I am writing an application which connects to a database. I want to create that db connection once, and then reuse that connection throughout the life of the application.
I also want to authenticate users. A user's auth will live for only the life of a request.
How can I differentiate between objects stored for the life of a flask app, versus specific to the request? Where would I store them so that all modules (and subsequent blueprints) have access to them?
Here is my sample app:
from flask import Flask, g
app = Flask(__name__)
#app.before_first_request
def setup_database(*args, **kwargs):
print 'before first request', g.__dict__
g.database = 'DATABASE'
print 'after first request', g.__dict__
#app.route('/')
def index():
print 'request start', g.__dict__
g.current_user = 'USER'
print 'request end', g.__dict__
return 'hello'
if __name__ == '__main__':
app.run(debug=True, port=6001)
When I run this (Flask 0.10.1) and navigate to http://localhost:6001/, here is what shows up in the console:
$ python app.py
* Running on http://127.0.0.1:6001/
* Restarting with reloader
before first request {}
after first request {'database': 'DATABASE'}
request start {'database': 'DATABASE'}
request end {'current_user': 'USER', 'database': 'DATABASE'}
127.0.0.1 - - [30/Sep/2013 11:36:40] "GET / HTTP/1.1" 200 -
request start {}
request end {'current_user': 'USER'}
127.0.0.1 - - [30/Sep/2013 11:36:41] "GET / HTTP/1.1" 200 -
That is, the first request is working as expected: flask.g is holding my database, and when the request starts, it also has my user's information.
However, upon my second request, flask.g is wiped clean! My database is nowhere to be found.
Now, I know that flask.g used to apply to the request only. But now that it is bound to the application (as of 0.10), I want to know how to bind variables to the entire application, rather than just a single request.
What am I missing?
edit: I'm specifically interested in MongoDB - and in my case, maintaining connections to multiple Mongo databases. Is my best bet to just create those connections in __init__.py and reuse those objects?
flask.g will only store things for the duration of a request. The documentation mentioned that the values are stored on the application context rather than the request, but that is more of an implementation issue: it doesn't change the fact that objects in flask.g are only available in the same thread, and during the lifetime of a single request.
For example, in the official tutorial section on database connections, the connection is made once at the beginning of the request, then terminated at the end of the request.
Of course, if you really wanted to, you could create the database connection once, store it in __init__.py, and reference it (as a global variable) as needed. However, you shouldn't do this: the connection could close or timeout, and you could not use the connection in multiple threads.
Since you didn't specify HOW you will be using Mongo in Python, I assume you will be using PyMongo, since that handles all of the connection pooling for you.
In this case, you would do something like this...
from flask import Flask
from pymongo import MongoClient
# This line of code does NOT create a connection
client = MongoClient()
app = Flask()
# This can be in __init__.py, or some other file that has imported the "client" attribute
#app.route('/'):
def index():
posts = client.database.posts.find()
You could, if you wish, do something like this...
from flask import Flask, g
from pymongo import MongoClient
# This line of code does NOT create a connection
client = MongoClient()
app = Flask()
#app.before_request
def before_request():
g.db = client.database
#app.route('/'):
def index():
posts = g.db.posts.find()
This really isn't all that different, however it can be helpful for logic that you want to perform on every request (such as setting g.db to a specific database depending on the user that is logged in).
Finally, you can realize that most of the work of setting up PyMongo with Flask is probably done for you in Flask-PyMongo.
Your other question deals with how you keep track of stuff specific to the user that is logged in. Well, in this case, you DO need to store some data that sticks around with the connection. flask.g is cleared at the end of the reuquest, so that's no good.
What you want to use is sessions. This is a place where you can store values that is (with the default implementation) stored in a cookie on the user's browser. Since the cookie will be passed along with every request the user's browser makes to your web site, you will have available the data you put in the session.
Keep in mind, though, that the session is NOT stored on the server. It is turned into a string that is passed back and forth to the user. Therefore, you can't store things like DB connections onto it. You would instead store identifiers (like user IDs).
Making sure that user authentication works is VERY hard to get right. The security concerns that you need to make sure of are amazingly complex. I would strongly recommend using something like Flask-Login to handle this for you. You can still use the session for storing other items as needed, or you can let Flask-Login handle determining the user ID and store the values you need in the database and retrieving them from the database in every request.
So, in summary, there are a few different ways to do what you want to do. Each have their usages.
Globals are good for items that are thread-safe (such as the PyMongo's MongoClient).
flask.g can be used for storing data in the lifetime of a request. With SQLAlchemy-based flask apps, a common thing to do is to ensure that all changes happen at once, at the end of a request using an after_request method. Using flask.g for something like this is very helpful.
