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Search found 119 results on 5 pages for 'traits'.

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  • Getting Started with Ruby & Ruby on Rails

    - by JakeTheSnake
    Some background: I'm a jack-of-all traits, one of which is programming. I learned VB6 through Excel and PHP for creating websites and so far it's worked out just fine for me. I'm not CS major or even mathematically inclined - logic is what interests me. Current status: I'm willing to learn new and more powerful languages; my first foray into such a route is learning Ruby. I went to the main Ruby website and did the interactive intro. (by the way, I'm currently getting redirected to google.com when I try the link...it's happening to other websites as well...is my computer infected?) I liked what I learned and wanted to get started using Ruby to create websites. I downloaded InstantRails and installed it; everything so far has been fine - the program starts up just fine, and I can test some Ruby code in the console. However my troubles begin when I try and view a web page with Ruby code present. Lastly, my problem: As in PHP, I can browse to the .php file directly and through using PHP tags and some simple 'echo' statements I can be on my way in making dynamic web pages. However with the InstantRails app working, accessing a .rb or .rhtml page doesn't produce similar results. I made a simple text file named 'test.rb' and put basic HTML tags in there (html, head, body) and the Ruby tags <%= and % with some ruby code inside. The web page actually shows the tags and the code - as if it's all just plain HTML. I take it Ruby isn't parsing the page before it is displayed to the user, but this is where my lack of understanding of the Ruby environment stops me short. Where do I go from here?

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  • Partial template specialization: matching on properties of specialized template parameter

    - by Kenzo
    template <typename X, typename Y> class A {}; enum Property {P1,P2}; template <Property P> class B {}; class C {}; Is there any way to define a partial specialization of A such that A<C, B<P1> > would be A's normal template, but A<C, B<P2> > would be the specialization? Replacing the Y template parameter by a template template parameter would be nice, but is there a way to partially specialize it based on P then? template <typename X, template <Property P> typename Y> class A {}; // template <typename X> class A<X,template<> Y<P2> > {}; <-- not valid Is there a way by adding traits to a specialization template<> B<P2> and then using SFINAE in A?

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  • Emulating Dynamic Dispatch in C++ based on Template Parameters

    - by Jon Purdy
    This is heavily simplified for the sake of the question. Say I have a hierarchy: struct Base { virtual int precision() const = 0; }; template<int Precision> struct Derived : public Base { typedef Traits<Precision>::Type Type; Derived(Type data) : value(data) {} virtual int precision() const { return Precision; } Type value; }; I want a function like: Base* function(const Base& a, const Base& b); Where the specific type of the result of the function is the same type as whichever of first and second has the greater Precision; something like the following pseudocode: template<class T> T* operation(const T& a, const T& b) { return new T(a.value + b.value); } Base* function(const Base& a, const Base& b) { if (a.precision() > b.precision()) return operation((A&)a, A(b.value)); else if (a.precision() < b.precision()) return operation(B(a.value), (B&)b); else return operation((A&)a, (A&)b); } Where A and B are the specific types of a and b, respectively. I want f to operate independently of how many instantiations of Derived there are. I'd like to avoid a massive table of typeid() comparisons, though RTTI is fine in answers. Any ideas?

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  • howto distinguish composition and self-typing use-cases

    - by ayvango
    Scala has two instruments for expressing object composition: original self-type concept and well known trivial composition. I'm curios what situations I should use which in. There are obvious differences in their applicability. Self-type requires you to use traits. Object composition allows you to change extensions on run-time with var declaration. Leaving technical details behind I can figure two indicators to help with classification of use cases. If some object used as combinator for a complex structure such as tree or just have several similar typed parts (1 car to 4 wheels relation) than it should use composition. There is extreme opposite use case. Lets assume one trait become too big to clearly observe it and it got split. It is quite natural that you should use self-types for this case. That rules are not absolute. You may do extra work to convert code between this techniques. e.g. you may replace 4 wheels composition with self-typing over Product4. You may use Cake[T <: MyType] {part : MyType} instead of Cake { this : MyType => } for cake pattern dependencies. But both cases seem counterintuitive and give you extra work. There are plenty of boundary use cases although. One-to-one relations is very hard to decide with. Is there any simple rule to decide what kind of technique is preferable? self-type makes you classes abstract, composition makes your code verbose. self-type gives your problems with blending namespaces and also gives you extra typing for free (you got not just a cocktail of two elements but gasoline-motor oil cocktail known as a petrol bomb). How can I choose between them? What hints are there? Update: Let us discuss the following example: Adapter pattern. What benefits it has with both selt-typing and composition approaches?

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  • Python: Beginning problems

    - by Blogger
    ok so basically i very new to programming and have no idea how to go about these problems help if you will ^^ Numerologists claim to be able to determine a person’s character traits based on the “numeric value” of a name. The value of a name is determined by summing up the values of the letters of the name, where ‘a’ is 1, ‘b’ is 2, ‘c’ is 3 etc., up to ‘z’ being 26. For example, the name “Zelle” would have the value 26 + 5 + 12 + 12 + 5 = 60 (which happens to be a very suspicious number, by the way). Write a program that calculates the numeric value of a single name provided as input. Word count. A common utility on Unix/Linux systems is a small program called “wc”. This program counts the number of lines, words (strings of characters separated by blanks, tabs, or new lines), and characters in a file. Write your own version of this program. The program should accept a file name as input and then print three numbers showing the count of lines, words, and characters in the file.

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  • Partial template specialization for more than one typename

    - by Matt Joiner
    In the following code, I want to consider functions (Ops) that have void return to instead be considered to return true. The type Retval, and the return value of Op are always matching. I'm not able to discriminate using the type traits shown here, and attempts to create a partial template specialization based on Retval have failed due the presence of the other template variables, Op and Args. How do I specialize only some variables in a template specialization without getting errors? Is there any other way to alter behaviour based on the return type of Op? template <typename Retval, typename Op, typename... Args> Retval single_op_wrapper( Retval const failval, char const *const opname, Op const op, Cpfs &cpfs, Args... args) { try { CallContext callctx(cpfs, opname); Retval retval; if (std::is_same<bool, Retval>::value) { (callctx.*op)(args...); retval = true; } else { retval = (callctx.*op)(args...); } assert(retval != failval); callctx.commit(cpfs); return retval; } catch (CpfsError const &exc) { cpfs_errno_set(exc.fserrno); LOGF(Info, "Failed with %s", cpfs_errno_str(exc.fserrno)); } return failval; }

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  • OSMF seek with Amazon Cloudfront

    - by giorrrgio
    I've written a little OSMF player that streams via RTMP from Amazon Cloudfront. There's a known issue, the mp3 duration is not correctly readed from metadata and thus the seek function is not working. I know there's a workaround implying the use of getStreamLength function of NetConnection, which I successfully implemented in a previous non-OSMF player, but now I don't know how and when to call it, in terms of OSMF Events and Traits. This code is not working: protected function initApp():void { //the pointer to the media var resource:URLResource = new URLResource( STREAMING_PATH ); // Create a mediafactory instance mediaFactory = new DefaultMediaFactory(); //creates and sets the MediaElement (generic) with a resource and path element = mediaFactory.createMediaElement( resource ); var loadTrait:NetStreamLoadTrait = element.getTrait(MediaTraitType.LOAD) as NetStreamLoadTrait; loadTrait.addEventListener(LoaderEvent.LOAD_STATE_CHANGE, _onLoaded); player = new MediaPlayer( element ); //Marker 5: Add MediaPlayer listeners for media size and current time change player.addEventListener( DisplayObjectEvent.MEDIA_SIZE_CHANGE, _onSizeChange ); player.addEventListener( TimeEvent.CURRENT_TIME_CHANGE, _onProgress ); initControlBar(); } private function onGetStreamLength(result:Object):void { Alert.show("The stream length is " + result + " seconds"); duration = Number(result); } private function _onLoaded(e:LoaderEvent):void { if (e.newState == LoadState.READY) { var loadTrait:NetStreamLoadTrait = player.media.getTrait(MediaTraitType.LOAD) as NetStreamLoadTrait; if (loadTrait && loadTrait.netStream) { var responder:Responder = new Responder(onGetStreamLength); loadTrait.connection.call("getStreamLength", responder, STREAMING_PATH); } } }

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  • How can I write a function template for all types with a particular type trait?

