Develop Reference

Rest assured equivalent in Python for Rest API automation testing

in Java there is a rest-assured framework to make the API requests and validate the responses with various ways.
Is there any alternative in Python?
Or I should use the requests library to make the API calls and validate the responses e.g using JsonPath, XmlPath and other libraries.
Thanks in advance.

In order to simulate the same functionality as in Java's rest-assured, you can use:
Option 1
requests module along with pytest
Option 2
Use an available module pyhttptest
Here you just need to define your test-cases and request in a json file and run all your test cases using command line
And Last
My favorite and recommended one is pyresttest
pyresttest is tool for testing RESTful HTTP requests. It’s written in Python (hence the py prefix) but unless you intend to write extensions this does not require any Python programming. It will work just fine in a Ruby, Go, Node, or PHP project.
As a command line tool it works by specifying a root URL (host) address and then the path to a YAML configuration file. The configuration file enumerates a list of URLs to request and tests against the expected status code.
Cheers!!

Python/Twisted-- Render Paramiko SFTPFile as if it were twisted.web.static.File

Before I pose the question, some background: I'm creating a web management tool that, among other things, allows the user to download, tail, email, and move and files between predefined directories via the management panel. Many of these directories are local to the server, but some are actually located on remote hosts and accessed via SSH--however, this is transparent to the user. I've used Twisted to create a pseudo-REST API for the client to access, but since I want to avoid revealing actual server paths to the client, it requests downloads of files using a POST with an arbitrary ID to the api, as such: "http://XXXX:8880/api/transfer/download"
with POST params similar to this: {"srckey":"5","srcfile":"solar2-windows-1.10.zip"}. The idea being the client only knows the key of the directory and filename.
Pardon the excessive background--I'm hoping it will make my question more clear: The issue I have is I'm trying to allow users to download a copy of a file from one of the "remote" hosts via the management server that hosts the web panel, all without caching the file locally. I've used Twisted's File() object to stream large static files before, but since the file resides on another server, I'm trying to accomplish the same using a file object provided by Paramiko's "open()" method.
I've tried setting up a consumer/producer system similar to that used in the render methods of twisted.web.static.File, plugging in the file pointer provided by Paramiko in the appropriate places, but only the smallest text files transfer successfully--all cases cause Paramiko to throw this error:
socket.error: Socket is closed
The contents of the relevant python files are here:
serve-project.py: http://pastebin.com/YcjsQHu3
WrapSSH.py:
http://pastebin.com/XaKXJwxb
In a nutshell, I'm trying to stream the data from a Paramiko SFTPFile to an HTTP client. I suspect that my approach is majorly faulty, due to my minimal familiarity with Twisted. Anyone have suggestions on a more intelligent way to accomplish this?

Python App Engine debug/dev mode

I'm working on an App Engine project (Python) where we'd like to make certain changes to the app's behavior when debugging/developing (most often locally). For example, when debugging, we'd like to disable our rate-limiting decorators, turn on the debug param in the WSGIApplication, maybe add some asserts.
As far as I can tell, App Engine doesn't naturally have any concept of a global dev-mode or debug-mode, so I'm wondering how best to implement such a mode. The options I've been able to come up with so far:
Use google.appengine.api.app_identity.get_default_version_hostname() to get the hostname and check if it begins with localhost. This seems... unreliable, and doesn't allow for using the debug mode in a deployed app instance.
Use os.environ.get('APPLICATION_ID') to get the application id, which according to this page is automatically prepended with dev~ by the development server. Worryingly, the very source of this information is in a box warning:
Do not get the App ID from the environment variable. The development
server simulates the production App Engine service. One way in which
it does this is to prepend a string (dev~) to the APPLICATION_ID
environment variable, which is similar to the string prepended in
production for applications using the High Replication Datastore. You
can modify this behavior with the --default_partition flag, choosing a
value of "" to match the master-slave option in production. Google
recommends always getting the application ID using get_application_id,
as described above.
Not sure if this is an acceptable use of the environment variable. Either way it's probably equally hacky, and suffers the same problem of only working with a locally running instance.
Use a custom app-id for development (locally and deployed), use the -A flag in dev_appserver.py, and use google.appengine.api.app_identity.get_application_id() in the code. I don't like this for a number of reasons (namely having to have two separate app engine projects).
Use a dev app engine version for development and detect with os.environ.get('CURRENT_VERSION_ID').split('.')[0] in code. When deployed this is easy, but I'm not sure how to make dev_appserver.py use a custom version without modifying app.yaml. I suppose I could sed app.yaml to a temp file in /tmp/ with the version replaced and the relative paths resolved (or just create a persistent dev-app.yaml), then pass that into dev_appserver.py. But that seems also kinda dirty and prone to error/sync issues.
Am I missing any other approaches? Any considerations I failed to acknowledge? Any other advice?

