Referential Sampling of Polygot Datasets via DAG
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Solution Overview
Problem
Existing data management techniques fail to ensure consistency in sampling across multiple datasets, particularly when referential constraints between tables are not honored, leading to inconsistent sampled datasets.
Innovation Solution
The approach involves referential sampling methods that create a directed acyclic graph (DAG) to determine the order of processing entities based on dependencies, allowing for topological sorting and optimized storage I/O to ensure that sampled datasets honor referential constraints, with support for both direct and referential sampling modes and native and non-native execution modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If independent sampling is applied to multiple datasets, then sampling efficiency is improved, but data consistency deteriorates
Solution Approach 1:
The system performs preliminary actions by first identifying all referential constraints between datasets before sampling begins. It creates a sampling plan that pre-determines which records to sample from parent datasets, ensuring that child datasets can be sampled consistently without violating referential integrity. This preliminary planning resolves the contradiction by establishing consistency rules before the sampling process affects productivity.
Solution Approach 2:
The sampling process is segmented into distinct phases: (1) identifying referential constraints between datasets, (2) creating a sampling plan that respects these constraints, and (3) executing sampling according to the plan. This segmentation allows the system to maintain data consistency through structured planning while achieving sampling efficiency through automated execution, resolving the contradiction between consistency and efficiency.
2Reliability
If referential constraints are honored across datasets, then data consistency is improved, but processing complexity increases
Solution Approach 1:
The system introduces an intermediary sampling plan that mediates between the referential constraints of multiple datasets. This sampling plan acts as a intermediary structure that encodes the relationships and dependencies between datasets, allowing the sampling process to honor referential constraints without directly managing the complexity of all inter-dataset relationships. The intermediary plan simplifies processing by pre-organizing the constraint information.
Solution Approach 2:
The system creates a simplified copy or representation of the referential constraint structure in the sampling plan. Instead of directly processing complex inter-dataset relationships during sampling execution, the system uses this copied structure to guide the sampling process, reducing processing complexity while maintaining data consistency through the encoded constraints.
3Measurement precision
If comprehensive sampling of all datasets is performed, then data representativeness is improved, but storage requirements increase
Solution Approach 1:
The system applies partial action by sampling only the necessary portion of each dataset required to maintain referential consistency, rather than uniformly sampling all datasets to the same extent. The sampling plan determines the appropriate sample size for each dataset based on its relationships with other datasets, achieving adequate representativeness while minimizing storage requirements by avoiding excessive sampling.
Data Source
AI summary
Approaches for referential sampling of disparate datasets. An execution mode and a sampling mode are determined for each entity in a plurality of disparate datasets. A directed acyclic graph (DAG) for each entity in the plurality of disparate datasets is created. The directed acyclic graph (DAG) is topologically sorted to produce a topologically sorted directed acyclic graph (DAG). One or more sampled datasets are retrieved from the plurality of disparate datasets using the topologically sorted directed acyclic graph (DAG). Advantageously, the one or more sampled datasets are a consistent sample that honors all referential constraints in the plurality of disparate datasets.