VictoriaMetrics/docs/CaseStudies.md

# Case studies and talks

Below please find public case studies and talks from VictoriaMetrics users. You can also join our [community Slack channel](http://slack.victoriametrics.com/)
where you can chat with VictoriaMetrics users to get additional references, reviews and case studies.

You can also read [articles about VictoriaMetrics from our users](https://victoriametrics.github.io/Articles.html#third-party-articles-and-slides).

Alphabetically sorted links to case studies:

* [adidas](#adidas)
* [Adsterra](#adsterra)
* [ARNES](#arnes)
* [Brandwatch](#brandwatch)
* [CERN](#cern)
* [COLOPL](#colopl)
* [Dreamteam](#dreamteam)
* [Idealo.de](#idealode)
* [MHI Vestas Offshore Wind](#mhi-vestas-offshore-wind)
* [Synthesio](#synthesio)
* [Wedos.com](#wedoscom)
* [Wix.com](#wixcom)
* [Zerodha](#zerodha)
* [zhihu](#zhihu)


## adidas

See our [slides](https://promcon.io/2019-munich/slides/remote-write-storage-wars.pdf) and [video](https://youtu.be/OsH6gPdxR4s)
from [Remote Write Storage Wars](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) talk at [PromCon 2019](https://promcon.io/2019-munich/).
VictoriaMetrics is compared to Thanos, Corex and M3DB in the talk.

## Adsterra

[Adsterra Network](https://adsterra.com) is a leading digital advertising agency that offers
performance-based solutions for advertisers and media partners worldwide.

We used to collect and store our metrics with Prometheus. Over time, the data volume on our servers
and metrics increased to the point that we were forced to gradually reduce what we were retaining. When our retention got as low as 7 days
we looked for alternative solutions. We chose between Thanos, VictoriaMetrics and Prometheus federation.

We ended up with the following configuration:

- Local instances of Prometheus with VictoriaMetrics as the remote storage on our backend servers.
- A single Prometheus on our monitoring server scrapes metrics from other servers and writes to VictoriaMetrics.
- A separate Prometheus that federates from other instances of Prometheus and processes alerts.

We learned that remote write protocol generated too much traffic and connections so after 8 months we started looking for alternatives.

Around the same time, VictoriaMetrics released [vmagent](https://victoriametrics.github.io/vmagent.html).
We tried to scrape all the metrics via a single instance of vmagent but it that didn't work because vmgent wasn't able to catch up with writes
into VictoriaMetrics. We tested different options and end up with the following scheme:

- We removed Prometheus from our setup.
- VictoriaMetrics [can scrape targets](https://victoriametrics.github.io/Single-server-VictoriaMetrics.html#how-to-scrape-prometheus-exporters-such-as-node-exporter) as well
so we removed vmagent. Now, VictoriaMetrics scrapes all the metrics from 110 jobs and 5531 targets.
- We use [Promxy](https://github.com/jacksontj/promxy) for alerting.

Such a scheme has generated the following benefits compared with Prometheus:

- We can store more metrics.
- We need less RAM and CPU for the same workload.

Cons are the following:

- VictoriaMetrics didn't support replication (it [supports replication now](https://victoriametrics.github.io/Cluster-VictoriaMetrics.html#replication-and-data-safety)) - we run an extra instance of VictoriaMetrics and Promxy in front of a VictoriaMetrics pair for high availability.
- VictoriaMetrics stores 1 extra month for defined retention (if retention is set to N months, then VM stores N+1 months of data), but this is still better than other solutions.

Here are some numbers from our single-node VictoriaMetrics setup:

- active time series: 10M
- ingestion rate: 800K samples/sec
- total number of datapoints: more than 2 trillion
- total number of entries in inverted index: more than 1 billion
- daily time series churn rate: 2.6M
- data size on disk: 1.5 TB
- index size on disk: 27 GB
- average datapoint size on disk: 0.75 bytes
- range query rate: 16 rps
- instant query rate: 25 rps
- range query duration: max: 0.5s; median: 0.05s; 97th percentile: 0.29s
- instant query duration: max: 2.1s; median: 0.04s; 97th percentile: 0.15s

VictoriaMetrics consumes about 50GB of RAM.

Setup:

We have 2 single-node instances of VictoriaMetrics. The first instance collects and stores high-resolution metrics (10s scrape interval) for a month.
The second instance collects and stores low-resolution metrics (300s scrape interval) for a month.
We use Promxy + Alertmanager for global view and alerts evaluation.


## ARNES

[The Academic and Research Network of Slovenia](https://www.arnes.si/en/) (ARNES) is a public institute that provides network services to research,
educational and cultural organizations enabling connections and cooperation with each other and with related organizations worldwide.

