Analyze Solution For Code Clones Missing In Visual Studio 2017

I was watching a video on Microsoft’s Channel 9 website regarding design patterns and during the video the presenter showed a feature of Visual Studio that analysed the currently loaded solution for “code clones” – sections of code that appear to be clones or duplicates in different methods.

This seemed like a very useful feature and something that I thought I could benefit from on a number of codebases that I’m currently working with.  So off I went to explore this handy feature for myself.

First thing to note this that, although this feature has been part of Visual Studio since Visual Studio 2012, it’s only available in the Enterprise SKU of the product (or the Premium or Ultimate versions for VS 2012/2013).

That was ok, as I’m using Visual Studio 2017 Enterprise, however, upon expanding the Analyze menu option, I was greeted with a distinct absence of the Analyze Solution For Code Clones option:


Hmm..  I was curious why the option was missing so went off doing my best googling to try to find out why.   And it turns out that there’s very little information out there on the internet regarding this feature, and even less information regarding troubleshooting why the option might not be appearing on the Analyze tools menu.

After some amount of aimlessly clicking around on the spurious search results, it suddenly dawned on me.   Perhaps it’s an “optional extra” component that you need to install?   Turns out I was right.

When I’d originally installed Visual Studio 2017, I’d selected one of the pre-configured “Workloads” to install.  In my case it was the “ASP.NET And Web Development” workload as that’s the sort of development work I do.  Turns out that, whilst this workload installs almost everything you might want or need for ASP.NET And Web Development, there’s a few crucial things missing, one of which is the “Architecture And Analysis Tools” component – which in turn includes the “Code Clone” tool:


In order to get the Code Clone tool installed, you can either select just that tool from the “Individual components” tab of the Visual Studio installer, or you can select the “Architecture and analysis tools” optional component (as seen on the right hand side in the above picture) which will select the Code Clone, Code Map and Live Dependency Validation tools.

Once this is done, restarting Visual Studio will give you the missing “Analyze Solution for Code Clones” menu option:


Easy when you know how, eh?

DDD East Anglia 2017 In Review

IMG_20170916_083642xThis past Saturday, 16th September 2017, the fourth DDD East Anglia event took place in Cambridge.  DDD East Anglia is a relatively new addition to the DDD event line-up but now it’s fourth event sees it going from strength to strength.

IMG_20170917_091108I’d made the long journey to Cambridge on the Friday evening and stayed in a local B&B to be able to get to the Hills Road College where DDD East Anglia was being held on the Saturday.  I arrived bright and early, registered at the main check-in desk and then proceeded to the college’s recital room just around the corner from the main building for breakfast refreshments.

After some coffee, it was soon time to head back to the main college building and up the stairs to the room where the first session of the day would commence.  My first session was to be Joseph Woodward’s Building A Better Web API With CQRS.

IMG_20170916_091329xJoseph starts his session by defining CQRS.  It’s an acronym, standing for Command Query Responsibility Segregation.  Fundamentally, it’s a pattern for splitting the “read” models from your “write” models within a piece of software.  Joseph points out that we should beware when googling for CQRS as google seems to think it’s a term relating to cars!

CQRS was first coined by Greg Young and it’s very closely related to a prior pattern called CQS (Command Query Separation), originally coined by Bertrand Meyer which states that every method should either be a command which performs an action, or a query which returns data to the caller, but never both.  CQS primarily deals with such separations at a very micro level, whilst CQRS primarily deals with the separations at a broader level, usually along the seams of bounded contexts.  Commands will mutate state and will often be of a “fire and forget” nature.  They will usually return void from the method call.  Queries will return state and, since they don’t mutate any state are idempotent and safe.  We learn that CQRS is not an architectural pattern, but is more of a programming style that simply adheres to the the separation of the commands and queries.

Joseph continues by asking what’s the problem with some of our existing code that CQRS attempts to address.   We look at a typical IXService (where X is some domain entity in a typical business application):

public class ICustomerService
     void MakeCustomerPreferred(int customerId);
     Customer GetCustomer(int customerId);
     CustomerSet GetCustomersWithName(string name);
     CustomerSet GetPreferredCustomers();
     void ChangeCustomerLocale(int cutomerId, Locale newLocale);
     void CreateCustomer(Customer customer);
     void EditCustomerDetails(CustomerDetails customerDetails);

The problem here is that the interface ends up growing and growing and our service methods are simply an arbitrary collection of queries, commands, actions and other functions that happen to relate to a Customer object.  At this point, Joseph shares a rather insightful quote from a developer called Rob Pike who stated:

“The bigger the interface, the weaker the abstraction”

And so with this in mind, it makes sense to split our interface into something a bit smaller.  Using CQRS, we can split out and group all of our "read" methods, which are our CQRS queries, and split out and group our "write" methods (i.e. Create/Update etc.) which are our CQRS commands.  This will simply become two interfaces in the place of one, an ICustomerReadService and an ICustomerWriteService.

There's good reasons for separating our concerns along the lines of reads vs writes, too.  Quite often, since reads are idempotent, we'll utilise heavy caching to prevent us from making excessive calls to the database and ensure our application can return data in as efficient a manner as possible, whilst our write methods will always hit the database directly.  This leads on to the ability to have entirely different back-end architectures between our reads and our writes throughout the entire application.  For example, we can scale multiple read replica databases independently of the database that is the target for writes.  They could even be entirely different database platforms.

From the perspective of Web API, Joseph tells us how HTTP verbs and CQRS play very nicely together.  The HTTP verb GET is simply one of our read methods, whilst the verbs PUT, POST, DELETE etc. are all of our write concerns.  Further to this, Joseph looks at how we can often end up with MVC or WebAPI controllers that require services to be injected into them and often our controller methods end up becoming bloated from having additional concerns embedded within them, such as validation.  We then look at the command dispatcher pattern as a way of supporting our separation of reads and writes and also as a way of keeping our controller action methods lightweight.

There are two popular frameworks that implement the command dispatcher pattern in the .NET world. MediatR and Brighter.  Both frameworks allow us to define our commands using a plain old C# object (that implements specific interfaces provided by the framework) and also to define a "handler" to which the commands are dispatched for processing.  For example:

public class CreateUserCommand : IRequest
     public string EmailAddress { get; set; }
     // Other properties...

public class CreateUserCommandHandler : IAsyncRequestHandler<CreateUserCommand>
     public CreateUserCommandHandler(IUserRepository userRepository, IMapper mapper)
          _userRepository = userRepository;
          _mapper = mapper;

     public Task Handle(CreateUserCommand command)
          var user = _userRepository.Map<CreateUserCommand, UserEntity>(command);
          await _userRepository.CreateUser(user);

Using the above style of defining commands and handlers along with some rudimentary configuration of the framework to allow specific commands and handlers to be connected, we can move almost all of the required logic for reading and writing out of our controllers and into independent, self-contained classes that perform a single specific action.  This enables further decoupling of the domain and business logic from the controller methods, ensuring the controller action methods remain incredibly lightweight:

public class UserController : Controller
     private readonly IMediator _mediator;
	 public UserController(IMediator mediator)
	      _mediator = mediator;
	 public async Task Create(CreateUserCommand user)
	      await _mediator.Send(user);

Above, we can see that the Create action method has been reduced down to a single line.  All of the logic of creating the entity is contained inside the handler class and all of the required input for creating the entity is contained inside the command class.

Both the MediatR and Brighter libraries allow for request and post-request pre-processors.  This allows defining another class, again deriving from specific interfaces/base classes within the framework, which will be invoked before the actual handler class or immediately afterwards.  Such pre-processing if often a perfect place to put cross-cutting concerns such as validation:

public class CreateUserCommandValidation : AbstractValidation<CreateUserCommand>
     public CreateUserCommandValidation()
	      RuleFor(x => x.EmailAddress).NotEmpty().WithMessage("Please specify an email address");

The above code shows some very simple example validation, using the FluentValidation library, that can be hooked into the command dispatcher framework's request pre-processing to firstly validate the command object prior to invoking the handler, and thus saving the entity to the database.

Again, we've got a very nice and clean separation of concerns with this approach, with each specific part of the process being encapsulated within it's own class.  The input parameters, the validation and the actual creation logic.

Both MediatR and Brighter have an IPipelineBehaviour interface, which allows us to write code that hooks into arbitrary places along the processing pipeline.  This allows us to implement other cross-cutting concerns such as logging.  Something that's often required at multiple stages of the entire processing pipeline.

At this point, Joseph shares another quote with us.  This one's from Uncle Bob:

"If your architecture is based on frameworks then it cannot be based on your use cases"

From here, Joseph turns his talk to discuss how we might structure our codebases in terms of files and folders such that separation of concerns within the business domain that the software is addressing are more clearly realised.  He talks about a relatively new style of laying out our projects called Feature Folders (aka Feature Slices).

