Add memory guard to cost_distance iterative Dijkstra + da.block assembly#1119
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brendancol merged 4 commits intomasterfrom Mar 31, 2026
Merged
Add memory guard to cost_distance iterative Dijkstra + da.block assembly#1119brendancol merged 4 commits intomasterfrom
brendancol merged 4 commits intomasterfrom
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Parallel subagent triage + ralph-loop workflow for auditing all xrspatial modules for performance bottlenecks, OOM risk under 30TB dask workloads, and backend-specific anti-patterns.
7 tasks covering command scaffold, module scoring, parallel subagent dispatch, report merging, ralph-loop generation, and smoke tests.
…1118) - Add memory guard before _preprocess_tiles: estimates ~3x dataset (source + friction cache + result) and raises MemoryError if it would exceed 80% of available RAM, suggesting finite max_cost. - Replace np.concatenate assembly with da.block to avoid building a monolithic numpy array from tile results. Tiles are now wrapped in dask.delayed and assembled lazily.
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Summary
_preprocess_tiles: estimates ~3x dataset size and raisesMemoryErrorif it would exceed 80% of available RAM, suggesting finitemax_costnp.concatenateresult assembly withda.blockto avoid building a monolithic numpy array from tile resultsContext
Found during performance sweep triage (#1118). The iterative tiled Dijkstra path (triggered when
max_cost=infor implied radius > chunk dimensions) caches all tiles in RAM viadask.compute(*blocks). The_assemble_resultthennp.concatenated everything into a single numpy array. At 30TB both are fatal.The
da.blockchange means the assembled result stays as a dask array with proper chunk structure, avoiding the second full materialization.Test plan