Project: Grafana Mimir (Observability and Analysis / Observability)
File: scaling-out.md

aliases: ../../operators-guide/running-production-environment/scaling-out/ ../../operators-guide/run-production-environment/scaling-out/ description: Learn how to scale out Grafana Mimir. menuTitle: Scaling out title: Scaling out Grafana Mimir weight: 30 Grafana Mimir can horizontally scale every component. Scaling out Grafana Mimir means that to respond to increased load, you can increase the number of replicas of each Grafana Mimir component. We have designed Grafana Mimir to scale up quickly, safely, and with no manual intervention. However, be careful when scaling down some of the stateful components as these actions can result in writes and reads failures, or partial query results. When running Grafana Mimir in monolithic mode, you can safely scale up to any number of instances. To scale down the Grafana Mimir cluster, see . When running Grafana Mimir in read-write mode, you can safely scale up any of the three components: You can safely scale the Mimir read component up or down because it is stateless. You could also use an autoscaler. You can safely scale the Mimir backend component down within one zone at a time. Because it contains the store-gateway, for more information see . To scale down the Mimir write component, see . When running Grafana Mimir in microservices mode, you can safely scale up any component. You can also safely scale down any stateless component. The following stateful components have limitations when scaling down: Alertmanagers Ingesters Store-gateways Scaling down can result in downtime. Consider the following guidelines when you scale down Alertmanagers: Scale down no more than two Alertmanagers at the same time. Ensure at least `-alertmanager.sharding-ring.replication-factor` Alertmanager instances are running (three when running Grafana Mimir with the default configuration). {{< admonition type="note" >}} If you enabled for Alertmanagers, you can, in parallel, scale down any number of Alertmanager instances within one zone at a time. {{< /admonition >}} store recently received samples in memory. When an ingester shuts down, because of a scale down operation, the samples stored in the ingester cannot be discarded in order to guarantee no data loss. You might experience the following challenges when you scale down ingesters: By default, when an ingester shuts down, the samples stored in the ingester are not uploaded to the long-term storage, which causes data loss. Ingesters expose an API endpoint that flushes in-memory time series data from ingester to the long-term storage and unregisters the ingester from the ring. After the `/ingester/shutdown` API endpoint successfully returns, the ingester does not receive write or read requests, but the process will not exit. You can terminate the process by sending a `SIGINT` or `SIGTERM` signal after the shutdown endpoint returns. To mitigate this challenge, ensure that the ingester blocks are uploaded to the long-term storage before shutting down. When you scale down ingesters, the querier might temporarily return partial results. The blocks an ingester uploads to the long-term storage are not immediately available for querying. It takes the and some time before a newly uploaded block is available for querying. If you scale down two or more ingesters in a short period of time, queries might return partial results. Complete the following steps to scale down ingesters deployed in a single

Configure the Grafana Mimir cluster to discover and query new uploaded blocks as quickly as possible. a. Configure queriers and rulers to always query the long-term storage and to disable ingesters on the read path: ``` -querier.query-store-after=0s -querier.shuffle-sharding-ingesters-enabled=false ``` b. Configure the compactors to frequently update the bucket index: ``` -compactor.cleanup-interval=5m ``` c. Configure the store-gateways to frequently refresh the bucket index and to immediately load all blocks: ``` -blocks-storage.bucket-store.sync-interval=5m -blocks-storage.bucket-store.ignore-blocks-within=0s ``` d. Configure queriers, rulers and store-gateways with reduced TTLs for the metadata cache: ``` -blocks-storage.bucket-store.metadata-cache.bucket-index-content-ttl=1m -blocks-storage.bucket-store.metadata-cache.tenants-list-ttl=1m -blocks-storage.bucket-store.metadata-cache.tenant-blocks-list-ttl=1m -blocks-storage.bucket-store.metadata-cache.metafile-doesnt-exist-ttl=1m ``` Scale down one ingester at a time: a. Send a POST request to the `/ingester/shutdown` API endpoint on the ingester to terminate. b. Wait until the API endpoint call has successfully returned and the ingester logged "finished flushing and shipping TSDB blocks". c. Send a `SIGINT` or `SIGTERM` signal to the process of the ingester to terminate. d. Wait 10 minutes before proceeding with the next ingester. The temporarily applied configuration guarantees newly uploaded blocks are available for querying within 10 minutes. Wait until the originally configured `-querier.query-store-after` period of time has elapsed since when all ingesters have been shutdown. Revert the temporary configuration changes done at the beginning of the scale down procedure. Grafana Mimir can tolerate a full-zone outage when you deploy ingesters in . A scale down of ingesters in one zone can be seen as a partial-zone outage. To simplify the scale down process, you can leverage ingesters deployed in multiple zones. For each zone, complete the following steps: Send a POST request to the `/ingester/shutdown` API endpoint on all ingesters that you want to terminate. Wait until the API endpoint calls have successfully returned and the ingester has logged "finished flushing and shipping TSDB blocks". Send a `SIGINT` or `SIGTERM` signal to the processes of the ingesters that you want to terminate. Wait until the blocks uploaded by terminated ingesters are available for querying before proceeding with the next zone. The required amount of time to wait depends on your configuration and it's the maximum value for the following settings: The configured `-querier.query-store-after` Two times the configured `-blocks-storage.bucket-store.sync-interval` Two times the configured `-compactor.cleanup-interval` To guarantee no downtime when scaling down , complete the following steps: Ensure at least `-store-gateway.sharding-ring.replication-factor` store-gateway instances are running (three when running Grafana Mimir with the default configuration). Scale down no more than two store-gateways at the same time. If you enabled for store-gateways, you can in parallel scale down any number of store-gateway instances in one zone at a time. Zone-aware replication is enabled by default in the `mimir-distributed` Helm chart. Stop the store-gateway instances you want to scale down. If you have set the value of `-store-gateway.sharding-ring.unregister-on-shutdown` to `false`, then remove the stopped instances from the store-gateway ring: In a browser, go to the `GET /store-gateway/ring` page that store-gateways expose on their HTTP port. Click Forget on the instances that you scaled down. Alternatively, wait for the duration of the value of `-store-gateway.sharding-ring.heartbeat-timeout` times 10. The default value of `-store-gateway.sharding-ring.heartbeat-timeout` is one minute. Proceed with the next two store-gateway replicas. If you are using zone-aware replication, the proceed with the next zone.