    - by TC
    Consider the following example: struct Scanner { template <typename T> T get(); }; template <> string Scanner::get() { return string("string"); } template <> int Scanner::get() { return 10; } int main() { Scanner scanner; string s = scanner.get<string>(); int i = scanner.get<int>(); } The Scanner class is used to extract tokens from some source. The above code works fine, but fails when I try to get other integral types like a char or an unsigned int. The code to read these types is exactly the same as the code to read an int. I could just duplicate the code for all other integral types I'd like to read, but I'd rather define one function template for all integral types. I've tried the following: struct Scanner { template <typename T> typename enable_if<boost::is_integral<T>, T>::type get(); }; Which works like a charm, but I am unsure how to get Scanner::get<string>() to function again. So, how can I write code so that I can do scanner.get<string>() and scanner.get<any integral type>() and have a single definition to read all integral types? Update: bonus question: What if I want to accept more than one range of classes based on some traits? For example: how should I approach this problem if I want to have three get functions that accept (i) integral types (ii) floating point types (iii) strings, respectively.

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  • Revolutionary brand powder packing machine price from affecting marketplace boom and put on uniform in addition to a lengthy service life

    - by user74606
    In mining in stone crushing, our machinery company's encounter becomes much more apparent. As a consequence of production capacity in between 600~800t/h of mining stone crusher, stone is mine Mobile Cone Crushing Plant Price 25~40 times, effectively solved the initially mining stone crusher operation because of low yield prices, no upkeep problems. Full chunk of mining stone crusher. Maximum particle size for crushing 1000x1200mm, an effective answer for the original side is mine stone provide, storing significant chunks of stone can not use complications in mines. Completed goods granularity is modest, only 2~15mm, an effective option for the original mine stone size, generally blocking chute production was an issue even the grinding machine. Two types of material mixed great uniformity, desulfurization of mining stone by adding weight considerably. Present quantity added is often reached 60%, effectively minimizing the cost of raw supplies. Electrical energy consumption has fallen. Dropped 1~2KWh/t tons of mining stone electrical energy consumption, annual electricity savings of one hundred,000 yuan. Efficient labor intensity of workers and also the atmosphere. Due to mine stone powder packing machine price a high degree of automation, with out human make contact with supplies, workers working circumstances enhanced significantly. Positive aspects, and along with mine for stone crushing, CS series cone Crusher has the following efficiency traits. CS series cone Crusher Chamber is divided into 3 unique designs, the user is usually chosen in accordance with the scenario on site crushing efficiency is high, uniform item size, grain shape, rolling mortar wall friction and put on uniform in addition to a extended service life of crushing cavity-. CS series cone Crusher utilizes a one of a kind dust-proof seal, sealing dependable, properly extend the service life of the lubricant replacement cycle and parts. CS series Sprial Sand washer price manufacture of important components to choose unique materials. Each and every stroke left rolling mortar wall of broken cone distances, by permitting a lot more products into the crushing cavity, as well as the formation of big discharge volume, speed of supplies by way of the crushing Chamber. This machine makes use of the principle of crushing cavity, also as unique laminated crushing, particle fragmentation, so that the completed product drastically improved the proportions of a cube, needle-shaped stones to lower particle levels extra evenly.

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  • Upgrading php from php 5.3 to 5.4 .7

    - by Takingsides
    So, quickly so to speak I have noticed this topic around, I have searched and there are plenty of solutions. However these solutions do not work for me, not only that but I'm intending to learn more about the Debian based OS. Questions I would like to know how to upgrade php5.3 to php 5.4.7 compiling it from source, myself without using a third-party ppa. Is the way (explained below) the correct way of configuring php5.4? I'm new to compiling from source. Set-up I run Ubuntu Server 12.04 64bit. I've currently got: PHP 5.3 MySQL-Server Apache2 Memcached The Problem So I initially installed php5.3 using apt-get. I now wish to upgrade the php 5.4 due to the advantage of traits in OOP and the struct with Arrays and all the other recent patches and such. Possible Solutions I've seen this ondrej/ppa repository, which I refuse to use, given the fact that it may work, but it's an unknown/untrusted source. ALso, i'm not learning how to administer from source, using configure, make and install accordingly. I've seen a solution compiling from source, which is essentially how I was hoping to go about it with some guidance. Conclusion So I didn't just expect to be spoon-fed, and I went out and did some manual reading and atleast started the ball rolling myself; this how far i've got. The first thing I did was su into root (to save the typing sudo all the darn time). $ sudo su The next thing I did was download the latest version of php (5.4.7) and extracted it's contents ready to configure before installing it. $ mkdir php5-new && cd !$ $ wget -O php-5.4.7.tar.bz2 http://php.net/get/php-5.4.7.tar.bz2/from/uk3.php.net/mirror $ bzip2 -d php-5.4.7.tar.bz2 $ tar xvf php-5.4.7.tar.gz $ cd php-5.4.7 $ ./configure --help Finally I decided to have a bash, I looked through the list of options and decided I needed to list ALL of the things I wanted to include in the configuration. $ ./configure --with-mysql --with-apache2 --with-libxml --with-openssl --with-zlib --with-bz2 --with-curl --with-dom --with-gd --with-imap --with-imap-ssl --with-mcrypt --with-mysqli --with-pdo-mysql --with-libxml --enable-ftp --enable-mbstring --enable-soap Finally, the results... When the configuration process had finished, it threw an error: configure: error: xml2-config not found. Please check your libxml2 installation.

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  • Book Review Charlene Li's New Book: Open Leadership

    - by david.talamelli
    A few weeks ago, I was surprised when I looked in our mail box. I had received an Advance Copy of Charlene Li's new book titled "Open Leadership: How Social Technology Can Transform the Way You Lead". Charlene sent a tweet a while back asking anyone interested in receiving the book to submit their details. I sent off my details and didn't think I would hear anything back, so it was a pleasant surprise. With that I almost feel bad that it has taken me 3 weeks to read her book. It took this long mainly because it has been hard to fit in some quality reading time for myself with work, the kids, volunteering, etc..... I am happy to report I have finished her book and wanted to run through my initial thoughts with you. I first came across Charlene Li after reading her book "Groundswell" a few years ago, her latest book "Open Leadership" is a follow on from Groundswell and to me it seems like a natural progression from the question "Ok the business landscape is changing, what do we do now?" For me these two books have a different writing style to them. Groundswell from memory spoke about broad social media concepts and adoption and alerted us to some of the changes taking place in the SM landscape. Open Leadership seems to be focussed on taking those broad concepts and finding ways to implement them into your environment. That is breaking broad concepts down into individual action items that can be measured and analysed. As the business world changes Leaders must change their approach and let go of control to more control. One of the things I love reading about is seeing real life examples of how people and organisations are making these things happen. In this book Charlene has collected some great collateral and case studies from companies such as Cisco, Best Buy, The Red Cross and The State Bank of India (as a side-note, I wish now that I submitted my input for the Leaders I work with here at Oracle - there are some great examples here of people who empower their staff). As society becomes more adept at using social media it is inevitable that Leaders must become open with their employees, clients and partners. From the book some of the key points I took away are (I actually took away a lot more from this book, this is just an overview) : 1) Organisations should encourage risk taking. Without being a "hacker", how can we improve ourselves, our processes, our business, etc... The old saying you only fail by not trying applies here. If Leaders create a culture where people are afraid to stick their neck out - how will you innovate? 2) Leaders need to lead by example - if you want to promote an open and transparent business, a Leader needs to exemplify the traits they would like to see out of their employees. 3) The definition of a Leader is changing, open leadership is about being a catalyst to change that uses networks to spread a vision as opposed to traditional leadership that is viewed as a role. 4) There is a cultural and business shift taking place. Information is more wide-spread and is being disseminated faster than any other time in the past. Leaders who are open and transparent will thrive in this new business environment. 5) Leadership is not defined by a title - it is defined by a person's actions. Also anyone can be a Leader or has Leadership potential in them- it is a matter of drawing that out of people. I found this book useful and I also found myself looking at my own actions and the actions of others around me (including my management) to see how open and transparent I am in my work. For me I am glad I read this book as it validated my own thoughts of the changes we are seeing take place. This book has certainly given me some new ideas and helped me push my own boundaries of what I can do. The book has a number of action plans at the end of some of the chapters such as "Conducting you Openness Audit" that I think have helped me take thoughts and ideas and turn them into concrete action items. I have included a link to the introduction of the book here if anyone wants to have a read of it. If anyone else has read this book, it would be great to hear your thoughts/comments/review. Leave your comments below. This article was originally posted on David Talamelli's Blog - David's Journal on Tap

