Making the Most of Historical Data: Preventing Future SQL Server Issues

by Nov 9, 2023


This blog post was authored by Pinal Dave from SQLAuthority.


Forecasting future SQL Server performance issues can often feel like gazing into a crystal ball. However, the key to predicting future troubles often lies in the past – specifically, historical data. In this post, we’ll explore the importance of historical data in preventing future SQL Server issues. We’ll discuss the native ways to access and analyze historical data and contrast this with how SQL Diagnostic Manager (SQL DM) provides an efficient way to examine historical trends and pinpoint when a problem first surfaced.

The Value of Historical Data

Historical data provides a treasure trove of information about the past performance of your SQL Server. By analyzing this data, you can identify patterns and trends, understand how changes to your system impact performance, and even predict potential future issues.

Built-in SQL Solutions

SQL Server provides several native mechanisms to access and analyze historical data. The Database Engine Tuning Advisor can use historical workloads to make index and partitioning recommendations, while SQL Server Profiler can trace and store events for later analysis.

Database Engine Tuning Advisor

The Database Engine Tuning Advisor (DETA) is a performance tuning tool that recommends what indexes, indexed views, and partitions could enhance the performance of your SQL Server applications. DETA uses workloads, which can be a series of SQL statements, to analyze historical data and make recommendations.

However, using DETA effectively requires a certain level of expertise. You need a good understanding of SQL Server and your workload to interpret the recommendations and decide which ones to implement. Additionally, DETA is not designed to continuously monitor performance or provide real-time alerts. Instead, it’s more of an ad-hoc tool that you use when you want to optimize your database.

In a real-world scenario, imagine a financial firm running complex calculations for risk assessment. Any changes to the database schema, such as the addition of new tables or indexes, could potentially affect these calculations’ performance. In this case, DETA could help by recommending optimal indexes and partitions based on the historical workload. Still, it requires manual intervention to analyze and apply the recommendations.

SQL Server Profiler

SQL Server Profiler is another native tool that can help with historical data analysis. It allows you to trace and record events in your SQL Server instance, such as the start of a batch or a transaction commit, which can be useful for troubleshooting performance issues or auditing activities.

However, Profiler has its limitations. For one, it has to be manually started and stopped, which means it’s not suitable for continuous monitoring. Additionally, while Profiler can capture a wealth of event data, analyzing this data can be time-consuming and complex, especially over longer periods.

Consider a situation where an e-commerce website experiences intermittent performance issues. SQL Server Profiler could help by capturing event data during the periods when the issues occur, but analyzing this data to find patterns or identify the root cause could be a complex and time-consuming process.

SQL DM: Efficient Historical Analysis

SQL Diagnostic Manager (SQL DM) fills these gaps by providing a user-friendly and comprehensive solution for historical data analysis.
SQL DM automatically collects and stores performance data from your SQL Server. This includes the performance metrics of queries, databases, and SQL Server instances, allowing you to focus on analysis rather than data collection. This automatic collection is handy in situations where continuous monitoring is necessary, such as a high-traffic e-commerce website where performance issues can directly impact sales.

Historical trend analysis is another powerful feature offered by SQL DM. By presenting data in easy-to-understand trend graphs, SQL DM allows you to spot patterns and trends in your SQL Server’s performance over time. This can help you to predict future performance issues and take proactive measures to prevent them. For instance, if a business intelligence application shows a steady increase in query execution time over several months, the trend analysis can help predict when the performance might degrade to unacceptable levels.

Moreover, SQL DM’s baseline comparison feature allows you to compare current performance to a baseline derived from historical data. This helps you to quickly identify any deviation from normal behavior and take immediate action. This feature could be invaluable for a financial firm that needs to ensure consistent performance for its risk assessment calculations. Any deviation from the baseline might indicate a potential issue that needs immediate attention.


Preventing future SQL Server issues is crucial to database administration, and historical data plays a vital role. While SQL Server’s native tools provide some means to access and analyze historical data, SQL Diagnostic Manager offers a more accessible and comprehensive solution.
With SQL DM, you can efficiently analyze historical trends and pinpoint the origin of problems, empowering you to maintain the health and performance of your SQL Server proactively. Remember, when it comes to preventing future SQL Server issues, history often holds the key.
Experience for yourself how SQL DM can help you prevent future SQL Server issues by making the most of historical data by downloading a free, 14-day trial (a credit card is not required).

About the Author

Pinal Dave is an SQL Server Performance Tuning Expert and independent consultant with over 21 years of hands-on experience. He holds a Masters of Science degree and numerous database certifications. Pinal has authored 13 SQL Server database books and 53 Pluralsight courses. To freely share his knowledge and help others build their expertise, Pinal has also written more than 5,600 database tech articles on his blog at SQLAuthority.