Integration Testing Behaviour with Mountebank

Developer’s machine > dev shared environment > staging environment > UAT > production.

Probably not exactly how everyone structures their delivery pipelines but probably not that far off. It allows instant feedback on whether what a developer is writing actually works with the code other developers are writing. And that’s a really good thing. Unfortunately, it misses something…

Each environment (other than the developer’s own machine) is shared with other developers who are also deploying new code at the same time. So how do you get an integration test for component A that relies on component B behaving in a custom manner (maybe even failing) to run automatically, without impacting the people who are trying to build and deploy component B?

If we were writing a unit test we would simply inject a mocked dependency. Fortunately there’s now a fantastic piece of kit available for doing exactly this but on an integration scale: enter Mountebank.

This clever piece of kit will intercept a network call for ANY protocol and respond in the way you ask it to. Transparent to your component and as easy to use as most mocking frameworks. I won’t go into detail about how to configure ‘Imposters’ as their own documentation is excellent, but suffice to say it can be easily configured in a TestFixtureSetup or similar.

So where does this fit into our pipeline? Personally, I think the flow should be:

Push code to repo > Code is pulled onto a build server > Build > Unit test > Integration test > Start deployment pipeline

The step where Mountebank comes in is obviously ‘integration testing’.

Keep in mind that installing the component and running it on the build agent is probably not a great idea, so make good use of the cloud or docker or both to spin up a temporary instance which has Mountebank already installed and running. Push your component to it, and run your integration tests. Once your tests have run then the instance can be blown away (or if constantly destroying environments gets a bit slow, maybe have them refreshing every night so they don’t get cluttered). Docker will definitely help keeping these processes efficient.

This principle of spinning up an isolated test instance can work in all kinds of situations, not just where Mountebank would be used. Calls to SQL Server can be redirected to a .mdf file for data dependent testing. Or DynamoDb tables can be generated specifically scoped to the running test.

What we end up with is the ability to test more behaviours than we can do in a shared environment where other people are trying to run their tests at the same time. Without this, our integration tests can get restricted to only very basic ‘check they talk to each other’ style tests which although have value do not cover everything we’d like.


My last two clients have had completely contrasting views on PaaS, specifically on whether it should be used at all. Both clients deploy to AWS and Azure. Both want to embrace software volatility. Neither want to introduce unnecessary complexity. Both have a similarly scaled online offering where traffic is subject to peaks and troughs which aren’t always predictable.

With such similar goals and problems to solve I’m intrigued by how different their approaches have been. Admittedly one client has a much more mature relationship with the cloud where the other is jumping in with both feet but still not sure how to swim. Perhaps that’s the crux of the matter and both will eventually become more similar in their approaches.

For this article I want to focus on the perceived issues with PaaS and try to explain why I think many concerns are unfounded.

The Concerns

My current client has raised a number of concerns about PaaS and I’ve dug around on the internet to find what has been worrying other people. Here’s a list of the most popular concerns I’ve seen.

  • Vendor lock in – the fear that if software makes use of PaaS from one cloud provider, it will be too difficult to move to a different provider in future.
  • Compliance – the fear of audit.
  • B.A.U. – the fear of managing a PaaS based solution after the developers have left the building.
  • Lack of published SLAs – the fear that a platform may not be as reliable as you need.
  • Confusing marketing message – the fear of relying on something that isn’t defined the same way by two different providers anywhere.
  • Lack of standard approach – the fear of ending up with software tightly coupled to a single platform.

This is certainly not an exhaustive list but I think it covers all the popular problems and those concerns raised directly to me from my clients. So now let’s try to address things.

Vendor Lock In

This sounds very scary. The idea that once we start allowing our software to make use of the APIs and services provided by one cloud provider, we’ll be unable to move to a different provider next year.

First of all, let’s talk about what’s driving this footloose requirement. At some level in the business, someone or some people are unsure that the currently chosen cloud provider is going to remain so. They may even want to review how suitable they are on an annual basis and have reserved the right to change their minds at that point. This isn’t unusual and it could be the right thing to do – any company that blindly continues to use the same vendors and service providers without questioning if they still offer the right solution is destined to find themselves hindered by a provider who can no longer meet the business needs. So for example, let’s assume that there is a distinct possibility that although AWS is the flavour of the month, this time next year might see a shift to Microsoft Azure.

