mincss version 0.8 is much much faster

February 27, 2013
0 comments Python

Remember mincss from last month? Well, despite it's rather crazy version number has only really had one major release. And it's never really been optimized.

So I took some metrics and was able to find out where all the time is spent. It's basically in this:


for body in bodies:
    for each in CSSSelector(selector)(body):
        return True

That in itself, on its own, is very fast. Just a couple of milliseconds. But the problem was that it happens so god damn often!

So, in version 0.8 it now, by default, first make a list (actually, a set) of every ID and every CLASS name in every node of every HTML document. Then, using this it gingerly tries to avoid having to use CSSSelector(selector) if the selector is quite simple. For example, if the selector is #container form td:last-child and if there is no node with id container then why bother.
It equally applies the same logic to classes.

And now, what you've all been waiting for; the results:

On a big document (20Kb) like my home page...

  1. BEFORE: 4.7 seconds

  2. AFTER: 0.85 seconds

(I ran it a bunch of times and averaged the times which had very little deviation)

So in the first round of optimization it suddenly becomes 500% faster. Pretty cool!

I've made it possible to switch this off just because I haven't yet tested it on equally many sites. All the unit tests pass of course.

mincss now support PhantomJS

February 2, 2013
3 comments Python

Remember mincss from a couple of days ago? Now it supports downloading the HTML, to analyze, using PhantomJS. That's pretty exciting because PhantomJS actually supports Javascript. It's a headless (a web browser without a graphical user interface) Webkit engine. What mincss does is that invokes a simple script like this:


var page = require('webpage').create();
page.open(phantom.args[0], function () {
  console.log(page.content);
  phantom.exit();
});

which will allow any window.onload events to fire which might create more DOM nodes. So, like in this example it'll spit out HTML that contains a <p class="bar"> tag which you otherwise wouldn't get with Python's urllib.urlopen().

The feature was just added (version 0.6.0) and I wouldn't be surprised if there are dragons there because I haven't tried it on a lot of sites. And at the time of writing, I was not able to compile it on my Ubuntu 64bit server so I haven't put it into production yet.

Anyway, with this you can hopefully sprinkle less of those /* no mincss */ comments into you CSS.

mincss in action - sample report from the wild

January 22, 2013
10 comments Python, Web development

First of all, to find out what mincss is read this blog post which explains what the heck this new Python tool is.

My personal website is an ideal candidate for using mincss because it uses an un-customized Bootstrap CSS which weighs over 80Kb (minified) and on every page hit, the rendered HTML is served directly from memcache so dynamic slowness is not a problem. With that, what I can do is run mincss just before the rendered (from Django) output HTML is stored in memcache. Also, what I can do is take ALL inline style blocks and all link tags and combine them into one big inline style block. That means that I can reduce any additional HTTP connections needed down to zero! Remember, "Minimize HTTP Requests" is the number one web performance optimization rule.

To get a preview of that, compare https://www.peterbe.com/about with https://www.peterbe.com/about3. Visually no difference. But view the source :)

Before:
Document size: Before

After:
Document size: After

Voila! One HTTP request less and 74Kb less!

Now, as if that wasn't good enough, let's now take into account that the browser won't start rendering the page until the HTML and ALL CSS is "downloaded" and parsed. Without further ado, let's look at how much faster this is now:

Before:
Waterfall view: Before
report

After:
Waterfall view: After
report

How cool is that! The "Start Render" event is fired after 0.4 seconds instead of 2 seconds!

Note how the "Content Download" isn't really changing. That's because no matter what the CSS is, there's still a tonne of images yet to download.

That example page is interesting too because it contains a piece of Javascript that is fired on the window.onload that creates little permalink links into the document and the CSS it needs is protected thanks to the /* no mincss */ trick as you can see here.

The code that actually implements mincss here is still very rough and is going to need some more polishing up until I publish it further.