This involves laying out our solutions so that instead of having a single top-level "Controllers" folder, as is common in almost all ASP.NET MVC web applications, we instead have multiple folders named such that they represent features or specific areas of functionality within our software.  We then have the requisite Controllers, Views and other folders underneath those.   This allows different areas of the software to be conceptually decoupled and kept separate from the other areas.  Whilst this is possible in ASP.NET MVC today, it's even easier with the newer ASP.NET Core framework, and a NuGet package called AddFeatureFolders already exists that enables this exact setup within ASP.NET Core.

Joseph wraps up his talk by suggesting that we take a look at some of his own code on GitHub for the DDD South West website (Joseph is one of the organisers for the DDD South West events) as this has been written using the the CQRS pattern along with using feature folders for layout.

IMG_20170916_102558After Joseph's talk it's time for a quick coffee break, so we head back to the recital room around the corner from the main building for some liquid refreshment.  This time also accompanied by some very welcome biscuits!

After our coffee break, it's time to head back to the main building for the next session.  This one was to be Bart Read's Client-Side Performance For Back-End Developers.

IMG_20170916_103032Bart's session is all about how to maximise performance of client-side script using tools and techniques that we might employ when attempting to troubleshoot and improve the performance of our back-end, server-side code.  Bart starts by stating that he's not a client-side developer, but is more of a full stack developer.  That said, as a full stack developer, one is expected to perform at least some client-side development work from time to time.  Bart continues that in other talks concerning client-side performance, the speakers tend to focus on the page load times and page lifecycle, which whilst interesting and important, is a more a technology-centric way of looking at the problem.  Instead, Bart says that he wants to focus on RAIL, which was originally coined by Google.  This is an acronym for Response, Animation, Idle and Load and is a far more user-centric way of looking at the performance (or perhaps even just perceived performance) issue.  In order to explore this topic, Bart states that he learnt JavaScript and built his own arcade game site,, which uses extensive JavaScript and other resources as part of the site.

We first look at Response.  For this we need to build a very snappy User Interface so that the user feels that the application is responding to them immediately.  Modern web applications are far more like desktop applications written using either WinForms or WPF than ever and users are very used to these desktop applications being incredibly responsive, even if lots of processing is happening in the background.  One way to get around this is to use "fake" pages.  These are pages that load very fast, usually without lots of dynamic data on them, that are shown to the user whilst asynchronous JavaScript loads the "real" page in the background.  Once fully loaded, the real page can be gracefully shown to the user.

Next, we look at Animation. Bart reminds us that animations help to improve the user perception of responsiveness of your user interface.  Even if your interface is performing some processing that takes a few milliseconds to run, loading and displaying an animation that the user can view whilst that processing to going on will greater enhance the perceived performance of the complete application.  We need to ensure that our animations always run at 60 fps (frames per second), anything less than this will cause them to look jerky and is not a good user experience.  Quite often, we need to perform some computation prior to invoking our animation and in this scenario we should ensure that the computation is ideally performed in less than 10 milliseconds.

Bart shares a helpful online utility called CanvasMark which provides benchmarking for HTML5 Canvas rendering.  This can be really useful in order to test the animations and graphics on your site and how they perform on different platforms and browsers

Bart then talks about using the Google Chrome Task Manager to monitor the memory usage of your page.  A lot of memory can be consumed by your page's JavaScript and other large resources.  Bart talks about his own site which uses 676MB of memory.  This might be acceptable on a modern day desktop machine, but it will not work so well on a more constrained mobile device.  He states that after some analysis of the memory consumption, most of the memory was consumed by the raw audio that was decompressed from loaded compressed audio in order to provide sound effects for the game.  By gracefully degrading the quality and size of the audio used by the site based upon the platform or device that is rendering the site, performance was vastly improved.

Another common pitfall is in how we write our JavaScript functions.  If we're going to be creating many instances of a JavaScript object, as can happen in a game with many individual items on the screen, we shouldn't attach functions directly to the JavaScript object as this creates many copies of the same function.  Instead, we should attach the function to the object prototype, creating a single copy of the function, which is then shared by all instances of the object and thus saving a lot of memory.  Bart also warns us to be careful of closures on our JavaScript objects as we may end up capturing far more than we actually need.

We now move onto Idle.   This is all about deferring work as the main concern for our UI is to respond to the user immediately.  One approach to this is to use Web Workers to perform work at a later time.  In Bart's case, he says that he wrote his own Task Executor which creates an array of tasks and uses the builtin JavaScript setTimeout function to slowly execute each of the queued tasks.  By staggering the execution of the queued tasks, we prevent the potential for the browser to "hang" with a white screen whilst background processing is being performed, as can often happen if excessive tasks and processing is performed all at once.

Finally, we look at Load.  A key take away of the talk is to always use HTTP/2 if possible.  Just by switching this on alone, Bart says you'll see a 20-30% improvement in performance for free.  In order to achieve this, HTTP/2 provides us with request multiplexing, which bundles requests together meaning that the browser can send multiple requests the the server in one go.  These requests won't necessarily respond any quicker, but we do save on the latency overhead we would incur if sending of each request separately.  HTTP/2 also provides server push functionality, stream priority and header compression.  It also has protocol encryption, which whilst not an official part of the HTTP/2 specification, is currently mandated by all browsers that support the HTTP/2 protocol, effectively making encryption compulsory.  HTTP/2 is widely supported across all modern browsers on virtually all platforms, with only Opera Mini being only browser without full support, and HTTP/2 is also fully supported within most of today's programming frameworks.  For example, the .NET Framework has supported HTTP/2 since version 4.6.0.  One other significant change when using HTTP/2 is that we no longer need to "bundle" our CSS and JavaScript resources.  This also applies to "spriting" of icons as a single large image.

Bart moves on to talk about loading our CSS resources and he suggests that one very effective approach is to inline the bare minimum CSS we would require to display and render our "above the fold" content with the rest of the CSS being loaded asynchronously.  The same applies to our JavaScript files, however, there can be an important caveat to this.  Bart explains how he loads some of his JavaScript synchronously, which itself negatively impacts performance, however, this is required to ensure that the asynchronously loaded 3rd-party JavaScript - over which you have no control - does not interfere with your own JavaScript as the 3rd-party JavaScript is loaded at the very last moment whilst Bart's own JavaScript is loaded right up front.  We should look into using DNS Prefetch to force the browser to perform DNS Lookups ahead of time for all of the domains that our site might reference for 3rd party resources.  This incurs a one off small performance impact as the page first loads, but makes subsequent requests for 3rd party content much quicker.

Bart warns us not to get too carried away putting things in the HEAD section of our pages and instead we should focus on getting the "above the fold" content to be as small as possible, ideally it should be all under 15kb, which is the size of data that can fit in a single HTTP packet.  Again, this is a performance optimization that may not have noticeable impact on desktop browsers, but can make a huge difference on mobile devices, especially if they're using a slow connection.  We should always check the payload size of our sites and ensure that we're being as efficient as possible and not sending more data than is required.  Further to this, we should use content compression if our web server supports it.  IIS has supported content compression for a long time now, however, we should be aware of a bug that affects IIS version 8 and possibly version 9 which turns off compression for chunked content. This bug was fixed in IIS version 10.

If we're using libraries or frameworks in our page, ensure we only deliver the required parts.  Many of today's libraries are componentized, allowing the developer to only include the parts of the library/framework that they actually need and use.  Use Content Delivery Networks if you're serving your site to many different geographic areas, but also be aware that, if your audience is almost exclusively located in a single geographic region, using a CDN could actually slow things down.  In this case, it's better to simply serve up your site directly from a server located within that region.

Finally, Bart re-iterates.  It's all about Latency.   It's latency that slows you down significantly and any performance optimizations that can be done to remove or reduce latency will improve the performance, or perceived performance, of your websites.

IMG_20170916_102542After Bart's talk, it's time for another coffee break.  We head back to the recital room for further coffee and biscuits and after a short while, it's time for the 3rd session of the day and the last one prior to lunch.  This session is to be a Visual Note Taking Workshop delivered by Ian Johnson.

As Ian's session was an actual workshop, I didn't take too many notes but instead attempted to take my notes visually using the technique of Sketch-Noting that Ian was describing.

Ian first states that Sketch-Noting is still mostly about writing words.  He says that most of us, as developers using keyboards all day long, have pretty terrible hand writing so we simply need to practice more at it.  Ian suggests to avoid all caps words and cursive writing, using a simple font and camel cased lettering (although all caps is fine for titles and headings).  Start bigger to get the practice of forming each letter correctly, then write smaller and smaller as you get better at it.  You'll need this valuable skill since Sketch-Noting requires you to be able to write both very quickly and legibly. 

