Tuning SQL can help accelerate overall database performance. However, effective database tuning can require understanding the top SQL statements, top wait types, SQL plans, blocked queries, resource contention, and the effect of missing indexes.
SolarWinds® Database Performance Analyzer (DPA) is a SQL query analyzer tool designed to provide you with the fastest way to analyze SQL statements and identify and resolve performance problems.
SolarWinds DPA SQL analyzer tool is designed to consolidate performance data into a unified view, so you can more easily isolate the root cause of a slow query in about three to four clicks.
With DPA, you gain round-the-clock monitoring of SQL Server, Oracle, DB2, and SAP ASE running on physical servers, and VMware, with less than a one percent load.
DPA helps align resource provisioning with database performance, giving users the information they need to be more prescriptive about hardware recommendations by helping to identify how resource provisioning may be affecting database response times. SolarWinds DPA can also provide developers with a unique perspective on VMware infrastructure and its impact on database performance.
Running a large database estate? The DPA REST API lets you automate the operational side — registering instances, starting and stopping monitors, allocating licenses, and adding annotations — using Python or PowerShell scripts. It connects securely to the DPA server and is fully documented through a built-in Swagger interface, so you can explore endpoints and test calls before writing a single line of automation.
DPA uses wait-time analytics and machine-learning–based anomaly detection to build a rich historical record of performance activity.
The Table Tuning Advisor built into DPA SQL query performance analyzer can also help developers identify problems requiring immediate attention and provide clear, actionable advice.
When advisors aren’t enough, AI Query Assist takes it a step further. It takes the query text and execution plan, runs it through SolarWinds Artificial Intelligence in a secure environment with PII masking, and returns a fully rewritten query — complete with a breakdown of what was changed, why it should perform better, and a side-by-side comparison of the original and optimized SQL. Less trial and error, faster mean time to resolution.
Accessing query performance on production servers can help improve SQL performance because it can help you incorporate application performance insights into development cycles.
DPA’s SQL Server query analyzer is designed to monitor development, test, and production servers with less than a one percent load. Within the DPA dashboard, developers can drill down into production databases to better understand how code is performing.
Do you find yourself asking…
SQL query analysis is versatile and not limited to just one purpose. Here are some areas where it proves valuable in various aspects of the work.
Troubleshooting slow queries under pressure
Your app is crawling, and users are pinging you. SolarWinds DPA’s wait-time analysis cuts through the noise and points directly at the queries consuming the most resources — whether the bottleneck is CPU, I/O, memory, or lock contention. You go from “something is slow” to “this specific query is the problem, and here’s why” in a few clicks.
Catching performance regressions after deployments
A release ships Friday. By Monday, a query that ran in 50ms is taking 2 seconds. DPA’s historical record — up to five years of performance data — lets you compare query behavior before and after the deployment window, so you can pinpoint what changed without relying on gut feel or war-room guesswork.
Auditing database activity for capacity planning
SQL query analysis provides a comprehensive view of execution frequency, resource consumption trends, and peak-load behavior over time. That data is invaluable when you’re making the case for new hardware, reallocating resources between host and guest, or trying to understand whether a performance problem is structural or situational.
Checking query modifications and index updates
After modifying a query or creating a new index, how can you tell if it made a difference? DPA provides side-by-side execution metrics from before and after your change, so you can rely on concrete data rather than anecdotal evidence or assumptions about performance improvements under load.
Proactive tuning before problems reach users
Not every SQL problem announces itself loudly. DPA’s machine learning anomaly detection surfaces queries with gradually increasing execution times or growing resource consumption — giving your team time to tune proactively rather than scramble reactively when things finally break.
SQL query analysis is versatile and not limited to just one purpose. Here are some areas where it proves valuable in various aspects of the work.
Troubleshooting slow queries under pressure
Your app is crawling, and users are pinging you. SolarWinds DPA’s wait-time analysis cuts through the noise and points directly at the queries consuming the most resources — whether the bottleneck is CPU, I/O, memory, or lock contention. You go from “something is slow” to “this specific query is the problem, and here’s why” in a few clicks.
Catching performance regressions after deployments
A release ships Friday. By Monday, a query that ran in 50ms is taking 2 seconds. DPA’s historical record — up to five years of performance data — lets you compare query behavior before and after the deployment window, so you can pinpoint what changed without relying on gut feel or war-room guesswork.
Auditing database activity for capacity planning
SQL query analysis provides a comprehensive view of execution frequency, resource consumption trends, and peak-load behavior over time. That data is invaluable when you’re making the case for new hardware, reallocating resources between host and guest, or trying to understand whether a performance problem is structural or situational.
Checking query modifications and index updates
After modifying a query or creating a new index, how can you tell if it made a difference? DPA provides side-by-side execution metrics from before and after your change, so you can rely on concrete data rather than anecdotal evidence or assumptions about performance improvements under load.
Proactive tuning before problems reach users
Not every SQL problem announces itself loudly. DPA’s machine learning anomaly detection surfaces queries with gradually increasing execution times or growing resource consumption — giving your team time to tune proactively rather than scramble reactively when things finally break.
Database Performance Analyzer for SQL Server