Streamlining Large-Scale Dataset Migrations with Background Coding Agents

Introduction

Managing thousands of datasets across a rapidly growing platform is no small feat. At Spotify, the engineering team faced a significant challenge: migrating downstream consumer datasets without disrupting services or overwhelming developers. The solution? A trio of powerful tools—Honk, Backstage, and Fleet Management—working in concert with background coding agents. This article explores how these components transformed a painful migration process into a smooth, automated operation.

What Are Background Coding Agents?

Background coding agents are autonomous processes that handle code generation, modification, and validation tasks in the background. Unlike interactive development, these agents run asynchronously, allowing engineers to focus on higher-level design while the agents handle repetitive or complex transformation scripts. In the context of dataset migrations, they automate the rewriting of schemas, queries, and access patterns to ensure downstream consumers adapt seamlessly.

Why Background Agents?

Traditional migration methods required manual intervention for each dataset—an impossibly slow process when dealing with thousands. Background coding agents eliminate bottlenecks by:

• Automatically detecting impacted datasets and their dependencies.
• Generating migration scripts that respect original data semantics.
• Running validations and error checks without developer oversight.
• Scaling horizontally to handle massive workloads in parallel.

Honk: The Core Agent Engine

Honk serves as the central orchestrator for these background agents. Originally developed for internal infrastructure tasks, Honk was adapted to coordinate dataset migrations across Spotify's ecosystem. It manages agent lifecycle, including deployment, execution, monitoring, and retries.

Key features of Honk in this migration context include:

• Task Queueing: Agents pull migration tasks from a prioritized queue, ensuring efficient resource use.
• Context Awareness: Honk agents understand the structure of both source and target datasets, reducing logic errors.
• Rollback Capabilities: If a migration fails validation, the agent can automatically revert changes.
• Audit Logging: Every action is logged for compliance and debugging.

By abstracting the complexity of dataset transformations, Honk allowed the team to focus on business logic rather than plumbing.

Backstage: The Developer Portal

While Honk handles the heavy lifting, Backstage provides the human interface. Spotify's instance of Backstage—a standardized developer portal—exposed migration statuses, logs, and triggers in a unified dashboard. This transparency was crucial for maintaining trust among teams whose datasets were being modified.

Integration Points

• Migration Catalog: Displays all datasets undergoing migration, with progress bars and error counts.
• Manual Override: Engineers can pause, skip, or escalate specific migrations via Backstage's UI.
• Notification Channels: Alerts are sent to Slack or email when a dataset requires human review.
• Documentation: Inline guides explain how agents work and what to do if something goes wrong.

By coupling Honk's agent results with Backstage's visibility, the team reduced cognitive load and accelerated decision-making.

Fleet Management: Coordinating Nodes

Running thousands of background agents requires robust infrastructure. Fleet Management, Spotify's internal system for managing compute resources, ensured that agents had enough capacity to run without starving other services.

Scalability and Reliability

• Dynamic Scaling: Fleet Management spins up additional agent nodes when migration queues are long, and tears them down during idle periods.
• Failure Isolation: If a node crashes, its tasks are redistributed to healthy nodes automatically.
• Resource Quotas: Each migration campaign gets a reserved slice of CPU and memory to prevent resource contention.
• Latency Optimization: Agents are placed geographically close to the datasets they need to access, reducing network overhead.

This infrastructure layer ensured that Honk agents ran efficiently, even during peak migration periods.

The Migration Workflow

1. Discovery: Honk scans the dataset catalog and identifies all downstream consumers.
2. Agent Assignment: For each consumer, a background coding agent is created with the appropriate transformation rules.
3. Simulation: The agent generates a dry-run migration and validates outputs against expected schemas.
4. Approval: Backstage displays the simulated changes; human reviewers can approve or modify.
5. Execution: Honk applies the migration in a controlled manner, often in phased rollouts.
6. Monitoring: Fleet Management tracks agent health, and Backstage updates dashboards in real time.
7. Rollback (if needed): Automated rollback mechanism reverts changes if error rate exceeds threshold.

This end-to-end automation turned what used to be a weeks-long manual process into a matter of hours.

Benefits and Lessons Learned

Key Outcomes

• Reduced Manual Effort: Engineers saved hundreds of hours previously spent writing custom scripts.
• Increased Reliability: Automated validation caught inconsistencies that humans might miss.
• Faster Iterations: Background agents could run overnight, enabling daily migration cycles.
• Improved Developer Experience: Backstage provided a single pane of glass for tracking progress.

Challenges Overcome

• Dependency Hell: Agents had to understand complex dataset dependencies to avoid breaking downstream pipelines.
• Scaling Agents: Fleet Management's dynamic scaling was essential to handle thousands of concurrent agents.
• Human Trust: The team invested in comprehensive testing and gradual rollouts to build confidence in automated migrations.

Conclusion

By combining Honk's background coding agents with Backstage's developer portal and Fleet Management's infrastructure, Spotify successfully transformed a painful dataset migration process into a streamlined, automated pipeline. This approach not only saved time but also improved data consistency and developer satisfaction. For organizations grappling with large-scale data changes, the principle of using autonomous agents alongside clear visualization and robust resource management offers a proven path forward.

Originally published on Spotify Engineering.