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@jordanstephens jordanstephens commented Nov 26, 2024

Changes in this PR

  • Create a "Deploy to your own cloud" tutorial
    • Create a "Deploy to your own AWS Account" tutorial
    • Create a "Deploy to your own Digital Ocean Account" tutorial
    • Create a stub for "Deploy to your own GCP Account"
  • After getting started, "Deploy to your own cloud" is the next recommended tutorial
  • Document Deployment modes
    • create a "rolling updates" concept page
    • create a "databases" concept page
  • Add a few more items to the FAQ
  • Add more linkable subheadings to the AWS and Digital Ocean Provider pages

@jordanstephens jordanstephens marked this pull request as ready for review November 26, 2024 22:05
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@Prakash-Sundaresan Prakash-Sundaresan left a comment

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See detailed comments.
Also, we should replace all references to "AI assistant" with "AI agent" - e.g. in https://docs.defang.io/docs/intro

| Feature | Development | Staging | Production |
|-|-|-|-|
| Build Resources | Builds will be run with 2x vCPUs | Builds will be run with 2x vCPUs | Builds will be run with 4x vCPUs |
| Databases | Defang will provision resources optimized for burstable memory | | Defang will provision resources optimized for production |
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Should we say something about behavior on 'compose down'?

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Yes. I think we should create a Databases concept topic where we can discuss the subtlety more carefully 👍

## Data Stores

Defang is not a good fit for data stores like databases, caches, and message queues. These types of services are designed to be long-lived and to store data. Defang is primarily designed for stateless workloads, and it's not a good fit for stateful workloads: if a container is replaced, all the data in it is lost. You could probably coerce a data store to work in a stateless environment with certain replication strategies, but it's not a good fit.
Defang is not a good fit for data stores like databases, caches, and message queues. These types of services are designed to be long-lived and to store data. Defang is primarily designed for stateless workloads, and it's not a good fit for stateful workloads: if a container is replaced, all the data in it is lost. You could probably coerce a data store to work in a stateless environment with certain replication strategies, but it's not a good fit.
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This is not accurate. We should mention the issue with stateful services and then point to Defang's support for managed storage options - https://docs.defang.io/docs/concepts/managed-storage

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@raphaeltm raphaeltm Nov 27, 2024

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@Prakash-Sundaresan I think you're right. This is no longer accurate because we support managed storage. I do think we should have a place where we caution people against trying to run unsupported data stores until we support volume mounts, though. Like, at the moment people shouldn't be using Defang to run Mongo, or ElasticSearch or anything like that.

@jordanstephens jordanstephens merged commit cdfe3ba into main Nov 28, 2024
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@jordanstephens jordanstephens deleted the jordan/launch-prep branch November 28, 2024 19:05
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5 participants