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  • Method parameters confusion

    - by elec
    Often time methods take more than 3 parameters which are all of the same type, eg. void mymethod (String param1, String param2, String param3) then it's very easy for the client to mix up the parameters orders, for instance inverting param1 and param2: mymethod (param2, param1, param3); ...which can be the cause of much time spent debugging what should be a trivial matter. Any tips on how to avoid this sort of mistake (apart from unit tests) ?

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  • How to make OSX application that just runs opens some file type and runs arbitrary Ruby code?

    - by taw
    It's trivial to make a program executable from shell - just put #!/usr/bin/ruby on top, chmod +x it and done. Unfortunately OSX won't let me associate file type with such scripts - it requires its .apps instead. This sort of distinction doesn't seem to exist on other operating systems. What's the simplest way of making such .app, which would merely execute some arbitrary Ruby code?

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  • How to calculate the latlng of a point a certain distance away from another?

    - by Rene Saarsoo
    To draw a circle on map I have a center GLatLng (A) and a radius (r) in meters. Here's a diagram: ----------- --/ \-- -/ \- / \ / \ / r \ | *-------------* \ A / B \ / \ / -\ /- --\ /-- ----------- How to calculate the GLatLng at position B? Assuming that r is parallel to the equator. Getting the radius when A and B is given is trivial using the GLatLng.distanceFrom() method - but doing it the other way around not so. Seems that I need to do some heavier math.

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  • kill a process in bash

    - by wyatt
    How do I kill a process which is running in bash - for example, suppose I open a file: $ gedit file.txt is there any way within the command prompt to close it? This example is fairly trivial, since I could just close the window, but it seems to come up a bit, particularly when I mistype commands. Also is there any way to escape an executable which is running? This probably has the same solution, but I thought I'd ask anyway. Thanks

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  • Type hinting for functions in Clojure

    - by mikera
    I'm trying to resolve a reflection warning in Clojure that seems to result from the lack of type inference on function return values that are normal Java objects. Trivial example code that demonstrates the issue: (set! *warn-on-reflection* true) (defn foo [#^Integer x] (+ 3 x)) (.equals (foo 2) (foo 2)) => Reflection warning, NO_SOURCE_PATH:10 - call to equals can't be resolved. true What is the best way to solve this? Can this be done with type hints?

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  • Ignoring certain chars globally

    - by shi kui
    Consider it that '_'s in a number doesn't change that number's value so 1000==1_000==10_00. The Problem: given numbers like 1_244_23 1412_2 1_1111 etc..., how could I decide whether certain number appears in that collection? For example: 1244_23 yes, 11_111 yes, 1412_1 no. How could using regex to solve this? I mean, if I could tell the regex engine just ignore these '_''s when matching then this problem becomes trivial? How could I do so?

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  • How to calculate a latlng on google map ceartain distance away from another?

    - by Rene Saarsoo
    To draw a circle on map I have a center GLatLng (A) and a radius (r) in meters. Here's a diagram: ----------- --/ \-- -/ \- / \ / \ / r \ | *-------------* \ A / B \ / \ / -\ /- --\ /-- ----------- How to calculate the GLatLng at position B? Assuming that r is parallel to the equator. Getting the radius when A and B is given is trivial using the GLatLng.distanceFrom() method - but doing it the other way around not so. Seems that I need to do some heavier math.

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  • Ptyhon date string to date object

    - by elif
    Hi all, How do I convert a string to a date object in python? The string would be: "24052010" (corresponding to the format: "%d%m%Y") I DON'T want a datetime object. I suspect that I'm asking a trivial question but I searched and couldn't find it neither on stackoverflow nor on google. Thank you, Elif

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  • using makefile targets to set build options

    - by leo grrr
    This is either trivial or runs counter to the philosophy of how make should be used, but I'd like to have a command line that reads as "make debug" rather than "make DEBUG=1". I tried creating a phony target called debug that did nothing except set the DEBUG variable, but then there was a difference between "make debug build" and "make build debug"--namely that in one case, the variable got set after the build happened. Is there a way to give certain targets precedence? Thanks for your help.

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  • How does Response.Redirect calculate the URL for "~/folder1/folder2/some.aspx"

    - by Chris Marisic
    This might sound like a trivial problem but for some reason it is not. Instead of needing to redirect Response.Redirect("~/folder1/folder2/some.aspx") I need the location as if it behaved like string navigatingUrl = Response.Redirect("~/folder1/folder2/some.aspx") Trying to replicate this I started with string navigatingUrl = new Uri(HttpContext.Current.Request.Url, new Uri("~/folder1/folder2/some.aspx", UriKind.Relative)); This instead generates "http://www.fullRequestUrl/~/folder1/folder2/some.aspx"

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  • How to get parallel behavior in Java Script ?

    - by Biswanath
    More or less I want to execute two functions in parallel. One way as I see is doing through SetTimeOut function. I have not completely gone through the ReactiveExentension, although it looks promising but may be overkill for my needs. Is there any framework which supports parallelism ? My use case is trivial, but I would like to know if anybody heavily needed parallelism in Java Script ? Thanks, Biswanath.

