Clarified installation docs around worker reqs

docs/installation.rst

@@ -154,14 +154,31 @@ variables and connections.
 
 Scaling Out with Celery
 '''''''''''''''''''''''
-CeleryExecutor is the way you can scale out the number of workers. For this
+``CeleryExecutor`` is one of the ways you can scale out the number of workers. For this
 to work, you need to setup a Celery backend (**RabbitMQ**, **Redis**, ...) and
 change your ``airflow.cfg`` to point the executor parameter to
-CeleryExecutor and provide the related Celery settings.
+``CeleryExecutor`` and provide the related Celery settings.
 
 For more information about setting up a Celery broker, refer to the
 exhaustive `Celery documentation on the topic <http://docs.celeryproject.org/en/latest/getting-started/brokers/index.html>`_.
 
+Here are a few imperative requirements for your workers:
+
+- ``airflow`` needs to be installed, and the CLI needs to be in the path
+- Airflow configuration settings should be homogeneous across the cluster
+- Operators that are executed on the worker need to have their dependencies
+  met in that context. For example, if you use the ``HiveOperator``,
+  the hive CLI needs to be installed on that box, or if you use the
+  ``MySqlOperator``, the required Python library needs to be available in
+  the ``PYTHONPATH`` somehow
+- The worker needs to have access to its ``DAGS_FOLDER``, and you need to
+  synchronize the filesystems by your own mean. A common setup would be to
+  store your DAGS_FOLDER in a Git repository and sync it across machines using
+  Chef, Puppet, Ansible, or whatever you use to configure machines in your
+  environment. If all your boxes have a common mount point, having your
+  pipelines files shared there should work as well
+
+
 To kick off a worker, you need to setup Airflow and kick off the worker
 subcommand
 
@@ -173,13 +190,19 @@ Your worker should start picking up tasks as soon as they get fired in
 its direction.
 
 Note that you can also run "Celery Flower", a web UI built on top of Celery,
-to monitor your workers.
+to monitor your workers. You can use the shortcut command ``airflow flower``
+to start a Flower web server.
+
 
 Logs
 ''''
-Users can specify a logs folder in ``airflow.cfg``. By default, it is in the ``AIRFLOW_HOME`` directory.
+Users can specify a logs folder in ``airflow.cfg``. By default, it is in
+the ``AIRFLOW_HOME`` directory.
 
-In addition, users can supply an S3 location for storing log backups. If logs are not found in the local filesystem (for example, if a worker is lost or reset), the S3 logs will be displayed in the Airflow UI. Note that logs are only sent to S3 once a task completes (including failure).
+In addition, users can supply an S3 location for storing log backups. If
+logs are not found in the local filesystem (for example, if a worker is
+lost or reset), the S3 logs will be displayed in the Airflow UI. Note that
+logs are only sent to S3 once a task completes (including failure).
 
 .. code-block:: bash
 
@@ -189,11 +212,11 @@ In addition, users can supply an S3 location for storing log backups. If logs ar
 
 Scaling Out on Mesos (community contributed)
 ''''''''''''''''''''''''''''''''''''''''''''
-MesosExecutor allows you to schedule airflow tasks on a Mesos cluster.
+``MesosExecutor`` allows you to schedule airflow tasks on a Mesos cluster.
 For this to work, you need a running mesos cluster and you must perform the following
 steps -
 
-1. Install airflow on a machine where webserver and scheduler will run,
+1. Install airflow on a machine where web server and scheduler will run,
    let's refer to this as the "Airflow server".
 2. On the Airflow server, install mesos python eggs from `mesos downloads <http://open.mesosphere.com/downloads/mesos/>`_.
 3. On the Airflow server, use a database (such as mysql) which can be accessed from mesos