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Vertical Slice Architecture

Thu, 19 Apr 2018 13:48:58 +0000

Many years back, we started on a new, long term project, and to start off with, we built the architecture around an onion architecture. Within a couple of months, the cracks started to show around this style and we moved away from that architecture and towards CQRS (before it had that name). Along with moving to CQRS, we started building our architectures around vertical slices instead of layers (whether flat or concentric, it's still layers). Since then, for the last 7-8 years or so, building around vertical slice architectures for all manners of applications and systems has been our exclusive approach and I can't imagine going back to the constraints of layered architecture approaches.

A traditional layered/onion/clean architecture is monolithic in its approach:

The problem is this approach/architecture is really only appropriate in a minority of the typical requests in a system. Additionally, I tend to see these architectures mock-heavy, with rigid rules around dependency management. In practice, I've found these rules rarely useful, and you start to get many abstractions around concepts that really shouldn't be abstracted (Controller MUST talk to a Service that MUST use a Repository).

Instead, I want to take a tailored approach to my system, where I treat each request as a distinct use case in how to approach its code. Because my system breaks down neatly into "command" requests and "query" requests (GET vs POST/PUT/DELETE in HTTP-land), moving towards a vertical slice architecture gives me CQRS out of the gate.

So what is a "Vertical Slice Architecture"? In this style, my architecture is built around distinct requests, encapsulating and grouping all concerns from front-end to back. You take a normal "n-tier" or hexagonal/whatever architecture and remove the gates and barriers across those layers, and couple along the axis of change:

When adding or changing a feature in an application, I'm typically touching many different "layers" in an application. I'm changing the user interface, adding fields to models, modifying validation, and so on. Instead of coupling across a layer, we couple vertically along a slice. Minimize coupling between slices, and maximize coupling in a slice.

With this approach, most abstractions melt away, and we don't need any kind of "shared" layer abstractions like repositories, services, controllers. Sometimes these are still required by our tools (like controllers or ORM units-of-work) but we keep our cross-slice logic sharing to a minimum.

With this approach, each of our vertical slices can decide for itself how to best fulfill the request:

The old Domain Logic patterns from the Patterns of Enterprise Architecture book no longer need to be an application-wide choice. Instead, we can start simple (Transaction Script) and simply refactor to the patterns that emerges from code smells we see in the business logic. New features only add code, you're not changing shared code and worrying about side effects. Very liberating!

There are some downsides to this approach, however, as it does assume that your team understands code smells and refactoring. If your team does not understand when a "service" is doing too much to push logic to the domain, this pattern is likely not for you.

If your team does understand refactoring, and can recognize when to push complex logic into the domain, into what DDD services should have been, and is familiar other Fowler/Kerievsky refactoring techniques, you'll find this style of architecture able to scale far past the traditional layered/concentric architectures.

Multi-Environment Deployments with React

Tue, 10 Apr 2018 12:54:17 +0000

If you are using Create-React-App to scaffold your react application there is built in support for changing environment variables based on the NODE_ENV values, this is done by using .env files. In short this process works by having a .env, .env.production, .env.development set of files. When you run/build your application CRA will set the NODE_ENV value to either development or production and based on these values the correct .env file will be used.

This works great, when you have a simple deploy setup. But many times in enterprise level applications you need support for more than just 2 environments, many times it is 3-4 environments. Common logic would suggest that you can accomplish this via the built in mechanism by having additional .env files and changing the NODE_ENV value to the value you care about. However, CRA does not support this with doing an eject, which will eject all the default conventions and leave it to you to configure your React application. Maybe this is a good idea, but in my case ejecting was not something I wanted to do.

Because I did not want to do an eject I needed to find another solution, and after a fair amount of searching I found a solution that seems to work for me and my needs and is about the amount of effort as I wanted </article>
<article>
<h1>Raspberry Pi Kubernetes Cluster - Part 1</h1>
<p>Sat, 07 Apr 2018 14:01:00 +0000</p>
<content:encoded>
<p>Howdy</p>

<p>This is going to be the first post about my setup of a Raspberry Pi Kubernetes Cluster. I saw a post by <a href= Hart Hoover and it finally motivated me to purchase his “grocery list” and do this finally. I’ve been using Minikube for local Kubernetes testing but it doesn’t give you multi-host testing abilities. I’ve also been wanting to get deeper into my Raspberry Pi knowledge. Lots of learning and winning.

The items I bought were:

Here is the tweet when it all arrived:

I blame @hhoover ;). I will be building my @kubernetesio cluster once the 6pi case shows up next Wednesday. The extra pi is to upgrade my @RetroPieProject. Touch screen is an addition I want to try. Side project here I come. pic.twitter.com/EebIKbsCeH
— Jason Meridth (@jmeridth) March 31, 2018

I spent this morning finally putting it together.

Here is me getting started on the “dogbone case” to hold all of the Raspberry Pis:

The bottom and one layer above:

And the rest:

Different angles completed:

And connect the power:

Next post will be on getting the 6 sandisk cards ready and putting them in and watching the Raspberry Pis boot up and get a green light. Stay tuned.

Cheers.

Raspberry Pi Kubernetes Cluster - Part 1 was originally published by Jason Meridth at Jason Meridth on April 07, 2018.

Building AWS Infrastructure with Terraform: S3 Bucket Creation

Fri, 06 Apr 2018 14:28:49 +0000

If you are going to be working with any cloud provider it is highly suggested that you script out the creation/maintenance of your infrastructure. In the AWS word you can use the native CloudFormation solution, but honestly I find this painful and the docs very lacking. Personally, I prefer Terraform by Hashicorp. In my experience the simplicity and easy of use, not to mention the stellar documentation make this the product of choice.

This is the initial post in what I hope to be a series of post about how to use Terraform to setup/build AWS Infrastructure.

Terrform Documentation on S3 Creation -> Here

In this post I will cover 2 things

Basic bucket setup
Bucket setup as Static website

Setting up a basic bucket we can use the following

resource "aws_s3_bucket" "my-bucket" {
    bucket = "my-bucket"
    acl    = "private"

    tags {
        Any_Tag_Name = "Tag value for tracking"
    }    
}

When looking at the example above the only 2 values that are required are bucket and acl.

I have added the use of Tags to show you can add custom tags to your bucket

Another way to setup an S3 bucket is to act as a Static Web Host. Setting this up takes a bit more configuration, but not a ton.

resource "aws_s3_bucket" "my-website-bucket" {
    bucket = "my-website-bucket"
    acl    = "public-read"

    website {
        index_document = "index.html"
        error_document = "index.html"
    }    

    policy = <<POLICY
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "AddPerm",
            "Effect": "Allow",
            "Principal": "*",
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::my-website-bucket/*"
        }
    ]
}
    POLICY
    tags {
          Any_Tag_Name = "Tag value for tracking"
    }
}

The example above has 2 things that need to be pointed out.

The website settings. Make sure you setup the correct pages here for index/error

The Policy settings. Here I am using just basic policy. You can of course setup any policy here you want/need.

As you can see, setting up S3 buckets is very simple and straight forward.

*** Reminder: S3 bucket names MUST be globally unique ***

Till next time,

SSH - Too Many Authentication Failures

Wed, 28 Mar 2018 05:00:00 +0000

Problem

I started seeing this error recently and had brain farted on why.

Received disconnect from 123.123.132.132: Too many authentication failures for hostname

After a bit of googling it came back to me. This is because I’ve loaded too many keys into my ssh-agent locally (ssh-add). Why did you do that? Well, because it is easier than specifying the IdentityFile on the cli when trying to connect. But there is a threshhold. This is set by the ssh host by the MaxAuthTries setting in /etc/ssh/sshd_config. The default is 6.

Solution 1

Clean up the keys in your ssh-agent.

ssh-add -l lists all the keys you have in your ssh-agent ssh-add -d key deletes the key from your ssh-agent

Solution 2

You can solve this on the command line like this:

ssh -o IdentitiesOnly=yes -i ~/.ssh/example_rsa foo.example.com

What is IdentitiesOnly? Explained in Solution 3 below.

Solution 3 (best)

Specifiy, explicitly, which key goes to which host(s) in your .ssh/config file.

You need to configure which key (“IdentityFile”) goes with which domain (or host). You also want to handle the case when the specified key doesn’t work, which would usually be because the public key isn’t in ~/.ssh/authorized_keys on the server. The default is for SSH to then try any other keys it has access to, which takes us back to too many attempts. Setting “IdentitiesOnly” to “yes” tells SSH to only try the specified key and, if that fails, fall through to password authentication (presuming the server allows it).

Your ~/.ssh/config would look like:

Host *.myhost.com
  IdentitiesOnly yes
  IdentityFile ~/.ssh/myhost
Host secure.myhost.com
  IdentitiesOnly yes
  IdentityFile ~/.ssh/mysecurehost_rsa
Host *.myotherhost.domain
  IdentitiesOnly yes
  IdentityFile ~/.ssh/myotherhost_rsa

Host is the host the key can connect to IdentitiesOnly means only to try this specific key to connect, no others IdentityFile is the path to the key

You can try multiple keys if needed

Host *.myhost.com
  IdentitiesOnly yes
  IdentityFile ~/.ssh/myhost_rsa
  IdentityFile ~/.ssh/myhost_dsa

Hope this helps someone else.

Cheers!

SSH - Too Many Authentication Failures was originally published by Jason Meridth at Jason Meridth on March 28, 2018.

Clear DNS Cache In Chrome

Tue, 27 Mar 2018 20:42:00 +0000

I’m blogging this because I keep forgetting how to do it. Yeah, yeah, I can google it. I run this blog so I know it is always available…..anywho.

Go To:

chrome://net-internals/#dns

Click “Clear host cache” button

Hope this helps someone else.

Cheers.

Clear DNS Cache In Chrome was originally published by Jason Meridth at Jason Meridth on March 27, 2018.

Create Docker Container from Errored Container

Mon, 26 Mar 2018 03:31:00 +0000

When I’m trying to “dockerize” an applciation I usually have to work through some wonkiness.

To diagnose a container that has errored out, I, obviously, look at the logs via docker logs -f [container_name]. That is sometimes helpful. It will, at minimum tell me where I need to focus on the new container I’m going to create.

Pre-requisites to being able to build a diagnosis container:

You need to use CMD, not ENTRYPOINT in the Dockerfile
- with CMD you’ll be able to start a shell, with ENTRYPOINT your diagnosis container will just keep trying to run that

To create a diagnosis container, do the following:

Check your failed container ID by docker ps -a
Create docker image form the container with docker commit
- example: docker commit -m "diagnosis" [failed container id]
Check the newly create docker image ID by docker images
docker run -it [new container image id] sh
- this takes you into a container immediately after the error occurred.

Hope this helps someone else.

Cheers.

Create Docker Container from Errored Container was originally published by Jason Meridth at Jason Meridth on March 25, 2018.

Log Early, Log Often… Saved my butt today

Wed, 21 Mar 2018 13:16:03 +0000

In a prior posting (AWS Lambda:Log Early Log often, Log EVERYTHING) I wrote about the virtues and value about having really in depth logging, especially when working with cloud services. Well today this logging saved my ASS a ton of detective work.

Little Background
I have a background job (Lambda that is called on a schedule) to create/update data cache in a DynamoDB table. Basically this job will pull data from one data source and attempt to push it as create/update/delete to our Dynamo table.

Today when I was running our application I noticed things were not loading right, in fact I had javascript errors because of null reference errors. I knew that the issue had to be in our data, but was not sure what was wrong. If I had not had a ton of logging (debug and info) I would have had to run our code locally and step though/debug code for hundreds of items of data.

However, because of in depth logging I was able to quickly go to CloudWatch and filter on a few key words and narrow hundreds/thousands of log entries down to 5. Once I had these 5 entries I was able to expand a few of those entries and found the error within seconds.

Total time to find the error was less than 5 minutes and I never opened a code editor or stepped into code.

The moral of this story, because I log everything, including data (no PII of course) I was able to quickly find the source of the error. Now to fix the code….

Till next time,

Containers - What Are They Good For? Build Environments?

Thu, 08 Mar 2018 15:37:27 +0000

In the last post, I looked at how containers could make local development easier for our typical projects, and mainly found they work well for dependencies.

Next up, I wanted to see if they could make our continuous integration server "better". Better, as in, faster, more reliable, more deterministic. But first, let's review our typical environment:

We have Continuous Integration servers running either on premise (with TFS, Jenkins, TeamCity) or in the cloud (VSTS, AppVeyor). We also run builds locally, as we're strong believers that you should be able to run your build locally before pushing your changes.

In either of our main options, on-prem or cloud, your build runs on a build agent. Most build servers also have the concepts of build queues/pools/requirements. In any case, when a build is triggered, the build server runs a build on a build agent.

Cloud build agents typically run your build in a purpose-built VM. AppVeyor and VSTS even have a few build images to choose from, but most importantly, these build images are pre-installed with a LOT of stuff:

This means that when our build agent starts up, we already have all of our "typical" build dependencies pre-installed, including popular services and databases. The hosted build servers cast a wide net with the software they install, as it's quicker to have this software pre-installed than it is to install it at build-time.

The other major advantage of course to this approach is your build is quicker if everything is pre-installed.

For on-prem build agents, you'll typically create a VM, install the build agent, and whatever software and services are needed to successfully run your build. Once this is installed, you don't need to re-do it. Some CI tools let you connect to Elastic Beanstalk with your own custom-built AMIs, giving you on-demand build agents. But this is still quite rare - most CI servers, for your own custom build agents, require those build agents to be running first, register, and only then will a build run on that agent.

Our starting point

With all this in mind, what do we actually get out of the box when we create a new project with Docker support in Visual Studio? When you create a new ASP.NET Core project, one of the configuration items available for you is to enable Docker support:

But what does this actually do. It turns out, from a delivery pipeline perspective, next to nothing. The Docker support in Visual Studio provides runtime support, not build pipeline support. It gives you the ability to run your .NET Core application inside a container, but nothing more. It does not provide an actual CI build environment in the form of a container. We can see more by looking at the dockerfile:

FROM microsoft/aspnetcore:2.0-nanoserver-1709 AS base  
WORKDIR /app  
EXPOSE 80

FROM microsoft/aspnetcore-build:2.0-nanoserver-1709 AS build  
WORKDIR /src  
COPY AspNetCoreApp.sln ./  
COPY AspNetCoreApp/AspNetCoreApp.csproj AspNetCoreApp/  
RUN dotnet restore -nowarn:msb3202,nu1503  
COPY . .  
WORKDIR /src/AspNetCoreApp  
RUN dotnet build -c Release -o /app

FROM build AS publish  
RUN dotnet publish -c Release -o /app

FROM base AS final  
WORKDIR /app  
COPY --from=publish /app .  
ENTRYPOINT ["dotnet", "AspNetCoreApp.dll"]

This multi-stage dockerfile uses two base images - first is the runtime image of microsoft/aspnetcore:2.0-nanoserver-1709, which since I chose Windows as my Docker image, uses the nanoserver-based image. The other base image, for actually building, is the microsoft/aspnetcore-build:2.0-nanoserver-1709 build image. It's also nanoserver-based, but the big difference between the two lies in what's actually included in the image. The ASP.NET Core "build" images base themselves first on the .NET Core SDK images, and add:

Node.js
npm
gulp
bower
git

Plus a "warmed up" cache of NuGet packages by creating a dummy project and running dotnet restore on it. All of this put together gives us the ability, and really this is it, to run dotnet build commands on them. So while this is a great environment to compile and run our application, it's nowhere near adequate as an actual build environment.

