Optimizing FileMaker Data API Performance

Expert

Identify and resolve the most common Data API performance bottlenecks: oversized payloads, session overhead, unindexed finds, and N+1 query patterns.

What you'll learn

How to identify N+1 query patterns in FM integrations
How to reduce payload size with lean layouts
How to verify that find fields are indexed
How to measure and benchmark API response times

The Data API is designed for transactional access, not analytics. Performance problems surface when integrations treat it like a bulk data warehouse query. The root causes are almost always the same: too many round trips, unindexed find fields, oversized response payloads, and session creation overhead. Fixing these can reduce response times by 10x.

1/4

Identifying and eliminating N+1 queries

N+1 occurs when you fetch N parent records and then make one API call per parent to get its children. Replace with a single find on the child layout filtered by a set of parent IDs.

FileMaker Script

// N+1 pattern (BAD -- 1 call per account):
const accounts = await fm.findRecords('Accounts', [{ Status: 'Active' }]);
for (const account of accounts) {
  const contacts = await fm.findRecords('Contacts', [{ AccountID: account.fieldData.AccountID }]);
  // ...
}

// Batched pattern (GOOD -- 2 calls total):
const accounts = await fm.findRecords('Accounts', [{ Status: 'Active' }]);
const ids = accounts.map(a => a.fieldData.AccountID);
// Build OR query for all IDs at once
const query = ids.map(id => ({ AccountID: id }));
const contacts = await fm.findRecords('Contacts', query, { limit: 1000 });

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