Content Addressable Query Engine for Disparate Database Federation
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Solution Overview
Problem
Conventional data storage and computing technologies face challenges in facilitating interoperability among disparate datasets due to differences in computing platforms, database technologies, and data formats, leading to inefficiencies in querying and data access, particularly as datasets grow in size and complexity.
Innovation Solution
A content addressable query engine is implemented to generate and execute queries across multiple data sources with different architectures, utilizing a collaborative dataset consolidation system that caches query results in memory, allowing for expedited data retrieval and reducing the need for repeated queries across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data storage and computing technologies are used to query disparate datasets, then data interoperability can be achieved through multiple architecture-specific queries, but computational resources are consumed and query performance deteriorates due to repeated queries and cascading effects
Solution Approach 1:
The system executes architecture-specific queries in advance to generate and cache query results before they are needed. This preliminary action stores the results in a content-addressable cache, so when a principal query is executed, the system can retrieve pre-computed results instead of re-executing the same queries, thereby improving query performance while maintaining data interoperability
Solution Approach 2:
The patent introduces a content-addressable cache as an intermediary layer between the principal query engine and the disparate data sources. This intermediary stores query results and enables the system to retrieve data without repeatedly querying the underlying databases, thus decoupling the principal query execution from the architecture-specific query execution and improving overall system productivity
2Reliability
If multiple architecture-specific queries are executed to ensure data consistency across disparate sources, then data interoperability is maintained, but computational resources are consumed irrespective of principal query execution
Solution Approach 1:
The system implements a self-service mechanism where the content-addressable cache automatically stores and retrieves query results based on content hashing. When data is inserted or updated in the underlying sources, the system automatically detects changes through content addressing and updates only the affected cached results, rather than re-executing all queries. This reduces computational resource consumption while maintaining data consistency across disparate sources
3Speed
If data is cached in memory for expedited retrieval, then data access speed is improved, but memory resources are consumed and cache management complexity increases
Solution Approach 1:
The patent replaces traditional key-based cache addressing with a content-addressable memory system that uses hashing of data content to determine storage locations. This substitution eliminates the need for complex key management, collision handling, and lookup operations associated with conventional caching mechanisms. The content-addressable cache automatically maps query results to their storage locations based on content hashes, simplifying cache management while maintaining fast data access speeds
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and wired and wireless network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform to implement computerized tools to facilitate expedited queries based on query results generated by disparate computing and database architectures, according to at least some examples. For example, a method may include generating multiple results of queries, storing the multiple results of queries in a memory, and linking each of the multiple results of queries to a portion of data stored in a graph. The method can include receiving data representing a query and accessing data representing quiescent data values to form a query result.