When developing RESTful web services, people are often confused with handling exceptions. This post will explain a powerful tactic for using exception mapper with the Dropwizard framework.

If you see an annotation, there must be some code somewhere to process it.

In this article, we will discuss how to configure "Quartz Scheduler" with a web application. Read on for more info.

What dark launches are and how they improve the future releases and stability of application infrastructure.

Monitoring, diagnosing, and troubleshooting are key activities in any enterprise application lifecycle, and logging is the core part of these activities. Meet Logback, a logging framework from the creator of Log4J.

We have all kinds of chief officers. Is now the time for Chief Performance Officers (CPO)? With more onus on performance, maybe so.

Groovy is a dynamic language with powerful capabilities for typing and compilation on the Java platform; use this snippet to debug it without compiling.

Need a custom authenticator for your WSO2 Identity Server SSO login? We've got you covered. Let's look at implementing a WSO2IS authenticator, and extend the basic authenticator.

Use the command line to do some efficient batch programming for your firmware boards

In this webinar David Norton, of Gartner Research, discusses findings on Technical Debt that estimates industry IT debt is at $500 billion—and on target to reach $1 trillion by 2015. Now that were in 2015, it's interesting to see him talk about the importance of software analysis and measurement in managing Technical Debt. He also touches on how to measure debt continuously in order to control total cost of ownership of the application life-cycle and include debt measurement in project management and prioritization. Visit here to watch the full webinar: http://goo.gl/yOJZn8

At Bloomfire’s User Conference in May, I had the pleasure of listening to City of Austin’s Chief Innovation Officer Kerry O’Connor present on how government knowledge management is changing. The Innovation Office focuses on internal and public service innovation, as well as open government. O’Connor has worked in the public sector for many years – at the U.S. Department of State, the Office of Management Policy Rightsizing and Innovation, and several U.S. Embassies. She talked about seeing firsthand that the government is changing from a “need to know organization” to a “need to share organization.” O’Connor argues that disruption is inevitable, and will come whether in the form of opportunity or threat – and there’s no script. “When there’s no script,” O’Connor says, “we have to be intentionally improvisational.” O’Connor defines innovation as any project that is new to you and has an uncertain outcome. She talked about how important knowledge is in supporting innovation. As the first person to ever fill this role, her goal for her first year in office was to set up an innovation infrastructure. This included putting into place the processes, teams, and skills and information to create an environment that fosters innovation. O’Connor recommends that to facilitate intentional improvisation, you must frame the problems you want to solve first. Once you know the goal, look for innovation technology infrastructure that helps you manage contacts, relationships, projects, knowledge, ideas, and insights. We live in a world that is increasingly interconnected and disrupted, and O’Connor says that organizations are naturally becoming more networked, human-centered, and improvisational. She encouraged attendees to “use what you have; we must connect, coach, mentor, share, and experiment.” To ensure that citizens can interact with the knowledge that city employees have, the City of Austin created online public spaces. These spaces, created on Bloomfire, offer the opportunity for citizens to participate in a conversation with employees around innovation, data, and city orientation. I was inspired by O’Connor’s presentation, and proud to live in a city that is so forward thinking about how information is shared. It made me want to get more involved in finding ways to solve some of problems Austin is facing as a result of our rapid growth. As a result of her talk, I’m going to try to make it to this weekend’s ATX Hack for Change. If you would like to watch O’Connor’s entire presentation, you can access it on the Bloomfire Community. Like this post? Click here to subscribe to our blog and receive the latest content on social learning, customer support, sales enablement, or all three.

When running a Maven build with many plugins (e.g. the jOOQ or Flyway plugins), you may want to have a closer look under the hood to see what’s going on internally in those plugins, or in your extensions of those plugins. This may not appear obvious when you’re running Maven from the command line, e.g. via: C:\Users\jOOQ\workspace>mvn clean install Luckily, it is rather easy to debug Maven. In order to do so, just create the following batch file on Windows: @ECHO OFF IF "%1" == "off" ( SET MAVEN_OPTS= ) ELSE ( SET MAVEN_OPTS=-Xdebug -Xnoagent -Djava.compile=NONE -Xrunjdwp:transport=dt_socket,server=y,suspend=y,address=5005 ) Of course, you can do the same also on a MacOS X or Linux box, by usingexport intead of SET. Now, run the above batch file and proceed again with building: C:\Users\jOOQ\workspace>mvn_debug C:\Users\jOOQ\workspace>mvn clean install Listening for transport dt_socket at address: 5005 Your Maven build will now wait for a debugger client to connect to your JVM on port 5005 (change to any other suitable port). We’ll do that now with Eclipse. Just add a new Remote Java Application that connects on a socket, and hit “Debug”: That’s it. We can now set breakpoints and debug through our Maven process like through any other similar kind of server process. Of course, things work exactly the same way with IntelliJ or NetBeans. Once you’re done debugging your Maven process, simply call the batch again with parameter off: C:\Users\jOOQ\workspace>mvn_debug off C:\Users\jOOQ\workspace>mvn clean install And your Maven builds will no longer be debugged. Happy debugging!

