Cache Invalidation Propagation and Dependency Reasoning Dataset
- Records
- 5000
- Size
- 0.4GB
- Format
- JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
This dataset contains supervised source code examples covering cache writes, updates, deletions, invalidation notifications, and replica synchronization. The examples focus on changed entities, dependency relationships, affected cache scope, invalidation conditions, notification channels and consumers, synchronization timing, and potential dirty-data paths. It supports code reasoning, cache consistency analysis, and evaluation for distributed systems development.
Overview
Dataset Insights
Preview
Sample Examples
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| reasoning | string | Detailed justification based on source code control flow, call relationships, and dependency propagation. |
| final_label | object | The cache consistency reasoning conclusion used for training and evaluation. |
| source_code | string | A source code snippet containing cache write, update, deletion, invalidation, notification propagation, or replica synchronization logic. |
| impact_summary | string | A summary of the overall impact of the write, update, or deletion on caches, notification flows, and replica synchronization scope. |
| operation_type | string | The primary data operation identified from the source code. |
| changed_entities | array | The data entities, tables, keyspaces, or sets of cache objects directly modified or deleted by the operation. |
| dependency_graph | array | The dependencies and propagation directions among changed entities, cache entries, notification topics, and replicas. |
| dirty_data_paths | array | The complete paths from a data change to dirty data in caches or replicas, with associated risk analysis. |
| invalidation_assessment | object | An assessment of whether cache invalidation is triggered, its triggering conditions, and the affected cache key scope. |
| replica_sync_assessment | object | An analysis of whether the change triggers replica synchronization, its targets, and inconsistency risks caused by synchronization timing. |
| notification_propagation | object | An analysis of whether invalidation or data-change notifications are emitted, which channels carry them, and which consumers process them. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What does the dataset contain?
- It contains source code examples involving cache writes, updates, and deletions, with annotations for operation types, changed entities, dependency propagation, invalidation and notification assessments, replica synchronization risks, and reasoning conclusions.
- Which capabilities can it support in training?
- It can support training models to understand control flow and dependencies in cache-related code, and to infer invalidation conditions, notification paths, affected cache scope, and potential causes of dirty data.
- How can I verify data quality and programming languages?
- Examples are organized as source code snippets with structured supervision fields. Refer to the product listing or accompanying data documentation for the specific programming languages, dataset size, and quality acceptance details.
- How is the data delivered, and what license applies?
- Delivery methods, available formats, and license scope depend on the specific product listing. Review its delivery documentation and license terms before use, especially for commercial training or redistribution.
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Cite this Work
@dataset{Mobiusi2026,
title={Cache Invalidation Propagation and Dependency Reasoning Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/957b0f35b35e126f68a6aa08d745fa3d?dataset_scene_cate_type=9},
urldate={2026-10-14},
keywords={cache invalidation dataset,cache consistency reasoning,cache dependency propagation,source code reasoning,cache invalidation analysis,dirty data paths,replica synchronization analysis,distributed caching},
version={1.0}
}Using this in research? Please cite us.