Search Index Update via Event Filtering
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Solution Overview
Problem
Existing search index systems face inefficiencies due to the need to process a high volume of events, many of which are irrelevant, leading to computational intensity and infeasibility, as they must interact with the search index for every action performed on content items, regardless of relevance.
Innovation Solution
Implementing a system that filters out irrelevant events using a Bloom Filter to determine if an action affects the search index, only updating the index for actions that impact its content, thereby reducing unnecessary interactions with the external search index platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If every action performed on content items is propagated to the search index, then the search index remains up-to-date with all content changes, but the computational intensity becomes infeasible and network resources are wasted on irrelevant events
Solution Approach 1:
The system performs preliminary filtering of events before propagating them to the search index. A filtering component analyzes incoming events and determines relevance to the search index content before allowing propagation, thus avoiding unnecessary computational overhead while maintaining index accuracy
Solution Approach 2:
A filtering component is introduced as an intermediary between the event source and the search index. This mediator selectively allows only relevant events to pass through to the search index, blocking irrelevant events and thereby reducing computational intensity and network resource consumption
2Loss of information
If the search index is updated for every action performed on content items, then the index reflects all current content states, but the number of interactions with the external search index platform becomes computationally intensive
Solution Approach 1:
The system performs preliminary filtering of events before propagating them to the search index. A filtering component analyzes incoming events and determines relevance to the search index content before allowing propagation, thus avoiding unnecessary computational overhead while maintaining index accuracy
Solution Approach 2:
The system extracts and removes irrelevant events from the event stream before they can interact with the search index. By taking out only the necessary update events and discarding irrelevant ones, the system reduces computational resource consumption while preserving content representation accuracy
Data Source
AI summary
Methods, systems, and programming for updating a search index are described herein. In a non-limiting embodiment, event data representing actions performed on a plurality of content items managed via the search index may be obtained. The event data may be filtered based on pre-determined criteria to generate updated event data, where the updated event data represents relevant actions performed on at least one of the plurality of content items and are associated with the search index. An updated search index based on the updated event data may then be generated.