I recently acquired a managed switch for my home office in order to segment a few devices off onto their own isolated vlan. As part of this, I want to expose these vlans on my desktop using Open vSwitch (OVS), and I wanted to implement the configuration using NetworkManager rather than either relying on the legacy /etc/sysconfig/network-scripts scripts or rolling my own set of services. These are my notes in case I ever have to do this again.

We’ve had some wacky weather recently. In the space of a week, the temperature went from a high of about 75°F to a low around 15°F. This got me to thinking about what constitutes “normal” weather here in the Boston area, and in particular, how common it is to have a string of consecutive days in which the high temperature stays below freezing. While this was an interesting question in itself, it also seemed like a great opportunity to learn a little about Pandas, the Python data analysis framework.

Literate programming is a programming paradigm introduced by Donald Knuth in which a program is combined with its documentation to form a single document. Tools are then used to extract the documentation for viewing or typesetting or to extract the program code so it can be compiled and/or run. While I have never been very enthusiastic about literate programming as a development methodology, I was recently inspired to explore these ideas as they relate to the sort of technical writing I do for this blog.

Introduction

A long time ago, I wrote an article all about OpenStack Neutron (which at that time was called Quantum). That served as an excellent reference for a number of years, but if you’ve deployed a recent version of OpenStack you may have noticed that the network architecture looks completely different. The network namespaces previously used to implement routers and dhcp servers are gone (along with iptables rules and other features), and have been replaced by OVN (“Open Virtual Network”). What is OVN? How does it work? In this article, I’d like to explore a minimal OVN installation to help answer these questions.

I recently acquired my Technician amateur radio license, and like many folks my first radio purchase was a Baofeng UV-5R. Due to its low cost, this is a very popular radio, and there is excellent open source software available for programming it in the form of the CHIRP project. After futzing around with the UV-5R for a while, I wanted to get something a little nicer for use at home, so I purchased a Kenwood TM-V71A. CHIRP claims to have support for this radio as well, but it turns out it’s not very good: it uses a “live” connection so every time you edit a channel it tries to update the radio. This result in a slow and flaky UI, especially when trying to make bulk changes (like relocating a block of channels). I ended up using Kenwood’s official MCP-2A software running on a Windows guest on my system, which works but isn’t ideal. I decided to learn more about how the radio interacts with the computer to see if I could improve the situation.

You’re working on a pull request. You’ve been working on a pull request for a while, and due to lack of sleep or inebriation you’ve been merging changes into your feature branch rather than rebasing. You now have a pull request that looks like this (I’ve marked merge commits with the text [merge]):

7e181479 Adds methods for widget sales
0487162 [merge] Merge remote-tracking branch 'origin/master' into my_feature
76ee81c [merge] Merge branch 'my_feature' of https://github.com/my_user_name/widgets into my_feature
981aab4 Adds api for the widget service.
b048836 Includes fixes suggested by reviewer.
3dd0c22 adds changes requested by reviewer
5891db2 [merge] fixing merge conflicts
2e226e4 fixes suggestions given by the reviewer
da1e85c Adds gadget related API spec
c555cc1 Adds doodad related API spec
e5beb3e Adds api for update and delete of widgets
c43bade Adds api for creating widgets
deaa962 Adds all get methods for listing widgets
9de79ab Adds api for showing a widget and simple data model
8288ab1 Adds api framework for widget service

You know that’s a mess, so you try to fix it by running git rebase -i master and squashing everything together…and you find yourself stuck in an endless maze of merge conflicts. There has to be a better way!

Always work on a branch (never commit on master)

When working with an upstream codebase, always make your changes on a feature branch rather than your local master branch. This will make it easier to keep your local master branch current with respect to upstream, and can help avoid situations in which you accidentally overwrite your local changes or introduce unnecessary merge commits into your history.

Rebase instead of merge

If you need to incorporate changes from the upstream master branch in the feature branch on which you are currently doing, bring in those changes using git rebase rather than git merge. This process will generally start by ensuring that your local copy of the upstream master is current:

In this article, we will look at what is necessary to run OpenStack’s Keystone service (and the requisite database server) in containers using Docker Compose.

Running MariaDB

The standard mariadb docker image can be configured via a number of environment variables. It also benefits from persistent volume storage, since in most situations you don’t want to lose your data when you remove a container. A simple docker command line for starting MariaDB might look something like:

A year or so ago I was diagnosed with sleep apnea, and since them I’ve been sleeping with a CPAP. This past weekend, I joined my daughter on a scout camping trip to a campground without readily accessible electricity. This would be the first time I found myself in this situation, and as the date approached, I realized I was going to have to build or buy some sort of battery solution for my CPAP. I opted for the “build” option because it seemed like a fun project.

I recently answered a question from Harsha Nalore on StackOverflow that involved using Ansible to extract the output of a command sent to a BigIP device of some sort. My solution – which I claim to be functional, but probably not optimal – involved writing an Ansible filter module to parse the output. That filter made use of a complex-looking regular expression. Harsha asked for some details on that regular expression works, and the existing StackOverflow answer didn’t really seem the write place for that: so, here we are.