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docs/deployment/continuous-deployment.md

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@@ -31,11 +31,9 @@ It has several significant benefits:
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## Manual production re-deployment
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1. Trigger `:prod` images builds
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- Run [scripts/move_stable.py](https://github.com/packit/deployment/blob/main/scripts/move_stable.py) to move `stable` branches to a newer commit.
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2. Import images -> re-deploy
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- If you don't want to wait for [it to be done automatically](#continuous-deployment), you can
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[do that manually](#manually-import-a-newer-image) once the images are built (check Actions in each repo).
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docs/deployment/resource-requirements.md

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1. Request adjustments of the quotas such that we can have some buffer (database
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migrations, higher load on service, etc.), but also could **permanently**
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scale up the workers if we find service to be more reliable that way
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- Based on the calculations above, 2× the current quotas on memory would be
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sufficient, but if we were to scale the workers up too (and account for
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possible adjustments, e.g., Redict) we should probably go for 3×
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1. Migrate tokman to different toolchain, it's a small self-contained app, so it
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is easy to migrate to either Rust or Go that should leave smaller footprint.
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- Opened an issue for testing out running without Tokman deployment
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https://github.com/packit/tokman/issues/72
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docs/deployment/specifics/packit-service.md

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- Since our infrastructure does not support shared volumes, we need to attach
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one volume with a cache to each worker and one to each corresponding sandbox
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pod.
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- In the case of workers, this is done during the deployment.
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- For sandboxes, there is an option in the service config (the environment
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variable needs to differ for each worker and is set during startup of the

docs/deployment/specifics/redict.md

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deployment. To reproduce:
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1. We have deployed Redict to our production cluster.
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- Defaults have been changed to:
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```yaml
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3. After the data exchange is done, change **all** references in variables to
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redis to point to the new hostname, in this case `redis → redict`.
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- Run
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```

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