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  • Simple-Talk development: a quick history lesson

    - by Michael Williamson
    Up until a few months ago, Simple-Talk ran on a pure .NET stack, with IIS as the web server and SQL Server as the database. Unfortunately, the platform for the site hadn’t quite gotten the love and attention it deserved. On the one hand, in the words of our esteemed editor Tony “I’d consider the current platform to be a “success”; it cost $10K, has lasted for 6 years, was finished, end to end in 6 months, and although we moan about it has got us quite a long way.” On the other hand, it was becoming increasingly clear that it needed some serious work. Among other issues, we had authors that wouldn’t blog because our current blogging platform, Community Server, was too painful for them to use. Forgetting about Simple-Talk for a moment, if you ask somebody what blogging platform they’d choose, the odds are they’d say WordPress. Regardless of its technical merits, it’s probably the most popular blogging platform, and it certainly seemed easier to use than Community Server. The issue was that WordPress is normally hosted on a Linux stack running PHP, Apache and MySQL — quite a difference from our Microsoft technology stack. We certainly didn’t want to rewrite the entire site — we just wanted a better blogging platform, with the rest of the existing, legacy site left as is. At a very high level, Simple-Talk’s technical design was originally very straightforward: when your browser sends an HTTP request to Simple-Talk, IIS (the web server) takes the request, does some work, and sends back a response. In order to keep the legacy site running, except with WordPress running the blogs, a different design is called for. We now use nginx as a reverse-proxy, which can then delegate requests to the appropriate application: So, when your browser sends a request to Simple-Talk, nginx takes that request and checks which part of the site you’re trying to access. Most of the time, it just passes the request along to IIS, which can then respond in much the same way it always has. However, if your request is for the blogs, then nginx delegates the request to WordPress. Unfortunately, as simple as that diagram looks, it hides an awful lot of complexity. In particular, the legacy site running on IIS was made up of four .NET applications. I’ve already mentioned one of these applications, Community Server, which handled the old blogs as well as managing membership and the forums. We have a couple of other applications to manage both our newsletters and our articles, and our own custom application to do some of the rendering on the site, such as the front page and the articles. When I say that it was made up of four .NET applications, this might conjure up an image in your mind of how they fit together: You might imagine four .NET applications, each with their own database, communicating over well-defined APIs. Sadly, reality was a little disappointing: We had four .NET applications that all ran on the same database. Worse still, there were many queries that happily joined across tables from multiple applications, meaning that each application was heavily dependent on the exact data schema that each other application used. Add to this that many of the queries were at least dozens of lines long, and practically identical to other queries except in a few key spots, and we can see that attempting to replace one component of the system would be more than a little tricky. However, the problems with the old system do give us a good place to start thinking about desirable qualities from any changes to the platform. Specifically: Maintainability — the tight coupling between each .NET application made it difficult to update any one application without also having to make changes elsewhere Replaceability — the tight coupling also meant that replacing one component wouldn’t be straightforward, especially if it wasn’t on a similar Microsoft stack. We’d like to be able to replace different parts without having to modify the existing codebase extensively Reusability — we’d like to be able to combine the different pieces of the system in different ways for different sites Repeatable deployments — rather than having to deploy the site manually with a long list of instructions, we should be able to deploy the entire site with a single command, allowing you to create a new instance of the site easily whether on production, staging servers, test servers or your own local machine Testability — if we can deploy the site with a single command, and each part of the site is no longer dependent on the specifics of how every other part of the site works, we can begin to run automated tests against the site, and against individual parts, both to prevent regressions and to do a little test-driven development In the next part, I’ll describe the high-level architecture we now have that hopefully brings us a little closer to these five traits.

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  • Mixed Emotions: Humans React to Natural Language Computer

    - by Applications User Experience
    There was a big event in Silicon Valley on Tuesday, November 15. Watson, the natural language computer developed at IBM Watson Research Center in Yorktown Heights, New York, and its inventor and principal research investigator, David Ferrucci, were guests at the Computer History Museum in Mountain View, California for another round of the television game Jeopardy. You may have read about or watched on YouTube how Watson beat Ken Jennings and Brad Rutter, two top Jeopardy competitors, last February. This time, Watson swept the floor with two Silicon Valley high-achievers, one a venture capitalist with a background  in math, computer engineering, and physics, and the other a technology and finance writer well-versed in all aspects of culture and humanities. Watson is the product of the DeepQA research project, which attempts to create an artificially intelligent computing system through advances in natural language processing (NLP), among other technologies. NLP is a computing strategy that seeks to provide answers by processing large amounts of unstructured data contained in multiple large domains of human knowledge. There are several ways to perform NLP, but one way to start is by recognizing key words, then processing  contextual  cues associated with the keyword concepts so that you get many more “smart” (that is, human-like) deductions,  rather than a series of “dumb” matches.  Jeopardy questions often require more than key word matching to get the correct answer; typically several pieces of information put together, often from vastly different categories, to come up with a satisfactory word string solution that can be rephrased as a question.  Smarter than your average search engine, but is it as smart as a human? Watson was especially fast at descrambling mixed-up state capital names, and recalling and pairing movie titles where one started and the other ended in the same word (e.g., Billion Dollar Baby Boom, where both titles used the word Baby). David said they had basically removed the variable of how fast Watson hit the buzzer compared to human contestants, but frustration frequently appeared on the faces of the contestants beaten to the punch by Watson. David explained that top Jeopardy winners like Jennings achieved their success with a similar strategy, timing their buzz to the end of the reading of the clue,  and “running the board”, being first to respond on about 60% of the clues.  Similar results for Watson. It made sense that Watson would be good at the technical and scientific stuff, so I figured the venture capitalist was toast. But I thought for sure Watson would lose to the writer in categories such as pop culture, wines and foods, and other humanities. Surprisingly, it held its own. I was amazed it could recognize a word definition of a syllogism in the category of philosophy. So what was the audience reaction to all of this? We started out expecting our formidable human contestants to easily run some of their categories; however, they started off on the wrong foot with the state capitals which Watson could unscramble so efficiently. By the end of the first round, contestants and the audience were feeling a little bit, well, …. deflated. Watson was winning by about $13,000, and the humans had gone into negative dollars. The IBM host said he was going to “slow Watson down a bit,” and the humans came back with respectable scores in Double Jeopardy. This was partially thanks to a very sympathetic audience (and host, also a human) providing “group-think” on many questions, especially baseball ‘s most valuable players, which by the way, couldn’t have been hard because even I knew them.  Yes, that’s right, the humans cheated. Since Watson could speak but not hear us (it didn’t have speech recognition capability), it was probably unaware of this. In Final Jeopardy, the single question had to do with law. I was sure Watson would blow this one, but all contestants were able to answer correctly about a copyright law. In a career devoted to making computers more helpful to people, I think I may have seen how a computer can do too much. I’m not sure I’d want to work side-by-side with a Watson doing my job. Certainly listening and empathy are important traits we humans still have over Watson.  While there was great enthusiasm in the packed room of computer scientists and their friends for this standing-room-only show, I think it made several of us uneasy (especially the poor human contestants whose egos were soundly bashed in the first round). This computer system, by the way , only took 4 years to program. David Ferrucci mentioned several practical uses for Watson, including medical diagnoses and legal strategies. Are you “the expert” in your job? Imagine NLP computing on an Oracle database.   This may be the user interface of the future to enable users to better process big data. How do you think you’d like it? Postscript: There were three little boys sitting in front of me in the very first row. They looked, how shall I say it, … unimpressed!

