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To rebuild Grafana after replacing a container or pod, preserve its data directory if you need instance state, and deliver provisioning files and dashboard definitions to every replacement instance if you need file-declared resources. These are separate recovery layers: a volume does not recreate dashboards from files, and provisioning does not preserve every part of Grafana’s database.
What disappears when you remove a Grafana container?
Grafana’s Docker documentation says it uses an embedded SQLite database by default to store configuration, users, dashboards, and other data. If those changes exist only on the container’s writable filesystem, removing the container removes that state too. Grafana recommends persistent storage, such as a Docker volume or bind mount, for data that must survive container replacement. Grafana’s Docker guide describes the default behavior.
The documented Docker data path is /var/lib/grafana. Mount persistent storage there, or at the configured data path if your deployment changes it. Check the actual Grafana configuration rather than assuming the default applies. Grafana’s Docker configuration reference lists the default paths.
Which recovery layer do you need?
| Recovery layer | What it does | What you must supply to a replacement |
|---|---|---|
| Persistent Grafana data | Retains database state written to Grafana’s data directory. | A persistent volume or bind mount at the active data path. |
| Provisioning files | Declares resources such as data sources and dashboard providers for Grafana to apply. | The provisioning configuration and any referenced dashboard definitions at paths Grafana can read. |
Use both when you need both retained instance state and repeatable resources declared in files. Neither mechanism substitutes for the other.
#1 Best Overall
How do you make a Docker replacement recover the setup?
Keep the data mount and provisioning delivery explicit in your deployment. Grafana documents /var/lib/grafana as the Docker data path and /etc/grafana/provisioning as the default provisioning path; customized paths must be handled accordingly. The Docker configuration reference covers these defaults.
- Choose persistent storage for data. A Docker named volume is managed by Docker; a bind mount uses a host path you choose. Both are documented persistence approaches. Select based on your deployment and storage-management needs rather than assuming one is universally preferable.
- Deliver provisioning files on every replacement. Include them in the image or mount them into the configured provisioning path. Also make dashboard JSON files available in the directory referenced by the dashboard provider configuration.
- Replace the container without discarding the data mount. Ensure the new container mounts the same persistent data storage at the active Grafana data path.
Classic provisioning uses YAML files, including data-source configuration under the provisioning data-sources directory and dashboard provider configuration under the dashboards directory. The provider points Grafana to the directory containing dashboard definitions. See Grafana’s provisioning documentation for the release-specific options and file structure.
Rank #2
How does the same idea apply to Kubernetes?
Separate storage for Grafana’s database state from delivery of provisioning configuration. Grafana’s Kubernetes guide demonstrates a PersistentVolumeClaim for provisioning storage, mounts the provisioning directory into the pod, and restarts the pod to apply resources. That example does not establish a universal production storage design: choose persistent storage for the data that must survive according to your workload and cluster setup, and ensure each replacement pod receives the provisioning content and dashboard definitions it needs. See Grafana’s Kubernetes deployment guide.
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What can provisioning change or delete?
Provisioning makes the files a source of truth for the resources they declare. That can be useful for repeatable recovery, but it has consequences when someone also edits resources in Grafana’s UI.
Dashboards
When a provisioned dashboard is updated from its file, Grafana can overwrite changes saved in the UI; it ignores the JSON version value for this reconciliation. If the provisioning source is removed, Grafana deletes the dashboard unless the provider configuration sets disableDeletion: true. Decide whether UI edits are allowed to be temporary or whether dashboard changes must be made in the source files. Grafana documents these dashboard provisioning behaviors.
Data sources
Grafana reconfigures an existing data source to match its provisioning file. A deleteDatasources list deletes named sources before configured sources are added or updated. With prune: true, provisioned data sources removed from the file are also removed from Grafana. Review these settings before deploying a change that removes or renames a source. See the data-source provisioning reference.
How do you keep the setup reproducible?
Keep provisioning configuration and dashboard definitions in version control, and use deployment automation to deliver them consistently. Grafana’s as-code overview describes Git-based collaboration and rollback, CI/CD, and infrastructure-as-code tooling as parts of this workflow; it is a workflow overview, not a detailed recipe for every deployment. See Grafana’s as-code documentation. Review changes to provisioning files as carefully as changes to application configuration, particularly when they alter dashboard providers or data-source deletion behavior.
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Replacement checklist
- Confirm the replacement uses the intended Grafana version and the expected data and provisioning paths.
- Verify that the same persistent storage is mounted at the active data path if database state must survive.
- Verify that provisioning YAML and referenced dashboard definitions are present at the paths configured for the replacement.
- Review dashboard deletion settings and data-source deletion or pruning options before applying changed files.
- Restart or otherwise apply provisioning using the procedure for your deployment, then inspect the resulting dashboards and data sources.
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