In regards to "detecting" localhost development we use the following in our applications settings / config file.
IS_DEV_APPSERVER = 'development' in os.environ.get('SERVER_SOFTWARE', '').lower()
That used in conjunction with the debug flag should do the trick for you.

Is there a better way to access my public api?

I am new to Flask.
I have a public api, call it api.example.com.
#app.route('/api')
def api():
name = request.args.get('name')
...
return jsonify({'address':'100 Main'})
I am building an app on top of my public api (call it www.coolapp.com), so in another app I have:
#app.route('/make_request')
def index():
params = {'name':'Fred'}
r = requests.get('http://api.example.com', params=params)
return render_template('really_cool.jinja2',address=r.text)
Both api.example.com and www.coolapp.com are hosted on the same server. It seems inefficient the way I have it (hitting the http server when I could access the api directly). Is there a more efficient way for coolapp to access the api and still be able to pass in the params that api needs?

Ultimately, with an API powered system, it's best to hit the API because:
It's user testing the API (even though you're the user, it's what others still access);
You can then scale easily - put a pool of API boxes behind a load balancer if you get big.
However, if you're developing on the same box you could make a virtual server that listens on localhost on a random port (1982) and then forwards all traffic to your api code.
To make this easier I'd abstract the API_URL into a setting in your settings.py (or whatever you are loading in to Flask) and use:
r = requests.get(app.config['API_URL'], params=params)
This will allow you to make a single change if you find using this localhost method isn't for you or you have to move off one box.
Edit
Looking at your comments you are hoping to hit the Python function directly. I don't recommend doing this (for the reasons above - using the API itself is better). I can also see an issue if you did want to do this.
First of all we have to make sure the api package is in your PYTHONPATH. Easy to do, especially if you're using virtualenvs.
We from api import views and replace our code to have r = views.api() so that it calls our api() function.
Our api() function will fail for a couple of reasons:
It uses the flask.request to extract the GET arg 'name'. Because we haven't made a request with the flask WSGI we will not have a request to use.
Even if we did manage to pass the request from the front end through to the API the second problem we have is using the jsonify({'address':'100 Main'}). This returns a Response object with an application type set for JSON (not just the JSON itself).
You would have to completely rewrite your function to take into account the Response object and handle it correctly. A real pain if you do decide to go back to an API system again...

Depending on how you structure your code, your database access, and your functions, you can simply turn the other app into package, import the relevant modules and call the functions directly.
You can find more information on modules and packages here.
Please note that, as Ewan mentioned, there's some advantages to using the API. I would advise you to use requests until you actually need faster requests (this is probably premature optimization).
Another idea that might be worth considering, depending on your particular code, is creating a library that is used by both applications.

