```json { "table_name": "IPL_Matches_Data_2008_2026", "primary_key": "season,match_number", "candidates": [ { "rank": 0, "partition_key": null, "partition_key_label": null, "partition_key_reasoning": "This is a dimension table containing IPL match metadata. With only 1,243 rows, it is small enough to not require partitioning. The entity_columns (season, match_number) form a composite natural key but are not suitable as partition keys: season has RPUV=65.42 (19 values, reasonable cardinality but low for partitioning), and match_number has RPUV=16.24 with 5.95% nulls. Neither provides the typical partitioning benefit for a table of this size. The table serves as a reference/lookup dimension for match details.", "clustering_key": null, "clustering_key_label": null, "clustering_key_reasoning": "Dimension tables typically do not require clustering keys as they are accessed by primary key lookups or full scans. The date column (event_time) has high uniqueness (76.2%) and could theoretically order rows chronologically, but for a 1,243-row dimension table, clustering provides negligible benefit.", "world_line": null, "session_column": null, "confidence": "HIGH", "notes": "Small dimension table (1,243 rows) containing IPL match reference data. The composite key (season, match_number) uniquely identifies each match, though match_number has 5.95% nulls. No partitioning needed due to small size. Many columns are constant metadata (event_name, match_type, overs_limit, balls_per_over, gender, team_type all have RPUV=1243) or ingestion metadata (_ingestion_timestamp, _batch_id, _source_file, _source_system). The table serves as a lookup for match details across 19 IPL seasons (2007/08 through 2026)." } ] } ```