Contract Engine Transaction Data Fidelity Management
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Solution Overview
Problem
Existing systems for online transaction processing face challenges in reducing latency and increasing availability, often resulting in higher latency and lower availability due to the requirement for highest fidelity transaction data, which can lead to system failures when network services are unavailable.
Innovation Solution
A contract engine that generates transaction data at varying fidelity levels, allowing for low fidelity data to be provided quickly and later replaced with higher fidelity data when network services are available, and uses default data in case of network service unavailability, enabling continuous transaction processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highest fidelity transaction data is required for all transactions, then data accuracy is improved, but latency increases and system availability decreases
Solution Approach 1:
The system applies partial action by providing low fidelity transaction data when network services are unavailable, rather than completely failing. This allows the transaction processing system to continue operating with reduced data quality, achieving 99.999% availability while maintaining acceptable functionality for time-sensitive transactions.
Solution Approach 2:
The system dynamically changes the fidelity parameter of transaction data based on network service availability. When network services are available, high fidelity data is used; when unavailable, the system transitions to low fidelity data generation, effectively adjusting the data quality parameter to maintain system operation under varying conditions.
2Measurement precision
If highest fidelity transaction data is required for all transactions, then data accuracy is improved, but processing speed decreases
Solution Approach 1:
The system provides sufficient rather than excessive fidelity by using low fidelity data for time-sensitive transactions where complete accuracy is not critical. This partial approach to data quality enables faster processing for urgent transactions while maintaining high fidelity for less time-sensitive operations.
Solution Approach 2:
The system segments transaction processing into different fidelity levels based on transaction characteristics and timing requirements. Time-sensitive transactions receive low fidelity data for immediate processing, while other transactions can wait for high fidelity data, effectively dividing the processing workload by data quality level.
3Measurement precision
If network services are made available for all transactions, then data quality is improved, but system complexity increases
Solution Approach 1:
The system uses self-service by automatically detecting network service availability and adapting its data generation strategy without external intervention. The contract engine autonomously determines whether to use high or low fidelity data based on the operational status of network services, reducing the need for complex external control mechanisms.
Data Source
AI summary
Techniques are described for generating transaction data for a transaction, the transaction data generated with varying fidelity levels based on various constraints. In response to a transaction request from a client, a contract engine may perform service call(s) that instruct network service(s) to generate transaction data. The service call(s) may include constraint(s) on the generation of the transaction data, such as a time limit or a requested fidelity. The network service(s) may generate the transaction data at a particular fidelity based on the time limit, requested fidelity, or other criteria such as current load on the network service(s). The transaction data may be communicated to the contract engine with a fidelity indicator. Lower fidelity transaction data may be sent to the client to enable a low latency user experience. The lower fidelity transaction data may subsequently be replaced with higher fidelity transaction data generated with higher latency.