Design Patterns
***************

There are a bunch of basic patterns that Eventlet usage falls into.
Here are a few examples that show their basic structure.


Client Pattern
==============

The canonical client-side example is a web crawler.  This use case is
given a list of urls and wants to retrieve their bodies for later
processing.  Here is a very simple example:

   import eventlet
   from eventlet.green import urllib2

   urls = ["http://www.google.com/intl/en_ALL/images/logo.gif",
          "https://www.python.org/static/img/python-logo.png",
          "http://us.i1.yimg.com/us.yimg.com/i/ww/beta/y3.gif"]

   def fetch(url):
       return urllib2.urlopen(url).read()

   pool = eventlet.GreenPool()
   for body in pool.imap(fetch, urls):
       print("got body", len(body))

There is a slightly more complex version of this in the web crawler
example.  Here's a tour of the interesting lines in this crawler.

"from eventlet.green import urllib2" is how you import a
cooperatively-yielding version of urllib2.  It is the same in all
respects to the standard version, except that it uses green sockets
for its communication.  This is an example of the Import Green
pattern.

"pool = eventlet.GreenPool()" constructs a "GreenPool" of a thousand
green threads.  Using a pool is good practice because it provides an
upper limit on the amount of work that this crawler will be doing
simultaneously, which comes in handy when the input data changes
dramatically.

"for body in pool.imap(fetch, urls):" iterates over the results of
calling the fetch function in parallel.  "imap" makes the function
calls in parallel, and the results are returned in the order that they
were executed.

The key aspect of the client pattern is that it involves collecting
the results of each function call; the fact that each fetch is done
concurrently is essentially an invisible optimization.  Note also that
imap is memory-bounded and won't consume gigabytes of memory if the
list of urls grows to the tens of thousands (yes, we had that problem
in production once!).


Server Pattern
==============

Here's a simple server-side example, a simple echo server:

   import eventlet

   def handle(client):
       while True:
           c = client.recv(1)
           if not c: break
           client.sendall(c)

   server = eventlet.listen(('0.0.0.0', 6000))
   pool = eventlet.GreenPool(10000)
   while True:
       new_sock, address = server.accept()
       pool.spawn_n(handle, new_sock)

The file echo server example contains a somewhat more robust and
complex version of this example.

"server = eventlet.listen(('0.0.0.0', 6000))" uses a convenience
function to create a listening socket.

"pool = eventlet.GreenPool(10000)" creates a pool of green threads
that could handle ten thousand clients.

"pool.spawn_n(handle, new_sock)" launches a green thread to handle the
new client.  The accept loop doesn't care about the return value of
the "handle" function, so it uses "spawn_n", instead of "spawn".

The difference between the server and the client patterns boils down
to the fact that the server has a "while" loop calling "accept()"
repeatedly, and that it hands off the client socket completely to the
handle() method, rather than collecting the results.


Dispatch Pattern
================

One common use case that Linden Lab runs into all the time is a
"dispatch" design pattern.  This is a server that is also a client of
some other services.  Proxies, aggregators, job workers, and so on are
all terms that apply here.  This is the use case that the "GreenPile"
was designed for.

Here's a somewhat contrived example: a server that receives POSTs from
clients that contain a list of urls of RSS feeds.  The server fetches
all the feeds concurrently and responds with a list of their titles to
the client.  It's easy to imagine it doing something more complex than
this, and this could be easily modified to become a Reader-style
application:

   import eventlet
   feedparser = eventlet.import_patched('feedparser')

   pool = eventlet.GreenPool()

   def fetch_title(url):
       d = feedparser.parse(url)
       return d.feed.get('title', '')

   def app(environ, start_response):
       pile = eventlet.GreenPile(pool)
       for url in environ['wsgi.input'].readlines():
           pile.spawn(fetch_title, url)
       titles = '\n'.join(pile)
       start_response('200 OK', [('Content-type', 'text/plain')])
       return [titles]

The full version of this example is in the Feed Scraper, which
includes code to start the WSGI server on a particular port.

This example uses a global (gasp) "GreenPool" to control concurrency.
If we didn't have a global limit on the number of outgoing requests,
then a client could cause the server to open tens of thousands of
concurrent connections to external servers, thereby getting
feedscraper's IP banned, or various other accidental-or-on-purpose bad
behavior.  The pool isn't a complete DoS protection, but it's the bare
minimum.

The interesting lines are in the app function:

   pile = eventlet.GreenPile(pool)
   for url in environ['wsgi.input'].readlines():
       pile.spawn(fetch_title, url)
   titles = '\n'.join(pile)

Note that in line 1, the Pile is constructed using the global pool as
its argument.  That ties the Pile's concurrency to the global's.  If
there are already 1000 concurrent fetches from other clients of
feedscraper, this one will block until some of those complete.
Limitations are good!

Line 3 is just a spawn, but note that we don't store any return value
from it.  This is because the return value is kept in the Pile itself.
This becomes evident in the next line...

Line 4 is where we use the fact that the Pile is an iterator.  Each
element in the iterator is one of the return values from the
fetch_title function, which are strings.  We can use a normal Python
idiom ("join()") to concatenate these incrementally as they happen.
