![dddnorthlogo]( "dddnorthlogo")
On Saturday 12th October 2013, in a slightly wet and windy Sunderland, the 3rd DDD North Developer conference took place. DDD North events are free one day conferences for .NET and the wider development community, run by developers for developers. This was the 3rd DDDNorth, and my 3rd DDD event in general (I’d missed the first DDD North, but did get to attend DDD East Anglia earlier this year) and this year’s DDDNorth was better than ever.
The day started when I arrived at the University Of Sunderland campus. I was travelling from Newcastle after having travelled to the North-East on the Friday evening beforehand. I’m lucky in that I have in-laws in Newcastle so was staying with them for the duration of the weekend making the journey to Sunderland fairly easy. Well, not that easy. I don’t really know Sunderland so I’d had to use my Sat-Nav which was great until we got close to the City Centre at which point my Sat-Nav took me on an interesting journey around Sunderland’s many roundabouts! :)
I eventually arrived at the Sir Tom Cowie Campus at the University of Sunderland and parked my car, thanks to the free (and ample) car parking provided by the university.
![20131012_083814]( "20131012_083814")
I’d arrived reasonably early for the registration, which opened at 8:30am, however there was still a small queue which I dutifully joined to wait to be signed in. Once I was signed in, it was time to examine the goodie bag that had been handed to me upon entrance to what was inside. There was some promotional material from some of the great sponsors of the events as well as a pen (very handy, as I always forget to bring pens to these events!) along with other interesting swag (the pen-cum-screwdriver was a particularly interesting item).
The very next task was to find breakfast! Again, thanks to some of the great sponsors of DDDNorth, the organisers were able to put on some sausage and bacon breakfast rolls for the attendees. This was a very welcome addition to the catering that was provided last time around at DDD North.
![20131012_084326]( "20131012_084326")
Once the bacon roll had been acquired, I was off to find perhaps the most important part of the morning’s requirements. Caffeine. Now equipped with a bacon roll and a cup of coffee, I was ready for the long but very exciting day of sessions ahead of me.
DDD North is somewhat larger than DDD East Anglia (although the latter will surely grow over time) so whereas DDD East Anglia had 3 parallel tracks of sessions, DDD North has 5! This can frequently lead to difficulties in deciding which session to attend but it is really testament to the variety and quality of the sessions at DDD North. So, having taken the difficult choices of which sessions to attend, I headed off the room for my first session.
![20131012_092813]( "20131012_092813")
The first session up was Phil Trelford’s F# Eye 4 the C# Guy. This session was one of three sessions during the day dedicated to F#. Phil’s session was aimed at developers currently using C# and he starts off by saying that, although F# offers some advantages over C#, there’s no “one true language” and it’s often the correct approach to use a combination of languages (both C# and F#) within a single application. Phil goes on to talk about the number and variety of companies that are currently using and taking advantage of the features of F#. F# was used within Halo 3 for the multi-player component which uses a self-improving machine learning algorithm to monitor, rate and intelligently match players with similar abilities together in games. This same algorithm was also tweaked and later used within the Bing search engine to match adverts to search queries. Phil also shares with us a quotation from a company called Kaggle who were previously predominantly a C# development team and who moved a lot of their C# code to F# with great success. They said, that their F# was “consistently shorter, easier to read, easier to refactor and contained far fewer bugs” compared to the equivalent C# code.
Phil talks about the the features of the F# language next. It’s statically typed and multi-paradigm. Phil states that it’s not entirely a functional language, but is really “functional first" and is also object-oriented. It’s also completely open source! Phil’s next step is to show a typical class in C#, the standard Person class with Name and Age properties:
public class Person
{
private string _Name;
private int _Age;
public Person(string name, int age)
{
_Name = name;
_Age = age;
}
public string Name
{
get { return _Name; }
set { _Name = value; }
}
public int Age
{
get { return _Age; }
set { _Age = value; }
}
public override string ToString()
{
return string.Format("{0} {1}", _Name, _Age);
}
}
Phil’s point here is that although this is a simple class with only two properties, the amount of times that the word “name” or “age” is repeated is excessive. Phil calls this the “Local Government Pattern” as everything has to be declared in triplicate! :) Here’s the same class, with the same functionality, but written in F#:
namespace People
type Person (name, age) =
member person.Name = name
member person.Age = age
override person.ToString() =
sprintf "%s %d" name age
Much shorter, and with far less repetition. But it can get far better than that. Here’s the same class again (albeit minus the .ToString() override) in a single line of F#:
type Person = { Name: string, Age: int }
Phil continues his talk to discuss how, being a fully-fledged, first class citizen of a language in the .NET world, F# code and components can fully interact with C# components, and vice-versa. F# also has the full extent of the .NET Framework at it’s disposal, too. Phil shows some more F# code, this one being something called a “discriminated union”:
type Shape =
| Circle of float
| Square of float * float
| Rectangle of float I’d come across the discriminated unions before, but as an F# newbie, I only barely understood them. Something that really helped me at least, as a C# guy, was when Phil explained the IL that is generated from the code. In the above example, the Shape class is defined as an abstract base class and the Circle, Square and Rectangle classes are concrete implementations of the abstract Shape class! Although thinking of these unions as base and derived classes isn’t strictly true when thinking of F# and it’s functional-style paradigm, it certainly helped me in mentally mapping something in F# back to the equivalent concept in C# (or a more OOP-style language).
Phil continues by mentioning some of the best ways to get up to speed with the F# language. One of Phil’s favourite methods for complete F# newbies, is the F# Koans GitHub repository. Based upon the Ruby Koans, this repository contains “broken” F# code that is covered by a number of unit tests. You run the unit tests to see them fail and your job is to “fix” the broken code, usually by “filling in the blanks” that are purposely left there, thereby allowing the test to pass. Each time you fix a test, you learn a little more about the F# syntax and the language constructs. I’ve already tried the first few of these and they’re a really good mechanism for a beginner to use to get to grips with F#. Phil states that he uses these Koans to train new hires in F# for the company he works for. Phil also gives a special mention to the tryfsharp.org website which also allows newbies to F# to play with the language. What’s special about tryfsharp.org is that you can try out the F# language entirely from within your web-browser, needing no other installed software on your PC. It even contains full IntelliSense!
Phil’s talk continues with a discussion of meta-programming and F#’s “quotations”. These are similar to C#’s Expressions but more powerful. They’re a more advanced subject (and worthy of a talk all of their own no doubt) but effectively allow you to represent F# code in an expression tree which can be evaluated at runtime. From here, we dive into BDD and testing of F# code in general. Phil talks about a BDD library (his own, called TickSpec) and how even text-based BDD test definitions are much more terse within F# rather than the equivalent C# BDD definitions (See the TickSpec homepage for some examples of this). Not only that, but Phil shows a remarkable ability to be able to debug his BDD text-based definitions within the Visual Studio IDE, including setting breakpoints, running the program in debug mode and breaking in his BDD text file! He also tells a story of how he was able, with a full suite of unit and BDD tests wrapped around the code, to convert a 30,000+ line C# code base into a 200 line F# program that not only perfectly replicated the behaviour of the C# program, but was actually able to deliver even more – all within less than 1/10th of the lines of code!