Our build environment requires far, far more things than this. I also saw that the build script in our dockerfile only builds one single csproj, but our actual CI builds build the solution, not just a single project. You might want to build the solution file - but you can't, the .NET Core SDK build system does not support those special Docker build projects (yet). Whoops.

Compare with the VSTS build agent dockerfile which includes:

Windows Server Core (not nanoserver)
git
node
JDK v8
curl
maven
gradle
ant
Docker
20 other things

Basically, it's the VM image of VSTS scripted out as a Docker image. Except, and this is important for us, any database images.

One other thing astute readers will notice is that the VSTS image uses Chocolatey, and the ASP.NET Core "build" images do not. There are a number of reasons why not, chiefly that the nanoserver image does NOT include:

Any full .NET Framework of any kind
Any version of Powershell

Chocolatey requires both, so we can't use basic tools to build our image.

I wouldn't let this stop me, so with all this in mind, let's create a dockerfile that can actually run our build.

A real CI Docker environment

Choices, choices. We need to decide what environment to build for our CI build, and how we compose our Lego docker bricks together. Our build requires a few dependencies:

Powershell (any version, including Powershell Core)
.NET Core SDK
A SQL database to run our tests against

What I hope we don't need is:

Full .NET
Visual Studio

Immediately we're presented with a problem. In our "normal" CI environments, we use a number of tools that require full .NET, including:

RoundhousE for database migrations
octo.exe for Octopus Deploy packaging/releasing

We either need to find new tools, or build a bigger image that includes the tools needed to run.

The other big decision we need to make is - do we bake our service dependencies (SQL Server) into our CI image? Or do we use docker compose to create a CI "environment" that is two or more docker images networked together to actually run our build?

And finally, how can we reliably run this build locally, and in some sort of CI environment? What CI environments support running docker images, and what does that actually look like?

In the next post, I'll walk through migrating our existing build infrastructure to various forms of Docker builds, and show how each of them, on Windows, still are not viable.

AWS Lambda: Log early, Log often, Log EVERYTHING

Tue, 06 Mar 2018 14:00:58 +0000

In the world of building client/server applications logs are important. They are helpful when trying to see what is going on in your application. I have always held the belief that your logs need to be detailed enough to allow you to determine the WHAT and WHERE without even looking at the code.

But lets be honest, in most cases when building client/server applications logs are an afterthought. Often this is because you can pretty easily (in most cases) debug your application and step through the code.

When building a serverless applications with technologies like AWS Lambda functions (holds true for Azure Functions as well) your logging game really needs to step up.

The reason for this is that you cannot really debug your Lambda in the wild (you can to some degree locally with AWS SAM or the Serverless framework). Because of this you need produce detailed enough logs to allow you to easily determine the WHAT and WHERE.

When I build my serverless functions I have a few guidelines I follow

Info Log calls to methods, output argument data (make sure no PII/PHI)
Error Log any failures (in try/catch or .catch for promises)
Debug Log any critical decision points
Info Log exit calls at top level methods

I also like to setup a simple and consistent format for my logs. The example I follow for my Lambda logs is as seen below

timestamp: [logLevel] : [Class.Method] - message {data points}

I have found that if I follow these general guidelines the pain of determine failure points in serverless environments is heavily reduced.

Till next time,

Sinon Error: Attempted to wrap undefined property ‘XYZ as function

Tue, 27 Feb 2018 13:45:29 +0000

I ran into a fun little error recently when working on a ReactJs application. In my application I was using SinonJs to setup some spies on a method, I wanted to capture the input arguments for verification. However, when I ran my test I received the following error.

Attempted to wrap undefined property handlOnAccountFilter as function

My method under test is setup as such

handleOnAccountFilter = (filterModel) => {
    // logic here
}

I was using the above syntax is the proposed class property feature, which will automatically bind the this context of the class to my method.

My sinon spy is setup as such

let handleOnAccountFilterSpy = null;
beforeEach(() => {
    handleOnAccountFilterSpy = sinon.spy(AccountsListingPage.prototype, 'handleOnAccountFilter');
});

afterEach(() => {
     handleOnAccountFilterSpy.restore();
});

Everything looked right, but I was still getting this error. It turns out that this error is due in part in the way that the Class Property feature implements the handlOnAccountFilter. When you use this feature the method/property is added to the class as an instance method/property, not as a prototype method/property. This means that sinon is not able to gain access to it prior to creating an instance of the class.

To solve my issue I had to make a change in the implementation to the following

handleOnAccountFilter(filterModel) {
    // logic here
}

After make the above change I needed to determine how I wanted to bind this to my method (Cory show 5 ways to do this here). I chose to bind this inside the constructor as below

constructor(props){
    super(props);

    this.handleOnAccountFilter = this.handleOnAccountFilter.bind(this);
}

I am not a huge fan of having to do this (pun intended), but oh well. This solved my issues.

Till next time

Ensuring componentDidMount is not called in Unit Tests

Thu, 22 Feb 2018 19:45:53 +0000

If you are building a ReactJs you will often times implement componentDidMount on your components. This is very handy at runtime, but can pose an issue for unit tests.

If you are building tests for your React app you are very likely using enzyme to create instances of your component. The issue is that when enzyme creates the component it invokes the lifecyle methods, like componentDidMount. Sometimes we do not want this to be called, but how to suppress this?

I have found 2 different ways to suppress/mock componentDidMount.

Method one is to redefine componentDidMount on your component for your tests. This could have interesting side effects so use with caution.

    describe('UsefullNameHere', () => {
        
        beforeAll(() => {
            YourComponent.prototype.componentDidMount = () => {
                // can omit or add custom logic
            };
        });
    });

Basically above I am just redefining the componentDidMount method on my component. This works and allows you to have custom logic. Be aware that when doing above you will have changed the implementation for your component for the lifetime of your test session.

Another solution is to use a mocking framework like SinonJs. With Sinon you can stub out the componentDidMount implementation as seen below

    describe('UsefullNameHere', () => {
        
        let componentDidMountStub = null;
        beforeAll(() => {
            componentDidMountStub = sinon.stub(YourComponent.prototype, 'componentDidMount').callsFake(function() {
                 // can omit or add custom logic
            });
        });

        afterAll(() => {
            componentDidMountStub.restore();
        });
    });

Above I am using .stub to redefine the method. I also added .callsFake() but this can be omitted if you just want to ignore the call. You will want to make sure you restore your stub via the afterAll, otherwise you will have stubbed out the call for the lifetime of your test session.

Till next time,

The False Dichotomy of Monoliths and Microservices

Wed, 21 Feb 2018 20:35:55 +0000

When learning about microservices, you're nearly always introduced to the concept of a monolith. If you're not doing microservices, you're building a monolith. If you're not building a monolith, you must go with microservices. If you're building a monolith, perhaps you're doing it well and it's a majestic monolith.

From my early encounters with microservices, this dichotomy bothered me. The discussion of a monolith was nearly always followed its purported problems. Difficult to develop. Difficult to deploy. Difficult to scale.

Which I always found odd, I've built applications that weren't microservices that had none of these properties, so what was different?

Redefining Microservices and Monoliths

The problem is that this is presented as a dichotomy. But it is a false dichotomy. It isn't an either/or choice. And we can get away from this false dichotomy by refining our definition of microservice (which, as even Wikipedia shows, doesn't have consensus about what the term means).

From my microservices FAQ:

A microservice is a service with a design focus towards the smallest autonomous boundary.

And a monolith:

A monolith is software whose design, information model, and interface combine multiple competing and interfering domains into one single application and data model. There is no longer consensus from users and designers of the software on terms, modeling, information design, or interface. The coupling between competing models makes future changes to the system difficult or impossible.

We can still build services that don't strive for the smallest autonomous boundary. And we can build monoliths out of distributed components. And our microservices can also be individual monoliths, if they can't meet the defined operational, business, and delivery objectives.

It's also why I see the question of "do I start with microservices or monoliths" to only have one answer: Mu. No one strives for a monolith, but they may not have a good enough understanding of the business to understand where the service boundaries should be. You're asking the wrong question, you need to unask the question and start over.

Migrating Contoso University Example to Razor Pages

Wed, 21 Feb 2018 15:24:16 +0000

A coworker noticed that it looked like Razor Pages were the new "recommended" way of building server-side rendered web applications in ASP.NET Core 2.0. I hadn't paid much attention because at first glance they looked like Web Forms.

However, that's not the case. I forked my Contoso University example (how I like to build MVC applications) and updated it to use Razor Pages instead. Razor Pages are similar to a controller-less action, and fit very well into the "feature folder" style we use on our projects here at Headspring. You can check out Steve Smith's MSDN Magazine article for more.

Back to our example, let's look first at our typical MVC application. We use:

AutoMapper
MediatR
HtmlTags
FluentValidation
Feature Folders

And it winds up looking something like:

We're able to move the controllers into the feature folder, but the controllers themselves are rather pointless. They're there basically to satisfy routing.

Years ago folks looked at building controller-less actions, but I'm hesitant to adopt divergence from the fundamental building blocks of the framework I'm on. Extend, configure, but not abandon.

So I left those controllers there. The files next to the views contain:

View Models
MediatR request/responses
MediatR handlers
Validators

Most of our applications don't actually use inner classes, but adjacent classes (instead of Foo.Query in Foo.cs, FooQuery.cs. But everything is together.

My initial skepticism with Razor Pages came from it looking like the examples having everything shoved in the View. That, combined with a deep distaste for the abomination that is Web Forms.

With that, I wanted to understand what our typical architecture looked like with Razor Pages.

Migrating to Razor Pages

Migrating is fairly straightforward, it was basically renaming the Features folder to Pages, and renaming my vertical slice files to have a cshtml.cs extension:

Next I needed to make my vertical slice class inherit from PageModel:

public class Create : PageModel

This class will now handle the GET/POST requests instead of my controller. I needed to move my original controller actions (very complicated):

public async Task<IActionResult> Edit(Edit.Query query)  
{
    var model = await _mediator.Send(query);

    return View(model);
}

[HttpPost]
[ValidateAntiForgeryToken]
public async Task<IActionResult> Edit(Edit.Command command)  
{
    await _mediator.Send(command);

    return this.RedirectToActionJson(nameof(Index));
}

Over to the Razor Pages equivalent (On<Method>Async):

public class Edit : PageModel  
{
    private readonly IMediator _mediator;

    [BindProperty]
    public Command Data { get; set; }

    public Edit(IMediator mediator) => _mediator = mediator;

    public async Task OnGetAsync(Query query) => Data = await _mediator.Send(query);

    public async Task<IActionResult> OnPostAsync()
    {
        await _mediator.Send(Data);

        return this.RedirectToPageJson(nameof(Index));
    }

The thing I needed to figure out was that model binding and view models are now just properties on my PageModel. I settled on a convention of having a property named Data rather than making up some property name for every page. This also kept with my convention of having only one model used in my views.

Links with Razor Pages are a little different, so I had to go through and replace my tags from asp-controller to asp-page. Not terrible, and I could incrementally move one controller at a time.

Finally, I moved the AutoMapper configuration inside this class too. With all this in place, my Razor Page includes:

Page request methods
MediatR Request/response models (view models)
View rendering
Validators
Mapping configuration
MediatR handlers

I'm not sure how I could make things more cohesive at this point. I will note that standard refactoring techniques still apply - if logic gets complicated in my command handlers, this should be pushed to the domain model to handle.

The final weird thing was around my views. The model for a "Razor Page" is the PageModel class, not my original ViewModel. This meant all my views broke. I needed to change all my extensions and tag helpers to include the Data. prefix on my markup, from:

<form asp-action="Edit">  
    @Html.ValidationDiv()
    <input-tag for="Id"/>

    <div class="form-group">
        <label-tag for="Id"/>
        <div><display-tag for="Id"/></div>
    </div>

    @Html.FormBlock(m => m.Title)
    @Html.FormBlock(m => m.Credits)
    @Html.FormBlock(m => m.Department)

To:

<form method="post">  
    @Html.ValidationDiv()
    <input-tag for="Data.Id" />

    <div class="form-group">
        <label-tag for="Data.Id" />
        <div><display-tag for="Data.Id" /></div>
    </div>

    @Html.FormBlock(m => m.Data.Title)
    @Html.FormBlock(m => m.Data.Credits)
    @Html.FormBlock(m => m.Data.Department)

This messed up my rendering, because our intelligent tag helpers use the property navigation to output text. I had to inform our tag helpers to NOT display the "Data" property name (our tag helpers automatically display text for Foo.Bar.Baz or FooBarBaz to "Foo Bar Baz").

So for property chains that start with Data., I remove that text from our labels:

Labels  
    .Always
    .ModifyWith(er => er.CurrentTag.Text(er.CurrentTag.Text().Replace("Data ", "")));

This could be made more intelligent, only looking for property chains that have an initial "Data" property. But for my sample this is sufficient.

I don't test controllers, nor would I test the action methods on my PageModel class. My tests only deal with MediatR requests/responses, so none of my tests needed to change. I could get rid of MediatR in a lot of cases, and make the methods just do the work on my PageModel, but I really like the consistency and simplicity of MediatR's model of "one-model-in, one-model-out".

All in all, I like the direction here. Those building with vertical slice architectures will find a very natural fit with Razor Pages. It's still up to you how much you use nested classes, but this truly makes everything related to a request (minus domain model) all part of a single modifiable location, highly cohesive.

Nice!

Los Techies Welcomes Derik Whittaker

Wed, 21 Feb 2018 11:00:00 +0000

Los Techies would like to introduce, and extend a welcome to Derik Whittaker. Derik is a C# MVP, member of the AspInsiders group, community speaker, and Pluralsight author. Derik was previously a contributor at CodeBetter.com. Welcome, Derik!

Ditch the Repository Pattern Already

Tue, 20 Feb 2018 21:00:00 +0000

One pattern that still seems particularly common among .Net developers is the Repository pattern. I began using this pattern with NHibernate around 2006 and only abandoned its use a few years ago.

I had read several articles over the years advocating abandoning the Repository pattern in favor of other suggested approaches which served as a pebble in my shoe for a few years, but there were a few design principles whose application seemed to keep motivating me to use the pattern. It wasn’t until a change of tooling and a shift in thinking about how these principles should be applied that I finally felt comfortable ditching the use of repositories, so I thought I’d recount my journey to provide some food for thought for those who still feel compelled to use the pattern.

Mental Obstacle 1: Testing Isolation

What I remember being the biggest barrier to moving away from the use of repositories was writing tests for components which interacted with the database. About a year or so before I actually abandoned use of the pattern, I remember trying to stub out a class derived from Entity Framework’s DbContext after reading an anti-repository blog post. I don’t remember the details now, but I remember it being painful and even exploring use of a 3rd-party library designed to help write tests for components dependent upon Entity Framework. I gave up after a while, concluding it just wasn’t worth the effort. It wasn’t as if my previous approach was pain-free, as at that point I was accustomed to stubbing out particularly complex repository method calls, but as with many things we often don’t notice friction to which we’ve become accustomed for one reason or another. I had assumed that doing all that work to stub out my repositories was what I should be doing.

Another principle that I picked up from somewhere (maybe the big xUnit Test Patterns book? … I don’t remember) that seemed to keep me bound to my repositories was that you shouldn’t write tests that depend upon dependencies you don’t own. I believed at the time that I should be writing tests for Application Layer services (which later morphed into discrete dispatched command handlers) and the idea of stubbing out either NHIbernate or Entity Framework violated my sensibilities.

