Simon Willison's Weblog: tornado

How do I choose between asynchronous web frameworks? My tech group is fairly language agnostic and we're trying to standardize on some technologies.

2013-07-31T10:15:00+00:00

My answer to How do I choose between asynchronous web frameworks? My tech group is fairly language agnostic and we're trying to standardize on some technologies. on Quora

Since they are all pretty close to each other and it sounds like your tech group's skills would support any of them, I would suggest having your tram build a simple prototype in all three so you can compare them for your own particular team and situation.

Tags: frameworks, javascript, nodejs, play, python, scala, tornado, webapps, quora

What server do I need to handle 1000+ users simultaneously while they can post messages, upload pictures, and other similar stuff on a website based on PHP and mySQL?

2012-01-10T09:37:00+00:00

My answer to What server do I need to handle 1000+ users simultaneously while they can post messages, upload pictures, and other similar stuff on a website based on PHP and mySQL? on Quora

You don't need to handle 1,000 users simultaneously: you need to build something and ship it and start the process of discovering what you can build that will attract that many users. Seriously: don't even start worrying about that kind of scale until you know you're going to need it.

(That said, there's no harm in keeping scaling issues in mind while you're building the software - try to avoid making dumb architectural decisions that will be impossible to scale later. But deciding on your 1,000+ simultaneous user serving architecture before you've Built Something People Want is a huge waste of your time).

Tags: concurrency, nodejs, tornado, webservers, quora

Quoting Dan Manges

2011-06-30T21:27:00+00:00

We can deploy new versions of our software, make database schema changes, or even rotate our primary database server, all without failing to respond to a single request. We can accomplish this because we gave ourselves the ability suspend our traffic, which gives us a window of a few seconds to make some changes before letting the requests through. To make this happen, we built a custom HTTP server and application dispatching infrastructure around Python’s Tornado and Redis.

— Dan Manges, Braintree

Tags: deployment, http, redis, tornado, recovered

What is the history of Django?

2010-08-24T17:31:00+00:00

What is the history of Django?

I’ve been playing with Quora—it’s a really neat twist on the question-and-answer format, which makes great use of friends, followers and topics and has some very neat live update stuff going on (using Comet on top of Tornado). I just posted quite a long answer to a question about the history of Django.

Tags: comet, django, history, tornado, quora, recovered, qna

Integrate Tornado in Django

2010-02-08T11:12:46+00:00

Integrate Tornado in Django

A handy ./manage.py runtornado management command for firing up a Tornado server that serves your Django application.

Tags: django, python, tornado

Web Sockets in Tornado

2009-12-31T11:54:48+00:00

Web Sockets in Tornado

Bret Taylor has a simple class making it trivial to experiment with the Web Sockets protocol (now shipping in Chrome) using the scalable Tornado application server. He also raises the million dollar question: what will existing load balancers and proxies make of the new protocol?

Tags: bret-taylor, chrome, comet, python, tornado, websockets

Socket Benchmark of Asynchronous Servers in Python

2009-12-22T22:34:01+00:00

Socket Benchmark of Asynchronous Servers in Python

A comparison of eight different asynchronous networking frameworks in Python. Tornado comes out on top in most of the benchmarks, but the post is most interesting for the direct comparison of simple code examples for each of the frameworks.

Tags: async, benchmarks, dieselweb, eventio, eventlet, gevent, orbited, python, stackless, tornado, twisted

Node.js is genuinely exciting

2009-11-23T12:50:22+00:00

I gave a talk on Friday at Full Frontal, a new one day JavaScript conference in my home town of Brighton. I ended up throwing away my intended topic (JSONP, APIs and cross-domain security) three days before the event in favour of a technology which first crossed my radar less than two weeks ago.

That technology is Ryan Dahl's Node. It's the most exciting new project I've come across in quite a while.

At first glance, Node looks like yet another take on the idea of server-side JavaScript, but it's a lot more interesting than that. It builds on JavaScript's excellent support for event-based programming and uses it to create something that truly plays to the strengths of the language.

Node describes itself as "evented I/O for V8 javascript". It's a toolkit for writing extremely high performance non-blocking event driven network servers in JavaScript. Think similar to Twisted or EventMachine but for JavaScript instead of Python or Ruby.

Evented I/O?

As I discussed in my talk, event driven servers are a powerful alternative to the threading / blocking mechanism used by most popular server-side programming frameworks. Typical frameworks can only handle a small number of requests simultaneously, dictated by the number of server threads or processes available. Long-running operations can tie up one of those threads - enough long running operations at once and the server runs out of available threads and becomes unresponsive. For large amounts of traffic, each request must be handled as quickly as possible to free the thread up to deal with the next in line.

This makes certain functionality extremely difficult to support. Examples include handling large file uploads, combining resources from multiple backend web APIs (which themselves can take an unpredictable amount of time to respond) or providing comet functionality by holding open the connection until a new event becomes available.

Event driven programming takes advantage of the fact that network servers spend most of their time waiting for I/O operations to complete. Operations against in-memory data are incredibly fast, but anything that involves talking to the filesystem or over a network inevitably involves waiting around for a response.

