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Update licenses for new versions after dependency upgrades
pin pycurl to version that does not break on install
implement new workflow for py2/3 requirements management
require twisted tls extras, resolve service-identity version
Upgrade celery to resolve importlib DeprecationWarning
use flags to resolve the unsafe and cache problems
Bump keystone auth to resolve problem with openstack script
Clarify code path, routing to template vs. managed injector
behavior is also now reflected in test data files
Refactor test data layout for inventory injector logic
Add developer docs for inventory plugins transition
Memoize only get_ansible_version with no parameters
Make inventory plugin injector enablement a separate
concept from the initial_version
switch tests to look for plugin_name as well
Add plugin injectors for tower and foreman.
Add jinja2 native types compat feature
move tower source license compare logic to management command
introduce inventory source compat mode
pin jinja2 for native Ansible types
Add parent group keys, and additional translations
manual dash sanitization for un-region-like ec2 groups
nest zones under regions using Ansible core feature just merged
implement conditionally only with BOTH group_by options
Make compat mode default be true
in API models, UI add and edit controllers
Add several additional hostvars to translation
Add Azure tags null case translation
Make Azure group_by key off source_vars
to be consistent with the script
support top-level ec2 boto_profile setting
Initialize some inventory plugin test data files
Implement openstack inventory plugin
This may be removed later:
- port non-JSON line strip method from core
Dupliate effort with AWX mainline devel
- Produce ansible_version related to venv
Refactor some of injector management, moving more
of this overhead into tasks.py, when it comes to
managing injector kwargs
Upgrade and move openstack inventory script
sync up parameters
Add extremely detailed logic to inventory file creation
for ec2, Azure, and gce so that they are closer to a
genuine superset of what the contrib script used to give.
this commit implements the bulk of `awx-manage run_dispatcher`, a new
command that binds to RabbitMQ via kombu and balances messages across
a pool of workers that are similar to celeryd workers in spirit.
Specifically, this includes:
- a new decorator, `awx.main.dispatch.task`, which can be used to
decorate functions or classes so that they can be designated as
"Tasks"
- support for fanout/broadcast tasks (at this point in time, only
`conf.Setting` memcached flushes use this functionality)
- support for job reaping
- support for success/failure hooks for job runs (i.e.,
`handle_work_success` and `handle_work_error`)
- support for auto scaling worker pool that scale processes up and down
on demand
- minimal support for RPC, such as status checks and pool recycle/reload
This was causing offline pip installs to fail for some weird reason:
The 'setuptools_scm>=1.15.0' distribution was not found and is required by the application
Even though it is there. v2.x still works.
The ansible-network-ui prototype project builds a standalone Network UI
outside of Tower as its own Django application. The original prototype
code is located here:
https://github.com/benthomasson/ansible-network-ui.
The prototype provides a virtual canvas that supports placing
networking devices onto 2D plane and connecting those devices together
with connections called links. The point where the link connects
to the network device is called an interface. The devices, interfaces,
and links may all have their respective names. This models physical
networking devices is a simple fashion.
The prototype implements a pannable and zoomable 2D canvas in using SVG
elements and AngularJS directives. This is done by adding event
listeners for mouse and keyboard events to an SVG element that fills the
entire browser window.
Mouse and keyboard events are handled in a processing pipeline where
the processing units are implemented as finite state machines that
provide deterministic behavior to the UI.
The finite state machines are built in a visual way that makes
the states and transitions clearly evident. The visual tool for
building FSM is located here:
https://github.com/benthomasson/fsm-designer-svg. This tool
is a fork of this project where the canvas is the same. The elements
on the page are FSM states and the directional connections are called
transitions. The bootstrapping of the FSM designer tool and
network-ui happen in parallel. It was useful to try experiemental
code in FSM designer and then import it into network-ui.
The FSM designer tool provides a YAML description of the design
which can be used to generate skeleton code and check the implementation
against the design for discrepancies.
Events supported:
* Mouse click
* Mouse scroll-wheel
* Keyboard events
* Touch events
Interactions supported:
* Pan canvas by clicking-and-dragging on the background
* Zooming canvas by scrolling mousewheel
* Adding devices and links by using hotkeys
* Selecting devices, interaces, and links by clicking on their icon
* Editing labels on devices, interfaces, and links by double-clicking on
their icon
* Moving devices around the canvas by clicking-and-dragging on their
icon
Device types supported:
* router
* switch
* host
* racks
The database schema for the prototype is also developed with a visual
tool that makes the relationships in the snowflake schema for the models
quickly evident. This tool makes it very easy to build queries across
multiple tables using Django's query builder.
See: https://github.com/benthomasson/db-designer-svg
The client and the server communicate asynchronously over a websocket.
This allows the UI to be very responsive to user interaction since
the full request/response cycle is not needed for every user
interaction.
The server provides persistence of the UI state in the database
using event handlers for events generated in the UI. The UI
processes mouse and keyboard events, updates the UI, and
generates new types of events that are then sent to the server
to be persisted in the database.
UI elements are tracked by unique ids generated on the client
when an element is first created. This allows the elements to
be correctly tracked before they are stored in the database.
The history of the UI is stored in the TopologyHistory model
which is useful for tracking which client made which change
and is useful for implementing undo/redo.
Each message is given a unique id per client and has
a known message type. Message types are pre-populated
in the MessageType model using a database migration.
A History message containing all the change messages for a topology is
sent when the websocket is connected. This allows for undo/redo work
across sessions.
This prototype provides a server-side test runner for driving
tests in the user interface. Events are emitted on the server
to drive the UI. Test code coverage is measured using the
istanbul library which produces instrumented client code.
Code coverage for the server is is measured by the coverage library.
The test code coverage for the Python code is 100%.
Last round of dependency updates showed that AWX
depended on packages which came implicitly from shade
decorator is added as an explicit dependency
and all of the rest of shade requirements are
added back in here.
Upgrades of minor dependency upgrades
Inventory scripts were upgraded in separate commit
Major exclusions from this update
- celery was already downgraded for other reasons
- Django / DRF major update already done, minor bumps here
- asgi-amqp has fixes coming independently, not touched
- TACACS plus added features not needed
Removals of note
- remove shade from AWX requirements
- remove kombu from Ansible requirements
Other notes
Add note about pinning setuptools and pip,
done but not mentioned previously
Stop pinning gevent-websocket and twisted
upgrade Azure to Ansible core requirements
more detailed notes
https://gist.github.com/AlanCoding/9442a512ab6977940bc7b5b346d4f70b
upgrade version of Django for Exception
update our event data search algorithm to be a bit lazier in event data
discovery; this drastically improves processing speeds for stdout >5MB
see: https://github.com/ansible/awx/issues/417