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  • Click() works in IE but not Firefox

    - by Tom Andrews
    I have code which is trivial but only works in IE not Firefox. $(document).ready(function(){ $('li#first').click(); }); I have also tried: document.getElementById('first').click(); But that doesn't work either. Is this an IE bug/feature or is click() not supported in the other browsers? Thanks in advance.

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  • WPF, databinding

    - by fsl
    Hi there In a given binding is it possible to specify the path on the source object? Seems like this could a void a lot of trivial converters.. Imagine this..: class foo { bool A int B } <ComboBox ItemsSource="ListOfFoos" SelectedItem="{Binding number, SourcePath=B}" />

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  • C++ How to copy text in string (from i.e. 8 letter to 12 letter)

    - by Alice90
    Hello This is not homework, I need this for my program :) I ask this question, because I searched for this in Google about 1 hour, and I don't find anything ready to run. I know that is trivial question, but if you will help me, you will make my day :) Question: How to copy text in string (from for example 8 letter to 12 letter) and send to other string? I have string: string s = "RunnersAreTheBestLovers"; and I want text from 8 letter to 17 letter in next string Alice90

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  • dropping characters from regular expression groups

    - by tcurdt
    The goal: I want to convert a number from the format "10.234,56" to "10234.56" Using this simple approach almost gets us there /([\d\.]+),(\d\d)/ => '\1.\2' The problem is that the first group of the match (of course) still contains the '.' character. So questions are: Is it possible to exclude a character from the group somehow? How would you solve this with a single regexp (I know this is a trivial problem when not using a single regexp)

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  • What do I need to be able to use Joomla as an online application development enbironment

    - by howdyworld
    I develop applications using PHP and MySQL. They're not trivial, though they're not too complex. As an example to that I have written an online application to enable football administrators manage the player, competition and judiciary processes. Separately to that I have used Joomla to create websites for small businesses. I'd like to be able to use Joomla as my online application development environment. Is there a way I can use Joomla for that?

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  • Premature-Optimization and Performance Anxiety

    - by James Michael Hare
    While writing my post analyzing the new .NET 4 ConcurrentDictionary class (here), I fell into one of the classic blunders that I myself always love to warn about.  After analyzing the differences of time between a Dictionary with locking versus the new ConcurrentDictionary class, I noted that the ConcurrentDictionary was faster with read-heavy multi-threaded operations.  Then, I made the classic blunder of thinking that because the original Dictionary with locking was faster for those write-heavy uses, it was the best choice for those types of tasks.  In short, I fell into the premature-optimization anti-pattern. Basically, the premature-optimization anti-pattern is when a developer is coding very early for a perceived (whether rightly-or-wrongly) performance gain and sacrificing good design and maintainability in the process.  At best, the performance gains are usually negligible and at worst, can either negatively impact performance, or can degrade maintainability so much that time to market suffers or the code becomes very fragile due to the complexity. Keep in mind the distinction above.  I'm not talking about valid performance decisions.  There are decisions one should make when designing and writing an application that are valid performance decisions.  Examples of this are knowing the best data structures for a given situation (Dictionary versus List, for example) and choosing performance algorithms (linear search vs. binary search).  But these in my mind are macro optimizations.  The error is not in deciding to use a better data structure or algorithm, the anti-pattern as stated above is when you attempt to over-optimize early on in such a way that it sacrifices maintainability. In my case, I was actually considering trading the safety and maintainability gains of the ConcurrentDictionary (no locking required) for a slight performance gain by using the Dictionary with locking.  This would have been a mistake as I would be trading maintainability (ConcurrentDictionary requires no locking which helps readability) and safety (ConcurrentDictionary is safe for iteration even while being modified and you don't risk the developer locking incorrectly) -- and I fell for it even when I knew to watch out for it.  I think in my case, and it may be true for others as well, a large part of it was due to the time I was trained as a developer.  I began college in in the 90s when C and C++ was king and hardware speed and memory were still relatively priceless commodities and not to be squandered.  In those days, using a long instead of a short could waste precious resources, and as such, we were taught to try to minimize space and favor performance.  This is why in many cases such early code-bases were very hard to maintain.  I don't know how many times I heard back then to avoid too many function calls because of the overhead -- and in fact just last year I heard a new hire in the company where I work declare that she didn't want to refactor a long method because of function call overhead.  Now back then, that may have been a valid concern, but with today's modern hardware even if you're calling a trivial method in an extremely tight loop (which chances are the JIT compiler would optimize anyway) the results of removing method calls to speed up performance are negligible for the great majority of applications.  Now, obviously, there are those coding applications where speed is absolutely king (for example drivers, computer games, operating systems) where such sacrifices may be made.  But I would strongly advice against such optimization because of it's cost.  Many folks that are performing an optimization think it's always a win-win.  That they're simply adding speed to the application, what could possibly be wrong with that?  What they don't realize is the cost of their choice.  For every piece of straight-forward code that you obfuscate with performance enhancements, you risk the introduction of bugs in the long term technical debt of the application.  It will become so fragile over time that maintenance will become a nightmare.  I've seen such applications in places I have worked.  There are times I've seen applications where the designer was so obsessed with performance that they even designed their own memory management system for their application to try to squeeze out every ounce of performance.  Unfortunately, the application stability often suffers as a result and it is very difficult for anyone other than the original designer to maintain. I've even seen this recently where I heard a C++ developer bemoaning that in VS2010 the iterators are about twice as slow as they used to be because Microsoft added range checking (probably as part of the 0x standard implementation).  To me this was almost a joke.  Twice as slow sounds bad, but it almost never as bad as you think -- especially if you're gaining safety.  The only time twice is really that much slower is when once was too slow to begin with.  Think about it.  2 minutes is slow as a response time because 1 minute is slow.  But if an iterator takes 1 microsecond to move one position and a new, safer iterator takes 2 microseconds, this is trivial!  The only way you'd ever really notice this would be in iterating a collection just for the sake of iterating (i.e. no other operations).  To my mind, the added safety makes the extra time worth it. Always favor safety and maintainability when you can.  I know it can be a hard habit to break, especially if you started out your career early or in a language such as C where they are very performance conscious.  But in reality, these type of micro-optimizations only end up hurting you in the long run. Remember the two laws of optimization.  I'm not sure where I first heard these, but they are so true: For beginners: Do not optimize. For experts: Do not optimize yet. This is so true.  If you're a beginner, resist the urge to optimize at all costs.  And if you are an expert, delay that decision.  As long as you have chosen the right data structures and algorithms for your task, your performance will probably be more than sufficient.  Chances are it will be network, database, or disk hits that will be your slow-down, not your code.  As they say, 98% of your code's bottleneck is in 2% of your code so premature-optimization may add maintenance and safety debt that won't have any measurable impact.  Instead, code for maintainability and safety, and then, and only then, when you find a true bottleneck, then you should go back and optimize further.