Mental Obstacle 2: The Dependency Inversion Principle Adherence

The Dependency Inversion Principle seems to be a source of confusion for many which stems in part from the similarity of wording with the practice of Dependency Injection as well as from the fact that the pattern’s formal definition reflects the platform from whence the principle was conceived (i.e. C++). One might say that the abstract definition of the Dependency Inversion Principle was too dependent upon the details of its origin (ba dum tss). I’ve written about the principle a few times (perhaps my most succinct being this Stack Overflow answer), but put simply, the Dependency Inversion Principle has at its primary goal the decoupling of the portions of your application which define policy from the portions which define implementation. That is to say, this principle seeks to keep the portions of your application which govern what your application does (e.g. workflow, business logic, etc.) from being tightly coupled to the portions of your application which govern the low level details of how it gets done (e.g. persistence to an Sql Server database, use of Redis for caching, etc.).

A good example of a violation of this principle, which I recall from my NHibernate days, was that once upon a time NHibernate was tightly coupled to log4net. This was later corrected, but at one time the NHibernate assembly had a hard dependency on log4net. You could use a different logging library for your own code if you wanted, and you could use binding redirects to use a different version of log4net if you wanted, but at one time if you had a dependency on NHibernate then you had to deploy the log4net library. I think this went unnoticed by many due to the fact that most developers who used NHibernate also used log4net.

When I first learned about the principle, I immediately recognized that it seemed to have limited advertized value for most business applications in light of what Udi Dahan labeled The Fallacy Of ReUse. That is to say, properly understood, the Dependency Inversion Principle has as its primary goal the reuse of components and keeping those components decoupled from dependencies which would keep them from being easily reused with other implementation components, but your application and business logic isn’t something that is likely to ever be reused in a different context. The take away from that is basically that the advertized value of adhering to the Dependency Inversion Principle is really more applicable to libraries like NHibernate, Automapper, etc. and not so much to that workflow your team built for Acme Inc.’s distribution system. Nevertheless, the Dependency Inversion Principle had a practical value of implementing an architecture style Jeffrey Palermo labeled the Onion Architecture. Specifically, in contrast to traditional 3-layered architecture models where UI, Business, and Data Access layers precluded using something like Data Access Logic Components to encapsulate an ORM to map data directly to entities within the Business Layer, inverting the dependencies between the Business Layer and the Data Access layer provided the ability for the application to interact with the database while also seemingly abstracting away the details of the data access technology used.

While I always saw the fallacy in strictly trying to apply the Dependency Inversion Principle to invert the implementation details of how I got my data from my application layer so that I’d someday be able to use the application in a completely different context, it seemed the academically astute and in vogue way of doing Domain-driven Design at the time, seemed consistent with the GoF’s advice to program to an interface rather than an implementation, and provided an easier way to write isolation tests than trying to partially stub out ORM types.

The Catalyst

For the longest time, I resisted using Entity Framework. I had become fairly proficient at using NHibernate and I just saw it as plain stupid to use a framework that was years behind NHibernate in features and maturity, especially when it had such a steep learning curve. A combination of things happened, though. A lot of the NHibernate supporters (like many within the Alt.Net crowd) moved on to other platforms like Ruby and Node; anything with Microsoft’s name on it eventually seems to gain market share whether it’s better or not; and Entity Framework eventually did seem to mostly catch up with NHibernate in features, and surpassed it in some areas. So, eventually I found it impossible to avoid using Entity Framework which led to me trying to apply the same patterns I’d used before with this newer-to-me framework.

To be honest, everything mostly worked, especially for the really simple stuff. Eventually, though, I began to see little ways I had to modify my abstraction to accommodate differences in how Entity Framework did things from how NHibernate did things. What I discovered was that, while my repositories allowed my application code to be physically decoupled from the ORM, the way I was using the repositories was in small ways semantically coupled to the framework. I wish I had kept some sort of record every time I ran into something, as the only real thing I can recall now were motivations with certain design approaches to expose the SaveChanges method for Unit of Work implementations I don’t want to make more of the semantic coupling argument against repositories than it’s worth, but observing little places where my abstractions were leaking, combined with the pebble in my shoe of developers who I felt were far better than me were saying I shouldn’t use them lead me to begin rethinking things.

More Effective Testing Strategies

It was actually a few years before I stopped using repositories that I stopped stubbing out repositories. Around 2010, I learned that you can use Test-Driven Development to achieve 100% test coverage for the code for which you’re responsible, but when you plug your code in for the first time with that team that wasn’t designing to the same specification and not writing any tests at all that things may not work. It was then that I got turned on to Acceptance Test Driven Development. What I found was that writing high-level subcutaneous tests (i.e. skipping the UI layer, but otherwise end-to-end) was overall easier, was possible to align with acceptance criteria contained within a user story, provided more assurance that everything worked as a whole, and was easier to get teams on board with. Later on, I surmised that I really shouldn’t have been writing isolation tests for components which, for the most part, are just specialized facades anyway. All an isolation test for a facade really says is “did I delegate this operation correctly” and if you’re not careful you can end up just writing a whole bunch of tests that basically just validate whether you correctly configured your mocking library.

So, by the time I started rethinking my use of repositories, I had long since stopped using them for test isolation.

Taking the Plunge

It was actually about a year after I had become convinced that repositories were unnecessary, useless abstractions that I started working with a new codebase I had the opportunity to steer. Once I eliminated them from the equation, everything got so much simpler. Having been repository-free for about two years now, I think I’d have a hard time joining a team that had an affinity for them.

Conclusion

If you’re still using repositories and you don’t have some other hangup you still need to get over like writing unit tests for your controllers or application services then give the repository-free lifestyle a try. I bet you’ll love it.

Using Manual Mocks to test the AWS SDK with Jest

Tue, 20 Feb 2018 13:56:45 +0000

Anytime you build Node applications it is highly suggested that your cover your code with tests. When your code interacts with 3rd party API’s such as AWS you will most certainly want to mock/stub your calls in order to prevent external calls (if you actually want to do external calls, these are called integration tests not unit tests.

If you are using Jest, one solution is utilize the built in support for manual mocks. I have found the usage of manual mocks invaluable while testing 3rd party API’s such as the AWS. Keep in mind just because I am using manual mocks this will remove the need for using libraries like SinonJs (a JavaScript framework for creating stubs/mocks/spies).

The way that manual mocks work in Jest is as follows (from the Jest website’s documentation).

Manual mocks are defined by writing a module in a __mocks__/ subdirectory immediately adjacent to the module. For example, to mock a module called user in the models directory, create a file called user.js and put it in the models/__mocks__ directory. Note that the __mocks__ folder is case-sensitive, so naming the directory __MOCKS__ will break on some systems. If the module you are mocking is a node module (eg: fs), the mock should be placed in the __mocks__ directory adjacent to node_modules (unless you configured roots to point to a folder other than the project root).

In my case I want to mock out the usage of the AWS-SDK for Node.

To do this I created a __mocks__ folder at the root of my solution. I then created a aws-sdk.js file inside this folder.

Now that I have my mocks folder created with a aws-sdk.js file I am able to consume my manual mock in my jest test by simply referencing the aws-sdk via a require('aws-sdk') command.

const AWS = require('./aws-sdk');

With declaration of AWS above my code is able to a use the NPM package during normal usage, or my aws-sdk.js mock when running under the Jest context.

Below is a small sample of the code I have inside my aws-sdk.js file for my manual mock.

const stubs = require('./aws-stubs');
const AWS = {};

// This here is to allow/prevent runtime errors if you are using 
//   AWS.config to do some runtime configuration of the library.
// If you do not need any runtime configuration you can omit this.
AWS.config = {
    setPromisesDependency: (arg) => {}
};

AWS.S3 = function() {

}

// Because I care about using the S3 service's which are part of the SDK
//  I need to setup the correct identifier.
// 
AWS.S3.prototype = {
    ...AWS.S3.prototype,

    // Stub for the listObjectsV2 method in the sdk
    listObjectsV2(params){
        const stubPromise = new Promise((resolve, reject) => {
            // pulling in stub data from an external file to remove the noise
            // from this file.  See the top line for how to pull this in
            resolve(stubs.listObjects);
        });

        return {
            promise: () => {
                return stubPromise;
            }
        }
    }
};

// Export my AWS function so it can be referenced via requires
module.exports = AWS;

A few things to point out in the code above.

I chose to use the promises implementation of the listObjectsV2. Because of this I need to return a promise method as my result on my listObjectsV2 function. I am sure there are other ways to accomplish this, but this worked and is pretty easy.
My function is returning stub data, but this data is described in a separate file called aws-stubs.js which sites along side of my aws-sdk.js file. I went this route to remove the noise of having the stub data inside my aws-adk file. You can see a full example of this here.

Now that I have everything setup my tests will no longer attempt to hit the actually aws-sdk, but when running in non-test mode they will.

Till next time,

Configure Visual Studio Code to debug Jest Tests

Fri, 16 Feb 2018 21:33:03 +0000

If you have not given Visual Studio Code a spin you really should, especially if you are doing web/javascript/Node development.

One super awesome feature of VS Code is the ability to easily configure the ability to debug your Jest (should work just fine with other JavaScript testing frameworks) tests. I have found that most of the time I do not need to actually step into the debugger when writing tests, but there are times that using console.log is just too much friction and I want to step into the debugger.

So how do we configure VS Code?

First you will need to install the Jest-Cli NPM package (I am assuming you already have Jest setup to run your tests, if you do not please read the Getting-Started docs). If you fail to do this step you will get the following error in Code when you try to run the debugger.

After you have Jest-Cli installed you will need to configure VS Code for debugging. To do this open up the configuration by clicking Debug -> Open Configurations. This will open up a file called launch.json.

Once launch.json is open add the following configuration

        {
            "name": "Jest Tests",
            "type": "node",
            "request": "launch",
            "program": "${workspaceRoot}/node_modules/jest-cli/bin/jest.js",
            "stopOnEntry": false,
            "args": ["--runInBand"],
            "cwd": "${workspaceRoot}",
            "preLaunchTask": null,
            "runtimeExecutable": null,
            "runtimeArgs": [
                "--nolazy"
            ],
            "env": {
                "NODE_ENV": "development"
            },
            "console": "internalConsole",
            "sourceMaps": false,
            "outFiles": []
        }

Here is a gist of a working launch.json file.

After you save the file you are almost ready to start your debugging.

Before you can debug you will want to open the debug menu (the bug icon on the left toolbar). This will show a drop down menu with different configurations. Make sure ‘Jest Test’ is selected.

If you have this setup correctly you should be able to set breakpoints and hit F5.

Till next time,

On Migrating Los Techies to Github Pages

Fri, 16 Feb 2018 20:00:00 +0000

We recently migrated Los Techies from a multi-site installation of WordPress to Github Pages, so I thought I’d share some of the more unique portions of the process. For a straightforward guide on migrating from WordPress to Github Pages, Tomomi Imura has published an excellent guide available here that covers exporting content, setting up a new Jekyll site (what Github Pages uses as its static site engine), porting the comments, and DNS configuration. The purpose of this post is really just to cover some of the unique aspects that related to our particular installation.

Step 1: Exporting Content

Having recently migrated my personal blog from WordPress to Github Pages using the aforementioned guide, I thought the process of doing the same for Los Techies would be relatively easy. Unfortunately, due to the fact that we had a woefully out-of-date installation of WordPress, migrating Los Techies proved to be a bit problematic. First, the WordPress to Jekyll Exporter plugin wasn’t compatible with our version of WordPress. Additionally, our installation of WordPress couldn’t be upgraded in place for various reasons. As a result, I ended up taking the rather labor-intensive path of exporting each author’s content using the default WordPress XML export and then, for each author, importing into an up-to-date installation of WordPress using the hosting site with which I previously hosting my personal blog, exporting the posts using the Jekyll Exporter plugin, and then deleting the posts in preparation for the next iteration. This resulted in a collection of zipped, mostly ready posts for each author.

Step 2: Configuring Authors

Our previous platform utilized the multi-site features of WordPress to facilitate a single site with multiple contributors. By default, Jekyll looks for content within a special folder in the root of the site named _posts, but there are several issues with trying to represent multiple contributors within the _posts folder. Fortunately Jekyll has a feature called Collections which allows you to set up groups of posts which can each have their own associated configuration properties. Once each of the author’s posts were copied to corresponding collection folders, a series of scripts were written to create author-specific index.html, archive.html, and tags.html files which are used by a custom post layout. Additionally, due to the way the WordPress content was exported, the permalinks generated for each post did not reflect the author’s subdirectory, so another script was written to strip out all the generated permalinks.

Step 3: Correcting Liquid Errors

Jekyll uses a language called Liquid as its templating engine. Once all the content was in place, all posts which contained double curly braces were interpreted as Liquid commands which ended up breaking the build process. For that, each offending post had to be edited to wrap the content in Liquid directives {% raw %} … {% endraw %} to keep the content from being interpreted by the Liquid parser. Additionally, there were a few other odd things which were causing issues (such as posts with non-breaking space characters) for which more scripts were written to modify the posts to non-offending content.

Step 4: Enabling Disqus

The next step was to get Disqus comments working for the posts. By default, Disqus will use the page URL as the page identifier, so as long as the paths match then enabling Disqus should just work. The WordPress Disqus plugin we were using utilized a unique post id and guid as the Disqus page identifier, so the Disqus javascript had to be configured to use these properties. These values were preserved by the Jekyll exporter, but unfortunately the generated id property in the Jekyll front matter was getting internally overridden by Jekyll so another script had to be written to modify all the posts to rename the properties used for these values. Properties were added to the Collection configuration in the main _config.yml to designate the Disqus shortname for each author and allow people to toggle whether disqus was enabled or disabled for their posts.

Step 5: Converting Gists

Many authors at Los Techies used a Gist WordPress plugin to embed code samples within their posts. Github Pages supports a jekyll-gist plugin, so another script was written to modify all the posts to use Liquid syntax to denote the gists. This mostly worked, but there were still a number of posts which had to be manually edited to deal with different ways people were denoting their gists. In retrospect, it would have been better to use JavaScript rather than the Jekyll gist plugin due to the size of the Los Techies site. Every plugin you use adds time to the overall build process which can become problematic as we’ll touch on next.

Step 6: Excessive Build-time Mitigation

The first iteration of the conversion used the Liquid syntax for generating the sidebar content which lists recent site-wide posts, recent author-specific posts, and the list of contributing authors. This resulted in extremely long build times, but it worked and who cares once the site is rendered, right? Well, what I found out was that Github has a hard cut off of 10 minutes for Jekyll site builds. If your site doesn’t build within 10 minutes, the process gets killed. At first I thought “Oh no! After all this effort, Github just isn’t going to support a site our size!” I then realized that rather than having every page loop over all the content, I could create a Jekyll template to generate JSON content one time and then use JavaScript to retrieve the content and dynamically generate the sidebar DOM elements. This sped up the build significantly, taking the build from close to a half-hour to just a few minutes.

Step 8: Converting WordPress Uploaded Content

Another headache that presented itself is how WordPress represented uploaded content. Everything that anyone had ever uploaded to the site for images and downloads used within their posts were stored in a cryptic folder structure. Each folder had to be interrogated to see which files contained therein matched what author, the folder structure had to be reworked to accommodate the nature of the Jekyll site, and more scripts had to be written to edit everyone’s posts to change paths to the new content. Of course, the scripts only worked for about 95% of the posts, a number of posts had to be edited manually to fix things like non-printable characters being used in file names, etc.

