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GraphQL Query Complexity Analysis & Cost-Based Rate Limiting #922

Description

@Daksh7785

Description

Title: Implement Query Complexity Analysis with Cost-Based Rate Limiting for GraphQL Operations
Detailed Description:
Currently, CommitPulse's GraphQL integration with GitHub's API doesn't implement query complexity analysis or cost-based rate limiting. This poses significant performance and cost risks when handling multiple concurrent requests.
Background Problem:
The GitHub GraphQL API enforces rate limiting based on "query cost" rather than simple request counts. Each GraphQL query has an associated cost based on its complexity (number of fields requested, pagination depth, aliases used, etc.). CommitPulse currently lacks a sophisticated mechanism to:

Calculate query complexity before execution
Track cumulative query costs per user/IP
Implement adaptive rate limiting based on cost rather than request count
Provide meaningful error messages when cost limits are exceeded

This can lead to unexpected API failures when users request complex data (e.g., users with very long contribution histories or large team repositories).
Objective:
Design and implement a comprehensive query complexity analysis system that:

Validates query cost before execution against GitHub's GraphQL API
Implements cost-aware caching strategies to prevent redundant expensive queries
Tracks per-user/IP cost consumption with sliding window rate limiting
Provides granular cost metrics in response headers for client-side optimization
Gracefully degrades service when approaching rate limits by implementing query batching and deferred execution queues

Files to Check:

app/api/streak/route.ts
lib/graphql/client.ts
lib/cache/ttl-cache.ts
middleware/rate-limit.ts

Implementation Details:

Create a GraphQL query analyzer using graphql-parse and graphql-query-complexity libraries
Implement a cost calculation engine that factors in field types, aliases, and nested depths
Develop a token bucket algorithm for cost-based rate limiting instead of simple request counting
Cache complex query results with longer TTLs
Implement query cost estimation hints in the API response headers

Testing Requirements:

Unit tests for query complexity calculations across various query patterns
Integration tests simulating concurrent requests with varying complexity levels
Load tests to ensure rate limiting doesn't cause cascading failures
Benchmark tests comparing old vs. new rate limiting strategies

Steps to Reproduce

na

Expected Behavior

na

Screenshots / Logs

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GitHub Username (If applicable)

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Environment

Chrome

Activity

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