The Flask session can be used to store simple data (strings and numbers, not connection objects) that can be used on subsequent requests that come from the same user. This is entirely dependent on using cookies, so at any point the user could delete the cookie and everything in the "session" will be lost. Therefore, you probably want to store much of your data in databases, with the session used to identify the data that relates to the user in the session.
"bound to the application" does not mean what you think it means. It means that g is bound to the currently running request. Quoth the docs:
Flask provides you with a special object that ensures it is only valid for the active request and that will return different values for each request.
It should be noted that Flask's tutorials specifically do not persist database objects, but that this is not normative for any application of substantial size. If you're really interested in diving down the rabbit hole, I suggest a database connection pooling tool. (such as this one, mentioned in the SO answer ref'd above)
I suggest you use session to manage user information. Sessions help you keep information b/w multiple requests and flask provides you a session framework already.
from flask import session
session['usename'] = 'xyz'
Look at the extension Flask-Login. It is well designed to handle user authentications.
For database, I suggest looking at Flask-SQLAlchemy extension. This takes care of initialization, pooling, teardowns etc. for you out of the box. All you need to do is define the database URI in a config and bind it to the application.
from flask.ext.sqlalchemy import SQLAlchemy
app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db'
db = SQLAlchemy(app)
Related
I'm building a data analysis Flask application which takes a ton of user inputs, performs some calculations then projects the results to various web pages. I'm using a pandas Dataframe to store the inputs and perform the calculations. Then I convert the DF to a dictionary and store that in the session object.
I'm having problems due to the fact that the session object can only hold ~4k bytes. Several pages read the data so I need a means to pass this large amount of data (~5k-50k) from one request to another (which the session object does perfectly but for smaller memory sizes).
Can I set the storage limit higher for the session object (I imagine I can't as 4k is the limit for a cookie and the session object is a cookie)? Or is there something else I should do here (store the dict in a database, etc.)?
EDIT:
I'm thinking a viable alternative would be to grab the data from the database (mongodb in my case), store it in a local variable and pass that variable to the template directly. Are there negatives to this? And is there a limit on how much memory I can pass directory to a template? See example below:
#app.route('/results')
def results():
# get data I need from database (~5k-50k bytes)
data = mongo.db[collection_name].find_one({'key': 'query'})
# pass directory to template (instead of storing in session object)
return render_template('results_page.html', data=data)
Yeah this definitely sounds like a case for server-side sessions.
There are code snippets on the official site for the most popular databases.
These shouldn't be hard to migrate to since they use the same SessionMixin interface as the cookie session system.
An even easier approach could be to use Flask-KVSession, which claims that
Integration with Flask is seamless, once the extension is loaded for a Flask application, it transparently replaces Flask’s own Session management. Any application working with sessions should work the same with Flask-KVSession
Flask-Session adds support to Server-side Session.
Here is how you can start Flask-Session (extracted from the official documentation).
from flask import Flask, session
from flask.ext.session import Session
app = Flask(__name__)
# Check Configuration section for more details
SESSION_TYPE = 'redis'
app.config.from_object(__name__)
Session(app)
#app.route('/set/')
def set():
session['key'] = 'value'
return 'ok'
#app.route('/get/')
def get():
return session.get('key', 'not set')
Regarding the size limit on rendering templates, the official Flask documentation has no mention of this.
I don't really believe there is any size limit actually.
If there is any limitation, that might be on Jinja's side.
Consider a web site that persists on disk the activitiy of its users, without requiring them to log-in/authenticate.
This would enable the user to return and find all their activity intact, even if the server had been relaunched.
Because the user session
from Flask import session
session['foo'] = 'bar'
is an ordinary dict, I'm assuming it gets wiped when the server is stopped and relaunched. It is hence not persistent if the user's two visits cross a server relaunch.
To do so using Flask, we'd use a database session
from flask.ext.sqlalchemy import SQLAlchemy
db = SQLAlchemy()
db.session.add(..)
db.session.commit()
and, since users are not logged-in, we'd distinguish between distinct users through their user sessions.
What is a unique ID that can be extracted from the user session to persist in a database session? The idea is that when a user returns, the cookie in their browser would identify them uniquely, which in turn would mean that the identifier we'd hash from the user session would remain intact.
From the terminology I assume you are using Flask -
So, the db.session does not refer to the "browsing" session, with date related to a single viewer- ratehr, it refers to a db connection session that is unique for that web view cycle (and not for the browsing session) - these are different things.