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  • Exception Handling And Other Contentious Political Topics

    - by Justin Jones
    So about three years ago, around the time of my last blog post, I promised a friend I would write this post. Keeping promises is a good thing, and this is my first step towards easing back into regular blogging. I fully expect him to return from Pennsylvania to buy me a beer over this. However, it’s been an… ahem… eventful three years or so, and blogging, unfortunately, got pushed to the back burner on my priority list, along with a few other career minded activities. Now that the personal drama of the past three years is more or less resolved, it’s time to put a few things back on the front burner. What I consider to be proper exception handling practices is relatively well known these days. There are plenty of blog posts out there already on this topic which more or less echo my opinions on this topic. I’ll try to include a few links at the bottom of the post. Several years ago I had an argument with a co-worker who posited that exceptions should be caught at every level and logged. This might seem like sanity on the surface, but the resulting error log looked something like this: Error: System.SomeException Followed by small stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace.   These were all the same exception. The problem with this approach is that the error log, if you run any kind of analytics on in, becomes skewed depending on how far up the stack trace your exception was thrown. To mitigate this problem, we came up with the concept of the “PreLoggedException”. Basically, we would log the exception at the very top level and subsequently throw the exception back up the stack encapsulated in this pre-logged type, which our logging system knew to ignore. Now the error log looked like this: Error: System.SomeException Followed by small stack trace. Much cleaner, right? Well, there’s still a problem. When your exception happens in production and you go about trying to figure out what happened, you’ve lost more or less all context for where and how this exception was thrown, because all you really know is what method it was thrown in, but really nothing about who was calling the method or why. What gives you this clue is the entire stack trace, which we’re losing here. I believe that was further mitigated by having the logging system pull a system stack trace and add it to the log entry, but what you’re actually getting is the stack for how you got to the logging code. You’re still losing context about the actual error. Not to mention you’re executing a whole slew of catch blocks which are sloooooooowwwww……… In other words, we started with a bad idea and kept band-aiding it until it didn’t suck quite so bad. When I argued for not catching exceptions at every level but rather catching them following a certain set of rules, my co-worker warned me “do yourself a favor, never express that view in any future interviews.” I suppose this is my ultimate dismissal of that advice, but I’m not too worried. My approach for exception handling follows three basic rules: Only catch an exception if 1. You can do something about it. 2. You can add useful information to it. 3. You’re at an application boundary. Here’s what that means: 1. Only catch an exception if you can do something about it. We’ll start with a trivial example of a login system that uses a file. Please, never actually do this in production code, it’s just concocted example. So if our code goes to open a file and the file isn’t there, we get a FileNotFound exception. If the calling code doesn’t know what to do with this, it should bubble up. However, if we know how to create the file from scratch we can create the file and continue on our merry way. When you run into situations like this though, What should really run through your head is “How can I avoid handling an exception at all?” In this case, it’s a trivial matter to simply check for the existence of the file before trying to open it. If we detect that the file isn’t there, we can accomplish the same thing without having to handle in in a catch block. 2. Only catch an exception if you can do something about it. Continuing with the poorly thought out file based login system we contrived in part 1, if the code calls a Login(…) method and the FileNotFound exception is thrown higher up the stack, the code that calls Login must account for a FileNotFound exception. This is kind of counterintuitive because the calling code should not need to know the internals of the Login method, and the data file is an implementation detail. What makes more sense, assuming that we didn’t implement any of the good advice from step 1, is for Login to catch the FileNotFound exception and wrap it in a new exception. For argument’s sake we’ll say LoginSystemFailureException. (Sorry, couldn’t think of anything better at the moment.) This gives us two stack traces, preserving the original stack trace in the inner exception, and also is much more informative to the calling code. 3. Only catch an exception if you’re at an application boundary. At some point we have to catch all the exceptions, even the ones we don’t know what to do with. WinForms, ASP.Net, and most other UI technologies have some kind of built in mechanism for catching unhandled exceptions without fatally terminating the application. It’s still a good idea to somehow gracefully exit the application in this case if possible though, because you can no longer be sure what state your application is in, but nothing annoys a user more than an application just exploding. These unhandled exceptions need to be logged, and this is a good place to catch them. Ideally you never want this option to be exercised, but code as though it will be. When you log these exceptions, give them a “Fatal” status (e.g. Log4Net) and make sure these bugs get handled in your next release. That’s it in a nutshell. If you do it right each exception will only get logged once and with the largest stack trace possible which will make those 2am emergency severity 1 debugging sessions much shorter and less frustrating. Here’s a few people who also have interesting things to say on this topic:  http://blogs.msdn.com/b/ericlippert/archive/2008/09/10/vexing-exceptions.aspx http://www.codeproject.com/Articles/9538/Exception-Handling-Best-Practices-in-NET I know there’s more but I can’t find them at the moment.

