```json { "table_name": "schedule_2026", "primary_key": "Date,Time,home_team,away_team", "candidates": [ { "rank": 0, "partition_key": null, "partition_key_label": null, "partition_key_reasoning": "This is a small dimension table (72 rows) representing scheduled World Cup matches. No partitioning is needed — the entire table fits comfortably in a single file. Date has RPUV=4.24 (17 unique dates for 72 rows), which is reasonable but unnecessary for this size. home_team and away_team each have RPUV=1.50 (48 unique values), also viable but overkill for 72 rows.", "clustering_key": null, "clustering_key_label": null, "clustering_key_reasoning": "No clustering key is needed. The table is already naturally ordered by Date (monotonicity=0.225), and queries will typically filter by date, team, or both. The small size makes any clustering overhead unnecessary.", "world_line": null, "session_column": null, "confidence": "HIGH", "notes": "This is a reference/dimension table for the 2026 World Cup schedule. The composite primary key (Date, Time, home_team, away_team) uniquely identifies each scheduled match. The table is too small (72 rows) to benefit from partitioning or clustering. Score, Referee, and Notes columns are empty (all matches are future/scheduled). The classifier correctly identified this as dimension type despite the presence of Date and event-like structure — it's a static schedule, not a log of occurrences." } ] } ```