Anyway, I'm really pleased with the results. I'm going to tune the implementation a bit further and eventually apply this to all pages here on my blog. Yes, I understand that the CSS, if implemented as a link, can be reused thanks to the browser's cache but visitors of my site rarely check out more than one page. In fact, the number of "pages per visit" on my blog is 1.17 according to Google Analytics. Even if this number was bigger I still think it would be a significant web performance boost.

UPDATE

Steve Souders points out a flaw in the test. See his full comment below. Basically, what appears to happen in the first report, IE8 downlads the file c98c3dfc8525.css twice even though it returns as a 200 the first time. No wonder that delays the "Start Render" time.

So, I re-ran the test with Firefox instead (still from the US East coast):

Before:
WebpageTest before (Firefox)
report

After:
WebpageTest after (Firefox)
report

That still shows a performance boost from 1.4 seconds down to 0.6 seconds when run using Firefox.

Perhaps it's a bug in Webpagetest or perhaps it's simply how IE8 works. In a sense it "simulates" the advantages of reducing the dependency on extra HTTP requests.

mincss "Clears the junk out of your CSS"

January 21, 2013
30 comments Python, Web development

A project I started before Christmas (i.e. about a month ago) is now production ready.

mincss (code on github) is a tool that when given a URL (or multiple URLs) downloads that page and all its CSS and compares each and every selector in the CSS and finds out which ones aren't used. The outcome is a copy of the original CSS but with the selectors not found in the document(s) removed. It goes something like this:


>>> from mincss.processor import Processor
>>> p = Processor()
>>> p.process_url('https://www.peterbe.com')
>>> p.process()
>>> p.inlines
[]
>>> p.links
[<mincss.processor.LinkResult object at 0x10a3bbe50>, <mincss.processor.LinkResult object at 0x10a4d4e90>]
>>> one = p.links[0]
>>> one.href
'//d1ac1bzf3lrf3c.cloudfront.net/static/CACHE/css/c98c3dfc8525.css'
>>> len(one.before)
83108
>>> len(one.after)
10062
>>> one.after[:70]
u'header {display:block}html{font-size:100%;-webkit-text-size-adjust:100'

To whet your appetite, running it on any one of my pages here on my blog it goes from: 82Kb down to 7Kb. Before you say anything; yes I know its because I using a massive (uncustomized) Twitter Bootstrap file that contains all sorts of useful CSS that I'm not using more than 10% of. And yes, those 10% on one page might be different from the 10% on another page and between them it's something like 15%. Add a third page and it's 20% etc. But, because I'm just doing one page at a time, I can be certain it will be enough.

One way of using mincss is to run it on the command line and look at the ouput, then audit it and give yourself an idea of selectors that aren't used. A safer way is to just do one page at a time. It's safer.

The way it works is that it parses the CSS payload (from inline blocks or link tags) with a relatively advanced regular expression and then loops over each selector one at a time and runs it with cssselect (which uses lxml) to see if the selector is used anywhere. If the selector isn't used the selector is removed.

I know I'm not explaining it well so I put together a little example implementation which you can download and run locally just to see how it works.

Now, regarding Javascript and DOM manipulations and stuff; there's not a lot you can do about that. If you know exactly what your Javascript does, for example, creating a div with class loggedin-footer you can prepare your CSS to tell mincss to leave it alone by adding /* no mincss */ somewhere in the block. Again, look at the example implementation for how this can work.

An alternative is to instead of using urllib.urlopen() you could use a headless browser like PhantomJS which will run it with some Javascript rendering but you'll never cover all bases. For example, your page might have something like this:


$(function() {
  $.getJSON('/is-logged-in', function(res) {
    if (res.logged_in) {
      $('<div class="loggedin-footer">').appendTo($('#footer'));
    }
  });
});

But let's not focus on what it can not do.

I think this can be a great tool for all of us who either just download a bloated CSS framework or you have a legacy CSS that hasn't been updated as new HTML is added and removed.

The code is Open Source (of course) and patiently awaiting your pull requests. There's almost full test coverage and there's still work to be done to improve the code such as finding more bugs and optimizing.

