Optimizing FileMaker Data API Performance
ExpertIdentify 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.
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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.
// 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 });Sign in to track your progress and pick up where you left off.
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