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270 lines
7.7 KiB
Markdown
270 lines
7.7 KiB
Markdown
# First Deployment Runbook
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## Purpose
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Execute the first real deployment with a repeatable sequence that covers infrastructure, secrets, webhook cutover, smoke checks, scheduler rollout, and rollback.
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## Preconditions
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- `main` is green in CI.
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- Terraform baseline has already been reviewed for the target environment.
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- You have access to:
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- GCP project
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- GitHub repo settings
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- Telegram bot token
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- Supabase project and database URL
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## Required Configuration Inventory
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### Terraform variables
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Required in your environment `*.tfvars`:
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- `project_id`
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- `region`
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- `environment`
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- `bot_api_image`
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- `mini_app_image`
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Recommended:
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- `database_url_secret_id = "database-url"`
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- `telegram_bot_token_secret_id = "telegram-bot-token"`
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- `openai_api_key_secret_id = "openai-api-key"`
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- `bot_mini_app_allowed_origins`
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- `scheduler_timezone`
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- `scheduler_paused = true`
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- `scheduler_dry_run = true`
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Household chat/topic bindings are no longer deployment config. Configure them in Telegram with
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`/setup`, `/bind_purchase_topic`, `/bind_feedback_topic`, and `/bind_payments_topic` after deploy.
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### Secret Manager values
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Create the secret resources via Terraform, then add secret versions for:
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- `telegram-bot-token`
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- `telegram-webhook-secret`
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- `scheduler-shared-secret`
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- `database-url`
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- optional `openai-api-key`
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- optional `supabase-url`
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- optional `supabase-publishable-key`
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### GitHub Actions secrets
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Required for CD:
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- `GCP_PROJECT_ID`
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- `GCP_WORKLOAD_IDENTITY_PROVIDER`
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- `GCP_SERVICE_ACCOUNT`
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Required for a real deploy:
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- `DATABASE_URL`
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### GitHub Actions variables
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Set if you do not want the defaults:
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- `GCP_REGION`
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- `ARTIFACT_REPOSITORY`
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- `CLOUD_RUN_SERVICE_BOT`
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- `CLOUD_RUN_SERVICE_MINI`
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## Phase 1: Local Readiness
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Run the quality gates locally from the deployment ref:
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```bash
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bun run format:check
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bun run lint
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bun run typecheck
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bun run test
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bun run build
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```
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If the release includes schema changes, also run:
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```bash
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bun run db:check
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E2E_SMOKE_ALLOW_WRITE=true bun run test:e2e
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```
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## Phase 2: Provision or Reconcile Infrastructure
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1. Prepare environment-specific variables:
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```bash
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cp infra/terraform/terraform.tfvars.example infra/terraform/dev.tfvars
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```
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2. Initialize Terraform with the correct state bucket:
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```bash
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terraform -chdir=infra/terraform init -backend-config="bucket=<terraform-state-bucket>"
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```
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3. Review and apply:
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```bash
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terraform -chdir=infra/terraform plan -var-file=dev.tfvars
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terraform -chdir=infra/terraform apply -var-file=dev.tfvars
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```
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4. Capture outputs:
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```bash
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BOT_API_URL="$(terraform -chdir=infra/terraform output -raw bot_api_service_url)"
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MINI_APP_URL="$(terraform -chdir=infra/terraform output -raw mini_app_service_url)"
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```
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5. If you did not know the mini app URL before the first apply, set `bot_mini_app_allowed_origins = [\"${MINI_APP_URL}\"]` in `dev.tfvars` and apply again.
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## Phase 3: Add Runtime Secret Versions
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Use the real project ID from Terraform variables:
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```bash
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echo -n "<telegram-bot-token>" | gcloud secrets versions add telegram-bot-token --data-file=- --project <project_id>
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echo -n "<telegram-webhook-secret>" | gcloud secrets versions add telegram-webhook-secret --data-file=- --project <project_id>
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echo -n "<scheduler-shared-secret>" | gcloud secrets versions add scheduler-shared-secret --data-file=- --project <project_id>
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echo -n "<database-url>" | gcloud secrets versions add database-url --data-file=- --project <project_id>
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```
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Add optional secret versions only if those integrations are enabled.
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For a functional household dev deployment, set `database_url_secret_id = "database-url"` in
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`dev.tfvars` before the apply that creates the Cloud Run services. Otherwise the bot deploys
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without `DATABASE_URL`, and finance commands, reminders, mini app auth/dashboard, and anonymous
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feedback remain disabled.
