Toxic Elephant

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Books for Programmers

Posted by matijs 19/02/2012 at 12h46

My list of all-time-favorite books for programmers. I’m not saying everyone should read these, but each of these had an important impact on my growth as a programmer. These are not necessarily in chronological order, by the way.

First, books that are mostly independent of your choice of programming language:

Design Patterns and Refactoring are not books to be read cover to cover, since they they devote quite a large part of their volume to catalogueing. The other two definitely are.

The following books are each really about a particular language. They’re well written, but it’s hard to separate the impact of the books from the impact of the languages.

  • Programming Perl (a.k.a. The Camel Book). This book made me grasp object-oriented programming for the first time by breaking it down to a very basic level. I did most of my learning Perl from this book.
  • Programming Ruby (a.k.a. The Pickaxe Book). I learned Ruby from the free online edition. It got me hooked.

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You Need Some Isolation

Posted by matijs 11/12/2011 at 18h08

Something weird just happened. While refactoring GirFFI, I had managed to remove all use of a particular module. So, I removed the corresponding file, ran the tests using

rake test

And the tests passed. Committed, done.

Then, I took a walk down to the library. By the time I got back, as soon as I looked at my code again, there it was: A giant require statement requiring the file I had just removed. Huh, why do my tests pass?

Well, duh, I have GirFFI installed as a gem, and my code is just picking up the missing file from there. So, I run

bundle exec rake test

The tests fail, showing me exactly the line I need to remove. Commit amended, done.

So, the moral of the story: If you’re developing a gem, use your isolation tool of choice, be it Bundler, Isolate, or something else, to shield your gem development environment from older installed versions.

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A tiny replacement for RVM

Posted by matijs 31/07/2011 at 17h31

Recently, there was a change in where Debian’s rubygems packages store each gem’s files. Instead of having a separate bin directory for each version of ruby, now both the 1.8 and the 1.9 version store scripts in /usr/local/bin. In fact, they will happily overwrite each other’s scripts. This can be very confusing when you think you’re running a script with Ruby 1.8, but in fact it’s running with 1.9, and hence, 1.9’s set of installed gems.

All this made me seriously consider using RVM. Which was quite shocking, as I consider it to be an ugly hack, both in concept and in execution. So, rather than admitting defeat, I decided to create my own hack.

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Choosing a Distributed File System

Posted by matijs 30/06/2011 at 18h15

It’s happening, like it happens to all of us: My hard disk is getting full, and although the free space would have seemed like an ocean just a decade ago, now it’s a worryingly small pool of tiny little gigabytes. I could try freeing up some by tediously going through all the photos I never bothered to cull before, but with Gb-sized videos being added on a regular basis, that isn’t a long term solution. Where long term is anything that will tide me over to my next laptop.

But, what if I could offload some of those files to some other storage medium? I’m not really that fond of external hard disks, but perhaps a file server? Great! You mount some remote directory, and it’s like it’s right there on your machine.

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GirFFI - An Introduction

Posted by matijs 10/05/2011 at 07h09

Over two years ago, I had the idea, that it should be possible to combine two great technologies, ruby-ffi, and GObject Introspection, to dynamically create bindings for GLib-based libraries.

This idea, like many, was born from frustration: The development of Ruby-GNOME2 is labour-intensive, and therefore, it lags behind the development of Gnome libraries. In particular, I wanted to use the Gio library, which had no bindings at the time, to fetch generated icons for images.

Serious development started in october 2009, with a basic proof-of-concept to show that it was at least possible to use FFI to bind Gtk+.

About a year later, GirFFI 0.0.1 was finally released as a gem. Several more releases followed, and now, with release 0.0.9, it seems be nearly feature-complete.

So, what can GirFFI do for you?

Given any GLib-based library with GObject Introspection data, it generates bindings for that library. These bindings are generated dynamically at runtime, meaning that methods and classes are not generated until first use.

Because GirFFI uses FFI to call into the C libraries, it is not tied to a particular Ruby, but is known to work with MRI 1.8.7 and 1.9.2, and with JRuby 1.6.1. Support for Rubinius is planned.

