In my Bottle app running on pythonanywhere, I want objects to be persisted between requests.
If I write something like this:
X = {'count': 0}
#route('/count')
def count():
X['count'] += 1
tpl = SimpleTemplate('Hello {{count}}!')
return tpl.render(count=X['count'])
The count increments, meaning that X persists between requests.
I am currently running this on pythonanywhere, which is a managed service where I have no control over the web server (nginx I presume?) threading, load balancing (if any) etc...
My question is, is this coincidence because it's only using one thread while on minimal load from me doing my tests?
More generally, at which point will this stop working? E.g. I have more than one thread/socket/instance/load-balanced server etc...?
Beyond that, what is my best options to make something like this work (sticking to Bottle) even if I have to move to a barebones server.
Here's what Bottle docs have to say about their request object:
A thread-safe instance of LocalRequest. If accessed from within a request callback, this instance always refers to the current request (even on a multi-threaded server).
But I don't fully understand what that means, or where global variables like the one I used stand with regards to multi-threading.
TL;DR: You'll probably want to use an external database to store your state.
If your application is tiny, and you're planning to always have exactly one server process running, then your current approach can work; "all" you need to do is acquire a lock around every (!) access to the shared state (the dict X in your sample code). (I put "all" in scare quotes there because it's likely to become more complicated than it sounds at first.)
But, since you're asking about multithreading, I'll assume that your application is more than a toy, meaning that you plan to receive substantial traffic and/or want to handle multiple requests concurrently. In this case, you'll want multiple processes, which means that your approach--storing state in memory--cannot work. Memory is not shared across processes. The (general) way to share state across processes is to store the state externally, e.g. in a database.
Are you familiar with Redis? That'd be on my short list of candidates.
I go the answers by contacting PythonAnywhere support, who had this to say:
When you run a website on a free PythonAnywhere account, just
one process handles all of your requests -- so a global variable like
the one you use there will be fine. But as soon as you want to scale
up, and get (say) a hacker account, then you'll have multiple processes
(not, not threads) -- and of course each one will have its own global
variables, so things will go wrong.
So that part deals with the PythonAnywhere specifics on why it works, and when it would stop working on there.
The answer to the second part, about how to share variables between multiple Bottle processes, I also got from their support (most helpful!) once they understood that a database would not work well in this situation.
Different processes cannot of course share variables, and the most viable solution would be to:
write your own kind of caching server to handle keeping stuff in memory [...] You'd have one process that ran all of the time, and web API requests would access it somehow (an internal REST API?). It could maintain stuff in memory [...]
Ps: I didn't expect other replies to tell me to store state in a database, I figured that the fact I'm asking this means I have a good reason not to use a database, apologies for time wasted!
Related
I have a code that contains a variable that I want to change manually when I want without stopping the main loop neither pause it (with input()). I can't find a library that allows me to set manually in the run, or access the RAM memory to change that value.
for now I set a file watcher that reads the parameters every 1 minutes but this is inefficient way I presume.
I guess you just want to expose API. You did it with files which works but less common. You can use common best practices such as:
HTTP web-server. You can do it quickly with Flask/bottle.
gRPC
pub/sub mechanism - Redis, Kafka (more complicated, requires another storage solution - the DB itself).
I guess that there are tons of other solution but you got the idea. I hope that's what you're looking for.
With those solution you can manually trigger your endpoint and change whatever you want in your application.
Hello I don't think this is in the right place for this question but I don't know where to ask it. I want to make a website and an api for that website using the same SQLAlchemy database would just running them at the same time independently be safe or would this cause corruption from two write happening at the same time.
SQLA is a python wrapper for SQL. It is not it's own database. If you're running your website (perhaps flask?) and managing your api from the same script, you can simply use the same reference to your instance of SQLA. Meaning, when you use SQLA to connect to a database and save to a variable, what is really happening is it saves the connection to a variable, and you continually reference that variable, as opposed to the more inefficient method of creating a new connection every time. So when you say
using the same SQLAlchemy database
I believe you are actually referring to the actual underlying database itself, not the SQLA wrapper/connection to it.
If your website and API are not running in the same script (or even if they are, depending on how your API handles simultaneous requests), you may encounter a race condition, which, according to Wikipedia, is defined as:
the condition of an electronics, software, or other system where the system's substantive behavior is dependent on the sequence or timing of other uncontrollable events. It becomes a bug when one or more of the possible behaviors is undesirable.
This may be what you are referring to when you mentioned
would this cause corruption from two write happening at the same time.
To avoid such situations, when a process accesses a file, (depending on the OS,) check is performed to see if there is a "lock" on that file, and if so, the OS refuses to open that file. A lock is created when a process accesses a file (and there is no other process holding a lock on that file), such as by using with open(filename): and is released when the process no longer holds an open reference to the file (such as when python execution leaves the with open(filename): indentation block.) This may be the real issue you might encounter when using two simultaneous connections to a SQLite db.
However, if you are using something like MySQL, where you connect to a SQL server process, and NOT a file, since there is no direct access to a file, there will be no lock on the database, and you may run in to that nasty race condition in the following made up scenario:
Stack Overflow queries the reputation an account to see if it should be banned due to negative reputation.
