The VictoriaMetrics Observability Blog
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Performance

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

by Roman Khavronenko on Feb 13, 2024

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.

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

by Roman Khavronenko / Aliaksandr Valialkin on Dec 8, 2023

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.

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Performance optimization techniques in time series databases: Limiting concurrency

by Roman Khavronenko / Aliaksandr Valialkin on Nov 24, 2023

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.

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Performance optimization techniques in time series databases: function caching

by Roman Khavronenko / Aliaksandr Valialkin on Nov 17, 2023

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.

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Performance optimization techniques in time series databases: strings interning

by Roman Khavronenko / Aliaksandr Valialkin on Nov 7, 2023

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.

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Save network costs with VictoriaMetrics remote write protocol

by Aliaksandr Valialkin on Mar 8, 2023

Save network bandwidth costs when transferring data to VictoriaMetrics starting from v1.88

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

by Roman Khavronenko on Jan 16, 2023

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.

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Cardinality explorer

by Dmytro Kozlov on Oct 4, 2022

In monitoring, the term cardinality defines the number of unique time series stored in TSDB. The higher is cardinality, the more resources are usually required for metrics processing and querying. Let’s see how Cardinality explorer can help us here.

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Grafana Mimir and VictoriaMetrics: performance tests

by Roman Khavronenko / Nikolay Khramchikhin on Sep 9, 2022

Benchmarking VictoriaMetrics and Grafana Mimir on the same hardware

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How to Choose a Scalable Open Source Time Series Database: The Cost of Scale

by Jean-Jerome Schmidt-Soisson / Roman Khavronenko on May 16, 2022

When looking for a most scalable open source time series database, what are the criteria to care about? Read this blog to get our recommendations.

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