With Twisted, EventMachine and Node, the solution lies in specifying I/O operations in conjunction with callbacks. A single event loop rapidly switches between a list of tasks, firing off I/O operations and then moving on to service the next request. When the I/O returns, execution of that particular request is picked up again.

(In the talk, I attempted to illustrate this with a questionable metaphor involving hamsters, bunnies and a hyperactive squid).

What makes Node exciting?

If systems like this already exist, what's so exciting about Node? Quite a few things:

JavaScript is extremely well suited to programming with callbacks. Its anonymous function syntax and closure support is perfect for defining inline callbacks, and client-side development in general uses event-based programming as a matter of course: run this function when the user clicks here / when the Ajax response returns / when the page loads. JavaScript programmers already understand how to build software in this way.
Node represents a clean slate. Twisted and EventMachine are hampered by the existence of a large number of blocking libraries for their respective languages. Part of the difficulty in learning those technologies is understanding which Python or Ruby libraries you can use and which ones you have to avoid. Node creator Ryan Dahl has a stated aim for Node to never provide a blocking API - even filesystem access and DNS lookups are catered for with non-blocking callback based APIs. This makes it much, much harder to screw things up.
Node is small. I read through the API documentation in around half an hour and felt like I had a pretty comprehensive idea of what Node does and how I would achieve things with it.
Node is fast. V8 is the fast and keeps getting faster. Node's event loop uses Marc Lehmann's highly regarded libev and libeio libraries. Ryan Dahl is himself something of a speed demon - he just replaced Node's HTTP parser implementation (already pretty speedy due to it's Ragel / Mongrel heritage) with a hand-tuned C implementation with some impressive characteristics.
Easy to get started. Node ships with all of its dependencies, and compiles cleanly on Snow Leopard out of the box.

With both my JavaScript and server-side hats on, Node just feels right. The APIs make sense, it fits a clear niche and despite its youth (the project started in February) everything feels solid and well constructed. The rapidly growing community is further indication that Ryan is on to something great here.

What does Node look like?

Here's how to get Hello World running in Node in 7 easy steps:

git clone git://github.com/ry/node.git (or download and extract a tarball)
./configure
make (takes a while, it needs to compile V8 as well)
sudo make install
Save the below code as helloworld.js
node helloworld.js
Visit http://localhost:8080/ in your browser

Here's helloworld.js:

var sys = require('sys'), 
  http = require('http');

http.createServer(function(req, res) {
  res.sendHeader(200, {'Content-Type': 'text/html'});
  res.sendBody('<h1>Hello World</h1>');
  res.finish();
}).listen(8080);

sys.puts('Server running at http://127.0.0.1:8080/');

If you have Apache Bench installed, try running ab -n 1000 -c 100 'http://127.0.0.1:8080/' to test it with 1000 requests using 100 concurrent connections. On my MacBook Pro I get 3374 requests a second.

So Node is fast - but where it really shines is concurrency with long running requests. Alter the helloworld.js server definition to look like this:

http.createServer(function(req, res) {
  setTimeout(function() {
    res.sendHeader(200, {'Content-Type': 'text/html'});
    res.sendBody('<h1>Hello World</h1>');
    res.finish();
  }, 2000);
}).listen(8080);

We're using setTimeout to introduce an artificial two second delay to each request. Run the benchmark again - I get 49.68 requests a second, with every single request taking between 2012 and 2022 ms. With a two second delay, the best possible performance for 1000 requests 100 at a time is 1000 requests / (1000 / 100) * 2 seconds = 50 requests a second. Node hits it pretty much bang on the nose.

The most important line in the above examples is res.finish(). This is the mechanism Node provides for explicitly signalling that a request has been fully processed and should be returned to the browser. By making it explicit, Node makes it easy to implement comet patterns like long polling and streaming responses - stuff that is decidedly non trivial in most server-side frameworks.

djangode

Node's core APIs are pretty low level - it has HTTP client and server libraries, DNS handling, asynchronous file I/O etc, but it doesn't give you much in the way of high level web framework APIs. Unsurprisingly, this has lead to a cambrian explosion of lightweight web frameworks based on top of Node - the projects using node page lists a bunch of them. Rolling a framework is a great way of learning a low-level API, so I've thrown together my own - djangode - which brings Django's regex-based URL handling to Node along with a few handy utility functions. Here's a simple djangode application:

var dj = require('./djangode');

var app = dj.makeApp([
  ['^/$', function(req, res) {
    dj.respond(res, 'Homepage');
  }],
  ['^/other$', function(req, res) {
    dj.respond(res, 'Other page');
  }],
  ['^/page/(\\d+)$', function(req, res, page) {
    dj.respond(res, 'Page ' + page);
  }]
]);
dj.serve(app, 8008);

djangode is currently a throwaway prototype, but I'll probably be extending it with extra functionality as I explore more Node related ideas.

nodecast

My main demo in the Full Frontal talk was nodecast, an extremely simple broadcast-oriented comet application. Broadcast is my favourite "hello world" example for comet because it's both simpler than chat and more realistic - I've been involved in plenty of projects that could benefit from being able to broadcast events to their audience, but few that needed an interactive chat room.