At this point, I put my laptop away and stopped taking written notes in my text editor and tried to actually sketch-note the rest of Ian's talk, which gave us many more pointers and advice on how to construct our Sketch Notes.  I don't consider myself artistic in the slightest, but Ian insists that Sketch Notes don't really rely on artistic skill, but more on the skill of being able to capture the relevant information from a fast-moving talk.  I didn't have proper pens for my Sketch Note and had to rely solely on my biro, but here in all its glory is my very first attempt at a Sketch Note:


IMG_20170916_130218After Ian's talk was over, it was time for lunch.  All the attendees reconvened in the recital room where we could help ourselves to the lunch kindly provided by the conference organizers and paid for by the sponsors.  Lunch was the usual brown bag affair consisting of a sandwich, some crisps a chocolate bar, a piece of fruit and a can of drink.  I took the various items for my lunch and the bag and proceeded to wander just outside the recital room to a small green area with some tables.   It was at this point that the weather decided to turn against us and is started raining very heavily.  I made a hasty retreat back inside the recital room where it was warm and dry and proceeded to eat my lunch there.

There were some grok talks taking place over the lunch time, but considering the weather and the fact the the grok talk were taking place in the theatre room which was the further point from the recital room, I decided against attending them and chose to remain warm and dry instead.

After lunch, it was time to head back to the main building for the next session, this one was to be Nathan Gloyn's Microservices - What I've Learned After A Year Building Systems.

IMG_20170916_135912Nathan first introduces himself and states that he's a contract developer.  As such, he's been involved in two different projects over the previous 12 months that have been developed using a microservices architecture.  We first asked to consider the question of why should we use microservices?  In Nathan's experience so far, he says, Don't!  In qualifying that statement, Nathan states that microservices are ideal if you need only part of a system to scale, however, for the majority of applications, the benefits to adopting a microservices architecture doesn't outweigh the additional complexity that is introduced.

Nathan state that building a system composed of microservices requires a different way of thinking.  With more monolithic applications, we usually scale them by scaling out - i.e. we use the same monolithic codebase for the website and simply deploy it to more machines which all target the same back-end database.  Microservices don't really work like this, and need to be individually small and simple.  They may even have their own individual database just for the individual service.

Microservices are often closely related to Domain-driven Design's Bounded Contexts so it's important to correctly identify the business domain's bounded contexts and model the microservices after those.  Failure to do this runs the risk that you'll create a suite of mini-monoliths rather than true microservices.

Nathan reminds us that we are definitely going to need a messaging system for an application built with a microservice architecture.  It's simply not an option not to use one as virtually all user interactions will be performed across multiple services.  Microservices are, by their very nature, entirely distributed.  Even simple business processes can often require multiple services and co-ordination of those services.  Nathan says that it's important not to build any messaging into the UI layer as you'll end up coupling the UI to the domain logic and the microservice which is something to be avoided.  One option for a messaging system is NServiceBus, which is what Nathan is currently using, however many other options exist.   When designing the messaging within the system, it's very important to give consideration to versioning of messages and message contracts.  Building in versioning from the beginning ensures that you can deploy individual microservices independently rather than being forced to deploy large parts of the system - perhaps multiple microservices - together if they're all required to use the exact same message version.

We next look at the difference between "fat" versus "thin" services.  Thin services generally only deal with data that they "own", if the thin service needs other data for processing, they must request it from the other service that owns that data.  Fat services, however, will hold on to data (albeit temporarily) that actually "belongs" to other services in order to perform their own processing.  This results in coupling between the services, however, the coupling of fat and thin services is different as fat services are coupled by data whereas thin services are coupled by service.

With microservices, cross-cutting concerns such as security and logging become even more important than ever.  We should always ensure that security is built-in to every service from the very beginning and is treated as a first class citizen of the service.  Enforcing the use of HTTPS across the board (i.e. even when running in development or test environments as well as production) helps to enforce this as a default choice.

We then look at how our system's source code can be structured for a microservices based system.  It's possible to use either one source control repository or multiple and there's trade-offs against both options.  If we use a single repository, that's really beneficial during the development phase of the project, but is not so great when it comes to deployment.  On the other hand, using multiple repositories, usually separated by microservice, is great for deployment since each service can be easily integrated and deployed individually, but it's more cumbersome during the development phase of the project.

It's important to remember that each microservice can be written using it's own technology stack and that each service could use an entirely different stack to others.  This can be beneficial if you have different team with different skill sets building the different services, but it's important to remember to you'll need to constantly monitor each of the technology stacks that you use for security vulnerabilities and other issues that may arise or be discovered over time.  Obviously, the more technology stacks you're using, the more time-consuming this will be.

It's also important to remember that even when you're building a microservices based system, you will still require shared functionality that will be used by multiple services.  This can be built into each service or can be separated out to become a microservice in it's own right depending upon the nature of the shared functionality.

Nathan talks about user interfaces to microservice based systems.  These are often written using a SPA framework such as Angular or React.  They'll often go into their own repository for independent deployment, however, you should be very careful that the front-end user interface part of the system doesn't become a monolith in itself.  If the back-end is nicely separated into microservice based on domain bounded contexts, the front-end should be broken down similarly too.

Next we look at testing of a microservice based system.  This can often be a double-edged sword as it's fairly easy to test a single microservice with its known good (or bad) inputs and outputs, however, much of the real-world usage of the system will be interactions that span multiple services so it's important to ensure that you're also testing the user's path through multiple services.  This can be quite tricky and there's no easy way to achieve this.  It's often done using automated integration testing via the user interface, although you should also ensure you do test the underlying API separately to ensure that security can't be bypassed.

Configuration of the whole system can often be problematic with a microservice based system.  For this reason, it's usually best to use a separate configuration management system rather than trying to implement things like web.config transforms for each service.  Tools like Consul or Spring Cloud Config are very useful here.

Data management is also of critical importance.  It should be possible to change data within the system's data store without requiring a deployment.  Database migrations are a key tool is helping with this.  Nathan mentions both Entity Framework Migrations and also FluentMigrator as two good choices.  He offers a suggestion for things like column renames and suggests that instead of a migration that renames the column, create a whole new column instead.  That way, if the change has to be rolled back, you can simply remove the new column, leaving the old column (with the old name) in place.  This allows other services that may not be being deployed to continue to use the old column until they're also updated.

Nathan then touches on multi-tenancy within microservice based systems and says that if you use the model of a separate database per tenant, this can lead to a huge explosion of databases if your microservices are using multiple databases for themselves.  It's usually much more manageable to have multi-tenancy by partitioning tenant data within a single database (or the database for each microservice).

Next, we look at logging and monitoring within our system.  Given the distributed nature of a microservice based system, it's important to be able to log and understand the complete user interaction even though logging is done individually by individual microservices.  To facilitate understanding the entire end-to-end interaction we can use a CorrelationID for this.  It's simply a unique identifier that travels through all services, passed along in each message and written to the logs of each microservice.  When we look back at the complete set of logs, combined from the disparate services, we can use the CorrelationID to correlate the log messages into a cohesive whole.  With regard to monitoring, it's also critically important to monitor the entire system and not just the individual services.  It's far more important to know how healthy the entire system is rather than each service, although monitoring services individually is still valuable.

Finally, Nathan shares some details regarding custom tools.  He says that, as a developer on a microservice based system, you will end up building many custom tools.  These will be such tools as bulk data loading whereby getting the data into the database requires processing by a number of different services and cannot simply be directly loaded into the database.  He says that despite the potential downsides of working on such systems, building the custom tools can often be some of the more enjoyable parts of building the complete system.

After Nathan's talk it was time for the last coffee break of the day, after which it was time for the final day's session.  For me, this was Monitoring-First Development by Benji Weber.

IMG_20170916_152036Benji starts his talk by introducing himself and says that he's mostly a Java developer but that he done some .NET and also writes JavaScript as well.  He works at an "ad-tech" company in London.  He wants to first start by talking about Extreme Programming as this is a style of programming that he uses in his current company.  We look at the various practices within Extreme Programming (aka XP) as many of these practices have been adopted within wider development styles, even for teams that don't consider themselves as using XP.  Benji says that, ultimately, all of the XP practices boils down to one key thing - Feedback.  They're all about getting better feedback, quicker.  Benji's company uses full XP with a very collaborative environment, collectively owning all the code and the entire end-to-end process from design/coding through to production, releasing small changes very frequently.

As part of this style adopted by the teams, it's lead onto the adoption of something they term Monitor-Driven Development.  This is simply the idea that monitoring of all parts of a software system is the core way to get feedback on that system, both when the system is being developed and as the system is running in production.  Therefore, all new feature development starts by asking the question, "How will we monitor this system?" and then ensuring that the ability to deeply monitor all aspects of the system being developed is a front and centre concern throughout the development cycle.