Using the proxy with '?MINCSS_STATS=1'
Also, there's a rough proxy server you can start that attempts to run it on any URL. You start it like this:

pip install Flask
cd mincss/proxy
python app.py

and then you just visit something like http://localhost:5000/www.peterbe.com/about and you can see it in action. That script needs some love since it's using lxml to render the processed output which does weird things to some DOM elements.

I hope it's of use to you.

UPDATE

Published a blog post about using mincss in action

UPDATE 2

cssmin now supports downloading using PhantomJS which means that Javascript rendering will work. See this announcement

UPDATE 3

Version 0.8 is 500% faster now for large documents. Make sure you upgrade!

Free business idea: An app for figuring out the best car for you

January 18, 2013
0 comments Wondering

Here's a business idea that I've not seen implemented and which I likely won't have time to attempt:

An app for statistically figuring out which car you should buy.

Like Hot or Not it shows you one car at a time (at random) with a variable (also at random). The variable will be turned into a question. The question will be something like: "What about the price of this?" and it's a picture of a Toyota Prius 2013 with its price. Three buttons to choose: "Too expensive", "About right", "Too cheap".

Next, it's a different car and a different variable. For example, a Volvo XC90 with the question "What about the looks of this?" and, again, three buttons: "Too ugly", "About right", "Too sexy".

Car salesman
On so on... You can keep going, answering more questions, or you can stop and check out your result. Obviously, the more you answer the better the suggestion. You might want to help the user with this so they don't answer too few.

Then when you present the result you can, on that page, show a bunch of affiliate links to various local dealerships where you can buy the ideal car for you. Additionally, if the app becomes successful I'm sure you can easily sell advertisement to car companies who would love to show their ads depending on certain variables. E.g. Honda Fits for those who answer that they want low MPG and small cars.

The algorithm shouldn't be too hard to figure out. I'm sure you can get a lot of mileage just by doing a weighted average on the totals. If you sit down and think about it some more I'm sure you can fit some better established algorithm or something from the neural networks if you lay out your results as a matrix.

That's about it. I don't know where to get the pictures and specs for each car but I'm sure one can scrape from various sites and/or seed some of it manually.

It's the kind of app where you can start small (assuming you have at least 100 cars and 3-6 facts about each car). Also, it doesn't depend on having a bunch of traffic already so you don't need to worry so much about the chicken & egg predicament.

Do you think it could fly?

All your images are belong to data uris

January 6, 2013
12 comments Web development

If the number 1 rule for making faster websites is to "Minimize HTTP Requests", then, let's try it.

On this site, almost all pages are served entirely from memcache. Django renders the template with the database content and the generated HTML is cached. So I thought I insert a little post processing script that converts all <img src="...something..."> into <img src="data:image/png;base64,iVBORw0KGgo..."> which basic means the HTML gets as fat as the sum of all referenced images combined.

It's either 10Kb HTML followed by (rougly) 10 x 30Kb images or it's 300Kb HTML and 0 images. The result is here: https://www.peterbe.com/about2 (open and view source)

You can read more about the Data URI scheme here if you're not familiar with how it works.

The code is a hack but that's after all what a personal web site is all about :)

So, how much slower is it to serve? Well, actual server-side render time is obviously slower but it's a process you only have to do a small fraction of the total time since the HTML can be nicely cached.