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  • The Incremental Architect&rsquo;s Napkin - #5 - Design functions for extensibility and readability

    - by Ralf Westphal
    Originally posted on: http://geekswithblogs.net/theArchitectsNapkin/archive/2014/08/24/the-incremental-architectrsquos-napkin---5---design-functions-for.aspx The functionality of programs is entered via Entry Points. So what we´re talking about when designing software is a bunch of functions handling the requests represented by and flowing in through those Entry Points. Designing software thus consists of at least three phases: Analyzing the requirements to find the Entry Points and their signatures Designing the functionality to be executed when those Entry Points get triggered Implementing the functionality according to the design aka coding I presume, you´re familiar with phase 1 in some way. And I guess you´re proficient in implementing functionality in some programming language. But in my experience developers in general are not experienced in going through an explicit phase 2. “Designing functionality? What´s that supposed to mean?” you might already have thought. Here´s my definition: To design functionality (or functional design for short) means thinking about… well, functions. You find a solution for what´s supposed to happen when an Entry Point gets triggered in terms of functions. A conceptual solution that is, because those functions only exist in your head (or on paper) during this phase. But you may have guess that, because it´s “design” not “coding”. And here is, what functional design is not: It´s not about logic. Logic is expressions (e.g. +, -, && etc.) and control statements (e.g. if, switch, for, while etc.). Also I consider calling external APIs as logic. It´s equally basic. It´s what code needs to do in order to deliver some functionality or quality. Logic is what´s doing that needs to be done by software. Transformations are either done through expressions or API-calls. And then there is alternative control flow depending on the result of some expression. Basically it´s just jumps in Assembler, sometimes to go forward (if, switch), sometimes to go backward (for, while, do). But calling your own function is not logic. It´s not necessary to produce any outcome. Functionality is not enhanced by adding functions (subroutine calls) to your code. Nor is quality increased by adding functions. No performance gain, no higher scalability etc. through functions. Functions are not relevant to functionality. Strange, isn´t it. What they are important for is security of investment. By introducing functions into our code we can become more productive (re-use) and can increase evolvability (higher unterstandability, easier to keep code consistent). That´s no small feat, however. Evolvable code can hardly be overestimated. That´s why to me functional design is so important. It´s at the core of software development. To sum this up: Functional design is on a level of abstraction above (!) logical design or algorithmic design. Functional design is only done until you get to a point where each function is so simple you are very confident you can easily code it. Functional design an logical design (which mostly is coding, but can also be done using pseudo code or flow charts) are complementary. Software needs both. If you start coding right away you end up in a tangled mess very quickly. Then you need back out through refactoring. Functional design on the other hand is bloodless without actual code. It´s just a theory with no experiments to prove it. But how to do functional design? An example of functional design Let´s assume a program to de-duplicate strings. The user enters a number of strings separated by commas, e.g. a, b, a, c, d, b, e, c, a. And the program is supposed to clear this list of all doubles, e.g. a, b, c, d, e. There is only one Entry Point to this program: the user triggers the de-duplication by starting the program with the string list on the command line C:\>deduplicate "a, b, a, c, d, b, e, c, a" a, b, c, d, e …or by clicking on a GUI button. This leads to the Entry Point function to get called. It´s the program´s main function in case of the batch version or a button click event handler in the GUI version. That´s the physical Entry Point so to speak. It´s inevitable. What then happens is a three step process: Transform the input data from the user into a request. Call the request handler. Transform the output of the request handler into a tangible result for the user. Or to phrase it a bit more generally: Accept input. Transform input into output. Present output. This does not mean any of these steps requires a lot of effort. Maybe it´s just one line of code to accomplish it. Nevertheless it´s a distinct step in doing the processing behind an Entry Point. Call it an aspect or a responsibility - and you will realize it most likely deserves a function of its own to satisfy the Single Responsibility Principle (SRP). Interestingly the above list of steps is already functional design. There is no logic, but nevertheless the solution is described - albeit on a higher level of abstraction than you might have done yourself. But it´s still on a meta-level. The application to the domain at hand is easy, though: Accept string list from command line De-duplicate Present de-duplicated strings on standard output And this concrete list of processing steps can easily be transformed into code:static void Main(string[] args) { var input = Accept_string_list(args); var output = Deduplicate(input); Present_deduplicated_string_list(output); } Instead of a big problem there are three much smaller problems now. If you think each of those is trivial to implement, then go for it. You can stop the functional design at this point. But maybe, just maybe, you´re not so sure how to go about with the de-duplication for example. Then just implement what´s easy right now, e.g.private static string Accept_string_list(string[] args) { return args[0]; } private static void Present_deduplicated_string_list( string[] output) { var line = string.Join(", ", output); Console.WriteLine(line); } Accept_string_list() contains logic in the form