To illustrate how the company came to adopt this methodology, Benji shares three short stories with us.  The first started with an email to the development team with the subject of "URGENT".  It was from some sales people trying to demonstrate some part of the software and they were complaining that the graphs on the analytics dashboard weren't loading for them.  Benji state that this was a feature that was heavily tested in development so the error seemed strange.   After some analysis into the problem, it was discovered that data was the root cause of the issue and that the development team had underestimated the way in which the underlying data would grow due to users doing unanticipated things on the system, which the existing monitoring that the team had in place didn't highlight.  The second story involves the discovery that 90% of the traffic their software was serving from the CDN was HTTP 500 server errors!  Again, after analysis it was discovered that the problem lay in some JavaScript code that a recently released new version of Internet Explorer was interpreting different from the old version and that this new version was caused the client-side JavaScript to continually make requested to a non-existent URL.  The third story involves a report from an irate client that the adverts being served up by the company's advert system was breaking the client's own website.  Analysis showed that this was again caused by a JavaScript issue and a line of code of self = this; that was incorrectly written using a globally-scoped variable, thereby overwriting variables that the client's own website relied upon.  The common theme throughout all of the stories was that the behaviour of the system had changed, even though no code had changed.   Moreover, all of the problems that arose from the changed behaviour were first discovered by the system's user and not the development team.

Benji references Google's own Site Reliability Engineering book (available to read online for free) which states that 70% of the reasons behind things breaking is because you've changed something.  But this leaves a large 30% of the time where the reasons are something that's outside of your control.  So how did Benji approach improving his ability to detect and respond to issues?  He started by looking at causes vs problems and concluded that they didn't have enough coverage of the problems that occurred.  Benji tells us that it's almost impossible to get sufficient coverage of the causes since there's an almost infinite number of things that could happen that could cause the problem.

To get better coverage of the problems, they universally adopted the "5 whys" approach to determining the root issues.  This involves starting with the problem and repeated asking "why?" to each cause to determine the root cause.  An example is, monitoring is hard. Why is it hard since we don't have the same issue when using Test-Driven Development during coding?  But TDD follows a Red - Green - Refactor cycle so you can't really write untestable code etc.

IMG_20170916_155043So Benji decided to try to apply the Test-Driven Development principles to monitoring.  Before even writing the feature, they start by determining how the feature will be monitored, then only after determining this, they start work on writing the feature ensuring that the monitoring is not negatively impacted.  In this way, the monitoring of the feature becomes the failing unit test that the actual feature implementation must make "go green".

Benji shares an example of show this is implemented and says that the "failing test" starts with a rule defined within their chosen monitoring tool, Nagios.  This rule could be something like "ensure that more adverts are loaded than reported page views", whereby the user interface is checked for specific elements or a specific response rendering.  This test will show as a failure within the monitoring system as the feature has not yet been implemented, however, upon implementation of the feature, the monitoring test will eventually pass (go green) and there we have a correct implementation of the feature driven by the monitoring system to guide it.  Of course, this monitoring system remains in place with these tests increasing over time and becoming an early warning system should any part of the software, within any environment, start to show any failures.  This ensure that the development team are the first to know of any potential issues, rather than the users being first to know.

Benji says they use a pattern called the Screenplay pattern for their UI based tests.  It's an evolution of the Page Objects pattern and allows highly decoupled tests as well as bringing the SOLID principles to the tests themselves.  He also states that they make use of Feature Toggles not only when implementing new features and functionality but also when refactoring existing parts of the system.  This allows them to test new monitoring scenarios without affecting older implementations.  Benji states that it's incredibly important to follow a true Red - Green - Refactor cycle when implementing monitoring rules and that you should always see your monitoring tests failing first before trying to make them pass/go green.

Finally, Benji says that adopting a monitoring-driven development approach ultimately helps humans too.  It helps in future planning and development efforts as it builds awareness of how and what to think about when designing new systems and/or functionality.

IMG_20170916_165131After Benji's session was over, it was time for all the attendees to gather back in the theatre room for the final wrap-up by the organisers and the prize draw.  After thanking the various people involved in making the conference what it is (sponsors, volunteers, organisers etc.) it was time for the prize draw.  There were some good prizes up for grabs, but alas, I wasn’t to be a winner on this occasion.  The DDD East Anglia 2017 event had been a huge success and it was all the more impressive given that the organisers shared the story that their original venue had pulled out only 5 weeks prior to the event!  The new venue had stepped in at the last minute and held an excellent conference which was  completely seamless to the attendees.  We would never have known of the last minute panic had it not been shared with us.  Here's looking forward to the next DDD East Anglia event next year.

DDD 12 In Review

IMG_20170610_085641On Saturday 10th June 2017 in sunny Reading, the 12th DeveloperDeveloperDeveloper event was held at Microsoft’s UK headquarters.  The DDD events started at the Microsoft HQ back in 2005 and after some years away and a successful revival in 2016, this year’s DDD event was another great occasion.

I’d travelled down to Reading the evening before, staying over in a B&B in order to be able to get to the Thames Valley Park venue bright and early on the Saturday morning.  I arrived at the venue and parked my car in the ample parking spaces available on Microsoft’s campus and headed into Building 3 for the conference.  I collected my badge at reception and was guided through to the main lounge area where an excellent breakfast selection awaited the attendees.  Having already had a lovely big breakfast at the B&B where I was staying, I simply decided on another cup of coffee, but the excellent breakfast selection was very well appreciated by the other attendees of the event.


I found a corner in which to drink my coffee and double-check the agenda sheets that we’d been provided with as we entered the building.   There’d been no changes since I’d printed out my own copy of the agenda a few nights earlier, so I was happy with the selections of talks that I’d chosen to attend.   It’s always tricky at the DDD events as there are usually at least 3 parallel tracks of talks and invariably there will be at least one or two timeslots where multiple talks that you’d really like to attend will overlap.

IMG_20170611_104806Occasionally, these talks will resurface at another DDD event (some of the talks and speakers at this DDD in Reading had given the same or similar talks in the recently held DDD South West in Bristol back in May 2017) and so, if you’re really good at scheduling, you can often catch a talk at a subsequent event that you’d missed at an earlier one!

As I finished off my coffee, more attendees turned up and the lounge area was now quite full.  It wasn’t too much longer before we were directed by the venue staff to make our way to the relevant conference rooms for our first session of the day.

Wanting to try something a little different, I’d picked Andy Pike’s Introducing Elixir: Self-Healing Applications at ZOMG scale as my first session.

Andy started his sessions by talking about where Elixir, the language came from.  It’s a new language that is built on top of the Erlang virtual machine (BEAM VM) and so has it’s roots in Erlang.  Like Erlang, Elixir compiles down to the same bytecode that the VM ultimately runs.  Erlang was originally created at the Ericsson company in order to run their telecommunications systems.  As such, Erlang is built on top of a collection of middleware and libraries known as OTP or Open Telecom Platform.  Due to its very nature, Erlang and thus Elixir has very strong concurrency, fault tolerance and distributed computing at it’s very core.  Although Elixir is a “new” language in terms perhaps of it’s adoption, it’s actually been around for approximately 5 years now and is currently on version 1.4 with version 1.5 not too far off in the future.

Andy tells of how Erlang is used by WhatsApp to run it’s global messaging system.  He provides some numbers around this.  WhatsApp have 1.2 billion users, process around 42 billion messages per day and can manage to handle around 2 million connections on each server!   That’s some impressive performance and concurrency figures and Andy is certainly right when he states that very few other, if any, platforms and languages can boast such impressive statistics.

Elixir is a functional language and its syntax is heavily inspired by Ruby.  The Elixir language was first designed by José Valim, who was a core contributor in the Ruby community.  Andy talks about the Elixir REPL that ships in the Elixir installation which is called “iex”.  He shows us some slides of simple REPL commands showing that Elixir supports all the same basic intrinsic types that you’d expect of any modern language, integers, strings, tuples and maps.  At this point, things look very similar to most other high level functional (and perhaps some not-quite-so-functional) languages, such as F# or even Python.

Andy then shows us something that appears to be an assignment operator, but is actually a match operator:

a = 1

Andy explains that this is not assigning 1 to the variable ‘a’, but is “matching” a against the constant value 1.  If ‘a’ has no value, Elixir will bind the right-hand side operand to the variable, then perform the same match.  An alternative pattern is:

^a = 1

which performs the matching without the initial binding.  Andy goes on to show how this pattern matching can work to bind variables to values in a list.  For example, given the code:

success = { :ok, 42 }
{ ;ok, result } = success

this will bind the value of 42 to the variable result and subsequently perform the match of the tuple to the variable ‘success’ which now matches.  We’re told how the colon in front of the ok variable makes it an “atom”.  This is similar to a constant, where the variables name is it’s own value.

