Adaptive Data Query Evaluation for API Change Resilience
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Solution Overview
Problem
Data collection systems face downtime and data loss due to structural or functional changes in server computer systems, such as modifications to Application Programming Interfaces (APIs), which can cause failure in data transfer without notification to the data collection systems, leading to unmonitored and prolonged issues.
Innovation Solution
A data query evaluation unit is implemented to search for and identify modified data collection queries that align with the updated API formats, iteratively refining search queries based on failure messages to ensure successful data transfer, thereby minimizing downtime and data loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data collection systems use fixed data collection queries based on existing API formats, then the systems can maintain stable operation and simple query structures, but the systems experience downtime and data loss when server computer systems undergo structural or functional changes
Solution Approach 1:
The system performs preliminary actions by proactively searching for and storing modified data collection queries in advance before failures occur. When a server system undergoes changes, the pre-stored modified queries are already available for immediate deployment, preventing downtime and data loss without requiring complex real-time adaptation mechanisms.
Solution Approach 2:
The system implements dynamic adaptability by enabling data collection queries to automatically adjust to changes in data providing systems. Through iterative searching and identification of modified queries based on failure messages, the system transforms static query structures into dynamic ones that can evolve with server system changes, maintaining reliability without permanent complexity.
2Measurement precision
If data collection systems manually monitor and update queries after server changes, then the query accuracy can be maintained, but the systems require significant manual intervention and time
Solution Approach 1:
The system implements automated feedback mechanisms by monitoring failure messages from server computer systems and using these messages to iteratively search for and identify modified data collection queries. This closed-loop feedback system automatically detects when server changes occur and retrieves the appropriate modified queries, maintaining data collection accuracy while eliminating manual intervention and associated downtime.
Solution Approach 2:
The system enables self-service by allowing data collection systems to automatically adapt to server changes without human intervention. The automated process of receiving failure messages, searching for modified queries, identifying appropriate replacements, and deploying them allows the system to maintain query accuracy and continue operation independently, eliminating both manual time investment and service interruption.
3Adaptability or versatility
If data collection systems perform iterative searching for modified queries using failure messages, then the systems can automatically adapt to server changes, but the search and identification process increases computational complexity
Solution Approach 1:
The system performs preliminary actions by pre-searching for and storing multiple modified data collection queries in advance before they are needed. This creates a ready repository of adapted queries that can be quickly retrieved and deployed when server changes occur, providing high adaptability while avoiding the need for complex real-time search and identification processes during actual failures.
Data Source
AI summary
Modifying data collection systems responsive to changes to data providing systems. When a data collection system receives a failure message from a data providing system in response to transmitting a data collection query, the data providing system can notify a data query evaluation system. The evaluation system can create a local version of the collection system and search computer-readable storage media for a modified data collection query. The evaluation system can execute the local version to provide the modified data collection query to the providing system. If the local version receives a success message in response to providing the modified data collection query, then an execution state of the local version is provided to the collection system. If the local version receives a failure message, then the data query evaluation system iteratively performs refined searches of different computer-readable storage media until the modified data collection query is identified.