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.

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) {
      issues(first: 50, orderBy: updatedAt) {
        nodes {
          id identifier title priority estimate updatedAt
          assignee { name email }
          state { name type }
          labels { nodes { name color } }
          project { name }
        }
        pageInfo { hasNextPage endCursor }
      }
    }
  }
`, { teamId: "team-uuid" });
// Complexity: ~50 * (10 fields * 0.1 + 4 objects) = ~275 pts

Step 2: Cache Static Data

Teams, workflow states, and labels change rarely. Cache them with appropriate TTLs.

interface CacheEntry<T> {
  data: T;
  expiresAt: number;
}

class LinearCache {
  private store = new Map<string, CacheEntry<any>>();

  get<T>(key: string): T | null {
    const entry = this.store.get(key);
    if (!entry || Date.now() > entry.expiresAt) {
      this.store.delete(key);
      return null;
    }
    return entry.data;
  }

  set<T>(key: string, data: T, ttlSeconds: number): void {
    this.store.set(key, { data, expiresAt: Date.now() + ttlSeconds * 1000 });
  }

  invalidate(key: string): void {
    this.store.delete(key);
  }
}

const cache = new LinearCache();

// Teams: 10 minute TTL (almost never change)
async function getTeams(client: LinearClient) {
  const cached = cache.get<any[]>("teams");
  if (cached) return cached;
  const teams = await client.teams();
  cache.set("teams", teams.nodes, 600);
  return teams.nodes;
}

// Workflow states: 30 minute TTL (rarely change)
async function getStates(client: LinearClient, teamId: string) {
  const key = `states:${teamId}`;
  const cached = cache.get<any[]>(key);
  if (cached) return cached;
  const team = await client.team(teamId);
  const states = await team.states();
  cache.set(key, states.nodes, 1800);
  return states.nodes;
}

// Labels: 10 minute TTL
async function getLabels(client: LinearClient) {
  const cached = cache.get<any[]>("labels");
  if (cached) return cached;
  const labels = await client.issueLabels();
  cache.set("labels", labels.nodes, 600);
  return labels.nodes;
}

Step 3: Webhook-Driven Cache Invalidation

Replace polling with webhooks. Invalidate cache when relevant entities change.

function handleCacheInvalidation(event: { type: string; action: string; data: any }) {
  switch (event.type) {
    case "Issue":
      cache.invalidate(`issue:${event.data.id}`);
      break;
    case "WorkflowState":
      cache.invalidate(`states:${event.data.teamId}`);
      break;
    case "IssueLabel":
      cache.invalidate("labels");
      break;
    case "Team":
      cache.invalidate("teams");
      break;
  }
}

Step 4: Batch Mutations

Combine multiple mutations into one GraphQL request.

// Instead of 100 separate updateIssue calls:
async function batchUpdatePriority(
  client: LinearClient,
  issueUpdates: Array<{ id: string; priority: number }>
) {
  const chunkSize = 20; // Keep complexity manageable
  for (let i = 0; i < issueUpdates.length; i += chunkSize) {
    const chunk = issueUpdates.slice(i, i + chunkSize);
    const mutations = chunk.map((u, j) =>
      `u${j}: issueUpdate(id: "${u.id}", input: { priority: ${u.priority} }) { success }`
    ).join("\n");

    await client.client.rawRequest(`mutation { ${mutations} }`);
  }
}

// Batch issue creation
async function batchCreate(
  client: LinearClient,
  teamId: string,
  issues: Array<{ title: string; priority?: number }>
) {
  const mutations = issues.map((issue, i) =>
    `c${i}: issueCreate(input: {
      teamId: "${teamId}",
      title: "${issue.title.replace(/"/g, '\\"')}",
      priority: ${issue.priority ?? 3}
    }) { success issue { id identifier } }`
  ).join("\n");

  return client.client.rawRequest(`mutation { ${mutations} }`);
}

Step 5: Efficient Pagination

// Stream all issues without loading everything into memory
async function* paginateIssues(
  client: LinearClient,
  teamId: string,
  pageSize = 50
) {
  let cursor: string | undefined;
  let hasNext = true;

  while (hasNext) {
    const result = await client.issues({
      first: pageSize,
      after: cursor,
      filter: { team: { id: { eq: teamId } } },
      orderBy: "updatedAt", // Fresh data first
    });

    yield result.nodes;
    hasNext = result.pageInfo.hasNextPage;
    cursor = result.pageInfo.endCursor;
  }
}

// Process in batches
for await (const batch of paginateIssues(client, "team-uuid")) {
  console.log(`Processing ${batch.length} issues`);
}

// Incremental sync: only fetch issues updated since last sync
const lastSync = "2026-03-20T00:00:00Z";
const updated = await client.issues({
  first: 100,
  filter: { updatedAt: { gte: lastSync } },
  orderBy: "updatedAt",
});

Step 6: Request Coalescing

Deduplicate concurrent identical requests.

const inflight = new Map<string, Promise<any>>();

async function coalesce<T>(key: string, fn: () => Promise<T>): Promise<T> {
  if (inflight.has(key)) return inflight.get(key)!;
  const promise = fn().finally(() => inflight.delete(key));
  inflight.set(key, promise);
  return promise;
}

// Multiple components requesting same team data simultaneously = 1 API call
const team = await coalesce("team:ENG", () =>
  client.teams({ filter: { key: { eq: "ENG" } } }).then(r => r.nodes[0])
);

Error Handling

Error	Cause	Solution
Query complexity too high	Deep nesting + large first	Use rawRequest() with flat fields, first: 50
HTTP 429	Burst exceeding rate budget	Add request queue with 100ms spacing
Stale cache	TTL too long	Shorten TTL or use webhook invalidation
Timeout	Query spanning too many records	Paginate with first: 50 + cursor

Examples

Performance Benchmark

async function benchmark(label: string, fn: () => Promise<any>) {
  const start = Date.now();
  await fn();
  console.log(`${label}: ${Date.now() - start}ms`);
}

await benchmark("Cold teams", () => client.teams());
await benchmark("Cached teams", () => getTeams(client));
await benchmark("50 issues (SDK)", () => client.issues({ first: 50 }));
await benchmark("50 issues (raw)", () => client.client.rawRequest(
  `query { issues(first: 50) { nodes { id identifier title priority } } }`
));

Resources

• Linear Best Practices
• Rate Limiting
• Pagination
• Filtering