Following my previous post, I now have a working config with PostgreSQL.

The aim of this is to have a postfix installation capable mainly of “aliasing” e-mails. Let’s say I have a domain “example.com” and I want to manage e-mail addresses for that domain. But, it’s a small domain with only a handful of users and I don’t want to store mails locally, just alias them to the users private e-mails addresses.

While the aim is only to alias e-maile, the config explained below is still capable of delivering mail locally (storing them directly to disk), but as there is no set-up to access those mails (POP/IMAP/webmail), it’s only marginally useful. But it gives you a working framework if you want to add these features yourself. Read the rest of this entry »

There are many postfix tutorials out there. I’ve always wondered what the hell I was copy/pasting onto my system and decided to start (nearly) from scratch. I took one of those tutorials (don’t remember which one) as inspiration, but based the final result on the official docs. I kept what I liked, changed some settings I did not like so much, and threw out a few other things which I deemed useless.

The main problem with those tutorials is that they show you a final result without telling you how they ended up with the result, sometimes it looks like an amalgam of other tutorials ending up in a huge “frankenconfig”. I don’t like deploying something where I don’t know what it’s doing…

The final result is a config which is stripped down to my most basic needs.

The first iteration will be an extremely simple config:

• No database will be used to store mail config. This however is something I will certainly implement.
• Only simple spam handling using blocklists.
• No antivirus.
• No webmail access.
• No POP access.
• No IMAP access.
• Not relay mails for other hosts (only local delivery)j.

For now, I only require e-mail aliasing. That is, I only want to handle e-mails destined for my domains, but I only want to “forward” them to other destinations. Local delivery (and access to those mails) may or may not be implemented later. It will be set up on an Ubuntu Precise Pangolin Server and should get you started for a basic mail server.

Here’s the main config:

The most interesting part of the config is at the end of the config after the “Virtual Mail” header. This part defines which e-mail addresses the MTA is handling and how. Will the mails be stored locally? Will they be “aliased” to another e-mail?

The config should be documented well-enough.

Examples for the hash files (for an explanation what they do, see above):

As of Python 3.2, the “ipaddress” module has been integrated into the stdlib. Personally, I find it a bit premature, as the library code does not look to be very PEP8 compliant. Still, it fills a huge gap in the stdlib.

In the last days, I needed to find a way to collapse consecutive IP networks into supernets whenever possible. Turns out, there’s a function for that: ipaddress.collapse_addresses. Unfortunately, I was unable to use it directly as-is because I don’t have a collection of networks, but rather object instances which have “network” as a member variable. And it would be impossible to extract the networks, collapse them and correlate the results back to the original instances.

So I decided to dive into the stdlib source code and get some “inspiration” to accomplish this task. To me personally, the code was fairly difficult to follow. About 60 lines comprised of two functions where one calls the other one recursively.

I thought I could do better. And preliminary tests are promising. It’s no longer recursive (it’s shift-reduceish if you will) and about 30 lines shorter. Now, the original code does some type checking which I might decide to add later on, increasing the number of lines a bit, and maybe even hit performance. I’m still confident.

A run with 30k IPv6 networks took 93 seconds with the new algorithm using up 490MB of memory. The old, stdlib code took 230 seconds to finish with a peak memory usage of 550MB. All in all, good results.

Note that in both cases, the 30k addresses had to be loaded into memory, so they will take up a considerable amount as well, but that size is the same in both runs.

I still have an idea in mind to improve the memory usage. I’ll give that a try.

Here are a few stats:

With the new algorithm:

and with the old algorithm:

I’ll add more details as I go… I’m too “into it” and keep forgetting time and to post fun stuff on-line… stay tuned.

I’ve seen my fair share of code fragments colourising console output. Especially when using logging. Sometimes the colour codes are directly embedded into the format string, which makes it really hairy to deal with different colours for different levels. Sometimes even the log message is wrapped in a colour string along the lines: LOG.info("{YELLOW}Message{NORMAL}") or something equally atrocious.

