# The Materialization Storm

> The graph started eating itself.

Canonical URL: <https://datadriven.io/problems/dagster-asset-fanout-cycle>

Domain: ai_coding · Difficulty: hard · Seniority: L6

## Problem

We added a new dataset to our Dagster repo last week. It declared 50 dynamic-partition assets that depend on shared upstream assets. Dagster scheduler CPU pegged at 100% within an hour and we're now seeing 12,000 materialization runs per hour for what should be ~50. Run tests/test_no_runaway_runs.py to reproduce. Find the cycle/fanout pattern in dagster_repo/assets.py and fix it so the partition fan-out is bounded and the run count is sane. Be ready to defend the trade-off your fix makes.

## Related

- [All practice problems](https://datadriven.io/problems)
- [Mock interview mode](https://datadriven.io/interview/dagster-asset-fanout-cycle)
- [Data Engineering Interview Prep Guide](https://datadriven.io/data-engineer-interview-prep)
- [Daily Challenge](https://datadriven.io/daily)

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