Scale InfluxDB Clustered (#5526)
* clustered scaling guide * Update content/influxdb/clustered/admin/scale-cluster.md Co-authored-by: Jason Stirnaman <jstirnaman@influxdata.com> * add information about ingester storage volumes to scaling guide * Update content/influxdb/clustered/reference/glossary.md Co-authored-by: Jason Stirnaman <jstirnaman@influxdata.com> --------- Co-authored-by: Jason Stirnaman <jstirnaman@influxdata.com>pull/5527/head
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@ -7,7 +7,7 @@ description: >
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menu:
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influxdb_clustered:
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parent: Administer InfluxDB Clustered
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weight: 207
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weight: 208
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---
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{{< product-name >}} generates a valid access token (known as the _admin token_)
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@ -0,0 +1,352 @@
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---
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title: Scale your InfluxDB cluster
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description: >
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InfluxDB Clustered lets you scale individual components of your cluster both
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vertically and horizontally to match your specific workload.
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menu:
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influxdb_clustered:
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parent: Administer InfluxDB Clustered
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name: Scale your cluster
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weight: 207
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influxdb/clustered/tags: [scale]
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related:
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- /influxdb/clustered/reference/internals/storage-engine/
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- https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#requests-and-limits, Kubernetes resource requests and limits
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---
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InfluxDB Clustered lets you scale individual components of your cluster both
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vertically and horizontally to match your specific workload.
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Use the `AppInstance` resource defined in your `influxdb.yml` to manage
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resources available to each component.
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- [Scaling strategies](#scaling-strategies)
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- [Vertical scaling](#vertical-scaling)
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- [Horizontal scaling](#horizontal-scaling)
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- [Scale components in your cluster](#scale-components-in-your-cluster)
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- [Horizontally scale a component](#horizontally-scale-a-component)
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- [Vertically scale a component](#vertically-scale-a-component)
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- [Apply your changes](#apply-your-changes)
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- [Scale your cluster as a whole](#scale-your-cluster-as-a-whole)
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- [Recommended scaling strategies per component](#recommended-scaling-strategies-per-component)
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- [Ingester](#ingester)
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- [Querier](#querier)
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- [Router](#router)
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- [Compactor](#compactor)
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- [Catalog](#catalog)
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- [Object store](#object-store)
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## Scaling strategies
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The following scaling strategies can be applied to components in your InfluxDB
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cluster.
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### Vertical scaling
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Vertical scaling (also known as "scaling up") involves increasing the resources
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(such as RAM or CPU) available to a process or system.
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Vertical scaling is typically used to handle resource-intensive tasks that
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require more processing power.
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{{< html-diagram/scaling-strategy "vertical" >}}
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### Horizontal scaling
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Horizontal scaling (also known as "scaling out") involves increasing the number of
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nodes or processes available to perform a given task.
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Horizontal scaling is typically used to increase the amount of workload or
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throughput a system can manage, but also provides additional redundancy and failover.
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{{< html-diagram/scaling-strategy "horizontal" >}}
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## Scale components in your cluster
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The following components of your InfluxDB cluster are scaled by modifying
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properties in your `AppInstance` resource:
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- Ingester
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- Querier
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- Compactor
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- Router
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{{% note %}}
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#### Scale your Catalog and Object store
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Your InfluxDB [Catalog](/influxdb/clustered/reference/internals/storage-engine/#catalog)
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and [Object store](/influxdb/clustered/reference/internals/storage-engine/#object-store)
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are managed outside of your `AppInstance` resource. Scaling mechanisms for these
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components depend on the technology and underlying provider used for each.
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{{% /note %}}
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Use the `spec.package.spec.resources` property in your `AppInstance` resource
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defined in your `influxdb.yml` to define system resource minimums and limits
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for each pod and the number of replicas per component.
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`requests` are the minimum that the Kubernetes scheduler should reserve for a pod.
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`limits` are the maximum that a pod should be allowed to use.
