Django and Celery Structured Event Monitoring

Implementation evidence

Django and Celery Structured Event Monitoring: from boundary to verified row

Use Django and Celery Structured Event Monitoring at a controlled application boundary, keep the event contract small, and verify a known outcome before building aggregate views.

1
Choose the outcome
Django API monitoring
2
Define the contract
task_name, task_id, queue_name, and status
3
Instrument the boundary
Emit one terminal event from Celery success and failure handlers instead of logging the full task payload.
4
Verify the evidence
Exercise a known fixture, then inspect job_completed for one correctly typed terminal row.

Before you start

Prerequisites and boundaries

A server-side TELEMETRY_API_KEY
telemetry-sh initialized once per process
A propagated request or workflow identifier

Delivery setup

Install and initialize server-side

Initialize Telemetry for synchronous code or TelemetryAsync for asyncio code once per process with a server-side key. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.

django-celery-install

pip installation

bash

python -m pip 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.

django-celery

Django and Celery Structured Event Monitoring event

python

@task_postrun.connect
def record_task(sender, task_id, state, retval, **kwargs):
    telemetry.log("job_completed", {
        "job_name": sender.name,
        "job_id": task_id,
        "queue_name": sender.request.delivery_info.get("routing_key"),
        "status": "success" if state == "SUCCESS" else "failed",
        "attempt": sender.request.retries + 1,
        "duration_ms": task_duration_ms(task_id),
        "error_type": categorize_error(retval) if state != "SUCCESS" else None,
    })

Event contract

task_name, task_id, queue_name, and status

attempt, duration_ms, error_type, and item_count

request_id, account_id, environment, and release

Implementation checkpoints

Checkpoint 1

Emit one terminal event from Celery success and failure handlers instead of logging the full task payload.

Checkpoint 2

Preserve the same task ID across retries and record the attempt separately.

Checkpoint 3

Keep Django request instrumentation independent from task completion so async latency remains visible.

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.

django-celery-verification

Django and Celery Structured Event Monitoring verification query

sql

SELECT *
FROM job_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.

Python SDK Queue worker guide Celery task signals Django logging

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.

Async reliability11 fields