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Comparing Performance and Resource Usage: Grafana Agent vs. Prometheus Agent Mode vs. VictoriaMetrics vmagent

Monitoring and observability are critical components of modern IT infrastructures, enabling organizations to gain insights into the performance, health, and security of their systems. Agents play a crucial role in gathering and forwarding telemetry from various sources to observability platforms.

VictoriaMetrics Machine Learning takes monitoring to the next level

Today we’re happy to announce our new VictoriaMetrics Anomaly Detection solution, which harnesses machine learning to make database alerts more relevant, accurate and actionable for enterprise customers. VictoriaMetrics Anomaly Detection lightens the load on overworked data engineers, focusing their scarce resources on the alerts that matter most to their organization.

How to reduce expenses on monitoring: Swapping in VictoriaMetrics for Prometheus

Monitoring can get expensive due to the huge quantities of data that need to be processed. In this blog post, you’ll learn the best ways to store and process monitoring metrics to reduce your costs, and how VictoriaMetrics can help. This blog post will only cover open-source solutions. VictoriaMetrics is proudly open source. You’ll get the most out of this blog post if you are familiar with Prometheus, Thanos, Mimir or VictoriaMetrics.

Performance optimization techniques in time series databases: sync.Pool for CPU-bound operations

Internally, VictoriaMetrics makes heavy use of sync.Pool, a data structure built into Go’s standard library. sync.Pool is intended to store temporary, fungible objects for reuse to relieve pressure on the garbage collector. If you are familiar with free lists, you can think of sync.Pool as a data structure that allows you to implement them in a thread-safe way.

VictoriaMetrics Enterprise, the World's Fastest Open-Source-Based Monitoring: Try It for Free

We’re happy to announce that we now offer a free trial of our VictoriaMetrics Enterprise solution! Designed to help solve an organisation’s monitoring and observability set ups, no matter the scale, VictoriaMetrics Enterprise provides reliable, secure and cost-efficient monitoring. The free trial of VictoriaMetrics Enterprise is perfect for organisations with large data loads, for whom cost-efficient monitoring is mission-critical.

Anomaly Detection for Time Series Data: Techniques and Models

Welcome to the third chapter of 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.

Performance optimization techniques in time series databases: function caching

Relabeling is an important feature that allows users to modify metadata (labels) of scraped metrics before they ever make it to the database. As an example, some of your scrape targets may generate metric labels with underscores (_), and some of your targets may generate labels with hyphens (-). Relabeling allows you to make this consistent, making database queries easier to write.