Phil shows us his “Cellz” spread sheet application written in F# next. He says it’s only a few hundred lines of code and is a good example of a medium sized F# program. He also states that his implementation of the code that parses and interprets user-defined functions within the spread sheet “cell” is sometimes as little as one line of code! We all ponder as to whether Excel’s implementations are as succinct! :) As well as Cellz, there’s a number of other project’s of Phil’s that he tells us about. One is a mocking framework, similar to C#’s Moq library, which of course, had to be called Foq! There is also a “Mario” style game that we are shown that was created with the FunScript set of type providers allowing JavaScript to be created from F# code. Phil also shows us a PacMan clone, running in the browser, created with only a few hundred lines of F# code.
Nearing the end of Phil’s talk, he shows us some further resources for our continued education, pointing out a number of books that cover the F# language. Some specific recommendations are “Programming F#” as well as Phil’s own book (currently in early-access), “F# Deep Dives” which is co-authored by Tomas Petricek (whom I’d seen give an excellent talk on F# at DDD East Anglia). Finally, Phil mentions that, although F# is a niche language with far fewer F# programmers than C# programmers, it’s a language that can command some impressive salaries! :) Phil shows us a slide that indicates the UK average salary of F# programmers is almost twice that of a C# programmer. So, there may not be as much demand for F# at the moment, but with that scarcity comes great rewards! :)
Overall, Phil’s talk was excellent and very enlightening. It certainly helped me as a predominantly C# developer to get my head around the paradigm shift that is functional programming. I’ve only scratched the surface so far, but I’m now curious to learn much more.
![20131012_115002]( "20131012_115002")
After a quick coffee break back in the main hall of the campus (during which time I was able to snaffle a sausage baguette which had been left over from the morning breakfast!), I headed off to one of the largest rooms being used during the entire conference for my next session. This one was Kendall Miller’sScaling Systems: Architectures That Grow.
Kendall opens his talk by saying that the entire session will be entirely technology agnostic. He says that what he’s about to talk about are concepts that can apply right across the board and across the complete technology spectrum. In fact, the concepts that Kendall is about to discuss regarding scalability in terms of how to achieve it and the things that can prevent you achieving it are not only technology agnostic, but they haven’t changed in over 30+ years of computing!
Kendall first asks, “What is scalability?” Scaling is the ability for a system to cope under a certain demand. That demand is clearly different for different systems. Kendall shows us some slides that differentiate between the “big boys” such as Amazon, Microsoft, Twitter etc., who are scaling to anything between 30-60 million unique visitors per day and those of us mere mortals that only need to scale to a few thousand or even hundred users per day. If we have a website that needs to handle 25,000 unique visitors per day, we can calculate that this is approximately 125,000 pages per day. In the USA, there’s around 11 “high traffic” hours (these are the daytime hours, but spread across the many time zones of North America). This gives us a requirement of around 12,000 pages/hour, and that divides down to only 3.3 pages per second. This isn’t such a large amount to expect of our webserver and, in the grand scheme of things, is effectively “small fry” and should be easily achievable in any technology. If we’re thinking about how we need our own systems to scale, it’s important to understand what we’re aiming for. We may actually not need all that much scalability! Scalability costs money, so we clearly don’t need to aim for scalability to millions of daily visitors to our site if we’re only likely to ever attract a few thousand.
We then ask, “What is availability?” Availability is having a request being completed in a given amount of time. It’s important to think about the different systems that this can apply to and the relative time that users of those systems will expect for a request to be completed. For example, simply accessing a website from your browser is a request/response cycle that’s expected to be completed within a very short amount of time. Delays here can turn people away from your website. Contrast this with (for example) sending an email. Here, it’s expected that email delivery won’t necessarily be instantaneous and the ability of the “system” in question to respond to the user’s request can take longer. Of course, it’s expected that the email will eventually be delivered otherwise the system couldn’t be said to be “available”!
Regarding websites, Kendall mentions that in order to achieve scalability we need only concern ourselves with the dynamic pages. Our static content should be inherently scalable in this day and age as scaling static content has long been a “solved problem”. Geo-located CDN’s can help in this regard and have been used for a long time. Kendall tells us that achieving scalability is simple in principle, but obviously much harder to implement in practice. That said, once we understand the principles required for scalability, we can seek to ensure our implementations adhere to them.
There’s only 3 things required to make us scale. And there’s only 1 thing that prevents us from scaling!
Kendall then introduces the 4 principles we need to be aware of: ACD/C.
This acronym is explained as Asynchronicity, Caching, Distribution & Consistency. The first three are the principles which, when applied, give us scalability. The last one, Consistency (or at least the need for our systems to remain in a consistent internal state) is the one that will stand in the way of scalability. Kendall goes on to elaborate on each of the 4 principles, but he also re-orders them in the order in which they should be applied when attempting to implement scalability in a system that perhaps has none already. We need to remember that scalability isn’t finite and that we need to ensure we work towards a scalability goal that makes sense for our application and it’s demands.
Kendall first introduces us to our own system’s architecture. All systems have this architecture he says…! Must admit, it’s a fairly popular one:
![arch1]( "arch1")
Kendall then talks about the principles we should apply, and the order in which we should apply them to an existing system in order to add scalability.
The first principle to add to a system is Caching. Caching is almost always the easiest to start with to introduce some scalability in a system/application that needs it. Caching is saving or storing the results of earlier work so that it can be reused at some later point in time. After all, the very best performing queries are those ones that never have to be run! Sometimes, caching alone can prevent around 99% of processing that really needn’t be done (i.e. a request for a specific webpage may well serve up the same page content over a long period of time, thus multiple requests within that time-scale can serve up the cached content). Caching should be applied in front of everything that is time consuming and it’s easiest to apply in a left-to-right order (working from adding a cache in front of the web server, through to adding one in front of the application server, then finally the database server).
Once in place, the caches can use very simple strategies, as these can be incredibly effective despite their simplicity. Microsoft’s Entity Framework uses a strategy that removes all cached entries as soon as a user commits a write (add/update/delete) to the database. Whilst on the surface this may seem excessive to eradicate all of the cache, it’s really not as in the vast majority of systems, reads from the database outnumber writes by an order of magnitude. For this reason, the cache is still incredibly effective and is still extensively re-used in real-world usage. We’re reminded that applications ask lots of repeated questions. Stateless applications even more so, but the answers to these questions rarely change. Authorative information, such as the logged on user’s name, is expensive to repeatedly query for as it’s required so often. Such information is the prime candidate to be cached.