of an API-call. Present_deduplicated_string_list() contains logic in the form of an expression and an API-call. And then repeat the functional design for the remaining processing step. What´s left is the domain logic: de-duplicating a list of strings. How should that be done? Without any logic at our disposal during functional design you´re left with just functions. So which functions could make up the de-duplication? Here´s a suggestion: De-duplicate Parse the input string into a true list of strings. Register each string in a dictionary/map/set. That way duplicates get cast away. Transform the data structure into a list of unique strings. Processing step 2 obviously was the core of the solution. That´s where real creativity was needed. That´s the core of the domain. But now after this refinement the implementation of each step is easy again:private static string[] Parse_string_list(string input) { return input.Split(',') .Select(s => s.Trim()) .ToArray(); } private static Dictionary<string,object> Compile_unique_strings(string[] strings) { return strings.Aggregate( new Dictionary<string, object>(), (agg, s) => { agg[s] = null; return agg; }); } private static string[] Serialize_unique_strings( Dictionary<string,object> dict) { return dict.Keys.ToArray(); } With these three additional functions Main() now looks like this:static void Main(string[] args) { var input = Accept_string_list(args); var strings = Parse_string_list(input); var dict = Compile_unique_strings(strings); var output = Serialize_unique_strings(dict); Present_deduplicated_string_list(output); } I think that´s very understandable code: just read it from top to bottom and you know how the solution to the problem works. It´s a mirror image of the initial design: Accept string list from command line Parse the input string into a true list of strings. Register each string in a dictionary/map/set. That way duplicates get cast away. Transform the data structure into a list of unique strings. Present de-duplicated strings on standard output You can even re-generate the design by just looking at the code. Code and functional design thus are always in sync - if you follow some simple rules. But about that later. And as a bonus: all the functions making up the process are small - which means easy to understand, too. So much for an initial concrete example. Now it´s time for some theory. Because there is method to this madness ;-) The above has only scratched the surface. Introducing Flow Design Functional design starts with a given function, the Entry Point. Its goal is to describe the behavior of the program when the Entry Point is triggered using a process, not an algorithm. An algorithm consists of logic, a process on the other hand consists just of steps or stages. Each processing step transforms input into output or a side effect. Also it might access resources, e.g. a printer, a database, or just memory. Processing steps thus can rely on state of some sort. This is different from Functional Programming, where functions are supposed to not be stateful and not cause side effects.[1] In its simplest form a process can be written as a bullet point list of steps, e.g. Get data from user Output result to user Transform data Parse data Map result for output Such a compilation of steps - possibly on different levels of abstraction - often is the first artifact of functional design. It can be generated by a team in an initial design brainstorming. Next comes ordering the steps. What should happen first, what next etc.? Get data from user Parse data Transform data Map result for output Output result to user That´s great for a start into functional design. It´s better than starting to code right away on a given function using TDD. Please get me right: TDD is a valuable practice. But it can be unnecessarily hard if the scope of a functionn is too large. But how do you know beforehand without investing some thinking? And how to do this thinking in a systematic fashion? My recommendation: For any given function you´re supposed to implement first do a functional design. Then, once you´re confident you know the processing steps - which are pretty small - refine and code them using TDD. You´ll see that´s much, much easier - and leads to cleaner code right away. For more information on this approach I call “Informed TDD” read my book of the same title. Thinking before coding is smart. And writing down the solution as a bunch of functions possibly is the simplest thing you can do, I´d say. It´s more according to the KISS (Keep It Simple, Stupid) principle than returning constants or other trivial stuff TDD development often is started with. So far so good. A simple ordered list of processing steps will do to start with functional design. As shown in the above example such steps can easily be translated into functions. Moving from design to coding thus is simple. However, such a list does not scale. Processing is not always that simple to be captured in a list. And then the list is just text. Again. Like code. That means the design is lacking visuality. Textual representations need more parsing by your brain than visual representations. Plus they are limited in their “dimensionality”: text just has one dimension, it´s sequential. Alternatives and parallelism are hard to encode in text. In addition the functional design using numbered lists lacks data. It´s not visible what´s the input, output, and state of the processing steps. That´s why functional design should be done using a lightweight visual notation. No tool is necessary to draw such designs. Use pen and paper; a flipchart, a whiteboard, or even a napkin is sufficient. Visualizing processes The building block of the functional design notation is a functional unit. I mostly draw it like this: Something is done, it´s clear what goes in, it´s clear what comes out, and it´s clear what the processing step requires in terms of state or hardware. Whenever input flows into a functional unit it gets processed and output is produced and/or a side effect occurs. Flowing data is the driver of something happening. That´s why I call this approach to functional design Flow Design. It´s about data flow instead of control