It’s very annoying for a program to crash, but it happens. When it happens if the system was configured to dump the core you’ll get a core file with hopefully enough information to debug the crash.

But we don’t know if the system will be configured to dump the core, so we may want to do this from our Python program. We can use the resource module in the standard library. The following example shows how to enable core dumps if —dump-core option is specified:

[gist]https://gist.github.com/772101[/gist]

PS: This worked just fine for the root user but didn’t seem to do the job for a normal user.

Registry is a quite common design pattern which I recently needed to implement and I found Python class decorators very useful so I thought I’d write about it. :–)

In the registry pattern we have a global object we call the registry or registrar which contains references to shared objects and it’s the only entity which can access those.

In my case, I had a plugin style architecture in which different plugins might be dynamically added without any configuration. The first implementation I made used a metaclass to add the plugin class to the registry:

[gist]https://gist.github.com/771884[/gist]

That worked, at least for the beginning. But later I found a limitation: plugins needed the Plugin metaclass, so I couldn’t use another metaclass on them if I needed to. And at some point I did feel the need to do that. I wanted the plugins to use the Singleton metaclass (to implement the Singleton pattern) but then I couldn’t use the Plugin metaclass because only one metaclass can be used (and I didn’t want to create a SingletonPlugin metaclass). And class decorators saved the day:

[gist]https://gist.github.com/771888[/gist]

Class decorators are supported only in Python >= 2.6, but that wasn’t a problem. :–)

:wq

In Python we have a way for creating anonymous functions at runtime: lambda. With lambda we can create a function that will not be bond to any name. That is, we don’t need to def a function, and lambda can be used instead.

[gist]https://gist.github.com/707898[/gist]

Lambda can also be used to call another function by fixing certain argument:

[gist]https://gist.github.com/707899[/gist]

However, lambda functions use late binding for the arguents they get. That is, when you create a lambda function passing a variable as an argument, it’s not immediately copied, a reference to the scope is kept and the value is resolved when the function is called. Thus, if the value of that argument changes within that scope the lambda function will be called with the changed value. Lets see it in action:

[gist]https://gist.github.com/707901[/gist]

Unexpected? It depends, but it was definitely unexpected when I run into it. The solution for this is to bind early, by copying the value at the time of the creation of the function. We can do this 2 ways:

Using a fixed parameter in the lambda function:

[gist]https://gist.github.com/707903[/gist]

Using functools.partial:

[gist]https://gist.github.com/707904[/gist]

I personally like the second approach, using partial, since its more explicit and I really see option one as a workaround to how lambda functions work.

In Python we have several types of objects for storing values in a
array-like way: lists, tuples, dictionaries (they are really more like
hash tables) and sets. Lists and sets might look alike but they are
different. Lets do a quick test:

[gist]https://gist.github.com/569053[/gist]

In the above snippet, we can see that iterating over all items in a

list takes just a little longer tan a a set: 8.99us vs 6.87us we are
talking micro-seconds here!

Now, if we look at how long it takes to verify is the list or set
contains an object, we can see the difference: 1.99us vs 140ns. Sets
are an order of magnitude faster!

Why is that? We can think that a python list is like a C linked list.
It’s time complexity when checking for a value is O(n). Sets, on the
other hand, are implemented as hash tables, so the key is hashed and
instantaneously found (or not) with a time complexity of O(1).

For the curious, this is the list object declaration in Include/listobject.h

[gist]https://gist.github.com/569078[/gist]

and this is the set object declaration, in Include/setobject.h

[gist]https://gist.github.com/569085[/gist]

More on python and hash tables here:

http://sites.google.com/site/usfcomputerscience/hash-tables-imp

:wq