Step 9: Handling Redirects

The final step to get the initial version of the conversion complete was to handle redirects which were formally being handled by .httpacess. The Los Techies site started off using Community Server prior to migrating to WordPress and redirects were set up using .httpaccess to maintain the paths to all the previous content locations. Github Pages doesn’t support .httpaccess, but it does support a Jekyll redirect plugin. Unfortunately, it requires adding a redirect property to each post requiring a redirect and we had several thousand, so I had to write another script to read the .httpaccess file and figure out which post went with each line. Another unfortunate aspect of using the Jekyll redirect plugin is that it adds overhead to the build time which, as discussed earlier, can become an issue.

Step 10: Enabling Aggregation

Once the conversion was complete, I decided to dedicate some time to figuring out how we might be able to add the ability to aggregate posts from external feeds. The first step to this was finding a service that could aggregate feeds together. You might think there would be a number of things that do this, and while I did find at least a half-dozen services, there were only a couple I found that allowed you to maintain a single feed and add/remove new feeds while preserving the aggregated feed. Most seemed to only allow you to do a one-time aggregation. For this I settled on a site named feed.informer.com. Next, I replaced the landing page with JavaScript that dynamically built the site from the aggregated feed along with replacing the recent author posts section that did the same and a special external template capable of making an individual post look like it’s actually hosted on Los Techies. The final result was a site that displays a mixture of local content along with aggregated content.

Conclusion

Overall, the conversion was way more work than I anticipated, but I believe worth the effort. The site is now much faster than it used to be and we aren’t having to pay a hosting service to host our site.

My Microservices FAQ

Thu, 15 Feb 2018 19:30:34 +0000

Mainly because I get asked all the time about microservices and I'm tired of having to remember on the spot:

What is a microservice?

A microservice is a service with a design focus towards the smallest autonomous boundary.

What is a service?

(From Clemens) A service is software that:

is owned, built, and run by an organization
is responsible for holding, processing, and/or distributing particular kinds of information within the scope of a system
can be built, deployed, and run independently, meeting defined operational objectives
communicates with consumers and other services, presenting information using conventions and/or contract assurances
protects itself against unwanted access, and its information against loss
handles failure conditions such that failures cannot lead to information corruption

In short, a service is software that exhibits autonomy. It's built, deployed, and run independently from other services. It manages its own information and how it communicates to/from the outside world. It handles failures so that its information isn't lost.

Do microservices require containers/Docker?

No.

Do microservices require Go/node.js/Elixir/.NET Core?

No.

Do microservices require any specific technology or stack?

No.

I put my app in a container, is it a microservice?

Containers have no bearing on whether or not software is a microservice or not.

Why do people talk about microservices along with containers/serverless/PaaS?

Those are technologies that can help remove technical barriers for the size of services.

What is a monolith?

A monolith is software whose design, information model, and interface combine multiple competing and interfering domains into one single monolithic application and data model. There is no longer consensus from users and designers of the software on terms, modeling, information design, or interface. The coupling between competing models makes future changes to the system difficult or impossible.

If I'm not doing microservices, am I building monoliths?

No, not necessarily. You can build services without a focus on the smallest but still autonomous boundary.

If I have a single application and database, is it a monolith?

No, not necessarily. You may have a single software system whose model is cohesive enough that there aren't competing/interfering domains. If that application meets the defined operational and business needs of the organization, then it does have the negative indicators of a monolith.

Similarly, not all data models are "anemic domain models". A domain model is only anemic if it's a data model masquerading as a domain model.

Can a microservice be too small?

Yes, if it no longer meets the characteristics of a service, the software is no longer a service, but a module, function, or data store.

Can a microservice be too big?

A microservice is a service with a design focus on the smallest possible autonomous boundary. What is possible and desirable is highly contextual on the domain, people, technology, and goals of the business.

Should I start with microservices or a monolith?

Microservices focus on the smallest autonomous boundary for a service. If the domain is ambiguous enough or business goals volatile enough, you may wind up building modules that can be independently deployed but not run because they depend on other modules to function.

Perhaps a better question is "should I start with a single application or modular architecture?" The answer is highly dependent on the team, business, and domain maturity.

How should microservices communicate?

In a manner and medium that does not violate the fundamental definition of a service. If a communication protocol introduces coupling that violates the design traits of a service, then you must re-evaluate that protocol, or re-evaluate your service boundaries.

As an example, services that solely communicate via RPC introduce process (and likely temporal coupling) such that the services are no longer autonomous and independent services, but modules within a larger service boundary.

Should I do have a single repository (monorepo) or repository per service?

This depends on your delivery pipeline. If you can own, build, deploy, and run your service in a single source control repository independent of others, this may be an option. You may decide to organize repositories around systems, applications, organizations, services, modules, etc. Repository boundaries are (somewhat) orthogonal to service boundaries. But it may make your life easier to just have repositories per service.

If you feel forced to build a single repository to host your services to enable changes easier, then you don't have microservices, but modules. Your services aren't independent or loosely coupled to other services, and therefore aren't services.

Should I do RESTful services or async, durable messaging?

These choices are orthogonal to microservices. Choose what makes sense for the communication protocol you are designing and the constraints you have, keeping in mind the fundamental definition of a service.

I typically chose event-driven architectures as inter-service communication, but this can be achieved via a variety of transport protocols, and is never a universal decision. Avoid making universal decisions such as "everything must use RESTful web services", especially because most "RESTful APIs" are merely RPC-over-HTTP.

Should I use streams or queues/brokers to dispatch events to others?

You can do both, as both have quite different designs and constraints, benefits and drawbacks. Fundamentally, though, just as your service should avoid strongly coupling yourself to others, you should avoid directly exposing internal state changes as external events. Contracts exposed to outside your service boundary will change at a different pace and reason than internal state/communication.

For example, exposing your event-sourced aggregate's events directly to outside subscribers is not much different than giving out a database connection string to a read-only view of your database.

Should I do microservices?

It depends, building services as small as their autonomy allows is a different way of building services, that usually results in more of them. It affects more than just a developer, to the entire value delivery chain from concept to production. It usually involves imbuing a DevOps culture and mentality, but the ramifications stretch upstream as well.

The bigger question to ask is - is our software delivery value stream delivering its value at the speed the business needs? And if not, is the cause of our bottleneck in delivery that we don't have small enough services to align agility more closely? If so, then microservices are a good choice to consider.

Containers - What Are They Good For? Local Dependencies

Wed, 14 Feb 2018 21:15:05 +0000

Containers, huh, good god
What is it good for?
Local Dependencies!
- Edwin Starr (also disputed)

In the last post, I walked through our typical development pipeline, from local dev to production:

Now for most of our developers, when we start a new project, we can just continue to work on our existing host machine. The development dependencies don't change that much from project to project, and we're on projects typically for 6-12 months. We're not typically switching from project to project, nor do we work on multiple projects as the norm.

So our "normal" dev dependencies are:

Some recent version Visual Studio (hopefully the latest)
Some recent version and any SKU of SQL Server (typically SQL Express)

The most we run into are connection strings being different because of different instance names. However, this isn't universal, and we sometimes have wonky VPN requirements and such, so a developer might do something like:

The entire development environment is in a virtual machine, so that its dependencies don't get mucked with the host, and vice-versa. The downside to this approach is maintaining these images is a bit of a pain - Visual Studio is large, and so is SQL Server, so it's not uncommon that these VMs get into the tens, even over 100 GB in size.

Then we have more exotic scenarios, such as dependencies that don't run too great on Windows, or are a pain to set up, or can't play nice with other versions. For this, we can look at Docker as a means to isolate our dependencies:

In this case, we've got the Elastic Stack we need to run for local development, but instead of installing it, we run it locally in Docker.

Docker compose for local dependencies

When we run our application like this, we're still running the main app on our host machine, but it connects to a set of Docker images for the dependencies. Docker-compose is a great tool for multi-container setups. If we wanted to just run one image, it's too bad with regular Docker, but docker-compose makes it dirt simple to do so.

For example, in my Respawn project, I support a number of different databases, and I don't really want to install them all. Instead, I have a simple docker-compose.yml file:

version: '3'

services:  
    postgres-db:
        image: postgres
        restart: always
        environment:
            POSTGRES_USER: docker
            POSTGRES_PASSWORD: Password12!
        ports:
            - 8081:5432
    mysql-db:
        image: mysql
        restart: always
        environment:
            MYSQL_ROOT_PASSWORD: testytest
        ports:
            - 8082:3306
    oracle:
        image: sath89/oracle-12c
        restart: always
        ports:
            - 8080:8080
            - 1521:1521
    adminer:
        image: adminer
        restart: always
        ports:
            - 8083:8083

I pull in Postgres, MySQL, and Oracle (as well as a simple Admin UI), and expose these services externally via some well-known ports. When I run my build, I start these services:

docker-compose up -d

Now when my build runs, the tests connect to these services through the configured ports.

So this is a big win for us - we don't have to install any 3rd-party services on our dev machines. Instead, we pull down (or build our own) Docker images and run those.

What about normal scenarios?

The above works great for "exotic" dependencies outside our normal toolbelt, like databases, queues, and services. But most systems we build are just on top of SQL Server, which most everyone has installed already. If not SQL Express, then SQL Local DB.

But I'm in an experimenting mood, what if we did this?

This is possible today - Microsoft have their full-blown SQL Server images on Docker Hub, both in the form of SQL Server Developer Edition, and Express. There is a Linux image, but we don't run SQL Server on Linux in production so I'm not sure if there would be a difference locally or not.

So this does work, we can ignore whatever SQL a user might have installed and just say "run the database through Docker".

There are some downsides to this approach, however:

It's running in Docker, not a Windows Service. You have to always run Docker to run your database
The images are huuuuuuuuge

Huge images are just a thing for Windows-based containers (unless you're running Nanoserver). Take a look at the image sizes for SQL Express:

SQL Server on Linux containers are smaller, but still large, around 450MB. Compare this to Postgres on Alpine Linux:

So the smallest SQL Linux image is over 30x bigger than the smallest Postgres image, and the smallest Windows SQL image is over 350x bigger. That's....ridiculous.

What does this actually mean for us locally? Docker images and layers are cached, so once you've got the base images that these SQL images are based on downloaded once, subsequent Docker pulls will only pull the additional images. For us, this would be Windows Server Core images (not Nanoserver), so you might already have this downloaded. Or not.

Startup time won't really be affected, it's about the same to start a Docker SQL container as it is to start say the SQL Server local service on the host machine, but that's still seconds.

Because of this, for our normal development experience, it's not really any better nor solving a pain that we have.

Taking it up to 11

I talked to quite a few developers who do a lot of local development in Docker, and it seemed to come down to that they are Node developers, and one thing Node is a bit lousy about is global dependencies. If you run npm install -g, then depending on what's already in your cache, or what version of node or npm running, you'll get different results.

And I also have talked to folks that have run their entire development environment inside of containers, so it looks something like:

The host machine might just include some caches, but we could run everything inside containers. Development environment, editors, our source, everything. This seemed like a great way to avoid Node/Ruby pollution on a host machine, but this person also did things like:

For a normal .NET developer, I'm likely not using vim all day, and I want to do things like use a debugger. It's unlikely then that I'd move all of my development environment into a container.

Going Async with Node AWS SDK with Express

Tue, 13 Feb 2018 13:00:30 +0000

When building applications in Node/Express you will quickly come to realize that everything is done asynchronously . But how you accomplish these tasks async can vary. The ‘old school’ way was to use call backs, which often led to callback hell. Than came along Promises which we thought was going to solve all the worlds problems, turned out they helped, but did not solve everything. Finally in Node 8.0 (ok, you could use them in Node 7.6) the support for async/await was introduced and this really has cleaned up and enhanced the readability of your code.

Having the ability to use async/await is great, and is supported out of the box w/ Express. But what do you do when you using a library which still wants to use promises or callbacks? The case in point for this article is AWS Node SDK.

By default if you read through the AWS SDK documentation the examples lead you to believe that you need to use callbacks when implementing the SDK. Well this can really lead to some nasty code in the world of Node/Express. However, as of v2.3.0 of the AWS SDK there is support for Promises. This is much cleaner than using callbacks, but still poses a bit of an issue if you want to use async/await in your Express routes.

However, with a bit of work you can get your promise based AWS calls to play nicely with your async/await based Express routes. Lets take a look at how we can accomplish this.

Before you get started I am going to make a few assumptions.

You already have a Node/Express application setup
You already have the AWS SDK for Node installed, if not read here

The first thing we are going to need to do is add reference to our AWS SDK and configure it to use promises.

const AWS = require('aws-sdk');
AWS.config.setPromisesDependency(null);

After we have our SDK configured we can implement our route handler. In my example here I am placing all the logic inside my handler. In a real code base I would suggest better deconstruction of this code into smaller parts.

const express = require('express');
const router = express.Router();
const s3 = new AWS.S3();

router.get('/myRoute', async (req, res) => {

    const controller = new sitesController();
    const params = req.params;
    const params = { 
       Bucket: "bucket_name_here"
    };

    let results = {};
    var listPromise = s3.listObjects(params).promise();
    listPromise.then((data) => {
        results = data;
    });

    await Promise.all([listPromise]);

    res.json({data: results })
})

module.exports = router;

Lets review the code above and call out a few important items.

The first thing to notice is the addition of the async keyword in my route handler. This is what allows us to use async/await in Node/Express.

The next thing to look at is how I am calling the s3.listObjects. Notice I am NOT providing a callback to the method, but instead I am chaining with .promise(). This is what instructs the SDK to use promises vs callbacks. Once I have my callback I chain a ‘then’ in order to handle my response.

The last thing to pay attention to is the line with await Promise.All([listPromise]); This is the magic forces our route handler to not return prior to the resolution of all of our Promises. Without this your call would exit prior to the listObjects call completing.

Finally, we are simply returning our data from the listObjects call via res.json call.

That’s it, pretty straight forward, once you learn that the AWS SDK supports something other than callbacks.

Till next time,

Package and Publish a Node site to AWS ElasticBeanstalk with Gulp

Sun, 11 Feb 2018 13:33:08 +0000

Say you are building a Node application which you want to host in AWS Elastic Beanstalk, but how do you automate this process? I mean sure, you could just open up the AWS console and manually upload your files, but lets be honest that is a royal pain in the ~~ass.~~ What we really want to do is setup some sort of automate to make everything easier. In this post we are going to walk though how to use Gulp to a publish your code to AWS S3 and then consume the package in Elastic Beanstalk (EB).

In this post we are going to learn how to use Gulp to create, upload and deploy a Node application. The flow will be as below

Before you get started I am going to make a few assumptions.

You already know basic Gulp.js usage, if not details on this here
You already have a React application, if not details on this here
You already have an AWS account setup, if not details on this here
- You will also need to make sure you have your CLI configured w/ credentials. See here for details
You already have a build npm task in your package.json file, if not look here
You already have the EB CLI installed, if not look here

To get started, the first thing we are going to need to install a few NPM packages

npm install --save-dev aws-sdk gulp-run gulp-zip run-sequence

After you have the npm packages installed we will need to start working on your gulpfile.js changes.

Add the reference to the package we added

const run = require('gulp-run');
const zip = require('gulp-zip');
const AWS = require('aws-sdk');
const runSequence = require('run-sequence');

After we reference our packages we need to create an instance of the S3 object, this will be used to push the files into our S3 bucket.

const s3 = new AWS.S3({apiVersion: '2006-03-01'});

Now that we have all the prerequisite stuff out of the way, time to get down to business.

