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, also compatible with Codex and OpenClaw

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..(, { :  });