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docs/Cluster-VictoriaMetrics.md: add links with the explanation of active time series and series churn rate

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Aliaksandr Valialkin 2021-10-24 18:39:12 +03:00
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README.md
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@ -303,7 +303,7 @@ Data replication can be used for increasing storage durability. See [these docs]
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).
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, series 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 determining bottlenecks in cluster setup. It is also recommended following [the troubleshooting docs](https://docs.victoriametrics.com/#troubleshooting).
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.html#what-is-active-time-series), [series churn rate](https://docs.victoriametrics.com/FAQ.html#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 determining bottlenecks in cluster setup. It is also recommended following [the troubleshooting docs](https://docs.victoriametrics.com/#troubleshooting).
The needed storage space for the given retention (the retention is set via `-retentionPeriod` command-line flag at `vmstorage`) can be extrapolated from disk space usage in a test run. For example, if the storage space usage is 10GB after a day-long test run on a production workload, then it will need at least `10GB*100=1TB` of disk space for `-retentionPeriod=100d` (100-days retention period). Storage space usage can be monitored with [the official Grafana dashboard for VictoriaMetrics cluster](#monitoring).
@ -316,7 +316,7 @@ It is recommended leaving the following amounts of spare resources:
Some capacity planning tips for VictoriaMetrics cluster:
* The [replication](#replication-and-data-safety) increases the amounts of needed resources for the cluster by up to `N` times where `N` is replication factor.
* Cluster capacity for active time series (e.g. time series with recently ingested samples) can be increased by adding more `vmstorage` nodes and/or by increasing RAM and CPU resources per each `vmstorage` node.
* Cluster capacity for [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-active-time-series) can be increased by adding more `vmstorage` nodes and/or by increasing RAM and CPU resources per each `vmstorage` node.
* Query latency can be reduced by increasing the number of `vmstorage` nodes and/or by increasing RAM and CPU resources per each `vmselect` node.
* The total number of CPU cores needed for all the `vminsert` nodes can be calculated from the ingestion rate: `CPUs = ingestion_rate / 100K`.
* The `-rpc.disableCompression` command-line flag at `vminsert` nodes can increase ingestion capacity at the cost of higher network bandwidth usage between `vminsert` and `vmstorage`.

4
docs/Cluster-VictoriaMetrics.md
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@ -307,7 +307,7 @@ Data replication can be used for increasing storage durability. See [these docs]
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).
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, series 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 determining bottlenecks in cluster setup. It is also recommended following [the troubleshooting docs](https://docs.victoriametrics.com/#troubleshooting).
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.html#what-is-active-time-series), [series churn rate](https://docs.victoriametrics.com/FAQ.html#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 determining bottlenecks in cluster setup. It is also recommended following [the troubleshooting docs](https://docs.victoriametrics.com/#troubleshooting).
The needed storage space for the given retention (the retention is set via `-retentionPeriod` command-line flag at `vmstorage`) can be extrapolated from disk space usage in a test run. For example, if the storage space usage is 10GB after a day-long test run on a production workload, then it will need at least `10GB*100=1TB` of disk space for `-retentionPeriod=100d` (100-days retention period). Storage space usage can be monitored with [the official Grafana dashboard for VictoriaMetrics cluster](#monitoring).
@ -320,7 +320,7 @@ It is recommended leaving the following amounts of spare resources:
Some capacity planning tips for VictoriaMetrics cluster:
* The [replication](#replication-and-data-safety) increases the amounts of needed resources for the cluster by up to `N` times where `N` is replication factor.
* Cluster capacity for active time series (e.g. time series with recently ingested samples) can be increased by adding more `vmstorage` nodes and/or by increasing RAM and CPU resources per each `vmstorage` node.
* Cluster capacity for [active time series](https://docs.victoriametrics.com/FAQ.html#what-is-active-time-series) can be increased by adding more `vmstorage` nodes and/or by increasing RAM and CPU resources per each `vmstorage` node.
* Query latency can be reduced by increasing the number of `vmstorage` nodes and/or by increasing RAM and CPU resources per each `vmselect` node.
* The total number of CPU cores needed for all the `vminsert` nodes can be calculated from the ingestion rate: `CPUs = ingestion_rate / 100K`.
* The `-rpc.disableCompression` command-line flag at `vminsert` nodes can increase ingestion capacity at the cost of higher network bandwidth usage between `vminsert` and `vmstorage`.