IMG_20170610_095341Andy shows how Elixir’s code is grouped into functions and functions can be contained within modules.  This is influenced by Ruby and how it also groups it’s code.  We then move on to look at lists.  These are handled in a very similar way to most other functional languages in that a list is merely seen as a “head” and a “tail”.  The “head” is the first value in the list and the “tail” is the entire rest of the list.  When processing the items in a list in Elixir, you process the head and then, perhaps recursively, call the same method passing the list “tail”.  This allows a gradual shortening of the list as the “head” is effectively removed with each pass through the list.  In order for such recursive processing to be performant, Elixir includes tail-call optimisation which allows the compiler to eliminate the necessity of maintaining state through each successive call to the method.  This is possible when the last line of code in the method is the recursive call.

Elixir also has guard clauses built right into the language.  Code such as:

def what_is(x) when is_number(x) and X > 0 do…

helps to ensure that code is more robust by only being invoked when ‘x’ is not only a number but also has some specific value too.  Andy states that, between the usage of such guard clauses and pattern matching, you can probably eliminate around 90-95% of all conditionals within your code (i.e. if x then y).

Elixir is very expressive within it’s allowed characters for function names, so functions can (and often do) have things like question marks in their name.  It’s a convention of the language that methods that return a Boolean value should end in a question mark, something shared with Ruby also, i.e. String.contains? "elixir of life", "of"  And of course, Elixir, like most other functional languages has a pipe operator(|>) which allows the piping of the result of one function call into the input of another function call, so instead of writing:

text = String.downcase(text)
text = String.reverse(text)
IO.puts text

Which forces us to continually repeat the “text” variable, we can instead write the same code like this:

|> String.downcase
|> String.reverse
|> IO.puts

Andy then moves on to show us an element of the Elixir language that I found particular intriguing, doctests.  Elixir makes function documentation a first class citizen within the language and not only does the doctest code provide documentation for the function – Elixir has a function, h, that when passed another function name as a parameter, will display the help for that function – but also serves as a unit test for the function, too!  Here’s a sample of an Elixir function containing some doctest code:

defmodule MyString do
   @doc ~S"""
   Converts a string to uppercase.

   ## Examples
       iex> MyString.upcase("andy")
   def upcase(string) do

The doctest code not only shows the textual help text that is shown if the user invokes the help function for the method (i.e. “h MyString”) but the examples contained within the help text can be executed as part of a doctest unit test for the MyString method:

defmodule MyStringTest do
   use ExUnit.Case, async: true
   doctest MyString

This above code uses the doctest code inside the MyString method to invoke each of the provided “example” calls and assert that the output is the same as that defined within the doctest!

After taking a look into the various language features, Andy moves on talk about the real power of Elixir which it inherits from it’s Erlang heritage – processes.  It’s processes that provide Erlang, and thus Elixir with the ability to scale massively, provide it with fault-tolerance and its highly distributed features.

Wen Elixir functions are invoked, they can effectively be “wrapped” within a process.  This involves spawning a process that contains the function.  Processes are not the same as Operating System processes, but are much more lightweight and are effectively only a C struct that contains a pointer to the function to call, some memory and mailbox (which will hold messages sent to the function).  Processes have a Process ID (PID) and will, once spawned, continue to run until the function contained within terminates or some error or exception occurs.  Processes can communicate with other processes by passing messages to those processes.  Here’s an example of a very simple module containing a single function and how that function can be called by spawning a separate process:

defmodule HelloWorld do
     def greet do
		IO.puts "Hello World"

HelloWorld.greet                 # This is a normal function call.
pid = spawn(HelloWorld, :greet)  # This spawns a process containing the function

Messages are sent to processes by invoking the “send” function, providing the PID and the parameters to send to the function:

send pid, { :greet, “Andy” }

This means that invoking functions in processes is almost as simple as invoking a local function.

Elixir uses the concept of schedulers to actually execute processes.  The Beam VM will supply one scheduler per core of CPU available, giving the ability to run highly concurrently.  Elixir also uses supervisors as part of the Beam VM which can monitor processes (or even monitor other supervisors) and can kill processes if they misbehave in unexpected ways.  Supervisors can be configured with a “strategy”, which allows them to deal with errant processes in specific ways.  One common strategy is one_for_one which means that if a given process dies, a single new one is restarted in it’s place.

Andy then talks about the OTP heritage of Elixir and Erlang and from this there is a concept of a “GenServer”.  A GenServer is a module within Elixir that provides a consistent way to implement processes.  The Elixir documentation states:

A GenServer is a process like any other Elixir process and it can be used to keep state, execute code asynchronously and so on. The advantage of using a generic server process (GenServer) implemented using this module is that it will have a standard set of interface functions and include functionality for tracing and error reporting. It will also fit into a supervision tree.

The GenServer provides a common set of interfaces and API’s that all processes can adhere to, allowing common conventions such as the ability to stop a process, which is frequently implemented like so:

GenServer.cast(pid, :stop)

Andy then talks about “nodes”.  Nodes are separate actual machines that can run Elixir and the Beam VM and these nodes can be clustered together.  Once clustered, a node can start a process not only on it’s own node, but on another node entirely.  Communication between processes, irrespective of the node that the process is running on is handled seamlessly by the Beam VM itself.  This provides Elixir solutions great scalability, robustness and fault-tolerance.

Andy mentions how Elixir has it’s own package manager called “hex” which gives access to a large range of packages providing lots of great functionality.  There’s “Mix”, which is a build tool for Elixir, OTP Observer for inspection of IO, memory and CPU usage by a node, along with “ETS”, which is an in-memory key-value store, similar to Redis, just to name a few.

Andy shares some book information for those of us who may wish to know more.  He suggests “Programming Elixir” and “Programming Phoenix”, both part of the Pragmatic Programmers series and also “The Little Elixir & OTP Guidebook” from Manning.

Finally, Andy wraps up by sharing a story of a US-based Sports news website called “Bleacher Report”.  The Bleacher Report serves up around 1.5 billion pages per day and is a very popular website both in the USA and internationally.  Their entire web application was originally built on Ruby on Rails and they required approximately 150 servers in order to meet the demand for the load.  Eventually, they re-wrote their application using Elixir.  They now serve up the same load using only 5 servers.  Not only have they reduced their servers by an enormous factor, but they believe that with 5 servers, they’re actually over-provisioned as the 5 servers are able to handle the load very easily.  High praise indeed for Elixir and the BeamVM.  Andy has blogged about his talk here.

After Andy’s talk, it was time for the first coffee break.  Amazingly, there were still some breakfast sandwiches left over from earlier in the morning, which many attendees enjoyed.  Since I was still quite full from my own breakfast, I decided a quick cup of coffee was in order before heading back to the conference rooms for the next session.  This one was Sandeep Singh’s Goodbye REST; Hello GraphQL.


Sandeep’s session is all about the relatively new technology of GraphQL.  It’s a query language for your API comprising of a server-side runtime for processing queries along with a client-side framework providing an in-browser IDE, called GraphiQL.  One thing that Sandeep is quick to point out is that GraphQL has nothing to do with Graph databases.  It can certainly act as a query layer over the top of a graph database, but can just as easily query any kind of underlying data such as RDBMS’s through to even flat-file data.

Sandeep first talks about where we are today with API technologies.  There’s many of them, XML-RPC, REST, ODATA etc. but they all have their pros and cons.  We explore REST in a bit more detail, as that’s a very popular modern day architectural style of API.  REST is all about resources and working with those resources via nouns (the name of the resource) and verbs (HTTP verbs such as POST, GET etc.), there’s also HATEOAS if your API is “fully” compliant with REST.

Sandeep talks about some of the potential drawbacks with a REST API.   There’s the problem of under-fetching.  This is seen when a given application’s page is required to show multiple resources at once, perhaps a customer along with recently purchased products and a list of recent orders.  Since this is three different resources, we would usually have to perform three distinct different API calls in order to retrieve all of the data required for this page, which is not the most performant way of retrieving the data.  There’s also the problem of over-fetching.  This is where REST API’s can take a parameter to instruct them to include additional data in the response (i.e. /api/customers/1/?include=products,orders), however, this often results in data that is additional to that which is required.  We’re also exposing our REST endpoint to potential abuse as people can add arbitrary inclusions to the endpoint call.  One way to get around the problems of under or over fetching is to create ad-hoc custom endpoints that retrieve the exact set of data required, however, this can quickly become unmaintainable over time as the sheer number of these ad-hoc endpoints grows.

