```json { "table_name": "dataset_lifecycle", "primary_key": "batch_id", "candidates": [ { "rank": 0, "partition_key": "dataset_id", "partition_key_label": "E", "partition_key_reasoning": "RPUV=2.00 indicates each dataset has ~2 batches on average, making it an ideal partition key. Uniqueness=0.500 with 99 unique values out of 198 rows. Top 3 coverage=24.75% (d_9e733a56 appears 22 times) shows reasonable distribution. Semantically, dataset_id is the primary entity whose lifecycle we are tracking across multiple batch submissions.", "clustering_key": "submitted_at", "clustering_key_label": "S", "clustering_key_reasoning": "submitted_at is the event_time identified by the classifier. Uniqueness=1.000 (all unique timestamps) provides perfect chronological ordering. This column sequences the lifecycle events for each dataset, showing when each batch was submitted over time.", "world_line": "dataset_id", "session_column": null, "confidence": "HIGH", "notes": "Primary perspective: track each dataset's lifecycle through its batch submissions over time. The path structure is clear: partition by dataset_id to group all batches for a given dataset, then order by submitted_at to see the temporal progression of submissions, status changes, and insights generation." }, { "rank": 1, "partition_key": "batch_id", "partition_key_label": "E", "partition_key_reasoning": "RPUV=1.00 with uniqueness=1.000 (198 unique values). Each batch_id appears exactly once, making it a valid but less useful partition key. Top 3 coverage=1.52% shows perfect distribution. Semantically, batch_id represents individual processing jobs.", "clustering_key": "submitted_at", "clustering_key_label": "S", "clustering_key_reasoning": "Same temporal ordering as rank=0. submitted_at sequences batches chronologically across all datasets.", "world_line": "batch_id", "session_column": null, "confidence": "MEDIUM", "notes": "Alternative perspective: track individual batch processing jobs chronologically. Less natural than dataset_id because each batch appears only once (RPUV=1.00), making partitioning less beneficial. This perspective would be useful for analyzing batch processing patterns across all datasets rather than individual dataset lifecycles." } ] } ```