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  • What Counts For a DBA: Fitness

    - by Louis Davidson
    If you know me, you can probably guess that physical exercise is not really my thing. There was a time in my past when it a larger part of my life, but even then never in the same sort of passionate way as a number of our SQL friends.  For me, I find that mental exercise satisfies what I believe to be the same inner need that drives people to run farther than I like to drive on most Saturday mornings, and it is certainly just as addictive. Mental fitness shares many common traits with physical fitness, especially the need to attain it through repetitive training. I only wish that mental training burned off a bacon cheeseburger in the same manner as does jogging around a dewy park on Saturday morning. In physical training, there are at least two goals, the first of which is to be physically able to do a task. The second is to train the brain to perform the task without thinking too hard about it. No matter how long it has been since you last rode a bike, you will be almost certainly be able to hop on and start riding without thinking about the process of pedaling or balancing. If you’ve never ridden a bike, you could be a physics professor /Olympic athlete and still crash the first few times you try, even though you are as strong as an ox and your knowledge of the physics of bicycle riding makes the concept child’s play. For programming tasks, the process is very similar. As a DBA, you will come to know intuitively how to backup, optimize, and secure database systems. As a data programmer, you will work to instinctively use the clauses of Transact-SQL DML so that, when you need to group data three ways (and not four), you will know to use the GROUP BY clause with GROUPING SETS without resorting to a search engine.  You have the skill. Making it naturally then requires repetition and experience is the primary requirement, not just simply learning about a topic. The hardest part of being really good at something is this difference between knowledge and skill. I have recently taken several informative training classes with Kimball University on data warehousing and ETL. Now I have a lot more knowledge about designing data warehouses than before. I have also done a good bit of data warehouse designing of late and have started to improve to some level of proficiency with the theory. Yet, for all of this head knowledge, it is still a struggle to take what I have learned and apply it to the designs I am working on.  Data warehousing is still a task that is not yet deeply ingrained in my brain muscle memory. On the other hand, relational database design is something that no matter how much or how little I may get to do it, I am comfortable doing it. I have done it as a profession now for well over a decade, I teach classes on it, and I also have done (and continue to do) a lot of mental training beyond the work day. Sometimes the training is just basic education, some reading blogs and attending sessions at PASS events.  My best training comes from spending time working on other people’s design issues in forums (though not nearly as much as I would like to lately). Working through other people’s problems is a great way to exercise your brain on problems with which you’re not immediately familiar. The final bit of exercise I find useful for cultivating mental fitness for a data professional is also probably the nerdiest thing that I will ever suggest you do.  Akin to running in place, the idea is to work through designs in your head. I have designed more than one database system that would revolutionize grocery store operations, sales at my local Target store, the ordering process at Amazon, and ways to improve Disney World operations to get me through a line faster (some of which they are starting to implement without any of my help.) Never are the designs truly fleshed out, but enough to work through structures and processes.  On “paper”, I have designed database systems to catalog things as trivial as my Lego creations, rental car companies and my audio and video collections. Once I get the database designed mentally, sometimes I will create the database, add some data (often using Red-Gate’s Data Generator), and write a few queries to see if a concept was realistic, but I will rarely fully flesh out the database since I have no desire to do any user interface programming anymore.  The mental training allows me to keep in practice for when the time comes to do the work I love the most for real…even if I have been spending most of my work time lately building data warehouses.  If you are really strong of mind and body, perhaps you can mix a mental run with a physical run; though don’t run off of a cliff while contemplating how you might design a database to catalog the trees on a mountain…that would be contradictory to the purpose of both types of exercise.

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  • Waterfall Model (SDLC) vs. Prototyping Model

    The characters in the fable of the Tortoise and the Hare can easily be used to demonstrate the similarities and differences between the Waterfall and Prototyping software development models. This children fable is about a race between a consistently slow moving but steadfast turtle and an extremely fast but unreliable rabbit. After closely comparing each character’s attributes in correlation with both software development models, a trend seems to appear in that the Waterfall closely resembles the Tortoise in that Waterfall Model is typically a slow moving process that is broken up in to multiple sequential steps that must be executed in a standard linear pattern. The Tortoise can be quoted several times in the story saying “Slow and steady wins the race.” This is the perfect mantra for the Waterfall Model in that this model is seen as a cumbersome and slow moving. Waterfall Model Phases Requirement Analysis & Definition This phase focuses on defining requirements for a project that is to be developed and determining if the project is even feasible. Requirements are collected by analyzing existing systems and functionality in correlation with the needs of the business and the desires of the end users. The desired output for this phase is a list of specific requirements from the business that are to be designed and implemented in the subsequent steps. In addition this phase is used to determine if any value will be gained by completing the project. System Design This phase focuses primarily on the actual architectural design of a system, and how it will interact within itself and with other existing applications. Projects at this level should be viewed at a high level so that actual implementation details are decided in the implementation phase. However major environmental decision like hardware and platform decision are typically decided in this phase. Furthermore the basic goal of this phase is to design an application at the system level in those classes, interfaces, and interactions are defined. Additionally decisions about scalability, distribution and reliability should also be considered for all decisions. The desired output for this phase is a functional  design document that states all of the architectural decisions that have been made in regards to the project as well as a diagrams like a sequence and class diagrams. Software Design This phase focuses primarily on the refining of the decisions found in the functional design document. Classes and interfaces are further broken down in to logical modules based on the interfaces and interactions previously indicated. The output of this phase is a formal design document. Implementation / Coding This phase focuses primarily on implementing the previously defined modules in to units of code. These units are developed independently are intergraded as the system is put together as part of a whole system. Software Integration & Verification This phase primarily focuses on testing each of the units of code developed as well as testing the system as a whole. There are basic types of testing at this phase and they include: Unit Test and Integration Test. Unit Test are built to test the functionality of a code unit to ensure that it preforms its desired task. Integration testing test the system as a whole because it focuses on results of combining specific units of code and validating it against expected results. The output of this phase is a test plan that includes test with expected results and actual results. System Verification This phase primarily focuses on testing the system as a whole in regards to the list of project requirements and desired operating environment. Operation & Maintenance his phase primarily focuses on handing off the competed project over to the customer so that they can verify that all of their requirements have been met based on their original requirements. This phase will also validate the correctness of their requirements and if any changed need to be made. In addition, any problems not resolved in the previous phase will be handled in this section. The Waterfall Model’s linear and sequential methodology does offer a project certain advantages and disadvantages. Advantages of the Waterfall Model Simplistic to implement and execute for projects and/or company wide Limited demand on resources Large emphasis on documentation Disadvantages of the Waterfall Model Completed phases cannot be revisited regardless if issues arise within a project Accurate requirement are never gather prior to the completion of the requirement phase due to the lack of clarification in regards to client’s desires. Small changes or errors that arise in applications may cause additional problems The client cannot change any requirements once the requirements phase has been completed leaving them no options for changes as they see their requirements changes as the customers desires change. Excess documentation Phases are cumbersome and slow moving Learn more about the Major Process in the Sofware Development Life Cycle and Waterfall Model. Conversely, the Hare shares similar traits with the prototyping software development model in that ideas are rapidly converted to basic working examples and subsequent changes are made to quickly align the project with customers desires as they are formulated and as software strays from the customers vision. The basic concept of prototyping is to eliminate the use of well-defined project requirements. Projects are allowed to grow as the customer needs and request grow. Projects are initially designed according to basic requirements and are refined as requirement become more refined. This process allows customer to feel their way around the application to ensure that they are developing exactly what they want in the application This model also works well for determining the feasibility of certain approaches in regards to an application. Prototypes allow for quickly developing examples of implementing specific functionality based on certain techniques. Advantages of Prototyping Active participation from users and customers Allows customers to change their mind in specifying requirements Customers get a better understanding of the system as it is developed Earlier bug/error detection Promotes communication with customers Prototype could be used as final production Reduced time needed to develop applications compared to the Waterfall method Disadvantages of Prototyping Promotes constantly redefining project requirements that cause major system rewrites Potential for increased complexity of a system as scope of the system expands Customer could believe the prototype as the working version. Implementation compromises could increase the complexity when applying updates and or application fixes When companies trying to decide between the Waterfall model and Prototype model they need to evaluate the benefits and disadvantages for both models. Typically smaller companies or projects that have major time constraints typically head for more of a Prototype model approach because it can reduce the time needed to complete the project because there is more of a focus on building a project and less on defining requirements and scope prior to the start of a project. On the other hand, Companies with well-defined requirements and time allowed to generate proper documentation should steer towards more of a waterfall model because they are in a position to obtain clarified requirements and have to design and optimal solution prior to the start of coding on a project.

