docs/CaseStudies.md: add case study for Idealo.de

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Aliaksandr Valialkin 2020-11-04 20:18:33 +02:00
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@ -38,6 +38,7 @@ See [features available for enterprise customers](https://github.com/VictoriaMet
* [Brandwatch](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#brandwatch)
* [Adsterra](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#adsterra)
* [ARNES](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#arnes)
* [Idealo.de](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/CaseStudies#idealode)
## Prominent features

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@ -6,6 +6,20 @@ and feel free asking for references, reviews and additional case studies from re
See also [articles about VictoriaMetrics from our users](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/Articles#third-party-articles-and-slides).
* [Adidas](#adidas)
* [CERN](#cern)
* [COLOPL](#colopl)
* [Zerodha](#zerodha)
* [Wix.com](#wixcom)
* [Wedos.com](#wedoscom)
* [Synthesio](#synthesio)
* [Dreamteam](#dreamteam)
* [Brandwatch](#brandwatch)
* [Adsterra](#adsterra)
* [ARNES](#arnes)
* [Idealo.de](#idealode)
## Adidas
See [slides](https://promcon.io/2019-munich/slides/remote-write-storage-wars.pdf) and [video](https://youtu.be/OsH6gPdxR4s)
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We are still in the process of migration, but we are really happy with the whole stack. It has proven as an essential piece
for insight into our services during COVID-19 and has enabled us to provide better service and spot problems faster.
## Idealo.de
[idealo.de](https://www.idealo.de/) is the leading price comparison website in Germany. We use Prometheus for metrics on our container platform.
When we introduced Prometheus at idealo we started with m3db as a longterm storage. In our setup m3db was quite unstable and consumed a lot of resources.
VictoriaMetrics runs very stable for us and uses only a fraction of the resources. Although we also increased our retention time from 1 month to 13 months.
Numbers:
- The number of active time series per VictoriaMetrics instance is 21M.
- Total ingestion rate 120k metrics per second.
- The total number of datapoints 3.1 trillion.
- The average time series churn rate is ~9M per day.
- The average query rate is ~20 per second. Response time for 99th quantile is 120ms.
- Retention: 13 months.
- Size of all datapoints: 3.5 TB