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"""megaeval — aggregate-first, risk-first evaluation layer for mega-scale populations.
Mental model (maps 1:1 to modules):
Dataset (immutable population) -> population.py (partitioned npz storage)
Evaluation Run (first-class job) -> runs.py (async workers -> partial stats -> reduce)
Population / Cohort -> cube.py (metric cube + query router + cache)
Container -> runs.py (container-level aggregate table)
Annotation (forensic drill-down) -> runs.py (per-object partitions, loaded on demand)
Single-node stand-ins for distributed infra (see ARCHITECTURE.md):
npz partitions ~ Parquet files in an Iceberg table
worker thread pool ~ Spark executors / Flink task slots
partial-stats+reduce~ map-side combine + shuffle reduce
QueryRouter ~ OLAP engine (Druid/Pinot) in front of the lakehouse
"""