```json { "table_name": "search_index_metrics", "primary_key": "id", "candidates": [ { "rank": 0, "partition_key": "dataset_id", "partition_key_label": "E", "partition_key_reasoning": "Primary entity perspective: dataset_id has RPUV=4.67 (95 unique values across 444 rows), Top 3 Coverage=21.40%, making it a well-distributed partition key. Each dataset has multiple indexing events tracked over time. Semantically represents the main entity whose search index build history is being traced.", "clustering_key": "created_at", "clustering_key_label": "S", "clustering_key_reasoning": "Temporal ordering axis: created_at is the event timestamp with 296 unique values (RPUV=1.50), providing chronological ordering of index build events within each dataset's timeline. Groups all indexing operations for a dataset together in time order.", "world_line": "dataset_id", "session_column": "batch_id", "confidence": "HIGH", "notes": "Primary perspective: tracks search index build events per dataset over time. The batch_id (RPUV=3.02) serves as a session/batch grouping within each dataset's timeline. Alternative perspectives by batch_id or engine are also viable but less natural for typical analytical queries." }, { "rank": 1, "partition_key": "batch_id", "partition_key_label": "E", "partition_key_reasoning": "Alternative entity perspective: batch_id has RPUV=3.02 (147 unique values), Top 3 Coverage=6.08%. Represents the batch/job execution context. Useful for analyzing all indexing operations that occurred within a single batch run across multiple datasets and engines.", "clustering_key": "created_at", "clustering_key_label": "S", "clustering_key_reasoning": "Same temporal ordering: created_at orders events within each batch chronologically, showing the sequence of index builds within that batch execution.", "world_line": "batch_id", "session_column": null, "confidence": "MEDIUM", "notes": "Batch-centric perspective: useful for operational monitoring of batch jobs and understanding which datasets/engines were processed together. Less common than dataset-centric analysis but valid for job execution tracking." }, { "rank": 2, "partition_key": "engine", "partition_key_label": "E", "partition_key_reasoning": "Engine comparison perspective: engine has only 3 unique values (whoosh, lucene, tantivy) with RPUV=148.00, Top 3 Coverage=100%. While this creates only 3 partitions (large files), it enables direct performance comparison across search engines for all datasets and batches.", "clustering_key": "created_at", "clustering_key_label": "S", "clustering_key_reasoning": "Temporal ordering: created_at orders all events for each engine type chronologically, enabling time-series analysis of engine performance trends.", "world_line": "engine", "session_column": null, "confidence": "LOW", "notes": "Engine benchmark perspective: creates only 3 large partitions but useful for comparative analysis of search engine performance across all datasets. The low cardinality makes this less optimal for general querying but valid for specific engine comparison use cases." } ] } ```