At the point of that shift to Azure, what is the expectation for existing systems? There has been a year of development effort pushing software into AWS, does the business think that it can be re-deployed into Azure ‘as is’? I would expect that there would be a plan for a period of transition. I would also expect that it would be recognised that there are some systems for which it isn’t worth spending the money to move. New development will undoubtedly happen in Azure with as little effort as possible focused on AWS. The business doesn’t expect a ‘big bang’ change (which would be incredibly high risk).

Now let’s think about how well your software currently running in AWS will run in Azure. Both AWS and Azure offer hosting with the same Operating Systems, so we’re off to a good start – you should at least be able to manually deploy and get something running. The catch is in the way that the virtual environments are delivered. If your app relies on local HD storage, then moving from AWS to Azure may mean quite a hit. At the time of writing this article, the best throughput you can get from Azure Premium storage is 200MB/s whereas AWS’ EBS Provisioned Volumes will give you a throughput of 320MB/s. So moving to Azure could impact your application’s performance under load, especially if it relies on a self managed instance of a database (Mongo DB for example). In fact, if you want high performance storage in Azure then Table Storage or DocumentDB are probably the best options – both of which are PaaS.

This is only one example of how moving cloud provider could impact your software, there are others. The virtual machine options are different – not just in hard disc size but in available memory, processor speeds and in how their performance changes with load. So what you’re deploying quite happily onto half a dozen instances with one cloud provider may require nine or ten instances on another, plus a few tweaks to how the software stores its data.

What I’m trying to highlight here isn’t that using PaaS won’t be a barrier to moving from one cloud provider to another, rather that it isn’t the one you would have to worry about. Changing the API that is used for caching data is a well defined problem with easily understood steps to implement. Understanding the impact of the subtle differences in how each cloud provider delivers your virtual environments – that’s hard.

That’s not the end of this issue. Lets look at this from the software side. How often do developers use 3rd party software? From my own experience, I don’t think I remember the last time I spent a day writing code which didn’t involve several NuGet Install-Package statements. Every time I’m always careful to prevent tight coupling between my code and the installed packages. Why wouldn’t I expect the same care to be taken when working with PaaS? It’s really good practice to write a client for your PaaS interaction that abstracts the detail of implementation away from the logical flow of your software. This is good programming 101. When moving to another cloud provider the impact of changing the API is predominantly limited to the client. By far not the biggest problem you’ll have to deal with.


Depending on what your business does, you may have restrictions on where you can store your data. Conversely, storing your data in some territories may incur restrictions on how that data must be encrypted. Some territories may just not allow certain types of data to be stored at all; or you may need to be certified in some way and prove correct storage policies by external audit.

These rules don’t change if you store your data in a traditional data-center. You still have to be aware of where your data is going and what that means. It isn’t just a cloud provider that might make use of geolocation for resilience purposes. So your problem exists either way.

Cloud providers are aware of this issue and are very clear on where their data is stored and what control you have over this. This is specifically for compliance reasons.


Once a system is in place and running, the developers are rarely interested in maintaining it from day to day. That job usually falls to a combination of Operations and Dev Ops. The concern with PaaS is that it will in some way be harder for a non-development team to manage than if something well known and self managed is used. I think this falls into the category of ‘fear of the unknown’ – the question I would ask is “will a service that is managed for you be harder to look after than something that you have to fully manage yourself?” Even if you have a dedicated team with a lot of expertise in managing a particular technology, they will still have to do the work to manage it. PaaS usually is configured and then left. With nothing else to do than respond to any alerts which might suggest a need to provision more resources. It’s made resilient and highly available by clicking those buttons during configuration or setting those values in an automation script.

