Using watchfiles and exec commands in bin scripts

README.md

@@ -1,3 +1,18 @@
 # FastAPI and Celery
 
 Learning distributed task queues by doing. Since it's a greenfield project this also uses a newer async web framework.
+
+## Debugging Celery
+
+### Check results in redis result backend
+
+```
+docker-compose exec redis sh
+redis-cli
+KEYS celery*
+MGET celery-task-meta-<UID of Task>
+```
+
+### Checking results in Flower
+
+`0.0.0.0:

compose/local/fastapi/celery/beat/start

@@ -4,4 +4,4 @@ set -o errexit
 set -o nounset
 
 rm -f './celerybeat.pid'
-celery -A main.celery beat -l info
+exec python -m celery -A main.celery beat -l info

compose/local/fastapi/celery/flower/start

@@ -13,6 +13,6 @@ until worker_ready; do
 done
 >&2 echo 'Celery workers is available'
 
-celery flower \
+exec celery flower \
     --app=main.celery \
     --broker="${CELERY_BROKER_URL}"

compose/local/fastapi/celery/worker/start

@@ -3,4 +3,11 @@
 set -o errexit
 set -o nounset
 
-celery -A main.celery worker --loglevel=info
+# Use watchfiles during development
+CELERY_WORKER_WATCH=${CELERY_WORKER_WATCH:-}
+
+if [ ! -z $CELERY_WORKER_WATCH ]; then
+    watchfiles --filter python 'celery -A main.celery worker --loglevel=info'
+else
+   exec python -m celery -A main.celery worker --loglevel=info
+fi

project/users/tasks.py

@@ -13,3 +13,11 @@ def divide(x, y):
 
     time.sleep(5)
     return x / y
+
+
+@shared_task
+def add(x, y):
+    import time
+
+    time.sleep(5)
+    return x + y

requirements.in

@@ -6,3 +6,4 @@ psycopg2-binary==2.9.3
 redis==4.3.4
 SQLAlchemy==1.4.40
 uvicorn[standard]==0.18.2
+watchfiles==0.17.0

requirements.txt

@@ -107,7 +107,9 @@ vine==5.0.0
     #   celery
     #   kombu
 watchfiles==0.17.0
-    # via uvicorn
+    # via
+    #   -r requirements.in
+    #   uvicorn
 wcwidth==0.2.5
     # via prompt-toolkit
 websockets==10.3