An interesting point that Kendall makes here is to question the conventional wisdom that “the fewest lines of code is the fastest”. He says that very often, that’s not really the case as very few lines of code in a method that is doing a lot of work implies that much of your processing is being off-loaded to other methods or classes that are doing your work for you. This can often slow things down, especially if those other methods and/or classes are not specifically built to utilise cached data. Very often, having more lines of code in a method can actually be the faster approach as your method is in total control of all of the processing work that needs to be done. You’re doing all of the work yourself and so can ensure that the processing uses your newly cached data rather than expecting to have to read (or re-read it) from disk or database.
Distribution is the next thing to tackle after Caching. Distribution is spreading the load around multiple servers and having many things doing your work for you rather than just one. It’s important to note that the less state that’s held within your system, the better (and wider) you can distribute the load. If we think of session state in a web application, such state will often prevent us from being able to fulfil user requests by any one of many different webservers. We’re often in a position where we’ll require at least “Server Affinity” (also known as “sticky sessions”) to ensure that each specific user’s requests are always fulfilled by the same server in a given session. Asynchronous code can really help here as it means that processing can be offloaded to other servers to be run in the background whilst the processing of the main work can continue to be performed in the foreground without having to wait for the response from the background processes.
Distribution is hardest when it comes to the database. Databases, and indeed other forms of storage, are fundamentally state and scaling state is very difficult. This is primarily due to the need to keep that state consistent across it’s distributed load. This is the same consistency, or the requirement of consistency, that can hinder all manner of scalability and is one of the core principles. One technique of scaling your storage layer is to use something called “Partitioned Storage Zones”. These are similar to the server affinity (or sticky sessions) used on the web server when state needs to be maintained except that storage partitioning is usually more permanent. We could have 5 separate database servers and split out (for example) 50 customers across those 5 database servers with 10 customers on each server. We don’t need to synchronize the servers as any single given customer will only ever use the one server to which they’ve been permanently assigned.
After distribution comes Asynchronicity. Asynchronicity (or Async for short) is always the hardest to implement and so is the last one to be attempted in order to provide scalability. Async is the decoupling of operations to ensure that the minimum amount of work is performed within the “critical path” of the system. The critical path is the processing that occurs to fulfil a user’s request end-to-end. A user request to a web server for a given resource will require processing of the request, retrieval and processing of data before returning to the user. If the retrieval and processing of data requires significant and time-consuming computation, it would be better if the user was not “held up” whilst waiting for the computation to complete, but for the response to be sent to the user in a more expedient fashion, with the results of the intensive computation delivered to the user at a later point in time. Work should always be “queued” in this manner so that load is smoothed out across all servers and applications within the system.
One interesting Async technique, which is used by Amazon for their “recommendation” engine, is “Speculative Execution”. This is some asynchronous processing that happens even though the user may never have explicitly requested such processing or may never even be around to see the results of such processing. This is a perfectly legitimate approach and, whilst seemingly contrary to the notion of not doing any work unless you absolutely have to, “speculative execution” can actually yield performance gains. It’s always done asynchronously so it’s never blocking the critical path of work being performed, and if the user does eventually require the results of the speculative execution, it’ll be pre-computed and cached so that it can be delivered to the user incredibly quickly. Another good async technique is “scheduled requests”. These are simply specific requests from the user for some computation work to be done, but the request is queued and the processing is performed at some later point in time. Some good examples of these techniques are an intensive report generation request from the user that will have it’s results available later, or a “nightly process” that runs to compute some daily total figures (for example, the day’s financial trading figures). When requested the next day, the previous day’s figures do not need to be computed in real-time at all and the system can simply retrieve the results from cache or persistent storage. This obviously improves the user’s perception of the overall speed of the system. Amazon uses an interesting trick that actually goes against async in that they actually “wait” for an order’s email confirmation to be sent before displaying the order confirmation web page to the user. It’s one of only a few areas of Amazon’s site that specifically isn’t async and is very intentionally done this way as the user’s perception of an order being truly finalized is of receiving the confirmation email in their inbox!
Kendall next talks about the final principle, which of the 4 principles is the one that actually prevents scalability, or at least complicates it significantly. It’s the principle of Consistency. Consistency is the degree to which all parties within the system observe some state that exists within the system at the same time. Of course, the other principles of distribution and asynchronicity that help to provide scalability will directly impact the consistency of a system. With this in mind, we need to recognize that scalability and scaling is very much about compromise.
There are numerous consistency challenges when scaling a system. Singleton data structures (such as a numbering system that must remain contiguous) are particularly challenging as having multiple separate parts of a system that can generate the next number in sequence would require locking and synchronicity around the number generation in order to prevent the same number being used twice. Kendall also talks about state that can be held at two separate endpoints of a process, such as a layer that reads some data from a database, and how this must be shared consistently – changes to the database after the data has been read must ideally be communicated to the layer that has previously read the data to be informed of the change. Within the database context, this consistency extends to ensuring multiple database servers are kept consistent in the data that they hold and queries across partitioned datasets must be kept in sync. All of these consistency challenges will cause compromise with the system, however, consistency can be achieved if the approach by the other 3 principles (Caching, Distribution & Async) are themselves implemented in a consistent manner and work towards the same goals.
Finally, Kendall discusses how we can actually implement all of these concepts within a real-world system. The key to this is to test your existing system and gather as many timings and metrics as you possibly can. Remember, scaling is about setting a realistic target that makes sense for your application. Once armed with metrics and diagnostic data, we can set specific targets that our scalability must reach. This could be something like, “all web pages must return to the user within 500ms”. You would then start to implement, working from left to right within your architecture, and implementing the principles in the order of simplicity and which will provide the biggest return on investment. Caching first, then Distribution, finally Async. But, importantly, when you hit your pre-defined target, you stop. You’re done.
![20131012_115812]( "20131012_115812")
After another coffee break back in the main hall, during which time I was able to browse through the various stalls set up by the conference’s numerous sponsors, chat with some of the folks running those stalls, and even grab myself some of the swag that was spread around, it was time for the final session before lunch. This one was Matthew Steeples’“You’ve Got Your Compiler In My Service”.
Matthew’s talk was about the functionality and features that the upcoming Microsoft Roslyn project will offer to .NET developers. Roslyn is a “compiler-as-a-service”. This means that the C# compiler offered by Roslyn will be available to be interacted with via other C# code. Traditionally, compilers – and the existing C# compiler is no exception – are effectively “black boxes” and operate in one direction only. Raw source code is fed in at one end, and after “magic” happening in the middle, compiled executable binary code came out from the other end. In the case of the C# compiler, it’s actually IL code that gets output, ready to be JIT’ed by the .NET runtime. But once that IL is output, there’s really no simple way to return from the IL back to the original source code. Roslyn will change that.
