Google Cloud Run Telemetry

Implementation evidence

Google Cloud Run Telemetry: from boundary to verified row

Use Google Cloud Run Telemetry at a controlled application boundary, keep the event contract small, and verify a known outcome before building aggregate views.

1
Choose the outcome
Serverless request reliability
2
Define the contract
service, revision, region, route_template, job_name, and release
3
Instrument the boundary
Emit after the request or job has a terminal outcome, and keep platform request logs for provider-native diagnostics.
4
Verify the evidence
Exercise a known fixture, then inspect cloud_run_request_completed for one correctly typed terminal row.

Before you start

Prerequisites and boundaries

A Cloud Run service or job with telemetry-sh initialized in trusted server code
Stable service, revision, region, route, and job names
Bounded delivery behavior that cannot extend request shutdown or fail a successful workload

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.

google-cloud-run-install

npm installation

bash

npm install telemetry-sh

1Prepare one reusable server-side delivery client with bounded network behavior.
2Add the outcome event at the success, failure, retry, or timeout boundary.
3Send controlled fixtures 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.

google-cloud-run

Google Cloud Run Telemetry event

javascript

await telemetry.log("cloud_run_request_completed", {
  service: process.env.K_SERVICE,
  revision: process.env.K_REVISION,
  region: process.env.GOOGLE_CLOUD_REGION,
  route_template: "/api/reports/:reportId",
  status: responseStatus < 500 ? "success" : "failed",
  status_code: responseStatus,
  duration_ms: Math.round(performance.now() - startedAt),
  request_id: requestId,
  cold_start_observed: coldStartObserved,
  environment: process.env.APP_ENV,
  release: process.env.APP_RELEASE,
});

Event contract

service, revision, region, route_template, job_name, and release

status, status_code, duration_ms, attempt, instance_id_category, and error_type

request_id, account_id, cold_start_observed, item_count, and environment

Implementation checkpoints

Checkpoint 1

Emit after the request or job has a terminal outcome, and keep platform request logs for provider-native diagnostics.

Checkpoint 2

Use K_REVISION, K_SERVICE, and K_CONFIGURATION where available; keep ephemeral instance identifiers out of grouping dimensions.

Checkpoint 3

Set a short delivery timeout and verify instance termination behavior so telemetry never delays a response or changes job success.

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.

google-cloud-run-verification

Google Cloud Run Telemetry verification query

sql

SELECT *
FROM cloud_run_request_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.

API reliability monitoring Background job monitoring Reliability by release SQL Cloud Run logging Cloud Run container runtime contract

Production boundary

Keep the outcome event small and recoverable

This pattern provides

A bounded, SQL-ready outcome beside the upstream workflow.
Stable fields for dashboards, alerts, and cross-event correlation.
A fixture-driven path for validating success, failure, retry, and timeout behavior.

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

Event schema starting points

Review the row grain, emit boundary, required types, privacy classes, example payload, and validation checklist before adapting a query or snippet to production.

API reliability10 fields