Database Query Translation Layer for Multi-Source Data
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Solution Overview
Problem
Current database systems face challenges in efficiently managing and querying data from multiple sources, particularly in preparing and executing complex database operations, such as ingesting, storing, indexing, searching, transforming, aggregating, and exporting data, due to the complexity of formulating queries and the need for optimizing query plans across diverse data sources.
Innovation Solution
The system provides techniques and constructs to improve database operations by receiving a job specification that abstracts the details of individual data sources, weighting data sources for relevance, and processing incoming data records to separate and store them with metadata, allowing for efficient query execution and data management across multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional query interfaces and data source management methods are used, then data can be retrieved from multiple sources, but the complexity of formulating queries and optimizing query plans increases significantly
Solution Approach 1:
The patent introduces a query translation layer that acts as an intermediary between the user's high-level query and the underlying diverse data sources. This translation layer automatically handles query plan generation and optimization, eliminating the need for users to manually manage the complexity of querying multiple different data source types.
Solution Approach 2:
The system implements a universal query interface that can handle multiple types of data sources (relational databases, NoSQL databases, data streams, files) through a single unified mechanism. This multi-functional approach allows the same interface and query language to work across diverse data sources without requiring source-specific query formulations.
2Measurement precision
If data from multiple sources is ingested and stored with full metadata, then query accuracy and relevance improve, but the time and resources required for data ingestion increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing metadata from data sources before queries are executed. This includes extracting schemas, data types, and relationships in advance, so that when queries arrive, the system can quickly match them against pre-organized metadata without performing heavy processing during query time.
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant metadata for each data source based on query patterns and data source importance. Rather than uniformly processing all metadata from all sources with equal depth, the system adjusts the level of metadata processing to balance accuracy requirements with ingestion time constraints.
3Productivity
If complex query plan optimization is performed, then query execution efficiency improves, but the computational resources and time required for optimization increase
Solution Approach 1:
The system performs query plan optimization in advance by pre-compiling and caching optimized execution plans for frequently executed queries. When the same or similar queries are executed again, the system retrieves the pre-optimized plans rather than performing full optimization, significantly reducing the computational resources required at query execution time.
Solution Approach 2:
The patent dynamically adjusts optimization parameters based on query characteristics, data source types, and system resource availability. For simple queries or resource-constrained environments, the system reduces optimization depth, while for complex queries with abundant resources, it applies more intensive optimization, thereby balancing execution efficiency with resource consumption.
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AI summary
In some examples, a computing device determines a data manipulation from a job specification. The device determines a corresponding data-processing instruction using data-source metadata, and determines and executes a corresponding query. In some examples, a device receives search keys. The device searches data-source metadata using the search keys. The device weights a first data source based on producer-consumer relationships between data sources, and ranks the first data source using the weight. In some examples, a device determines structural and content information of a data record. The device determines a data-source identifier from the structural information and stores the content information with the data-source identifier in a database. In some examples, via a user interface, a device receives a job specification and annotation data. The device stores the spec and the annotation data in a metadata repository.