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Atlantis workflow without a backend

Hacker News

Atlantis workflow without a backend

Last week we created a TF cloud alternative that could just run in GH actions and got an overwhelming response on Reddit. A lot of people recommended Atlantis as a way to run terraform plan and apply jobs in your CI. It is used by many great teams (Lyft for example) - however, we see the following issues: - You need to deploy and maintain an Atlantis backend in your infrastructure - It runs terraform commands locally on the same server it is installed in. This makes it tricky to achieve high levels of isolation and repeatability that is typically needed in CI/CD scenarios So we thought: does this really need a backend? Can we somehow make it work without a need to deploy a dedicated service and with terraform jobs running natively in Github Actions with proper isolation? Actually, the only need that makes Atlantis backend irreplaceable is code-level locks (not to be confused with state locks). But these can be stored in a database, accessed directly from the action - it can even be the same DB that is used by Terraform for state locks! So we’ve built a proof-of-concept that does just that: it stores higher-level locks in DynamoDB, so there’s no need for any backend. It works like this : - create a PR - this will create a lock - comment digger plan - terraform plan output will be added as comment - create another PR - plan or apply won’t work in this PR until the first lock is released - you will get Locked by PR #1 comment This proof-of-concept is very much a WIP - for example, there’s no support for apply and then there are things like one PR applied making plans from other PRs thinking new resources need to be deleted; so you need to merge main before re-running plan - and other things like that. Would love to hear what the HN community thinks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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