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  • Augmenting your Social Efforts via Data as a Service (DaaS)

    - by Mike Stiles
    The following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo. In this post, we will discuss the approach and value of integrating additional “public” data via a cloud-based Data-as-as-Service platform (or DaaS) to augment your Socially Enabled Big Data Analytics and CX Management. Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application. What is the next step? Augmenting Social Data with other Public Data for More Advanced Analytics When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced: It can vary across structured, semi-structured, and unstructured data It can span across content, profile, and communities of profiles data It is increasingly public, curated and user generated The key is not just getting the data, but making it value-added data and using it to help discover the insights to connect to and improve your KPIs. As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications. The following diagram shows the requirements for this next generation data and insights service or (DaaS): Some quick points on these requirements: Public Data, which in this context is about Common Business Entities, such as - Customers, Suppliers, Partners, Competitors (all are organizations) Contacts, Consumers, Employees (all are people) Products, Brands This data can be broadly categorized incrementally as - Base Utility data (address, industry classification) Public Master Reference data (trade style, hierarchy) Social/Web data (News, Feeds, Graph) Transactional Data generated by enterprise process, workflows etc. This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes - Change management of Public Reference Data across all categories Applied Big Data to extract statics as well as real-time insights Knowledge Diagnostics and Data Mining As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is: Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply Cost-effective: Pre-integrated into applications so customers don’t have to Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place Data-as-a-Service (DaaS) Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS). Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration. Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur. How do you decide the best approach? Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value. The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.). Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved. Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined capabilities to extract and integrate the right data from the right sources with the right factoring at the right time for faster decision-making and action within your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage. Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications. In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM. In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc. This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with. DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.Photo: Erik Araujo, stock.xchng

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  • EM12c Release 4: Database as a Service Enhancements

    - by Adeesh Fulay
    Oracle Enterprise Manager 12.1.0.4 (or simply put EM12c R4) is the latest update to the product. As previous versions, this release provides tons of enhancements and bug fixes, attributing to improved stability and quality. One of the areas that is most exciting and has seen tremendous growth in the last few years is that of Database as a Service. EM12c R4 provides a significant update to Database as a Service. The key themes are: Comprehensive Database Service Catalog (includes single instance, RAC, and Data Guard) Additional Storage Options for Snap Clone (includes support for Database feature CloneDB) Improved Rapid Start Kits Extensible Metering and Chargeback Miscellaneous Enhancements 1. Comprehensive Database Service Catalog Before we get deep into implementation of a service catalog, lets first understand what it is and what benefits it provides. Per ITIL, a service catalog is an exhaustive list of IT services that an organization provides or offers to its employees or customers. Service catalogs have been widely popular in the space of cloud computing, primarily as the medium to provide standardized and pre-approved service definitions. There is already some good collateral out there that talks about Oracle database service catalogs. The two whitepapers i recommend reading are: Service Catalogs: Defining Standardized Database Service High Availability Best Practices for Database Consolidation: The Foundation for Database as a Service [Oracle MAA] EM12c comes with an out-of-the-box service catalog and self service portal since release 1. For the customers, it provides the following benefits: Present a collection of standardized database service definitions, Define standardized pools of hardware and software for provisioning, Role based access to cater to different class of users, Automated procedures to provision the predefined database definitions, Setup chargeback plans based on service tiers and database configuration sizes, etc Starting Release 4, the scope of services offered via the service catalog has been expanded to include databases with varying levels of availability - Single Instance (SI) or Real Application Clusters (RAC) databases with multiple data guard based standby databases. Some salient points of the data guard integration: Standby pools can now be defined across different datacenters or within the same datacenter as the primary (this helps in modelling the concept of near and far DR sites) The standby databases can be single instance, RAC, or RAC One Node databases Multiple standby databases can be provisioned, where the maximum limit is determined by the version of database software The standby databases can be in either mount or read only (requires active data guard option) mode All database versions 10g to 12c supported (as certified with EM 12c) All 3 protection modes can be used - Maximum availability, performance, security Log apply can be set to sync or async along with the required apply lag The different service levels or service tiers are popularly represented using metals - Platinum, Gold, Silver, Bronze, and so on. The Oracle MAA whitepaper (referenced above) calls out the various service tiers as defined by Oracle's best practices, but customers can choose any logical combinations from the table below:  Primary  Standby [1 or more]  EM 12cR4  SI  -  SI  SI  RAC -  RAC SI  RAC RAC  RON -  RON RON where RON = RAC One Node is supported via custom post-scripts in the service template A sample service catalog would look like the image below. Here we have defined 4 service levels, which have been deployed across 2 data centers, and have 3 standardized sizes. Again, it is important to note that this is just an example to get the creative juices flowing. I imagine each customer would come up with their own catalog based on the application requirements, their RTO/RPO goals, and the product licenses they own. In the screenwatch titled 'Build Service Catalog using EM12c DBaaS', I walk through the complete steps required to setup this sample service catalog in EM12c. 2. Additional Storage Options for Snap Clone In my previous blog posts, i have described the snap clone feature in detail. Essentially, it provides a storage agnostic, self service, rapid, and space efficient approach