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all: replace old https://docs.victoriametrics.com/CaseStudies.html url with the new one - https://docs.victoriametrics.com/casestudies/
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Aliaksandr Valialkin
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README.md
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README.md
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<img src="docs/logo.webp" width="300" alt="VictoriaMetrics logo">
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VictoriaMetrics is a fast, cost-effective and scalable monitoring solution and time series database.
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/CaseStudies.html).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/casestudies/).
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VictoriaMetrics is available in [binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest),
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[Docker images](https://hub.docker.com/r/victoriametrics/victoria-metrics/), [Snap packages](https://snapcraft.io/victoriametrics)
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@ -48,7 +48,7 @@ VictoriaMetrics has the following prominent features:
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* It can be used as long-term storage for Prometheus. See [these docs](#prometheus-setup) for details.
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* It can be used as a drop-in replacement for Prometheus in Grafana, because it supports the [Prometheus querying API](#prometheus-querying-api-usage).
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* It can be used as a drop-in replacement for Graphite in Grafana, because it supports the [Graphite API](#graphite-api-usage).
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VictoriaMetrics allows reducing infrastructure costs by more than 10x comparing to Graphite - see [this case study](https://docs.victoriametrics.com/CaseStudies.html#grammarly).
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VictoriaMetrics allows reducing infrastructure costs by more than 10x comparing to Graphite - see [this case study](https://docs.victoriametrics.com/casestudies/#grammarly).
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* It is easy to setup and operate:
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* VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d)
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without external dependencies.
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@ -104,7 +104,7 @@ VictoriaMetrics has the following prominent features:
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* It can store data on [NFS-based storages](https://en.wikipedia.org/wiki/Network_File_System) such as [Amazon EFS](https://aws.amazon.com/efs/)
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and [Google Filestore](https://cloud.google.com/filestore).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/CaseStudies.html) and [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/casestudies/) and [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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## Components
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@ -1648,9 +1648,9 @@ with scrape intervals exceeding `5m`.
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## Capacity planning
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html).
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/casestudies/).
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VictoriaMetrics capacity scales linearly with the available resources. The needed amounts of CPU and RAM highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), series [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics for your production workload and iteratively scaling CPU and RAM resources until it becomes stable according to [troubleshooting docs](#troubleshooting). A single-node VictoriaMetrics works perfectly with the following production workload according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html):
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VictoriaMetrics capacity scales linearly with the available resources. The needed amounts of CPU and RAM highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), series [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics for your production workload and iteratively scaling CPU and RAM resources until it becomes stable according to [troubleshooting docs](#troubleshooting). A single-node VictoriaMetrics works perfectly with the following production workload according to [our case studies](https://docs.victoriametrics.com/casestudies/):
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* Ingestion rate: 1.5+ million samples per second
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* Active time series: 50+ million
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@ -12,7 +12,7 @@ aliases:
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# Articles
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See also [case studies](https://docs.victoriametrics.com/CaseStudies.html).
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See also [case studies](https://docs.victoriametrics.com/casestudies/).
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## Third-party articles and slides about VictoriaMetrics
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@ -582,7 +582,7 @@ created by v1.90.0 or newer versions. The solution is to upgrade to v1.90.0 or n
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* SECURITY: upgrade base docker image (alpine) from 3.17.2 to 3.17.3. See [alpine 3.17.3 release notes](https://alpinelinux.org/posts/Alpine-3.17.3-released.html).
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* SECURITY: upgrade Go builder from Go1.20.2 to Go1.20.3. See [the list of issues addressed in Go1.20.3](https://github.com/golang/go/issues?q=milestone%3AGo1.20.3+label%3ACherryPickApproved).
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* FEATURE: open source [Graphite Render API](https://docs.victoriametrics.com/#graphite-render-api-usage). This API allows using VictoriaMetrics as a drop-in replacement for Graphite at both data ingestion and querying sides and reducing infrastructure costs by up to 10x comparing to Graphite. See [this case study](https://docs.victoriametrics.com/CaseStudies.html#grammarly) as an example.
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* FEATURE: open source [Graphite Render API](https://docs.victoriametrics.com/#graphite-render-api-usage). This API allows using VictoriaMetrics as a drop-in replacement for Graphite at both data ingestion and querying sides and reducing infrastructure costs by up to 10x comparing to Graphite. See [this case study](https://docs.victoriametrics.com/casestudies/#grammarly) as an example.
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* FEATURE: release Windows binaries for [single-node VictoriaMetrics](https://docs.victoriametrics.com/), [VictoriaMetrics cluster](https://docs.victoriametrics.com/cluster-victoriametrics/), [vmbackup](https://docs.victoriametrics.com/vmbackup/) and [vmrestore](https://docs.victoriametrics.com/vmrestore/). See [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236), [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821) and [this](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70) issues. This release of VictoriaMetrics for Windows cannot delete [snapshots](https://docs.victoriametrics.com/#how-to-work-with-snapshots) due to Windows constraints. See [this comment](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70#issuecomment-1491529183) for details. This issue should be resolved in future releases.