flow. Control flow like in algorithms is of no concern to functional design. Thinking about control flow simply is too low level. Once you start with control flow you easily get bogged down by tons of details. That´s what you want to avoid during design. Design is supposed to be quick, broad brush, abstract. It should give overview. But what about all the details? As Robert C. Martin rightly said: “Programming is abot detail”. Detail is a matter of code. Once you start coding the processing steps you designed you can worry about all the detail you want. Functional design does not eliminate all the nitty gritty. It just postpones tackling them. To me that´s also an example of the SRP. Function design has the responsibility to come up with a solution to a problem posed by a single function (Entry Point). And later coding has the responsibility to implement the solution down to the last detail (i.e. statement, API-call). TDD unfortunately mixes both responsibilities. It´s just coding - and thereby trying to find detailed implementations (green phase) plus getting the design right (refactoring). To me that´s one reason why TDD has failed to deliver on its promise for many developers. Using functional units as building blocks of functional design processes can be depicted very easily. Here´s the initial process for the example problem: For each processing step draw a functional unit and label it. Choose a verb or an “action phrase” as a label, not a noun. Functional design is about activities, not state or structure. Then make the output of an upstream step the input of a downstream step. Finally think about the data that should flow between the functional units. Write the data above the arrows connecting the functional units in the direction of the data flow. Enclose the data description in brackets. That way you can clearly see if all flows have already been specified. Empty brackets mean “no data is flowing”, but nevertheless a signal is sent. A name like “list” or “strings” in brackets describes the data content. Use lower case labels for that purpose. A name starting with an upper case letter like “String” or “Customer” on the other hand signifies a data type. If you like, you also can combine descriptions with data types by separating them with a colon, e.g. (list:string) or (strings:string[]). But these are just suggestions from my practice with Flow Design. You can do it differently, if you like. Just be sure to be consistent. Flows wired-up in this manner I call one-dimensional (1D). Each functional unit just has one input and/or one output. A functional unit without an output is possible. It´s like a black hole sucking up input without producing any output. Instead it produces side effects. A functional unit without an input, though, does make much sense. When should it start to work? What´s the trigger? That´s why in the above process even the first processing step has an input. If you like, view such 1D-flows as pipelines. Data is flowing through them from left to right. But as you can see, it´s not always the same data. It get´s transformed along its passage: (args) becomes a (list) which is turned into (strings). The Principle of Mutual Oblivion A very characteristic trait of flows put together from function units is: no functional units knows another one. They are all completely independent of each other. Functional units don´t know where their input is coming from (or even when it´s gonna arrive). They just specify a range of values they can process. And they promise a certain behavior upon input arriving. Also they don´t know where their output is going. They just produce it in their own time independent of other functional units. That means at least conceptually all functional units work in parallel. Functional units don´t know their “deployment context”. They now nothing about the overall flow they are place in. They are just consuming input from some upstream, and producing output for some downstream. That makes functional units very easy to test. At least as long as they don´t depend on state or resources. I call this the Principle of Mutual Oblivion (PoMO). Functional units are oblivious of others as well as an overall context/purpose. They are just parts of a whole focused on a single responsibility. How the whole is built, how a larger goal is achieved, is of no concern to the single functional units. By building software in such a manner, functional design interestingly follows nature. Nature´s building blocks for organisms also follow the PoMO. The cells forming your body do not know each other. Take a nerve cell “controlling” a muscle cell for example:[2] The nerve cell does not know anything about muscle cells, let alone the specific muscel cell it is “attached to”. Likewise the muscle cell does not know anything about nerve cells, let a lone a specific nerve cell “attached to” it. Saying “the nerve cell is controlling the muscle cell” thus only makes sense when viewing both from the outside. “Control” is a concept of the whole, not of its parts. Control is created by wiring-up parts in a certain way. Both cells are mutually oblivious. Both just follow a contract. One produces Acetylcholine (ACh) as output, the other consumes ACh as input. Where the ACh is going, where it´s coming from neither cell cares about. Million years of evolution have led to this kind of division of labor. And million years of evolution have produced organism designs (DNA) which lead to the production of these different cell types (and many others) and also to their co-location. The result: the overall behavior of an organism. How and why this happened in nature is a mystery. For our software, though, it´s clear: functional and quality requirements needs to be fulfilled. So we as developers have to become “intelligent designers” of “software cells” which we put together to form a “software organism” which responds in satisfying ways to triggers from it´s environment. My bet is: If nature gets complex organisms working by following the PoMO, who are we to not apply this recipe for success to our much simpler “machines”? So my rule is: Wherever there is functionality to be delivered, because there is a clear Entry