Perhaps there is a concern that in future, it will be harder to find development resource to make changes. This is a baseless fear. No-one debates this problem when referencing 3rd party libraries via NuGet – there really isn’t any difference. Sure there may be some more subtle behaviours of a PaaS service which a developer may not be aware of but any problems should be caught by testing. Often the documentation for PaaS services is pretty good and quite to the point; I’d expect any developer working with a PaaS service to spend as much time in their documentation as they would for any 3rd party library they used.

Take a look at the AWS docs for DynamoDB – the behaviour of the database when spikes take reads or writes beyond what has been provisioned is a pretty big gotcha, but it’s described really well and is pretty obvious just from a quick read through.

There is definitely going to be a need to get some monitoring in place but that is true for the whole system anyway. When establishing the monitoring and alerts, there will have to be some decisions made around what changes are worthy of monitoring and what warrant alerts. Thinking of the utilised PaaS as just something else pushing monitoring events is a pretty good way to make sure the right people will know well in advance if any problems are going to be encountered.

Lack of Published SLAs

This can cause some worries and it’s something that annoys me a lot about AWS in particular. I don’t see any reason why an SLA wouldn’t be published – people want to know what they’re buying and that’s an important part of it. But let’s get our sensible heads on – we’re talking pretty damned decent up times even if it isn’t always 99.999%.

In my opinion, worrying about the SLA for a PaaS service provided by people such as Amazon, Microsoft or Google doesn’t always make much sense. These guys have massive resources behind them – you’re far more likely to mess it up than they are. But let’s think about how failures in any service should be handled. There should always be a failure state which defaults to something which at least isn’t broken, otherwise your SLA is tied to a multiple of the SLAs of every 3rd party. Your system has to be resilient to outages of services you rely on. Also, let’s remember where you system is hosted – in the same data centre as the PaaS service is running in. If there is an outage of the PaaS service, it could be also impacting your own system. Leveraging the flexibility of geolocation and availability zones allows you to get around those kinds of outages. I’m not saying you’re guaranteed constant availability but how often have you seen go down?

Given the nature of cloud hosting coupled with a resilient approach to calling 3rd party services, a lack of published SLA isn’t as terrifying as it seems. Code for outages and do some research about what problems have occurred in the past for any given service.

Confusing Marketing Message

This is an interesting one. What is PaaS? Where does infrastructure end and platform begin? That might be pretty easy to answer in a world of traditional data-centers, but in the cloud things are a bit more fluffy. Take Autoscaling Groups, for example, or more specifically the ability to automatically scale the number of instances of your application horizontally across new instances based on some measure. I’ve heard this described as IaaS, PaaS and once as ‘IaaS plus’.

The line between IaaS and PaaS is being continuously blurred by cloud providers who I don’t think are particularly worried about the strict categorisation of the services they provide. With services themselves consisting of several elements, some of which might or might not fall neatly into either PaaS or IaaS, the result is neither.

I think this categorisation is causing an amount of analysis paralysis among some people who feel the need for services to be pigeon holed in some way. Perhaps being able to add a service into a nice, pre-defined category makes it somehow less arduous to decide whether it’s something that could be useful. “Oh, IaaS – yeah, we like that! Use it everywhere.” Such categorisations give comfort with an ivory tower, fully top down approach but don’t change the fundamental usefulness of any given service.

This feels a little 1990’s to me. Architecture is moving on and people are becoming more comfortable with the idea of transferring responsibility for the problematic bits to our cloud provider’s solution. We don’t have to do everything for ourselves to have confidence that it’s as good as it could be – in fact that idea is being turned on its head.

I love the phrase “do the hard things often”, well no-one does any of this as often as the people who provide your cloud infrastructure. Way more often than you do and they’re far better at it, which is fine – your company isn’t a cloud provider, it’s good at something else.

So should we worry that a service might or might not be neatly described as either PaaS or IaaS? I think it would be far more sensible to ask the question “is it useful?” or even “how much risk is being removed from our architecture by using it?” and that isn’t going anywhere near the cost savings involved.

Lack of Standard Aproach

In my mind, this could be a problem as it does seem to push toward vendor lock in. But, let’s consider the differing standards across cloud providers – where are they the same? The different mechanisms for providing hard disks for VM’s results in Amazon being half as fast again as Azure’s best offering. What about the available VM types? I’m not sure there is much correlation. What about auto-scaling mechanisms? Now they are definitely completely different. Code deployment services? Definitely not the same.

