Data Shapes for Efficient Object-Relational Mapping
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Solution Overview
Problem
Existing data retrieval systems face inefficiencies due to long latencies and inconsistencies in loading data from relational databases, particularly when using ad-hoc loading approaches that result in unnecessary data transfer and inconsistent data views across queries.
Innovation Solution
The introduction of a 'data shape' concept that defines policies for efficient data retrieval, including eager loading and filtering policies, to standardize and optimize data loading across multiple queries, ensuring consistent and efficient data access in object-relational mapping systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ad-hoc data loading approaches are used, then flexibility in querying is improved, but data retrieval efficiency deteriorates due to long latencies and repeated data transfers
Solution Approach 1:
The patent applies preliminary action by pre-defining data shapes that specify exactly which related data should be loaded with each query. This allows the system to pre-calculate and pre-configure the optimal data retrieval plan before execution, eliminating the need for ad-hoc decisions during query processing and reducing repeated data transfers while maintaining query flexibility.
2Adaptability or versatility
If ad-hoc data loading approaches are used, then query flexibility is improved, but data consistency deteriorates due to inconsistent data views across queries
Solution Approach 1:
The patent applies parameter changes by transforming the data retrieval process from ad-hoc to parameterized through data shapes. These data shapes define fixed parameters for data loading behavior that can be applied consistently across multiple queries, ensuring that the same data retrieval rules are applied uniformly while still allowing flexibility in defining different data shapes for different query patterns.
3Reliability
If eager loading is applied to all queries, then data availability is improved, but data transfer volume increases due to unnecessary data retrieval
Solution Approach 1:
The patent applies local quality by making data loading behavior specific to each data shape definition rather than applying eager loading uniformly to all queries. Each data shape can specify exactly which related data should be loaded eagerly and which should use lazy loading, allowing the system to optimize data transfer volume by loading only the locally necessary data for each specific query context while maintaining data availability where needed.
4Loss of information
If data is loaded without filtering policies, then completeness of data is improved, but memory usage increases due to unwanted data transfer
Solution Approach 1:
The patent applies the taking out principle by extracting and isolating only the necessary data elements through filtering policies defined in data shapes. Instead of loading complete datasets, the system extracts only the specific data fields and related entities that are actually needed for each query, removing unnecessary data from the retrieval process and reducing memory usage while preserving data completeness for the required information.
Data Source
AI summary
The subject disclosure pertains to systems and methods that facilitate retrieval of data. In particular, an application defined data shape can regulate loading of data for use in an object relational mapping system, enabling the object-oriented application to access data in a relational data base system. Data shapes define policies that control data retrieval, enabling efficient data loading. A data shape allows users to declaratively specify a set of policies to be applied to all queries associated with the data shape. In particular, data shapes can include policies that control eager loading. In addition, data shapes can also include filtering policies intended to reduce or limit data retrieved from a relational database.