MongoDB operation telemetry

Test the integration

Send and verify events with MongoDB operation telemetry

Use MongoDB operation telemetry where your app knows the final result. Collect only the fields you need, then verify a test event before building charts.

1
Choose the outcome
Slow-operation analysis
2
Define the contract
database_system, operation, collection_name, query_fingerprint, and release
3
Log the final result
Name the application operation separately from the database command so dashboards remain stable as query implementations change.
4
Check the stored event
Exercise a known fixture, then inspect mongodb_operation_completed for one correctly typed terminal row.

Before you start

The MongoDB Node.js driver and telemetry-sh initialized server-side
A fixed list of operation names that excludes collection documents and filter values
A reviewed distinction between application timing and database-profiler diagnostics

Delivery setup

Install and initialize server-side

Import telemetry-sh in server-only code and initialize it once with process.env.TELEMETRY_API_KEY. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.

mongodb-install

npm installation

bash

npm install telemetry-sh

1Create one reusable server-side client. Set its timeout and retry limit.
2Log an event when the operation succeeds, fails, retries, or times out.
3Send test events with known results and inspect the stored rows before enabling an alert.

Snippet

Start with one structured event

Add this shape where the workflow completes, fails, or retries. Then build the dashboard from real fields.

mongodb

MongoDB operation telemetry event

javascript

await telemetry.log("mongodb_operation_completed", {
  database_system: "mongodb",
  operation: "workspace_activity.aggregate",
  collection_name: "workspace_activity",
  query_fingerprint: "workspace_activity_summary_v3",
  status: "success",
  duration_ms: Math.round(performance.now() - startedAt),
  result_count: results.length,
  retry_count: 0,
  transaction_status: "not_started",
  read_preference: "primaryPreferred",
  route_template: "/api/workspaces/:workspaceId/activity",
  release: process.env.APP_RELEASE,
});

Event schema

database_system, operation, collection_name, query_fingerprint, and release

status, duration_ms, result_count, retry_count, and transaction_status

read_preference, write_concern_category, route_template, and error_type

Check your setup

Checkpoint 1

Name the application operation separately from the database command so dashboards remain stable as query implementations change.

Checkpoint 2

Keep filters, update documents, aggregation pipelines, returned documents, connection strings, and unrestricted driver errors out of events.

Checkpoint 3

Use the MongoDB database profiler selectively for native query diagnostics; its captured data can include sensitive query details and needs its own access and retention review.

Verification

Prove the event arrived

Run this after exercising known success and failure cases. Replace the fallback table name if your final event contract differs from the snippet.

mongodb-verification

MongoDB operation telemetry verification query

sql

SELECT *
FROM mongodb_operation_completed
ORDER BY timestamp_utc DESC
LIMIT 20;

Confirm one terminal row per logical outcome, with the expected status, identifiers, units, and UTC time.

Inspect the inferred schema and verify that retries do not change field types or generate a new logical event ID.

Search the stored fields for credentials, raw payloads, prompts, private content, and unbounded error messages.

Exercise a provider timeout, ingestion rejection, and process shutdown before treating the dashboard as complete.

Implementation references

Review the event contract, data-safety guidance, and upstream primary documentation before enabling a new production path.

Database reliability monitoring Database-query impact SQL Redacting sensitive data MongoDB database profiler MongoDB query performance guide

Where to log

Keep the outcome event small and recoverable

This pattern provides

Record the outcome as an event you can query with SQL.
Stable fields for dashboards, alerts, and cross-event correlation.
Test events for checking success, failure, retries, and timeouts.

This pattern does not provide

An OTLP exporter, automatic collection pipeline, or replacement for detailed traces and diagnostic logs.
Exactly-once delivery merely because the payload contains an event ID.
Permission to collect raw provider payloads, user content, credentials, or regulated data.

Delivery and idempotency Batching and shutdown Troubleshoot ingestion

Example event schemas

Check what each event records, when to send it, and which field types it needs. Review the example payload and privacy checklist before using it in production.

Database reliability9 fields