#338: optimize for one core setting

also sets celery worker scaling back to one per core as previous test results proved diminishing returns of multiple workers per core (at least in one core setting)

Author: Wolkenfarmer

backend/dps_training_k/configuration/settings.py

@@ -184,6 +184,7 @@
         "schedule": 1.0,
     },
 }
+CELERY_WORKER_CONCURRENCY = env.int("CELERY_WORKER_CONCURRENCY", default=1)
 
 DATA_ROOT = os.path.join(BASE_DIR, "data")
 

backend/dps_training_k/deployment/django/celery/worker/start

@@ -3,16 +3,16 @@
 set -o errexit
 set -o nounset
 
-# Allow override via env var, otherwise 4x CPU cores for I/O-bound tasks,
-# capped at 32 to stay within PostgreSQL's default max_connections (100) with headroom for Django and CeleryBeat.
+# Use override if set, otherwise default to nproc (1 worker per core), capped at 32
+# to stay within PostgreSQL's default max_connections (100) with headroom for Django and CeleryBeat.
 if [ -z "${CELERY_WORKER_CONCURRENCY:-}" ]; then
-    CPU_COUNT=$(nproc)
-    CONCURRENCY=$(( CPU_COUNT * 4 ))
+    CONCURRENCY=$(nproc)
     if [ "$CONCURRENCY" -gt 32 ]; then
         CONCURRENCY=32
     fi
 else
     CONCURRENCY=$CELERY_WORKER_CONCURRENCY
 fi
 
+export CELERY_WORKER_CONCURRENCY=$CONCURRENCY
 watchgod celery.__main__.main --args -A configuration.celery_app worker -l INFO -c $CONCURRENCY

backend/dps_training_k/game/tasks.py

@@ -38,8 +38,14 @@ def check_for_updates():
         event_ids = list(claimed.values_list('id', flat=True))
         ScheduledEvent.objects.filter(id__in=event_ids).update(enqueued=True)
 
-    for event_id in event_ids:
-        try:
-            run_event.apply_async(args=[event_id]) # add event to celery queue for a worker to pick up
-        except Exception as e:
-            logging.error(f"failed to enqueue event {event_id}: {e}")
+    if getattr(settings, 'CELERY_WORKER_CONCURRENCY', 0) == 1:
+        # Single worker — execute inline to avoid async dispatch overhead
+        for event_id in event_ids:
+            run_event.apply(args=[event_id])
+    else:
+        # Multiple workers — dispatch for parallel processing
+        for event_id in event_ids:
+            try:
+                run_event.apply_async(args=[event_id])
+            except Exception as e:
+                logging.error(f"failed to enqueue event {event_id}: {e}")