Added documentation page on how to reduce memory usage.

api/compose/django/daphne.sh

@@ -1,3 +1,3 @@
 #!/bin/bash -eux
 python /app/manage.py collectstatic --noinput
-/usr/local/bin/daphne -b 0.0.0.0 -p 5000 config.asgi:application
+/usr/local/bin/daphne -b 0.0.0.0 -p 5000 config.asgi:application --proxy-headers

changes/changelog.d/optimization.doc

@@ -0,0 +1,11 @@
+Added documentation for optimizing Funkwhale and reduce its memory
+footprint.
+
+Changelog
+^^^^^^^^^
+
+For non-docker deployments, add ``--proxy-headers`` at the end of the ``daphne``
+command in :file:`/etc/systemd/system/funkwhale-server.service`.
+
+This will ensure the application receive the correct IP address from the client
+and not the proxy's one.

deploy/funkwhale-server.service

@@ -8,7 +8,7 @@ User=funkwhale
 # adapt this depending on the path of your funkwhale installation
 WorkingDirectory=/srv/funkwhale/api
 EnvironmentFile=/srv/funkwhale/config/.env
-ExecStart=/srv/funkwhale/virtualenv/bin/daphne -b ${FUNKWHALE_API_IP} -p ${FUNKWHALE_API_PORT} config.asgi:application
+ExecStart=/srv/funkwhale/virtualenv/bin/daphne -b ${FUNKWHALE_API_IP} -p ${FUNKWHALE_API_PORT} config.asgi:application --proxy-headers
 
 [Install]
 WantedBy=multi-user.target

docs/installation/index.rst

@@ -28,10 +28,16 @@ On a dockerized instance with 2 CPUs and a few active users, the memory footprin
    funkwhale_postgres_1        22.73 MiB
    funkwhale_redis_1           1.496 MiB
 
+Some users have reported running Funkwhale on Raspberry Pis with a memory
+consuption of less than 200MiB.
+
 Thus, Funkwhale should run fine on commodity hardware, small hosting boxes and
 Raspberry Pi. We lack real-world exemples of such deployments, so don't hesitate
 do give us your feedback (either positive or negative).
 
+Check out :doc:`optimization` for advices on how to tune your instance on small
+configurations.
+
 Software requirements
 ---------------------
 

docs/installation/optimization.rst

@@ -0,0 +1,37 @@
+Optimizing your Funkwhale instance
+==================================
+
+Depending on your requirements, you may want to reduce as much as possible
+Funkwhale's footprint.
+
+Reduce workers concurrency
+--------------------------
+
+Asynchronous tasks are handled by a celery worker, which will by default
+spawn a worker process per CPU available. This can lead to a higher
+memory usage.
+
+You can control this behaviour using the ``--concurrency`` flag.
+For instance, setting ``--concurrency=1`` will spawn only one worker.
+
+This flag should be appended after the ``celery -A funkwhale_api.taskapp worker``
+command in your :file:`docker-compose.yml` file if your using Docker, or in your
+:file:`/etc/systemd/system/funkwhale-worker.service` otherwise.
+
+.. note::
+
+    Reducing concurrency comes at a cost: asynchronous tasks will be processed
+    more slowly. However, on small instances, this should not be an issue.
+
+
+Switch from prefork to solo pool
+--------------------------------
+
+Using a different pool implementation for Celery tasks may also help.
+
+Using the ``solo`` pool type should reduce your memory consumption.
+You can control this behaviour using the ``--pool=solo`` flag.
+
+This flag should be appended after the ``celery -A funkwhale_api.taskapp worker``
+command in your :file:`docker-compose.yml` file if your using Docker, or in your
+:file:`/etc/systemd/system/funkwhale-worker.service` otherwise.