Social Network Search Query Rewriting for Processing Efficiency
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Solution Overview
Problem
The inefficiency in social networking systems to retrieve a sufficient number of search results due to mismatch between static scores and final scores of objects, leading to excessive data retrieval and processing power usage.
Innovation Solution
The social networking system optimizes static scores by comparing them with final scores from archived search queries, adjusting the scoring algorithm and parsing configuration parameters to reduce the number of objects retrieved while maintaining search result quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the social-networking system retrieves a large number of objects from data stores to ensure sufficient search results, then the search result quantity and quality are improved, but the processing power usage and data retrieval overhead increase excessively
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing static scores for objects in data stores before search queries are executed. This allows the system to quickly retrieve and rank objects based on pre-computed scores without performing extensive real-time calculations, thereby reducing processing power usage during actual search operations while maintaining search result quality
Solution Approach 2:
The system changes parameters by introducing static scores as a new parameter for object ranking in data stores. Instead of relying solely on dynamic scoring during query processing, the system uses static scores that are pre-determined and stored, allowing for more efficient retrieval and reducing the computational overhead during search operations
2Speed
If the social-networking system uses static scores for object retrieval from data stores, then the data retrieval process is simplified and faster, but the static scores do not match the final scores leading to excessive retrieval
Solution Approach 1:
The system performs preliminary actions by pre-calculating static scores that are designed to correlate with final scores. These static scores are computed in advance and stored in data stores, enabling fast retrieval while maintaining accuracy through careful design of the scoring algorithm that anticipates final score requirements
Solution Approach 2:
The system implements feedback by comparing static scores with final scores to continuously optimize the scoring algorithm. This feedback mechanism allows the system to adjust and refine static score calculations to better match final scores, improving both retrieval efficiency and accuracy over time
3Reliability
If the social-networking system retrieves excessive objects to compensate for score mismatches, then sufficient search results are guaranteed, but the processing overhead and system resource consumption increase
Solution Approach 1:
The system changes parameters by optimizing the static score calculation algorithm to better predict final scores. By adjusting the parameters and weights in the static scoring algorithm, the system reduces the gap between static and final scores, allowing for more precise retrieval with fewer objects while maintaining result sufficiency
Solution Approach 2:
The system substitutes mechanical retrieval processes by replacing brute-force retrieval of large numbers of objects with a more intelligent approach using static scores. Instead of retrieving and then filtering excessive objects, the system uses pre-computed static scores to directly identify and retrieve only the most relevant objects, reducing processing complexity
Data Source
AI summary
In one embodiment, a method includes accessing a data set including a list of objects matching a query command and a score for each of the listed objects, where the query command is generated by parsing a query using a parsing algorithm, and where the score for each of the listed objects is calculated based on a scoring algorithm. The method also includes generating multiple subsets of the data set, each subset including one or more of the listed objects, and calculating, for each subset, a measure of score-quality associated with the scores of the objects in the subset and a measure of CPU-power associated with an amount of processing power required for retrieving the objects in the subset. The method also includes revising the parsing algorithm based on a comparison of the measures of score-quality and the measures of CPU-power associated with one or more of the subsets.