And as such, the code above do persist your object on the DB in a permanent, unique way. If you can't see them when restarting the Flask application it must be because you are using a transient database. Just adjust your configurations to use a permanent - in disk - database, instead of either an in-memory sqlite instance, or a database inside a docker container that is re-created every time.
The "session" you are thinking of, as "browsing" session does not exist out-of the box in Flask- you either roll yur own, or use one of the plug-ins such as https://pythonhosted.org/Flask-Session/ - this is what is related to PHP's "session".
I'm building a small website and I already have all my models in SQLAlchemy. The website is to publish some information from some calculations which are done offline. Only the results will be published to a slimmed down database i.e. it contains the results, not the raw data, but the website needs to query the results.
I'm going to use Flask, as my models are already driven with Python (and some heavy lifting in C++ via SWIG) and I don't want to use Django.
Now this has been asked before I'm sure and the usual mantra without much justification is to 'use Flask-SQLAlchemy'. The question is why?
If I write some session handling myself, why do I have to go through the additional layer of redefining my database in Flask-SQLAlchemy. Other than having to write some code like here in my Flask app somewhere:
#app.before_request
def before_request():
g.db = connect_db()
#app.teardown_request
def teardown_request(exception):
db = getattr(g, 'db', None)
if db is not None:
db.close()
What else do I need to worry about? SQLAlchemy even does connection pooling for me by default.
Actually, you are building a web application using Flask which do database related kinds of stuff by sqlalchemy. So, when you are dealing with database session with multiple requests which your application handles, then you have to ascertain that, you are creating and closing sessions cautiously.
If you read SQLAlchemy docs, they recommend to keep the lifecycle of the session separate and external from functions and objects that access and/or manipulate database data. This will greatly help with achieving a predictable and consistent transactional scope.
A web application is the easiest case because such an application is already constructed around a single, consistent scope - this is the request, which represents an incoming request from a browser, the processing of that request to formulate a response, and finally the delivery of that response back to the client. Integrating web applications with the Session is then the straightforward task of linking the scope of the Session to that of the request. The Session can be established as the request begins, or using a lazy initialization pattern which establishes one as soon as it is needed. The request then proceeds, with some system in place where application logic can access the current Session in a manner associated with how the actual request object is accessed. As the request ends, the Session is torn down as well, usually through the usage of event hooks provided by the web framework. The transaction used by the Session may also be committed at this point, or alternatively the application may opt for an explicit commit pattern, only committing for those requests where one is warranted, but still always tearing down the Session unconditionally at the end.
In laymen terms, i mean to say that
In SQLAlchemy, above action is mentioned because sessions in web application should be scoped, meaning that each request handler creates and destroys its own session.
This is necessary because web servers can be multi-threaded, so multiple requests might be served at the same time, each working with a different database session.
This means that if you are using SqlAlchemy with Flask, you have to manually handle session like creating scoped session and also remove them on each request cautiously otherwise you may be in deep shit, which adds extra layer of complexity to your web application.
But, there comes Flask-SqlAlchemy (an extension of sqlalchemy library for Flask app) which provides infrastructure to assist in the task of aligning the lifespan of a Session with that of each web request. Actually, you can also found that in SqlAlchmey docs, they also recommend to use this with Flask.
Flask-SQLAlchemy creates a fresh/new scoped session for each request. If you dig further it you will find out here, it also installs a hook on app.teardown_appcontext (for Flask >=0.9), app.teardown_request (for Flask 0.7-0.8), app.after_request (for Flask <0.7) and here is where it calls db.session.remove().
The code you put in the question is not actually valid for Sqlalchemy integration is Flask. I know it is just example, but saying that just in case.
For Sqlalchemy integration all you need to do is to make sure current DbSession is cleaned up at the end of request via something like this:
#app.teardown_appcontext
def shutdown_session(exception=None):
DbSession.remove()
where DbSession is scoped session.
Here is documentation for the case when you dont want to use Flask-Sqlalchemy package.
Scenario:
Major web app w. Python+Flask
Flask login and Flask.session for basic session variables (user-id and session-id)
Flask.session and limitations? (Cookies)
Cookie based and basically persist only at the client side.
For some session variables that will be regularly read (ie, user permissions, custom application config) it feels awkward to carry all that info around in a cookie, at every single page request and response.
Database is too much?
Since the session can be identified at the server side by introducing unique session id at login, some server-side session variable management can be used. Reading this data at the server side from a database also feels like unnecessary overhead.
Question
What is the most efficient way to handle the session variables at the server side?
Perhaps that could be a memory-based solution, but I am worried that different Flask app requests could be executed at different threads that would not share the memory-stored session data, or cause conflicts in case of simultaneous reading-writing.
I am looking for advice and best practice for planning the basic level architecture.
