```json { "table_name": "job_logs", "primary_key": "log_id", "candidates": [ { "rank": 0, "partition_key": "job_id", "partition_key_label": "E", "partition_key_reasoning": "job_id is the entity whose timeline we trace. RPUV = 508.06 indicates each job has ~508 log entries on average, which is ideal for partitioning. Top 3 coverage = 16.70% shows good distribution across 598 unique jobs. Semantically, this groups all log events for a single job execution together.", "clustering_key": "ts", "clustering_key_label": "S", "clustering_key_reasoning": "ts is the event timestamp with perfect uniqueness (1.000) and monotonicity (1.000 ascending). It orders log entries chronologically within each job's partition, enabling efficient temporal queries and log replay.", "world_line": "job_id", "session_column": null, "confidence": "HIGH", "notes": "Primary perspective: job-centric log analysis. Each job_id partition contains the complete chronological log sequence for that job execution. This is the natural organization for debugging, auditing, and job performance analysis." }, { "rank": 1, "partition_key": "dataset_id", "partition_key_label": "E", "partition_key_reasoning": "dataset_id is an alternative entity perspective. RPUV = 2712.70 indicates each dataset has ~2713 log entries on average. Top 3 coverage = 42.23% shows concentration but still viable. This perspective groups all job logs related to a specific dataset.", "clustering_key": "ts", "clustering_key_label": "S", "clustering_key_reasoning": "ts orders events chronologically within each dataset's partition, enabling dataset-level activity tracking and cross-job analysis for the same dataset.", "world_line": "dataset_id", "session_column": "job_id", "confidence": "MEDIUM", "notes": "Alternative perspective: dataset-centric analysis. Useful for tracking all processing activity (across multiple jobs) for a specific dataset. The session_column=job_id allows grouping events by job within the dataset partition." } ] } ```