Context Aggregator Component for Dependent Query Consolidation
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Solution Overview
Problem
Current query languages, such as GraphQL, do not natively support dependent queries, leading to increased latency and network congestion due to multiple calls to contextual data services, and lack of native support for conditional queries forces clients to implement client-side conditions and make additional calls.
Innovation Solution
The implementation of a context aggregator component that enables dependent queries by allowing multiple subqueries and their dependencies to be defined in a single call to a contextual data service, and the introduction of conditional queries that can be evaluated by the contextual data service without returning intermediate results, reducing the need for client-side logic and subsequent calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate calls are made to contextual data services to retrieve dependent queries, then the queries can be executed with current query languages, but latency increases and network congestion occurs
Solution Approach 1:
The patent combines multiple separate query calls into a single consolidated call to the contextual data service. The system merges dependent queries, conditional queries, and sibling queries into one unified request, eliminating multiple network round trips and reducing latency while maintaining full query execution capability.
Solution Approach 2:
The system performs preliminary parsing and planning of the query structure before execution. By analyzing the query graph and identifying dependencies upfront, the system prepares the consolidated request structure in advance, allowing efficient single-call execution without repeated network communication.
2Adaptability or versatility
If multiple separate calls are made to handle conditional queries, then client-side logic can be implemented, but network traffic increases
Solution Approach 1:
The patent merges conditional query logic with the main query execution flow by consolidating all queries (including conditional and dependent queries) into a single call. This eliminates the need for separate follow-up calls to handle conditional results, reducing network traffic while preserving full conditional query functionality.
Solution Approach 2:
The system introduces an intermediary query planning component that analyzes query dependencies and structures the consolidated request appropriately. This intermediary layer handles the complexity of conditional logic evaluation without requiring additional network communication, acting as a mediator between the client and contextual data service.
3Adaptability or versatility
If client-side conditions are implemented with additional calls, then query flexibility is maintained, but device complexity increases
Solution Approach 1:
The patent extracts conditional logic and dependency evaluation from the client device and transfers it to the server-side query processing system. By moving this logic to the server, the client device's complexity is reduced while query flexibility is maintained through the server's ability to handle complex query graphs and conditional evaluations.
Solution Approach 2:
The system introduces a server-side intermediary query planning component that handles the complexity of conditional logic and dependency management. This intermediary processes the query graph, determines execution order, and evaluates conditions without requiring complex client-side logic, thereby reducing device complexity while maintaining query flexibility.
Data Source
AI summary
Devices and techniques are generally described for query enhancements for a contextual data aggregator. In some examples, a context aggregator component may receive from a first computing device, a first query directed to a graph database. In some examples, the first query may include a first sub-query and a second sub-query. In some cases, the context aggregator component may determine that a first input to the first sub-query includes first output data from the second sub-query. In various examples, the first output data may be determined from the second sub-query. In further examples, the first output data may be provided from the second sub-query as the first input to the first sub-query to generate first result data. In various examples, the first result data may be sent to the first computing device.