IMG_20170610_110327GraphQL isn’t an architectural style, but is a query language that sits on top of your existing API. This means that the client of your API now has access to a far more expressive way of querying your API’s data.  GraphQL responses are, by default, JSON but can be configured to return XML instead if required, the input queries themselves are similarly structured to JSON.  Here’s a quick example of a GraphQL query and the kind of data the query might return:

	customer {
	"data" : {
		"customer" : {
			"name" : "Acme Ltd.",
			"address" : ["123 High Street", "Anytown", "Anywhere"]

GraphQL works by sending the query to the server where it’s translated by the GraphQL server-side library.  From here, the query details are passed on to code that you have written on the server in order that the query can be executed.  You’ll write your own types and resolvers for this purpose.  Types provide the GraphQL queries with the types/classes that are available for querying – this means that all GraphQL queries are strongly-typed.  Resolvers tell the GraphQL framework how to turn the values provided by the query into calls against your underlying API.  GraphQL has a complete type system within it, so it supports all of the standard intrinsic types that you’d expect such as strings, integers, floats etc. but also enums, lists and unions.

Sandeep looks at the implementations of GraphQL and explains that it started life as a NodeJS package.  It’s heritage is therefore in JavaScript, however, he also states that there are many implementations in numerous other languages and technologies such as Python, Ruby, PHP, .NET and many, many more.  Sandeep says how GraphQL can be very efficient, you’re no longer under or over fetching data, and you’re retrieving the exact fields you want from the tables that are available.  GraphQL allows you to version your queries and types also by adding deprecated attributes to fields and types that are no longer available.

IMG_20170610_110046We take a brief looks at the GraphiQL GUI client which is part of the GraphQL client side library.  It displays 3 panes within your browser showing the schemas, available types and fields and a pane allowing you to type and perform ad-hoc queries.  Sandeep explains that the schema and sample tables and fields are populated within the GUI by performing introspection over the types configured in your server-side code, so changes and additions there are instantly reflected in the client.  Unlike REST, which has obvious verbs around data access, GraphQL doesn't really have these.  You need introspection over the data to know how you can use that data, however, this is a very good thing.  Sandeep states how introspection is at the very heart of GraphQL – it is effectively reflection over your API – and it’s this that leads to the ability to provide strongly-typed queries.

We’re reminded that GraphQL itself has no concept of authorisation or any other business logic as it “sits on top of” the existing API.  Such authorisation and business logic concerns should be embedded within the API or a lower layer of code.  Sandeep says that the best way to think of GraphQL is like a “thin wrapper around the business logic, not the data layer”.

IMG_20170610_111157GraphQL is not just for reading data!  It has full capabilities to write data too and these are known as mutations rather than queries.  The general principle remains the same and a mutation is constructed using JSON-like syntax and sent to the server for resolving to a custom method that will validate the data and persist it the data store by invoking the relevant API endpoint.  Sandeep explains how read queries can be nested, so you can actually send one query to the server that actually contains syntax to perform two queries against two different resources.    GraphQL has a concept of "loaders".  These can batch up actual queries to the database to prevent issues when asking for such things all Customers including Orders.  Doing something like this normally results in a N+1 issue where by Orders are retrieved by issuing a separate query for each customer, resulting in degraded performance.  GraphQL loaders works by enabling the rewriting of the underlying SQL that can be generated for retrieving the data so that all Orders are retrieved for all of the required Customers in a single SQL statement.  i.e. Instead of sending queries like the following to the database:

SELECT CustomerID, CustomerName FROM Customer
SELECT OrderID, OrderNumber, OrderDate FROM Order WHERE CustomerID = 1
SELECT OrderID, OrderNumber, OrderDate FROM Order WHERE CustomerID = 2
SELECT OrderID, OrderNumber, OrderDate FROM Order WHERE CustomerID = 3

We will instead send queries such as:

SELECT CustomerID, CustomerName FROM Customer
SELECT OrderID, OrderNumber, OrderDate FROM Order WHERE CustomerID IN (1,2,3)

IMG_20170610_110644Sandeep then looks at some of the potential downsides of using GraphQL.  Caching is somewhat problematic as you can no longer perform any caching at the network layer as each query is now completely dynamic.  Despite the benefits, there are also performance considerations if you intend to use GraphQL as your underlying data needs to be correctly structured in order to work with GraphQL in the most efficient manner.  GraphQL Loaders should also be used to ensure N+1 problems don’t become an issue.  There’s security considerations too. You shouldn’t expose anything that you don’t want to be public since everything through the API is available to GraphQL and you’ll need to be aware of potentially malicious queries that attempt to retrieve too much data.  One simple solution to such queries is to use a timeout.  If a given query takes longer than some arbitrarily defined timeout value, you simply kill the query, however this may not be the best approach.  Other approaches taken by big websites currently using such functionality is to whitelist all the acceptable queries.  If a query is received that isn’t in the whitelist, it doesn’t get run.  You also can’t use HTTP codes to indicate status or contextual information to the client.  Errors that occur when processing the GraphQL query are contained within the GraphQL response text itself which is returned to the client with 200 HTTP success code.  You’ll need to have your own strategy for exposing such data to the user in a friendly way.   Finally Sandeep explains that, unlike other querying technologies such as ODATA, GraphQL has no intrinsic ability to paginate data.  All data pagination must be built into your underlying API and business layer, GraphQL will merely pass the paging data – such as page number and size – onto the API and expect the API to correctly deal with limiting the data returned.

IMG_20170610_103123After Sandeep’s session, it’s time for another coffee break.  I quickly grabbed another coffee from the main lounge area of the conference, this time accompanied by some rather delicious biscuits, before consulting my Agenda sheet to see which room I needed to be in for my next session.  Turned out that the next room I needed to be in was the same one I’d just left!  After finishing my coffee and biscuits I headed back to Chicago 1 for the next session and the last one before the lunch break.  This one was Dave Mateer’s Fun With Twitter Stream API, ELK Stack, RabbitMQ, Redis and High Performance SQL.

Dave’s talk is really all about performance and how to process large sets of data in the most efficient and performant manner.  Dave’s talk is going to be very demo heavy and so to give us a large data set to work with, Dave starts by looking at Twitter, and specifically, it’s Stream API.  Dave explains that the full Twitter firehose, which is reserved only for Twitter’s own use, currently has 1.3 million tweets per second flowing through it.  As a consumer, you can get access to a deca-firehose which contains 1/10th of the full firehose (i.e. 130000 tweets per second) but this costs money, however, Twitter does expose a freely available Stream API although it’s limited to 50 tweets per second.  This is still quite a sizable amount of data to process.

IMG_20170610_120247Dave starts by showing us a demo of a simple C# console application that uses the Twitter Stream API to gather real-time tweets for the hashtag of #scotland which are then echoed out to the console.   Our goal is to get the tweet data into a SQL Server database as quickly and as efficiently as possible.  Dave now says that to simulate a larger quantity of data, he’s going to read in a number of pre-saved text files containing tweet data that he’s previously collected.  These files represent around 6GB of raw tweet data, containing approximately 1.9 million tweets.  He then adds to the demo to start saving the tweets into SQL Server.  Dave mentions that he's using Dapper to access SQL Server and that previously he tried such things using Entity Framework, which was admittedly some time in the past, but that it was a painful experience and not very performant.   Dave likes Dapper as it's a much simpler abstraction over the database, so therefore much more performant.  It's also a lot easier to optimize your queries when using Dapper as the abstraction isn’t so great and you’re not hiding the implementation details too much.

IMG_20170610_121958Next, Dave shows us a Kibana interface.  As well as writing to SQL Server, he's also saving the tweets to a log file using SeriLog and then using LogStash to send those logs to ElasticSearch allowing viewing the raw log data with Kibana (also known as the ELK stack).  Dave then shows us how easy it is to really leverage the power of tools like Kibana by creating a dashboard for all of the data.

From here, Dave begins to talk about performance and just how fast we can process the large quantity of tweet data.  The initial run of the application, which was simply reading in each tweet from the file and performing an INSERT via Dapper to insert the data to the SQL Server database was able to process approximately 420 tweets per second.  This isn’t bad, but it’s not a great level of performance.  Dave digs out SQL Server Profiler to see where the bottle-necks are within the data access code, and this shows that there are some expensive reads – the data is normalized so that the user that a tweet belongs to is stored in a separate table and looked up when needed.  It’s decided that adding indexes on the relevant columns used for the lookup data might speed up the import process.  Sure enough, after adding the indexes and re-running the tweet import, we improve from 420 to 1600 tweets per second.  A good improvement, but not an order of magnitude improvement.  Dave wants to know if we can change the architecture of our application and go even faster.  What if we want to try to achieve a 10x level of increase in performance?