Roslyn represents a deconstruction of the existing C# compiler. It’s exposes all of the compiler’s functionality publically allowing a developer to use Roslyn to construct new C# code with C# code! Traditional compilers will follow a series of steps to convert the raw text-based source code into something that the compiler can understand in order to convert it into working machine code. These steps can vary from one compiler to another, but generally consist of a step to first breakdown the text into individual words and characters that can be further processed. This step is known as “parsing”. Next, the parsed text must be examined for language keywords that the compiler understands as being part of the language, as well as user-defined variable names and other tokens. This is known as “lexical analysis”. This is followed by “syntax analysis”, which is the understanding of (and verification against) the syntactical rules of the language. Next comes the “semantic analysis” which is the checking of the semantics of the languages expression (for example, ensuring that the expression with an if statement’s condition evaluates to a boolean). Finally, after all of this analysis, “code generation” can take place.
Roslyn, on the other hand, takes a different approach, and effectively turns the compiler of both the C# and VB languages into a large object model, exposing an API that programmers can easily interact with (For example: An object called “CatchClause” exists within the Roslyn.Compiler namespace that effectively represents the “catch” statement from within the try..catch block).
Creating code via Roslyn is achieved by creating a top-level object known as a Syntax Tree. Syntax Trees contain a vast hierarchy of child objects, literally as a tree data structure and usually contain multiple Compilation Units (a compilation unit is a single class or “module” of code). Each compilation unit, in turn, contains further objects and nodes that represent (for example) a complete C# class, starting with the class declaration itself including its scope and modifiers, drilling down the the methods (and their scoping and modifiers) and ultimately the individual lines of code contained within. These syntax trees ultimately represent an entire C# (or VB!) program and can either be declared and created within other C# code, or parsed from raw text. Specifically, Syntax Trees have three important attributes. They represent all of the source code in full fidelity meaning every keyword, every variable name, every operator. In fact, they’ll represent everything right down to the whitespace. The second important attribute of a Syntax Tree is that, due to the first attribute, they’re completely reversible. This means that code parsed from a raw text file into the SyntaxTree object model, is completely reversible back to the raw text source code. The third and final attribute is that of immutability. Once created, Syntax Trees cannot be changed. This means they’re completely thread-safe.
Syntax Trees break down all source code into only three types of object. Nodes, Tokens and Trivia. Nodes are syntactic constructs of the language like declarations, statements, clauses and expressions. Nodes generally also act as parent objects for other child objects and nodes within the Syntax Tree. Tokens are the individual language grammar keywords but can also be identifiers, literals and punctuation. Tokens have properties that represent (for example) their type (a token representing a string literal in code will have a property that represents the fact that the literal is of type string) as well as other meta-data for the token, but tokens can never be parents of other objects within the Syntax Tree. Finally, trivia, is everything else within the source code and are primarily concerned with largely insignificant text such as whitespace, comments, pre-processor directives etc.
The following bit of C# code shows how we can use Roslyn to parse a literal text representation of a simple “Hello World” application:
var tree = SyntaxTree.ParseText(@"
using System;
namespace HelloRoslyn
{
class Program
{
static void Main(string[] args)
{
Console.WriteLine(""Hello World"");
}
}
}
");Once this code has been executed, the tree variable will hold a complete syntax tree that represents the entire program as defined in the string literal. Once created, tree variable’s syntax tree can be executed (i.e. the “Hello World” program can be run), it can be turned into IL (Intermediate Language), or turned back into the same source code!
The following C# code is the equivalent of the code above, except that here we’re not just parsing from the raw source code text, we’re actually creating and building up the syntax tree by hand using the built-in Roslyn objects that represent the various facets of the C# language:
using System;
using Roslyn.Compilers.CSharp;
namespace HelloRoslyn
{
class Program
{
static void Main()
{
string program = Syntax.CompilationUnit(
usings: Syntax.List(Syntax.UsingDirective(name: Syntax.ParseName("System"))),
members: Syntax.List<MemberDeclarationSyntax>(
Syntax.NamespaceDeclaration(
name: Syntax.ParseName("HelloRoslyn"),
members: Syntax.List<MemberDeclarationSyntax>(
Syntax.ClassDeclaration(
identifier: Syntax.Identifier("Program"),
members: Syntax.List<MemberDeclarationSyntax>(
Syntax.MethodDeclaration(
returnType: Syntax.PredefinedType(Syntax.Token(SyntaxKind.VoidKeyword)),
modifiers: Syntax.TokenList(Syntax.Token(SyntaxKind.StaticKeyword)),
identifier: Syntax.ParseToken("Main"),
parameterList: Syntax.ParameterList(),
bodyOpt: Syntax.Block(
statements: Syntax.List<StatementSyntax>(
Syntax.ExpressionStatement(
Syntax.InvocationExpression(
Syntax.MemberAccessExpression(
kind: SyntaxKind.MemberAccessExpression,
expression: Syntax.IdentifierName("Console"),
name: Syntax.IdentifierName("WriteLine"),
operatorToken: Syntax.Token(SyntaxKind.DotToken)),
Syntax.ArgumentList(
arguments: Syntax.SeparatedList(
Syntax.Argument(
expression: Syntax.LiteralExpression(
kind: SyntaxKind.StringLiteralExpression,
token: Syntax.Literal("\"Hello world\"", "Hello world")
)
)
)
)
)
)
)
)
)
)
)
)
)
));
}
}
}Phew! That’s quite some code there to create the Syntax Tree for a simple “Hello World” console application! Although Roslyn can be quite verbose, and building up syntax trees in code can be incredibly cumbersome, the functionality offered by Roslyn is incredibly powerful. So, why on earth would we need this kind of functionality?
Well, one current simple usage of Roslyn is to create a “plug-in” for the Visual Studio IDE. This plug-in can interact with the source code editor window to dynamically interrogate the current user edited source and perform alterations. These could be refactoring and code generation, similar to the functionality that’s currently offered by the ReSharper or JustCode tools. Of course, those tools can perform a myriad of interactions with the code editor windows of the Visual Studio IDE, however they probably currently have to implement their own parsing and translation engine over the code that’s edited by the user. Roslyn makes this incredibly easy to accomplish within your own plug-in utilities. Other usages of Roslyn include the ability for an application to dynamically “inject” code into itself. At this point Matthew shows us a demo of a simple Windows Forms application with a simple textbox on the form. He proceeds to type out a C# class declaration into the form’s textbox. He ensures that this class declaration implements a specific interface that the Windows Forms application already knows about. Once entered, the running WinForms app can take the raw text from the textbox, and using Roslyn, convert this text into a Syntax Tree. This Syntax Tree can then be invoked as actual code, as though it were simply a part of the running application. In this case, Matthew’s example has an interface the defines a single “GetDate” method that returns a string. Matthew types his class into the WinForms textbox and returns the current Date and Time in the current locale. This is then executed and invoked by the running application and the result is displayed on the Form. Matthew then shows how the code within the textbox can be easily altered to return the same Date and Time but in the UTC time zone. One click of a button and the new code is parsed, interpreted and invoked using Roslyn to immediately show the new result on the Windows Form.
