Query Processing Apparatus for Candidate Ranking Latency
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Solution Overview
Problem
Existing systems for determining applicant competitiveness in job rankings face significant latency and load issues due to multiple calls to data stores and machine learning models, leading to increased computation and overhead in processing queries for candidate rankings.
Innovation Solution
The implementation of a query-processing apparatus that identifies similarities among queries to batch calls, utilizes pre-computed scores from an offline data store, and makes additional calls to online services for recent candidates, optimizing batch sizes and caching results to reduce latency and system load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple calls are made to data stores and machine learning models for each query, then accurate applicant rankings can be generated, but system latency and computational load increase significantly
Solution Approach 1:
The system pre-computes and stores applicant scores in an offline data store before queries are received. When a query arrives, the system retrieves pre-computed scores instead of calculating them from scratch, significantly reducing query latency while maintaining ranking accuracy.
Solution Approach 2:
The system differentiates between cold-start queries (requiring full computation) and warm queries (using pre-computed scores). By applying different processing strategies based on query characteristics, the system optimizes the balance between accuracy and latency for different scenarios.
2Reliability
If multiple calls are made to data stores and machine learning models for each query, then complete candidate evaluation is achieved, but system load and computational overhead increase
Solution Approach 1:
The system merges multiple data sources (offline pre-computed scores, online service scores, candidate profiles, job descriptions) into a unified ranking framework. By consolidating these sources and using batch processing for similar queries, the system reduces redundant computations and lowers overall system load.
Solution Approach 2:
The system performs preliminary processing by pre-computing applicant scores and storing them offline. This advance preparation reduces the computational burden during query processing, maintaining evaluation completeness while reducing real-time system load.
3Speed
If pre-computed scores and caching are used to reduce latency, then query processing speed improves, but system complexity increases
Solution Approach 1:
The system segments the scoring architecture into offline pre-computation components and online query processing components. This segmentation allows each component to be optimized independently, with the offline layer handling heavy computation and the online layer focusing on fast retrieval and composition, thereby managing complexity through modular design.
Solution Approach 2:
The system introduces an intermediary caching layer between the data stores and the query processing logic. This caching layer stores pre-computed scores and intermediate results, acting as a buffer that simplifies the interaction between components and reduces the complexity of direct real-time computations.
Data Source
AI summary
The disclosed embodiments provide a system for processing a query for a ranking of candidates for an opportunity. During operation, the system obtains a set of parameters associated with a query for a ranking of candidates for an opportunity, wherein the set of parameters include a candidate, the opportunity, and a ranking category. Next, the system uses the set of parameters to obtain a set of pre-computed scores for use in generating the ranking and determine a number of online scores required to complete the ranking. The system then generates a set of calls to produce the determined number of the online scores. Finally, the system uses the pre-computed scores and the online scores to generate a result of the query.