Entries Tagged 'software' ↓

With the recent release of Rails 2.0, many of us are reviewing our approaches to common problems. Many new features have been added to Rails, and some old tricks are either no longer necessary or no longer work.

I am developing a project with Rails 2.0 and am getting close to putting it into production. A recurring issue for today’s web developers is that of asset packaging, or the combination of multiple site assets into a single file. Specifically, we’re talking about Javascript and CSS.

A given “Web 2.0” (a term I wish had recently been found dead in a cramped apartment in Brooklyn) site might have a half dozen Javascript or CSS files to deliver to a user, and web browsers are not all that efficient at retrieving them. Each one requires a separate TCP connection to the server, and many browsers are only capable of getting two of these files concurrently. This means delays for your users.

In Rails 2.0 (and previously in Edge Rails), it’s possible to combine multiple Javascript and CSS files using the javascript_include_tag and stylesheet_link_tag functions in your html.erb files; simply add :cache => true to the parameters like this:

<%= javascript_include_tag 'prototype', 'effects', :cache => true %><%= stylesheet_link_tag 'main', 'shop', 'form', :cache => true %>

With :cache => true and when running in your production environment, Rails will automatically combine your Javascript and CSS assets into single files (all.js and all.css, respectively) and significantly reduce your site’s load time.

However, this really only solves part of the problem. A common technique used to further improve site performance is to compress Javascript and CSS by removing unnecessary whitespace and comments. I am not sure why this wasn’t included as part of Rails’ built-in caching features, but it seemed to me it should be easy to add.

Turns out I was mostly right. Google “javascript minimization” (or minification) and you’ll see it’s a pretty hot topic. The Asset Packager plugin from Scott Becker does this, as well as CSS compression, but is targeted at Rails 1.x and doesn’t really make sense in the face of Rails 2.0.

So I set out to solve this problem in an elegant way for Rails 2.0. Asset Packager uses a Ruby script called jsmin.rb by Uladzislau Latynski which is based on jsmin.c by Douglas Crockford. The thing is, jsmin.rb is not a class or library, but rather a standalone executable that operates on stdin and stdout. Asset Pacakger actually forks a ruby shell process to do its Javascript minimization, and this seemed like folly if it could be done internal to Rails.

Accordingly, I modified jsmin.rb to operate as a singleton class and with a class method you could pass Javascript data to. Then it was simply a matter of monkey patching this function into ActionView::Helpers::AssetTagHelper, home of javascript_include_tag and stylesheet_link_tag.

I also wanted to add in CSS compression, which turned out to be easy. The javascript_include_tag and stylesheet_link_tag functions both use the same underlying functions to package their assets, so it was a simple case of replacing them with equivalents that do compression appropriately, based on whether we are dealing with CSS or JS.

config/initializers/javascript_minimization.rb:

module ActionView  module Helpers    module AssetTagHelper      require 'jsminlib'

      def compress_css(source)        source.gsub!(/\s+/, " ")           # collapse space        source.gsub!(/\/\*(.*?)\*\/ /, "") # remove comments        source.gsub!(/\} /, "}\n")         # add line breaks        source.gsub!(/\n$/, "")            # remove last break        source.gsub!(/ \{ /, " {")         # trim inside brackets        source.gsub!(/; \}/, "}")          # trim inside brackets      end

      def get_file_contents(filename)        contents = File.read(filename)        if filename =~ /\.js$/          JSMin.minimize(contents)        elsif filename =~ /\.css$/          compress_css(contents)        end      end

      def join_asset_file_contents(paths)        paths.collect { |path|          get_file_contents(File.join(ASSETS_DIR, path.split("?").first)) }.join("\n\n")      end

    end  endend

By simply modifying join_asset_file_contents to use our new function get_file_contents instead of File.read, we quickly get to the heart of the matter. CSS files get compress_css run on them, while Javascript files get JSMin.minimize run on them. Your :cache => true Javascript and CSS assets will now be gloriously combined and compressed!

Note that the above monkey patch requires jsminlib.rb, which you can download here. It is just a modified version of the original jsmin.rb, and you will want to put it into your Rails lib directory.

A good next step would be to further enhance get_file_contents to do Javascript obfuscation, which allows for the replacement of variable names and thus even further compression; it also tends to make Javascript code nearly incomprehensible and thus harder to steal, which may be desirable for some developers. I haven’t found any native Ruby ways to do this yet, but it seems to me that this would be a good place for a C extension (or similar), and that this should all be put into a tiny and lightweight plugin.

I’m always amazed at how easy it is to bend Rails (and Ruby) to one’s will, and in this case it’s really quite elegant and straightforward. I’d love to hear your ideas about how to take this idea forward, potentially even including it in Rails itself.

Download the files here:

• lib/jsminlib.rb
• config/initializers/javascript_minimization.rb

While I was working on some changes to Twittervision yesterday, I saw someone mention freerice.com, a site where you can go quiz yourself on vocabulary words and help feed the world. How? Each word you get right gives 10 grains of rice to, one hopes, someone who needs it.