Roslyn, as a new C# compiler, is itself written in C#. Some of the current complexities with the Roslyn toolkit is that the current C# compiler, which is written in C++, doesn’t entirely conform to the C# specification. This makes it fairly tricky to reproduce the compiler in accordance with the C# specification, and the current dilemma is whether Roslyn should embrace the C# specification entirely (thus making it slightly incompatible with the existing C# compiler) or whether to faithfully reproduce the existing C# compiler’s behaviour even though it doesn’t strictly conform to the specification.
Matthew wraps up his talk with a summary of the Roslyn compiler’s abilities, which are extensive and powerful despite it still only being a CTP (Community Technology Preview) of the final functionality, and offers the link to the area on MSDN where you can download Roslyn and learn all about this new “compiler-as-a-service” which will, eventually, become a standard part of Visual Studio and C# (and VB!) development in general.
![20131012_131316]( "20131012_131316")
After Matthew’s talk it was time for lunch. Lunch at DDD North this year was just as great as last year. We all wandered off to the main entrance hall where the staff of the venue were frantically trying to put out as many bags with a fantastic variety of sandwiches, fruit and chocolate bars as they could before the hoards of hungry developers came along to whisk them away. The catering really was excellent as it was possible to pre-order specific lunches for those with specific dietary requirements, as well as ensuring there was a wide range of vegetarian options available too.
I examined the available options, which took a little while as I, too, have specific dietary requirements in that I’m a fussy bugger as I don’t like mayonnaise! It took a little while to find a sandwich that didn’t come loaded with mayo, but after only a short while, I found one. And a lovely sandwich it was too! Along with my crisps, chocolate and fruit, I found a place to sit down and quietly eat my lunch whilst contemplating the quantity and quality of the information I’d learned so far.
![20131012_131448]( "20131012_131448")
During the lunch break, there were a number of “grok talks” taking place in the largest of the lecture theatres that were being used for the conference (this was the same theatre where Kendall Miller had given his talk earlier). Whilst I always try to take in at least one or two (if not all) of the grok talks that take place during the DDD (and other) conferences, unfortunately on this occasion I was too busy stuffing my face, wandering around the main hall and browsing the many sponsors stands as well as chatting away to some old and new friends that I’d met up with there. By the time I realised the grok talks were talking place, it was too late to attend.
After an lovely lunch, it was time for the first of the afternoon’s sessions, one of two remaining in the day. This session saw us gathering in one of the lecture halls only to find that the projector had decided to stop working. The DDD volunteers tried frantically to get the thing working again, but ultimately, it proved to be a futile endeavour. Eventually, we were told to head across the campus to the other building that was being used for the conference and to a “spare room”, apparently reserved for such an eventuality.
After a brisk, but slightly soggy walk across the campus forecourt (the weather at this point was fairly miserable!) we entered the David Goldman Informatics Centre and trundled our way to the spare room. We quickly sat ourselves down and the speaker quickly set himself up as we were now running slightly behind schedule. So, without further ado, we kicked off the first afternoon session which was MongoDB For C# Developers, given by Simon Elliston Ball.
Simon’s talk was an introduction to the MongoDB No-SQL database and specifically how we as C# developers can utilise the functionality provided by MongoDB. Mongo is a document-oriented database and stores it’s data as a collection of key/value pairs within a document. These documents are then stored together as collections within a database. A document can be thought of as a single row in a RDBMS database table, and the collection of documents can be thought of as the table itself, finally multiple collections are grouped together as a database, however, this analogy isn’t strictly correct. This is very different from the relational structure you can can find in today’s popular database systems such as Microsoft’s SQL Server, Oracle, MySQL & IBM’s DB2 to name just a few of them. Document oriented databases usually store their data represented in JSON format, and in the case of MongoDB, it uses a flavour of JSON known as BSON which is Binary JSON. An example JSON document could something as simple as:
{
"firstName": "John",
"lastName": "Smith",
"age": 25
}
However, the same document could be somewhat more complex, like this:
{
"firstName": "John",
"lastName": "Smith",
"age": 25,
"address": {
"streetAddress": "21 2nd Street",
"city": "New York",
"state": "NY",
"postalCode": 10021
},
"phoneNumbers": [
{
"type": "home",
"number": "212 555-1234"
},
{
"type": "fax",
"number": "646 555-4567"
}
]
}
This gives us an ability that RDBMS database don’t have and that’s the ability to nest multiple values for a single “key” in a single document. RDBMS’s would require multiple tables joined together by a foreign key in order to represent this kind of data structure, but for document-oriented databases, this is fairly standard. Furthermore, MongoDB is a schema-less database which means that documents within the same collection don’t even need to have the same structure. We could take our two JSON examples from above and safely store them within the exact same collection in the same database! Of course, we have to be careful when we’re reading them back out again, especially if we’re trying to deserialize the JSON into a C# class. Importantly, as MongoDB uses BSON rather than JSON, it can offer strong typing of the values that are assigned to keys. Within the .NET world, the MongoDB client framework allows decorating POCO classes with annotations that will aid in the mapping between the .NET data types and the BSON data types.
So, given this incredible flexibility of a document-oriented database, what are the downsides? Well, there are no joins within MongoDB. This means we can’t join documents (or records) from one collection with another as you could do with different tables within a RDBMS system. If your data is very highly relational, a document-oriented database is probably not the right choice, but but a lot of data structures can be represented by documents. MongoDB allows an individual document to be up to 16MB in size, and given that we can have multiple values for a given key within the document, we can probably represent an average hierarchical data/object graph using a single document.
Simon makes a comparison between MongoDB and another popular document-oriented database, RavenDB. Simon highlights how RavenDB, being the newer document-oriented database offers ACID-compliance and transactions that stretch over multi-documents. He states that MongoDB’s transactions are only per document. MongoDB’s replication supports a Master-Slave configuration, but Raven’s replication is Master-Master and that MongoDB supports being used from within many different languages with native client libraries for JavaScript, Java, Python, Ruby, .NET, Scala, Erlang and many more. RavenDB is effectively .NET only (at least as far as native client libraries go) however RavenDB does offer a REST-based API and is thus callable from any language that can reach a URI.
