SolarWinds Database Performance Analyzer (DPA)

Database performance has always been an area of ​​focus for IT departments. It plays a crucial role in optimizing the cost of an organization’s cloud infrastructure spend, a factor keenly observed by the accounting department. Sales and marketing departments should also be concerned with database performance, as it determines how quickly data will be delivered to different locations and supports applications that improve user experience, adoption and acceptance. use.

Slow customer experiences can lead to lost customers, which has never been truer for web applications, as highlighted by Google’s adoption of Core Web Vitals, a report measuring the speed and user experience of your website that drives your search engine rankings.

The database performance monitoring scene has long been dominated by database manufacturers themselves, with tools that typically only work on their solutions.

This is where SolarWinds comes in. The Austin, Texas-based software vendor, long synonymous with network monitoring, offers an intuitive and comprehensive database monitoring solution in its portfolio called SolarWinds® Database Performance Analyzer (DPA).

Its features include support for multiple database platforms such as Oracle, SQL Server, Azure SQL Database, Aurora, PostgreSQL, Db2, SAP ASE, MySQL, and MariaDB. And in each of these databases, many flavors and versions are supported.

SolarWinds DPA is available on Linux and Windows Server and can be used in Azure, Google and AWS cloud environments.

We got our hands on DPA and tested it on our own PostgreSQL databases in a Linux environment.

No software is installed on the monitored server; instead, DPA connects remotely to each database to collect data and then stores it in a separate repository. This connection is managed through Java Database Connectivity (JDBC).

DPA can remotely connect to every VMware vCenter Server, ESX, or ESXi host in a virtual environment. Again, no software is installed on the database server VMs.

DPA runs a default “quick poll” query once per second to collect information about wait events. This information is encrypted to protect the organization’s data. When comparing queries, DPA calculates the total wait time for all executions so that DBAs can focus on improving the most impactful queries first.

DPA offers wait-based analytics, which pinpoint why a query is slow, such as a virtual machine lagging resource consumption for CPU or storage. DPA turns tuning from reactive to proactive by identifying the best opportunities for optimization based on the actual workload.

IT teams can access DPA through a secure web interface. They can perform the installation and configure the repository to store the organization’s collected data. The repository database can be configured automatically or IT teams can set it up themselves.

Setup through the web interface is simple, and once complete, teams can run analytics and drill down into historical and real-time data.

Machine learning (ML) algorithms analyze DPA data to find anomalies and make recommendations. This prediction improves over time with more data collected.

After analyzing our database configuration, DPA advised us to focus mainly on table tuning (indexing changes) and inefficient queries that we need to resolve. DPA was able to easily troubleshoot our worst performing apps and boards, allowing us to focus on them and quickly take corrective action. Each supported database system includes specific watch areas; for example, PostgreSQL support enables monitoring of cache eviction, checkpoints, replication, voiding, row operations, license compliance, and more.

CPU, I/O and VMware vSphere performance monitoring is also included in DPA analysis. Some competing tools only analyze SQL statements and transactions.

Targeted and customizable email alerts can be configured, and internal developers can also use the built-in APIs to access DPA data to integrate with other systems or interfaces.

For a broader monitoring perspective, support is included for aggregating organization DPA data into the SolarWinds Orion platform or the latest SolarWinds hybrid cloud observability platform.

While all of these analyzes are useful, the key capability offered by DPA is the ability for IT teams to be “evidence-driven” to estimate how increases in demand and scaling will affect application performance over time. ‘coming.

We found that installing DPA had no noticeable resource overhead on our database server, which coincides with SolarWinds’ statement: “DPA causes less than 1% overhead on the instance” .

In summary, DPA monitors, diagnoses, and fixes database performance issues. It has a lightweight, agentless architecture and uses ML to make actionable recommendations. We found DPA simple, powerful, and relatively easy to install and use.

And SolarWinds DPA recently won the Bronze Stevie Award at the 2022 Asia-Pacific Stevie Awards. More information about DPA can be found online hereand the tool is available as a perpetual license or on a subscription basis with a free trial available here. The trial is non-binding and can be easily uninstalled if you don’t like the product as much as we do.

Maria H. Underwood