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Bug hunting with the scientific method

I've never come across a problem I couldn't solve using the following technique / problem solving methodology, and I've come across some tough ones.

http://en.wikipedia.org/wiki/Scientific_method

v14.0 stable release - Massive Community Effort!

Drum roll please... May I proudly introduce: The TurnKey Linux v14.0 release!

turnkey 14.0 banner

A long time coming...

Wow is it mid September already!? What has happened to the year?!

Marching towards v14.0 RC2

Update: v14.0 stable is available in all build types: OVA & VMDK, Proxmox, OpenNode & Docker (Proxmox build is somewhat generic LXC/OpenVZ container) and Xen & OpenStack.

How to build a TurnKey appliance ISO from source

[UPDATE:] This blog post has been converted to a Doc page and has had some updates since this post was published.

TurnKey v14.0 RC1 is LIVE! (aka we need YOU!)

Update: v14.0 stable is available in all build types: OVA & VMDK, Proxmox, OpenNode & Docker (Proxmox build is somewhat generic LXC/OpenVZ container) and Xen & OpenStack.

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Invalidating the disk cache on Linux

Here's a super easy way to invalidate the disk cache, which is useful for testing IO performance in the real world, where you can't rely on all of your reads being served up from a super-fast RAM cache rather than a vastly slower physical disk drive.

This will free up everything in the disk cache:

echo 3 > /proc/sys/vm/drop_caches

Or if you want more control over exactly what is being freed...

  1. This frees up the pagecache (e.g., cache of contents of files):

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pyproject-pub: A simple Python project template

I hate repeating myself. It's boring. Life is too short. Like any self respecting hacker I will go out of my way to avoid it, even when I suspect it would cost me more to automate something away than to just do it by hand.

On the other hand, doing stuff I've done before by hand is no fun, while writing scripts is fun. Even when it does take longer, time is relative, or so Einstein said.

Getting started with Python and Lisp

A few weeks ago I talked with a friend studying computer science who I discovered had never experienced the joy of programming with a high level language. Not only that but he didn't have the first clue what he was missing. I feared without my immediate intervention another perfectly good mind would be wasted in programming hell. At his university they were using Java for nearly everything so he had somehow gotten the terribly mistaken idea that it didn't really matter what programming language one used. I carefully explained that:

Why parallel programming is hard

Implementing Cloudtask took more time than I had planned due mainly to the challenges of parallel programming, which I hadn't done that much of before. Also, parallel programming really is inherently far more difficult than serial programming.

In my mind there are three major challenges:

Parallelize - a simple yet powerful high-level interface to multiprocessing

When I was developing Cloudtask, I discovered none of the interfaces in the Python multiprocessing module were powerful enough for my needs so I had to roll my own. The result is the generically useful multiprocessing_utils module in turnkey-pylib which from my totally subjective perspective provides a far superior interface to parallelization than the built-in multiprocessing interfaces.

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