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docs/Single-server-VictoriaMetrics.md: impromve formatting for prominent features chapter

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@ -41,19 +41,36 @@ VictoriaMetrics has the following prominent features:
* It can be used as a drop-in replacement for Prometheus in Grafana, because it supports [Prometheus querying API](#prometheus-querying-api-usage).
* It can be used as a drop-in replacement for Graphite in Grafana, because it supports [Graphite API](#graphite-api-usage).
* It features easy setup and operation:
* VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d) without external dependencies.
* VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d)
without external dependencies.
* All the configuration is done via explicit command-line flags with reasonable defaults.
* All the data is stored in a single directory pointed by `-storageDataPath` command-line flag.
* Easy and fast backups from [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282) to S3 or GCS can be done with [vmbackup](https://docs.victoriametrics.com/vmbackup.html) / [vmrestore](https://docs.victoriametrics.com/vmrestore.html) tools. See [this article](https://medium.com/@valyala/speeding-up-backups-for-big-time-series-databases-533c1a927883) for more details.
* It implements PromQL-based query language - [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html), which provides improved functionality on top of PromQL.
* Easy and fast backups from [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282)
can be done with [vmbackup](https://docs.victoriametrics.com/vmbackup.html) / [vmrestore](https://docs.victoriametrics.com/vmrestore.html) tools.
See [this article](https://medium.com/@valyala/speeding-up-backups-for-big-time-series-databases-533c1a927883) for more details.
* It implements PromQL-like query language - [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html), which provides improved functionality on top of PromQL.
* It provides global query view. Multiple Prometheus instances or any other data sources may ingest data into VictoriaMetrics. Later this data may be queried via a single query.
* It provides high performance and good vertical and horizontal scalability for both [data ingestion](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b) and [data querying](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4). It [outperforms InfluxDB and TimescaleDB by up to 20x](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
* It [uses 10x less RAM than InfluxDB](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) and [up to 7x less RAM than Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f) when dealing with millions of unique time series (aka [high cardinality](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality)).
* It provides high performance and good vertical and horizontal scalability for both
[data ingestion](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b)
and [data querying](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4).
It [outperforms InfluxDB and TimescaleDB by up to 20x](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
* It [uses 10x less RAM than InfluxDB](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893)
and [up to 7x less RAM than Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f)
when dealing with millions of unique time series (aka [high cardinality](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality)).
* It is optimized for time series with [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
* It provides high data compression, so [up to 70x more data points](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4) may be crammed into limited storage comparing to TimescaleDB and [up to 7x less storage space is required compared to Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f).
* It is optimized for storage with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc). See [disk IO graphs from these benchmarks](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b).
* A single-node VictoriaMetrics may substitute moderately sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB. See [vertical scalability benchmarks](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae), [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/).
* It protects the storage from data corruption on unclean shutdown (i.e. OOM, hardware reset or `kill -9`) thanks to [the storage architecture](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
* It provides high data compression, so up to 70x more data points may be stored into limited storage comparing to TimescaleDB
according to [these benchmarks](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4)
and up to 7x less storage space is required compared to Prometheus, Thanos or Cortex
according to [this benchmark](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f).
* It is optimized for storage with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc).
See [disk IO graphs from these benchmarks](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b).
* A single-node VictoriaMetrics may substitute moderately sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB.
See [vertical scalability benchmarks](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae),
[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/).
* It protects the storage from data corruption on unclean shutdown (i.e. OOM, hardware reset or `kill -9`) thanks to
[the storage architecture](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
* It supports metrics' scraping, ingestion and [backfilling](#backfilling) via the following protocols:
* [Metrics scraping from Prometheus exporters](#how-to-scrape-prometheus-exporters-such-as-node-exporter).
* [Prometheus remote write API](#prometheus-setup).
@ -67,10 +84,13 @@ VictoriaMetrics has the following prominent features:
* [Native binary format](#how-to-import-data-in-native-format).
* [DataDog agent or DogStatsD](#how-to-send-data-from-datadog-agent).
* It supports metrics [relabeling](#relabeling).
* It can deal with [high cardinality issues](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality) and [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate) issues via [series limiter](#cardinality-limiter).
* It ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various [Enterprise workloads](https://docs.victoriametrics.com/enterprise.html).
* It can deal with [high cardinality issues](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality) and
[high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate) issues via [series limiter](#cardinality-limiter).
* It ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data
and various [Enterprise workloads](https://docs.victoriametrics.com/enterprise.html).
* It has open source [cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster).
* 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/) and [Google Filestore](https://cloud.google.com/filestore).
* 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/)
and [Google Filestore](https://cloud.google.com/filestore).
See also [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).

