old_contents: # Configuration for running the Avalon Music Server under Gunicorn
  # http://docs.gunicorn.org
  
  # Note that this configuration omits a bunch of features that Gunicorn
  # has (such as running as a daemon, changing users, error and access
  # logging) because it is designed to be used when running Gunicorn
  # with supervisord and a separate public facing web server (such as
  # Nginx).
  
  # Bind the server to an address only accessible locally. We'll be
  # running Nginx which will proxy to Gunicorn and act as the public-
  # facing web server.
  bind = 'localhost:8000'
  
  # Use three workers in addition to the master process. Since the Avalon
  # Music Server is largely CPU bound, you can increase the number of
  # request that can be handled by increasing this number (up to a point!).
  # The Gunicorn docs recommend 2N + 1 where N is the number of CPUs you
  # have.
  workers = 3
  
  # Make sure to load the application only in the main process before
  # spawning the worker processes. Necessary when the server is scanning
  # a music collection on start up to prevent multiple workers from
  # stomping all over the database at the same time.
  preload_app = True
  
new_contents: # Configuration for running the Avalon Music Server under Gunicorn
  # http://docs.gunicorn.org
  
  # Note that this configuration omits a bunch of features that Gunicorn
  # has (such as running as a daemon, changing users, error and access
  # logging) because it is designed to be used when running Gunicorn
  # with supervisord and a separate public facing web server (such as
  # Nginx).
  
  # Bind the server to an address only accessible locally. We'll be
  # running Nginx which will proxy to Gunicorn and act as the public-
  # facing web server.
  bind = 'localhost:8000'
  
  # Use three workers in addition to the master process. Since the Avalon
  # Music Server is largely CPU bound, you can increase the number of
  # request that can be handled by increasing this number (up to a point!).
  # The Gunicorn docs recommend 2N + 1 where N is the number of CPUs you
  # have.
  workers = 3
  
  # Make sure to load the application only in the main process before
  # spawning the worker processes. This will save us memory when using
  # multiple worker processes since the OS will be be able to take advantage
  # of copy-on-write optimizations.
  preload_app = True
  
subject: Fix comment about why we use preload