VictoriaMetrics Observability Blog

Roman Khavronenko
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Performance optimization techniques in time series databases: strings interning

This blog post is a first in the series of the blog posts based on the talk about ‘Performance optimizations in Go’, GopherCon 2023. It is dedicated to various optimization techniques used in VictoriaMetrics for improving performance and resource usage.

Turbocharge Your Data Monitoring Whilst Slashing Costs

Never-firing alerts: What they are and how to deal with them

Read how vmalert helps to find alerting rules which don’t match any time series. Such rules will never fire and only trick users with a false sense of protection.

Rules backfilling via vmalert

Read how to use vmalert’s replay mode to retroactively evaluate recording and alerting rules with SLO objective as example.

Monitoring benchmark: how to generate 100 million samples/s of production-like data

One of the latest benchmarks we made was ‘VictoriaMetrics: scaling to 100 million metrics per second’. While the fact of such scale for VictoriaMetrics is noteworthy on its own, the benchmark tool used to generate that load is usually overlooked. In this blog post I’ll explain in more details the challenge of running such benchmarks.

VictoriaMetrics Monitoring

VictoriaMetrics is a monitoring solution. It was designed to collect and process telemetry from many systems, provide a retrospective view, and forecast metrics for capacity planning. But what about monitoring VictoriaMetrics itself?

Pricing comparison for Managed Prometheus

We’ll have to see what would be the cost of serving the same workload at Amazon Managed Service for Prometheus, Google Cloud Managed Service for Prometheus and VictoriaMetrics Cloud.