We are going to need to create 5 different tasks in our gulp file.

deploy -> This will be used to kick off the entire process
zip-build -> We want to zip our entire solution into a single file for pushing to S3 -> EB
copy-to-bucket -> Will copy our zip file to S3 for store-and-forward to EB
publish-version-to-eb -> Will create the actual version in EB for usage
publish-environment-to-eb -> Will publish the provisioned version we created in the prior step

gulp.task('deploy', deployTest);

gulp.task('copy-to-bucket', copyToS3Bucket);
gulp.task('publish-version-to-eb', publishVersionToEB);
gulp.task('publish-environment-to-eb', publishEnvironmentToEB);
gulp.task('zip-build', zipBuild)

function copyToS3Bucket() {
    const options = s3Options.active;
    const packageNameAndLocation = "./deploy/deploymentPackageName.zip";
    const s3BucketPath = "s3://your-s3-bucket-name";
    
    return run("aws s3 cp " + packageNameAndLocation + " " + s3BucketPath ).exec();
}

function deploy(done) {
    runSequence('zip-build',
                'copy-to-bucket',
                'publish-version-to-eb',
                'publish-environment-to-eb',
                done);
}

function publishVersionToEB(){
    const createAppCommand = "aws elasticbeanstalk create-application-version --application-name application-name-from-eb --version-label deployment-version-label --source-bundle S3Bucket=your-bucket-name,S3Key=full-name-of-deploy-zip.zip";
    return run(createAppCommand).exec();
}

function publishEnvironmentToEB(){
    const updateEnvironmentCommand = "aws elasticbeanstalk update-environment --environment-name your-environemnt-name-in-eb --version-label your-version-name-just-created";
    return run(updateEnvironmentCommand).exec();
}

function zipBuild(){
    const options = s3Options.active;

    // when zipping the files we want to grab all files and sub folders
    // we want to omit our deploy and test files (no need for those in EB)
    return gulp.src(["./**/*.*",             
            "!./node_modules/**/*.*",
            "!./deploy/**/*.*",
            "!./test/**/*.*"
        ])
        .pipe(zip(options.deployPackageName))
        .pipe(gulp.dest("deploy"));

}

After you have implemented the changes above you can run gulp on the command line and watch the magic unfold.

gulp deploy

If you would like to see a full working copy of my gulpfile.js checkout this gist.

Containers - What Are They Good For?

Tue, 06 Feb 2018 21:21:00 +0000

Containers, huh, good god
What is it good for?
Probably something?
- Edwin Starr (disputed)

Here at Headspring, we're seeing more and more usage of Docker for local development. Having not really touched Docker or containers, I wanted to understand how Docker could help make our lives easier for development, whether it's just local development, our CI/CD pipeline, production, anything really.

I hadn't touched containers mainly because I really didn't have the problems that I (thought) it solved. I've done automated builds and deployments, scripted local dev setup, for over a decade now, so I wasn't sure how containers could streamline what I saw as a fairly streamlined process at was today. In my previous company, now over 10 years ago, we were doing blue-green deployments well before it had that sort of name, for a large (in the billions of $ in yearly revenue) e-commerce website, scripting out VM deployments before even Powershell was around to help script. That, and I don't do any Linux work, nor do any of my clients.

That being said, I wanted to see what containers could do for me.

Learning containers

First and foremost was really understanding what containers are. I'm not a Linux person, so when I hear "containers are just cgroups and namespaces" I'm already behind because I don't know what cgroups or namespaces are. It also doesn't help that evidently containers aren't actually a thing. So...yeah.

And on Windows, there are things called containers, but like Linux, it's not really a thing either, and there are different flavors.

Luckily, between the Docker website and the Windows website, there are tons of great resources for learning Docker and containers. Looking at our current build/deploy pipeline, I wanted to build a picture of what the Docker terms mean for how we typically build apps.

Semi-not-really-accurate Docker terminology

Docker has quite a few terms and the world is similar, but not the same, as what I'm used to. The mapping I had in my head is roughly:

Dockerfile =/= "build.ps1"
Image =/= "foo.exe"
Container =/= "$ foo.exe"
Docker-compose =/= "foo.sln"
Docker Hub =/= NuGet server

Our projects have a build script that produces a runnable instance of the app. It's not quite "F5" so more or less it's equivalent to the build instructions of a dockerfile. Built images are kinda like our built app, and a running instance is similar to a container as a running image.

A solution pulls together many different projects and applications into a single "run" experience, similar to a docker-compose configuration. And finally, we use Octopus Deploy and NuGet as our build artifacts, similar to Docker and Docker Hub.

Semi-quasi-sorta-probably-not-accurate equivalent terminology, but close enough for my mental model.

Pipeline before Docker

I wanted to understand how Docker could help our development, but first, let's look at what my typical CI/CD pipeline looks today:

In this picture, I'm using some sort of Git source control server, Bitbucket, VSTS, whatever. For local development, it's usually just Visual Studio (or Code) and a database. The database is usually SQL Express, sometimes SQL Server Developer Edition if there are additional things going on.

Because we can't assume everyone has the same instance name, our app would use environment variables for things like instance names/connection strings so that two developers wouldn't need to have the exact same environment.

For continuous integration, we'll use AppVeyor or VSTS for cloud builds, or TeamCity or Jenkins for on-prem builds. Rarely we'd use TFS for on-prem, but it is there.

The output of our build is an artifact, whose design matches our deployment needs. For Octopus Deploy, this would be one or more versioned NuGet packages, or with VSTS it'd just be the build artifact (typically a zip file).

Finally, for continuous delivery, promoting packages between environments, we default to Octopus Deploy. We're looking at VSTS too, but it's somewhat similar. You promote immutable build artifacts from environment to environment, with environment configuration usually inside our CD pipeline configuration. Deployments would be Azure, AWS, or some bespoke on-prem servers.

Everything is automated from end-to-end, local setup, builds, deployments.

Given this current state, how could containers help our process, from local dev to production?

In the next few posts, I'll walk through my journey of adding Docker/containers to each of these stages, to see where it works well for our typical clients, and where it still needs work.

Unable To Access Mysql With Root and No Password After New Install On Ubuntu

Wed, 31 Jan 2018 00:13:00 +0000

This bit me in the rear end again today. Had to reinstall mysql-server-5.7 for other reasons.

You just installed mysql-server locally for your development environment on a recent version of Ubuntu (I have 17.10 artful installed). You did it with a blank password for root user. You type mysql -u root and you see Access denied for user 'root'@'localhost'.

Issue: Because you chose to not have a password for the root user, the auth_plugin for my MySQL defaulted to auth_socket. That means if you type sudo mysql -u root you will get in. If you don’t, then this is NOT the fix for you.

Solution: Change the auth_plugin to mysql_native_password so that you can use the root user in the database.

$ sudo mysql -u root

mysql> USE mysql;
mysql> UPDATE user SET plugin='mysql_native_password' WHERE User='root';
mysql> FLUSH PRIVILEGES;
mysql> exit;

$ sudo systemctl restart mysql
$ sudo systemctl status mysql

NB ALWAYS set a password for mysql-server in staging/production.

Cheers.

Unable To Access Mysql With Root and No Password After New Install On Ubuntu was originally published by Jason Meridth at Jason Meridth on January 30, 2018.

How Respawn Works

Tue, 30 Jan 2018 22:20:14 +0000

This post is mainly a reminder to myself when inevitably I forget what I was doing when designing Respawn. The general problem space is something I've covered quite a lot, but I haven't really walked through how Respawn works internally.

The general problem is trying to find the correct order of deletion for tables when you have foreign key constraints. You can do something like:

ALTER TABLE [Orders] NOCHECK CONSTRAINT ALL;  
ALTER TABLE [OrderLineItems] NOCHECK CONSTRAINT ALL;  
DELETE [Orders];  
DELETE [OrderLineItems];  
ALTER TABLE [Orders] WITH CHECK CHECK CONSTRAINT ALL;  
ALTER TABLE [OrderLineItems] WITH CHECK CHECK CONSTRAINT ALL;

You just ignore what order you can delete things in by just disabling all constraints, deleting, then re-enabling. Unfortunately, this results in 3x as many SQL statements over just DELETE, slowing down your entire test run. Respawn fixes this by building the list of DELETE intelligently, and with 3.0, detecting circular relationships. But how?

Traversing a graph

Assuming we've properly set up foreign key constraints, we can view our schema through its relationships:

Another way to think about this is to imagine each table as a node, and each foreign key as an edge. But not just any kind of edge, there's a direction. Putting this together, we can construct a directed graph:

There's a special kind of graph, a directed acyclic graph, where there are no cycles, but we can't make that assumption. We only know there are directed edges.

So why do we need this directed graph? Assuming we don't have any kind of cascades set up for our foreign keys, the order in which we delete tables should start tables with no foreign keys, then tables that reference those, then those that reference those, and so on until we reach the last table. The table that we delete first are those with no foreign keys pointing to it - because no tables depend on it.

In directed graph terms, this is known as a depth-first search. The ordered list of tables is found by conducting a depth-first search, adding the nodes at the deepest first until we reach the root(s) of the graph. When we're done, the list to delete is just the reversed list of nodes. As we traverse, we keep track of the visited nodes, exhausting our not-visited list until it's empty:

The code for traversing is fairly straightforward, first we need to kick off the traversal based on the overall (unordered) list of tables:

private static List<Table> BuildDeleteList(  
    HashSet<Table> tables)
{
    var toDelete = new List<Table>();
    var visited = new HashSet<Table>();

    foreach (var table in tables)
    {
        BuildTableList(table, visited, toDelete);
    }

    return toDelete;
}

Then we conduct our DFS, adding tables to our toDelete list once we've searched all its relationships recursively:

private static void BuildTableList(  
    Table table, HashSet<Table> visited, List<Table> toDelete)
{
    if (visited.Contains(table))
        return;

    foreach (var rel in table.Relationships)
    {
        BuildTableList(rel.ReferencedTable, visited, toDelete);
    }

    toDelete.Add(table);
    visited.Add(table);
}

Not pictured - constructing our Table with its Relationships, which is just a matter of querying the schema metadata for each database, and building the graph.

To actually execute the deletes, we just build DELETE statements in the reverse order of our list, and we should have error-free deletions.

This technique works, as we've shown, as long as there are no cycles in our graph. If our tables somehow create a cycle (self-referencing tables don't count, databases can handle a single DELETE on a table with relationships with itself), then our DFS will continue to traverse the cycle infinitely, until we hit a StackOverflowException.

With Respawn 3.0, I wanted to fix this.

Dealing with cycles

There's lots of literature and examples out there on detecting cycles in a directed graph, but I don't just need to detect a cycle. I have to understand, what should I do when I detect a cycle? How should this affect the overall deletion strategy?

For this, it really depends on the database. The overall problem is the foreign key constraints prevent us from deleting with orphans, but with a cycle in our graph, I can't delete tables one-by-one.

We could try to re-model our schema to remove the cycle, but above, the cycle might make the most sense. A project as a single project lead, and for a user, they have a primary project they work on. Ignoring how we'd figure it out, what would be the right set of commands to do? We still have to worry about the rest of the tables, too.

What we'd like to do is find the problematic tables and just disable the constraints for those tables (or disable those constraints). This depends on the database, however, on how we can disable constraints. Each database is slightly different:

SQL Server - per constraint or table
PostgreSQL - per table
Oracle - per constraint
MySQL - entire session

For MySQL, this makes our job a bit easier. We can disable constraints at the beginning of our deletion, and the order of the tables no longer matters.

The other databases can disable individual constraints and/or constraints for the entire table. Given the choice, it's likely fewer SQL statements just to disable for tables, but Oracle lets us only disable individual constraints. So that means that our algorithm must:

Find ALL cycles (not just IF there is a cycle)
Find all tables and relationships involved the cycles
Identify these problematic relationships when traversing

Our updated graph now looks like:

We need to detect the relationship between Projects and Users, keep those in a list, and finally note those relationships when traversing the entire graph.

In order to find all the cycles, once we find one, we can note it, continuing on instead of repeating the same paths:

We need to alter our algorithm a bit, first to keep track of our new lists for not visited, visiting, and visited. We also need to return not just the ordered list of tables to delete via a stack, but the set of relationships that we identified as causing a cycle:

private static bool HasCycles(Table table,  
    HashSet<Table> notVisited,
    HashSet<Table> visiting,
    HashSet<Table> visited,
    Stack<Table> toDelete,
    HashSet<Relationship> cyclicalRelationships
    )
{
    if (visited.Contains(table))
        return false;

    if (visiting.Contains(table))
        return true;

    notVisited.Remove(table);
    visiting.Add(table);

    foreach (var relationship in table.Relationships)
    {
        if (HasCycles(relationship.ReferencedTable, 
            notVisited, visiting, visited, toDelete, cyclicalRelationships))
        {
            cyclicalRelationships.Add(relationship);
        }
    }

    visiting.Remove(table);
    visited.Add(table);
    toDelete.Push(table);

    return false;
}

Walking through the code, we're examining one table at a time. If the set of visited tables already contains this table, there's nothing to do and we can just return false.

If, however, the set of currently visiting tables contains the table then we've detected a cycle, so return true.

Next, we move our table from the set of notVisited to currently visiting tables. We loop through our relationships, to see if any complete a cycle. If that relationship does, we add that relationship to the set of cyclical relationships.

Finally, once we're done navigating our relationships, we move our table from the set of visiting to visited tables, and push our table to the stack of tables to delete. We switched to a stack so that we can just pop off the tables and that becomes the right order to delete.

To kick things off, we just loop through the collection of all our tables, checking each for a cycle and building up our list of tables to delete and bad relationships:

private static (HashSet<Relationship> cyclicRelationships, Stack<Table> toDelete)  
    FindAndRemoveCycles(HashSet<Table> allTables)
{
    var notVisited = new HashSet<Table>(allTables);
    var visiting = new HashSet<Table>();
    var visited = new HashSet<Table>();
    var cyclicRelationships = new HashSet<Relationship>();
    var toDelete = new Stack<Table>();

    foreach (var table in allTables)
    {
        HasCycles(table, notVisited, visiting, visited, toDelete, cyclicRelationships);
    }

    return (cyclicRelationships, toDelete);
}

It's expected that we'll visit some tables twice, but that's a quick check in our set and we only traverse the entire graph once.

Now that we have our list of bad tables/relationships, we can apply the right SQL DDL to disable/enable constraints intelligently. By only targeting the bad relationships, we can minimize the extra DDL statements by adding just add a single DDL statement for each cycle found.

Respawn 3.0 Released

Mon, 29 Jan 2018 22:11:25 +0000

Respawn, the intelligent database deleter, reached the 3.0 milestone today. In this release, Respawn now supports complex circular/cyclical relationships.

When Respawn detects a cycle in the graph, it substitutes a separate deletion strategy by disabling/enabling foreign key constraints just for those tables affected.

This release also adds support for Oracle, and drops support for SQL Server CE, bringing the supported databases to:

SQL Server
PostgreSQL
MySQL/MariaDB
Oracle

Download via NuGet:

Install-Package Respawn

Or your favorite package manager.

Enjoy!

Designing Microservice Messages: A Primer

Mon, 22 Jan 2018 22:15:20 +0000

When you move from monoliths to microservices, and your services aren't 100% isolated from each other, eventually you need your microservices to communicate. They need to expose their capabilities to other applications and systems, and when you get to this point, you need to design their means of communication.

Microservices doesn't prescribe a specific mode of messaging (nor should it), and I personally like the presentation "Messaging and Microservices" (slides) from Clemens Vasters as a great overall primer to the problem space, so start there.

Once we understand the overall landscape of messaging and microservices, the next part is design. How do we design the communication between different services? How do we build the channels so that we maintain high cohesion, low coupling, and ultimately, autonomy for our services? What are some general guidelines and principles for ensuring our success here?

Luckily, the names of things of changed over the years but the rules stay largely the same. For (micro)services-based systems I've run into that haven't been as successful, designing the boundaries and interactions goes a long way into ensuring the success of your overall architecture.

