Playlist Update Aggregator Service Push Pull Logic
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing playlist update systems inefficiently distribute updates to subscribers, leading to resource wastage and delayed updates, as all subscribers receive notifications regardless of their activity or proximity to the curator.
Innovation Solution
A computer system determines whether to send push or pull updates based on subscriber and playlist scores, considering factors like subscription timing, proximity, and usage history, using an Aggregator Service to selectively send push notifications to active and relevant subscribers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If push notifications are sent to all playlist subscribers, then all subscribers receive updates, but network traffic and resource usage increase significantly
Solution Approach 1:
The system applies different update delivery strategies to different subscribers based on their individual characteristics. Subscribers are evaluated using scoring mechanisms that consider their proximity to the curator, subscription duration, and engagement history. High-scoring subscribers receive push notifications while lower-scoring subscribers receive updates through pull mechanisms, optimizing resource usage while maintaining update delivery effectiveness.
2Speed
If push notifications are sent to all subscribers, then updates are delivered quickly, but resource wastage occurs due to notifying inactive or distant subscribers
Solution Approach 1:
The system dynamically changes the delivery parameter (push vs. pull) based on subscriber scores that are calculated from multiple factors including proximity to curator, subscription timing, and historical engagement. This parameter change allows the system to optimize between speed and resource usage by matching the delivery mechanism to the subscriber's likelihood of utilizing the update.
3Loss of energy
If selective push notifications are sent only to high-scoring subscribers, then resource usage decreases, but some active subscribers may not receive timely updates
Solution Approach 1:
The system implements a feedback mechanism where subscriber behavior is continuously monitored and incorporated into score calculations. Engagement metrics, proximity data, and subscription history are fed back into the scoring system, allowing the system to learn and improve its predictions about which subscribers will utilize updates. This feedback loop ensures that resource-efficient selective notification does not compromise delivery reliability.
Data Source
AI summary
Some implementations allow users to create and share data including playlists. Such playlists may be subscribed to by other users. Changes to such playlists, such as updates by the curator of the playlist, must be propagated to subscribers in a timely manner while efficiently utilizing resources. Some implementations allow updates to be propagated using a push notification to cause client devices to request the update (push). Alternatively, updates may be made when the client devices of the subscribers next accesses the system (pull). An aggregator system may be used to make decisions on which users to send push notifications, and which users to wait to pull updates on their own. Additionally, the aggregator may determine that certain playlist updates should be distributed using push notifications. The aggregator may determine that only certain playlist subscribers should receive push notifications regarding playlist updates.