lablup / lablup/backend.ai

Load deployments, auto scaling rules and revision presets through bulk get

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Dominant language
Python
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Forks
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Avg merge
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Description

deployment_loader (deployment.global_search), auto_scaling_rule_loader (deployment.search_auto_scaling_rules) and revision_preset_loader (deployment_revision_preset.global_search) read through SUPERADMIN-gated global searches. The deployment child rows (revisions, replicas, routes, access tokens) already load through bulk gets; follow the same shape. Confirm first whether an auto scaling rule is wired as a field row of the deployment or as an entity.

## Success Criteria
- [ ] deployments and revision presets load through entity partial bulk gets
- [ ] auto scaling rules load through batch_load_fields or an entity bulk get, matching their wiring
- [ ] an unreadable id errors only its own field; a missing id resolves to None
- [ ] pants test passes for affected packages

JIRA Issue: BA-7890

Contributor guide

Open the contributing guide

Research direction

Locate deployment_loader, auto_scaling_rule_loader, and revision_preset_loader, then compare their current global-search paths with the existing bulk-get loading for deployment revisions, replicas, routes, and access tokens. Confirm whether auto scaling rules are field rows or entities, run the affected packages with pants test, and verify the listed missing-id and unreadable-id behaviors.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
api, backend
Issue type
Refactor
Difficulty
4/5
Estimated time
3-5 days
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
55/100

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