December 11, 2009 § Leave a comment
As of late November 2009 – December 2009, It appears that Reddit is using Tornado web server. Interesting.
You can get the same type of information by using curl (curl -I http://www.reddit.com)
September 20, 2009 § Leave a comment
First of all, there are several wiki posts at pylonshq.com already, below are some of them:
I believe there’s not a single best solution in choosing any of these strategies. This post is intended as sharing my experience in some of these. I’m definitely not set on particular one and will change my mind once I gained better understanding.
My testing methodology
I’m currently testing multiple deployment configurations on the same app, thus giving me the opportunity to blog about it. I use Apache Benchmark (on client side), top and ps afx on server side. My machine is 1 Linode 1440 instance.
My AB setup are:
ab -n 500 -c 50 -k http://rootapp.com/ ab -n 1200 -c 50 -k http://rootapp.com/ ab -n 1500 -c 50 -k http://rootapp.com/ ab -n 800 -c 800 -k http://rootapp.com/
The results varied insignificantly with some subtle interesting differences. I’ll explain those below.
Note: I am testing these configuration on dynamic AJAX-y web application, thus reporting hard numbers is not very useful.
CherryPy vs Paste HTTP Server behind NGINX
One thing that I noticed immediately is that CherryPy has better performance than Paste’s HTTP server. On both, having multiple processes does not help much on overall performance, but significantly reduces number of failed requests. When run under multiple processes, CherryPy consistently have the least number of failed requests.
For my setup having 7-8 processes is the sweet spot. When I have more than that, top is telling me that the latter processes are under utilized.
Setting up CherryPy on your production.ini is painless:
use = egg:PasteScript#cherrypy numthreads = 20 request_queue_size = 512 host = %(http_host)s port = %(http_port)s
By just comparing the two, CherryPy is easily the winner.
Lighttpd and SCGI
This gist is basic configuration to get SCGI up and running on lighttpd while the following is setup for your production.ini:
use = egg:Flup#scgi_thread host = %(http_host)s port = %(http_port)s
given the same AB configuration as CherryPy and Paste counterpart, lighttpd and SCGI consistently capable of handling 30 requests/seconds. About 8-10 requests/seconds more than CherryPy. Even though this setup is better, I noticed that memory consumption continues to go up after 2 weeks. I haven’t spend much time in investigating why. The reason I didn’t choose this path is more because I simply like NGINX better.
If only SCGI module on NGINX isn’t so experimental.
NGINX and FastCGI
This gist is basic configuration to get FastCGI running on NGINX while the following is setup for your production.ini:
use = egg:Flup#fcgi_thread host = %(http_host)s port = %(http_port)s
With this configuration, I consistently get about 25 requests/seconds. It’s a bit behind lighttpd and SCGI configuration. Interestingly, when run under ab -n 1500 -c 50 -k, this configuration hangs NGINX requiring it to be restarted. It only happen once though.
Again, when load balanced properly (depending on your app), any one of these configurations would work well. Hopefully this post can help others to get up to speed in Pylons deployment.
September 6, 2009 § Leave a comment
Someone dear to me put together this not too long ago:
pink, lightpink, palevioletred, hotpink, deeppink, red, tomato, crimson, firebrick, indianred
darkred, maroon, brown, sienna, saddlebrown, rosybrown, tan, darkkhaki, BurlyWood, chocolate, peru, darkgoldenrod
lightcoral, coral, lightsalmon, salmon, darksalmon,
orangered, darkorange, orange, sandybrown, goldenrod, khaki, gold, yellow
greenyellow, lightgreen, lawngreen, chartreuse, lime, springgreen, mediumspringgreen,
limegreen, green, forestgreen, darkgreen,
seagreen, mediumseagreen, darkseagreen,
olive, olivedrab, darkolivegreen
azure, aliceblue, lightcyan, paleturquoise, lightblue, lightsteelblue, powderblue, cyan, aqua, aquamarine, turquoise, lightskyblue, skyblue, mediumaquamarine,
mediumturquoise, darkturquoise, deepskyblue, cadetblue, cornflowerblue, steelblue, slateblue, mediumslateblue,royalblue, dodgerblue, lightseagreen, teal,darkcyan, blue, mediumblue,darkslateblue, navy, darkblue, midnightblue
indigo, blueviolet, mediumpurple, mediumorchid, purple, darkmagenta, darkviolet, darkorchid, lavender, thistle, plum, violet, orchid, magenta, fuchsia, mediumvioletred
darkslategray, dimgray, gray, slategray, darkgray, silver
August 25, 2009 § Leave a comment
First of all, I love Tokyo and already use Tokyo as secondary database for Pylons development and so far it has been a great success.