to solving your data cloning problems. The net benefit is that you get incredible amounts of storage savings (on average 90%) all while cloning databases in a matter of minutes. Space and Time, two things enterprises would love to save on. This feature has been designed with the goal of providing data cloning capabilities while protecting your existing investments in server, storage, and software. With this in mind, we have pursued with the dual solution approach of Hardware and Software. In the hardware approach, we connect directly to your storage appliances and perform all low level actions required to rapidly clone your databases. While in the software approach, we use an intermediate software layer to talk to any storage vendor or any storage configuration to perform the same low level actions. Thus delivering the benefits of database thin cloning, without requiring you to drastically changing the infrastructure or IT's operating style. In release 4, we expand the scope of options supported by snap clone with the addition of database CloneDB. While CloneDB is not a new feature, it was first introduced in 11.2.0.2 patchset, it has over the years become more stable and mature. CloneDB leverages a combination of Direct NFS (or dNFS) feature of the database, RMAN image copies, sparse files, and copy-on-write technology to create thin clones of databases from existing backups in a matter of minutes. It essentially has all the traits that we want to present to our customers via the snap clone feature. For more information on cloneDB, i highly recommend reading the following sources: Blog by Tim Hall: Direct NFS (DNFS) CloneDB in Oracle Database 11g Release 2 Oracle OpenWorld Presentation by Cern: Efficient Database Cloning using Direct NFS and CloneDB The advantages of the new CloneDB integration with EM12c Snap Clone are: Space and time savings Ease of setup - no additional software is required other than the Oracle database binary Works on all platforms Reduce the dependence on storage administrators Cloning process fully orchestrated by EM12c, and delivered to developers/DBAs/QA Testers via the self service portal Uses dNFS to delivers better performance, availability, and scalability over kernel NFS Complete lifecycle of the clones managed by EM12c - performance, configuration, etc 3. Improved Rapid Start Kits DBaaS deployments tend to be complex and its setup requires a series of steps. These steps are typically performed across different users and different UIs. The Rapid Start Kit provides a single command solution to setup Database as a Service (DBaaS) and Pluggable Database as a Service (PDBaaS). One command creates all the Cloud artifacts like Roles, Administrators, Credentials, Database Profiles, PaaS Infrastructure Zone, Database Pools and Service Templates. Once the Rapid Start Kit has been successfully executed, requests can be made to provision databases and PDBs from the self service portal. Rapid start kit can create complex topologies involving multiple zones, pools and service templates. It also supports standby databases and use of RMAN image backups. The Rapid Start Kit in reality is a simple emcli script which takes a bunch of xml files as input and executes the complete automation in a matter of seconds. On a full rack Exadata, it took only 40 seconds to setup PDBaaS end-to-end. This kit works for both Oracle's engineered systems like Exadata, SuperCluster, etc and also on commodity hardware. One can draw parallel to the Exadata One Command script, which again takes a bunch of inputs from the administrators and then runs a simple script that configures everything from network to provisioning the DB software. Steps to use the kit: The kit can be found under the SSA plug-in directory on the OMS: EM_BASE/oracle/MW/plugins/oracle.sysman.ssa.oms.plugin_12.1.0.8.0/dbaas/setup It can be run from this default location or from any server which has emcli client installed For most scenarios, you would use the script dbaas/setup/database_cloud_setup.py For Exadata, special integration is provided to reduce the number of inputs even further. The script to use for this scenario would be dbaas/setup/exadata_cloud_setup.py The database_cloud_setup.py script takes two inputs: Cloud boundary xml: This file defines the cloud topology in terms of the zones and pools along with host names, oracle home locations or container database names that would be used as infrastructure for provisioning database services. This file is optional in case of Exadata, as the boundary is well know via the Exadata system target available in EM. Input xml: This file captures inputs for users, roles, profiles, service templates, etc. Essentially, all inputs required to define the DB services and other settings of the self service portal. Once all the xml files have been prepared, invoke the script as follows for PDBaaS: emcli @database_cloud_setup.py -pdbaas -cloud_boundary=/tmp/my_boundary.xml -cloud_input=/tmp/pdb_inputs.xml          The script will prompt for passwords a few times for key users like sysman, cloud admin, SSA admin, etc. Once complete, you can simply log into EM as the self service user and request for databases from the portal. More information available in the Rapid Start Kit chapter in Cloud Administration Guide.  4. Extensible Metering and Chargeback  Last but not the least, Metering and Chargeback in release 4 has been made extensible in all possible regards. The new extensibility features allow customer, partners, system integrators, etc to : Extend chargeback to any target type managed in EM Promote any metric in EM as a chargeback entity Extend list of charge items via metric or configuration extensions Model abstract entities like no. of backup requests, job executions, support requests, etc  A slew of emcli verbs have also been added that allows administrators to create, edit, delete, import/export charge plans, and assign cost centers all via the command line. More information available in the Chargeback API chapter in Cloud Administration Guide. 5. Miscellaneous Enhancements There are other miscellaneous, yet important, enhancements that are worth a mention. These mostly have been asked by customers like you. These are: Custom naming of DB Services Self service users can provide custom names for DB SID, DB service, schemas, and tablespaces Every custom name is validated for uniqueness in EM 'Create like' of Service Templates Now creating variants of a service template is only a click away. This would be vital when you publish service templates to represent different database sizes or service levels. Profile viewer View the details of a profile like datafile, control files, snapshot ids, export/import files, etc prior to its selection in the service template Cleanup automation - for failed and successful requests Single emcli command to cleanup all remnant artifacts of a failed request Cleanup can be performed on a per request bases or by the entire pool As an extension, you can also delete successful requests Improved delete user workflow Allows administrators to reassign cloud resources to another user or delete all of them Support for multiple tablespaces for schema as a service In addition to multiple schemas, user can also specify multiple tablespaces per request I hope this was a good introduction to the new Database as a Service enhancements in EM12c R4. I encourage you to explore many of these new and existing features and give us feedback. Good luck! References: Cloud Management Page on OTN Cloud Administration Guide [Documentation] -- Adeesh Fulay (@adeeshf)