GirFFI supports both Gtk+ 2, and the new Gtk+ 3. You can choose between them by specifying the version when you set up the bindings.

The bindings GirFFI generates are less Ruby-like than those of Ruby-GNOME2. For example, it uses the standard method names provided by the library, and doesn’t try to change, e.g., get_name and set_name to name and name=. This also means it is not a drop-in replacement for Ruby-GNOME2.

Of course, some work remains to be done.

GirFFI is currently very conservative when it comes to freeing allocated memory. As a result, it leaks memory. There are tools to at least test this, but I have no experience using these. Help would definitely be appreciated.

Also, documentation is rather lacking. In particular, there’s not much information on how to translate from the C function calls described in the Gtk+ documentation to the Ruby method calls needed for GirFFI. Ideally, GirFFI could generate Ruby-oriented documentation straight from the GObject-introspection data.

Finally, there are certain functions that are deemed ‘unintrospectable’ by GObject Introspection. These include functions taking varargs or generic pointers. These will need to be hand-bound. GirFFI includes several such hand-bound methods, but the set is far from complete.

Do give GirFFI a try. The gem’s name is gir_ffi, and you can fork the code on GitHub. Comments, bug reports, and pull requests are all welcome.

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Benchmarking Dynamic Method Creation in Ruby

Posted by matijs 22/04/2011 at 09h30

Let’s look at dynamic method generation. I need it for GirFFI, and if you do any kind of metaprogramming, you probably need it too. It was already shown a long time ago that using string evaluation is preferable to using define_method with a block.

That is, if you care at all about speed.

How preferable? About a factor of 1.8 on Ruby 1.8.7:

                    user     system      total        real
regular:        0.270000   0.000000   0.270000 (  0.267164)
string eval:    0.270000   0.000000   0.270000 (  0.273170)
define_method:  0.490000   0.000000   0.490000 (  0.493258)

These numbers are relatively easy to explain. The string evaluation basically has the exact same result as regular method definition, because you actually do the same thing: You define a method using def. With define_method, you have a lot of overhead because a block is more than a bunch of code. It’s actually a closure, and Ruby has to set up the closure’s binding every time you call the method.

(Aside: There are of course other factors to consider. Using string evaluation gives you much more power to build exactly the method you want, while the fact that the blocks passed to define_method are closures allows you to do things now ordinary method can do.)

On Ruby 1.9.2, the results are quite similar, although the difference is now only a factor of 1.5:

                    user     system      total        real
regular:        0.140000   0.010000   0.150000 (  0.142998)
string eval:    0.140000   0.000000   0.140000 (  0.141439)
define_method:  0.210000   0.000000   0.210000 (  0.211936)

Too bad. It seems we’re stuck with string eval. Let’s look at JRuby:

                    user     system      total        real
regular:        0.324000   0.000000   0.324000 (  0.324000)
string eval:    0.208000   0.000000   0.208000 (  0.208000)
define_method:  0.690000   0.000000   0.690000 (  0.690000)

Wait, that can’t be right. Let’s run that again:

                    user     system      total        real
regular:        0.424000   0.000000   0.424000 (  0.424000)
string eval:    0.241000   0.000000   0.241000 (  0.241000)
define_method:  0.756000   0.000000   0.756000 (  0.756000)

Hm, it got a little slower, but the pattern is the same: The method defined using string eval is the fastest of the lot. What is going on here?

Quick, let’s try rubinius.

                    user     system      total        real
regular:        0.348022   0.000000   0.348022 (  0.183686)
string eval:    0.380024   0.004000   0.384024 (  0.196908)
define_method:  0.412026   0.008001   0.420027 (  0.215781)

Uh-huh. Again please.

                    user     system      total        real
regular:        0.376023   0.004000   0.380023 (  0.192683)
string eval:    0.356022   0.000000   0.356022 (  0.189258)
define_method:  0.164010   0.000000   0.164010 (  0.138058)

Huh? Like, maybe the benchmark is wrong?