The source code for the version I demoed can be found on GitHub in the no-redis branch. It's a very simple application - the client-side JavaScript simply uses jQuery's getJSON method to perform long-polling against a simple URL endpoint:

function fetchLatest() {
  $.getJSON('/wait?id=' + last_seen, function(d) {
    $.each(d, function() {
      last_seen = parseInt(this.id, 10) + 1;
      ul.prepend($('<li></li>').text(this.text));
    });
    fetchLatest();
  });
}

Doing this recursively is probably a bad idea since it will eventually blow the browser's JavaScript stack, but it works OK for the demo.

The more interesting part is the server-side /wait URL which is being polled. Here's the relevant Node/djangode code:

var message_queue = new process.EventEmitter();

var app = dj.makeApp([
  // ...
  ['^/wait$', function(req, res) {
    var id = req.uri.params.id || 0;
    var messages = getMessagesSince(id);
    if (messages.length) {
      dj.respond(res, JSON.stringify(messages), 'text/plain');
    } else {
      // Wait for the next message
      var listener = message_queue.addListener('message', function() {
        dj.respond(res, 
          JSON.stringify(getMessagesSince(id)), 'text/plain'
        );
        message_queue.removeListener('message', listener);
        clearTimeout(timeout);
      });
      var timeout = setTimeout(function() {
        message_queue.removeListener('message', listener);
        dj.respond(res, JSON.stringify([]), 'text/plain');
      }, 10000);
    }
  }]
  // ...
]);

The wait endpoint checks for new messages and, if any exist, returns immediately. If there are no new messages it does two things: it hooks up a listener on the message_queue EventEmitter (Node's equivalent of jQuery/YUI/Prototype's custom events) which will respond and end the request when a new message becomes available, and also sets a timeout that will cancel the listener and end the request after 10 seconds. This ensures that long polls don't go on too long and potentially cause problems - as far as the browser is concerned it's just talking to a JSON resource which takes up to ten seconds to load.

When a message does become available, calling message_queue.emit('message') will cause all waiting requests to respond with the latest set of messages.

Talking to databases

nodecast keeps track of messages using an in-memory JavaScript array, which works fine until you restart the server and lose everything. How do you implement persistent storage?

For the moment, the easiest answer lies with the NoSQL ecosystem. Node's focus on non-blocking I/O makes it hard (but not impossible) to hook it up to regular database client libraries. Instead, it strongly favours databases that speak simple protocols over a TCP/IP socket - or even better, databases that communicate over HTTP. So far I've tried using CouchDB (with node-couch) and redis (with redis-node-client), and both worked extremely well. nodecast trunk now uses redis to store the message queue, and provides a nice example of working with a callback-based non-blocking database interface:

var db = redis.create_client();
var REDIS_KEY = 'nodecast-queue';

function addMessage(msg, callback) {
  db.llen(REDIS_KEY, function(i) {
    msg.id = i; // ID is set to the queue length
    db.rpush(REDIS_KEY, JSON.stringify(msg), function() {
      message_queue.emit('message', msg);
      callback(msg);
    });
  });
}

Relational databases are coming to Node. Ryan has a PostgreSQL adapter in the works, thanks to that database already featuring a mature non-blocking client library. MySQL will be a bit tougher - Node will need to grow a separate thread pool to integrate with the official client libs - but you can talk to MySQL right now by dropping in DBSlayer from the NY Times which provides an HTTP interface to a pool of MySQL servers.

Mixed environments

I don't see myself switching all of my server-side development over to JavaScript, but Node has definitely earned a place in my toolbox. It shouldn't be at all hard to mix Node in to an existing server-side environment - either by running both behind a single HTTP proxy (being event-based itself, nginx would be an obvious fit) or by putting Node applications on a separate subdomain. Node is a tempting option for anything involving comet, file uploads or even just mashing together potentially slow loading web APIs. Expect to hear a lot more about it in the future.

Quoting Bret Taylor

2009-09-11T17:31:57+00:00

We experimented with different async DB approaches, but settled on synchronous at FriendFeed because generally if our DB queries were backlogging our requests, our backends couldn't scale to the load anyway. Things that were slow enough were abstracted to separate backend services which we fetched asynchronously via the async HTTP module.

— Bret Taylor

Tags: async, bret-taylor, friendfeed, http, tornado

Tornado Web Server

2009-09-10T21:32:57+00:00

Tornado Web Server

An extremely exciting addition to the Python web landscape, Tornado is the open sourced version of FriendFeed’s custom web stack. It’s a non-blocking (epoll) Python web server designed for handling thousands of simultaneous connections, perfect for building Comet applications. The web framework is cosmetically similar to web.py or App Engine’s webapp but has decorators for writing asynchronous request handlers. The template language uses Django-style syntax but allows you to use full Python expressions. FriendFeed have benchmarked it handling 8,000 requests a second running as four load-balanced processes on a 4 core server.

Via Bret Taylor

Tags: bret-taylor, comet, django, epoll, friendfeed, google-app-engine, python, tornado, webapp, webpy