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  • Abstracting functionality

    - by Ralf Westphal
    Originally posted on: http://geekswithblogs.net/theArchitectsNapkin/archive/2014/08/22/abstracting-functionality.aspxWhat is more important than data? Functionality. Yes, I strongly believe we should switch to a functionality over data mindset in programming. Or actually switch back to it. Focus on functionality Functionality once was at the core of software development. Back when algorithms were the first thing you heard about in CS classes. Sure, data structures, too, were important - but always from the point of view of algorithms. (Niklaus Wirth gave one of his books the title “Algorithms + Data Structures” instead of “Data Structures + Algorithms” for a reason.) The reason for the focus on functionality? Firstly, because software was and is about doing stuff. Secondly because sufficient performance was hard to achieve, and only thirdly memory efficiency. But then hardware became more powerful. That gave rise to a new mindset: object orientation. And with it functionality was devalued. Data took over its place as the most important aspect. Now discussions revolved around structures motivated by data relationships. (John Beidler gave his book the title “Data Structures and Algorithms: An Object Oriented Approach” instead of the other way around for a reason.) Sure, this data could be embellished with functionality. But nevertheless functionality was second. When you look at (domain) object models what you mostly find is (domain) data object models. The common object oriented approach is: data aka structure over functionality. This is true even for the most modern modeling approaches like Domain Driven Design. Look at the literature and what you find is recommendations on how to get data structures right: aggregates, entities, value objects. I´m not saying this is what object orientation was invented for. But I´m saying that´s what I happen to see across many teams now some 25 years after object orientation became mainstream through C++, Delphi, and Java. But why should we switch back? Because software development cannot become truly agile with a data focus. The reason for that lies in what customers need first: functionality, behavior, operations. To be clear, that´s not why software is built. The purpose of software is to be more efficient than the alternative. Money mainly is spent to get a certain level of quality (e.g. performance, scalability, security etc.). But without functionality being present, there is nothing to work on the quality of. What customers want is functionality of a certain quality. ASAP. And tomorrow new functionality needs to be added, existing functionality needs to be changed, and quality needs to be increased. No customer ever wanted data or structures. Of course data should be processed. Data is there, data gets generated, transformed, stored. But how the data is structured for this to happen efficiently is of no concern to the customer. Ask a customer (or user) whether she likes the data structured this way or that way. She´ll say, “I don´t care.” But ask a customer (or user) whether he likes the functionality and its quality this way or that way. He´ll say, “I like it” (or “I don´t like it”). Build software incrementally From this very natural focus of customers and users on functionality and its quality follows we should develop software incrementally. That´s what Agility is about. Deliver small increments quickly and often to get frequent feedback. That way less waste is produced, and learning can take place much easier (on the side of the customer as well as on the side of developers). An increment is some added functionality or quality of functionality.[1] So as it turns out, Agility is about functionality over whatever. But software developers’ thinking is still stuck in the object oriented mindset of whatever over functionality. Bummer. I guess that (at least partly) explains why Agility always hits a glass ceiling in projects. It´s a clash of mindsets, of cultures. Driving software development by demanding small increases in functionality runs against thinking about software as growing (data) structures sprinkled with functionality. (Excuse me, if this sounds a bit broad-brush. But you get my point.) The need for abstraction In the end there need to be data structures. Of course. Small and large ones. The phrase functionality over data does not deny that. It´s not functionality instead of data or something. It´s just over, i.e. functionality should be thought of first. It´s a tad more important. It´s what the customer wants. That´s why we need a way to design functionality. Small and large. We need to be able to think about functionality before implementing it. We need to be able to reason about it among team members. We need to be able to communicate our mental models of functionality not just by speaking about them, but also on paper. Otherwise reasoning about it does not scale. We learned thinking about functionality in the small using flow charts, Nassi-Shneiderman diagrams, pseudo code, or UML sequence diagrams. That´s nice and well. But it does not scale. You can use these tools to describe manageable algorithms. But it does not work for the functionality triggered by pressing the “1-Click Order” on an amazon product page for example. There are several reasons for that, I´d say. Firstly, the level of abstraction over code is negligible. It´s essentially non-existent. Drawing a flow chart or writing pseudo code or writing actual code is very, very much alike. All these tools are about control flow like code is.[2] In addition all tools are computationally complete. They are about logic which is expressions and especially control statements. Whatever you code in Java you can fully (!) describe using a flow chart. And then there is no data. They are about control flow and leave out the data altogether. Thus data mostly is assumed to be global. That´s shooting yourself in the foot, as I hope you agree. Even if it´s functionality over data that does not mean “don´t think about data”. Right to the contrary! Functionality only makes sense with regard to data. So data needs to be in the picture right from the start - but it must not dominate the thinking. The above tools fail on this. Bottom line: So far we´re unable to reason in a scalable and abstract manner about functionality. That´s why programmers are so driven to start coding once they are presented with a problem. Programming languages are the only tool they´ve learned to use to reason about functional solutions. Or, well, there might be exceptions. Mathematical notation and SQL may have come to your mind already. Indeed they are tools on a higher level of abstraction than flow charts etc. That´s because they are declarative and not computationally complete. They leave out details - in order to deliver higher efficiency in devising overall solutions. We can easily reason about functionality using mathematics and SQL. That´s great. Except for that they are domain specific languages. They are not general purpose. (And they don´t scale either, I´d say.) Bummer. So to be more precise we need a scalable general purpose tool on a higher than code level of abstraction not neglecting data. Enter: Flow Design. Abstracting functionality using data flows I believe the solution to the problem of abstracting functionality lies in switching from control flow to data flow. Data flow very naturally is not about logic details anymore. There are no expressions and no control statements anymore. There are not even statements anymore. Data flow is declarative by nature. With data flow we get rid of all the limiting traits of former approaches to modeling functionality. In addition, nomen est omen, data flows include data in the functionality picture. With data flows, data is visibly flowing from processing step to processing step. Control is not flowing. Control is wherever it´s needed to process data coming in. That´s a crucial difference and needs some rewiring in your head to be fully appreciated.[2] Since data flows are declarative they are not the right tool to describe algorithms, though, I´d say. With them you don´t design functionality on a low level. During design data flow processing steps are black boxes. They get fleshed out during coding. Data flow design thus is more coarse grained than flow chart design. It starts on a higher level of abstraction - but then is not limited. By nesting data flows indefinitely you can design functionality of any size, without losing sight of your data. Data flows scale very well during design. They can be used on any level of granularity. And they can easily be depicted. Communicating designs using data flows is easy and scales well, too. The result of functional design using data flows is not algorithms (too low level), but processes. Think of data flows as descriptions of industrial production lines. Data as material runs through a number of processing steps to be analyzed, enhances, transformed. On the top level of a data flow design might be just one processing step, e.g. “execute 1-click order”. But below that are arbitrary levels of flows with smaller and smaller steps. That´s not layering as in “layered architecture”, though. Rather it´s a stratified design à la Abelson/Sussman. Refining data flows is not your grandpa´s functional decomposition. That was rooted in control flows. Refining data flows does not suffer from the limits of functional decomposition against which object orientation was supposed to be an antidote. Summary I´ve been working exclusively with data flows for functional design for the past 4 years. It has changed my life as a programmer. What once was difficult is now easy. And, no, I´m not using Clojure or F#. And I´m not a async/parallel execution buff. Designing the functionality of increments using data flows works great with teams. It produces design documentation which can easily be translated into code - in which then the smallest data flow processing steps have to be fleshed out - which is comparatively easy. Using a systematic translation approach code can mirror the data flow design. That way later on the design can easily be reproduced from the code if need be. And finally, data flow designs play well with object orientation. They are a great starting point for class design. But that´s a story for another day. To me data flow design simply is one of the missing links of systematic lightweight software design. There are also other artifacts software development can produce to get feedback, e.g. process descriptions, test cases. But customers can be delighted more easily with code based increments in functionality. ? No, I´m not talking about the endless possibilities this opens for parallel processing. Data flows are useful independently of multi-core processors and Actor-based designs. That´s my whole point here. Data flows are good for reasoning and evolvability. So forget about any special frameworks you might need to reap benefits from data flows. None are necessary. Translating data flow designs even into plain of Java is possible. ?

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  • build error with boost spirit grammar (boost 1.43 and g++ 4.4.1) part II