The idea is that you will sit there for hours and look at the advertising from the do-gooder multinationals who sponsor it. Which I did for a while. I got up to level 44 or so and got to feeling pretty good about Toshiba and Macy’s.

It occurred to me though that my computer could also play this game, and has a much better memory for words than I do. In fact, once it learns something, it always chooses the right answer.

So I wrote a program to play the freerice.com vocabulary game. In parallel. 50 browsers at a time. Sharing what they learn with each other. Cumulatively.

It’s a multithreaded Ruby program using WWW::Mechanize and Hpricot. Nothing terribly fancy, but it does learn from each right and wrong answer, and after just a few minutes seems to hit a stride of about 75-80% accuracy. And a rate of about 200,000 grains of rice per hour (depending on the speed of your connection).

UPDATE: With some tuning, the script is now able to push out about 600,000 grains of rice per hour, which according to the statistic of 20,000 grains per person per day, is enough to feed over 720 people per day! If one thousand people run this script, it will (allegedly) generate enough to feed 720,000 people per day.

Before you go off on me, disclaimer: Yes, I realize this program subverts the intent of the freerice.com site. I’ve released this not to “game” freerice.com but simply to show a flaw in their design and have a little fun at the same time. If what they are after is human interaction, this design doesn’t mandate it. That’s all I’m saying.

Run it for a while and see how many people you can feed!

Prerequisites:

• Ruby (Linux, OS X, Other)
• Rubygems
• gem install mechanize –include-dependencies

Download the code

Continuing my theme of lifting ideas from Dave Thomas’ blog posts, in our last episode we built a somewhat broken program to sequentially fetch feed information from Youtube’s XML API.

I had some trouble understanding why I wasn’t getting #xmlText records back from xmerl_xpath. Thanks to a comment by Ulf Wiger, I now understand what was going wrong.

As many of us do, I was using the shell to play around with ideas before committing them to my program, and in the shell the record format is not defined because xmerl.hrl is not referenced. This *was* being included in my program, but I wasn’t running the program in my testing in the shell.

I took his advice, used the force, and got my patterns to match #xmlText records.

I also copied Dave Thomas’ design pattern for parallel spawning of the fetch process to produce this program which 1) grabs a feed of the most_viewed videos on YouTube, and then 2) grabs in parallel the user profiles for each of those videos.

While I still only have a rudimentary understanding of the language, I at least understand everything that’s going on in this program. It’s amazing how quick concurrent programs in Erlang can be. The fetch_parallel function in this program runs in about 3 seconds, while the fetch_sequential version takes about 20 seconds.

If you think about what this means for API developers, it has scary implications. In short, they will need a lot more bandwidth and processing capacity to deal with concurrent clients than are presently needed to deal with a sequential universe. Most API developers are accustomed to interacting with programs that make a single request, do some processing, and then make another related request.

A world of Erlang-derived, concurrent API clients likely calls for Erlang-derived concurrent API servers. Today’s API interactions are timid, one-off requests compared to what’s possible in a concurrent API interaction.

Imagine a recursive program designed to spider through API data until it finds the results it’s looking for. You could easily write a program that grabs a set of N search results, which in turn generates N concurrent API queries, which in turn generates N^2 concurrent API requests, which in turn generates N^3 requests.

You get the idea. Rather than being simple request & response mechanisms, APIs in fact expose all of the data they contain — in parallel and all at once. A single concurrent Erlang client can easily create as much simultaneous load as 10,000 individual sequential API consumers do now.

API developers should start pondering the answer to this question. Right now, there are no standards for enforcing best practices on most APIs. There’s nothing to stop a developer from requesting the same data over and over again from an API, other than things like the Google maps geolocation API limit of 50,000 requests per day. But what about caching and expiring data, refusing repetitive requests, enforcing bandwidth limits or other strategies?

Many people do all of these things in different ways, but we’re at the tip of the iceberg in terms of addressing these kinds of issues. A poorly designed sequential API client is one thing; a badly designed concurrent API client is another thing altogether and could constitute a kind of DoS (denial of service) attack.

Start thinking now about how you’re going to deal with the guy who polls you every 10 seconds for the latest status of all 142,000 of his users — in parallel, 15,000 at a time.

And for you would-be API terrorists out there, here’s some code:

-module(youtube).  -export([fetch_sequential/0, fetch_parallel/0]).  -include_lib("xmerl/include/xmerl.hrl").

 get_feed() ->  { ok, {_Status, _Headers, Body }} = http:request("http://gdata.youtube.com/feeds/standardfeeds/most_viewed"),  { Xml, _Rest } = xmerl_scan:string(Body),    xmerl_xpath:string("//author/name/text()", Xml).

 get_user_profile(User) ->  #xmlText{value = Name} = User,  URL = "http://gdata.youtube.com/feeds/users/" ++ Name,  { ok, {_Status, _Headers, Body} } = http:request(URL),  { Xml, _Rest } = xmerl_scan:string(Body),  [#xmlText{value = Id}] = xmerl_xpath:string("//id/text()", Xml),  [#xmlText{value = Published}] = xmerl_xpath:string("//published/text()", Xml),  { Name, Id, Published }.

 fetch_sequential() ->  lists:map(fun get_user_profile/1, get_feed()).

 fetch_parallel() ->    inets:start(),  Users = get_feed(),  lists:foreach(fun background_fetch/1, Users),    gather_results(Users).

  background_fetch(User) ->    ParentPID = self(),    spawn(fun() ->              ParentPID ! { ok, get_user_profile(User) }          end).

  gather_results(Users) ->          lists:map(fun(_) ->            receive                 { ok, Anything } -> Anything            end          end, Users).

