```json { "table_name": "llm_calls", "primary_key": "id", "candidates": [ { "rank": 0, "partition_key": "purpose", "partition_key_label": "E", "partition_key_reasoning": "RPUV=1898.46 is excellent for partitioning. 26 unique purposes with top-3 coverage of 65.50% indicates good distribution. Semantically represents the entity/context of each LLM call (e.g., 'api.analytical.agent.decide', 'column_tags.measure_properties'). Groups related calls by functional purpose, enabling efficient queries by use-case.", "clustering_key": "created_at", "clustering_key_label": "S", "clustering_key_reasoning": "Timestamp column with perfect uniqueness (1.000) and high monotonicity (implied by sequential id). Orders events chronologically within each purpose partition, supporting time-range queries and temporal analysis of LLM usage patterns.", "world_line": "purpose", "session_column": "conversation_id", "confidence": "HIGH", "notes": "Primary recommendation: partition by purpose (the functional entity/context) and sort by created_at (event time). This aligns with the event table classification where purpose represents the entity perspective and created_at is the temporal axis. The id column is the unique primary key. conversation_id could serve as a session grouping for conversational flows (98.92% null, so only relevant for chat-based calls)." }, { "rank": 1, "partition_key": "dataset_id", "partition_key_label": "E", "partition_key_reasoning": "RPUV=219.88 is viable for partitioning. 217 unique datasets with top-3 coverage of 20.64% shows reasonable distribution. Only 3.33% null. Represents an alternative entity perspective: analyzing LLM calls by the dataset being processed. Useful for dataset-specific cost/usage analysis.", "clustering_key": "created_at", "clustering_key_label": "S", "clustering_key_reasoning": "Same temporal ordering as rank=0. Orders LLM calls chronologically within each dataset's processing timeline.", "world_line": "dataset_id", "session_column": "job_id", "confidence": "MEDIUM", "notes": "Alternative perspective: partition by dataset_id to analyze LLM usage per dataset. job_id (RPUV=9.23, 4.17% null) could serve as session grouping for batch processing jobs. This perspective is useful for cost attribution and dataset-level performance analysis." }, { "rank": 2, "partition_key": "job_id", "partition_key_label": "E", "partition_key_reasoning": "RPUV=9.23 is acceptable for partitioning. 5124 unique jobs with top-3 coverage of 18.09%. Only 4.17% null. Represents job-level entity perspective: grouping all LLM calls within a processing job. Useful for job-level cost tracking and debugging.", "clustering_key": "created_at", "clustering_key_label": "S", "clustering_key_reasoning": "Same temporal ordering. Orders LLM calls chronologically within each job's execution timeline.", "world_line": "job_id", "session_column": null, "confidence": "MEDIUM", "notes": "Alternative perspective: partition by job_id to analyze LLM usage per processing job. This is useful for operational monitoring, job cost analysis, and debugging failed jobs. Lower confidence than rank=1 due to lower RPUV, but still a valid analytical perspective." } ] } ```