Yes in most cases. VictoriaMetrics can substitute Prometheus in the following aspects:
* Prometheus-compatible service discovery and target scraping can be done with [vmagent](https://docs.victoriametrics.com/vmagent.html) and with single-node VictoriaMetrics - see [these docs](https://docs.victoriametrics.com/#how-to-scrape-prometheus-exporters-such-as-node-exporter).
* Prometheus-compatible alerting rules and recording rules can be processed with [vmalert](https://docs.victoriametrics.com/vmalert.html).
* Prometheus-compatible querying in Grafana is supported by VictoriaMetrics. See [these docs](https://docs.victoriametrics.com/#grafana-setup).
according to the provided Prometheus-compatible [scrape configs](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#scrape_config)
and send data to multiple remote storage systems, vmagent has the following additional features:
- vmagent usually requires lower amounts of CPU, RAM and disk IO comparing to Prometheus when scraping big number of targets (more than 1000)
or targets with big number of exposed metrics.
- vmagent provides independent disk-backed buffers per each configured remote storage (aka `-remoteWrite.url`). This means that slow or temporarily unavailable storage
doesn't prevent from sending data to healthy storage in parallel. Prometheus uses a single shared buffer for all the configured remote storage systems (aka `remote_write->url`)
with the hardcoded retention of 2 hours.
- vmagent may accept, relabel and filter data obtained via multiple data ingestion protocols additionally to data scraped from Prometheus targets.
I.e. it supports both `pull` and `push` protocols for data ingestion.
## How does VictoriaMetrics compare to other remote storage solutions for Prometheus such as [M3 from Uber](https://eng.uber.com/m3/), [Thanos](https://github.com/thanos-io/thanos), [Cortex](https://github.com/cortexproject/cortex), etc.?
VictoriaMetrics is simpler, faster, more cost-effective and it provides [MetricsQL query language](MetricsQL) based on PromQL. The simplicity is twofold:
- It is simpler to configure and operate. There is no need in configuring [sidecars](https://github.com/thanos-io/thanos/blob/master/docs/components/sidecar.md),
or setting up third-party systems such as [Consul](https://github.com/cortexproject/cortex/issues/157), [Cassandra](https://cortexmetrics.io/docs/production/cassandra/),
[DynamoDB](https://cortexmetrics.io/docs/production/aws/) or [Memcached](https://cortexmetrics.io/docs/production/caching/).
- VictoriaMetrics has simpler architecture. This means less bugs and more useful features in the long run comparing to competing TSDBs.
See [comparing Thanos to VictoriaMetrics cluster](https://medium.com/@valyala/comparing-thanos-to-victoriametrics-cluster-b193bea1683)
and [Remote Write Storage Wars](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) talk from [PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/).
VictoriaMetrics also [uses less RAM than Thanos components](https://github.com/thanos-io/thanos/issues/448).
- QuestDB needs more than 20x storage space than VictoriaMetrics. This translates to higher storage costs and slower queries over historical data, which must be read from the disk.
- QuestDB is much harder to setup and operate than VictoriaMetrics. Compare [setup instructions for QuestDB](https://questdb.io/docs/get-started/binaries) to [setup instructions for VictoriaMetrics](https://docs.victoriametrics.com/#how-to-start-victoriametrics).
- VictoriaMetrics provides [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) query language, which is better suited for typical queries over time series data than SQL-like query language provided by QuestDB. See [this article](https://valyala.medium.com/promql-tutorial-for-beginners-9ab455142085) for details.
- Thanks to PromQL support, VictoriaMetrics [can be used as a drop-in replacement for Prometheus in Grafana](https://docs.victoriametrics.com/#grafana-setup), while QuestDB needs full rewrite of existing dashboards in Grafana.
- Thanks to Prometheus remote_write API support, VictoriaMetrics can be used as a long-term storage for Prometheus or for [vmagent](https://docs.victoriametrics.com/vmagent.html), while QuestDB has no integration with Prometheus.
- QuestDB [supports smaller range of popular data ingestion protocols](https://questdb.io/docs/develop/insert-data) compared to VictoriaMetrics (compare to [the list of supported data ingestion protocols for VictoriaMetrics](https://docs.victoriametrics.com/#how-to-import-time-series-data)).
- [VictoriaMetrics supports backfilling (e.g. storing historical data) out of the box](https://docs.victoriametrics.com/#backfilling), while QuestDB provides [very limited support for backfilling](https://questdb.io/blog/2021/05/10/questdb-release-6-0-tsbs-benchmark#the-problem-with-out-of-order-data).
- Both systems support multi-tenancy out of the box. See [the corresponding docs for VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multitenancy).