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Your `AppInstance` resource can include the following properties to define
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resource minimums and limits per pod and replicas per component:
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- `spec.package.spec.resources`
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- `ingester`
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- `requests`
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- `cpu`: Minimum CPU resource units to assign to ingesters
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- `memory`: Minimum memory resource units to assign to ingesters
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- `replicas`: Number of ingester replicas to provision
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- `limits`
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- `cpu`: Maximum CPU resource units to assign to ingesters
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- `memory`: Maximum memory resource units to assign to ingesters
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- `compactor`
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- `requests`
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- `cpu`: Minimum CPU resource units to assign to compactors
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- `memory`: Minimum memory resource units to assign to compactors
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- `replicas`: Number of compactor replicas to provision
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- `limits`
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- `cpu`: Maximum CPU resource units to assign to compactors
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- `memory`: Maximum memory resource units to assign to compactors
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- `querier`
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- `requests`
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- `cpu`: Minimum CPU resource units to assign to queriers
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- `memory`: Minimum memory resource units to assign to queriers
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- `replicas`: Number of querier replicas to provision
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- `limits`
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- `cpu`: Maximum CPU resource units to assign to queriers
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- `memory`: Maximum memory resource units to assign to queriers
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- `router`
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- `requests`
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- `cpu`: Minimum CPU resource units to assign to routers
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- `memory`: Minimum memory resource units to assign to routers
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- `replicas`: Number of router replicas to provision
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- `limits`
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- `cpu`: Maximum CPU Resource units to assign to routers
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- `memory`: Maximum memory resource units to assign to routers
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{{< expand-wrapper >}}
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{{% expand "View example `AppInstance` with resource requests and limits" %}}
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{{% code-placeholders "(INGESTER|COMPACTOR|QUERIER|ROUTER)_(CPU_(MAX|MIN)|MEMORY_(MAX|MIN)|REPLICAS)" %}}
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```yml
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apiVersion: kubecfg.dev/v1alpha1
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kind: AppInstance
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# ...
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spec:
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package:
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spec:
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# The following settings tune the various pods for their cpu/memory/replicas
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# based on workload needs. Only uncomment the specific resources you want
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# to change. Anything left commented will use the package default.
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resources:
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ingester:
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requests:
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cpu: INGESTER_CPU_MIN
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memory: INGESTER_MEMORY_MIN
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replicas: INGESTER_REPLICAS # Default is 3
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limits:
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cpu: INGESTER_CPU_MAX
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memory: INGESTER_MEMORY_MAX
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compactor:
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requests:
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cpu: COMPACTOR_CPU_MIN
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memory: COMPACTOR_MEMORY_MIN
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replicas: COMPACTOR_REPLICAS # Default is 1
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limits:
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cpu: COMPACTOR_CPU_MAX
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memory: COMPACTOR_MEMORY_MAX
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querier:
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requests:
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cpu: QUERIER_CPU_MIN
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memory: QUERIER_MEMORY_MIN
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replicas: QUERIER_REPLICAS # Default is 1
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limits:
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cpu: QUERIER_CPU_MAX
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memory: QUERIER_MEMORY_MAX
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router:
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requests:
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cpu: ROUTER_CPU_MIN
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memory: ROUTER_MEMORY_MIN
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replicas: ROUTER_REPLICAS # Default is 1
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limits:
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cpu: ROUTER_CPU_MAX
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memory: ROUTER_MEMORY_MAX
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```
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{{% /code-placeholders %}}
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{{% /expand %}}
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{{< /expand-wrapper >}}
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{{% note %}}
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Applying resource limits to pods is optional, but provides better resource
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isolation and protects against pods using more resources than intended. For
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information, see [Kubernetes resource requests and limits](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#requests-and-limits).
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{{% /note %}}
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##### Related Kubernetes documentation
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- [CPU resource units](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu)
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- [Memory resource units](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#meaning-of-memory)
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### Horizontally scale a component
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To horizontally scale a component in your InfluxDB cluster, increase or decrease
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the number of replicas for the component in the `spec.package.spec.resources`
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property in your `AppInstance` resource and [apply the change](#apply-your-changes).
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{{% warn %}}
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#### Only use the AppInstance to scale component replicas
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Only use the `AppInstance` resource to scale component replicas.