Running..
ab -n 1000 -c 10 https://www.peterbe.com/about

BEFORE:

Document Path:          /about
Document Length:        12512 bytes

Concurrency Level:      10
Time taken for tests:   0.314 seconds
Complete requests:      1000
Failed requests:        0
Write errors:           0
Total transferred:      12779000 bytes
HTML transferred:       12512000 bytes
Requests per second:    3181.36 [#/sec] (mean)
Time per request:       3.143 [ms] (mean)
Time per request:       0.314 [ms] (mean, across all concurrent requests)
Transfer rate:          39701.75 [Kbytes/sec] received

AFTER:

Document Path:          /about2
Document Length:        306965 bytes

Concurrency Level:      10
Time taken for tests:   1.089 seconds
Complete requests:      1000
Failed requests:        0
Write errors:           0
Total transferred:      307117000 bytes
HTML transferred:       306965000 bytes
Requests per second:    918.60 [#/sec] (mean)
Time per request:       10.886 [ms] (mean)
Time per request:       1.089 [ms] (mean, across all concurrent requests)
Transfer rate:          275505.06 [Kbytes/sec] received

So, it's basically 292Mb transferred instead of 12Mb in the test and the requests per second is a third of what it used to be. But it's not too bad. And with web site optimization, what matters is the individual user's impression, not how much or how little the server can serve multiple users.

Next, how does the waterfall of this look?

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Gamification for me as a software developer

December 21, 2012
3 comments Web development

"Gamification is the use of game-thinking and game mechanics in non-game contexts in order to engage users and solve problems" -- wikipedia

Gamification sneaks into a software developer's life whether he/she likes it or not. Some work for me, some don't.

What works for me

  1. PyPI downloads on my packages
    Although clouded with inaccuracies and possible false positives (someone's build script could be pip installing over zealously), seeing your download count go up means that people actually depend on your code. Most likely, they're not just downloading to awe, they download to use it.

  2. Github followers and Starred projects
    Being followed on Github means people see your activity on their dashboard (aka. home page). Every commit and every gist you push gets potential eyes on it.
    When people star your project it probably means that they're thinking "oh neat! this could come in handy some day
    so I'll star it for now". That's kinda flattering to be honest.

  3. Twitter followers
    This doesn't apply to everyone of course but to me it does. I really try my best to write about work or code related stuff on Twitter and personal stuff on Facebook. Whenever a blog post of mine gets featured on HN or if I present at some conference I get a couple of new followers.
    Some people do a great job curating their followers, responding and keeping it very relevant. They deserve their followers.
    Yes, there are a lot of bogus Twitter accounts that follow you but since that happens to everyone it's easy to oversee. Since you probably skim through most of the "You have new follower(s)" emails, it's quite flattering when it's a real human being who does what you do or somewhat similar.

  4. Activity on Github projects
    This one is less about fame and fortune and more of a "damage prevention". Clicking into a project and seeing that the last commit was 3 years ago most definitely means the project is dead.
    I have some projects that I don't actively work on but the code might still be relevant and doesn't need much more maintenance. For those kind of projects it's good to have some sporadic activity just to signal to people it's not completely abandoned.

  5. Hacker News posts and comments "Show HN: ..."
    I've now had quite a few posts to HN that get promoted to the front page. Whenever this happens you get those almost [embarrassing spikes in your Google Analytics account/static/cache/7c/3b/7c3be91fa89401add4f423e944878706.jpg).
    However, it happened. Enough people thought it was interesting to vote it up to the front page.
    It's important to not count the number of comments as a measure of "success" because oftentimes comments aren't simply constructive feedback but just comments on other comments.
    Keep this one simple, the fact that you have built something that is "Show HN:..." means you probably have worked hard.

What does NOT work for me

  1. Unit test code coverage metrics
    Test coverage percentages are quite a private matter. Kinda like your stool. Unless something amazing happened, keep it to yourself.
    It's nice to see a general increase of the total percentage but do not dare to obsess about it. What matters is that you look through the report and take note that what matters is covered. Coverage on code that is allowed to break and isn't embarrassing if it does, does not need to be green all the way. Who are you trying to impress? The intern you're mentoring or the family you don't have time to spend time with because you're hunting perfection?
    I must, however, admit that I too have in the past inserted pragma: no cover in my code. Also, being able to say that you have 100% test coverage on a lib can be good "advertisement" in your README as it instills confidence in your potential users.