44
docs/Single-server-VictoriaMetrics.md
View file

@ -44,19 +44,36 @@ VictoriaMetrics has the following prominent features:
* It can be used as a drop-in replacement for Prometheus in Grafana, because it supports [Prometheus querying API](#prometheus-querying-api-usage).
* It can be used as a drop-in replacement for Graphite in Grafana, because it supports [Graphite API](#graphite-api-usage).
* It features easy setup and operation:
* VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d) without external dependencies.
* VictoriaMetrics consists of a single [small executable](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d)
without external dependencies.
* All the configuration is done via explicit command-line flags with reasonable defaults.
* All the data is stored in a single directory pointed by `-storageDataPath` command-line flag.
* Easy and fast backups from [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282) to S3 or GCS can be done with [vmbackup](https://docs.victoriametrics.com/vmbackup.html) / [vmrestore](https://docs.victoriametrics.com/vmrestore.html) tools. See [this article](https://medium.com/@valyala/speeding-up-backups-for-big-time-series-databases-533c1a927883) for more details.
* It implements PromQL-based query language - [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html), which provides improved functionality on top of PromQL.
* Easy and fast backups from [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282)
can be done with [vmbackup](https://docs.victoriametrics.com/vmbackup.html) / [vmrestore](https://docs.victoriametrics.com/vmrestore.html) tools.
See [this article](https://medium.com/@valyala/speeding-up-backups-for-big-time-series-databases-533c1a927883) for more details.
* It implements PromQL-like query language - [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html), which provides improved functionality on top of PromQL.
* It provides global query view. Multiple Prometheus instances or any other data sources may ingest data into VictoriaMetrics. Later this data may be queried via a single query.
* It provides high performance and good vertical and horizontal scalability for both [data ingestion](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b) and [data querying](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4). It [outperforms InfluxDB and TimescaleDB by up to 20x](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
* It [uses 10x less RAM than InfluxDB](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) and [up to 7x less RAM than Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f) when dealing with millions of unique time series (aka [high cardinality](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality)).
* It provides high performance and good vertical and horizontal scalability for both
[data ingestion](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b)
and [data querying](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4).
It [outperforms InfluxDB and TimescaleDB by up to 20x](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae).
* It [uses 10x less RAM than InfluxDB](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893)
and [up to 7x less RAM than Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f)
when dealing with millions of unique time series (aka [high cardinality](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality)).
* It is optimized for time series with [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate).
* It provides high data compression, so [up to 70x more data points](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4) may be crammed into limited storage comparing to TimescaleDB and [up to 7x less storage space is required compared to Prometheus, Thanos or Cortex](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f).
* It is optimized for storage with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc). See [disk IO graphs from these benchmarks](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b).
* A single-node VictoriaMetrics may substitute moderately sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB. See [vertical scalability benchmarks](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae), [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/).
* It protects the storage from data corruption on unclean shutdown (i.e. OOM, hardware reset or `kill -9`) thanks to [the storage architecture](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
* It provides high data compression, so up to 70x more data points may be stored into limited storage comparing to TimescaleDB
according to [these benchmarks](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4)
and up to 7x less storage space is required compared to Prometheus, Thanos or Cortex
according to [this benchmark](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f).
* It is optimized for storage with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc).
See [disk IO graphs from these benchmarks](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b).
* A single-node VictoriaMetrics may substitute moderately sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB.
See [vertical scalability benchmarks](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae),
[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/).
* It protects the storage from data corruption on unclean shutdown (i.e. OOM, hardware reset or `kill -9`) thanks to
[the storage architecture](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
* It supports metrics' scraping, ingestion and [backfilling](#backfilling) via the following protocols:
* [Metrics scraping from Prometheus exporters](#how-to-scrape-prometheus-exporters-such-as-node-exporter).
* [Prometheus remote write API](#prometheus-setup).
@ -70,10 +87,13 @@ VictoriaMetrics has the following prominent features:
* [Native binary format](#how-to-import-data-in-native-format).
* [DataDog agent or DogStatsD](#how-to-send-data-from-datadog-agent).
* It supports metrics [relabeling](#relabeling).
* It can deal with [high cardinality issues](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality) and [high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate) issues via [series limiter](#cardinality-limiter).
* It ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various [Enterprise workloads](https://docs.victoriametrics.com/enterprise.html).
* It can deal with [high cardinality issues](https://docs.victoriametrics.com/FAQ.html#what-is-high-cardinality) and
[high churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate) issues via [series limiter](#cardinality-limiter).
* It ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data
and various [Enterprise workloads](https://docs.victoriametrics.com/enterprise.html).
* It has open source [cluster version](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/cluster).
* 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/) and [Google Filestore](https://cloud.google.com/filestore).
* 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/)
and [Google Filestore](https://cloud.google.com/filestore).
See also [various Articles about VictoriaMetrics](https://docs.victoriametrics.com/Articles.html).