- Both systems support data replication. See [replication in Cortex](https://github.com/cortexproject/cortex/blob/fe56f1420099aa1bf1ce09316c186e05bddee879/docs/architecture.md#hashing) and [replication in VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety).
- Both systems scale horizontally to multiple nodes. See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#cluster-resizing-and-scalability) for details.
- Both systems support alerting and recording rules via the corresponding tools such as [vmalert](https://docs.victoriametrics.com/vmalert.html).
- 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).
- 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).
- VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to Prometheus remote_write protocol - InfluxDB, OpenTSDB, Graphite, CSV, JSON, native binary.
- Thanos re-uses Prometheus source code, while VictoriaMetrics is written from scratch.
- VictoriaMetrics accepts data via [standard remote_write API for Prometheus](https://prometheus.io/docs/practices/remote_write/),
while Thanos uses non-standard [Sidecar](https://github.com/thanos-io/thanos/blob/master/docs/components/sidecar.md), which must run alongside each Prometheus instance.
- Thanos Sidecar requires disabling data compaction in Prometheus, which may hurt Prometheus performance and increase RAM usage. See [these docs](https://thanos.io/components/sidecar.md/) for more details.
- Thanos stores data in object storage (Amazon S3 or Google GCS), while VictoriaMetrics stores data in block storage
([GCP persistent disks](https://cloud.google.com/compute/docs/disks#pdspecs), Amazon EBS or bare metal HDD).
While object storage is usually less expensive, block storage provides much lower latencies and higher throughput.
VictoriaMetrics works perfectly with HDD-based block storage - there is no need in using more expensive SSD or NVMe disks in most cases.
- Thanos may lose up to 2 hours of recent data, which wasn't uploaded yet to object storage. VictoriaMetrics may lose only a few seconds of recent data,
which isn't synced to persistent storage yet. See [this article for details](https://medium.com/@valyala/wal-usage-looks-broken-in-modern-time-series-databases-b62a627ab704).
- Thanos may be harder to setup and operate comparing to VictoriaMetrics, since it has more moving parts, which can be connected with less reliable networks.
See [this article for details](https://medium.com/faun/comparing-thanos-to-victoriametrics-cluster-b193bea1683).
- Thanos 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/).
- VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to Prometheus remote_write protocol - InfluxDB, OpenTSDB, Graphite, CSV, JSON, native binary.
- VictoriaMetrics requires [10x less RAM](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) and it [works faster](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
- VictoriaMetrics needs lower amounts of storage space than InfluxDB on production data.
- VictoriaMetrics provides better query language - [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) - than InfluxQL or Flux. See [this tutorial](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085) for details.
- VictoriaMetrics accepts data in multiple popular data ingestion protocols additionally to InfluxDB - Prometheus remote_write, OpenTSDB, Graphite, CSV, JSON, native binary.
- TimescaleDB insists on using SQL as a query language. While SQL is more powerful than PromQL, this power is rarely required during typical TSDB usage. Real-world queries usually [look clearer and simpler when written in PromQL than in SQL](https://medium.com/@valyala/promql-tutorial-for-beginners-9ab455142085).
- VictoriaMetrics requires [up to 70x less storage space comparing to TimescaleDB](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4) for storing the same amount of time series data. The gap in storage space usage can be lowered from 70x to 3x if [compression in TimescaleDB is properly configured](https://docs.timescale.com/latest/using-timescaledb/compression) (it isn't an easy task in general case :)).
- VictoriaMetrics requires up to 10x less CPU and RAM resources than TimescaleDB for processing production data. See [this article](https://abiosgaming.com/press/high-cardinality-aggregations/) for details.
- TimescaleDB is [harder to setup, configure and operate](https://docs.timescale.com/timescaledb/latest/how-to-guides/install-timescaledb/self-hosted/ubuntu/installation-apt-ubuntu/) than VictoriaMetrics (see [how to run VictoriaMetrics](https://docs.victoriametrics.com/#how-to-start-victoriametrics)).
- VictoriaMetrics accepts data in multiple popular data ingestion protocols - InfluxDB, OpenTSDB, Graphite, CSV, while TimescaleDB supports only SQL inserts.
## Does VictoriaMetrics use Prometheus technologies like other clustered TSDBs built on top of Prometheus such as [Thanos](https://github.com/thanos-io/thanos) or [Cortex](https://github.com/cortexproject/cortex)?
No. VictoriaMetrics core is written in Go from scratch by [fasthttp](https://github.com/valyala/fasthttp) [author](https://github.com/valyala).
