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Within the confines of a single machine, these mechanisms can get you started on writing application components as scalable producers and consumers Finally, if you are writing a large application that is sending huge amounts of data in one direction using the pipeline pattern, then you might also want to check out this resource: http://wikipythonorg/moin/FlowBasedProgramming It will point you toward resources related to Python and flow-based programming, which steps back from the idea of messages to the more general idea of information flowing downstream from an origin, through various processing steps, and finally to a destination that saves or displays the result This can be a very natural way to express various scientific computations, as well as massively datadriven tasks like searching web server log files for various patterns.

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Listing 7-13. The class declaration of the custom widget class CircleWidget : public QWidget { Q_OBJECT public: CircleWidget( QWidget *parent=0 ); QSize sizeHint() const; private slots: void timeout(); protected: void paintEvent( QPaintEvent* ); void mousePressEvent( QMouseEvent* ); void mouseMoveEvent( QMouseEvent* ); void mouseReleaseEvent( QMouseEvent* ); private: int x, y, r; QColor color;

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Some flow-based systems even support the use of a graphical interface, which can let scientists and other researchers who might be unfamiliar with programming build quite sophisticated data processing stacks One final note: do not let the recent popularity of message queues mislead you into thinking that the messaging pattern itself is a recent phenomenon! It is not Message queues are merely the formalization of an ages-old architecture that would originally have involved piles of punch cards waiting for processing, and that in more recent incarnations included things like incoming FTP folders full of files that were submitted for processing The modern libraries are simply a useful and general implementation of a very old wheel that has been re-invented countless times..

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int mx, my; QTimer timer; }; The constructor shown in Listing 7-14 initializes the radius of the current circle, r, to 0, meaning no circle. It then configures and connects a QTimer object. The timer interval is set to 50 milliseconds, meaning that the circle will be updated roughly 20 times per second (this is often enough to imitate a continuous motion). Listing 7-14. Initializing the custom widget CircleWidget::CircleWidget( QWidget *parent ) : QWidget( parent ) { r = 0; timer.setInterval( 50 ); connect( &timer, SIGNAL(timeout()), this, SLOT(timeout()) ); } The sizeHint method is the simplest one of the entire class; it simply returns a static size (see Listing 7-15). Listing 7-15. Returning a static size QSize CircleWidget::sizeHint() const { return QSize( 200, 200 ); } Listing 7-16 shows the three methods used to track the mouse activity. Before looking too closely at the methods it is important to know that mouse movements are reported only when the mouse buttons are pressed. This means that mouseMoveEvent will not be called unless a mouse button is pressed.

Traditionally, if you wanted to distribute a large task across several racks of machine-room servers, then you faced two quite different problems. First, of course, you had to write code that could be assigned a small part of the problem and solve it, and then write code that could assemble the various answers from each node back into one big answer to the original question. But, finally, you would also have wound up writing a lot of code that had little to do with your problem at all: the scripts that would push your code out to all of the servers in the cluster, then run it, and then finally collect the data back together using the network or a shared file system. The idea of a map-reduce system is to eliminate that last step in distributing a large computation, and to offer a framework that will distribute data and execute code without your having to worry about the underlying distribution mechanisms. Most frameworks also implement precautions that are often not present in homemade parallel computations, like the ability to seamlessly re-submit tasks to other nodes if some of the cluster servers fail during a particular computation. In fact, some map-reduce frameworks will happily let you unplug and reboot machines for routine maintenance even while the

if (userItems[l].nodeType != 1) { } else if (userItems[l].firstChild != null) { switch (userItems[l].nodeName) { case "id" : { userID = userItems[l].firstChild.nodeValue; break; } case "screen_name" : { screenName = userItems[l] .firstChild.nodeValue; break; } case "location" : { location = userItems[l] .firstChild.nodeValue; break; } case "description" : { description = userItems[l] .firstChild.nodeValue; break; } case "profile_image_url" : { imageUrl = userItems[l] .firstChild.nodeValue; // HACK: twitter sometimes return url as blah.comsystem/blah // instead of blah.com/system/blah imageUrl = imageUrl .replace("comsystem", "com/system"); break; }

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