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Manually scaling replicas may cause errors.
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{{% /warn %}}
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For example--to horizontally scale your
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[Ingester](/influxdb/clustered/reference/internals/storage-engine/#ingester):
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```yaml
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apiVersion: kubecfg.dev/v1alpha1
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kind: AppInstance
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# ...
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spec:
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package:
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spec:
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resources:
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ingester:
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requests:
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# ...
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replicas: 6
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```
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### Vertically scale a component
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To vertically scale a component in your InfluxDB cluster, increase or decrease
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the CPU and memory resource units to assign to component pods in the
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`spec.package.spec.resources` property in your `AppInstance` resource and
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[apply the change](#apply-your-changes).
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```yaml
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apiVersion: kubecfg.dev/v1alpha1
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kind: AppInstance
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# ...
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spec:
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package:
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spec:
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resources:
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ingester:
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requests:
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cpu: "500m"
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memory: "512MiB"
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# ...
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limits:
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cpu: "1000m"
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memory: "1024MiB"
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```
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### Apply your changes
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After modifying the `AppInstance` resource, use `kubectl apply` to apply the
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configuration changes to your cluster and scale the updated components.
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```sh
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kubectl apply \
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--filename myinfluxdb.yml \
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--namespace influxdb
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```
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## Scale your cluster as a whole
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Scaling your entire InfluxDB Cluster is done by scaling your Kubernetes cluster
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and is managed outside of InfluxDB. The process of scaling your entire Kubernetes
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cluster depends on your underlying Kubernetes provider. You can also use
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[Kubernetes autoscaling](https://kubernetes.io/docs/concepts/cluster-administration/cluster-autoscaling/)
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to automatically scale your cluster as needed.
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## Recommended scaling strategies per component
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- [Ingester](#ingester)
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- [Querier](#querier)
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- [Router](#router)
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- [Compactor](#compactor)
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- [Catalog](#catalog)
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- [Object store](#object-store)
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### Ingester
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The Ingester can be scaled both [vertically](#vertical-scaling) and
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[horizontally](#horizontal-scaling).
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Vertical scaling increases write throughput and is typically the most effective
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scaling strategy for the Ingester.
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#### Ingester storage volume
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Ingesters use an attached storage volume to store the
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[Write-Ahead Log (WAL)](/influxdb/clustered/reference/glossary/#wal-write-ahead-log).
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With more storage available, Ingesters can keep bigger WAL buffers, which
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improves query performance and reduces pressure on the Compactor.
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Storage speed also helps with query performance.
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Configure the storage volume attached to Ingester pods in the
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`spec.package.spec.ingesterStorage` property of your `AppInstance` resource.
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{{< expand-wrapper >}}
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{{% expand "View example Ingester storage configuration" %}}
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{{% code-placeholders "STORAGE_(CLASS|SIZE)" %}}
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```yml
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apiVersion: kubecfg.dev/v1alpha1
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kind: AppInstance
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# ...
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spec:
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package:
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spec:
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# ...
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ingesterStorage:
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# (Optional) Set the storage class. This will differ based on the K8s
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#environment and desired storage characteristics.
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# If not set, the default storage class is used.
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storageClassName: STORAGE_CLASS
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# Set the storage size (minimum 2Gi recommended)
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storage: STORAGE_SIZE
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```
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{{% /code-placeholders %}}
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{{% /expand %}}
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{{< /expand-wrapper >}}
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### Querier
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The Querier can be scaled both [vertically](#vertical-scaling) and
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[horizontally](#horizontal-scaling).
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Horizontal scaling increases query throughput to handle more concurrent queries.
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Vertical scaling improves the Querier’s ability to process computationally
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intensive queries.
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### Router
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The Router can be scaled both [vertically](#vertical-scaling) and
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[horizontally](#horizontal-scaling).
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Horizontal scaling increases request throughput and is typically the most effective
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scaling strategy for the Router.
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### Compactor
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The Compactor can be scaled both [vertically](#vertical-scaling) and
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[horizontally](#horizontal-scaling).