I suppose what I’m trying to get at is that each cloud provider has come up with their own service which does things in its own specific way. Not surprising really. We don’t complain when an Android device doesn’t have a Windows style Start button, why would we expect two huge feats of engineering which are cloud services to obey the same rules? They were created by different people with different ideas and to initially solve different problems.

So there is a lack of standards, but this doesn’t just impact PaaS. If this is a good reason to fear PaaS then it must be a good reason to fear the cloud altogether. I think we’ve found the 1990’s again.

Round Up

I’m not in any way trying to say that PaaS is some kind of silver bullet, or that it is inherently always less risky than a self managed solution. What I am trying to make clear is that much of the fear around PaaS is from a lack of understanding. The further away an individual is from dealing with the different implementations (writing the code), the harder it is to see the truth of the detail. We’ve had decades of indoctrination telling us that physical architecture forms a massive barrier to change but the cloud and associated technologies (such as Dev Ops) removes that barrier. We don’t have less points of contact with external systems, we actually have more, but each of those points is far more easily changed than was once true.

Some Useful Links


Going Deep Enough with Microservices

Moving from a monolith architecture to microservices is a widely debated process, with many recommendations and nuggets of advice available on the web in blogs like this. There are so many different opinions out there mainly because where an enterprise finds their main complexities lay depends on the skillsets of their technologists, the domain knowledge within the business and the existing code base. During the years I’ve spent as a contractor in a very wide range of enterprises, I’ve seen lots of monolith architectures – all of them causing slightly different headaches because those responsible for developing them let different aspects of the architecture slip. After all, the thing that is often forgotten is that if a monolith is maintained well, then it can work. The reverse is also true – if a microservice architecture is left to evolve on its own, it can cause as many problems as a poorly maintained monolith.


One popular way to break things down is using Domain Driven Design. Two books which cover most concepts involved in this process are ‘Building Microservices’ by Sam Newton ( and ‘Implementing Domain Driven Design’ by Vaughn Vernon ( which largely references ‘Domain Driven Design: Tackling Complexity in the Heart of Software’ by Eric Evans ( I recommend Vaughn’s book over Evans’ as the latter is a little dry.

If you take on board even just half the content covered in these books, you’ll be on a reasonable footing to get started. You’ll make mistakes but as Sam Newton points out (and I’ve seen for myself) that’s inevitable.

Something that seems to be left out of a lot of domain driven discussions is what happens beyond the basic CRUD processes and domain logic in the application layer. Attention sits primarily with the thin interaction between a web interface and the domain processing by the aggregate in question. When dismantelling a monolith architecture into microservices, focus on just the application layer can give the impression of fast progress but in reality half the picture is missing. It’s likely that in a few months there will be several microservices but instead of them operating solely in their sub-domains, they’ll still be tied to the database that the original monolith was using.


It’s hugely important to pull the domain data out of the monolith store. This is for the very same reasons we segregate service responsibilities into sub-domains. Data pertaining to a given domain may exist in other domains as well but changes will not necessarily be subjected to the same domain rules and individual records may have different properties. There may be a User record in several sub-domains, each with a Username property but the logic around how duplicate Usernames are prevented should sit firmly in a single sub-domain. If a service in a different sub-domain needs to update the username, it should either call a public service from the Profile sub-domain or raise a ‘Username Updated’ event that the Profile sub-domain would handle, process and possibly respond with a ‘Username Update Failed’ event of its own.

This example may be a little contrived – checking for duplicates could be something that’s implemented everywhere it’s needed. But consider what would happen if it became necessary to check for duplicates within another external system every time a Username is updated. That logic could easily be encapsulated behind the call to the Profile service but having to update every service that updates Usernames wouldn’t be good practice.

So if we are now happy that the same data represented in different sub-domains could at any one time be different (given the previous two paragraphs) then we shouldn’t store the data for both sub-domains in the same table.