Simon continues by telling us about how we can get to play with MongoDB as C# developers. The native C# MongoDB client library is distributed as a NuGet package which is easily installable from within any Visual Studio project. The NuGet package contains the client library which enables easy access to a MongoDB Server instance from .NET as well as containing types that provides the aforementioned annotations to decorate your POCO classes to enable easy mapping of your .NET types to the MongoDB BSON types. Once installed, accessing some data within a MongoDB database can be performed quite easily:
var client = new MongoClient(connectionString);
var server = client.GetServer();
var database = server.GetDatabase("MyDatabase");
var collection = database.GetCollection("MyCollection");
One of the nice things with MongoDB is that we don’t have to worry about explicitly closing or disposing of the resources that we’ve acquired with the above code. Once these objects fall out of scope, the MongoDB client library will automatically close the database connection and release the connection back to the connection pool. Of course, this can be done explicitly too, but it’s nice to know that failure to do so won’t leak resources.
Simon explains that all of Mongo’s operations are as “lazy” as they possibly can be, thus in the code above, we’re only going to hit the database to retrieve the documents from “MyCollection” once we start iterating over the collection variable. The code above shows a simple query that simply returns all of the documents within a collection. We can compose more complex queries in a number of ways, but perhaps the way that will be most familiar to C# developers is with LINQ-style query:
var readQuery = Query<Person>.EQ(p => p.PersonID == 2);
Person thePerson = personCollection.FindOne(readQuery);
This style of query allows retrieving a strongly-typed “Person” object using a Lambda expression as the argument to the EQ function of the Query object. The resulting configured query object is then passed to the .FindOne method of the collection to allow retrieval of one specific Person object based upon the predicate of the query. The newer versions of MongoDB support most of the available LINQ operators and expressions and collections can easily be exposed to the client code as an IQueryable:
var query =
from person in personCollection.AsQueryable()
where person.LastName == "Smith"
select person;
foreach (var person in query)
// ....[snip]....
We can also create cursors to iterate over an entire collection of documents using the MongoCursor object:
MongoCursor<Person> personCursor = personCollection.FindAll();
personCursor.Skip = 100;
personCursor.Limit = 10;
foreach(var person in personCursor)
// .....[snip]....
Simon further explains how Mongo’s Update operations are trivially simple to perform too, often merely requiring the setting of the object properties, and calling the .Save method against the collection, passing in the updated object:
person.LastName = "Smith";
personCollection.Save(person);
Simon tells us that MongoDB supports something known as “write concerns”. This mechanism allows us to return control to our code only after the master database and all slave servers have been successfully updated with our changes. Without these write concerns, control will return to our code before the changes have persisted across all database servers, returning control to our code after only the master server has been updated whilst the slaves continue to update asynchronously in the background. Unlike most RDBMS systems, UPDATEs to MongoDB will, by default, only ever affect one document, and this is usually the first document that the update query finds. If you wish to perform a multi document update, you must explicitly tell MongoDB to perform such an update.
As stated earlier, documents are limited to 16MB in size however MongoDB provides a way to store a large “blob” of data (for example, if you needed to store a video file) using a technology called GridFS. GridFS sits on top of MongoDB and allows you to store a large amount of binary data in “chunks”, even if this data exceeds the 16MB document limit. Large files are committed to the database with a simple command such as:
database.GridFS.Upload(filestream, "mybigvideo.wmv").
This will upload the large video file to the database, which will break down the file into many small chunks. Querying and retrieving this data is as simple as retrieving a normal document, and the database and the database driver are responsible for re-combining all of the chunks of the file to allow you to retrieve the file correctly with no further work required on the developers behalf.
MongoDB supports GeoSpatial functionality which allows querying location and geographic data for results that are “near” or within a certain distance of a specific geographic location:
database = server.GetDatabase("MyDatabase");
var collection = database.GetCollection("MyCollection");
var query = Query.EQ("Landmarks.LandMarkType", new BsonString("Statue"));
double lon = 54.9117468;
double lat = -1.3737675;
var earthRadius = 6378.0; // km
var rangeInKm = 100.0; // km
var options = GeoNearOptions
.SetMaxDistance(rangeInKm / earthRadius /* to radians */)
.SetSpherical(true);
var results = collection.GeoNear(query, lat, lon, 10, options);The above code sample would find all documents within the Landmarks collection that have a LandMarkType of Statue and which are also within 10 kilometres of our defined Latitude and Longitude position.
MongoDB also supports the ability to query and transform data using a “MapReduce” algorithm. MapReduce is a very powerful way in which a large set of data can be filtered, sorted (the “map” part) and summarised (the “reduce” part) using hand-crafted map and reduce functions. These functions are written in JavaScript and are interpreted by the MongoDB database engine, which contains a full JavaScript interpreter and execution engine. Using this MapReduce mechanism, a developer can perform many of the same kinds of complicated “grouping” and aggregation queries that RDBMS systems perform. For example, the following sample query would iterate over the collection within the database and sum the count of documents, grouped together by the key:
var map =
"function() {" +
" for (var key in this) {" +
" emit(key, { count : 1 });" +
" }" +
"}";
var reduce =
"function(key, emits) {" +
" total = 0;" +
" for (var i in emits) {" +
" total += emits[i].count;" +
" }" +
" return { count : total };" +
"}";
var mr = collection.MapReduce(map, reduce);Finally, Simon wraps up his talk by telling us about a Glimpse plug-in that he’s authored himself which can greatly help to understand exactly what is going on between the client-side code that talks to the MongoDB client library and the actual requests that are sent to the server, as well as being able to inspect the resulting responses.
After a short trip back across the campus to grab a coffee in the other building that contains the main entrance hall, as well as an array of crisps, chocolate and fruit (these were the “left-overs” from the lunch bags of earlier in the afternoon!) to keep us developers well fed and watered, I trundled back across the campus to the same David Goldman Informatics Centre building I’d been in previously to watch the final session of the day. This session was another F# session (F# was a popular subject this year) called “You’ve Learned The Basics Of F#, What’s Next?” and given by Ian Russell.
The basis of Ian’s talk was to examine two specific features of F# that Ian thought offered a fantastic amount of productivity over other languages, and especially over other .NET languages. These two features were Type Providers and the MailboxProcessor.
First up, Ian takes a look at Type Providers. First introduced in F# 3.0, Ian starts by explaining that Type Providers provide type inference over third party data. What this essentially means is that a type provider for something like (say) a database can give the F# IDE type inference over what types you’ll be working with from the database as soon as you’ve typed in the line of code that specifies the connection string! Take a look at the sample code below:
open System.Linq
open Microsoft.FSharp.Data.TypeProviders
type SqlConnection =
SqlDataConnection<ConnectionString = @"Data Source=.\sql2008r2;Initial Catalog=chinook;Integrated Security=True">
let db = SqlConnection.GetDataContext()
let table =
query { for r in db.Artist do
select r }
The really important line of code from the sample above is this one:
query { for r in db.Artist doNote the db.Artist part. There’s no type within the code that defines what artist is. The FSharp Data Type Provider has asynchronously and in the background of the IDE quietly opened the SQL Server connection as soon as the connection string was specified in the code. It’s examined the database referred to in the connection string and it has automatically generated the types base upon the tables and their columns within the database!