For those of you who haven’t heard about Erlang yet, it is a functional programming language (like Lisp or Prolog) developed quietly over the last 20 years by telecoms giant Ericsson for use in telco switches.

Ericsson has been using it for roughly the last 14 years; it has several properties that make it particularly relevant to many of the problems facing developers today. It’s one of the few languages that is particularly good at letting programmers take advantage of multi-core/multi-CPU systems, and distribute services across multiple boxes. YAWS, a webserver written in Erlang and a poster child of its efficiencies, kicks Apache’s tail from a scalability standpoint. This is no small accomplishment for a high level language.

Today’s scaling strategies revolve less around faster clock speeds and more around adding cores. Scaling out to many machines is also important, but power and space considerations are also more of an issue than ever before.

So Erlang is gaining ground because it addresses scalability for this new age of multi-core systems. Today you might have a dual Clovertown Xeon box with 8 cores, but very little software to take advantage of it. Once you get past 2 or 4 cores, that extra capacity provides little to no benefit. Enter a language like Erlang, and suddenly all that power becomes available to the programmer.

Some of my coder buddies, (Jay Phillips, Rich Kilmer and Marcel Molina) have been looking at Erlang for various tasks, and Dave Thomas’ blog posts on Erlang have also inspired me to take a look at the language for some of my own work.

I picked up Joe Armstrong’s book, Programming Erlang from Pragmatic Bookshelf and started reading it on a recent airplane flight.

Today I put together my first Erlang program, based on knowledge gleaned from Dave Thomas’ postings and from the book.

This very simple program grabs the top_rated feed of videos from YouTube (an XML RSS feed) and then iterates through the result set to get the profile URL for each user. It is a fairly useless and trivial example, but if I can make this work then there are other things I can do down the line.

-module(youtube).  -export([fetch_each_user/0]).  -include_lib("xmerl/include/xmerl.hrl").

 get_feed() ->  { ok, {_Status, _Headers, Body }} = http:request("http://gdata.youtube.com/feeds/standardfeeds/top_rated"),  { Xml, _Rest } = xmerl_scan:string(Body),    xmerl_xpath:string("//author/name/text()", Xml).

 get_user_profile(User) ->  {_,[A|B],_,[],Name,_} = User,  URL = "http://gdata.youtube.com/feeds/users/" ++ Name,  { ok, {_Status, _Headers, Body }} = http:request(URL),  { Xml, _Rest } = xmerl_scan:string(Body),  [{_,[C|D],_,[],Id,_}] = xmerl_xpath:string("//id/text()", Xml),  { Name, Id }.

 fetch_each_user() ->  lists:map(fun get_user_profile/1, get_feed()).

I am pretty sure I am doing this All Wrong ™.

My biggest area of confusion comes in the pattern matching that’s required to match (and thus read) the results from the xmerl_xpath:string parsing. According to Dave Thomas’ examples, xmerl_xpath should produce a #xmlText record (or a set of them) that can then be matched with the #xmlText{} syntax.

In practice, and with the Youtube API data I used, I see no such #xmlText records. Instead I get a flattened tuple from such parsing, along the lines of:

>xmerl_xpath:string("//location/text()", Xml)[{xmlText,[{'yt:location',14},{entry,1}],1,[],"GB",text}]

The only way I can find to match this is something like this:

[{_,[A|B],_,[],Location,_}] = xmerl_xpath:string("//location/text()", Xml)

I am sure I am missing some key step or concept, but that’s how we learn new languages — stumble along til we figure out how to solve the things we want to solve.

There’s an incredible amount of functionality packed into these 19 lines of code. It’ll be even more amazing when I figure out my initial questions and then add concurrent processing of the user profile URLs. In theory I can simultaneously process dozens of URL feeds from Youtube and spider their API data as though through a fire hose. Stay tuned.

Meantime if anyone has any suggestions on my current puzzlements I’d love to hear them.

Erlang is a cool language. It doesn’t give me the aesthetic fuzzies I receive from programming in Ruby, but I do get pretty jazzed up thinking about what should be possible from a performance and economy standpoint. Erlang doesn’t allow for mutable state; variables have fixed values once assigned, and algorithms are generally handled via recursion. This is how it scales out to so many cores/cpus/machines so readily. It’s kinda weird if you’re used to “normal” mutable state languages.

Whenever I learn a new language (human or computer) I generally have weird and overactive dreams. I attribute this to my brain shuffling things around to accommodate new grammar and semantics.

The last few days have produced particularly vivid dreams.