After using Cacti, Graphite and StatsD for years, we wanted to upgrade our monitoring stack to something that:

- has native alerting support
- can be run on-prem
- has multi-dimensional metrics
- has lower hardware requirements
- is scalable
- has a simple client that allows for provisioning and discovery with Puppet

We hed been running Prometheus for about a year in a test environment and it was working well but there was a need/wish for a few more years of retention than the old system provided. We tested Thanos which was a bit resource hungry but worked great for about half a year.
Then we discovered VictoriaMetrics. Our scale isn't that big so we don't have on-prem S3 and no Kubernetes. VM's single node instance provided
the same result with far less maintenance overhead and lower hardware requirements.

After testing it a few months and with great support from the maintainers on [Slack](http://slack.victoriametrics.com/),
we decided to go with it. VM's support for the ingestion of InfluxDB metrics was an additional bonus as our hardware team uses
SNMPCollector to collect metrics from network devices and switching from InfluxDB to VictoriaMetrics required just a simple change in the config file. 

Numbers:

- 2 single node instances per DC (one for Prometheus and one for InfluxDB metrics)
- Active time series per VictoriaMetrics instance: ~500k (Prometheus) + ~320k (InfluxDB)
- Ingestion rate per VictoriaMetrics instance: 45k/s (Prometheus) / 30k/s (InfluxDB)
- Query duration: median ~5ms, 99th percentile ~45ms
- Total number of datapoints per instance: 390B (Prometheus), 110B (InfluxDB)
- Average datapoint size on drive: 0.4 bytes
- Disk usage per VictoriaMetrics instance: 125GB (Prometheus), 185GB (InfluxDB)
- Index size per VictoriaMetrics instance: 1.6GB (Prometheus), 1.2GB (InfluxDB)

We are running 1 Prometheus, 1 VictoriaMetrics and 1 Grafana server in each datacenter on baremetal servers, scraping 350+ targets
(and 3k+ devices collected via SNMPCollector sending metrics directly to VM). Each Prometheus is scraping all targets
so we have all metrics in both VictoriaMetrics instances. We are using [Promxy](https://github.com/jacksontj/promxy) to deduplicate metrics from both instances.
Grafana has an LB infront so if one DC has problems we can still view all metrics from both DCs on the other Grafana instance.

We are still in the process of migration, but we are really happy with the whole stack. It has proven to be an essential tool
for gathering insights into our services during COVID-19 and has enabled us to provide better service and identify problems faster.

## Brandwatch

[Brandwatch](https://www.brandwatch.com/) is the world's pioneering digital consumer intelligence suite,
helping over 2,000 of the world's most admired brands and agencies to make insightful, data-driven business decisions.

The engineering department at Brandwatch has been using InfluxDB to store application metrics for many years
but when End-of-Life of InfluxDB version 1.x was announced we decided to re-evaluate our entire metrics collection and storage stack.

The main goals for the new metrics stack were:
- improved performance
- lower maintenance
- support for native clustering in open source version
- the less metrics shipment had to change, the better
- longer data retention time period would be great but not critical

We initially tested CrateDB and TimescaleDB wand found that both had limitations or requirements in their open source versions
that made them unfit for our use case. Prometheus was also considered but it's push vs. pull metrics was a big change we did not want
to include in the already significant change.

Once we found VictoriaMetrics it solved the following problems:
- it is very lightweight and we can now run virtual machines instead of dedicated hardware machines for metrics storage
- very short startup time and any possible gaps in data can easily be filled in using Promxy
- we could continue using Telegraf as our metrics agent and ship identical metrics to both InfluxDB and VictoriaMetrics during the migration period (migration just about to start)
- compression im VM is really good. We can store more metrics and we can easily spin up new VictoriaMetrics instances
for new data and keep read-only nodes with older data if we need to extend our retention period further
than single virtual machine disks allow and we can aggregate all the data from VictoriaMetrics with Promxy

High availability is done the same way we did with InfluxDB by running parallel single nodes of VictoriaMetrics.

Numbers:

- active time series: up to 25 million
- ingestion rate: ~300 000
- total number of datapoints: 380 billion and growing
- total number of entries in inverted index: 575 million and growing
- daily time series churn rate: ~550 000
- data size on disk: ~660GB and growing
- index size on disk: ~9,3GB and growing
- average datapoint size on disk: ~1.75 bytes

Query rates are insignificant as we have concentrated on data ingestion so far.

