PHPMem v2.0.1
Version
1.6.45
Uptime
7 days 18 hours 58 minutes 53 seconds
Memory
Total
512MB
Used
8,15MB (1.59%)
Free
503,85MB
Keys
Current
6 252
Total (since start)
11 098
Evictions
0
Reclaimed
299
Expired Unfetched
0
Evicted Unfetched
0
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4 / 1 024 max
Total
72 363
Rejected
0
llm:3ec8087a83fb00a6af617ef984d674e00941dce3c36a8f858f2e30cb0c47ba9b
Edit
```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 implied by unique timestamps). Sorting by ts within each job_id partition creates a chronological log timeline for each job, which is the natural access pattern for log analysis.",
"world_line": "job_id",
"session_column": null,
"confidence": "HIGH",
"notes": "Primary recommendation: partition by job_id to group all log events for each job execution, sorted chronologically by ts. This is the canonical organization for event logs where each job produces a sequence of timestamped log entries."
},
{
"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 ~2,713 log entries on average. Top 3 coverage = 42.23% shows reasonable distribution across 112 unique datasets. This perspective groups all job logs related to a specific dataset.",
"clustering_key": "ts",
"clustering_key_label": "S",
"clustering_key_reasoning": "ts provides chronological ordering of all log events for a dataset across multiple jobs. This enables dataset-centric analysis of processing history.",
"world_line": "dataset_id",
"session_column": "job_id",
"confidence": "MEDIUM",
"notes": "Alternative perspective: partition by dataset_id to analyze all processing activity for a specific dataset across multiple jobs. Useful for dataset-centric monitoring and debugging, though less natural than job-centric organization."
}
]
}
```