Monitoring and Logging FileMaker API Integrations at Scale

Expert

Build a comprehensive observability layer for FileMaker integrations: structured logging, distributed tracing, alerting pipelines, and SLO dashboards.

What you'll learn

How to structure logs for aggregation in tools like Datadog or Loki
How to add trace IDs to correlate FM API calls across services
How to define and measure SLOs for FM-backed APIs
How to build an alerting pipeline that pages on FM-specific error conditions

At scale, individual error logs are insufficient -- you need aggregated metrics, distributed traces that span multiple systems, and SLOs that quantify reliability goals. FileMaker integrations require special handling because errors can be FM engine errors, data errors, or infrastructure failures, and each category needs different alerting and runbook responses.

1/4

Structured JSON logs with FM-specific fields

Include FM-specific metadata in every log entry: layout, FM error code, session token prefix, and record count. This enables filtering and aggregation in log tools.

FileMaker Script

interface FmLogEntry {
  ts: string;
  traceId: string;
  service: string;
  action: 'find' | 'create' | 'update' | 'delete' | 'script';
  layout: string;
  fmCode: string | null;
  httpStatus: number;
  durationMs: number;
  foundCount: number | null;
  error: string | null;
  tokenPrefix: string;
}

function logFmCall(entry: FmLogEntry) {
  console.log(JSON.stringify(entry));
}

Optimizing FileMaker Data API Performance API Design Patterns for FileMaker-Backed Services