Local Data

In fact, we’re now pretty well removed from needing a classic relational database for storing data that’s local to the sub-domain. We’re dealing with data that is limited in scope and is intended for use solely by the microservices built to sit in that sub-domain. NoSQL databases are ideal for this scenario and no matter which platform you’ve chosen to build on there are excellent options available. One piece of advice I think is pretty sound is that if you are working in the cloud, you’ll usually get the best performance by using the data services provided by your cloud provider. Make sure you do your homework, though – some have idiosyncracies that can impact performance if you don’t know about them.

So now we have data stored locally to the sub-domain, but this isn’t where the work stops. It’s likely there’s a team of DBA’s jumping around wondering why their data warehouse isn’t getting any new data.

The problem is that the relational database backing the monolith wasn’t just acting as a data-store for the application. There were processes feeding other data-stores for things like customer reporting, machine learning platforms and BI warehouses. In fact, anything that requires a historical view of things will be reading it from one or more stores that are loaded incrementally from the monolith’s relational database. Now data is being stored in a manner best suiting any given sub-domain, there isn’t a central source for that data to be pulled from into these downstream stores.

Shift of Responsibility

Try asking a team of dba’s if they fancy writing CLR based stored procedures to detect changes and pull new records into their warehouse by querying whatever data-store technologies have been decided on in each case – I doubt they’ll be too receptive. The responsibility for getting data out of each local data-store now has to move closer to the application services.

The data guys are interested in recording historical and aggregated records, which is convenient as there is a useful well known tool for informing different systems that something has happened – an event.

It’s been argued that using events to communicate across sub-domains is miss-using an event stream as a message bus. My argument in this case is that the back-end historical data-store is still within the original sub-domain. The data being stored belongs specifically to that sub-domain and still holds the same context as when it was saved. There has been a transition to a new medium of storage but that’s all.

So we are now free to raise events from our application microservices into eventstreams which are then handled by a service specifically designed to transfer data from events into whatever downstream stores were originally being fed from the monolith database. This gives us full extraction from the monolithic architecture and breaks the sub-domain’s dependency on the monolith database.

There is also the possibility that we can now give more fine grained detail of changes than was being recorded previously.

Gaps in the Monolith Database

Of course back end data-stores aren’t the only consumers of the sub-domain’s data. Most likely there will be other application level queries that used to read the data you’re now saving outside of the monolith database. How you manage these dependencies will depend on whether the read requests are coming from the same sub-domain or another. If they’re from the same sub-domain then it’s equally correct to either pull the data from an event stream or from microservices within that sub-domain. Gradually, a sub-domain’s dependency on the monolith database will die. If the queries are coming from a different sub-domain then it’s better to continue to update the monolith database as a consumer of the data stored locally to the sub-domain. The original table no longer containing data that is relevant to the sub-domain you’re working on.


Obviously we don’t want to have any gaps in the data being sent to our back-end stores, so as we pull functionality into microservices and add new data-stores local to the sub-domain, we also need to build the pipeline for our new back end processing of domain events into the warehouse. As this gets switched on, the loading processes from the original monolith can be switched off.

External Keys

Very few enterprise systems function in isolation. Most businesses make use of off-the-shelf packages or cloud based services such as Salesforce. Mapping records into these systems usually means using the primary key of each record to create a reference. If this has happened then the primary key from the monolith is most likely being relied on to hold things together. Moving away from the monolith database means the primary key generation has probably been lost.

There are two options here and I’d suggest going with whatever is the easiest – they both have their merits and problems.

  1. Continue to generate unique id’s in the same way as the monolith database did and continue to use these id’s for reference across different systems. Don’t rely on the monolith for id generation here, create a new process in the microservice that continues the same pattern.
  2. Start generating a new version of id generation and copy the new keys out to the external systems for reference. The original keys can eventually be lost.

Deeper than Expected

When planning the transition from monolithic architecture to microservices, there may well be promises from the management team that time will be given to build each sub-domain out properly. Don’t take this at face value – Product Managers will still have their roadmaps to fulfill and unfortunately there is maybe only 30% of any given slice of functionality being pulled out of a monolith that an end user will ever see. Expect the process to be difficult no matter what promises are made.

