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Understanding Artifact Lifecycle Management

This topic explores how Infrahub's artifact system works with Nornir to enable sophisticated configuration management workflows. Understanding artifact lifecycle management is crucial for implementing robust, auditable network automation.

Introduction​

Artifacts in Infrahub are generated outputs based on your infrastructure data and templates. They represent the "rendered truth" of your infrastructure at any point in time. This document explains:

  • What artifacts are and why they matter
  • How artifacts flow through generation, storage, and deployment
  • The relationship between artifacts, templates, and infrastructure data
  • How version control applies to generated configurations

What are artifacts?​

Definition​

An artifact is any generated content that represents infrastructure state:

  • Device configurations
  • Compliance reports
  • Documentation
  • API payloads
  • Validation results

Key characteristics​

Artifacts are:

  • Generated: Created from templates + data, not manually written
  • Versioned: Each generation creates a new version
  • Immutable: Once generated, artifacts don't change
  • Traceable: Linked to the exact data and templates used

References​

For detailed information about artifacts in Infrahub:

Artifact management with Nornir​

Task plugin functions​

The Nornir-Infrahub plugin provides three key functions:

generate_artifacts()​

Triggers generation for all targets in a definition:

# Generate configs for all edge devices
nr.run(task=generate_artifacts, artifact="Startup Config for Edge devices")

This is typically used:

  • After template updates
  • On scheduled intervals
  • Before deployment windows

regenerate_host_artifact()​

Regenerates artifact for specific hosts:

# Regenerate after device-specific change
changed_devices = nr.filter(name="atl1-edge1")
changed_devices.run(task=regenerate_host_artifact, artifact="Startup Config for Edge devices")

Use cases:

  • After individual device updates
  • Testing template changes
  • Troubleshooting specific devices

get_artifact()​

Retrieves stored artifact content:

# Get current config for deployment
result = nr.run(task=get_artifact, artifact="Startup Config for Edge devices")
for host, task_result in result.items():
    config = task_result.result
    # Deploy config to device

Use cases:

  • Deploying configurations to devices
  • Comparing current vs desired state
  • Feeding into validation pipelines

Version control integration​

Branch-aware artifacts​

Artifacts are generated per branch:

main branch:
  atl1-edge1 startup-config v3 (current)
  atl1-edge1 startup-config v2
  atl1-edge1 startup-config v1

feature branch:
  atl1-edge1 startup-config v2 (testing changes)
  atl1-edge1 startup-config v1

This enables:

  • Safe testing in feature branches
  • Comparison between branches
  • Controlled promotion to production

Practical workflows​

Configuration deployment​

  1. Generate: Create new configs from current data
  2. Validate: Check configs before deployment
  3. Deploy: Push to devices
  4. Verify: Confirm successful application

Compliance auditing​

  1. Define: Create compliance report template
  2. Generate: Run against all devices
  3. Review: Identify non-compliant devices
  4. Remediate: Fix issues and regenerate

Disaster recovery​

  1. Regular Generation: Keep artifacts current
  2. Backup Storage: Replicate artifact store
  3. Rapid Recovery: Deploy last-known-good configs

Advanced concepts​

Conditional generation​

Generate only when needed:

# Check if regeneration needed
if device.last_modified > artifact.generated_at:
    nr.run(task=regenerate_host_artifact, artifact="Startup Config for Edge devices")

Further reading​