AT THE EXACT SAME TIME, Someone upvotes an answer made by that account that sets it one point under the account ban threshold.
The outcome is now determined by the speed of execution of these 2 tasks.
If the upvoter has, say, a slow computer, and the "upvote" does not get processed by StackOverflow before the reputation query completes, the account will be banned. However, if there is some lag on Stack Overflow's end, and the upvote processes before the account query finishes, the account will not get banned.
The key concept behind this example is that all of these steps can occur within fractions of a second, and the outcome depends of the speed of execution on both ends.
To address the issue of data corruption, most databases have a system in place that properly order database read and writes, however, there are still semantic issues that may arise, such as the example given above.
Two applications can use the same database as the DB is a separate application that will be accessed by each flask app.
What you are asking can be done and is the methodology used by many large web applications, specially when the API is written in a different framework than the main application.
Since SQL databases are ACID compliant, they have a system in place to queue the multiple read/write requests put to it and perform them in the correct order while ensuring data reliability.
One question to ask though is whether it is useful to write two separate applications. For most flask-only projects the best approach would be to separate the project using blueprints, having a “main” blueprint and a “api” blueprint.
First of all to begin with 'Yes' i checked and googled this topic but can't find anything that gives me a clear answer to my question? I am a beginner in Djagno and studying its documentation where i read about the Thread Safety Considerations for render method of nodes for Templates Tags. Here is the link to the documentation Link. My question lies where it states that Once the node is parsed the render method for that node might be called multiple times i am confused whether it is talking about the use of the template tag in the same document at different places for the same user at the single instance level of the user on the server or the use of the template tag for multiple request coming from users all around the world sharing the same django instance in memory? If its the latter one does't django create a new instance at the server level for every new user request and have separate resources for every user in the memory or am i wrong about this?
It's the latter.
A WSGI server usually runs a number of persistent processes, and in each process it runs a number of threads. While some automatic scaling can be applied, the number of processes and threads is more or less constant, and determines how many concurrent requests Django can handle. The days where each request would create a new CGI process are long gone, and in most cases persistent processes are much more efficient.
Each process has its own memory, and the communication between processes is usually handled by the database, the cache etc. They can't communicate directly through memory.
Each thread within a process shares the same memory. That means that any object that is not locally scoped (e.g. only defined inside a function), is accessible from the other threads. The cached template loader parses each template once per process, and each thread uses the same parsed nodes. That also means that if you set e.g. self.foo = 'bar' in one thread, each thread will then read 'bar' when accessing self.foo. Since multiple threads run at the same time, this can quickly become a huge mess that's impossible to debug, which is why thread safety is so important.
As the documentation says, as long as you don't store data on self, but put it into context.render_context, you should be fine.
When using Django, how long does the Python process used to service requests stay alive? Obviously, a given Python process services an entire request, but is it guaranteed to survive across across requests?
The reason I ask is that I perform some expensive computations at when I import certain modules and would like to know how often the modules will be imported.
This is not a function of Django at all, but of whatever system is being used to serve Django. Usually that'll be wsgi via something like mod_wsgi or a standalone server like gunicorn, but it might be something completely different like FastCGI or even plain CGI.
The point is that all these different systems have their own models that determines process lifetime. In anything other than basic CGI, any individual process will certainly serve several requests before being recycled, but there is absolutely no general guarantee of how many - the process might last several days or weeks, or just a few minutes.
One thing to note though is that you will almost always have several processes running concurrently, and you absolutely cannot count on any particular request being served by the same one as the previous one. That means if you have any user-specific data you want to persist between requests, you need to store it somewhere like the session.
I got a lot scripts running: scrappers, checkers, cleaners, etc. They have some things in common:
they are forever running;
they have no time constrain to finish their job;
they all access the same MYSQL DB, writting and reading.
Accumulating them, it's starting to slow down the website, which runs on the same system, but depends on these scripts.
I can use queues with Kombu to inline all writtings.
But do you know a way to make the same with reading ?
E.G: if one script need to read from the DB, his request is sent to a blocking queue, et it resumes when it got the answer ? This way everybody is making request to one process, and the process is the only one talking to the DB, making one request at the time.
I have no idea how to do this.
Of course, in the end I may have to add more servers to the mix, but before that, is there something I can do at the software level ?
You could use a connection pooler and make the connections from the scripts go through it. It would limit the number of real connections hitting your DB while being transparent to your scripts (their connections would be held in a "wait" state until a real connections is freed).
I don't know what DB you use, but for Postgres I'm using PGBouncer for similiar reasons, see http://pgfoundry.org/projects/pgbouncer/
You say that your dataset is <1GB, the problem is CPU bound.
Now start analyzing what is eating CPU cycles:
Which queries are really slow and executed often. MySQL can log those queries.
What about the slow queries? Can they be accelerated by using an index?
Are there unused indices? Drop them!
Nothing helps? Can you solve it by denormalizing/precomputing stuff?
You could create a function that each process must call in order to talk to the DB. You could re-write the scripts so that they must call that function rather than talk directly to the DB. Within that function, you could have a scope-based lock so that only one process would be talking to the DB at a time.