Point into software, design the functionality like nature would do it. Build it from mutually oblivious functional units. That´s what Flow Design is about. In that way it´s even universal, I´d say. Its notation can also be applied to biology: Never mind labeling the functional units with nouns. That´s ok in Flow Design. You´ll do that occassionally for functional units on a higher level of abstraction or when their purpose is close to hardware. Getting a cockroach to roam your bedroom takes 1,000,000 nerve cells (neurons). Getting the de-duplication program to do its job just takes 5 “software cells” (functional units). Both, though, follow the same basic principle. Translating functional units into code Moving from functional design to code is no rocket science. In fact it´s straightforward. There are two simple rules: Translate an input port to a function. Translate an output port either to a return statement in that function or to a function pointer visible to that function. The simplest translation of a functional unit is a function. That´s what you saw in the above example. Functions are mutually oblivious. That why Functional Programming likes them so much. It makes them composable. Which is the reason, nature works according to the PoMO. Let´s be clear about one thing: There is no dependency injection in nature. For all of an organism´s complexity no DI container is used. Behavior is the result of smooth cooperation between mutually oblivious building blocks. Functions will often be the adequate translation for the functional units in your designs. But not always. Take for example the case, where a processing step should not always produce an output. Maybe the purpose is to filter input. Here the functional unit consumes words and produces words. But it does not pass along every word flowing in. Some words are swallowed. Think of a spell checker. It probably should not check acronyms for correctness. There are too many of them. Or words with no more than two letters. Such words are called “stop words”. In the above picture the optionality of the output is signified by the astrisk outside the brackets. It means: Any number of (word) data items can flow from the functional unit for each input data item. It might be none or one or even more. This I call a stream of data. Such behavior cannot be translated into a function where output is generated with return. Because a function always needs to return a value. So the output port is translated into a function pointer or continuation which gets passed to the subroutine when called:[3]void filter_stop_words( string word, Action<string> onNoStopWord) { if (...check if not a stop word...) onNoStopWord(word); } If you want to be nitpicky you might call such a function pointer parameter an injection. And technically you´re right. Conceptually, though, it´s not an injection. Because the subroutine is not functionally dependent on the continuation. Firstly continuations are procedures, i.e. subroutines without a return type. Remember: Flow Design is about unidirectional data flow. Secondly the name of the formal parameter is chosen in a way as to not assume anything about downstream processing steps. onNoStopWord describes a situation (or event) within the functional unit only. Translating output ports into function pointers helps keeping functional units mutually oblivious in cases where output is optional or produced asynchronically. Either pass the function pointer to the function upon call. Or make it global by putting it on the encompassing class. Then it´s called an event. In C# that´s even an explicit feature.class Filter { public void filter_stop_words( string word) { if (...check if not a stop word...) onNoStopWord(word); } public event Action<string> onNoStopWord; } When to use a continuation and when to use an event dependens on how a functional unit is used in flows and how it´s packed together with others into classes. You´ll see examples further down the Flow Design road. Another example of 1D functional design Let´s see Flow Design once more in action using the visual notation. How about the famous word wrap kata? Robert C. Martin has posted a much cited solution including an extensive reasoning behind his TDD approach. So maybe you want to compare it to Flow Design. The function signature given is:string WordWrap(string text, int maxLineLength) {...} That´s not an Entry Point since we don´t see an application with an environment and users. Nevertheless it´s a function which is supposed to provide a certain functionality. The text passed in has to be reformatted. The input is a single line of arbitrary length consisting of words separated by spaces. The output should consist of one or more lines of a maximum length specified. If a word is longer than a the maximum line length it can be split in multiple parts each fitting in a line. Flow Design Let´s start by brainstorming the process to accomplish the feat of reformatting the text. What´s needed? Words need to be assembled into lines Words need to be extracted from the input text The resulting lines need to be assembled into the output text Words too long to fit in a line need to be split Does sound about right? I guess so. And it shows a kind of priority. Long words are a special case. So maybe there is a hint for an incremental design here. First let´s tackle “average words” (words not longer than a line). Here´s the Flow Design for this increment: The the first three bullet points turned into functional units with explicit data added. As the signature requires a text is transformed into another text. See the input of the first functional unit and the output of the last functional unit. In between no text flows, but words and lines. That´s good to see because thereby the domain is clearly represented in the design. The requirements are talking about words and lines and here they are. But note the asterisk! It´s not outside the brackets but inside. That means it´s not a stream of words or lines, but lists or sequences. For each text a sequence of words is output. For each sequence of words a sequence of lines is produced. The asterisk is used to abstract from the concrete implementation. Like with