linear-performance-tuning

# Linear Performance Tuning ## Overview Optimize Linear API usage for minimal latency and efficient resource consumption. The three main levers are: (1) query flattening to avoid N+1 and reduce complexity, (2) caching static data with webhook-driven invalidation, and (3) batching mutations into single GraphQL requests. **Key numbers:** - Query complexity budget: 250,000 pts/hour, max 10,000 per query - Each property: 0.1 pt, each object: 1 pt, connections: multiply by `first` - Best practice: sort by `updatedAt` to get fresh data first ## Prerequisites - Working Linear integration with `@linear/sdk` - Understanding of GraphQL query structure - Optional: Redis for distributed caching ## Instructions ### Step 1: Eliminate N+1 Queries The SDK lazy-loads relations. Accessing `.assignee` on 50 issues makes 50 separate API calls. ```typescript import { LinearClient } from "@linear/sdk"; const client = new LinearClient({ apiKey: process.env.LINEAR_API_KEY! }); // BAD: N+1 — 1 query for issues + 50 for assignees + 50 for states = 101 requests const issues = await client.issues({ first: 50 }); for (const i of issues.nodes) { const assignee = await i.assignee; // API call! const state = await i.state; // API call! console.log(`${i.identifier}: ${assignee?.name} [${state?.name}]`); } // GOOD: 1 request — use rawRequest with exact field selection const response = await client.client.rawRequest(` query TeamDashboard($teamId: String!) { team(id: $teamId) { ...