Using CherryPy/Cherryd to launch multiple Flask instances

Per suggestions on SO/SF and other sites, I am using CherryPy as the WSGI server to launch multiple instances of a Python web server I built with Flask. Each instance runs on its own port and sits behind Nginx. I should note that the below does work for me, but I'm troubled that I have gone about things the wrong way and it works "by accident".
Here is my current cherrypy.conf file:
[global]
server.socket_host = '0.0.0.0'
server.socket_port = 8891
request.dispatch: cherrypy.dispatch.MethodDispatcher()
tree.mount = {'/':my_flask_server.app}
Without diving too far into my Flask server, here's how it starts:
import flask
app = flask.Flask(__name__)
#app.route('/')
def hello_world():
return "hello"
And here is the command I issue on the command line to launch with Cherryd:
cherryd -c cherrypy.conf -i my_flask_server
Questions are:
Is wrapping Flask inside CherryPy still the preferred method of using Flask in production? https://stackoverflow.com/questions/4884541/cherrypy-vs-flask-werkzeug
Is this the proper way to use a .conf file to launch CherryPy and import the Flask app? I have scoured the CherryPy documentation, but I cannot find any use cases that match what I am trying to do here specifically.
Is the proper way to launch multiple CherryPy/Flask instances on a single machine to execute multiple cherryd commands (daemonizing with -d, etc) with unique .conf files for each port to be used (8891, 8892, etc)? Or is there a better "CherryPy" way to accomplish this?
Thanks for any help and insight.

I can't speak for Flask, but I can for CherryPy. That looks like the "proper way"...mostly. That line about a MethodDispatcher is a no-op since it only affects CherryPy Applications, and you don't appear to have mounted any (just a single Flask app instead).
Regarding point 3, you have it right. CherryPy allows you to run multiple Server objects in the same process in order to listen on multiple ports (or protocols), but it doesn't have any sugar for starting up multiple processes. As you say, multiple cherryd commands with varying config files is how to do it (unless you want to use a more integrated cluster/config management tool like eggmonster).

Terminology: Mounting vs Grafting
In principle this is a proper way to serve a flask app through cherrypy, just a quick note on your naming:
It is worth noting here that tree.mount is not a configuration key by itself - tree will lead to cherrypy._cpconfig._tree_config_handler(k, v) being called with the arguments 'mount', {'/': my_flask_server.app}.
The key parameter is not used at all by the _tree_config_handler so in your config "mount" is just an arbitrary label for that specific dict of path mappings. It also does not "mount" the application (it's not a CherryPy app after all). By that I mean, it does not cherrypy.tree.mount(…) it but rather cherrypy.tree.grafts an arbitrary WSGI handler onto your "script-name" (paths, but in CherryPy terminology) namespace.
Cherrypy's log message somewhat misleadingly says "Mounted <app as string> on /"]
This is a somewhat important point since with graft, unlike mount, you cannot specify further options such as static file service for your app or streaming responses on that path.
So I would recommend changing the tree.mount config key to something descriptive that does not invite reading too much semantics about what happens within CherryPy (since there is the cherrypy.tree.mount method) due to that config. E.g., tree.flask_app_name if you're just mapping that one app in that dict (there can be many tree directives, all of them just getting merged into the paths namespace) or tree.wsgi_delegates if you map many apps in that dict.
Using CherryPy to serve additional content without making an app of it
Another side note, if you want cherrypy to e.g. provide static file service for your app, you don't have to create a boilerplate cherrypy app to hold that configuration. You just have to mount None with the appropriate additional config. The following files would suffice to have CherryPy to serve static content from the subdirectory 'static' if they are put into the directory where you launch cherryd to serve static content (invoke cherryd as cherryd -c cherrypy.conf -i my_flask_server -i static:
static.py
import cherrypy
# next line could also have config as an inline dict, but
# file config is often easier to handle
cherrypy.tree.mount(None, '/static-path', 'static.conf')
static.conf
# static.conf
[/]
tools.staticdir.on = True
tools.staticdir.root = os.getcwd()
tools.staticdir.dir = 'static'
tools.staticdir.index = 'index.html'