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Because compaction is a compute-heavy process, vertical scaling (especially
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increasing the available CPU) is the most effective scaling strategy for the
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Compactor. Horizontal scaling increases compaction throughput, but not as
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efficiently as vertical scaling.
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### Catalog
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Scaling strategies available for the Catalog depend on the PostgreSQL-compatible
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database used to run the catalog. All support [vertical scaling](#vertical-scaling).
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Most support [horizontal scaling](#horizontal-scaling) for redundancy and failover.
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### Object store
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Scaling strategies available for the Object store depend on the underlying
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object storage services used to run the object store. Most support
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[horizontal scaling](#horizontal-scaling) for redundancy, failover, and
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increased capacity.
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@ -757,21 +757,21 @@ your anticipated workload.
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in your `myinfluxdb.yml`. If omitted, your cluster will use the default scale settings.
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- `spec.package.spec.resources`
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- `.ingester.requests`
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- `.cpu`: CPU Resource units to assign to ingesters
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- `.memory`: Memory resource units to assign to ingesters
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- `ingester.requests`
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- `cpu`: CPU resource units to assign to ingesters
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- `memory`: Memory resource units to assign to ingesters
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- `replicas`: Number of ingester replicas to provision
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- `.compactor.requests`
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- `.cpu`: CPU Resource units to assign to compactors
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- `.memory`: Memory resource units to assign to compactors
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- `compactor.requests`
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- `cpu`: CPU resource units to assign to compactors
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- `memory`: Memory resource units to assign to compactors
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- `replicas`: Number of compactor replicas to provision
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- `.querier.requests`
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- `.cpu`: CPU Resource units to assign to queriers
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- `.memory`: Memory resource units to assign to queriers
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- `querier.requests`
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- `cpu`: CPU resource units to assign to queriers
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- `memory`: Memory resource units to assign to queriers
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- `replicas`: Number of querier replicas to provision
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- `.router.requests`
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- `.cpu`: CPU Resource units to assign to routers
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- `.memory`: Memory resource units to assign to routers
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- `router.requests`
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- `cpu`: CPU resource units to assign to routers
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- `memory`: Memory resource units to assign to routers
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- `replicas`: Number of router replicas to provision
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###### Related Kubernetes documentation
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@ -1131,15 +1131,17 @@ A statement that sets or updates the value stored in a variable.
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## W
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### WAL (Write Ahead Log) - enterprise
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### WAL (Write-Ahead Log)
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The temporary cache for recently written points.
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To reduce the frequency that permanent storage files are accessed, InfluxDB
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caches new points in the WAL until their total size or age triggers a flush to
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more permanent storage. This allows for efficient batching of the writes into the TSM.
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more permanent storage. This allows for efficient batching of the writes into
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the storage engine.
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Points in the WAL can be queried and persist through a system reboot.
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On process start, all points in the WAL must be flushed before the system accepts new writes.
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Points in the WAL are queryable and persist through a system reboot.
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On process start, all points in the WAL must be flushed before the system
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accepts new writes.
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Related entries:
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[tsm](#tsm-time-structured-merge-tree)
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@ -28,9 +28,6 @@ queries, and is optimized to reduce storage cost.
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- [Catalog](#catalog)
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- [Object store](#object-store)
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- [Compactor](#compactor)
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- [Scaling strategies](#scaling-strategies)
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- [Vertical scaling](#vertical-scaling)
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- [Horizontal scaling](#horizontal-scaling)
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## Storage engine diagram
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##### Ingester scaling strategies
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The Ingester can be scaled both [vertically](#vertical-scaling) and
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[horizontally](#horizontal-scaling).
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The Ingester can be scaled both [vertically](/influxdb/clustered/admin/scale-cluster/#vertical-scaling)
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and [horizontally](/influxdb/clustered/admin/scale-cluster/#horizontal-scaling).
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Vertical scaling increases write throughput and is typically the most
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effective scaling strategy for the Ingester.
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##### Querier scaling strategies
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The Querier can be scaled both [vertically](#vertical-scaling) and
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[horizontally](#horizontal-scaling).
