Deploying a Single Page App to AWS S3

There are a million ways to deploy a Single Page App (SPA) to S3. However, this is a very lightway way, and is based on aws-architect.js. I don’t want to repeat the great readme of this repository, but nevertheless, some introduction.

Bootstrap the deployment

To bootstrap the deployment, run the following code:

After that, adjust package.json and make.js to only contain the S3 relevant options since aws-architect.js would also allow to create AWS Lambda based web services.

Add AWS CloudFront distribution

The most declarative way with a straight forward deployment option is to leverage AWS CloudFormation to configure additional resources, notably an SSL Certificate attached to your domain, as well as the AWS CloudFront distribution. An easy way is to add that to the deploy command of make.js, but since it’s rarely changing items, you could as well apply those manually.

First, you’ll need an AWS Certificate that can be used for AWS CloudFront later. The CloudFormation template could look like this, allowing for multiple domains, for example to deploy a test or pull request as well as the production version to the same AWS S3 Bucket:

Now we can take the ACM Certificate, and create an AWS CloudFront Distribution with it, which includes two URLs, one for production, and one for test or pull requests. In this example, it’s assumed that pull requests are deployed to S3 using a path that starts with PR. The configuration looks like this:

That’s all. Slightly painful to get the ACM Certificate and CloudFront distribution settings correctly set to your needs. But afterwards, just execute make.js as part of your build, and aws-architect.js does the rest for you.

Delete your local master

I’ve been involved in many pull requests where the pull request got discarded and re-created from scratch with a reason of “merge conflicts”. Or people taking half a day to solve “unexpected” or “weird” behavior in their git repository. While reasons are various, one pattern emerged: Most of the time people were committing to the local master, potentially pushing it and having an out-of-sync master on their fork repository, and further along merging from a branch called “master” that doesn’t represent what they think master should represent.

One of the simplest solution is to just not have a local master.

Further, besides a lot of git features that can be leveraged, I usually get along with the following commands to get the essntial branching right:

There are some repositories where I keep a local master, for example when working with forked repositories on GitHub. But even then, I only use the master to merge the latest changes from upstream, but follow the pattern described abover.

Using Nginx on Docker to redirect HTTP to HTTPS

I had a website running using HTTPS behind a load balancer, and didn’t want to bother setting up HTTP as well. Instead, I configured the load balancer to point to a very simple Nginx webserver that does nothing else than redirecting HTTP to HTTPS. So from the application side I only had to take care of HTTPS and could ignore additional configuration. As a nice side-effect, the Nginx redirection is generic so that I only need to run a single instance for all my applications.

Since I don’t need anything else than Nginx on the Docker image, I used Alpine Linux as a base and added Nginx, or more precisely the preconfigured Nginx alpine-stable docker image from The Dockerfile looks like the following:

And the related nginx.conf file, which gets copied when the docker image is created like this:

Assuming the Dockerfile and nginx.conf are in the same directory, a simple docker build command creates the docker image which can be loaded into your docker host. Writing a simple script to include this step in your build automation should be fairly trivial, depending on your needs.

LambdaWrap blog post at LifeInVistaprint

LambdaWrapRecently, I’ve worked on LambdaWrap as part of deploying an AWS Lambda based microservice. LambdaWrap is an open source ruby gem that allows to easily publish Lambda functions and its associated API Gateway configuration during automated deployment when using rakefiles.

I got the opportunity to write a larger blog post than this note at LiveInVistaprint. The blog post “LambdaWrap, a Ruby GEM for AWS Lambda” is available at

xUnit2NUnit web service

There are various easy ways to convert one XML file into another XML file, usually through an XSL transformation. Most languages support this with little code. For example, in C# it could be something like this (simplified):

However, I decided to create a simple web service to achieve a few things. For one, to demonstrate that such repetitive code can be wrapped into a service and accessed from everywhere, for example as part of build scripts. Other reasons include that I wanted to play with ASP.NET 5, experimenting with more complex options than just request-response despite the simple use case and host the service on an Azure Web App.

The current version of the service can be found on github at It builds with AppVeyor and as part of the build, I start the web service in the background and run a very basic service level test against it (I’ve written a dedicated blog post on service level testing recently).

Logging an exception and structured message data with ASP.NET 5

Once you started using ASP.NET 5 (aka. ASP.NET vNext), it’s surprisingly difficult to add a message that contains structured data to an exception. The LoggerExtensions either supports a flat message (which is probably good for a lot of cases), but it doesn’t support structured data for more sophisticated analysis.


Let’s quickly look into a simple workaround, which is a 2-liner instead of a one-liner:


The logging extensions only support structured log messages when FormattedLogValues are provided, but don’t provide an interface to do the formatting behind the scenes. This is what I actually want to do:

I have created a pull request to the aspnet/Logging repository, but as of late October 2015, this wasn’t accepted. For the time being, you can simply copy/paste my changes to your project and start using the simplified logging capabilities.


Why am I so obsessed with structured data in messages? Probably simply because I got used to it. I started using Elasticsearch as a logging sink over a year ago, and I mostly look into analyzing fields instead of full text messages. The detailed story is long, and probably worth a few posts, but it’s mostly around analyzing numeric data like performance data, or in the case of errors, by looking into categories of errors (outside of a hard to read error numbers).