Most logging frameworks support this use-case with “Formatters”. Use them! Here’s a quick example of how to do it “the right way™”:

Clicky clicky → https://gist.github.com/exhuma/8147910

I’m about to embark on a quest to understand the development for custom google-closure components (UI widgets if you will). Reading through the relevant section in “Closure – The Definitive Guide” makes me believe, it’s not all too difficult. But there are still a bunch of concepts which I need to familiarize myself with. This article briefly outlines my aim for this “learning trail”, and starts of with a tiny HelloWorld project using plovr. This article assume a minimal knowledge of google closure (you should know what “provides” and “requires”. “exportSymbol” should also not surprise you) Read the rest of this entry »

According to the Python documentation about __repr__, a call to repr() should give you a valid Python expression if possible. This is a very useful guideline. And it is also something I I like to implement in my Python projects as much as possible.

Now, for mapped database entities, you might argue that it makes sense to have a default constructor as long as it accepts the primary key columns.

By default, it is possible to create new instances by specifying column values in SQLAlchemy. For example:

It should be possible to create such “repr” values automatically for primary keys. All the required meta info is available. Digging through the SA docs, I found that it is possible the customize Base in order to add behaviour to all mapped entities!

Here’s the result:

With this in place, all representations of DB entities will finally make sense and be copy/pasteable directly into your code.

Of course, by nature of ORMs, the new instances created this way will be detached from the session and need to be merged before you can do any DB related operations on them! A simple example:

More than once I needed to create files on the staging/production box which I had no need of on the local development box (For example complex logging configuration).

This fragment contains a simple function which tries to do this in a safe manner, and also ensuring proper cleanup in case of failure.

The problem

Today I needed to keep an eye on PostgreSQL logs. Luckily, I decided upon installation to log everything using the “csvlog” format. But there’s a small catch, depending how you read that log. This catch is newline characters in database queries.

This has nothing to do with PostgreSQL directly. In fact, it does the right thing, in that it quotes all required fields. Now, a quoted field can contain a newline character. But if you read the file on a line-by-line basis (using methods like file_handle.readline, this will case problems. No matter what programming language you use, if you call readline, it will read up to the next newline character and return that. So, let’s say you have the following CSV record:

If you read this naïvely with “readline” calls, you will get the following:

Now, this is really annoying if you want to parse the file properly.

The solution

Read the file byte-by-byte, and feed a line to the CSV parser only if you hit a newline outside of quoted text. Obviously you should consider the newline style (\n, \r or \r\n) and the proper quote and escape characters when doing this.

What about Python?

It turns out, Python’s csv module suffers from this problem. The builtin CSV module reads files line-by-line. However, it is possible to override the default behavior.

For my own purpose, I wrote a simple script, reading from the postgres log until interrupted.

You are free to use this for your own purpose, modify or extend it as you like.

You can find it here: exhuma/postgresql-logmon

I do a lot of work on the go. Offline. Sometimes it takes a long time to push changes to a remote repository. As always, Murphy’s law applies, and the one repo that explodes into my face is the one with ten days worth of work in it.

While working, suddenly my laptop hang. Music looping. No mouse movement. Nothing. The only possible solution was to do a cold-reboot. I was not worried. Everything was saved, and I only changed a few lines and can easily recover if something went awry. So I rebooted.

Once back in the system, I immediately wanted to do a git status and git diff. Git spat back the following error message:

My Laptop had shown some strange and erratic behaviour over the last few days already. I already left a memtest running for about 24 hours earlier this week without errors. The only possible explanation left was the hard-disk.

Fun times ahead! 10 days of work at risk… 10 days of important changes! Sweat building up my forehead. Bloody sweat!

I trust my tools to keep my code safe. I trust git. I trust vim. I do microscopic commits, and I knew my current uncommitted changes only involved a few lines. So maybe only the last commit got corrupted? Let’s see…

Read the rest of this entry »