What I really want to get across here is that extracting even a small amount of functionality into microservices carries with it a much deeper dive into the enterprise’s tech stack than just creating a couple of application services. It requires time and focus from more than just the Dev team and before it can even be started, there has to be a architectural plan spanning the full vertical slice of a sub-domain, from front end to warehoused historical data.

Consequences of Not Going Deep Enough

How difficult do you find it in your organisation to get approval for technical upgrade work, or for dealing with technical debt as a project (which I’m not advocating is a good strategy), or for doing anything which doesn’t have a directly measurable positive impact on new product? In my experience, it isn’t easy and I’m not sure it should be, but that’s for another post.

Imagine you’ve managed to extract maybe 70% of your application layer away from your monolith but you’re still tied to the same data model. Have you achieved what you set out to do? You certainly don’t have loose coupling because everything is tied at the data level. You don’t have domain isolation. You are preventing your data team from getting access to the juicy new events you don’t really need to be raising (because the changed data is already available everywhere). You’ve turned a monolith into an abomination – it isn’t really microservices and it isn’t a classic monolith, it isn’t really any desired pattern at all. Even worse, the work you are missing is pretty big and may not directly carry with it any new features. Will you get agreement to remove coupling with the database as a project itself?

How are your developers doing? How many of them see that the strategy is only going half way? How many are moaning about paying lip service to the architecture? Wasn’t that one of the reasons you started with microservices in the first place?

Can you deploy the microservices without affecting other sub-domains? What if there are schema changes? What if there are schema changes in 2 sub-domains and one needs to be rolled back after release because it wasn’t quite right? Wasn’t this something microservices was supposed to prevent?

How many dodgy hacks or ‘surprises’ are there in your new code where devs have managed to make domain isolated services work with a single relational data model? How many devs waste time hand wrangling when they know they’re building something that is going to be technical debt the moment it goes live?

Ok, so I’m painting a darker picture than you’ll probably feel, but each of these scenarios will almost certainly come up, you just might not get to hear about it.

The crux for me is thinking about the reasons for pursuing a microservice architecture. The flexibility, loose coupling, technology agnosticity (if that’s a real term), the speed of continuous delivery that you’re looking for. Unless you go deeper than the low lying fruit of the application layer, you’ll be cheating yourself out of these benefits. Sure, you’ll see improvements short term but you are building something which is already technical debt. No matter what architecture you choose, if you don’t invest in maintaining it properly (or even building it properly in the first place) then it will ultimately become your albatross.

Events vs Commands

In the world of service oriented architectures and CQRS style processes there is a tendancy for nearly everything to raise events. Going back a few years however, before REST became fashionable many interactions were by RPC and often the result of processing commands from a queue.

So when did commands become an anti-pattern? Well of course, they never did. These days we just have to understand when it’s more appropriate to send a command or raise an event.

Here’s a table to help you decide what you should be using:

Events Commands
An event is all about something that has already happened A command is all about something that the originating service wants to happen (although it might not be successful)
A service raising an event doesn’t care what happens to it. Something consuming an event is not critical to the service’s function. A service sending a command needs that command to be processed as part of it’s functionality.
An event could be consumed by one, many or no consumers. A command is intended for one specific consumer.
An event can suggest loose coupling between services. A command definitely indicates tight coupling – the originating service knows about the command target.
A service prevented from raising an event can only report that the event was not raised. A service prevented from sending a command can report the failure to a team with specific domain knowledge about what will happen down stream if the command is not processed. The service may be designed to fail its own process if the command fails.

A really good example of the right use of an event is communicating between services within a bounded context that something has happened. The originating service will have successfully completed its function before raising the event. Consumers of the event do something else in addition that the originating service doesn’t really care about.

A good example of the right use of a command is where two different platforms need to be kept in sync with each other. When data is updated in one system a sync command is sent to update the other. If something stops that command getting sent (e.g. an auth issue between the service and a message queue) then the service can react and alert people to the issue, or it may be that the update in the originating service needs to fail.

Both events and commands are important in a distributed system. Using them in the right places makes your intent much clearer and helps keep your system structured.

