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linear-performance-tuning 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".
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langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
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3 archivos 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 <jeremy@intentsolutions.io> 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 ! });
const issues = await client.issues ({ first : 50 });
for (const i of issues.nodes ) {
const assignee = await i.assignee ;
const state = await i.state ;
console .log (`${i.identifier} : ${assignee?.name} [${state?.name} ]`
const
await
client
rawRequest
`
query TeamDashboard($teamId: String!) {
team(id: $teamId) {
issues(first: 50, orderBy: updatedAt) {
nodes {
id identifier title priority estimate updatedAt
assignee { name email }
state { name type }
labels { nodes { name color } }
project { name }
}
pageInfo { hasNextPage endCursor }
}
}
}
`
teamId
"team-uuid"
Step 2: Cache Static Data Teams, workflow states, and labels change rarely. Cache them with appropriate TTLs.
interface CacheEntry <T> {
data : T;
expiresAt : number ;
}
class LinearCache {
private store = new Map <string , CacheEntry <any >>();
get<T>(key : string ): T | null {
const entry = this .store .get (key);
if (!entry || Date .now () > entry.expiresAt ) {
this .store .delete (key);
return null ;
}
return entry.data ;
}
set<T>(key : string , data : T, ttlSeconds : number ): void {
this .store .set (key, { data, expiresAt : Date .now () + ttlSeconds * 1000 });
}
invalidate (key : string ): void {
this .store .delete (key);
}
}
const cache = new LinearCache ();
async function getTeams (client : LinearClient const cached = cache.get <any []>("teams" );
if (cached) return cached;
const teams = await client.teams ();
cache.set ("teams" , teams.nodes , 600 );
return teams.nodes ;
}
async function getStates (client : LinearClient , teamId : string const key = `states:${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 ;
}
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.
async function batchUpdatePriority (
client : LinearClient ,
issueUpdates : Array <{ id: string ; priority: number }>
) {
const chunkSize = 20 ;
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 { ${mutations} }` );
}
}
async function batchCreate (
client : LinearClient ,
teamId : string ,
issues : Array <{ title: string ; priority?: number }>
) {
const mutations = issues.map ((issue, i ) =>
`c${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
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" ,
});
yield result.nodes ;
hasNext = result.pageInfo .hasNextPage ;
cursor = result.pageInfo .endCursor ;
}
}
for await (const batch of paginateIssues (client, "team-uuid" )) {
console .log (`Processing ${batch.length} issues` );
}
const lastSync = "2026-03-20T00:00:00Z" ;
const updated = await client.issues ({
first : 100 ,
filter : { updatedAt : { gte : lastSync } },
orderBy : "updatedAt" ,
});
Step 6: Request Coalescing Deduplicate concurrent identical requests.
const inflight = new Map <string , Promise <any >>();
async function coalesce<T>(key : string , fn : () => Promise <T>): Promise <T> {
if (inflight.has (key)) return inflight.get (key)!;
const promise = fn ().finally (() => inflight.delete (key));
inflight.set (key, promise);
return promise;
}
const team = await coalesce ("team:ENG" , () =>
client.teams ({ filter : { key : { eq : "ENG" } } }).then (r =>nodes [0 ])
);
Error Handling Error Cause Solution Query complexity too highDeep nesting + large first Use rawRequest() with flat fields, first: 50 HTTP 429 Burst exceeding rate budget Add request queue with 100ms spacing Stale cache TTL too long Shorten TTL or use webhook invalidation Timeout Query spanning too many records Paginate with first: 50 + cursor
Examples
Performance Benchmark async function benchmark (label : string , fn : () => Promise <any >const start = Date .now ();
await fn ();
console .log (`${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 