```json { "table_name": "places", "primary_key": "place_id", "candidates": [ { "rank": 0, "partition_key": "place_id", "partition_key_label": "primary_identifier", "partition_key_reasoning": "place_id is the unique identifier for this dimension table with RPUV=1.00, 86,061 unique values for 86,061 rows, perfect uniqueness=1.000, and monotonicity=1.000 indicating it's a sequential primary key. As a dimension table, the primary identifier serves as the natural partition key.", "clustering_key": null, "clustering_key_label": null, "clustering_key_reasoning": "Dimension tables typically do not require clustering keys as they are reference tables looked up by their primary identifier. No temporal or hierarchical ordering is needed within this table.", "world_line": null, "session_column": null, "confidence": "HIGH", "notes": "This is a dimension table representing places (venues, stores, breweries, etc.) in a beer rating system. The place_id is the definitive unique identifier. The table contains descriptive attributes (name, address, city, state, country, type, features like has_taps/has_bottles, ratings aggregates). Dimension tables are typically small-to-medium reference tables partitioned by their primary key for efficient lookups. With 86K rows, this is appropriately sized for a dimension table. The created_at and updated_at timestamps indicate when records were added/modified but do not represent a time-series or state evolution—they are metadata. No alternative partitioning perspectives are viable for a dimension table." } ] } ```