Since my box does not have a lot of memory, and a lot more disk space, it make sense to use Tokyo as caching solution instead of memcache.
Quick googling revealed that Jack Hsu has already implemented beaker’s Tokyo extension. His snippet works out of the box.
For my use case, I change the serializing strategy to using pickle instead of json. My reasoning is that pickle allows serializing complex object and I don’t have requirement for portability on cache data.
You can find the Tokyo extension here. I added Redis extension as well since it is very similar to Tokyo.
Edit (08/26/2009): Added extension for Dynomite
Edit (08/26/2009): Added extension for Ringo
[fixing bad layout]
August 2, 2009 § Leave a comment
I have this nagging problem SSH from OS X to ubuntu on VPS environment.
The delete key does FORWARD DELETE!
While it is not a big deal, but it does bother me. This post told me the simple step to mitigate that problem:
1. Go to the terminal preferences
2. Go to settings
3. Under advanced click Delete sends Ctrl-H
July 14, 2009 § Leave a comment
In order to have Beaker’s session applied to all subdomains, set cookie_domain to: .yourdomain.com (notice the dot in front).
In pylons, the cookie domain is available as: beaker.session.cookie_domain.
Also, in Google Group, Cezary mentioned to set also set sub_domain in routing.py
- This howto won’t work in Firefox. As far as I know, only Safari allows me to do this.
July 13, 2009 § 54 Comments
Edit (2009/08/04): It is not obvious for many readers that this blog and its articles are meant as opinion piece. It is also not obvious that I like both languages. Just to be clear, I do like both languages.
Not too long ago on Reddit, someone posted Python vs Ruby. This kind of comparisons would be more interesting if the author take some time to actually highlights the merit of both languages.
So, let me take a stab at it.
Python has map, reduce, lambda, and list comprehension. Ruby has select, collect, reject, inject, and block, (and lambda).
Edit (2009/08/04): Thanks to everyone who reminded me that Ruby has lambda too.
These techniques allow programmers to perform operations on lists (or dict/hash) effectively. Some of these are not optimized for speed, so do not expect much on speed gain.
Both have set type, collection of distinct values. Set and List/Array are cast able bi-directionally.
Edit (2009/08/08): I forgot about Generator Expression in Python. It looks more or less like list comprehension, but it works using iterator as opposed to containing all values inside in-memory list.
Edit (2009/08/04): I mention lambda as one of the tools that help me manipulating list, not to point out that Python is cool for having lambda (Ruby is cool for having lambda too).
Python does not have anonymous function, but anonymous function maybe out of scope in this section. Because I can simply use lambda.
- I would be interested to read on Ruby’s block performance.
Reflection and Meta Programming and Monkey Patching
Edit (2009/08/04): Thanks to readers for pointing it out that these are not the same thing. Although they do serve the same purpose for my use cases.
Both languages supports reflection (and meta-programming and monkey patching). That means you have access to the inner working of an object. Python gives you a lot of access via __these_kind_of_methods__ (I never knew what these are called), while Ruby gives you access to everything inside object.
Example of Python monkey patching: You can swapped out object.__class__ with a completely different class. You can also added extra methods to object.__dict__
You can manipulate Python classes on run-time, but not basic classes such as int or basestring. While in Ruby, you can manipulate everything, including replacing/adding methods inside Integer or String. Even though by default attributes are private, Ruby does not try to stop me from accessing them (use send).
As many of you might already know, Rails monkey-patch global object (e.g. object.blank?). I’m glad that Django and Pylons does NOT do that.
eval()/exec() are simply evil (annoys me) in both languages. They make debugging more difficult.
Manipulating files in Python is horrible. The whole os, os.path, shutil, filecmp, tempfile business is convoluted and inconsistent. IMHO, Ruby wins big time.
It’s not that easy to read Python documentation because it’s written like a narration. Whereas Ruby documentation follows Javadoc (which is my personal favorite) style. Use apidock.com for even better RTFM experience.
Edit (2009/08/04): Yes. That is my personal preference. If that’s not clear.