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The Querier can be scaled both [vertically](/influxdb/clustered/admin/scale-cluster/#vertical-scaling)
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and [horizontally](/influxdb/clustered/admin/scale-cluster/#horizontal-scaling).
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Horizontal scaling increases query throughput to handle more concurrent queries.
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Vertical scaling improves the Querier's ability to process computationally intensive queries.
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Vertical scaling improves the Querier's ability to process computationally
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intensive queries.
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### Catalog
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|
@ -117,8 +115,10 @@ It fulfills the following roles:
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##### Catalog scaling strategies
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Scaling strategies available for the Catalog depend on the PostgreSQL-compatible
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database used to run the catalog. All support [vertical scaling](#vertical-scaling).
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Most support [horizontal scaling](#horizontal-scaling) for redundancy and failover.
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database used to run the catalog. All support
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[vertical scaling](/influxdb/clustered/admin/scale-cluster/#vertical-scaling).
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Most support [horizontal scaling](/influxdb/clustered/admin/scale-cluster/#horizontal-scaling)
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for redundancy and failover.
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### Object store
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|
@ -132,8 +132,8 @@ Data in each Parquet file is sorted, encoded, and compressed.
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Scaling strategies available for the Object store depend on the underlying
|
||||
object storage services used to run the object store.
|
||||
Most support [horizontal scaling](#horizontal-scaling) for redundancy, failover,
|
||||
and increased capacity.
|
||||
Most support [horizontal scaling](/influxdb/clustered/admin/scale-cluster/#horizontal-scaling)
|
||||
for redundancy, failover, and increased capacity.
|
||||
|
||||
### Compactor
|
||||
|
||||
|
@ -143,48 +143,9 @@ It then updates the [Catalog](#catalog) with locations of compacted data.
|
|||
|
||||
##### Compactor scaling strategies
|
||||
|
||||
The Compactor can be scaled both [vertically](#vertical-scaling) and
|
||||
[horizontally](#horizontal-scaling).
|
||||
The Compactor can be scaled both [vertically](/influxdb/clustered/admin/scale-cluster/#vertical-scaling)
|
||||
and [horizontally](/influxdb/clustered/admin/scale-cluster/#horizontal-scaling).
|
||||
Because compaction is a compute-heavy process, vertical scaling (especially
|
||||
increasing the available CPU) is the most effective scaling strategy for the Compactor.
|
||||
Horizontal scaling increases compaction throughput, but not as efficiently as
|
||||
vertical scaling.
|
||||
|
||||
---
|
||||
|
||||
## Scaling strategies
|
||||
|
||||
The following scaling strategies can be applied to components of the InfluxDB v3
|
||||
storage architecture.
|
||||
|
||||
{{% note %}}
|
||||
|
||||
<!-- Cloud Dedicated-specific -->
|
||||
|
||||
For information about scaling your {{< product-name >}} infrastructure,
|
||||
[contact InfluxData support](https://support.influxdata.com).
|
||||
{{% /note %}}
|
||||
|
||||
### Vertical scaling
|
||||
|
||||
Vertical scaling (also known as "scaling up") involves increasing the resources
|
||||
(such as RAM or CPU) available to a process or system.
|
||||
Vertical scaling is typically used to handle resource-intensive tasks that
|
||||
require more processing power.
|
||||
|
||||
{{< html-diagram/scaling-strategy "vertical" >}}
|
||||
|
||||
### Horizontal scaling
|
||||
|
||||
Horizontal scaling (also known as "scaling out") involves increasing the number of
|
||||
nodes or processes available to perform a given task.
|
||||
Horizontal scaling is typically used to increase the amount of workload or
|
||||
throughput a system can manage, but also provides additional redundancy and failover.
|
||||
|
||||
{{% warn %}}
|
||||
#### Only use the AppInstance to scale component replicas
|
||||
|
||||
Manually scaling resources may cause errors.
|
||||
{{% /warn %}}
|
||||
|
||||
{{< html-diagram/scaling-strategy "horizontal" >}}
|
||||
|
|
Loading…
Reference in New Issue