User Secrets in asp.NET 5

Accidentally pushing credentials to a public repo has never happened to me, but I know a few people for whom it has. AWS have an excellent workaround for this by using credential stores that can be configured via the CLI or IDE but this technique only works for IAM user accounts, it doesn’t allow you to connect to anything outside of the AWS estate.

Welcome to User Secrets in asp.NET 5 – and they’re pretty cool.

User Secrets are a part of the new asp.NET configuration mechanism. If you open Visual Studio 2015 and create a new Web API project, for example, you’ll be presented with something somewhat different to previous versions. Configuration is carried out in Startup.cs, where we can conditionally load configuration from one or many sources including .config and .json files, environment variables and the User Secret store. To access User Secrets, you want to modify the constructor like so:

public Startup(IHostingEnvironment env, IApplicationEnvironment appEnv)
    var builder = new ConfigurationBuilder(appEnv.ApplicationBasePath)

    Configuration = builder.Build();

In this example, the order of calls to AddJsonFile(), AddUserSecrets() and AddEnvironmentVariables() makes a difference. If the property ‘Username’ is defined in config.json and also as a secret then the value in config.json will be ignored in favour of the secret. Similarly, if there is a ‘Username’ environment variable set, that would win over the other two. The order loaded dictates which wins.

To create a secret, first open a Developer Command Prompt for VS2015. This is all managed via the command line tool ‘user-secret’. To check if you have everything installed, at the prompt, type ‘user-secret -h’.

C:Program Files (x86)Microsoft Visual Studio 14.0>user-secret -h

If user-secret isn’t recognised then you may need to install the SecretManager command in the .NET Development Utilities (DNU). Do this by typing ‘dnu command install SecretManager’.

C:Program Files (x86)Microsoft Visual Studio 14.0>dnu command install SecretManager

In my case, this was again not recognised, even though I had just completed a full install of every component of Visual Studio 2015 Professional. If this is still not working for you, then you need to update the .NET Version Manager (DNVM). Do this by typing ‘dnvm upgrade’.

C:Program Files (x86)Microsoft Visual Studio 14.0>dnvm upgrade

Hopefully, you should get a similar response to this:

C:Program Files (x86)Microsoft Visual Studio 14.0>dnvm upgrade
Determining latest version
Downloading dnx-clr-win-x86.1.0.0-beta6 from
Installing to C:UsersPeter.dnxruntimesdnx-clr-win-x86.1.0.0-beta6
Adding C:UsersPeter.dnxruntimesdnx-clr-win-x86.1.0.0-beta6bin to process PATH
Adding C:UsersPeter.dnxruntimesdnx-clr-win-x86.1.0.0-beta6bin to user PATH
Native image generation (ngen) is skipped. Include -Ngen switch to turn on native image generation to improve application startup time.
Setting alias 'default' to 'dnx-clr-win-x86.1.0.0-beta6'

Now try installing the command. You should see all of your registered NuGet sources being queried for updates and then a whole host of System.* packages being installed. The very end of the response should look something like this:

    10 package(s) to C:UsersPeter.dnxbinpackages
    56 package(s) to C:UsersPeter.dnxbinpackages
The following commands were installed: user-secret

Now when you run ‘user-secret -h’ you should get this:

Usage: user-secret [options] [command]

  -?|-h|--help  Show help information
  -v|--verbose  Verbose output

  set     Sets the user secret to the specified value
  help    Show help information
  remove  Removes the specified user secret
  list    Lists all the application secrets
  clear   Deletes all the application secrets

Use "user-secret help [command]" for more information about a command.

You can see five possible commands listed, and getting help on any particular one is also explained. As an example, if you want to set a property ‘Username’ to ‘Guest’ then type this:

C:Program Files (x86)Microsoft Visual Studio 14.0>cd MyProjectFolder
C:MyProjectFolder>user-secret set Username Guest

Where MyProjectFolder is the location of a project.json file.

So there you have it. You’re ready to create secrets that can never be accidentally pushed into a public repo or shared anywhere they shouldn’t be. Just remember that emailing them to the dev sitting next to you might not be much better.

Useful links: