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maintainx-performance-tuning Optimize MaintainX API integration performance.
Use when experiencing slow API responses, optimizing data fetching,
or improving integration throughput with MaintainX.
Trigger with phrases like "maintainx performance", "maintainx slow",
"optimize maintainx", "maintainx caching", "maintainx faster".
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Download Zip Downloading... More from this repository 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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approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
name maintainx-performance-tuning description Optimize MaintainX API integration performance.
Use when experiencing slow API responses, optimizing data fetching,
or improving integration throughput with MaintainX.
Trigger with phrases like "maintainx performance", "maintainx slow",
"optimize maintainx", "maintainx caching", "maintainx faster".
allowed-tools Read, Write, Edit, Bash(npm:*) version 1.11.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","maintainx","api","performance"] compatibility Designed for Claude Code
MaintainX Performance Tuning
Overview
Optimize MaintainX integration performance with caching, connection pooling, efficient pagination, and request deduplication.
Prerequisites
MaintainX integration working
Node.js 18+
Redis (recommended for production caching)
Performance baseline measurements
Instructions
Step 1: Connection Pooling with Keep-Alive
import axios from 'axios' ;
import http from 'node:http' ;
import https from 'node:https' ;
const httpAgent = new http.Agent ({ keepAlive : true , maxSockets : 10 });
const httpsAgent = new https.Agent ({ keepAlive : true , maxSockets : 10 });
const client = axios.create ({
baseURL : 'https://api.getmaintainx.com/v1' ,
headers : {
Authorization : `Bearer ${process.env.MAINTAINX_API_KEY} ` ,
'Content-Type' : 'application/json' ,
},
httpAgent,
httpsAgent,
timeout : 30_000 ,
});
Step 2: Multi-Level Caching
interface CacheLayer <T> {
get (key : string ): Promise <T | undefined >;
set (key : string , value : T, ttlMs : number ): Promise <void >;
}
class MemoryCache <T> implements CacheLayer <T> {
private store = new Map <string , { value : T; expiresAt : number }>();
async get (key : string const entry = this .store .get (key);
if (entry && entry.expiresAt > Date .now ()) return entry.value ;
this .store .delete (key);
return undefined ;
}
async set (key : string , value : T, ttlMs : number this .store .set (key, { value, expiresAt : Date .now () + ttlMs });
}
}
class RedisCache <T> implements CacheLayer <T> {
constructor (private redis : any async get (key : string const data = await this .redis .get (`mx:${key} ` );
return data ? JSON .parse (data) : undefined ;
}
async set (key : string , value : T, ttlMs : number await this .redis .setex (`mx:${key} ` , Math .ceil (ttlMs / 1000 ), JSON .stringify (value));
}
}
class MultiCache <T> {
constructor (private l1 : CacheLayer <T>, private l2 : CacheLayer <T>async getOrFetch (key : string , ttlMs : number , fetcher : () => Promise <T>): Promise <T> {
let value = await this .l1 .get (key);
if (value !== undefined ) return value;
value = await this .l2 .get (key);
if (value !== undefined ) {
await this .l1 .set (key, value, ttlMs / 2 );
return value;
}
value = await fetcher ();
await this .l1 .set (key, value, ttlMs / 2 );
await this .l2 .set (key, value, ttlMs);
return value;
}
}
Step 3: DataLoader for Batch Loading When multiple parts of your app need the same work order, batch and deduplicate:
import DataLoader from 'dataloader' ;
const workOrderLoader = new DataLoader <number , any >(
async (ids : readonly number []) => {
const results = await Promise .all (
ids.map ((id ) =>
client.get (`/workorders/${id} ` ).then ((r ) => r.data )
),
);
return ids.map ((id ) => results.find ((r ) => r.id === id) || null );
},
{
maxBatchSize : 25 ,
cacheKeyFn : (id ) => String (id),
},
);
const [wo1, wo2, wo3] = await Promise .all ([
workOrderLoader.load (100 ),
workOrderLoader.load (200 ),
workOrderLoader.load (100 ),
]);
Step 4: Efficient Pagination
async function efficientFetchAll (client : any , endpoint : string , key : string const all = [];
let cursor : string | undefined ;
let pageCount = 0 ;
const startTime = Date .now ();
do {
const { data } = await client.get (endpoint, {
params : { limit : 100 , cursor },
});
all.push (...data[key]);
cursor = data.cursor ;
pageCount++;
} while (cursor);
const elapsed = Date .now () - startTime;
console .log (`Fetched ${all.length} items in ${pageCount} pages (${elapsed} ms)` );
return all;
}
async function fetchAllResources (client : any const [workOrders, assets, locations] = await Promise .all ([
efficientFetchAll (client, '/workorders' , 'workOrders' ),
efficientFetchAll (client, '/assets' , 'assets' ),
efficientFetchAll (client, '/locations' , 'locations' ),
]);
return { workOrders, assets, locations };
}
Step 5: Request Deduplication
class RequestDeduplicator {
private inflight = new Map <string , Promise <any >>();
async dedupe<T>(key : string , fetcher : () => Promise <T>): Promise <T> {
if (this .inflight .has (key)) {
return this .inflight .get (key)! as Promise <T>;
}
const promise = fetcher ().finally (() => {
this .inflight .delete (key);
});
this .inflight .set (key, promise);
return promise;
}
}
const dedup = new RequestDeduplicator ();
async function getWorkOrder (id : number return dedup.dedupe (`wo:${id} ` , () => client.get (`/workorders/${id} ` ));
}
Performance Benchmarks Optimization Before After Improvement Connection pooling 350ms/req 150ms/req 57% faster L1 cache (hot path) 150ms/req < 1ms/req 99% faster DataLoader batching 10 calls 1 call 90% fewer requests Max page size (100) 50 pages 10 pages 5x fewer round trips Request dedup N calls 1 call (N-1) saved
Output
Connection pooling with keep-alive (reuses TCP connections)
Multi-level cache (L1 in-memory + L2 Redis)
DataLoader for batching and deduplication of entity fetches
Efficient pagination with max page sizes
Request deduplication preventing redundant concurrent calls
Error Handling Issue Cause Solution Stale cache data TTL too long Reduce TTL, invalidate on writes Memory growth Unbounded cache Set max size, use LRU eviction DataLoader errors One item in batch fails Handle per-item errors in batch function Connection pool exhaustion Too many concurrent requests Increase maxSockets or add queue
Resources
Next Steps For cost optimization, see maintainx-cost-tuning.
Examples Benchmark your API response times :
for i in $(seq 1 10); do
curl -s -o /dev/null -w "Request $i : %{time_total}s\n" \
"https://api.getmaintainx.com/v1/workorders?limit=1" \
-H "Authorization: Bearer $MAINTAINX_API_KEY "
done