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* FEATURE: log metrics with truncated labels if the length of label value in the ingested metric exceeds `-maxLabelValueLen`. This should simplify debugging for this case.
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* FEATURE: [vmagent](https://docs.victoriametrics.com/vmagent/): show target URL when debugging [target relabeling](https://docs.victoriametrics.com/vmagent/#relabel-debug). This should simplify target relabel debugging a bit. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/3882).
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@ -629,7 +629,7 @@ Data replication can be used for increasing storage durability. See [these docs]
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## Capacity planning
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html).
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/casestudies/).
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Each node type - `vminsert`, `vmselect` and `vmstorage` - can run on the most suitable hardware. Cluster capacity scales linearly with the available resources. The needed amounts of CPU and RAM per each node type highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), [series churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics cluster for your production workload and iteratively scaling per-node resources and the number of nodes per node type until the cluster becomes stable. It is recommended setting up [monitoring for the cluster](#monitoring). It helps to determine bottlenecks in cluster setup. It is also recommended following [the troubleshooting docs](https://docs.victoriametrics.com/#troubleshooting).
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@ -19,7 +19,7 @@ To provide the best monitoring solution.
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## Who uses VictoriaMetrics?
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See [case studies](https://docs.victoriametrics.com/CaseStudies.html).
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See [case studies](https://docs.victoriametrics.com/casestudies/).
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## Which features does VictoriaMetrics have?
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@ -152,7 +152,7 @@ The main differences between Cortex and VictoriaMetrics:
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* Cortex may lose up to 12 hours of recent data on Ingestor failure – see [the corresponding docs](https://github.com/cortexproject/cortex/blob/fe56f1420099aa1bf1ce09316c186e05bddee879/docs/architecture.md#ingesters-failure-and-data-loss).
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VictoriaMetrics may lose only a few seconds of recent data, which isn't synced to persistent storage yet.
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See [this article for details](https://medium.com/@valyala/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
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* Cortex is usually slower and requires more CPU and RAM than VictoriaMetrics. See [this talk from adidas at PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) and [other case studies](https://docs.victoriametrics.com/CaseStudies.html).
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* Cortex is usually slower and requires more CPU and RAM than VictoriaMetrics. See [this talk from adidas at PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) and [other case studies](https://docs.victoriametrics.com/casestudies/).
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* VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to Prometheus remote_write protocol – InfluxDB, OpenTSDB, Graphite, CSV, JSON, native binary.
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See [these docs](https://docs.victoriametrics.com/single-server-victoriametrics/#how-to-import-time-series-data) for details.
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* VictoriaMetrics provides the [MetricsQL](https://docs.victoriametrics.com/metricsql/) query language, while Cortex provides the [PromQL](https://prometheus.io/docs/prometheus/latest/querying/basics/) query language.
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@ -382,7 +382,7 @@ Both [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-serve
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Single-node VictoriaMetrics is able to handle quite big workloads in production
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with tens of millions of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series)
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at the ingestion rate of million of samples per second. See [this case study](https://docs.victoriametrics.com/CaseStudies.html#wixcom).
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at the ingestion rate of million of samples per second. See [this case study](https://docs.victoriametrics.com/casestudies/#wixcom).
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Single-node VictoriaMetrics requires lower amounts of CPU and RAM for handling the same workload comparing
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to cluster version of VictoriaMetrics, since it doesn't need to pass the encoded data over the network
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@ -15,7 +15,7 @@ title: VictoriaMetrics
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<img src="logo.webp" width="300" alt="VictoriaMetrics logo">
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VictoriaMetrics is a fast, cost-effective and scalable monitoring solution and time series database.
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/CaseStudies.html).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/casestudies/).
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VictoriaMetrics is available in [binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest),
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[Docker images](https://hub.docker.com/r/victoriametrics/victoria-metrics/), [Snap packages](https://snapcraft.io/victoriametrics)
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* It can be used as long-term storage for Prometheus. See [these docs](#prometheus-setup) for details.
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* It can be used as a drop-in replacement for Prometheus in Grafana, because it supports the [Prometheus querying API](#prometheus-querying-api-usage).
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* It can be used as a drop-in replacement for Graphite in Grafana, because it supports the [Graphite API](#graphite-api-usage).
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VictoriaMetrics allows reducing infrastructure costs by more than 10x comparing to Graphite - see [this case study](https://docs.victoriametrics.com/CaseStudies.html#grammarly).
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VictoriaMetrics allows reducing infrastructure costs by more than 10x comparing to Graphite - see [this case study](https://docs.victoriametrics.com/casestudies/#grammarly).
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* It is easy to setup and operate:
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* VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d)
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without external dependencies.
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@ -107,7 +107,7 @@ VictoriaMetrics has the following prominent features:
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* It can store data on [NFS-based storages](https://en.wikipedia.org/wiki/Network_File_System) such as [Amazon EFS](https://aws.amazon.com/efs/)
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and [Google Filestore](https://cloud.google.com/filestore).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/CaseStudies.html) and [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/casestudies/) and [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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## Components
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@ -1651,9 +1651,9 @@ with scrape intervals exceeding `5m`.