Ian highlights the fact that F#’s SQL Server type provider requires to mapping code to go from F# type in code to SQL Server entities. The equivalent C# code using Entity Framework would be significantly more verbose.
Ian also shows how it’s easy to take the “raw” types captured by the type provider and wrap them up into a nicer pattern, in this case a repository:
type ChinookRepository () =
member x.GetArtists () =
use context = SqlConnection.GetDataContext()
query { for g in context.Artist do
select g }
|> Seq.toList
let artists =
ChinookRepository().GetArtists()
Ian explains how F# supports a “query” syntax that is very similar (but much better than) C# and LINQ’s query syntax, ie:
from x in y select new { TheID = x.Id, TheName = x.FirstName }
The reason that F#’s query syntax is far superior is that F# allow you to define your own query syntax keywords. For example, you can define your own keyword, “top” which would implement “Select Top X” style functionality. This effectively allows you to define your own DSL (Domain-Specific Language) within F#!
After the data type provider, Ian goes on to show us how the same functionality of early-binding and type inference to a third-party data source works equally well with local CSV data in a file. He shares the following code with us:
open FSharp.Data
let csv = new CsvProvider<"500-uk.csv">()
let data =
csv.Data
|> Seq.iter (fun t -> printf "%s %s\n" t.``First Name`` t.``Last Name``)
This code shows how you can easily express the columns from the CSV that you wish to work with by simply specifying the column name as a property of the type. The actual type of this data is inferred from the data itself (numeric, string etc.) however, you can always explicitly specify the types should you desire. Ian also shows how the exact same mechanism can even pull down data from an internet URI and infer strong types against it:
open FSharp.Data
let data = WorldBankData.GetDataContext()
data.Countries.``United Kingdom``.Indicators.``Central government debt, total (% of GDP)``
|> Seq.maxBy fst
The above code shows how simple and easy it is to consume data from the World Bank’s online data store in a strong, type inferred way.
This is all made possible thanks to the FSharp.Data library which is available as a NuGet package and is fully open-source and available on GitHub. This library has the type providers for the World Bank and Freebase online data sources already built-in along with generic type providers for dealing with any CSV, JSON or XML file. Ian tells us about a type provider that’s currently being developed to generically work against any REST service and will type infer the required F# objects and properties all in real-time simply from reading the data retrieved by the REST service. Of course, you can create your own type providers to work with your own data sources in a strongly-typed, eagerly-inferred magical way!
After this quick lap around type providers, Ian moves on to show us another well used and very useful feature of F#, the MailboxProcessor. A MailboxProcessor is also sometimes known as an “Agent” (this name is frequently used in other functional languages) and effectively provides a stateless, dedicated message queue. The MailboxProcessor consists of a lightweight message queue (the mailbox) and a message handler (the processor). For code interacting with the MailboxProcessor, it’s all asynchronous, code can post messages to the message queue asynchronously (or synchronously if you prefer), however, internally the MailboxProcessor itself will only process it’s messages in a strictly synchronous manner and in a strict FIFO (First in, First Out) order, one message at a time. This helps to maintain consistency of the queue. Due to the MailboxProcessor exposing it’s messages asynchronously (but maintaining strict synchronicity internally), we don’t need to acquire locks when we’re dealing with the messages going in or coming out. So, why is the MailboxProcessor so useful?
Well, Ian shows us a sample chat application that consists of simply posting messages to a MailboxProcessor. The entire functionality of the chat application is contained within a single type/class:
type ChatMessage =
| GetContent of AsyncReplyChannel<string>
| SendMessage of string
let agent = Agent<_>.Start(fun agent ->
let rec loop messages = async {
// Pick next message from the mailbox
let! msg = agent.Receive()
match msg with
| SendMessage msg ->
// Add message to the list & continue
return! loop (msg :: messages)
| GetContent reply ->
// Generate HTML with messages
let sb = new StringBuilder()
sb.Append("<ul>\n") |> ignore
for msg in messages do
sb.AppendFormat(" <li>{0}</li>\n", msg) |> ignore
sb.Append("</ul>") |> ignore
// Send it back as the reply
reply.Reply(sb.ToString())
return! loop messages }
loop [] )
agent.Post(SendMessage "Welcome to F# chat implemented using agents!")
agent.Post(SendMessage "This is my second message to this chat room...")
agent.PostAndReply(GetContent)
The code above creates a single type (ChatRoom) that encapsulates all of the functionality required to “post” and “receive” messages from a MailboxProcessor – effectively mimicking the back and forth chat messages of a chat room. Further code shows how this can be exposed over a webpage by utilising a HttpListener with another type:
let root = @"C:\Temp\Demo.ChatServer\"
let cts = new CancellationTokenSource()
HttpListener.Start
("http://localhost:8082/", (fun (request, response) -> async {
match request.Url.LocalPath with
| "/post" ->
// Send message to the chat room
room.SendMessage(request.InputString)
response.Reply("OK")
| "/chat" ->
// Get messages from the chat room (asynchronously!)
let! text = room.AsyncGetContent()
response.Reply(text)
| s ->
// Handle an ordinary file request
let file =
root + (if s = "/" then "chat.html" else s.ToLower())
if File.Exists(file) then
let typ = contentTypes.[Path.GetExtension(file)]
response.Reply(typ, File.ReadAllBytes(file))
else
response.Reply(sprintf "File not found: %s" file) }),
cts.Token)
cts.Cancel()
This code shows how an F# type can be written to create a server which listens on a specific HTTP address and port and accepts messages to URL endpoints as part of the HTTP payload. These messages are stored within the internal MailboxProcessor and subsequently retrieved to display on the webpage. We can imagine two (or more) separate users with the same webpage open in their browser’s and each person’s messages getting both echoed back to themselves as well as being shown on each other user’s browsers.
Ian has actually coded up such a web application, with a slightly nicer UI, and ends off his demonstrations of the power of the MailboxProcessor by firing up two separate browsers on the same machine (mimicking two different users) and showing how chat messages from one user instantly and easily appear on the other user’s browser. Amazingly, there’s a minimum of JavaScript involved in this demo, and even the back-end code that maintains the list of users and the list of messages is no more than a few screens full!
Ian wrapped up his talk by recapping the power of both Type Providers and the MailboxProcessor, and how both techniques build upon your existing F# knowledge and make the consumption and processing of data incredibly easy.
