syntax issue in migration

[icecrasher321]

Summary

Attempting to fix memory issue with github runner for large migration.

[vercel]

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Project	Deployment	Preview	Comments	Updated (UTC)
sim (staging)	Building			Aug 18, 2025 7:59am

1 Skipped Deployment

Project	Deployment	Preview	Comments	Updated (UTC)
docs	Skipped			Aug 18, 2025 7:59am

[greptile-apps]

Greptile Summary

This PR fixes memory issues with GitHub runners during a large data migration by making the migration more resilient to varying database schema states. The primary changes focus on migration file 0076_damp_vector.sql, which appears to be a complex data migration involving workflow logs and user statistics.

The key modifications include:

1. Added RECURSIVE to CTE: The Common Table Expression (CTE) now uses WITH RECURSIVE candidate AS to properly handle hierarchical traversal of trace spans and their children, which is essential for processing nested execution data.

2. Switched to JSON-based column access: Instead of direct column references like l.total_cost, the migration now uses (to_jsonb(l)->>'total_cost')::numeric to safely extract values. This prevents failures when columns might not exist in certain schema states.

3. Implemented JSON existence checks: Rather than direct column checks, the migration uses JSON operators like (to_jsonb(l) ? 'total_tokens') to verify field existence before accessing values.

These changes make the migration more robust across different deployment environments and schema states, particularly important for what appears to be a "one-shot data migration" that needs to be safe on reruns. The migration likely processes large amounts of workflow execution data to populate user statistics, which explains the memory pressure on GitHub runners.

Confidence score: 3/5

• This PR addresses a specific technical issue but introduces complexity that could impact performance
• Score reflects the high-risk nature of modifying complex data migrations and potential performance implications
• Pay close attention to the migration file and thoroughly test with production-sized datasets

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