The Entrance Fee - Good Boundaries

Designing microservice boundaries well is our first goal. I often find that when messages are designed poorly, it's simply because the original boundaries weren't designed well. Well-defined microservice boundaries tend to be self-evident, because they achieve the goal of autonomy. Poorly-defined microservice boundaries tend to lose their autonomy, relying on a group of services to actually perform a business goal.

If you have to run a dozen microservices for your service to even run, it's a sign you've chosen the wrong boundaries. Instead of focusing on the nouns in your system (Orders, Customers, Products), you instead should focus on capabilities (Catalog, Checkout). Services that just manage a thing tend to quite data-oriented and RPC-focused. Rather than focusing on tiers, we look at vertical slices that stretch all the way to the front-end.

But once we have those boundaries, how do we actually design the communication between services? In the next few posts, I'll walkthrough designing messages both inside and between services, looking at the benefits and tradeoffs of each approach, as well as some general rules of thumb and patterns that tend to let us fall into the pit of microservice success.

New Job

Mon, 08 Jan 2018 18:13:00 +0000

Well, it is a new year and I’ve started a new job. I am now a Senior Software Engineer at True Link Financial.

After interviewing with the co-founders Kai and Claire and their team, I knew I wanted to work here.

TL;DR: True Link: We give elderly and disable (really, anyone) back their financial freedom where they may not usually have it.

Longer Version: Imagine you have an elderly family member who may start showing signs of dimensia. You can give them a True Link card and administer their card. You link it to their bank account or another source of funding and you can set limitations on when, where and how the card can be used. The family member feels freedom by not having to continually ask for money but is also protected by scammers and non-friendly people (yep, they exist).

The customer service team, the marketing team, the product team, the engineering team and everyone else at True Link are amazing.

For any nerd readers, the tech stack is currently Rails, React, AWS, Ansible. We’ll be introducing Docker and Kubernetes soon hopefully, but always ensuring the right tools for the right job.

Looking forward to 2018.

Cheers.

New Job was originally published by Jason Meridth at Jason Meridth on January 08, 2018.

The Gemba Walk for Designing Software

Thu, 04 Jan 2018 16:39:29 +0000

The Gemba Walk is a common lean practice for understanding a current process as-is before taking any action to improve that process. Traditionally, you'd see this in lean manufacturing, where you'd walk through a process, in-person, to understand the purpose, process, and people involved in creating some sort of value or product.

For whatever reason, the "Gemba Walk" is dismissed more or less from lean software engineering as a means of walking the value chain of the software development process. I'm dubious about this - with all new clients, I walk the value chain from concept to cash.

But that's not the walk what I'm referring to. Not walking the software delivery process, but walking the process for whom the software we're building.

Traditional Gemba Walk

Gemba (or genba) is a term that refers to the place where work is done. The idea behind a Gemba Walk is to literally go to where the work is done, inspect, and understand the actual physical process happening in order to make any sort of process improvements.

Rather than discussing how to improve a shipment packing process in a sterile conference room on a whiteboard, you first do a Gemba Walk. The walk consists of three parts:

Go See
Ask Why
Show Respect

The goal is not to put blame on any one person for why the process is bad, but to fully understand (with respect) the people, process, and purpose. During the walk, we don't talk about any future improvements or anything of the like. Just focus on understanding.

You'll often do two Gemba Walks, where the first just to generate the value stream map, and the second to understand how well the value stream performs (see Value Stream Mapping for more).

There's way more to this practice, but let's look at how a Gemba Walk applies to software development.

Gemba Walks for Designing Software

During a Gemba Walk, I'm faced with many different "truths" to a process:

What managements believes/wants the process to be
What the process is documented to be
What we aspire for the process to be
What the process actually is

We build software for a purpose. And to fully achieve that purpose, we need to have a deep understanding of the problem we're trying to solve. We can help this understanding by having a product owner or subject matter expert or domain expert, but in my experience, those people only really give us the information for #1-#3 above. We don't see what the existing process actually is.

Seeing, especially with outside eyes, and removing inattentional blindness is absolutely critical to building software that actually solves the underlying problem. It's not enough to just ask the 5 whys. We have to see the process we're trying to improve. We'll completely miss the deeper level of understanding that's so critical to building software that can truly transform an organization.

Inattentional blindness is one of the leading factors for bad requirements and rework, in my experience. Someone inside of a process all day doesn't notice that that simple 3-step process is actually 10 steps. Or they might describe the process only in terms of the exceptional cases, instead of the typical case. Or skip over all of the manual steps they perform and only describe their process in terms of interfacing with their existing legacy system. Or the process is not in any one person's head, but in everyone's. I've been on multiple Gemba Walks where the client itself argues amongst themselves about the process and they learn what their process actually is.

You can't do a Gemba Walk without the 3rd pillar - Show Respect. The Gemba Walk is not about me "not believing" or "not trusting" the domain expert, and it's important that they understand this as well. It's not a judgement on their ability to explain. Some things just can't be verbally explained, and having many eyes on a process results in the greatest understanding.

Gemba Walk in Practice

My Gemba Walks are the very first thing I do on a new project. Kickoffs aren't just about meet and greets, I need to see and understand the problem at hand to provide the appropriate context for what I'm about to build. It's so important that we at Headspring insist on doing these walks, at the start of a project and at the start of any new area of software we're building.

Besides just understanding the process, the second part (looking for improvements), is focused on how the software we're building can improve that process. I'm also looking at ways to improve the process without software as well, because any software I don't write is software I don't have to maintain.

In the second walk, I'm focusing on process improvements, specifically:

How long does it take you to perform this step?
How often do you do this?
How after you're done until the next step?
Do you do one-at-a-time, or do you batch up work?
What are the exceptional cases?
Look for the Lean wastes
What is the value (monetary/time/etc) of the work?

I carry a little notepad to write down the steps and answers to these (and more) questions as we walk. I can't keep everything in my head, so after the walk we'll go back and review to make sure our collective understanding was the same, capturing the results in a more permanent format.

When trying to show the value of your software, it's great to have that empirical data to show improvements (lead time for acquisitions went from 90 days to 7, etc.) and the Gemba Walk provides the means for doing so. I'm looking at lead time, cycle time, and throughput. It doesn't have to be 100% accurate, but it can give us that extra set of information for management to fully understand the potential and realized value of their software.

So, as software developers, we have to get out of our chairs and go to the work being done if we want to build applications and systems that can truly transform an organization. Without a Gemba Walk, we're merely guessing.

Respawn 2.0 released

Wed, 03 Jan 2018 19:00:03 +0000

A small release for Respawn but there's a breaking change in the underlying extension API, hence the major version bump. A couple things added here:

Support for MySQL
Support for Amazon RDS (from a bug fixed)

The API change was added so that database adapters can specify the quote character to use. With this, we now have 4 databases supported:

MS SQL Server
MS SQL Server Compact Edition
PostgreSQL
MySQL

Download from NuGet

Enjoy!

Docker Daemon Error When Running Docker Compose

Tue, 02 Jan 2018 18:11:00 +0000

TL;DR Make sure you don’t have any old mounted volumes around if you see the error below.

I just got the following error when trying to run docker-compose up -d

ERROR: Couldn't connect to Docker daemon at http+docker://localunixsocket - is it running?

If it's at a non-standard location, specify the URL with the DOCKER_HOST environment variable.

Please note I have a database using a mounted volume. The issue was that the previous mount was still present. Once I deleted that, docker-compose up -d would work jut fine.

If anyone knows a more elegant way to handle this, I’m open to it.

Cheers.

Docker Daemon Error When Running Docker Compose was originally published by Jason Meridth at Jason Meridth on January 02, 2018.

Trunk-Based Development or Pull Requests - Why Not Both?

Wed, 06 Dec 2017 17:37:17 +0000

Trunk-Based Development movement is often proposed as the alternative to Git Flow. I understand this distinction - managing streams of work through long-lived branches can be trouble. For those new to TBD, it can look like this means throwing away all branches and everyone commits literally to trunk. Typically this comes out as "feature branches are bad". But I've seen the opposite - it's not that feature branches are bad, but that long-lived branches are bad.

While there are plenty in the Agile/TBD movement that see this as true, I've never seen it as an either/or. First, why is TBD good and long-running branches bad? To see why TBD works in a lot of cases, we can see the reasons all the way back to Lean/Kanban principles.

Limit WIP

When you have many commits in a branch, or lots of work in a branch, we're building up inventory of work. The more our inventory builds, the more difficult it becomes to move that work through the development pipeline. This is why when I was on teams that did 6-month deployments, our deployments were so incredibly painful. We had months of work to test, verify, validate, and ship across many different departments. We literally brought sleeping bags to work.

Most see limiting WIP strictly in terms of the development activities. Don't work on more than one ticket at a time. But what's missing is a visualization of the entire value stream, that goes from concept to production. When we have long-running branches, that's WIP piling up before production.

And if your development value stream doesn't include "deploy to production", you're missing the most valuable step in the process - shipping!

The more we can limit WIP across the entire pipeline, the lower our cycle time can get. For that, long-lived branches that hold WIP go directly against the Lean concept of limiting WIP.

Reducing batch size

Going along with reducing WIP is reducing the size of each item in our process. For software development, this means working in small chunks, and each small chunk represents a shippable item. We call these "features" or "stories", but the main goal is to reduce the size of these features/stories so that the items go through the system more quickly and with less variation.

Interestingly enough, the side effect of small features and common-sizing work means that estimation no longer becomes a value-add activity (if it ever was). The #NoEstimates movement captured this explicitly - that it's more valuable to measure the cycle time, lead time, and throughput as those serve as better predictors for future delivery than committed estimates (because that's what estimatese are - commitments).

With these two items together - limiting WIP and reducing batch size, it leads us to the conclusion that we want our "features" or "stories" to be small, and therefore no long-lived branches.

Bringing Pull Requests In

Finally, we come to pull requests. If our features are small, then feature branching by itself isn't bad. We've tackled the problem at its root - driving to smaller features instead of just saying "branches are bad".

For us, pull requests are a means of driving quality into the process. Inspections at the point in time mean we don't have defects further down the process that become more expensive the later we find it. Pull requests answer two questions - "is the code right?" and "is it the right code?". Checking to see that the feature meets the expectations and understanding before it hits production (or the next step even) can be critical on our projects.

An alternative is pair programming, but in my experience, pairing just isn't for every person or every task. We tend to pair when we need to, and work alone when we need to, with most work being alone.

So drive to small features first, limit WIP and reduce batch size, and the rest will follow.

MediatR 4.0 Released

Fri, 01 Dec 2017 15:40:56 +0000

The last major release of MediatR brought a simplification in design. Instead of having several different IRequest types and IRequestHandler implementations with several flavors, MediatR would try at runtime to determine how a single IRequest should resolve to different IRequestHandler implementations (synchronous, async, async with a cancellation token). In practice, this proved problematic as not all containers support this sort of 'try-resolve' behavior. MediatR relied on try...catch to resolve, which can work, has unintended side effects.

MediatR 4.0 consolidates these design decisions. We'll still have a single IRequest type, but will now have only a single IRequestHandler interface. This simplifies the registrations quite a bit:

// Before (StructureMap)
cfg.Scan(scanner =>  
{
    scanner.AssemblyContainingType<Ping>();
    scanner.ConnectImplementationsToTypesClosing(typeof(IRequestHandler<,>));
    scanner.ConnectImplementationsToTypesClosing(typeof(IRequestHandler<>));
    scanner.ConnectImplementationsToTypesClosing(typeof(IAsyncRequestHandler<,>));
    scanner.ConnectImplementationsToTypesClosing(typeof(IAsyncRequestHandler<>));
    scanner.ConnectImplementationsToTypesClosing(typeof(ICancellableAsyncRequestHandler<,>));
    scanner.ConnectImplementationsToTypesClosing(typeof(ICancellableAsyncRequestHandler<>));
    scanner.ConnectImplementationsToTypesClosing(typeof(INotificationHandler<>));
    scanner.ConnectImplementationsToTypesClosing(typeof(IAsyncNotificationHandler<>));
    scanner.ConnectImplementationsToTypesClosing(typeof(ICancellableAsyncNotificationHandler<>));
});

// After
cfg.Scan(scanner =>  
{
    scanner.AssemblyContainingType<Ping>();
    scanner.ConnectImplementationsToTypesClosing(typeof(IRequestHandler<,>));
    scanner.ConnectImplementationsToTypesClosing(typeof(IRequestHandler<>));
    scanner.ConnectImplementationsToTypesClosing(typeof(INotificationHandler<>));
});

This new default interface is the previous ICancellableAsyncRequestHandler interface:

public interface IRequestHandler<in TRequest, TResponse>  
    where TRequest : IRequest<TResponse>
{
    Task<TResponse> Handle(TRequest request, CancellationToken cancellationToken);
}

public interface IRequestHandler<in TRequest>  
    where TRequest : IRequest
{
    Task Handle(TRequest message, CancellationToken cancellationToken);
}

Instead of multiple interfaces, MediatR 4.0 (re)introduces helper base classes:

RequestHandler - for synchronous actions
AsyncRequestHandler - for async actions that ignore the cancellation token

With the simplified interfaces, it's much less possible to "forget" a registration in your container.

Another small but breaking change is the behavior pipeline and pre-processor now have the cancellation token as part of the interface.

Both the MediatR and MediatR.Extensions.Microsoft.DependencyInjection packages are released with the simplified API.

Since these are breaking changes to the API (hence the major version bump), migration to the new API is manual. However, to ease the transition, you can follow the 3.0 to 4.0 migration guide. I've also updated the docs to walk through the new classes, and the samples include the updated registrations for the major containers out there:

Microsoft DI
Autofac
DryIoc
LightInject
Ninject
Simple Injector
StructureMap
Unity
Windsor

A big move for MediatR to break the API, but necessary to remove the complexity in multiple interfaces and registration. The only other option would be to make MediatR responsible for all registration and wiring, which is more than I want MediatR to do. The design goal of MediatR is to lean on the power of DI containers for registration, and MediatR merely resolves its interfaces.

Enjoy!

Lenovo Thinkpad - Swap ctrl and fn keys

Wed, 22 Nov 2017 16:18:00 +0000

I just got a new laptop. It is a Lenovo Thinkpad X1 Carbon 5th Gen.

Ubuntu 17.10 16 GB RAM 1 TB SSD 64-bit i7 Pentium USB-C power

I’m in love.

My only gripe is that the Fn key is on the far left bottom of the keyboard. I prefer that to be the Ctrl key due to copy/paste and other keyboard commands I use often. I also have very big hands and my pinky isn’t made to “find” the Ctrl key to the right of the Fn key.

I currently use Dconf for mapping of keys in Ubuntu (currently using 17.10). I learned today that the fn key on keyboards is not managed by the operating system, which makes sense. Lenovo in all of its glorious-ness has a BIOS option to swap the Fn and Ctrl keys.

Thank you Lenovo.

Cheers.

Lenovo Thinkpad - Swap ctrl and fn keys was originally published by Jason Meridth at Jason Meridth on November 22, 2017.

Ubuntu - set caps lock to escape

Wed, 22 Nov 2017 16:05:00 +0000

I just got a new laptop and had to google again on how to set caps lock key to escape (I’m a Vim user).

sudo apt-get install dconf-tools
dconf write /org/gnome/desktop/input-sources/xkb-options "['caps:escape']"

To know your options, use the following command:

man xkeyboard-config

(MAN pages are your friend; man is short for manual)

You can also now use the Dconf GUI editor if you must (SHAME! ;) )

Type Dconf in Unity or Gnome app opener and go to the following location:

`org` →- `gnome` → `desktop` →  `input-sources` →  `xkb-options`

Add ['caps:escape'] to Custom Value textbox.

