GraphQL Query Rate Limiting via Content Analysis

The patent segments the rate limiting approach from endpoint-based to query-content-based analysis. Instead of treating all requests to an endpoint uniformly, the system parses and analyzes individual query contents, identifying specific data fields, relationships, and complexity metrics. This segmentation enables differentiated rate limiting for different query types while maintaining a unified rate limiting framework.

The patent implements dynamic rate limiting where limits are not fixed per endpoint but adapt based on query content analysis. The system dynamically adjusts rate limits by evaluating query complexity, data sensitivity, user profiles, and historical usage patterns. This dynamic approach allows the system to be more restrictive for complex queries targeting sensitive data while being more permissive for simple queries, thereby improving security without overly restricting legitimate access.

The patent applies preliminary action by performing query parsing and content analysis before the query is executed against the data source. Rate limit decisions are made in advance based on pre-computed query metrics such as complexity score, data sensitivity classification, and estimated resource consumption. This preliminary analysis prevents wasteful execution of queries that would exceed rate limits, thereby reducing actual computational overhead from query processing while maintaining security.

The system implements self-service by automatically analyzing query content and making rate limiting decisions without requiring manual configuration or intervention. The query parsing engine autonomously extracts relevant features, compares them against defined policies and user profiles, and determines whether to allow or reject the query. This automated self-service approach reduces the need for manual security rule maintenance while keeping computational overhead manageable through efficient algorithm design.

The patent applies local quality by implementing differentiated rate limiting policies for different query types, users, and data sensitivity levels rather than applying a uniform strict limit to all queries. Simple queries, users with higher trust levels, and queries accessing public data can proceed with higher rate limits, while complex queries, new users, and queries targeting sensitive data are subject to stricter limits. This localized approach protects critical resources while maintaining high throughput for legitimate, low-risk queries.

The system dynamically changes rate limiting parameters based on query content analysis results. Instead of fixed thresholds, the rate limits are adjusted as a function of query complexity metrics, user profile attributes, historical behavior patterns, and current system load. This parameter adaptation allows the system to maintain resource protection by imposing stricter limits when necessary while increasing throughput capacity during periods of low risk or high available resources, thereby optimizing the balance between security and productivity.