Anders Bomberg, Monitoring and Infrastructure Team Lead, brandwatch.com

## CERN

The European Organization for Nuclear Research better known as [CERN](https://home.cern/) uses VictoriaMetrics for real-time monitoring
of the [CMS](https://home.cern/science/experiments/cms) detector system.
According to [published talk](https://indico.cern.ch/event/877333/contributions/3696707/attachments/1972189/3281133/CMS_mon_RD_for_opInt.pdf)
VictoriaMetrics is used for the following purposes as a part of the "CMS Monitoring cluster":

* As a long-term storage for messages ingested from the [NATS messaging system](https://nats.io/). Ingested messages are pushed directly to VictoriaMetrics via HTTP protocol
* As a long-term storage for Prometheus monitoring system (30 days retention policy. There are plans to increase it up to ½ year)
* As a data source for visualizing metrics in Grafana.

R&D topic: Evaluate VictoraMetrics vs InfluxDB for large cardinality data.

Please also see [The CMS monitoring infrastructure and applications](https://arxiv.org/pdf/2007.03630.pdf) publication from CERN with details about their VictoriaMetrics usage.


## COLOPL

[COLOPL](http://www.colopl.co.jp/en/) is Japanese game development company. It started using VictoriaMetrics
after evaulating the following remote storage solutions for Prometheus:

* Cortex
* Thanos
* M3DB
* VictoriaMetrics

See [slides](https://speakerdeck.com/inletorder/monitoring-platform-with-victoria-metrics) and [video](https://www.youtube.com/watch?v=hUpHIluxw80)
from `Large-scale, super-load system monitoring platform built with VictoriaMetrics` talk at [Prometheus Meetup Tokyo #3](https://prometheus.connpass.com/event/157721/).

## Dreamteam

[Dreamteam](https://dreamteam.gg/) successfully uses single-node VictoriaMetrics in multiple environments.

Numbers:

* Active time series: from 350K to 725K.
* Total number of time series: from 100M to 320M.
* Total number of datapoints: from 120 billion to 155 billion.
* Retention period: 3 months.

VictoriaMetrics in production environment runs on 2 M5 EC2 instances in "HA" mode, managed by Terraform and Ansible TF module.
2 Prometheus instances are writing to both VMs, with 2 [Promxy](https://github.com/jacksontj/promxy) replicas
as the load balancer for reads.

## 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 our longterm storage. In our setup, m3db was quite unstable and consumed a lot of resources.

VictoriaMetrics in poroduction is very stable for us and uses only a fraction of the resources even though we also increased our retention period 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


## MHI Vestas Offshore Wind

The mission of [MHI Vestas Offshore Wind](http://www.mhivestasoffshore.com) is to co-develop offshore wind as an economically viable and sustainable energy resource to benefit future generations.

MHI Vestas Offshore Wind is using VictoriaMetrics to ingest and visualize sensor data from offshore wind turbines. The very efficient storage and ability to backfill was key in choosing VictoriaMetrics. MHI Vestas Offshore Wind is running the cluster version of VictoriaMetrics on Kubernetes using the Helm charts for deployment to be able to scale up capacity as the solution is rolled out.

Numbers with current, limited roll out:

- Active time series: 270K
- Ingestion rate: 70K/sec
- Total number of datapoints: 850 billion
- Data size on disk: 800 GiB
- Retention period: 3 years


## Synthesio

[Synthesio](https://www.synthesio.com/) is the leading social intelligence tool for social media monitoring and analytics.

> We fully migrated from [Metrictank](https://grafana.com/oss/metrictank/)  to VictoriaMetrics

Numbers:
- Single node
- Active time series - 5 Million
- Datapoints: 1.25 Trillion
- Ingestion rate - 550k datapoints per second
- Disk usage - 150gb
- Index size - 3gb
- Query duration 99th percentile - 147ms
- Churn rate - 100 new time series per hour

## Wedos.com

> [Wedos](https://www.wedos.com/) is the biggest hosting provider in the Czech Republic. We have our own private data center that holds our servers and technologies. We are in the process of building a second, stae of the art data center where the servers will be cooled in an oil bath. We started using [cluster VictoriaMetrics](https://victoriametrics.github.io/Cluster-VictoriaMetrics.html) to store Prometheus metrics from all our infrastructure after receiving positive references from people who had successfully used VictoriaMetrics.

Numbers:

* The number of acitve time series: 5M.
* Ingestion rate: 170K data points per second.
* Query duration: median is ~2ms, 99th percentile is ~50ms.

> We like that VictoriaMetrics is simple to configuree and requires zero maintenance. It works right out of the box and once it's set up you can just forget about it. 

## Wix.com

[Wix.com](https://en.wikipedia.org/wiki/Wix.com) is the leading web development platform.