![20131012_170122]( "20131012_170122")
After Ian’s talk it was time for the final announcements of the day and the prize give away! We all made our way back to the main building, and to the largest room, the Tom Cowie Lecture Theatre.
After a short while all of the DDD North attendees along with the speakers, and sponsors had assembled in the lecture theatre. The main organiser of DDD North, Andy Westgarth, gave a short speech thanking the attendees and the sponsors. I’d like to offer my thanks to the sponsors here also, because as Andy said, if it wasn’t for them there wouldn’t be a DDD North. After Andy’s short speech a number of the sponsors took to the microphone to both offer their thanks to the organisers of the event and to give away some prizes! One of the first was Rachel Hawley who had been manning the Telerik stand all day, and who lead the call for applause and thanks for Andy and his team. After Rachel had given away a prize, Steve from Tinamous was up to thank everyone involved and to give away more prizes. After Steve had given away his prize, Andy mentioned that Steve had generously put some money behind the bar for the after event Geek Dinner that was taking place later in the evening and that everyone’s first drink was on him! Thanks Steve!
Steve was followed by representatives from the NDC Conference, a representative from Sage and various other speakers and sponsor staff, all giving a quick speech to thank to organisers and to state how much they’ve enjoyed sponsoring such a great community event as DDD North.
Of course, each of these sponsors had prizes to give away. Each time, Andy would offer a bag of our feedback forms which we’d submitted at the end of each session and the sponsor would draw out a winning entry. As is usual for me, I didn’t win anything, however, lots of people did and there were some great prizes on offer, including a stack of various books, some Camtasia software licenses along with a complete copy of Visual Studio Premium with MSDN!
After a final closing speech by Andy thanking everyone again and telling us that, although there’s no confirmed date or location for the next DDD North, it will definitely happen and it’ll be in a North-West location, as the intention is to alternate the location each time between a North East location and one in the North West in order to cover the entire “north” of England.
![20131012_175549]( "20131012_175549")
And with that, another fantastic DDD North event was over… Except that it wasn’t. Not quite yet! Courtesy of Make It Sunderland and Sunderland Software City, they had agreed to host a “drinks reception” at the Sunderland Software City offices! The organisers of DDD North had laid on a free bus transfer service for the short ride from Sunderland University to the location of the Sunderland Software City offices closer to Sunderland city centre. Since I was in the car, I drove the short 10 minutes drive to the Sunderland Software City offices. Of course, being in the car meant that my drinking was severely limited.
Around 80 of the 300+ attendees from DDD North made the trip to the drinks reception and we we’re treated with a small bar with two hand-pulled ales from the Maxim Brewery. One was the famous Double Maxim and the other, Swedish Blonde. Two fine ales and they were free all night long, for as long as the cask lasted (or at least for the 2 hours that the drinks reception event lasted)!
Being a big fan of real ales, it was at this point that I was kicking myself for having brought the car with me to DDD North. I could have relatively easily taken the Metro train service from Newcastle to Sunderland, but alas, I was not to know this fantastic drinks reception would be so great or that there would be copious amounts of real-ale on offer. In hindsight though, it was probably for the best that my ability to drink the endless free ale was curtailed! :)
![20131012_175818]( "20131012_175818")
I made my way to a comfy seating area and was joined by Phil Trelford who had given the first talk of the day that I attended and who I had been chatting with off and on throughout the day, and also Sean Newham. Later we were joined by another guy who’s name I forget (sorry). We chatted about various things and had a really fun time. It was here that Phil showed us an F# Type Provider that himself and his friend had written in a moment of inspiration that mimics the old “Choose Your Own Adventure” style books from the 1980’s by offering up the entire story within the Visual Studio IDE!
Not only were we supplied with free drinks for the evening, we were also supplied with a seemingly endless amount of nibbles, Hors d'oeuvre and tiny desserts and cakes. These were brought to us with such an alarming frequency and never seemed to end! Not that I’m complaining… Oh no. They were delicious, but there was a real fear that the sheer amount of these lovely nibbles would ruin everyone’s appetite for the impending Geek Dinner.
There’s a tradition at DDD events to have a “geek dinner” after the event where attendees that wish to hang around can all go to a local restaurant and have their evening dinner together. I’d never been to one of these geek dinner’s before, but on this occasion, I was able to attend. Andy had selected a Chinese buffet restaurant, the Panda Oriental Buffet, mainly because it was a very short walk from the Sunderland Software City offices, and also presumably because they use Windows Azure to host their website!
After the excellent drinks reception was finished, we all wandered along the high street in Sunderland city centre to the restaurant. It took a little while for us all to be seated, but we were all eventually in and were able to enjoy some nice Chinese food and continue to chat with fellow geeks and conference attendees. I managed to speak with a few new faces, some guys who worked at Sage in Newcastle, some guys who worked at Black Marble in Yorkshire and a few other guys who’d travelled from Leeds.
After the meal, and with a full belly, I bid goodbye to my fellow geeks and set off back towards my car which I’d left parked outside the Sunderland Software City offices to head back to what was my home for that weekend, my in-law’s place in Newcastle. A relatively short drive (approx. 30-40 minutes) away.
And so ended another great DDD event. DDD North 2013 was superb. The talks and the speakers were superb, and Andy and his team of helpers had, once again, arranged a conference with superb organisation. So, many thanks to those involved in putting on this conference, and of course, thanks to the sponsors without whom there would be no conference. Here’s looking forward to another great DDD North in 2014. I can’t wait!
![]()
_thumb.png "MS(rgb)")
_1477_thumb.png "MCSD_2013(rgb)_1477")
_1509_thumb.png "MCSD_2013(rgb)_1509")
_thumb_1.jpg "image(4)")
_thumb_2.jpg "image(2)")
_thumb.jpg "image(7)")
_thumb.jpg "image(10)")
_thumb.jpg "image(9)")
_thumb.jpg "image (15)")
_thumb.jpg "image (19)")
_thumb.jpg "image (1)")
_thumb.jpg "image (3)")
Gary proceeds to explain that a Neural Network is really a weighted directed graph. This is a graph of nodes and the connections between those nodes. The connections are in a specific direction, from one node to another, and that same node can have a connection back to the originating node. Each connection has a “weight” or a probability. In the diagram to the left we can see that node A has a connection to node E and also a separate connection to node F. The “weight” of the connection to node F is 0.9 whilst the weight of the connection to node E is 0.1. This means there a 10% chance that a message or data coming from node A will be directed to node E and a 90% chance that a message coming from node A will be directed to node F. The overall combination of nodes and connections between the nodes overall gives us the Neural Network. 
_thumb.png "2016-06-07 21_42_25-I5xLz.png (766×432)")
