Cloud API Request Scoring for Backend Load Protection
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Solution Overview
Problem
Cloud systems face inefficiencies due to customers querying web services with excessive data demands, leading to high loads on backend servers and degraded performance, as users often fetch unnecessary data without considering the impact on backend resources.
Innovation Solution
Implement a request scoring system that evaluates queries based on multiple impact factors, providing a total score and suggestions to optimize queries, reducing the load on backend resources and improving performance by suggesting modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users query web services with excessive data demands, then users can obtain comprehensive data, but backend servers experience high loads and degraded performance
Solution Approach 1:
The system performs preliminary scoring of incoming queries before they are executed against the backend. By evaluating the request score based on query complexity, data volume, and resource requirements in advance, the system can identify and optimize high-impact queries before they consume backend resources, thereby preventing performance degradation while still allowing comprehensive data retrieval when appropriate.
Solution Approach 2:
The system implements a feedback mechanism where query results and backend performance metrics are analyzed to continuously refine request scoring. The scoring model learns from historical data about which queries cause excessive loads and adjusts weights accordingly, creating a closed-loop system that optimizes the balance between data retrieval completeness and backend performance over time.
2Reliability
If users fetch unnecessary data, then users can ensure complete information retrieval, but computational resources are wasted
Solution Approach 1:
The system applies different scoring thresholds and optimization strategies to different types of queries based on their specific characteristics. Rather than uniformly reducing data retrieval, the system identifies queries that are likely to return unnecessary data and applies targeted optimizations only to those cases, preserving data completeness for queries where it is genuinely needed while eliminating waste for queries where it is not.
Solution Approach 2:
The system dynamically adjusts query parameters based on the calculated request score. For high-scoring queries, the system modifies parameters such as result limits, filtering strictness, or data format to reduce computational waste while maintaining adequate data completeness. This allows the system to adapt the level of optimization based on the specific query's impact potential.
3Reliability
If the system evaluates all queries with scoring, then backend protection is improved, but system complexity increases
Solution Approach 1:
The query evaluation system is segmented into distinct components: a lightweight pre-scoring stage that quickly evaluates basic query characteristics, and a more sophisticated analysis stage that is only invoked for queries exceeding certain thresholds. This hierarchical segmentation allows the system to protect the backend from obvious problematic queries with minimal overhead while reserving complex analysis for cases where it is truly necessary.
Solution Approach 2:
The request scoring system acts as an intermediary layer between users and the backend services. Rather than having users directly interact with complex backend systems, the scoring intermediary translates diverse query types into standardized risk assessments, simplifying the protection mechanism while maintaining comprehensive backend coverage.
Data Source
AI summary
Methods, systems, and computer-readable storage media for receiving a request through a web services API, the request comprising a query to query a database system, retrieving a set of weights that is specific to the web services, determining a factor score for each impact factor in a set of impact factors to provide a set of factor scores, providing a score total for the query based on the set of weights and the set of factor scores, returning a score response including the total score and at least one query suggestion, and receiving a modified request through the web services API, the modified request including the query modified to include at least a portion of the at least one query suggestion.