Okay, let’s try bmbm instead of bm. For rubinius:

Rehearsal --------------------------------------------------
regular:         0.332020   0.004001   0.336021 (  0.172166)
string eval:     0.352022   0.004000   0.356022 (  0.185557)
define_method:   0.192012   0.000000   0.192012 (  0.152656)
----------------------------------------- total: 0.884055sec

                     user     system      total        real
regular:         0.052003   0.000000   0.052003 (  0.050464)
string eval:     0.052003   0.000000   0.052003 (  0.050471)
define_method:   0.076005   0.000000   0.076005 (  0.076912)

That looks more sane. Let’s try JRuby:

Rehearsal --------------------------------------------------
regular:         0.408000   0.000000   0.408000 (  0.408000)
string eval:     0.196000   0.000000   0.196000 (  0.196000)
define_method:   0.657000   0.000000   0.657000 (  0.657000)
----------------------------------------- total: 1.261000sec

                     user     system      total        real
regular:         0.095000   0.000000   0.095000 (  0.096000)
string eval:     0.109000   0.000000   0.109000 (  0.109000)
define_method:   0.416000   0.000000   0.416000 (  0.416000)

Much better. Notice that the difference between string eval and define_method is a stunning factor of four!

Now go back to rubinius. Did you notice how fast it was? That’s stunning. So what’s the difference there between define_method and string eval? Not so big. But the numbers are small so there may be some influence from the environment. Let’s look at how things scale: 10 million iterations:

Rehearsal --------------------------------------------------
regular:         1.240078   0.004000   1.244078 (  1.034290)
string eval:     1.076067   0.000000   1.076067 (  1.058813)
define_method:   0.848053   0.000000   0.848053 (  0.838424)
----------------------------------------- total: 3.168198sec

                     user     system      total        real
regular:         0.496031   0.000000   0.496031 (  0.496565)
string eval:     0.528033   0.000000   0.528033 (  0.500421)
define_method:   0.864054   0.000000   0.864054 (  0.863875)

That’s a factor of about 1.6, somewhere between MRI 1.8.7 and 1.9.2.


The basic conclusion holds: String evaluation leads to faster methods. How much of a difference it makes depends on which Ruby you’re using, and will probably depend on the particular method you’re creating.

Benchmarking is tricky: If you don’t watch out, you could draw the wrong conclusions.

Finally, Rubinius is fast. Really fast. Wow.

The Code

Benchmarks were generated with the following program:

<typo:code lang=”ruby”> require ‘benchmark’

class Foo def regular; end

eval “def stringeval; end”

define_method(:block) {} end

foo =

n = 10000000

Benchmark.bmbm do |x|“regular: “) { n.times { foo.regular } }“string eval: “) { n.times { foo.stringeval } }“define_method:”) { n.times { foo.block } } end </typo:code>

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Materialized Path to Nested Set

Posted by matijs 13/12/2010 at 23h14

On twitter, @clemensk asks:

Hey SQL experts, is it somehow possible in pure (My)SQL to extract a nested set from a table full of paths (think: Category 1 > Category 2)?

To do this, you need to do two things: Extract the names of the nodes, and calculate values for lft and rgt. Here’s my take on the latter part:

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Redefined Accessors

Posted by matijs 10/12/2010 at 09h29

If you’re going to do this:

<typo:code lang=”ruby”> def foo= f

@foo = f + " bar"

end </typo:code>

Then don’t first do this:

<typo:code lang=”ruby”> attr_accessor :foo </typo:code>

But instead do this:

<typo:code lang=”ruby”> attr_reader :foo </typo:code>

That way, there won’t be “method redefined” warnings all over the place.

Let’s make this more general: Before you release your gem, make sure it runs without warnings. They should stick out like a sore thumb when you run your tests, anyway.


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If you have Oops commits, you're doing it wrong

Posted by matijs 16/09/2010 at 17h22

If you still have commits with messages like ‘Oops, I forgot this file’, you’re doing something wrong. Just use git commit --amend.

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Posted by matijs 08/09/2010 at 05h22

Sometimes we say something that sounds right but is in fact the exact opposite of what we mean.

So it’s entirely possible that a talk you started presenting a year and a half ago is so different now as to be almost indistinguishable from the original.

Andy Budd – 7 Ways to improve your public speaking

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