The architecture is [optimized for storing and querying large amounts of time series data with high cardinality](https://medium.com/devopslinks/victoriametrics-creating-the-best-remote-storage-for-prometheus-5d92d66787ac). VictoriaMetrics storage uses [certain ideas from ClickHouse](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282). Special thanks to [Alexey Milovidov](https://github.com/alexey-milovidov).
Remote read API requires transferring all the raw data for all the requested metrics over the given time range. For instance,
if a query covers 1000 metrics with 10K values each, then the remote read API had to return `1000*10K`=10M metric values to Prometheus.
This is slow and expensive.
Prometheus remote read API isn't intended for querying foreign data aka `global query view`. See [this issue](https://github.com/prometheus/prometheus/issues/4456) for details.
VictoriaMetrics is able to handle data from hundreds of millions of IoT sensors and industrial sensors.
It supports [high cardinality data](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b),
perfectly [scales up on a single node](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae)
See [these docs](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#multitenancy). Multitenancy is supported only by [cluster version](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) of VictoriaMetrics.
All the VictoriaMetrics component provide command-line flags to control the size of internal buffers and caches: `-memory.allowedPercent` and `-memory.allowedBytes` (pass `-help` to any VictoriaMetrics component in order to see the description for these flags). These limits don't take into account additional memory, which may be needed for processing incoming queries. Hard limits may be enforced only by the OS via [cgroups](https://en.wikipedia.org/wiki/Cgroups), Docker (see [these docs](https://docs.docker.com/config/containers/resource_constraints)) or Kubernetes (see [these docs](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers)).
Memory usage for VictoriaMetrics components can be tuned according to the following docs:
* [Capacity planning for single-node VictoriaMetrics](https://docs.victoriametrics.com/#capacity-planning)
* [Capacity planning for cluster VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#capacity-planning)
* [Troubleshooting for vmagent](https://docs.victoriametrics.com/vmagent.html#troubleshooting)
* [Troubleshooting for single-node VictoriaMetrics](https://docs.victoriametrics.com/#troubleshooting)
VictoriaMetrics is included in FreeBSD ports, so just install it from there. See [this link](https://www.freebsd.org/cgi/ports.cgi?query=victoria&stype=all).
A time series is uniquely identified by its name plus a set of its labels. For example, `temperature{city="NY",country="US"}` and `temperature{city="SF",country="US"}` are two distinct series, since they differ by `city` label. A time series is considered active if it receives at least a single new sample during the last hour.
If old time series are constantly substituted by new time series at a high rate, then such a state is called `high churn rate`. High churn rate has the following negative consequences:
* Increased total number of time series stored in the database.
* Increased size of inverted index, which is stored at `<-storageDataPath>/indexdb`, since the inverted index contains entries for every label of every time series with at least a single ingested sample
The solution against high churn rate is to identify and eliminate labels with frequently changed values. The [/api/v1/status/tsdb](https://docs.victoriametrics.com/#tsdb-stats) page can help determining these labels.
High cardinality usually means high number of [active time series](#what-is-active-time-series). High cardinality may lead to high memory usage and/or to high percentage of [slow inserts](#what-is-slow-insert). The source of high cardinality is usually a label with big number of unique values, which presents in big share of the ingested time series. The solution is to identify and remove the source of high cardinality with the help of [/api/v1/status/tsdb](https://docs.victoriametrics.com/#tsdb-stats).
VictoriaMetrics maintains in-memory cache for mapping of [active time series](#what-is-active-time-series) into internal series ids. The cache size depends on the available memory for VictoriaMetrics in the host system. If the information about all the active time series doesn't fit the cache, then VictoriaMetrics needs to read and unpack the information from disk on every incoming sample for time series missing in the cache. This operation is much slower than the cache lookup, so such insert is named `slow insert`. High percentage of slow inserts on the [official dashboard for VictoriaMetrics](https://docs.victoriametrics.com/#monitoring) indicates on memory shortage for the current number of [active time series](#what-is-active-time-series). Such a condition usually leads to significant slowdown for data ingestion, to significantly increased disk IO and CPU usage. The solution is to add more memory or to reduce the number of [active time series](#what-is-active-time-series). The `/api/v1/status/tsdb` page can be helpful for locating the source of high number of active time seriess - see [these docs](https://docs.victoriametrics.com/#tsdb-stats).
[MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) provides better user experience than PromQL. It fixes a few annoying issues in PromQL. This prevents MetricsQL to be 100% compatible with PromQL. See [this article](https://medium.com/@romanhavronenko/victoriametrics-promql-compliance-d4318203f51e) for details.
Please use [whisper-to-graphite](https://github.com/bzed/whisper-to-graphite) tool for reading the data from Graphite and pushing it to VictoriaMetrics via [Graphite import API](https://docs.victoriametrics.com/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd).
