuming queries and long-running operations.

We regularly review the Query Performance Insight dashboard to spot trends in query execution times and resource utilization. This practice has allowed us to pinpoint problematic queries before they significantly impact our overall system performance. When we identify a query that needs optimization, we use the execution plan analysis tools in SQL Server Management Studio to fine-tune its performance.

By implementing these automated maintenance techniques and leveraging built-in performance monitoring tools, we’ve been able to maintain consistently high performance across our SQL Server environment. Regular reviews of our maintenance plans and performance metrics have allowed us to adapt our strategies as our workload evolves, ensuring that our databases continue to meet the demands of our growing business.

Conclusion

Finally, I’ll offer a note of wrapping up around optimizing SQL Server performance, and it is an ongoing journey that always includes a healthy mix of smart SQL Server configuration, DAMN good database design, and smart query optimization. By taking the above techniques into action, we’ve had a big win in database speed and efficiency. Such an operation has a positive impact on user experience and system productivity as a whole, providing a genuine competitive advantage to businesses in today’s data-driven world.

For the future, we need to watch performance metrics and be flexible when optimizing. There is always something new in the world of database management – challenges and solutions are sprouting up all the time. Stay on top of these changes and continue to get those systems tuned precisely and SQL Server environments will stay speedy, reliable, and ready to take on some task that comes their way.

FAQs

1. How can we make SQL queries perform better?

In order to enhance SQL query performance, we can indirectly increase performance by having some indexing for hitting data out quickly. Some other strategies to handle data are to use EXISTS instead of IN; choosing good data types; and, when dealing with large amounts of data LIMIT and OFFSET as preferred means of selection. On a side note, it is also advisable to avoid the use of SELECT DISTINCT, minimize the number of subqueries, optimize the database design avoid UNION, and hope for UNION ALL instead.

2. How do I improve the performance of SQL Server?

There are some more best practice ways to enhance SQL Server performance such as following indexes appropriately, pulling only the required columns with SELECT instead of SELECT *, optimizing JOINs, and keeping the subqueries at the lowest. Also, do not bring unnecessary data in, use stored procedures, and think about database strategies such as partitioning, sharding, and normalizing.

3. What is the most crucial technique for optimizing SQL performance?

Indexing is fundamental in SQL performance optimization as it significantly speeds up the retrieval of data from a table. Effective use of both clustered and non-clustered indexes is crucial. Understanding the intent of your query will help in selecting the most appropriate type of index for your specific scenario.