Cheers.

Ubuntu - set caps lock to escape was originally published by Jason Meridth at Jason Meridth on November 22, 2017.

AutoMapper 6.2.1 Released

Thu, 16 Nov 2017 15:02:16 +0000

Release notes here.

The previous release introduced some inadvertent breaking behavior changes in convention-based map creation and some DI scenarios. This maintenance release fixes these two issues by:

Find this release on NuGet:

https://www.nuget.org/packages/AutoMapper/

Cleanup Docker

Mon, 13 Nov 2017 03:15:00 +0000

I keep having friends who have experienced the no space left on device when trying to build images.

I have aliases for most of my container/image/volume cleanup:

lias dka='dkc;dki;dkv'
alias dkc='docker ps -aq | xargs docker rm -f'
alias dki='docker images -aq | xargs docker rmi -f'
alias dkv='docker volume ls -qf dangling=true | xargs docker volume rm'

I use dka all the time.

There is also the docker system prune -a command that works.

I’ve also had to unmount my local aufs volume on my ubuntu laptop via:

sudo umount -l /var/lib/docker/aufs && sudo rm -rf /var/lib/docker/aufs

and all things are cleaned up. Hope this helps someone else.

Cheers.

Cleanup Docker was originally published by Jason Meridth at Jason Meridth on November 12, 2017.

OnePlus 5

Mon, 03 Jul 2017 17:02:00 +0000

I’ve had a Nexus 6 for the last 2 years and was finally due for a phone upgrade. I went through a pretty good fiasco with Google store trying to purchase a Google Pixel XL earlier this year so I decided to wait for the One Plus 5 (release late June). I ordered it the first hour it was announced.

Features that I’m loving:

8 GB RAM
128 GB hard drive
dash charge
USB C
OxygenOS (Android fork)
headphone jack
fingerprint authentication
dual camera (allows for portrait mode) 16 MP
front camera 16 MP
Dual SIM support

More detailed specs can be found here

Features I’m adjusting to:

no Google Phone app install allowed
no Google Contacts app install allowed

I’ll adjust to those over time.

I did entertain the iPhone 7 for a bit also but am not a fan of iTunes. The iPhone integration with Google apps has gotten much better since the last time I looked though.

Cheers.

OnePlus 5 was originally published by Jason Meridth at Jason Meridth on July 03, 2017.

Hello, React! – A Beginner’s Setup Tutorial

Thu, 25 May 2017 08:00:32 +0000

React has been around for a few years now and there are quite a few tutorials available. Unfortunately, many are outdated, overly complex, or gloss over configuration for getting started. Tutorials which side-step configuration by using jsfiddle or code generator options are great when you’re wanting to just focus on the framework features itself, but many tutorials leave beginners struggling to piece things together when you’re ready to create a simple react application from scratch. This tutorial is intended to help beginners get up and going with React by manually walking through a minimal setup process.

A Simple Tutorial

This tutorial is merely intended to help walk you through the steps to getting a simple React example up and running. When you’re ready to dive into actually learning the React framework, a great list of tutorials can be found here.

There are a several build, transpiler, or bundling tools from which to select when working with React. For this tutorial, we’ll be using be using Node, NPM, Webpack, and Babel.

Step 1: Install Node

Download and install Node for your target platform. Node distributions can be obtained here.

Step 2: Create a Project Folder

From a command line prompt, create a folder where you plan to develop your example.

$> mkdir hello-react

Step 3: Initialize Project

Change directory into the example folder and use the Node Package Manager (npm) to initialize the project:

$> cd hello-react
$> npm init --yes

This results in the creation of a package.json file. While not technically necessary for this example, creating this file will allow us to persist our packaging and runtime dependencies.

Step 4: Install React

React is broken up into a core framework package and a package related to rendering to the Document Object Model (DOM).

From the hello-react folder, run the following command to install these packages and add them to your package.json file:

$> npm install --save-dev react react-dom

Step 5: Install Babel

Babel is a transpiler, which is to say it’s a tool from converting one language or language version to another. In our case, we’ll be converting EcmaScript 2015 to EcmaScript 5.

From the hello-react folder, run the following command to install babel:

$> npm install --save-dev babel-core

Step 6: Install Webpack

Webpack is a module bundler. We’ll be using it to package all of our scripts into a single script we’ll include in our example Web page.

From the hello-react folder, run the following command to install webpack globally:

$> npm install webpack --global

Step 7: Install Babel Loader

Babel loader is a Webpack plugin for using Babel to transpile scripts during the bundling process.

From the hello-react folder, run the following command to install babel loader:

$> npm install --save-dev babel-loader

Step 8: Install Babel Presets

Babel presets are collections of plugins needed to support a given feature. For example, the latest version of babel-preset-es2015 at the time this writing will install 24 plugins which enables Babel to transpile ECMAScript 2015 to ECMAScript 5. We’ll be using presets for ES2015 as well as presets for React. The React presets are primarily needed for processing of JSX.

From the hello-react folder, run the following command to install the babel presets for both ES2015 and React:

$> npm install --save-dev babel-preset-es2015 babel-preset-react

Step 9: Configure Babel

In order to tell Babel which presets we want to use when transpiling our scripts, we need to provide a babel config file.

Within the hello-react folder, create a file named .babelrc with the following contents:

{                                    
  "presets" : ["es2015", "react"]    
}

Step 10: Configure Webpack

In order to tell Webpack we want to use Babel, where our entry point module is, and where we want the output bundle to be created, we need to create a Webpack config file.

Within the hello-react folder, create a file named webpack.config.js with the following contents:

const path = require('path');
 
module.exports = {
  entry: './app/index.js',
  output: {
    path: path.resolve('dist'),
    filename: 'index_bundle.js'
  },
  module: {
    rules: [
      { test: /\.js$/, loader: 'babel-loader', exclude: /node_modules/ }
    ]
  }
}

Step 11: Create a React Component

For our example, we’ll just be creating a simple component which renders the text “Hello, React!”.

First, create an app sub-folder:

$> mkdir app

Next, create a file named app/index.js with the following content:

import React from 'react';
import ReactDOM from 'react-dom';
 
class HelloWorld extends React.Component {
    render() {
          return (
                  <div>
                    Hello, React!
                  </div>
                )
        }
};
 
ReactDOM.render(<HelloWorld />, document.getElementById('root'));

Briefly, this code includes the react and react-dom modules, defines a HelloWorld class which returns an element containing the text “Hello, React!” expressed using JSX syntax, and finally renders an instance of the HelloWorld element (also using JSX syntax) to the DOM.

If you’re completely new to React, don’t worry too much about trying to fully understand the code. Once you’ve completed this tutorial and have an example up and running, you can move on to one of the aforementioned tutorials, or work through React’s Hello World example to learn more about the syntax used in this example.

Note: In many examples, you will see the following syntax:

var HelloWorld = React.createClass( {
    render() {
          return (
                  <div>
                    Hello, React!
                  </div>
                )
        }
});

This syntax is how classes were defined in older versions of React and will therefore be what you see in older tutorials. As of React version 15.5.0 use of this syntax will produce the following warning:

Warning: HelloWorld: React.createClass is deprecated and will be removed in version 16. Use plain JavaScript classes instead. If you’re not yet ready to migrate, create-react-class is available on npm as a drop-in replacement.

Step 12: Create a Webpage

Next, we’ll create a simple html file which includes the bundled output defined in step 10 and declare a <div> element with the id “root” which is used by our react source in step 11 to render our HelloWorld component.

Within the hello-react folder, create a file named index.html with the following contents:

<html>
  <div id="root"></div>
  <script src="./dist/index_bundle.js"></script>
</html>

Step 13: Bundle the Application

To convert our app/index.js source to ECMAScript 5 and bundle it with the react and react-dom modules we’ve included, we simply need to execute webpack.

Within the hello-react folder, run the following command to create the dist/index_bundle.js file reference by our index.html file:

$> webpack

Step 14: Run the Example

Using a browser, open up the index.html file. If you’ve followed all the steps correctly, you should see the following text displayed:

Hello, React!

Conclusion

Congratulations! After completing this tutorial, you should have a pretty good idea about the steps involved in getting a basic React app up and going. Hopefully this will save some absolute beginners from spending too much time trying to piece these steps together.

Up into the Swarm

Sat, 08 Apr 2017 20:59:26 +0000

Last Thursday evening I had the opportunity to give a presentation at the Docker Meetup in Austin TX about how to containerize a Node JS application and deploy it into a Docker Swarm. I also demonstrated techniques that can be used to reduce friction in the development process when using containers.

The meeting was recorded but unfortunately sound only is available after approximately 16 minutes. You might want to just scroll forward to this point.

Video: https://youtu.be/g786WiS5O8A

Slides and code: https://github.com/gnschenker/pets-node

New Year, New Blog

Thu, 26 Jan 2017 03:39:05 +0000

One of my resolutions this year was to take ownership of my digital content, and as such, I’ve launched a new blog at jimmybogard.com. I’m keeping all my existing content on Los Techies, where I’ve been humbled to be a part of for the past almost 10 years. Hundreds of posts, thousands of comments, and innumerable wrong opinions on software and systems, it’s been a great ride.

If you’re still subscribed to my FeedBurner feed – nothing to change, you’ll get everything as it should. If you’re only subscribed to the Los Techies feed…well you’ll need to subscribe to my feed now.

Big thanks to everyone at Los Techies that’s put up with me over the years, especially our site admin Jason, who has become far more knowledgable about WordPress than he ever probably wanted.

Containers – Cleanup your house revisited

Mon, 12 Dec 2016 21:10:02 +0000

In version 1.13 Docker has added some useful commands to the CLI that make it easier to keep our environment clean. As you might have experienced yourself over time our development environment gets really cluttered with unused containers, dangling Docker images, abandoned volumes and forgotten networks. All these obsolete items take aways precious resources and ultimately lead to an unusable environment. In a previous post I have shown how we can keep our house clean by using various commands like

docker rm -f $(docker ps -aq)

to forcibly remove all running, stopped and terminated containers. Similarly we learned commands that allowed us to remove dangling images, networks and volumes.

Although the commands I described solved the problem they were proprietary, verbose or difficult to remember. The new commands introduced are straight forward and easy to use. Let’s try them out.

If you like this article then you can find more posts about containers in general and Docker in specific in this table of content.

Management Commands

To un-clutter the CLI a bit Docker 1.13 introduces new management commands. The list of those are

system
container
image
plugin
secret

Older versions of Docker already had network, node, service, swarm and volume.

These new commands group subcommands that were previously directly implemented as root commands. Let me give an example

docker exec -it [container-name] [some-command]

The exec command is now a subcommand under container. Thus the equivalent of the above command is

docker container exec -it [container-name] [some-command]

I would assume that for reasons of backwards compatibility the old syntax will stick around with us for the time being.

Docker System

There is a new management command system. It has 4 possible subcommands df, events, info and prune. The command

docker system df

gives us an overview of the overall disk usage of Docker. This include images, containers and (local) volumes. So we can now at any time stay informed about how much resources Docker consumes.

If the previous command shows us that we’re using too much space we might as well start to cleanup. Our next command does exactly that. It is a do-it-all type of command

docker system prune

This command removes everything that is currently not used, and it does it in the correct sequence so that a maximum outcome is achieved. First unused containers are removed, then volumes and networks and finally dangling images. We have to confirm the operation though by answering with y. If we want to use this command in a script we can use the parameter --force or -f to instruct Docker not to ask for confirmation.

Docker Container

We already know many of the subcommands of docker container. They were previously (and still are) direct subcommands of docker. We can get the full list of subcommands like this

docker container --help

In the list we find again a prune command. If we use it we only remove unused containers. Thus the command is much more limited than the docker system prune command that we introduced in the previous section. Using the --force or -f flag we can again instruct the CLI not to ask us for confirmation

docker container prune --force

Docker Network

As you might expect, we now also have a prune command here.

docker network prune

removes all orphaned networks

Docker Volume

And once again we find a new prune command for volumes too.

docker volume prune

removes all (local) volumes that are not used by at least one container.

Docker Image

Finally we have the new image command which of course gets a prune subcommand too. We have the flag --force that does the same job as in the other samples and we have a flag --all that not just removes dangling images but all unused ones. Thus

docker image prune --force --all

removes all images that are unused and does not ask us for confirmation.

Summary

Not only has Docker v 1.13 brought some needed order into the zoo of Docker commands by introducing so called admin commands but also we find some very helpful commands to clean up our environment from orphaned items. My favorite command will most probably be the docker system prune as I always like an uncluttered environment.

Dealing with Duplication in MediatR Handlers

Mon, 12 Dec 2016 20:37:57 +0000

We’ve been using MediatR (or some manifestation of it) for a number of years now, and one issue that comes up frequently is “how do I deal with duplication”. In a traditional DDD n-tier architecture, you had:

Controller
Service
Repository
Domain

It was rather easy to share logic in a service class for business logic, or a repository for data logic (queries, etc.) When it comes to building apps using CQRS and MediatR, we remove these layer types (Service and Repository) in favor of request/response pairs that line up 1-to-1 with distinct external requests. It’s a variation of the Ports and Adapters pattern from Hexagonal Architecture.

Recently, going through an exercise with a client where we collapsed a large project structure and replaced the layers with commands, queries, and MediatR handlers brought this issue to the forefront. Our approaches for tackling this duplication will highly depend on what the handler is actually doing. As we saw in the previous post on CQRS/MediatR implementation patterns, our handlers can do whatever we like. Stored procedures, event sourcing, anything. Typically my handlers fall in the “procedural C# code” category. I have domain entities, but my handler is just dumb procedural logic.

Starting simple

Regardless of my refactoring approach, I ALWAYS start with the simplest handler that could possibly work. This is the “green” step in TDD’s “Red Green Refactor” step. Create a handler test, get the test to pass in the simplest means possible. This means the pattern I choose is a Transaction Script. Procedural code, the simplest thing possible.

Once I have my handler written and my test passes, then the real fun begins, the Refactor step!

WARNING: Do not skip the refactoring step

At this point, I start with just my handler and the code smells it exhibits. Code smells as a reminder are indication that the code COULD exhibit a problem and MIGHT need refactoring, but is worth a decision to refactor (or not). Typically, I won’t hit duplication code smells at this point, it’ll be just standard code smells like:

Large Class
Long Method

Those are pretty straightforward refactorings, you can use:

Extract Class
Extract Subclass
Extract Interface
Extract Method
Replace Method with Method Object
Compose Method

I generally start with these to make my handler make more sense, easier to understand and the like. Past that, I start looking at more behavioral smells:

Combinatorial Explosion
Conditional Complexity
Feature Envy
Inappropriate Intimacy
and finally, Duplicated Code

Because I’m freed of any sort of layer objects, I can choose whatever refactoring makes most sense.

Dealing with Duplication

If I’m in a DDD state of mind, my refactorings in my handlers tend to be as I would have done for years, as I laid out in my (still relevant) blog post on strengthening your domain. But that doesn’t really address duplication.

In my handlers, duplication tends to come in a couple of flavors:

Behavioral duplication
Data access duplication

Basically, the code duplicated either accesses a DbContext or other ORM thing, or it doesn’t. One approach I’ve seen for either duplication is to have common query/command handlers, so that my handler calls MediatR or some other handler.