Dave states that, since his laptop has multiple cores, we should start by changing the application architecture to make better use of parallel processing across all of the available cores in the machine.  We set up an instance of the RabbitMQ message queue, allowing us to read the tweet data in from the files and send it to a RabbitMQ queue.  The queue is explicitly set to durable and each message is set to persistent in order to ensure we have the ability to continue where we left off from in the event of a crash or server failure.  From here, we can have multiple instances of another application that pull messages off the queue, leveraging RabbitMQ’s ability to effectively isolate each of the client consumers, ensuring that the same message is not sent to more than one client.  Dave then sets up Redis.  This will be used for "lookups" that are required when adding tweet data.  So users (and other data) is added to the DB first, then all data is cached in Redis, which is an in-memory key/value store and is often used for caching scenarios.  As tweets are added to the RabbitMQ message queue, required ID/Key lookups for Users and other data are done using the Redis cache rather than performing a SQL Server query for the data, thus improving the performance.  Once processed and the relevant values looked up from Redis, Dave uses SQL Server Bulk Copy to get the data into SQL Server itself.  SQL Server Bulk Copy provides a significant performance benefit over using standard INSERT T-SQL statements.   For Dave’s purposes, he Bulk Copies the data into temporary tables within SQL Server and then, at the end of the import, runs a single T-SQL statement to copy the data from the temporary tables to the real tables. 

IMG_20170610_125524Having re-architected the solution in this manner, Dave then runs his import of 6GB of tweet data again.  As he’s now able to take advantage of the multiple CPU cores available, he runs 3 console applications in parallel to process all of the data.  Each console application completes their jobs within around 30 seconds, and whilst they’re running, Dave shows us the Redis dashboard which is indicating that Redis is receiving around 800000 hits to the cache per second!  The result, ultimately, is that the application’s performance has increased from processing around 1600 tweets per second to around 20000!  An impressive improvement indeed!

Dave then looks at some potential downsides of such re-architecture and performance gains.  He shows how he’s actually getting duplicated data imported into his SQL Server and this is likely due to race conditions and concurrency issues at one or more points within the processing pipeline.  Dave then quickly shows us how he’s got around this problem with some rather ugly looking C# code within the application (using temporary generic List and Dictionary structures to pre-process the data).  Due to the added complexity that this performance improvement brings, Dave argues that sometimes slower is actually faster in that, if you don't absolutely really need so much raw speed, you can remove things like Redis from the architecture, slowing down the processing (albeit to a still acceptable level) but allowing a lot of simplification of the code.

IMG_20170610_130715After Dave’s talk was over, it was time for lunch.  The attendees made their way to the main conference lounge area where we could choose between lunch bags of sandwiches (meat, fish and vegetarian options available) or a salad option (again, meat and vegetarian options).

Being a recently converted vegetarian, I opted for a lovely Greek Salad and then made my way to the outside area along with, it would seem, most of the other conference attendees.

IMG_20170610_132208It had turned into a glorious summer’s day in Reading by now and the attendees and some speakers and other conference staff were enjoying the lovely sunshine outdoors while we ate our lunch.  We didn’t have too long to eat, though, as there were a number of Grok talks that would be taking place inside one of the major conference rooms over lunchtime.  After finishing my lunch (food and drink was not allowed in the session rooms themselves) I heading back towards Chicago 1 where the Grok Talks were to be held.

I’d managed to get there early enough in order to get a seat (these talks are usually standing room only) and after settling myself in, we waited for the first speaker.  This was to be Gary Short with a talk about Markov, Trump and Countering Radicalisation.

Gary starts by asking, “What is radicalisation?”.  It’s really just persuasion – being able to persuade people to hold an extreme view of some information.  Counter-radicalisation is being able to persuade people to hold a much less extreme belief.  This is hard to achieve, and Gary says that it’s due to such things as Cognitive Dissonance and The “Backfire” Effect (closely related to Confirmation Bias) – two subjects that he doesn’t have time to go into in a 10 minute grok talk, but he suggests we Google them later!  So, how do we process text to look for signs of radicalisation?  Well, we start with focus groups and corpus's of known good text as well as data from previously de-radicalised people (Answers to questions like “What helped you change?” etc.)  Gary says that Markov chains are used to process the text.  They’re a way of “flowing” through a group of words in such a way that the next word is determined based upon statistical data known about the current word and what’s likely to follow it.  Finally, Gary shows us a demo of some Markov chains in action with a C# console application that generates random tweet-length sentences based upon analysis of a corpus of text from Donald Trump.  His application is called TweetLikeTrump and is available online.

The next Grok talk is Ian Johnson’s Sketch Notes.  Ian starts by defining Sketch Notes.  They’re visual notes that you can create for capturing the information from things like conferences, events etc.   He states that he’s not an artist, so don’t think that you can’t get started doing Sketch Noting yourself.  Ian talks about how to get started with Sketch Notes.  He says it’s best to start by simply practising your handwriting!   Find a clear way of quickly and clearly writing down text using pen & paper and then practice this over and over.  Ian shared his own structure of a sketch note, he places the talk title and the date in the top left and right corners and the event title and the speaker name / twitter handle in the bottom corners.  He goes on to talk more about the component parts of a sketch note.   Use arrows to create a flow between ideas and text that capture the individual concepts from the talk.  Use colour and decoration to underline and underscore specific and important points – but try to do this when there’s a “lull” in the talk and you don’t necessarily have to be concentrating on the talk 100% as it’s important to remember that the decoration is not as important as the content itself.  Ian shares a specific example.  If the speaker mentions a book, Ian would draw a little book icon and simply write the title of the book next to the icon.  He says drawing people is easy and can be done with a simple circle for the head and no more than 4 or 5 lines for the rest of the body.  Placing the person’s arms in different positions helps to indicate expression.  Finally, Ian says that if you make a mistake in the drawing (and you almost certainly will do, at least when starting out) make a feature of it!  Draw over the mistake to create some icon that might be meaningful in the entire context of the talk or create a fancy stylised bullet point and repeat that “mistake” to make it look intentional!  Ian has blogged about his talk here.

Next up is Christos Matskas’s grok talk on Becoming an awesome OSS contributor.   Christos starts by asking the audience who is using open source software?  After a little thought, virtually everyone in the room raises their hand as we’re all using open source software in one way or another.  This is usually because we'll be working on projects that are using at least one NuGet package, and NuGet packages are mostly open source.  Christos shares the start of his own journey into becoming an OSS contributor which started with him messing up a NuGet Package restore on his first day at a new contract.  This lead to him documenting the fix he applied which was eventually seen by the NuGet maintainers and he was offered to write some documentation for them.  Christos talks about major organisations using open source software including Apple, Google, Microsoft as well as the US Department of Defence and the City of Munich to name but a few.  Getting involved in open source software yourself is a great way to give back to the community that we’re all invariably benefiting from.  It’s a great way to improve your own code and to improve your network of peers and colleagues.  It’s also great for your CV/Resume.  In the US, its almost mandatory that you have code on a GitHub profile.  Even in the UK and Europe, having such a profile is not mandatory, but is a very big plus to your application when you’re looking for a new job.  You can also get free software tools to help you with your coding.  Companies such as JetBrains, RedGate, and many, many others will frequently offer many of their software products for free or a heavily discounted price for open source projects.   Finally, Christos shares a few websites that you can use to get started in contributing to open source, such as, and the twitter account @yourfirstPR.

The final grok talk is Rik Hepworth’s Lability.  Lability is a Powershell module, available via Github, that uses Azure’s DSC (Desired State Configuration) feature to facilitate the automated provisioning of complete development and testing environments using Windows Hyper-V.  The tool extends the Powershell DSC commands to add metadata that can be understood by the Lability tool to configure not only the virtual machines themselves (i.e. the host machine, networking etc.) but also the software that is deployed and configured on the virtual machines.  Lability can be used to automate such provisioning not only on Azure itself, but also on a local development machine.

IMG_20170610_141738After the grok talks were over, I had a little more time available in the lunch break to grab another quick drink before once again examining the agenda to see where to go for the first session of the afternoon, and penultimate session of the day.  This one was Ian Russell’s Strategic Domain-Driven Design.

Ian starts his talk by mentioning the “bible” for Domain Driven Design and that’s the book by Eric Evans of the same name.  Ian asks the audience who has read the book to which quite a few people raise their hands.  He then asks who has read past Chapter 14, to which a lot of people put their hands down.  Ian states that, if you’ve never read the book, the best way to get the biggest value from the book is to read the last 1/3rd of the book first and only then return to read the first 2/3rds!

So what is Domain-Driven Design?  It’s an abstraction of reality, attempting to model the real-world we see and experience before us in a given business domain.  Domain-Driven Design (DDD) tries to break down the domain into what is the “core” business domain – these are the business functions that are the very reason for a business’s being – and other domains that exist that are there to support the “core” domain.  One example could be a business that sells fidget spinners.  The actual domain logic involved with selling the spinners to customers would be the core domain, however, the same company may need to provide a dispute resolution service for unhappy customers.  The dispute resolution service, whilst required by the overall business, would not be the core domain but would be a supporting or generic domain.

