MySQL is the trusted open-source database management system for many desktop, mobile, web, and cloud applications. Monitoring the performance of MySQL is critical but as the applications expand over multi-cloud, cloud-native, and hybrid cloud, monitoring also grows in complexity. Continuous monitoring and scaling help applications take advantage of MySQL’s capabilities such as reliability, security, flexibility, availability, and performance scalability.

There are several high-performing database services available on Amazon Web Services (AWS). When you need to handle caching, Online Transaction Processing (OLTP), real-time data, session stores, or personalization, you can choose one of these options. Examples of these high-performance database services include Amazon RDS, Aurora, MemoryDB, DynamoDB, Neptune, and ElastiCache. We often get questions about which AWS database service is best between RDS and Aurora.

Microsoft Azure offers a choice of relational and non-relational database services to support a wide range of application needs and demands. Built-in intelligence helps automate management tasks like high availability, scaling, and query performance tuning to provide users with services that ensure applications are always available and performant. Many services offer essentially limitless database scale and SLAs (Service Level Agreements) usually range between 99.9-99.999% availability.

OpenTelemetry libraries can be used to monitor MongoDB interactions. In this tutorial, we will learn how we can monitor MongoDB with OpenTelemetry libraries to analyze query execution and identify performance bottlenecks. Most modern applications have distributed architecture thanks to cloud and containerization. In cloud-native applications, it is necessary to track user requests across services and components like databases.

Welcome to the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 1) is focused on.

Most development teams know that testing the application layer of a system (a.k.a the codebase) is of vital importance. Testing the data layer (the database) is just as important. To perform database testing, you construct queries to assert and validate the database operations, structures, and attributes required by the application connecting to the database.

IT pros need tools designed to ingest large volumes of data, correlate events across data sources, detect problems, and resolve them with new technologies to support more efficient IT systems. This is the function of AIOps. AIOps or Artificial Intelligence for IT Operations, is the use of artificial intelligence (AI) and machine learning (ML) technologies to enhance and automate various aspects of IT.

We release VictoriaMetrics several times a month, including at least one major update. However, because these new releases often introduce new features, they may be less stable. That’s why we also regularly publish Long-term support releases (LTS) alongside our regular releases. These LTS versions focus exclusively on bug fixes without new features and performance improvements. We committed to publishing LTS versions every six months and supporting them for one year.

The story of how we simplified database system monitoring for generalists while making it flexible enough for specialists.

In my first post, I talked about how SQL Sentry has taken shape over the years. Now we announced SQL Sentry 2023.3, and a lot is changing. In the past, customers could only work with SQL Sentry through an installed, Windows-based client. It’s a feature-rich interface with access to everything you need with no limit to how many users install it. But we heard our customers; they wanted a web-based interface with a simplified user experience.