Get a guided tour on advanced Oracle monitoring based on Microsoft System Center Operations Manager.

We recently got this great feedback from a dear user in our Discord: This is great and exactly what we want, a clear problem or improvement we could make to help make that users monitoring life a little easier. This is also where the beauty of open source comes in and being able to build on the shoulders of giants - adding such a feature turned out to be pretty easy by just extending our existing Pandas collector to support SQL queries leveraging its read_sql() capabilities.

Microsoft SQL Server is a popular relational database management system that provides a wide range of performance and reliability features (e.g., AlwaysOn availability groups) to support business-critical applications. As your SQL Server workloads scale and increase in complexity, it can be difficult to monitor all of their components and pinpoint the exact issues that are degrading your databases’ performance.

As database administrators and developers, we need to know how to tune SQL queries and databases. Tuning SQL queries and databases is one of the most powerful tools in our arsenal for achieving the best possible performance results. This post will help you understand more about SQL tuning. I’ll start by explaining what SQL performance tuning is. Then, I’ll go over how to conduct SQL performance tuning in MySQL, Microsoft SQL Server, and Oracle.

Share: VictoriaMetrics is always improving, with frequent updates adding new features, performance improvements and bug fixes listed at the CHANGELOG page. We usually make at least a single release every month. All the new features and bug fixes go to the latest release. That’s why we recommend periodically upgrading VictoriaMetrics components to the latest available release. But the latest release may also contain bugs in the latest features.

The MongoDB document-oriented database is one of the most popular database tools available today. Developed as an open-source project, MongoDB is highly scalable and can be set up in your environment in just a few simple steps. When running and managing databases, monitoring is a key requirement.

The database is one of the most critical components here at Rollbar and its performance ripples across most of our SLOs. One of our goals last months has been to remove unnecessary or repetitive load from it as much as possible. Caching queries is the 101 solution whenever you need to keep scaling up while maintaining, or even reducing, database costs (very relevant these days). Here’s an example of how just one query has been drastically reduced thanks to caching.

As part of a recent Redgate Summit which focused on digital transformation and data modernization, I was fortunate enough to be able to interview Pramod Sadalage, a Director at Thoughtworks. There, and I’m quoting here, he enjoys the rare role of bridging the divide between database professionals and application developers.

Redis is a widely used in-memory database in the industry. As a consequence of its in-memory database, it can concurrently serve data as a key-value-oriented NoSQL database. Due to the use of in-memory data storage in Redis, you can achieve performance that is challenging with conventional databases. It is crucial to monitor Redis' resource usage since it is an in-memory data store.

When it comes to your SQL Server, performance degradation is a costly, time-consuming issue. There may be any number of reasons why your SQL Server performance may be diminishing, but one of the biggest culprits tends to be memory usage.