    - by lurscher
    I'm having issues getting a small spirit/qi grammar to compile. i am using boost 1.43 and g++ 4.4.1. the input grammar header: inputGrammar.h #include <boost/config/warning_disable.hpp> #include <boost/spirit/include/qi.hpp> #include <boost/spirit/include/phoenix_core.hpp> #include <boost/spirit/include/phoenix_operator.hpp> #include <boost/spirit/include/phoenix_fusion.hpp> #include <boost/spirit/include/phoenix_stl.hpp> #include <boost/fusion/include/adapt_struct.hpp> #include <boost/variant/recursive_variant.hpp> #include <boost/foreach.hpp> #include <iostream> #include <fstream> #include <string> #include <vector> namespace sp = boost::spirit; namespace qi = boost::spirit::qi; using namespace boost::spirit::ascii; //using namespace boost::spirit::arg_names; namespace fusion = boost::fusion; namespace phoenix = boost::phoenix; using phoenix::at_c; using phoenix::push_back; template< typename Iterator , typename ExpressionAST > struct InputGrammar : qi::grammar<Iterator, ExpressionAST(), space_type> { InputGrammar() : InputGrammar::base_type( block ) { tag = sp::lexeme[+(alpha) [sp::_val += sp::_1]];//[+(char_ - '<') [_val += _1]]; block = sp::lit("block") [ at_c<0>(sp::_val) = sp::_1] >> "(" >> *instruction[ push_back( at_c<1>(sp::_val) , sp::_1 ) ] >> ")"; command = tag [ at_c<0>(sp::_val) = sp::_1] >> "(" >> *instruction [ push_back( at_c<1>(sp::_val) , sp::_1 )] >> ")"; instruction = ( command | tag ) [sp::_val = sp::_1]; } qi::rule< Iterator , std::string() , space_type > tag; qi::rule< Iterator , ExpressionAST() , space_type > block; qi::rule< Iterator , ExpressionAST() , space_type > function_def; qi::rule< Iterator , ExpressionAST() , space_type > command; qi::rule< Iterator , ExpressionAST() , space_type > instruction; }; the test build program: #include <iostream> #include <string> #include <vector> using namespace std; //my grammar #include <InputGrammar.h> struct MockExpressionNode { std::string name; std::vector< MockExpressionNode > operands; typedef std::vector< MockExpressionNode >::iterator iterator; typedef std::vector< MockExpressionNode >::const_iterator const_iterator; iterator begin() { return operands.begin(); } const_iterator begin() const { return operands.begin(); } iterator end() { return operands.end(); } const_iterator end() const { return operands.end(); } bool is_leaf() const { return ( operands.begin() == operands.end() ); } }; BOOST_FUSION_ADAPT_STRUCT( MockExpressionNode, (std::string, name) (std::vector<MockExpressionNode>, operands) ) int const tabsize = 4; void tab(int indent) { for (int i = 0; i < indent; ++i) std::cout << ' '; } template< typename ExpressionNode > struct ExpressionNodePrinter { ExpressionNodePrinter(int indent = 0) : indent(indent) { } void operator()(ExpressionNode const& node) const { cout << " tag: " << node.name << endl; for (int i=0 ; i < node.operands.size() ; i++ ) { tab( indent ); cout << " arg "<<i<<": "; ExpressionNodePrinter(indent + 2)( node.operands[i]); cout << endl; } } int indent; }; int test() { MockExpressionNode root; InputGrammar< string::const_iterator , MockExpressionNode > g; std::string litA = "litA"; std::string litB = "litB"; std::string litC = "litC"; std::string litD = "litD"; std::string litE = "litE"; std::string litF = "litF"; std::string source = litA+"( "+litB+" ,"+litC+" , "+ litD+" ( "+litE+", "+litF+" ) "+ " )"; string::const_iterator iter = source.begin(); string::const_iterator end = source.end(); bool r = qi::phrase_parse( iter , end , g , space , root ); ExpressionNodePrinter< MockExpressionNode > np; np( root ); }; int main() { test(); } finally, the build error is the following: (the full error trace is 20 times bigger than the allowed size for a stackoverflow question, so i posted the full version of it at http://codepad.org/Q74IVCUc) /usr/bin/make -f nbproject/Makefile-linux_amd64_devel.mk SUBPROJECTS= .build-conf make[1]: se ingresa al directorio `/home/mineq/NetBeansProjects/InputParserTests' /usr/bin/make -f nbproject/Makefile-linux_amd64_devel.mk dist/linux_amd64_devel/GNU-Linux-x86/vpuinputparsertests make[2]: se ingresa al directorio `/home/mineq/NetBeansProjects/InputParserTests' mkdir -p build/linux_amd64_devel/GNU-Linux-x86 rm -f build/linux_amd64_devel/GNU-Linux-x86/tests_main.o.d g++ `llvm-config --cxxflags` `pkg-config --cflags unittest-cpp` `pkg-config --cflags boost-1.43` `pkg-config --cflags boost-coroutines` -c -g -I../InputParser -MMD -MP -MF build/linux_amd64_devel/GNU-Linux-x86/tests_main.o.d -o build/linux_amd64_devel/GNU-Linux-x86/tests_main.o tests_main.cpp from /home/mineq/third_party/boost_1_43_0/boost/spirit/include/phoenix_operator.hpp:11, from ../InputParser/InputGrammar.h:14, from tests_main.cpp:14: /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/operator/self.hpp: In instantiation of ‘const int boost::phoenix::result_of_assign<MockExpressionNode&, boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>::size’: In file included from /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/operator.hpp:16, /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/operator/self.hpp:27: instantiated from ‘const int boost::phoenix::result_of_assign<MockExpressionNode&, boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>::index’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/operator/self.hpp:27: instantiated from ‘boost::phoenix::result_of_assign<MockExpressionNode&, boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>’ /home/mineq/third_party/boost_1_43_0/boost/mpl/eval_if.hpp:38: instantiated from ‘boost::mpl::eval_if<boost::mpl::or_<boost::phoenix::is_actor<MockExpressionNode&>, boost::phoenix::is_actor<boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>, mpl_::bool_<false>, mpl_::bool_<false>, mpl_::bool_<false> >, boost::phoenix::re_curry<boost::phoenix::assign_eval, MockExpressionNode&, boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_>, boost::phoenix::result_of_assign<MockExpressionNode&, boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&> >’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/operator/self.hpp:69: instantiated from ‘boost::phoenix::assign_eval::result<boost::phoenix::basic_environment<boost::fusion::vector1<boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>, boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, bool, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_>, boost::spirit::attribute<0>, boost::spirit::argument<0> >’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/core/detail/composite_eval.hpp:89: instantiated from ‘boost::phoenix::detail::composite_eval<2>::result<boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> >, boost::phoenix::basic_environment<boost::fusion::vector1<boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>, boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, bool, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> >’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/core/composite.hpp:61: instantiated from ‘boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> >::result<boost::phoenix::basic_environment<boost::fusion::vector1<boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>, boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, bool, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> >’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/core/actor.hpp:56: instantiated from ‘boost::phoenix::eval_result<boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> >, boost::phoenix::basic_environment<boost::fusion::vector1<boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>, boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, bool, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> >’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/phoenix/core/actor.hpp:65: instantiated from ‘boost::phoenix::actor<boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> > >::result<boost::phoenix::actor<boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> > >(boost::fusion::vector1<boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>&>&, boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >&, bool&)>’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/support/action_dispatch.hpp:44: instantiated from ‘bool boost::spirit::traits::action_dispatch<Component>::operator()(const boost::phoenix::actor<Eval>&, Attribute&, Context&) [with Eval = boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> >, Attribute = boost::variant<MockExpressionNode, std::basic_string<char, std::char_traits<char>, std::allocator<char> >, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_>, Context = boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, Component = boost::spirit::qi::alternative<boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, MockExpressionNode(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::string(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::nil> > >]’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/qi/action/action.hpp:62: instantiated from ‘bool boost::spirit::qi::action<Subject, Action>::parse(Iterator&, const Iterator&, Context&, const Skipper&, Attribute&) const [with Iterator = __gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, Context = boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, Skipper = boost::spirit::qi::char_class<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, Attribute = const boost::fusion::unused_type, Subject = boost::spirit::qi::alternative<boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, MockExpressionNode(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::string(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::nil> > >, Action = boost::phoenix::actor<boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> > >]’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/qi/nonterminal/detail/parser_binder.hpp:33: instantiated from ‘bool boost::spirit::qi::detail::parser_binder<Parser, Auto>::call(Iterator&, const Iterator&, Context&, const Skipper&, mpl_::true_) const [with Iterator = __gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, Skipper = boost::spirit::qi::char_class<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, Context = boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, Parser = boost::spirit::qi::action<boost::spirit::qi::alternative<boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, MockExpressionNode(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::string(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::nil> > >, boost::phoenix::actor<boost::phoenix::composite<boost::phoenix::assign_eval, boost::fusion::vector<boost::spirit::attribute<0>, boost::spirit::argument<0>, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_, boost::fusion::void_> > > >, Auto = mpl_::bool_<false>]’ /home/mineq/third_party/boost_1_43_0/boost/spirit/home/qi/nonterminal/detail/parser_binder.hpp:53: instantiated from ‘bool boost::spirit::qi::detail::parser_binder<Parser, Auto>::operator()(Iterator&, const Iterator&, Context&, const Skipper&) const [with Iterator = __gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, Skipper = boost::spirit::qi::char_class<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, Context = boost::spirit::context<boost::fusion::cons<MockExpressionNode&, boost::fusion::nil>, boost::fusion::vector0<void> >, Parser = boost::spirit::qi::action<boost::spirit::qi::alternative<boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, MockExpressionNode(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::cons<boost::spirit::qi::reference<const boost::spirit::qi::rule<__gnu_cxx::__normal_iterator<const char*, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::string(), boost::proto::exprns_::expr<boost::proto::tag::terminal, boost::proto::argsns_::term<boost::spirit::tag::char_code<boost::spirit::tag::space, boost::spirit::char_encoding::ascii> >, 0l>, boost::fusion::unused_type, boost::fusion::unused_type> >, boost::fusion::nil> > >, ... ... more errors but i had to truncate to fit the 30k limit make[2]: *** [build/linux_amd64_devel/GNU-Linux-x86/tests_main.o] Error 1 make[2]: se sale del directorio `/home/mineq/NetBeansProjects/InputParserTests' make[1]: *** [.build-conf] Error 2 make[1]: se sale del directorio `/home/mineq/NetBeansProjects/InputParserTests' make: *** [.build-impl] Error 2 BUILD FAILED (exit value 2, total time: 2m 13s)

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