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HTTPServer with greater degree of control in Python

I have a fairly simple problem that I would like to solve in Python.
I would like a webserver that has the following behavior: if it receives a POST request for /work, then it should add it to a work queue and execute some function on the data attached. If it receives a POST request for /cancel it should cancel whatever its current task is.
Unfortunately, the only way I can seem to get a BaseHTTPRequestHandler to handle multiple requests is to use a ThreadingMixIn, but that seems unecessarily complicated as I then have to use a set of locks to prevent multiple work tasks from executing concurrently.
I tried to use a BaseHTTPRequestHandler without a ThreadingMixIn and just spin off threads in do_POST, but that didn't work since apparently BaseHTTPRequestHandler closes its connection when the do_POST function returns.
Ideally, I'm looking for an interface that gives me the ability to close the connection to the client on my own terms, so I can do it in a worker thread, and manage the queue myself, rather than working around the ThreadingMixIn's behavior in this regard.

Python Threading vs Gevent for High Volume Web Scraping

I'm trying to decide if I should use gevent or threading to implement concurrency for web scraping in python.
My program should be able to support a large (~1000) number of concurrent workers. Most of the time, the workers will be waiting for requests to come back.
Some guiding questions:
What exactly is the difference between a thread and a greenlet? What is the max number of threads \ greenlets I should create in a single process (with regard to the spec of the server)?

The python thread is the OS thread which is controlled by the OS which means it's a lot heavier since it needs context switch, but green threads are lightweight and since it's in userspace the OS does not create or manage them.
I think you can use gevent, Gevent = eventloop(libev) + coroutine(greenlet) + monkey patch. Gevent give you threads but without using threads with that you can write normal code but have async IO.
Make sure you don't have CPU bound stuff in your code.

I don't think you have thought this whole thing through. I have done some considerable lightweight thread apps with Greenlets created from the Gevent framework. As long as you allow control to switch between Greenlets with appropriate sleep's or switch's -- everything tends to work fine. Rather than blocking or waiting for a reply, it is recommended that the wait or block timeout, raise and except and then sleep (in except part of your code) and then loop again - otherwise you will not switch Greenlets readily.
Also, take care to join and/or kill all Greenlets, since you could end up with zombies that cause copious effects that you do not want.
However, I would not recommend this for your application. Rather, one of the following Websockets extensions that use Gevent... See this link
Websockets in Flask
and this link
https://www.shanelynn.ie/asynchronous-updates-to-a-webpage-with-flask-and-socket-io/
I have implemented a very nice app with Flask-SocketIO
https://flask-socketio.readthedocs.io/en/latest/
It runs through Gunicorn with Nginx very nicely from a Docker container. The SocketIO interfaces very nicely with Javascript on the client side.
(Be careful on the webscraping - use something like Scrapy with the appropriate ethical scraping enabled)

How to handle a burst of connection to a port?

I've built a server listening on a specific port on my server using Python (asyncore and sockets) and I was curious to know if there was anything possible to do when there is too many people connecting at once on my server.
The code in itself cannot be changed, but will adding more process works? or is it from an hardware perspective and I should focus on adding a load balancer in front and balancing the requests on multiple servers?
This questions is borderline StackOverflow (code/python) and ServerFault (server management). I decided to go with SO because of the code, but if you think ServerFault is better, let me know.

1.
asyncore relies on operating system for whole connection handling, therefore what you are asking is OS dependent. It has very little to do with Python. Using twisted instead of asyncore wouldn't solve your problem.
On Windows, for example, you can listen only for 5 connections coming in simultaneously.
So, first requirement is, run it on *nix platform.
The rest depends on how long your handlers are taking and on your bandwith.
2.
What you can do is combine asyncore and threading to speed-up waiting for next connection.
I.e. you can make Handlers that are running in separate threads. It will be a little messy but it is one of possible solutions.
When server accepts a connection, instead of creating new traditional handler (which would slow down checking for following connection - because asyncore waits until that handler does at least a little bit of its job), you create a handler that deals with read and write as non-blocking.
I.e. it starts a thread and does the job, then, when it has data ready, only then sends it upon following loop()'s check.
This way, you allow asyncore.loop() to check the server's socket more often.
3.
Or you can use two different socket_maps with two different asyncore.loop()s.
You use one map (dictionary), let say the default one - asyncore.socket_map to check the server, and use one asyncore.loop(), let say in main thread, only for server().
And you start the second asyncore.loop() in a thread using your custom dictionary for client handlers.
So, One loop is checking only server that accepts connections, and when it arrives, it creates a handler which goes in separate map for handlers, which is checked by another asyncore.loop() running in a thread.
This way, you do not mix the server connection checks and client handling. So, server is checked immediately after it accepts one connection. The other loop balances between clients.
If you are determined to go even faster, you can exploit the multiprocessor computers by having more maps for handlers.
For example, one per CPU and as many threads with asyncore.loop()s.
Note, sockets are IO operations using system calls and select() is one too, therefore GIL is released while asyncore.loop() is waiting for results. This means, that you will have total advantage of multithreading and each CPU will deal with its number of clients in literally parallel way.
What you would have to do is make the server distributing the load and starting threading loops upon connection arrivals.
Don't forget that asyncore.loop() ends when the map empties. So the loop() in a thread that manages clients must be started when new connection is accepted and restarted if at some time there are no more connections present.
4.
If you want to be able to run your server on multiple computers and use them as a cluster, then you install the process balancer in front.
I do not see the serious need for it if you wrote the asyncore server correctly and want to run it on single computer only.

