Stream Listening Cache Updater for Fresh Data Delivery
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Solution Overview
Problem
Service providers face challenges in balancing consumer data storage to minimize stale data while managing processing burdens, leading to consumer dissatisfaction and overengineering storage systems due to spiky demand.
Innovation Solution
Implementing a stream listening cache updater that refreshes application caches based on data streams and machine-learning models to predict user interactions, reducing reliance on data stores and enhancing timely data delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If longer TTL values are used in the cache, then data availability is improved, but data freshness deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-warming the cache with data before actual consumer requests occur. The stream listening service continuously monitors data streams and proactively populates the cache with upcoming data, ensuring that when consumers request data, it is already available and fresh, thus resolving the contradiction between data availability and freshness.
Solution Approach 2:
The system implements feedback mechanisms where the stream listening service receives updates from data streams, processes them, and feeds back into the cache. This continuous feedback loop ensures that the cache is dynamically updated with the latest data while maintaining availability, allowing the system to adapt to changing data requirements in real-time.
2Loss of information
If shorter TTL values are used in the cache, then data freshness is improved, but storage system processing burden increases
Solution Approach 1:
The system extracts the cache updating function from the storage system by introducing a dedicated stream listening service. This service independently monitors data streams and manages cache population, separating the freshness management task from the storage system's primary data retrieval function. As a result, the storage system experiences reduced processing burden while the cache maintains data freshness through the specialized stream listening service.
3Loss of information
If consumers request data frequently to ensure freshness, then data timeliness is improved, but network traffic and processing overhead increase
Solution Approach 1:
The stream listening service performs preliminary actions by proactively populating the cache with data before consumers request it. By continuously monitoring data streams and pre-loading data into the cache, the system eliminates the need for frequent consumer-initiated requests, thereby reducing network traffic and processing overhead while ensuring data timeliness.
Solution Approach 2:
The cache system becomes self-sufficient through the stream listening service that automatically updates and maintains data freshness without requiring continuous consumer requests. The system serves itself by autonomously monitoring data streams and refreshing cache entries, reducing the dependency on frequent external requests and minimizing associated network traffic and processing overhead.
Data Source
AI summary
A system, process, and computer-readable medium for updating an application cache using a stream listening service is described. A stream listening service may monitor one or more data streams for content relating to a user. The stream listening service may forward the content along with time-to-live values to an application cache. A user may use an application to obtain information regarding the user's account, where the application obtains information from a data store and/or cached information from the application cache. The stream listening service, by forwarding current account information, obtained from listening to one or more streams, to the application cache, reduces traffic at the data store by providing current information from the data stream to the application cache.