> We needed to redesign our metrics infrastructure from the ground up after the move to Kubernetes. We had tried out a few different options before landing on this solution which is working great. We have a Prometheus instance in every datacenter with 2 hours retention for local storage and remote write into [HA pair of single-node VictoriaMetrics instances](https://victoriametrics.github.io/Single-server-VictoriaMetrics.html#high-availability).

Numbers:

* The number of active time series per VictoriaMetrics instance is 50 millios.
* The total number of time series per VictoriaMetrics instance is 5000 million.
* Ingestion rate per VictoriaMetrics instance is 1.1 millions data points per second.
* The total number of datapoints per VictoriaMetrics instance is 8.5 trillion.
* The average churn rate is 150 millions new time series per day.
* The average query rate is ~150 per second (mostly alert queries).
* Query duration: median is ~1ms, 99th percentile is ~1sec.
* Retention period: 3 months.

> The alternatives that we tested prior to choosing VictoriaMetrics were: Prometheus federated, Cortex, IronDB and Thanos.
> The items that were critical to us central tsdb, in order of importance were as follows:

* At least 3 month worth of retention.
* Raw data, no aggregation, no sampling.
* High query speed.
* Clean fail state for HA (multi-node clusters may return partial data resulting in false alerts).
* Enough headroom/scaling capacity for future growth which is planned to be up to 100M active time series.
* Ability to split DB replicas per workload. Alert queries go to one replica and user queries go to another (speed for users, effective cache).

> Optimizing for those points and our specific workload, VictoriaMetrics proved to be the best option. As icing on the cake we’ve got [PromQL extensions](https://victoriametrics.github.io/MetricsQL.html) - `default 0` and `histogram` are my favorite ones. We really like having a lot of tsdb params easily available via config options which makes tsdb easy to tune for each specific use case. We've also found a great community in [Slack channel](http://slack.victoriametrics.com/) and responsive and helpful maintainer support.

Alex Ulstein, Head of Monitoring, Wix.com

## Zerodha

[Zerodha](https://zerodha.com/) is India's largest stock broker. The monitoring team at Zerodha had the following requirements:

* Multiple K8s clusters to monitor
* Consistent monitoring infra for each cluster across the fleet
* The ability to handle billions of timeseries events at any point of time
* Easy to operate and cost effective

Thanos, Cortex and VictoriaMetrics were evaluated as a long-term storage for Prometheus. VictoriaMetrics has been selected for the following reasons:

* Blazingly fast benchmarks for a single node setup.
* Single binary mode. Easy to scale vertically with far fewer operational headaches.
* Considerable [improvements on creating Histograms](https://medium.com/@valyala/improving-histogram-usability-for-prometheus-and-grafana-bc7e5df0e350).
* [MetricsQL](https://victoriametrics.github.io/MetricsQL.html) gives us the ability to extend PromQL with more aggregation operators.
* The API is compatible with Prometheus and nearly all standard PromQL queries work well out of the box.
* Handles storage well, with periodic compaction which makes it easy to take snapshots.

Please see [Monitoring K8S with VictoriaMetrics](https://docs.google.com/presentation/d/1g7yUyVEaAp4tPuRy-MZbPXKqJ1z78_5VKuV841aQfsg/edit) slides,
[video](https://youtu.be/ZJQYW-cFOms) and [Infrastructure monitoring with Prometheus at Zerodha](https://zerodha.tech/blog/infra-monitoring-at-zerodha/) blog post for more details.


## zhihu

[zhihu](https://www.zhihu.com) is the largest Chinese question-and-answer website. We use VictoriaMetrics to store and use Graphite metrics. We shared the [promate](https://github.com/zhihu/promate) solution in our [单机 20 亿指标，知乎 Graphite 极致优化！](https://qcon.infoq.cn/2020/shenzhen/presentation/2881)([slides](https://static001.geekbang.org/con/76/pdf/828698018/file/%E5%8D%95%E6%9C%BA%2020%20%E4%BA%BF%E6%8C%87%E6%A0%87%EF%BC%8C%E7%9F%A5%E4%B9%8E%20Graphite%20%E6%9E%81%E8%87%B4%E4%BC%98%E5%8C%96%EF%BC%81-%E7%86%8A%E8%B1%B9.pdf)) talk at [QCon 2020](https://qcon.infoq.cn/2020/shenzhen/).

Numbers:

- Active time series: ~2500 Million
- Datapoints: ~20 Trillion
- Ingestion rate: ~1800k/s
- Disk usage: ~20 TB
- Index size: ~600 GB
- The average query rate is ~3k per second (mostly alert queries).
- Query duration: median is ~40ms, 99th percentile is ~100ms.