linear-performance-tuning

name	linear-performance-tuning
description	Optimize Linear API queries, caching, and batching for performance. Use when improving response times, reducing API calls, or implementing caching strategies for Linear data. Trigger: "linear performance", "optimize linear", "linear caching", "linear slow queries", "speed up linear", "linear N+1".
allowed-tools	Read, Write, Edit, Grep
version	1.12.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","linear","api","performance"]
compatibility	Designed for Claude Code

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.

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();
}


 response =  client..(, { :  });