Dataset Revision Control Through Provenance-Aware Cataloging
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Solution Overview
Problem
Traditional large-scale data analytic systems lack comprehensive revision control for data and job results, limiting their functionality in managing data integrity and provenance.
Innovation Solution
Implement a data revision control system that records dataset versions, transformations, and provenance, using a catalog service to manage dataset versions and permissions, and a build service to ensure driver programs are executed in the correct order, with support for branching and fallbacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional large-scale data analytic systems execute data processing jobs on a cluster of commodity computing hardware with parallel task execution and distributed file system storage, then processing efficiency and data movement minimization are improved, but revision control functionality for data and job results is lost or limited
Solution Approach 1:
The system integrates multiple functions into a unified architecture: the catalog service not only stores metadata about datasets and jobs but also manages version control, permissions, and provenance tracking. The data analytic system thus serves both as a high-performance processing engine and as a comprehensive data management platform with revision control capabilities.
Solution Approach 2:
The catalog service acts as an intermediary layer between the distributed file system and the data processing jobs. It mediates access to data and results, providing revision control functionality without interfering with the efficient parallel execution of jobs on the cluster. The catalog service translates high-level revision control operations into underlying file system operations.
2Ease of operation
If data and job results are stored in distributed file system locations specified by users, then storage flexibility and accessibility are improved, but extensive revision control management is not provided
Solution Approach 1:
The system separates data storage from data management. Physical data remains in the distributed file system for accessibility, while the catalog service segments and manages the metadata, version information, and revision control logic. This segmentation allows user-specified storage locations to maintain accessibility while the catalog service provides robust revision control and integrity management.
Solution Approach 2:
The catalog service implements feedback mechanisms by tracking all changes to datasets and job results, maintaining version histories, and providing provenance information. This feedback loop ensures that any modification to data is recorded, tracked, and can be reviewed, thereby maintaining data integrity control while preserving storage flexibility.
3Reliability
If a catalog service records dataset versions, transformations, and provenance information, then data integrity and provenance are improved, but system complexity increases
Solution Approach 1:
The catalog service serves as an intermediary that abstracts the complexity of version control and provenance tracking from the core data processing engine. It handles all metadata management, version recording, and permission control operations, allowing the distributed file system and processing jobs to remain relatively simple while still achieving robust data integrity through the catalog service's comprehensive tracking capabilities.
Data Source
AI summary
A method comprises storing first transaction data for a first transaction producing a first version of a first dataset, second transaction data for a second transaction transforming a first version of the first dataset to a second version of the first dataset, and third transaction data for a third transaction transforming the second version of the first dataset to a third version of the first dataset; storing dependency information indicating a first dependency of the third transaction on the second transaction and a second dependency of the second transaction on the first transaction; receiving a first instruction to revoke a first permission of a first user to access the second version of the first dataset; automatically revoking a certain permission of the first user to access the third version of the first dataset based on the dependency information.