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## Capacity planning
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html).
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/casestudies/).
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VictoriaMetrics capacity scales linearly with the available resources. The needed amounts of CPU and RAM highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), series [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics for your production workload and iteratively scaling CPU and RAM resources until it becomes stable according to [troubleshooting docs](#troubleshooting). A single-node VictoriaMetrics works perfectly with the following production workload according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html):
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VictoriaMetrics capacity scales linearly with the available resources. The needed amounts of CPU and RAM highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), series [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics for your production workload and iteratively scaling CPU and RAM resources until it becomes stable according to [troubleshooting docs](#troubleshooting). A single-node VictoriaMetrics works perfectly with the following production workload according to [our case studies](https://docs.victoriametrics.com/casestudies/):
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* Ingestion rate: 1.5+ million samples per second
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* Active time series: 50+ million
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@ -23,7 +23,7 @@ aliases:
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<img src="logo.webp" width="300" alt="VictoriaMetrics logo">
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VictoriaMetrics is a fast, cost-effective and scalable monitoring solution and time series database.
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/CaseStudies.html).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/casestudies/).
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VictoriaMetrics is available in [binary releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest),
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[Docker images](https://hub.docker.com/r/victoriametrics/victoria-metrics/), [Snap packages](https://snapcraft.io/victoriametrics)
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@ -59,7 +59,7 @@ VictoriaMetrics has the following prominent features:
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* It can be used as long-term storage for Prometheus. See [these docs](#prometheus-setup) for details.
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* It can be used as a drop-in replacement for Prometheus in Grafana, because it supports the [Prometheus querying API](#prometheus-querying-api-usage).
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* It can be used as a drop-in replacement for Graphite in Grafana, because it supports the [Graphite API](#graphite-api-usage).
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VictoriaMetrics allows reducing infrastructure costs by more than 10x comparing to Graphite - see [this case study](https://docs.victoriametrics.com/CaseStudies.html#grammarly).
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VictoriaMetrics allows reducing infrastructure costs by more than 10x comparing to Graphite - see [this case study](https://docs.victoriametrics.com/casestudies/#grammarly).
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* It is easy to setup and operate:
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* VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d)
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without external dependencies.
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@ -115,7 +115,7 @@ VictoriaMetrics has the following prominent features:
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* It can store data on [NFS-based storages](https://en.wikipedia.org/wiki/Network_File_System) such as [Amazon EFS](https://aws.amazon.com/efs/)
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and [Google Filestore](https://cloud.google.com/filestore).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/CaseStudies.html) and [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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See [case studies for VictoriaMetrics](https://docs.victoriametrics.com/casestudies/) and [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).
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## Components
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@ -1659,9 +1659,9 @@ with scrape intervals exceeding `5m`.
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## Capacity planning
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html).
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VictoriaMetrics uses lower amounts of CPU, RAM and storage space on production workloads compared to competing solutions (Prometheus, Thanos, Cortex, TimescaleDB, InfluxDB, QuestDB, M3DB) according to [our case studies](https://docs.victoriametrics.com/casestudies/).
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VictoriaMetrics capacity scales linearly with the available resources. The needed amounts of CPU and RAM highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), series [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics for your production workload and iteratively scaling CPU and RAM resources until it becomes stable according to [troubleshooting docs](#troubleshooting). A single-node VictoriaMetrics works perfectly with the following production workload according to [our case studies](https://docs.victoriametrics.com/CaseStudies.html):
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VictoriaMetrics capacity scales linearly with the available resources. The needed amounts of CPU and RAM highly depends on the workload - the number of [active time series](https://docs.victoriametrics.com/faq/#what-is-an-active-time-series), series [churn rate](https://docs.victoriametrics.com/faq/#what-is-high-churn-rate), query types, query qps, etc. It is recommended setting up a test VictoriaMetrics for your production workload and iteratively scaling CPU and RAM resources until it becomes stable according to [troubleshooting docs](#troubleshooting). A single-node VictoriaMetrics works perfectly with the following production workload according to [our case studies](https://docs.victoriametrics.com/casestudies/):
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* Ingestion rate: 1.5+ million samples per second
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* Active time series: 50+ million
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@ -20,8 +20,8 @@ sometimes old known solutions just can't keep up with the new expectations.
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VictoriaMetrics is a high-performance opensource time series database specifically designed to deal with huge volumes of
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monitoring data while remaining cost-efficient at the same time. Many companies are choosing to migrate from InfluxDB to
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VictoriaMetrics specifically for performance and scalability reasons. Along them see case studies provided by
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[ARNES](https://docs.victoriametrics.com/CaseStudies.html#arnes)
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and [Brandwatch](https://docs.victoriametrics.com/CaseStudies.html#brandwatch).
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[ARNES](https://docs.victoriametrics.com/casestudies/#arnes)
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and [Brandwatch](https://docs.victoriametrics.com/casestudies/#brandwatch).
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This guide will cover the differences between two solutions, most commonly asked questions, and approaches for migrating
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from InfluxDB to VictoriaMetrics.
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