How can I offer concurrency with Pika in long-working consumers?

Short version: How can I prevent blocking Pika in a Remote Procedure Call situation?
Long version:
None of the Pika examples demonstrate my use case.
I have a Tornado server which communicates with other processes/machines over AMQP (RabbitMQ, Pika). These other processes are not very well-defined, but they will, for the most part, be returning data (see the RPC example on RabbitMQ's website). Sometimes, a process might need to take an extremely long time to process a large amount of information, but it shouldn't completely block smaller requests from being taken by the process. Or maybe the remote server is blocking because it sent out a web request. Think of it like a web server, but using AMQP instead of HTTP.
Since Pika documentation claims that it's not thread-safe, I cannot pass the connection to multiple threads (or processes, for that matter). What I want to do is start a new process, and add a socket event (for the pipe to that program) to the Pika IOLoop, as I would be able to do with Tornado. The Pika IOLoop is much different from the Tornado IOLoop, and it doesn't seem to support adding multiple handlers; it seems to operate using one "poller" on one socket.
I'd like to avoid requiring the Tornado package for this package, because I would only be using the IOLoop. It's not out of the question, but I want to see what my other options are, or if there is a solution to my problem by somehow connecting multiple Pika IOLoops/Pollers. RabbitMQ's documentation says that workers can often be "scaled up" by adding more. I'd like to avoid creating a connection for every request that comes in (if they're coming in fast).

From what you described, I believe you unfortunately either need a different communication model or need multiple Pika IOLoops/Pollers/Redundant Connections.
It sounds like from documentation and from other sites that RPC in Pika is always a blocking statement and unable to be passed around between threads. See http://www.rabbitmq.com/tutorials/tutorial-six-python.html where the author points out that RPC in Pika is inherently blocking once you actually call the ioloop.
"When in doubt avoid RPC. If you can, you should use an asynchronous pipeline - instead of RPC-like blocking"
If you want to keep sending multiple RPC calls on the same connection before one completes, you'll need a different Asynchronous model. Multiple RPC calls on the same connection before completion isn't the usual implementation of the RPC model, though it's not technically forbidden ( http://pic.dhe.ibm.com/infocenter/aix/v6r1/index.jsp?topic=%2Fcom.ibm.aix.progcomm%2Fdoc%2Fprogcomc%2Frpc_mod.htm ). I don't think Pika operates with this model, though it does have asynchronous support via callbacks (not what you are looking for I think).
If you just want to easily be able to generate new connections on the fly you could use a thread or process wrapper on a connection, where you create and block on the RPC in the other context and push to a common Queue which the main thread can monitor. Tornado might give you this, but I agree that it's a bit of overkill, and making such a connection wrapper shouldn't be all that difficult as I've done something similar for other I/O ops in less than 100 lines of Python (see Queue package for Threaded wrapper version). I think you already saw this possibility though based on your talk of multiple IOLoops.

Multi-Threading and Asynchronous sockets in python

I'm quite new to python threading/network programming, but have an assignment involving both of the above.
One of the requirements of the assignment is that for each new request, I spawn a new thread, but I need to both send and receive at the same time to the browser.
I'm currently using the asyncore library in Python to catch each request, but as I said, I need to spawn a thread for each request, and I was wondering if using both the thread and the asynchronous is overkill, or the correct way to do it?
Any advice would be appreciated.
Thanks
EDIT:
I'm writing a Proxy Server, and not sure if my client is persistent. My client is my browser (using firefox for simplicity)
It seems to reconnect for each request. My problem is that if I open a tab with http://www.google.com in it, and http://www.stackoverflow.com in it, I only get one request at a time from each tab, instead of multiple requests from google, and from SO.

I answered a question that sounds amazingly similar to your, where someone had a homework assignment to create a client server setup, with each connection being handled in a new thread: https://stackoverflow.com/a/9522339/496445
The general idea is that you have a main server loop constantly looking for a new connection to come in. When it does, you hand it off to a thread which will then do its own monitoring for new communication.
An extra bit about asyncore vs threading
From the asyncore docs:
There are only two ways to have a program on a single processor do
“more than one thing at a time.” Multi-threaded programming is the
simplest and most popular way to do it, but there is another very
different technique, that lets you have nearly all the advantages of
multi-threading, without actually using multiple threads. It’s really
only practical if your program is largely I/O bound. If your program
is processor bound, then pre-emptive scheduled threads are probably
what you really need. Network servers are rarely processor bound,
however.
As this quote suggests, using asyncore and threading should be for the most part mutually exclusive options. My link above is an example of the threading approach, where the server loop (either in a separate thread or the main one) does a blocking call to accept a new client. And when it gets one, it spawns a thread which will then continue to handle the communication, and the server goes back into a blocking call again.
In the pattern of using asyncore, you would instead use its async loop which will in turn call your own registered callbacks for various activity that occurs. There is no threading here, but rather a polling of all the open file handles for activity. You get the sense of doing things all concurrently, but under the hood it is scheduling everything serially.