Roman Khavronenko
How to reduce expenses on monitoring: be smarter about data
This blog post is the second in the series of the blog posts based on the talk about ‘How to reduce expenses on monitoring’, stackconf 2023. It is about open-source instruments and techniques from the VictoriaMetrics ecosystem for improving cost-efficiency of monitoring.
How to reduce expenses on monitoring: Swapping in VictoriaMetrics for Prometheus
This blog post is the first in the series of the blog posts based on the talk about ‘How to reduce expenses on monitoring’, stackconf 2023. It is about open-source instruments and techniques from theVictoriaMetrics ecosystem for improving cost-efficiency of monitoring.
Performance optimization techniques in time series databases: sync.Pool for CPU-bound operations
This blog post is the fourth in the series of 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.
Performance optimization techniques in time series databases: Limiting concurrency
This blog post is a third 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.
Performance optimization techniques in time series databases: function caching
This blog post is a second 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.
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
Open source monitoring solutions must strive to achieve scalability that approaches the limits of infinity in order to make a lasting impact.
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.