Anticipatory Request Processing for E-Commerce Latency
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Solution Overview
Problem
Electronic commerce systems face delays due to time-consuming database inserts and data caching issues, which negatively impact customer experience.
Innovation Solution
The system anticipates potential client requests by issuing anticipatory requests to services, marking results as 'pending' for database inserts, and processing delay-causing actions in parallel or post-processing, while also optimizing timeouts and load balancing based on processing loads and request weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database inserts are performed synchronously during client requests, then data consistency is maintained, but processing delays increase and customer satisfaction decreases
Solution Approach 1:
The system performs database inserts in advance before the client actually submits the request. When a client views a product or adds it to cart, the system anticipates the potential purchase and performs the database insert beforehand, marking the result as 'pending'. When the client actually submits the order, the pending result is already available, eliminating the processing delay while maintaining data consistency through the pending/active status mechanism.
Solution Approach 2:
The patent segments the database insert operation from the client request processing. The insert is performed as a separate anticipatory action, and its result is stored with a 'pending' status. The client request then only needs to retrieve and activate the pending result, rather than waiting for the insert to complete. This segmentation allows the heavy insert operation to be decoupled from the client-facing response time.
2Speed
If data is cached for faster access, then response time improves, but data may become stale or inconsistent with the database
Solution Approach 1:
The system dynamically manages cache data validity through status flags. Anticipatory database inserts mark their results as 'pending' rather than immediately active. The cache system can serve these pending results quickly to clients, and when the actual client request arrives, the system activates the pending data, ensuring consistency. This dynamic status management allows the cache to be both fast and consistent.
Solution Approach 2:
The system implements a feedback mechanism where the completion of anticipatory database inserts is tracked and communicated back to the cache system. When a database insert completes, its result is marked as 'pending' and this status is fed back to the cache. When the client request arrives, the system checks the feedback status and activates the data, ensuring the cache reflects the current database state while maintaining fast access.
3Loss of time
If anticipatory requests are issued for all potential client actions, then processing delays are minimized, but system resource consumption and complexity increase
Solution Approach 1:
The system applies anticipatory requests selectively rather than universally. Instead of issuing anticipatory requests for all potential client actions, the system identifies specific high-value scenarios such as product views, add-to-cart actions, and recommended items. This localized application of anticipatory requests reduces unnecessary system complexity and resource consumption while still providing significant performance benefits for the most critical customer interactions.
Solution Approach 2:
The system performs partial anticipatory action by issuing requests for only the most likely client actions rather than all possible actions. The system anticipates product purchases when items are viewed or added to cart, but doesn't anticipate every possible client interaction. This partial action approach balances the benefits of reduced processing delay with the costs of system complexity, applying optimization only where it provides the most value.
Data Source
AI summary
The systems and techniques described herein optimize processing of requests, such as external requests, internal requests or inter-service requests. In some examples, a service may interact with a client device and determine, based at least in part on a state of the interaction with the client device, a potential client request that may subsequently be received from the client device. The service may then perform one or more actions to at least partially fulfill the potential client request.