streams. Whether the list of words gets implemented as an array or an IEnumerable is not important during design. It´s an implementation detail. Does any processing step require further refinement? I don´t think so. They all look pretty “atomic” to me. And if not… I can always backtrack and refine a process step using functional design later once I´ve gained more insight into a sub-problem. Implementation The implementation is straightforward as you can imagine. The processing steps can all be translated into functions. Each can be tested easily and separately. Each has a focused responsibility. And the process flow becomes just a sequence of function calls: Easy to understand. It clearly states how word wrapping works - on a high level of abstraction. And it´s easy to evolve as you´ll see. Flow Design - Increment 2 So far only texts consisting of “average words” are wrapped correctly. Words not fitting in a line will result in lines too long. Wrapping long words is a feature of the requested functionality. Whether it´s there or not makes a difference to the user. To quickly get feedback I decided to first implement a solution without this feature. But now it´s time to add it to deliver the full scope. Fortunately Flow Design automatically leads to code following the Open Closed Principle (OCP). It´s easy to extend it - instead of changing well tested code. How´s that possible? Flow Design allows for extension of functionality by inserting functional units into the flow. That way existing functional units need not be changed. The data flow arrow between functional units is a natural extension point. No need to resort to the Strategy Pattern. No need to think ahead where extions might need to be made in the future. I just “phase in” the remaining processing step: Since neither Extract words nor Reformat know of their environment neither needs to be touched due to the “detour”. The new processing step accepts the output of the existing upstream step and produces data compatible with the existing downstream step. Implementation - Increment 2 A trivial implementation checking the assumption if this works does not do anything to split long words. The input is just passed on: Note how clean WordWrap() stays. The solution is easy to understand. A developer looking at this code sometime in the future, when a new feature needs to be build in, quickly sees how long words are dealt with. Compare this to Robert C. Martin´s solution:[4] How does this solution handle long words? Long words are not even part of the domain language present in the code. At least I need considerable time to understand the approach. Admittedly the Flow Design solution with the full implementation of long word splitting is longer than Robert C. Martin´s. At least it seems. Because his solution does not cover all the “word wrap situations” the Flow Design solution handles. Some lines would need to be added to be on par, I guess. But even then… Is a difference in LOC that important as long as it´s in the same ball park? I value understandability and openness for extension higher than saving on the last line of code. Simplicity is not just less code, it´s also clarity in design. But don´t take my word for it. Try Flow Design on larger problems and compare for yourself. What´s the easier, more straightforward way to clean code? And keep in mind: You ain´t seen all yet ;-) There´s more to Flow Design than described in this chapter. In closing I hope I was able to give you a impression of functional design that makes you hungry for more. To me it´s an inevitable step in software development. Jumping from requirements to code does not scale. And it leads to dirty code all to quickly. Some thought should be invested first. Where there is a clear Entry Point visible, it´s functionality should be designed using data flows. Because with data flows abstraction is possible. For more background on why that´s necessary read my blog article here. For now let me point out to you - if you haven´t already noticed - that Flow Design is a general purpose declarative language. It´s “programming by intention” (Shalloway et al.). Just write down how you think the solution should work on a high level of abstraction. This breaks down a large problem in smaller problems. And by following the PoMO the solutions to those smaller problems are independent of each other. So they are easy to test. Or you could even think about getting them implemented in parallel by different team members. Flow Design not only increases evolvability, but also helps becoming more productive. All team members can participate in functional design. This goes beyon collective code ownership. We´re talking collective design/architecture ownership. Because with Flow Design there is a common visual language to talk about functional design - which is the foundation for all other design activities.   PS: If you like what you read, consider getting my ebook “The Incremental Architekt´s Napkin”. It´s where I compile all the articles in this series for easier reading. I like the strictness of Function Programming - but I also find it quite hard to live by. And it certainly is not what millions of programmers are used to. Also to me it seems, the real world is full of state and side effects. So why give them such a bad image? That´s why functional design takes a more pragmatic approach. State and side effects are ok for processing steps - but be sure to follow the SRP. Don´t put too much of it into a single processing step. ? Image taken from www.physioweb.org ? My code samples are written in C#. C# sports typed function pointers called delegates. Action is such a function pointer type matching functions with signature void someName(T t). Other languages provide similar ways to work with functions as first class citizens - even Java now in version 8. I trust you find a way to map this detail of my translation to your favorite programming language. I know it works for Java, C++, Ruby, JavaScript, Python, Go. And if you´re using a Functional Programming language it´s of course a no brainer. ? Taken from his blog post “The Craftsman 62, The Dark Path”. ?

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