DDD has a ubiquitous language.  This is the language of the business domain and not the technical domain.  Great care should be taken to not use technical terms when discussing domains with business stake holders, and the terms within the language should be ubiquitous across all domains and across the entire business.  Having this ubiquitous language reduces the chance of ambiguity and ensures that everyone can relate to specific component parts of the domain using the same terminology.  DDD has sub-domains, too.  These are the core domain – the main business functionality, the supporting domains – which exist solely to support the core, and generic domains - such as the dispute resolution domain, which both supports the core domain but is also generic and could apply to other businesses too.  DDD has bounded contexts.  These are like sub-domains but don’t necessarily map directly to sub-domains.  They are explicitly boundaries from other areas of the business.  Primarily, bounded contexts can be developed in software independently from each other.  They could be written by different development teams and could even use entirely different architectures and technologies in their construction.

Ian talks about driving out the core concepts by creating a “shared kernel”.  These are the concepts that exist in the overlaps between bounded contexts.  These concepts don’t have to be shared between the bounded contexts and they may be different – the concept of an “account” might mean different things within the finance bounded context to the concept of an “account” within the shipping bounded context, for example.  Ian talks about the concept of an “anti-corruption layer” as part of each bounded context.  This allows bounded contexts to communicate with items from the shared kernel but where those concepts actually do differ between contexts, the anti-corruption layer will prevent corruption from incorrect implementations of concepts being passed to it.  Ian mentions domain events next.  He says that these are something that are not within Eric Evans’ book but are often documented in other DDD literature.  Domain events are essentially just ”things that occur” within the domain. For example, a new customer registered on the company’s website is an domain event.  Events can be created by users, by the passage of time, by external system, or even by other domain events.

All of this is Strategic domain-driven design.  It’s the modelling and understanding of the business domain without ever letting any technological considerations interfere with the modelling and understanding.  It’s simply good architectural practice and the understanding of how different parts of the business domain interact with, and communicate with other parts of the business domain.

Ian suggests that there’s three main ways in which to achieve strategic domain-driven design.  These are Behavioural Driven Design (BDD), Prototyping and Event Storming.  BDD involves writing acceptance tests for our software application using a domain-specific language within our application code that allows the tests to use the ubiquitous language of the domain, rather than the explicitly technical language of the software solution.  BDD facilitates engagement with business stakeholders in the development of the acceptance tests that form part of the software solution.  One common way to do this is to run three amigos sessions which allow developers, QA/testers and domain experts to write the BDD tests, usually in the standard Given-When-Then style.   Prototyping consists of producing images and “wireframes” that give an impression of how a completed software application could look and feel.  Prototypes can be low-fidelity, just simple static images and mock-ups, but it’s better if you can produce high-fidelity prototypes, which allows varying levels of interaction with the prototype. Tools such as Balsamiq and InVision amongst others can help with the production of high-fidelity prototypes.  Event storming is a particular format of meeting or workshop that has developers and domain experts collaborating on the production of a large paper artefact that contains numerous sticky notes of varying colours.  The sticky notes’ colour represent various artifacts within the domain such as events, commands, users, external systems and others.  Sticky notes are added, amended, removed and moved around the paper on the wall by all meeting attendees.  The resulting sticky notes tends to naturally cluster into the various bounded contexts of the domain, allowing the overall domain design to emerge.  If you run your own Event Storming session, Ian suggests to start by trying to drive out the domain events first, and for each event, attempt to first work backwards to find the cause or causes of that event, then work forwards, investigating what this event causes - perhaps further events or the requirement for user intervention etc.

IMG_20170610_145159Ian rounds off his talk by sharing some of his own DDD best practices.  We should strive for creative collaboration between developers and domain experts at all stages of the project, and a fostering of an environment that allows exploration and experimentation in order to find the best model for the domain, which may not be the first model that is found.  Ian states that determining the explicit context boundaries are far more important than finding a perfect model, and that the focus should always be primarily on the core domain, as this is the area that will bring the greatest value to the business.

After Ian’s talk was over, it was time for another coffee break, the last of the day.  I grabbed a coffee and once again checked my agenda sheet to see where to head for the final session of the day.   This last session was to be Stuart Lang’s Async in C#, the Good, the Bad and the Ugly.

IMG_20170610_153023Stuart starts his session by asking the audience to wonder why he’s doing a session on Async in C#' today.  It’s 2017 and Async/Await has been around for over 6 years!  Simply put, Stuart says that, whilst Async/Await code is fairly ubiquitous these days, there are still many mistakes made with some implementations and the finer points of asynchronous code are not as well understood.  We learn how Async is really an abstraction, much like an ORM tool.  If you use it in a naïve way, it’s easy to get things wrong.

Stuart mentions Async’s good parts.  We can essentially perform non-blocking waiting for background I/O operations, thus allowing our user interfaces to remain responsive.  But then comes the bad parts.  It’s not always entirely clear what is asynchronous code.  If we call an Async method in someone else’s library, there’s no guarantee that what we’re really executing is asynchronous code, even if the method returns a Task<T>.  Async can sometimes leads to the need to duplicate code, for example, when your code has to provide both asynchronous and synchronous versions of each method.  Also, Async can’t be used everywhere.  It can’t be used in a class constructor or inside a lock statement.

IMG_20170610_155205One of the worst side-effects of poor Async implementation is deadlocks.  Stuart states that, when it comes to Async, doing it wrong is worse than not doing it at all!  Stuart shows us a simple Async method that uses some Task.Delay methods to simulate background work.  We then look at what the Roslyn compiler actually translates the code to, which is a large amount of code that effectively implements a complete state machine around the actual method’s code. 

IMG_20170610_160038Stuart then talks about Synchronization Context.  This is an often misunderstood part of Async that allows awaited code, that can be resumed on a different thread, to synchronize with other threads.  For example, if awaited code needs to update some element on the user interface in a WinForms or WPF application, it will need to synchronize that change to the UI thread, it can’t be performed by a thread-pool thread that the awaited code would be running on.  Stuart talks about blocking on Async code, for example:

var result = MyAsyncMethod().Result;

We should try to never do this!   Doing so can cause code within the Async method to block on the synchronization context as awaited code within the Async method may be trying to restart on the same synchronization context that is already blocked by the "outer" code that is performing the .Result on the main Async method.

Stuart then shows us some sample code that runs as an ASP.NET page with the Async code being called from within the MVC controller.  He outputs the threads used by the Async code to the browser, and we then examine variations of the code to see how each awaited piece of code uses either the same or different threads.  One way of overcoming the blocking issue when using .Result at the end of an Async method call is to write code similar to:

var result = Task.Run(() => MyAsyncMethod().Result).Result;

It's messy code, but it works.  However, code like this should be heavily commented because if someone removes that outer Task.Run(); the code will start blocking again and will fail miserably.

Stuart then talks about the vs-threading library which provides a JoinableTaskFactory.   Using code such as:

jtf.Run(() => MyAsyncMethod())

ensures that awaited code resumes on the same thread that's blocked, so Stuart shows the output from his same ASP.NET demo when using the JoinableTaskFactory and all of the various awaited blocks of code can be seen to always run and resume on the same thread.

IMG_20170610_163019Finally, Stuart shares some of the best practices around deadlock prevention.  He asserts that the ultimate goal for prevention has to be an application that can provide Async code from the very “bottom” level of the code (i.e. that which is closest to I/O) all the way up to the “top” level, where it’s exposed to other client code or applications.

IMG_20170610_164756After Stuart’s talk is over, it’s time for the traditional prize/swag giving ceremony.  All of the attendees start to gather in the main conference lounge area and await the attendees from the other sessions which are finishing shortly afterwards.  Once all sessions have finished and the full set of attendees are gathered, the main organisers of the conference take time to thank the event sponsors.  There’s quite a few of them and, without them, there simply wouldn’t be a DDD conference.  For this, the sponsors get a rapturous round of applause from the attendees.

After the thanks, there’s a small prize giving ceremony with many of the prizes being given away by the sponsors themselves.  Like most times, I don’t win a prize – given that there was only around half a dozen prizes, I’m not alone!

It only remained for the organisers to announce the next conference in the DDD calendar, which although doesn’t have a specific date at the moment, will take place in October 2017.  This one is DDD North.  There’s also a surprise announcement of a DDD in Dublin to be held in November 2017 which will be a “double capacity” event, catering for around 600 – 800 conference delegates.  Now there’s something to look forward to!

So, another wonderful DDD conference was wrapped up, and there’s not long to wait until it’s time to do it all over again!

UPDATE (20th June 2017):

As last year, Kevin O'Shaughnessy was also in attendance at DDD12 and he has blogged his own review and write-up of the various sessions that he attended.  Many of the sessions attended by Kevin were different than the sessions that I attended, so check out his blog for a fuller picture of the day's many great sessions.