  2. Number of tests
    When you realize that 1 nicely packaged integration test can test just as much as 22 anally verbose unit tests you realize that number of tests is a stupid measure.
    A lot of junior test driven developers write tests that cover circumstances that are just absurd. For example "what if I pass a floating point number instead of a URL string which it's supposed to be??".
    Remember, results and quality count. Having too many tests also means more things to slow you down when you refactor.

  3. Commit counts
    On projects with multiple contributors commit counts is not a measure of anything. It has no valuable implications or deductions. Adding a newline character to a README can be 1 count.
    If you skim through the commit log on a Github project you'll notice that surprisingly many commits are trivial stuff such as style semantics or updating a CREDITS file.
    Yes, someone has to do that stuff too and we're always appreciative of that but it's not a measure of excellence over others. It's just a count.

  4. Resolved bugs/issues count
    If this mattered and was a measure of anything you could simple just swallow everything with a quick turnaround and resolve or close it.
    But not every bug deserves your attention. Even if it is a genuine bug it might still be really low priority which working on costs time and focus distraction away from much more important work.

  5. Number of releases
    It's nice to see projects making releases (or tags) but don't measure things by this. There's so much good quality software that doesn't really fit the release model.

My new web marketing strategy: Begging

December 9, 2012
13 comments Web development

From one of the monthly summary emails
Building a side project is fun. Launching it is fun. Improving and measuring it is fun. But marketing it is aweful!

Marketing your side project means you're not coding, instead you're walking around the interwebs with your pants down trying your hardest to get people to not only try your little project but to also get beyond that by tweeting about it, Facebook status update about it, blog about it or use whatever devices inside it to help the viral spread. Now that! ...is freckin hard.

I'm struggling to even get my best friends and my wife to even try my side projects. I can't blame them, unlike a lemonade stand at a farmers market it's very impersonal. When I tried to get my buddies to try Around The World several did but only very briefly and granted some few did give me feedback but it's really not much to go by.

So, today I'm launching the start of my new web marketing strategy: Begging

Or rather, politely asking people to help me. Instead of using the usual "we" or "our" language I'm referring to it in first person instead. The platform for this strategy experiment is on HUGEpic and it looks like this: hugepic.io/yourhelp/

I'm recently built a feature into HUGEpic that once a month emails everyone who uploaded a picture a little summary of their upload and the number of hits and comments and boldly in the footer of this email there's a link to the /yourhelp/ page (see screenshot above).

Let's see how this works out. Mostly likely it'll be just another noise in the highways of peoples' internet lifes but perhaps it can become successful too.

Mind you, the motives of all of this is for my "insert-sideproject-name-here" to become successful. And by successful I mean popular and lots of traffic. None of my side projects make me any money which makes it easier to beg. However, none of them make any money for the people I'm asking for help. Perhaps that's what could be the version 2.0 of my web marketing strategy.

Introducing: HUGEpic - a web app for showing massive pictures

November 3, 2012
19 comments Python

So here's my latest little fun side-project: HUGEpic.io http://hugepic.io

Zoomed in on Mona Lisa
It's a web app for uploading massive pictures and looking at them like maps.

The advantages with showing pictures like this are:

  • you only download what you need
  • you can send a permanent link to a picture at a particular location with a particular zoom level
  • you can draw annotations on a layer on top of the image

All the code is here on Github and as you can see it's a Tornado that uses two databases: MongoDB and Redis and when it connects to MongoDB it uses the new Tornado specific driver called Motor which is great.

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Fastest way to thousands-commafy large numbers in Python/PyPy

October 13, 2012
15 comments Python

Here are two perfectly good ways to turn 123456789 into "123,456,789":



import locale

def f1(n):
    locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')
    return locale.format('%d', n, True)

def f2(n):
    r = []
    for i, c in enumerate(reversed(str(n))):
        if i and (not (i % 3)):
            r.insert(0, ',')
        r.insert(0, c)
    return ''.join(r)

assert f1(123456789) == '123,456,789'
assert f2(123456789) == '123,456,789'    

Which one do you think is the fastest?

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