SuiteScript Integration Optimization — Reducing API Calls & Improving Throughput

Introduction

Even well-designed integrations can slow down if every transaction hits the API repeatedly.
By optimizing how data is fetched, cached, and processed, you can cut run-time by half — or more — and dramatically reduce governance usage.

This post shows how to:

• Batch API calls efficiently.
• Cache configuration and metadata.
• Use Map/Reduce properly for large jobs.
• Measure and compare results before vs. after optimization.

1️⃣ Before vs. After Example

2️⃣ Batch Record Operations

Instead of loading or submitting one record at a time:

const ids = [201,202,203,204];
record.submitFields({
  type: record.Type.SALES_ORDER,
  id: ids,
  values: { custbody_synced:true }
});

✅ Use submitFields() or record.submitBulk() (where available) to update multiple records simultaneously.

3️⃣ Cache Reference Data

Configuration data (item mappings, tax codes, currencies) rarely changes — so cache it.

define(['N/cache'], (cache) => {
  const configCache = cache.getCache({name:'integration_cfg'});
  const getConfig = (key, loader) => configCache.get({
    key, loader
  });
  return { getConfig };
});

✅ Reduces repeated search.lookupFields() calls for static values.

4️⃣ Consolidate Searches

Don’t run a new search inside each loop. Build one paged search and reuse results.

const paged = search.load({id:'customsearch_pending_orders'}).runPaged({pageSize:1000});
paged.pageRanges.forEach(range => {
  paged.fetch({index:range.index}).data.forEach(process);
});

✅ 1 search call → 1000 records processed.

5️⃣ Bundle External API Requests

If integrating with Shopify / Salesforce, send bulk payloads:

Before: 100 POST requests × 1 record
After: 1 POST request × 100 records

Example payload structure:

{
 "orders":[
  {"id":301,"total":250.50},
  {"id":302,"total":180.75}
 ]
}

✅ Cuts network overhead and authentication costs dramatically.

6️⃣ Leverage Map/Reduce Parallelization

Split large jobs into smaller chunks automatically.

define(['N/search','N/record'], (search,record)=>({
  getInputData:() => search.load({id:'customsearch_orders'}),
  map:(ctx)=>{
    const order = JSON.parse(ctx.value);
    record.submitFields({
      type:record.Type.SALES_ORDER,
      id:order.id,
      values:{custbody_synced:true}
    });
  }
}));

✅ Each map stage runs in parallel → high throughput with automatic yielding.

7️⃣ Optimize Field Access

Use search.lookupFields() to read just the needed data:

const cust = search.lookupFields({
  type:'customer',
  id:123,
  columns:['email','salesrep']
});

✅ 1 call instead of record.load() (20–40 units saved).

8️⃣ Use Conditional Processing

Skip unnecessary work:

if (!order.syncRequired) return;

✅ Simple check = major time savings for high-volume runs.

9️⃣ Track and Measure Governance

Add usage tracking at key points:

const usage = runtime.getCurrentScript().getRemainingUsage();
if (usage < 200) log.audit('Yielding to avoid timeout');

✅ Helps identify heavy sections for future optimization.

🔧 10️⃣ Best Practices Summary

Conclusion

Integration optimization is about doing more with less.
By batching, caching, and parallelizing your SuiteScript operations, you’ll cut processing time, stay under governance limits, and deliver integrations that scale effortlessly as data grows.

Efficient code = reliable automation.