The VictoriaMetrics Observability Blog

Traditional concurrent Go tests can be flaky due to non-deterministic scheduler behavior and timing. Go 1.24’s experimental synctest feature provides deterministic testing by running goroutines in isolated ‘bubbles’ where a synthetic clock only advances when all internally managed goroutines are durably blocked.

Seeing that VictoriaMetrics products are this popular with engineers worldwide is fantastic: Just a little over a year ago, we hit 10K stars, and with the adoption of VictoriaLogs, the star count now went beyond 14K. Read about most recent achievements in this blog post.

Turn alerts into action, not noise. Our May 22nd Tech Talk (10 AM PDT) dives into vmalert best practices. Learn to configure, write effective rules, and optimize your VictoriaMetrics alerting strategy. Don’t miss out!

VictoriaMetrics is a fast, scalable monitoring system made of modular components like vminsert, vmstorage, and vmselect. It supports both single-node and clustered setups, along with tools for backup, restore, alerting, access control, and data migration. Data can be ingested, stored, queried, backed up, and restored with high performance and minimal resource use.

We have just got back from an incredible weekend representing VictoriaMetrics at LinuxFest Northwest 2025 in beautiful Bellingham, WA! 🌲🏔️

Go applications can implement graceful shutdown by handling termination signals (SIGTERM, SIGINT) via os/signal or signal.NotifyContext. Shutdown must complete within a specified timeout (e.g., Kubernetes’ terminationGracePeriodSeconds)…

In our fourth VictoriaMetrics Tech Talks episode, we wrapped up our deep dive into logs and summarized the key takeaways for effective log management. This installment concluded our log series, featuring Co-founder and CTO Alex who joined to share some exciting news and updates from VictoriaMetrics.

This post recaps the latest VictoriaMetrics Cloud updates, including new organization management features, OpenTelemetry integration, a powerful Explore tab, and expanded API capabilities, making your monitoring experience even more seamless and efficient.

Prometheus querying involves multiple moving parts: functions, operators, modifiers, and subqueries. Small differences in how vectors are handled or rollups are applied can lead to completely different outcomes or errors. MetricsQL extends PromQL by handling these edge cases more gracefully with features like default rollups.

We believe microservices shouldn’t be the default and that companies should start with monoliths until reality actually demands they scale and shift from one node to a cluster of nodes. As a result, we offer clustering on an open source basis because we want to support realistic growth. Read on for details!