Flask-Caching
What you need is a server-side caching package that's Flask-Caching.
A simple setup:
from flask import Flask
from flask_caching import Cache
app = Flask(__name__)
app.config['CACHE_TYPE'] = 'SimpleCache'
cache = Cache(app)
Then a explicitly use of a cached variable:
#app.route('/')
def load():
cache.set("foo", foo)
bar = cache.get("foo")
There is much more in Flask-Caching and that's the recommended approach by Flask.
In case of a multithread server with gunicorn from here you better use ['CACHE_TYPE'] = 'FileSystemCache'
Your instinct is correct, it's probably not the way to do it.
Session data should only be ephemeral information that is not too troublesome to lose and recreate. For example, the user will just have to login again to restore it.
Configuration data or anything else that's necessary on the server and that must survive a logout is not part of the session and should be stored in a DB.
Now, if you really need to easily keep this information client-side and it's not too much of a problem if it's lost, then use a session cookie for logged in/out state and a permanent cookie with a long lifespan for the rest of the configuration information.
If the information is too much size-wise, then the only option I can think of is to store the data, other than the logged in/out state, in a DB.
I have a Pylons-based web application which connects via Sqlalchemy (v0.5) to a Postgres database. For security, rather than follow the typical pattern of simple web apps (as seen in just about all tutorials), I'm not using a generic Postgres user (e.g. "webapp") but am requiring that users enter their own Postgres userid and password, and am using that to establish the connection. That means we get the full benefit of Postgres security.
Complicating things still further, there are two separate databases to connect to. Although they're currently in the same Postgres cluster, they need to be able to move to separate hosts at a later date.
We're using sqlalchemy's declarative package, though I can't see that this has any bearing on the matter.
Most examples of sqlalchemy show trivial approaches such as setting up the Metadata once, at application startup, with a generic database userid and password, which is used through the web application. This is usually done with Metadata.bind = create_engine(), sometimes even at module-level in the database model files.
My question is, how can we defer establishing the connections until the user has logged in, and then (of course) re-use those connections, or re-establish them using the same credentials, for each subsequent request.
We have this working -- we think -- but I'm not only not certain of the safety of it, I also think it looks incredibly heavy-weight for the situation.
Inside the __call__ method of the BaseController we retrieve the userid and password from the web session, call sqlalchemy create_engine() once for each database, then call a routine which calls Session.bind_mapper() repeatedly, once for each table that may be referenced on each of those connections, even though any given request usually references only one or two tables. It looks something like this:
# in lib/base.py on the BaseController class
def __call__(self, environ, start_response):
# note: web session contains {'username': XXX, 'password': YYY}
url1 = 'postgres://%(username)s:%(password)s#server1/finance' % session
url2 = 'postgres://%(username)s:%(password)s#server2/staff' % session
finance = create_engine(url1)
staff = create_engine(url2)
db_configure(staff, finance) # see below
... etc
# in another file
Session = scoped_session(sessionmaker())
def db_configure(staff, finance):
s = Session()
from db.finance import Employee, Customer, Invoice
for c in [
Employee,
Customer,
Invoice,
]:
s.bind_mapper(c, finance)
from db.staff import Project, Hour
for c in [
Project,
Hour,
]:
s.bind_mapper(c, staff)
s.close() # prevents leaking connections between sessions?
So the create_engine() calls occur on every request... I can see that being needed, and the Connection Pool probably caches them and does things sensibly.
But calling Session.bind_mapper() once for each table, on every request? Seems like there has to be a better way.
Obviously, since a desire for strong security underlies all this, we don't want any chance that a connection established for a high-security user will inadvertently be used in a later request by a low-security user.
Binding global objects (mappers, metadata) to user-specific connection is not good way. As well as using scoped session. I suggest to create new session for each request and configure it to use user-specific connections. The following sample assumes that you use separate metadata objects for each database:
binds = {}
finance_engine = create_engine(url1)
binds.update(dict.fromkeys(finance_metadata.sorted_tables, finance_engine))
# The following line is required when mappings to joint tables are used (e.g.
# in joint table inheritance) due to bug (or misfeature) in SQLAlchemy 0.5.4.
# This issue might be fixed in newer versions.
binds.update(dict.fromkeys([Employee, Customer, Invoice], finance_engine))
staff_engine = create_engine(url2)
binds.update(dict.fromkeys(staff_metadata.sorted_tables, staff_engine))
# See comment above.
binds.update(dict.fromkeys([Project, Hour], staff_engine))
session = sessionmaker(binds=binds)()
I would look at the connection pooling and see if you can't find a way to have one pool per user.
You can dispose() the pool when the user's session has expired