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+---
+title: Celery
+weight: 60
+---
+
+## Celery
+
+When using Celery, a common approach is to instrument tasks with `prometheus_client` metrics and expose them via a small sidecar exporter process on each node. Prometheus scrapes the sidecar HTTP endpoint.
+
+This keeps metric exporting independent from Django/FastAPI and avoids relying on any web server running inside the Celery worker process.
+
+### 1) Instrument Celery tasks
+
+Define metrics in a module that is imported by your Celery app/tasks:
+
+```python
+from prometheus_client import Counter
+
+CELERY_TASKS_TOTAL = Counter(
+    "celery_tasks_total",
+    "Total number of Celery tasks processed",
+    ["task_name", "status"],
+)
+```
+
+Update them from tasks:
+
+```python
+from celery import shared_task
+from .metrics import CELERY_TASKS_TOTAL
+
+@shared_task(bind=True)
+def example_task(self, *args, **kwargs):
+    try:
+        # ... task logic ...
+        CELERY_TASKS_TOTAL.labels(task_name=self.name, status="success").inc()
+    except Exception:
+        CELERY_TASKS_TOTAL.labels(task_name=self.name, status="failure").inc()
+        raise
+```
+
+### 2) Run a sidecar exporter
+
+Run a small process alongside your Celery workers to expose `/metrics`:
+
+```python
+from prometheus_client import start_http_server
+import time
+
+def main():
+    # Expose metrics on http://0.0.0.0:8000/metrics
+    start_http_server(8000, addr="0.0.0.0")
+    while True:
+        time.sleep(60)
+
+if __name__ == "__main__":
+    main()
+```
+
+Start this exporter on each node (e.g. as a systemd service, container sidecar, or process supervisor entry).
+
+### Notes
+
+- The exporter process does not depend on Django/FastAPI; it simply exposes metrics from the same Python environment where task code runs.
+- If you run Celery with multiple worker processes and need aggregation across processes, see [Multiprocess mode]({{< ref "/multiprocess" >}}) and use a dedicated `PROMETHEUS_MULTIPROC_DIR`.
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