Infrastructure as Code and Automation Questions

I would make the module interface small, typed, and caller-driven. A module should accept inputs through variables and return only the outputs another team needs.

Inputs

• env for environment name like dev, staging, prod
• name_prefix for naming consistency
• tags as a map(string) so teams can add ownership and cost-center labels
• sizing inputs, such as instance_class or cpu_count
• network inputs like subnet_ids and vpc_id

Design rules

• Use defaults only for safe values, not for important architecture choices
• Add validation so bad values fail early, such as rejecting an empty subnet list
• Use stable names and tags inside the module, but let callers control the prefix
• Output IDs, ARNs, DNS names, or endpoints, not whole resources

Example
Dev can pass instance_class = "t3.small" and prod can pass instance_class = "m6i.large", while the module code stays unchanged. That lets each environment differ in size and naming without forking the module.

Decision framework
I first determine whether Terraform should own the object. A resource should be imported only if the team wants Terraform to manage its lifecycle and we can make the current cloud object match code with low risk. If ownership is unclear or another team runs it, I leave it alone and document the boundary.

How I decide

• Import when the resource already exists, is needed long term, and its current settings are close enough to model in code.
• Recreate when the live object is too drifted, unsafe, or impossible to express cleanly, and downtime or cutover is acceptable.
• Leave alone when it is intentionally outside this stack, or the business risk of taking ownership is higher than the benefit.

Concrete example
Say an existing prod-logs bucket was created manually. If the app depends on it and the bucket policy, versioning, and encryption are acceptable, I would import it, then update the module so the code matches the real settings. If the bucket has years of data and a clean import is possible, import is safest. If the bucket name, policy, or region is wrong and fixing it in place would be risky, I would create a new bucket, migrate traffic or data, then delete the old one after validation.

What I check first
I compare state, config, and live attributes, then run a plan to see whether the desired change is truly just drift or a bigger ownership problem.

I would put both human review and automation around the plan. A Terraform plan is a preview of create, update, destroy, and replace actions. Replace is especially risky because Terraform deletes and recreates a resource.

Guardrails

• Run terraform fmt -check and terraform validate in CI
• Save the plan as an artifact and require approval before apply
• Fail the pipeline if the plan includes unexpected destroys or replacements on critical resources
• Use policy checks for things like public exposure, open security groups, or deletion of databases
• Add prevent_destroy to the few resources that should almost never be removed

Example
If the plan shows aws_db_instance.main will be replaced because of an engine change, I would force manual review from an engineer and, ideally, a service owner. If the plan only adds an autoscaling instance, that can follow the normal path.

This catches surprises before users feel them, which is the real goal.

I would move production to a remote backend that supports both shared access and state locking. State is Terraform’s record of what it created, and locking means only one writer can change that record at a time.

Design choices

• Use one backend per environment, so prod does not share state with dev
• Turn on encryption at rest for the backend storage
• Restrict write access to CI and a small break-glass admin group
• Give most engineers read-only access to plans and state history
• Require an exclusive lock for apply, with a short timeout and clear failure message

Recovery
I would also enable versioning or history so I can recover a prior state file if an apply fails halfway through. If a run breaks, I would not edit state first. I would inspect the lock, compare the current cloud resources with terraform plan -refresh-only, and then decide whether to fix drift, import an orphan, or roll back.

Example
If a colleague applies from their workstation while CI is planning, the lock should stop the second write. That prevents two people from corrupting the same prod state.

I would separate shared logic from environment-specific entrypoints. Right now one large root module is forcing unrelated changes into the same file set, which creates merge conflicts and increases blast radius.

New structure

• modules/network
• modules/app
• modules/database
• envs/dev
• envs/staging
• envs/prod

Each environment folder has its own backend and only calls the modules it needs. Shared values like tags or account IDs can live in small local files or a common variable file.

Why this helps

• Two engineers can edit dev and prod in parallel with less conflict
• A change to app compute does not force a network review unless inputs change
• Cross-environment mistakes are harder because each root has its own state

Example
Dev can use t3.small and prod can use m6i.large, but both call the same modules/app. That gives consistency without a monolith. I would also keep module outputs explicit so one environment cannot accidentally consume another environment’s state.