Streamlining Large-Scale Dataset Migrations with Background Agents: A Practical Guide

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Overview

Migrating thousands of datasets across a distributed system is a daunting task. Each dataset may have unique schemas, dependencies, and downstream consumers. Performing migrations synchronously can cause downtime, race conditions, and resource contention. At Spotify, we faced this exact challenge and developed a solution using background coding agents—specifically our internal tool Honk—integrated with Backstage for service discovery and Fleet Management for orchestration. This guide walks you through setting up a similar system, enabling you to supercharge your own dataset migrations with minimal friction.

Streamlining Large-Scale Dataset Migrations with Background Agents: A Practical Guide — Source: engineering.atspotify.com

By the end of this tutorial, you’ll understand how to configure a background agent that processes migration tasks asynchronously, track progress via Backstage, and scale the operation using Fleet Management. This approach reduces manual effort, prevents cascading failures, and provides transparency into the migration lifecycle.

Prerequisites

Before diving into the implementation, ensure your environment meets the following requirements:

Honk Agent Framework: Honk is our background task execution platform. You need access to a Honk deployment and the ability to create agents that listen for migration jobs.
Backstage Instance: Backstage serves as the service catalog and developer portal. Your datasets should be registered as entities in Backstage, with associated metadata (schema version, consumer info).
Fleet Management System: This orchestrates agent deployment across your infrastructure. We use it to push Honk agents to worker nodes and scale them based on queue depth.
Migration Schema: A predefined schema describing the transformation rules for each dataset. This could be a simple JSON map or a more complex DSL.
Access Credentials: Appropriate permissions to read/write to source and target databases, and to interact with Honk queues and Backstage APIs.

Step-by-Step Instructions

1. Define Your Migration Blueprint in Backstage

The first step is to encode your migration logic as a Backstage entity. This ensures every dataset has a clear, versioned migration path. Create a new entity type e.g., MigrationPlan in your Backstage catalog.

apiVersion: backstage.io/v1alpha1
kind: Component
metadata:
  name: customer-dataset-v2-migration
  annotations:
    honk/queue: dataset-migrations
spec:
  type: migration-plan
  lifecycle: production
  owner: team-infra
  system: data-platform
  dependsOn:
    - component:default/source-dataset
  leadsTo:
    - component:default/target-dataset

This entity defines a migration from source to target dataset, and links to the Honk queue that will process it. The honk/queue annotation tells Backstage where to send migration jobs.

2. Create Your Honk Agent

Honk agents are lightweight processes that poll a queue and execute tasks. Here’s a Python-based agent that reads migration plans from Backstage and applies transformations.

import honk
from backstage import BackstageClient
from migration_engine import apply_transform

@honk.agent(queue="dataset-migrations")
def migration_worker(task):
    # Fetch migration metadata from Backstage
    client = BackstageClient(base_url="https://backstage.example.com")
    plan = client.get_entity(task["entity_ref"])
    
    # Execute the migration step by step
    for step in plan.spec.steps:
        apply_transform(step)
    
    return {"status": "done", "dataset": plan.metadata.name}

The @honk.agent decorator registers the function as a consumer for the dataset-migrations queue. The agent fetches the full migration plan from Backstage using the entity reference provided in the task payload.

3. Register the Agent with Fleet Management

Fleet Management allows you to deploy the Honk agent across many workers. Create a deployment manifest:

apiVersion: fleet/v1
kind: Deployment
metadata:
  name: migration-agent-v1
spec:
  replicas: 10
  template:
    spec:
      containers:
        - name: honk-worker
          image: myregistry/migration-agent:1.0
          env:
            - name: HONK_QUEUE
              value: dataset-migrations
            - name: BACKSTAGE_URL
              value: https://backstage.example.com

This deploys 10 replicas, each running the Honk agent. The queue name is passed as an environment variable. Fleet Management will handle scaling up or down based on unprocessed task count.

4. Trigger a Migration Task

Now you can kick off a migration by sending a job to the Honk queue. Use Backstage’s API to create a task:

curl -X POST https://backstage.example.com/api/honk/tasks \
  -H "Content-Type: application/json" \
  -d '{
    "queue": "dataset-migrations",
    "payload": {
      "entity_ref": "component:default/customer-dataset-v2-migration"
    }
  }'

Honk will distribute the task to an available agent, which then executes the migration asynchronously.

5. Monitor Progress via Backstage

Add a custom plugin in Backstage to show migration status. Each agent outcome can be written to a dedicated table:

| Dataset                 | Status | Started           | Completed         |
|-------------------------|--------|-------------------|-------------------|
| customer-dataset-v2     | done   | 2024-03-15 10:00  | 2024-03-15 10:12  |
| inventory-dataset       | running| 2024-03-15 10:05  | -                 |

This visibility helps teams track migration health and identify stuck tasks.

Common Mistakes

Ignoring Idempotency: Migration tasks may run multiple times. Ensure your transformations are idempotent (e.g., use unique transaction IDs).
Missing Backpressure Handling: If the target database can’t keep up, agents will retry indefinitely. Implement exponential backoff and dead-letter queues.
Not Versioning Schemas: Always include schema version in your Backstage entities. Otherwise, agents might apply outdated transformations.
Underestimating Agent Resource Needs: Each migration may consume significant memory or CPU. Use Fleet Management’s resource limits to avoid cluster overload.
Neglecting Error Reporting: When an agent fails, log the full context (dataset, step, error). Without this, debugging becomes a nightmare.
Testing on Production Data: Always run migrations against a staging environment first. Validate that the transformation logic works as expected.

Summary

By combining Honk agents, Backstage, and Fleet Management, you can automate dataset migrations at scale. This approach decentralizes the migration workload, provides a single source of truth in Backstage, and allows elastic scaling through Fleet Management. The key takeaways are: define your migration plans as Backstage entities, write idempotent Honk agents, deploy them via Fleet Management, and monitor progress in the developer portal. Adopting this pattern reduces manual overhead and accelerates your data platform evolution.

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