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Keep `telegram_bot_token_secret_id = "telegram-bot-token"` aligned with the actual bot token
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secret name. CD uses that secret to sync the Telegram command menu after deploy.
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## Phase 4: Configure GitHub CD
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Populate GitHub repository secrets with the Terraform outputs:
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- `GCP_PROJECT_ID`
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- `GCP_WORKLOAD_IDENTITY_PROVIDER`
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- `GCP_SERVICE_ACCOUNT`
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- `DATABASE_URL`
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If you prefer the GitHub CLI:
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```bash
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gh secret set GCP_PROJECT_ID
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gh secret set GCP_WORKLOAD_IDENTITY_PROVIDER
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gh secret set GCP_SERVICE_ACCOUNT
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gh secret set DATABASE_URL
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```
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Set GitHub repository variables if you want to override the defaults used by `.github/workflows/cd.yml`.
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- optional `TELEGRAM_BOT_TOKEN_SECRET_ID`
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- only needed if your bot token secret name is not `telegram-bot-token`
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## Phase 5: Trigger the First Deployment
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You have two safe options:
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- Merge the deployment ref into `main` and let `CD` run after successful CI.
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- Trigger `CD` manually from the GitHub Actions UI with `workflow_dispatch`.
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The workflow will:
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- run `bun run db:migrate` before deploy
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- build and push bot and mini app images
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- deploy both Cloud Run services
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## Phase 6: Telegram Webhook Cutover
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After the bot service is live, set the webhook explicitly:
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```bash
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export TELEGRAM_BOT_TOKEN="$(gcloud secrets versions access latest --secret telegram-bot-token --project <project_id>)"
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export TELEGRAM_WEBHOOK_SECRET="$(gcloud secrets versions access latest --secret telegram-webhook-secret --project <project_id>)"
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export TELEGRAM_WEBHOOK_URL="${BOT_API_URL}/webhook/telegram"
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bun run ops:telegram:webhook set
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bun run ops:telegram:webhook info
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```
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If you want to discard queued updates during cutover:
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```bash
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export TELEGRAM_DROP_PENDING_UPDATES=true
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bun run ops:telegram:webhook set
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```
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## Phase 7: Post-Deploy Smoke Checks
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Run the smoke script:
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```bash
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export BOT_API_URL
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export MINI_APP_URL
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export TELEGRAM_EXPECTED_WEBHOOK_URL="${BOT_API_URL}/webhook/telegram"
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bun run ops:deploy:smoke
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```
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The smoke script verifies:
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- bot health endpoint
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- mini app root delivery
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- mini app auth endpoint is mounted
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- scheduler endpoint rejects unauthenticated requests
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- Telegram webhook matches the expected URL when bot token is provided
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## Phase 8: Scheduler Enablement
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First release:
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1. Keep `scheduler_paused = true` and `scheduler_dry_run = true` on initial deploy.
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2. After smoke checks pass, set `scheduler_paused = false` and apply Terraform.
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3. Trigger one job manually:
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```bash
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gcloud scheduler jobs run household-dev-utilities --location <region> --project <project_id>
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```
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4. Verify the reminder request succeeded and produced `dryRun: true` logs.
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5. Set `scheduler_dry_run = false` and apply Terraform.
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6. Trigger one job again and verify the delivery side behaves as expected.
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## Rollback
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If the release is unhealthy:
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1. Pause scheduler jobs again in Terraform:
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```bash
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terraform -chdir=infra/terraform apply -var-file=dev.tfvars -var='scheduler_paused=true'
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```
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2. Move Cloud Run traffic back to the last healthy revision:
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```bash
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gcloud run revisions list --service <bot-service-name> --region <region> --project <project_id>
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gcloud run services update-traffic <bot-service-name> --region <region> --project <project_id> --to-revisions <previous-revision>=100
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gcloud run revisions list --service <mini-service-name> --region <region> --project <project_id>
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gcloud run services update-traffic <mini-service-name> --region <region> --project <project_id> --to-revisions <previous-revision>=100
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```
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3. If webhook traffic must stop immediately:
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```bash
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bun run ops:telegram:webhook delete
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```
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4. If migrations were additive, leave schema in place and roll application code back.
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5. If a destructive migration failed, stop and use the rollback SQL prepared in that PR.
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## Dev-to-Prod Promotion Notes
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- Repeat the same sequence in a separate `prod.tfvars` and Terraform state.
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- Keep separate GCP projects for `dev` and `prod` when possible.
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- Do not unpause production scheduler jobs until prod smoke checks are complete.
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