Ruby wins a lot of TDD practitioners. There are plethora of Ruby modules created for making testing experience truly wonderful. See: RSpec, Shoulda, Factory Girl, Selenium.
Python mocking libraries are still not trivial to use. Testing is an area where Python can learn from Ruby (Yes, Selenium also supports Python).
Edit (2009/08/04): Thanks for telling me about windmill!
Visitor pattern is a technique of decoupling logic from object. Often times, there are logic which needs to be shared among objects that do not share the same parent. Decorator is Python’s implementation to visitor pattern, while in Ruby, this could be done by including module/mixin.
Edit (2009/08/04): Example on why I think decorator is visitor pattern (See @InputEvaluator below):
class InputEvaluator(object): def __init__(self, func): self.func = func def __call__(self): # add functionality before self.func is executed self.func() # add functionality after self.func is executed # While I'm at it, I can manipulate things inside self.func.__class__, or __name__
They are not PHP
Both do not have GOTO and are general purpose language. They have real objects and objects can persist longer than the life cycle of HTTP request.
As general purpose language, both have interactive console (plus debugger). Useful for testing features that I forgot. PHP5 does have CLI, but seriously…
Although, I have to say PHP’s require_once is nice. That’s 1 thing I have to gripe about in Python, circular import.
A lot of pythonistas say that if programmer have circular import, then s/he usually have bad design. That’s likely to be correct. But, on those rare cases where the design is good, circular import becomes a huge pain in the neck. A good example of this would be:
2 SqlAlchemy model classes which have classmethod that calls the other class. Perfectly legitimate use case, but now both of those model classes have to be put under the same file because of circular import (To NOT have to do this, create a method that calls the other classmethod). I believe this ruins code maintainability.
Edit (2009/08/04): See commentary’s input on how to avoid this situation.
HTTP and other basic networking
Python comes with webbrowser, urllib2, smtp, http, SocketServer, HttpServer, and more, while Ruby only has net/HTTP
With all those tools, building things like web spider is trivial in Python.
Edit (2009/08/04): This section is just about standard library. I don’t have enough material to elaborate on this. Thus, it’s fair to criticize this section.
Both are terrible in threading. Python has GIL which limits its threading performance, while Ruby’s threading is leaking memory. (I think 1.9 address this issue. Anyone can confirm this?)
Edit (2009/08/04): Yes! yes! yes! for those who said that Jython and JRuby do not have these problems.
Both does not have daemonize as part of standard library, although it’s very easy to roll my own.
Jython and JRuby exists and both are making using Java significantly more productive.
- JRuby is actually really nice and have “real” threading implementation.
- Using Jython for manipulating Swing objects is surprisingly a happy experience.
Modules (for Web Apps)
Both have so many useful modules for building web applications.
Python have: Django, Pylons, web.py, Beautiful Soup, SqlAlchemy, Paste, Werkzeug, Routes (totally “inspired” by Rails), Shove, Pygments, a dozen or so template languages (my favorite is Mako), 4 different JSON modules (cjson is faster than simple-json when looping through 10,000 times. I don’t actually know if this is the best way to benchmark the two), various performance improvement modules (psyco, pyrex, cython)
Ruby have: Rails, Merb, Sinatra, HPricot, DataMapper, Mongrel, ActiveSupport, Moneta, erb, json, RubyInline
Edit (2009/08/04): If it’s not obvious, I am making direct comparison between Python and Ruby here. Yes, I have used all these modules (except Merb and DataMapper. They look awesome though.)
IMHO, outside web app realm, Python is better positioned. See: Pyglet, WxWidget, SciPy, etc.
Python surprisingly lacks of mature library that handles online payments (Python people are not worried about paying customer?). I would appreciate it if anyone can point me to a good payment API in Python.
Edit (2009/08/04): See comments below for Python payment module.
Big Companies Backing
I believe Python is winning here. Google, Youtube, Yelp, Nasa, Honeywell, etc. use Python. On the other hand, yellowpages.com, AboutUs, and these guys use Ruby. I heard that Amazon Fresh uses RoR, can anyone confirm?
Edit (2009/08/05): Some have suggested that Apple is leaning towards Ruby camp, especially with MacRuby project (link).
These languages are interchangeable for building web application. Neither are more awesome. They get the job done and they make programmers happy.
Thanks HN visitors for giving thoughtful comments! I’ll try my best to keep up with you guys in updating this article.