I’m not a fan of this approach – it gets quite confusing. Instead, I want MediatR to serve as the outermost window into the actual domain-specific behavior in my application:

Excluding sub-handlers or delegating handlers, where should my logic go? Several options are now available to me:

Its own class (named appropriately)
Domain service (as was its original purpose in the DDD book)
Base handler class
Extension method
Method on my DbContext
Method on my aggregate root/entity

As to which one is most appropriate, it naturally depends on what the duplicated code is actually doing. Common query? Method on the DbContext or an extension method to IQueryable or DbSet. Domain behavior? Method on your domain model or perhaps a domain service. There’s a lot of options here, it really just depends on what’s duplicated and where those duplications lie. If the duplication is within a feature folder, a base handler class for that feature folder would be a good idea.

In the end, I don’t really prefer any approach to the another. There are tradeoffs with any approach, and I try as much as possible to let the nature of the duplication to guide me to the correct solution.

Docker and Swarmkit – Part 6 – New Features of v1.13

Fri, 25 Nov 2016 15:14:15 +0000

In a few days version 1.13 of Docker will be released and among other it contains a lot of new features for the Docker Swarmkit. In this post I want to explore some of these new capabilities.

In the last few parts of this series of posts about the Docker Swarmkit we have used version 1.12.x of Docker. You can find those post here

Part 1, Part 2, Part 3, Part 4 and Part 5

For a full index of all Docker related post please refer to this post

Preparating for Version 1.13

First we need to prepare our system to use Docker 1.13. I will be using VirtualBox and the Boot2Docker ISO to demonstrate the new features. This is what I have done to get going. Note that at the time of this writing Docker just released Docker v1.13 rc2.

First I am going to install the newest version of docker-machine on my Mac. The binaries can be downloaded from here. In my case the package I download is docker-machine-Darwin-x86_64 v0.9.0-rc1

From the download folder move the binaries to the target folder

mv ~/Downloads/docker-machine-Darwin-x86_64 /usr/local/bin/docker-machine

and then make it executable

chmod +x /usr/local/bin/docker-machine

finally we can double check that we have the expected version

docker-machine -v

and in my case I see this

docker-machine version 0.9.0-rc1, build ed849a7

Now let’s download the newest boot2docker.iso image. At the time of this writing it is v1.13rc2. We can get it from here. Once downloaded move the image to the correct location

mv ~/Downloads/boot2docker.iso ~/.docker/machine/cache/

And we’re ready to go…

Creating a Docker Swarm

Preparing the Nodes

Now we can create a new swarm with Docker at version 1.13. We use the very same approach as described in part x of this series. Please read that post for more details.

Let’s clean up any pre-existing nodes called node1, node2, …, noneX with e.g. the following command

for n in $(seq 1 5); do
  docker-machine rm node$n
done;

and then we create 5 new nodes with Docker version 1.13rc2

for n in $(seq 1 5); do
  docker-machine create --driver virtualbox node$n
done;

Once this is done (takes about 2 minutes or so) we can double check the result

docker-machine ls

which in my case shows this

Now we can SSH into node1

docker-machine ssh node1

and we should see this

and indeed, we are now having a Docker host running at version 1.13.0-rc2.

Creating the Swarm

Now lets first initialize a swarm. node1 will be the leader and node2 and node3 will be additional master nodes whilst node4 and node5 will be worker nodes (Make sure you are in a terminal on your Mac).

First let’s get the IP address of the future swarm leader

export leader_ip=`docker-machine ip node1`

Then we can initialize the swarm

docker-machine ssh node1 docker swarm init --advertise-addr $leader_ip

Now let’s get the swarm join token for a worker node

export token=`docker-machine ssh node1 docker swarm join-token worker -q`

We can now use this token to have the other 4 nodes join as worker nodes

for n in $(seq 2 5); do
  docker-machine ssh node$n docker swarm join --token $token $leader_ip:2377
done;

what we should see is this

Let’s promote nodes 2 and 3 to masters

docker-machine ssh node1 docker node promote node2 node3

And to make sure everything is as expected we can list all nodes on the leader

docker-machine ssh node1 node ls

In my case I see this

Adding everything to one script

We can now aggregate all snippets into one single script which makes it really easy in the future to create a swarm from scratch

Analyzing the new Features

Secrets

One of the probably most requested features is support for secrets managed by the swarm. Docker supports a new command secret for this. We can create, remove, inspect and list secrets in the swarm. Let’s try to create a new secret

echo '1admin2' | docker secret create 'MYSQL_PASSWORD'

The value/content of a secret is provided via stdin. In this case we pipe it into the command.

When we run a service we can map secrets into the container using the --secret flag. Each secret is mapped as a file into the container at /run/secrets. Thus, if we run a service like this

docker service create --name mysql --secret MYSQL_PASSWORD \
      mysql:latest ls /run/secrets

and then observe the logs of the service (details on how to use logs see below)

docker service logs mysql

we should see this

The content of each file corresponds to the value of the secret.

Publish a Port

When creating an new service and want to publish a port we can now instead of only using the somewhat condensed --publish flag use the new --port flag which uses a more descriptive syntax (also called ‘csv’ syntax)

docker service create --name nginx --port mode=ingress,target=80,published=8080,protocol=tcp nginx

In my opinion, altough the syntax is more verbous it makes things less confusing. Often people with the old syntax forgot in which order the target and the published port have to be declard. Now it is evident without having to consult the documentation each time.

Attachable Network support

Previously it was not possible for containers that were run in classical mode (via docker run ...) to run on the same network as a service. With version 1.13 Docker has introduced the flag --attachable to the network create command. This will allow us to run services and individual containers on the same network. Let’s try that and create such a network called web

docker network create --attachable --driver overlay web

and let’s run Nginx on as a service on this network

docker service create --name nginx --network web nginx:latest

and then we run a conventional container on this network that tries to acces the Nginx service. First we run it without attaching it to the web network

docker run --rm -it appropriate/curl nginx

and the result is as expected, a failure

And now let’s try the same again but this time we attach the container to the web network

docker run --rm -it --network web appropriate/curl nginx:8080

Run Docker Deamon in experimental mode

In version 1.13 the experimental features are now part of the standard binaries and can be enabled by running the Deamon with the --experimental flag. Let’s do just this. First we need to change the dockerd profile and add the flag

docker-machine ssh node-1 -t sudo vi /var/lib/boot2docker/profile

add the --experimental flag to the EXTRA_ARGS variable. In my case the file looks like this after the modification

EXTRA_ARGS='
--label provider=virtualbox
--experimental

'
CACERT=/var/lib/boot2docker/ca.pem
DOCKER_HOST='-H tcp://0.0.0.0:2376'
DOCKER_STORAGE=aufs
DOCKER_TLS=auto
SERVERKEY=/var/lib/boot2docker/server-key.pem
SERVERCERT=/var/lib/boot2docker/server.pem

Save the changes as reboot the leader node

docker-machine stop node-1
docker-machine start node-1

After the node is ready SSH into it

docker-machine ssh node-1

Aggregated logs of a service (experimental!)

In this release we can now easily get the aggregated logs of all tasks of a given service in a swarm. That is neat. Lets quickly try that. First we need to run Docker in experimental mode on the node where we execute all commands. Just follow the steps in the previous section.

Now lets create a sample service and run 3 instances (tasks) of it. We will be using Redis in this particular case, but any other service should work.

docker service create --name Redis --replicas 3 redis:latest

after giving the service some time to initialize and run the tasks we can now output the aggregated log

docker service logs redis

and we should see something like this (I am just showing the first few lines)

We can clearly see how the output is aggregated from the 3 tasks running on nodes 3, 4 and 5. This is a huge improvement IMHO and I can’t wait until it is part of the stable release.

Summary

In this post we have created a Docker swarm on VirtualBox using the new version 1.13.0-rc2 of Docker. This new release offers many new and exciting features. In this post I have concentrated on some of the features concerning the Swarmkit. My post is getting too long and I have still so many interesting new features to explore. I will do that in my next post. Stay tuned.

Docker and SwarmKit – Part 5 – going deep

Fri, 11 Nov 2016 11:52:39 +0000

In this post we will work with the SwarmKit directly and not use the Docker CLI to access it. For that we have to first build the necessary components from source which we can find on GitHub.

You can find the links to the previous 4 parts of this series here. There you will also find links to my other container related posts.

Build the infrastructure

Once again we will use VirtualBox to create a few virtual machines will be the members of our cluster. First make sure that you have no existing VM called nodeX where X is a number between 1 and 5. Otherwise used docker-machine rm nodeX to remove the corresponding nodes. Once we’re ready to go lets build 5 VMs with this command

for n in $(seq 1 5); do
  docker-machine create --driver VirtualBox node$n
done;

As always buildling the infrastructure is the most time consuming task by far. On my laptop the above command takes a couple of minutes. The equivalent on say AWS or Azure would also take a few minutes.

Luckily we don’t have to do that very often. On the other hand, what I just said sounds a bit silly if you’re an oldie like me. I still remember the days when we had to wait weeks to get a new VM or even worse months to get a new physical server. So, we are totally spoiled. (Rant)

Once the VMs are built use

docker-machine ls

to verify that all machines are up and running as expected

Build SwarmKit Binaries

To build the binaries of the SwarmKit we can either use an existing GO environment on our Laptop and follow the instructions here or use the golang Docker container to build the binaries inside a container without the need to have GO natively installed

We can SSH into node1 which later should become the leader of the swarm.

docker-machine ssh node1

On our leader we first create a new directory, e.g.

mkdir /swarmkit

now cd into the swarmkit folder

cd swarmkit

we then clone the source from GitHub using Go

docker run --rm -t -v $(pwd):/go golang:1.7 go get -d github.com/docker/swarmkit    

this will put the source under the directory /go/src/github.com/docker/swarmkit. Finally we can build the binaries, again using the Go container

docker run --rm -t \ 
    -v $(pwd):/go \
    -w /go/src/github.com/docker/swarmkit \
    golang:1.7 bash -c "make binaries"

We should see something like this

and voila, you should find the binaries in the subfolder bin of the swarmkit folder.

Using the SwarmCtl Utility

To make the swarmd and swarmctl available everywhere we can create a symlink to these two binaries into the /usr/bin folder

sudo ln -s ~/swarmkit/src/github.com/docker/swarmkit/bin/swarmd /usr/bin/swarmd
sudo ln -s ~/swarmkit/src/github.com/docker/swarmkit/bin/swarmctl /usr/bin/swarmctl

now we can test the tool by entering

swarmctl version

and we should see something along the lines of

swarmctl github.com/docker/swarmkit v1.12.0-714-gefd44df

Create a Swarm

Initializing the Swarm

Similar to what we were doing in part 1 we need to first initialize a swarm. Still logged in to node we can execute this command to do so

swarmd -d /tmp/node1 --listen-control-api /tmp/node1/swarm.sock --hostname node1

Let’s open a new ssh session to node1 and assign the socket to the swarm to the environment variable SWARM_SOCKET

export SWARM_SOCKET=/tmp/node1/swarm.sock

Now we can use the swarmctl to inspect the swarm

swarmctl cluster inspect default

and we should see something along the line of

Please note the two swarm tokens that we see at the end of the output above. We will be using those tokens to join the other VMs (we call them nodes) to the swarm either as master or as worker nodes. We have a token for each role.

Copy Swarmkit Binaries

To copy the swarm binaries (swarmctl and swarmd) to all the other nodes we can use this command

 for n in $(seq 2 5); do
   docker-machine scp node1:swarmkit/src/github.com/docker/swarmkit/bin/swarmd node$n:/home/docker/
   docker-machine scp node1:swarmkit/src/github.com/docker/swarmkit/bin/swarmctl node$n:/home/docker/
 done;

Joining Worker Nodes

Now let’s ssh into e.g. node2 and join it to the cluster as a worker node

./swarmd -d /tmp/node2 --hostname node2 --join-addr 192.168.99.100:4242 --join-token <Worker Token>

In my case the <Worker Token> is SWMTKN-1-4jz8msqzu2nwz7c0gtmw7xvfl80wmg2gfei3bzpzg7edlljeh3-285metdzg17jztsflhg0umde8. The join-addr is the IP address of node1 of your setup. You can get it via

docker-machine ip node

in my case it is 192.168.99.100.

Repeat the same for node3. Make sure to replace node2 with node3 in the join command.

On node1 we can now execute the command

swarmctl node ls

and should see something like this

As you can see, we now have a cluster of 3 nodes with one master (node1) and two workers (node2 and node3). Please join the remaining two nodes 4 and 5 with the same approach as above.

Creating Services

Having a swarm we can now create services and update them using the swarmctl binary. Let’s create a service using the nginx image

swarmctl service create --name nginx --image nginx:latest

This will create the service and run one container instance on a node of our cluster. We can use

swarmctl service ls

to list all our services that are defined for this cluster. We should see something like this

If we want to see more specific information about a particular service we can use the inspect command

swarmctl service inspect nginx

and should get a much more detailed output.

We can see a lot of details in the above output. I want to specifically point out the column Node which tells us on which node the nginx container is running. In my case it is node2.

Now if we want to scale this service we can use the update command

swarmctl service update nginx --replicas 2

after a short moment (needed to download the image on the remaining node) we should see this when executing the inspect command again

As expected nginx is now running on two nodes of our cluster.

Summary

In this part we have used the Docker swarmkit directly to create a swarm and define and run services on this cluster. In the previous posts of this series we have used the Docker CLI to execute the same tasks. But under the hood the CLI just calls or uses the swarmd and swarmctl binaries.

If you are interested in more articles about containers in general and Docker in specific please refer to this index post

Mapping Caps Lock to Esc Is Native to OSX Now

Mon, 31 Oct 2016 20:12:00 +0000

I have been using Seil for a few years now on OSX to map Caps Lock to Esc. I use Vim for my development and letting my left pinky tap the Caps Lock key instead of Esc allows me to keep my hands on the home row and move much quicker. I also can’t remember the last time I actually needed the Caps Lock key. Well as of 10.12.1 (macOS Sierra Update) you can do this mapping in System Preferences.

Thank you to my co-worker Dedi for letting me know about this.

Go to System Preferences from the Apple menu:

Go to keyboard:

Go to “Modifier Keys” button on bottom right:

Change Caps Lock Key to Escape:

Mapping Caps Lock to Esc Is Native to OSX Now was originally published by Jason Meridth at Jason Meridth on October 31, 2016.

Details HTML Section In Github Issues and Gists

Thu, 28 Jul 2016 16:01:00 +0000

I recently became aware of using the <details></details> and <summary>...</summary> tags in Github issues and Gists.

Here is an example.

I will definitely be using this more when posting big logs or stack traces.

Kudos:

Laura Frank for showing me this feature
Keith Dahlby for letting me know it doesn’t currently work in Firefox
Matt Hinze for showing me this gist where I learned of <summary>...</summary>
Eric Clemmons for the awesome gist that does a great job explaining the feature

Details HTML Section In Github Issues and Gists was originally published by Jason Meridth at Jason Meridth on July 28, 2016.

Stop and Remove All Docker Containers

Wed, 06 Jul 2016 21:44:00 +0000

Command remove all docker containers:

docker stop '$(docker ps -a -q)' && docker rm '$(docker ps -a -q)'

docker ps -a -q lists all container IDs

Stop and Remove All Docker Containers was originally published by Jason Meridth at Jason Meridth on July 06, 2016.

Get Repository Root Path

Tue, 05 Jul 2016 01:52:00 +0000

I needed to find a way to get the path to the root of my Git repository. I found the answer in the following command:

git rev-parse --show-toplevel

Example:

->$ git rev-parse --show-toplevel
/Users/jmeridth/dev/jmeridth.github.io

Get Repository Root Path was originally published by Jason Meridth at Jason Meridth on July 04, 2016.