Having mentored and invested in startups I have come to learn what works and doesn’t websites. The reality of getting out of startup mode and scaling takes vision and the ability to anticipate how you may need to pivot. There are two main reasons why startups do not scale. The first is a lack of experience and mentorship. This is closely followed by a lack of a working capital. An effective entrepreneur understands not only how to pivot but how to utilize capital investment. This article aims to demystify the need for investing in site infrastructure. When scaling a business there are several huge issues that executives tend to forget. whether you are a startup or a fortune 500 company your website is the organizations public face. Maintaining a clean and secure site will help to avoid deep routed problems that could potentially destroy not only you site by your reputation. Hackers can be devastating and end up costing you millions. Another aspect of your site to consider is funnel optimization. By making this a priority you will be able to effective guide customers step by step into a conversion. American With Disabilities Act (ADA) The ADA can be a freighting legal area for many entrepreneurs. Many think that ADA only applies to physical boundaries such as implementing ramps for wheelchairs and accessible bathroom stalls. However this is far from the case. Few people know that the digital world also counts. Is your website written in HTML5? If not you are in violation of ADA. This newest version of the HTML coding language allows for an audible version of a web site for those facing impaired vision. However there are some quick an easy ways to gain some ADA points to allow for more leeway in other more long-term solutions. Implementing closed captioning into your promotional videos will allow for increased accessibility for those facing an auditory disability. Investing in infrastructure is not a single step but a constant to building a good company culture where employees can feel proud of where they work and remain safe. Please comment on this article if you have any additional suggestions.

Last month we shared the news about the debut Opsmatic and their live state monitoring service, a solution that delivers a precise, real-time picture of the detailed configuration – as well as changes that affect a computing infrastructure. We wanted to let you know that Opsmatic continues to expand its service capabilities with the announcement of two new versions. The Enterprise version of the Opsmatic service includes all the features we discussed last month in their Professional edition with the addition of single sign-on and dedicated support. The On-Premises edition embodies features contained in the Enterprise version in a solution designed for customers with an isolated infrastructure who require an internally-deployed solution. Details of Opsmatic’s new service offerings are outlined in the press release below. Should you have any questions, Opsmatic would be happy to respond to them.

in “ semihosting with kinetis design studio ” i used printf() to exchange text and data between the target board and the host using the debug connection. kinetis design studio (kds) has that semihosting baked into its libraries. what about if using the gnu arm embedded (launchpad) tools and libraries (see “ switching arm gnu tool chain and libraries in kinetis design studio “)? actually it requires two more steps, but is very easy too. semihosting output there are three things to be in place to use semihosting with the gnu arm embedded (launchpad) libraries: option in the gnu linker settings enabling semihosting in the debugger settings initializing the gnu libraries linker option to enable semihosting for the gnu arm embedded ( launchpad ) libraries, i need to add --specs=rdimon.specs to the linker options: linker option to enable semihosting in case i’m using newlib-nano and want to use printf() and/or scanf() with floating point support, i need to pull in some symbols explicitly with the linker options ‘u': -u _scanf_float -u _printf_float debugger settings in the gnu arm eclipse plugins, i need to enable semihosting. segger j-link for segger j-link, i enable the console in the launch configuration: allocated semihosting console for segger additionally i enable semihosting options in the startup options of the debugger: enabled semihosting in the startup options for segger p&e multilink for p&e the following settings are used: semihosting settings for pne settings for openocd the following settings are used for openocd: openocd semihosting settings initializing the gnu libraries if you would now try to use semihosting with running the debugger, you probably will get error messages like this (e.g. from segger j-link): warning: semihosting command sys_flen failed. handle is 0. warning: semihosting command sys_write failed. handle is 0. warning: semihosting command sys_write failed. handle is 0. warning: semihosting command sys_write failed. handle is 0. the reason is that the semihosting needs to be enabled by the application. i need to call initialise_monitor_handles() before i’m using printf() : 1 2 3 4 5 6 7 8 extern void initialise_monitor_handles( void ); /* prototype */ int main( void ) { initialise_monitor_handles(); /* initialize handles */ for (;;) { printf ( "hello world!\r\n" ); } } with this, i can use printf() and scanf() through a debugger connection. semihosting printf output summary while i don’t like printf() for many reasons, sometimes it is useful to exchange data with the host. using semihosting no physical connection is required, as the communication goes through the debugger. it is somewhat intrusive, and adds code and data overhead, but the gnu arm embedded (launchpad) libraries (both newlib and newlib-nano) have semihosting built-in. it is a matter to enable it in the linker and debugger settings, and to initialize the handles in the application. happy semihosting :-)

Jump into Sonar with this tutorial that provides installation instructions for SonarQube and the Code Analyzer, followed by a Java example.

JSF is not what we often think it is. It's also a framework that can be somewhat tricky to debug, especially when first encountered. In this post let's go over on why that is and provide some JSF debugging techniques. We will go through the following topics: JSF is not what we often think The difficulties of JSF debugging How to debug JSF systematically How JSF Works - The JSF lifecycle Debugging an Ajax request from browser to server and back Debugging the JSF frontend Javascript code Final thoughts - alternatives? (questions to the reader) JSF is not what we often think JSF looks on first look like an enterprise Java/XML frontend framework, but under the hood it really isn't. It's really a polyglot Java/Javascript framework, where the client Javascript part is non-neglectable and also important to understand it. It also has good support for direct HTML/CSS use. JSF developers are on ocasion already polyglot developers, whose primary language is Java but still need to use ocasionally Javascript. The difficulties of JSF debugging When comparing JSF to GWT and AngularJS in a previous post, I found that the (most often used) approach that the framework takes of abstracting HTML and CSS from the developer behind XML adds to the difficulty of debugging, because it creates an extra level of indirection. A more direct approach of using HTML/CSS directly is also possible, but it seems enterprise Java developers tend to stick to XML in most cases, because it's a more familiar technology. Also another problem is that the client side Javascript part of the framework/libraries is not very well documented, and it's often important to understand what is going on. The only way to debug JSF systematically When first encountering JSF, I first tried to approach it from a Java, XML and documentation only. While I could do a part of the work that way, there where frequent situations where that approach was really not sufficient. The conclusion that I got to is that in order to be able to debug JSF applications effectively, an understanding of the following is needed: HTML CSS Javascript HTTP Chrome Dev Tools, Firebug or equivalent The JSF Lifecycle This might sound surprising to developers that work mostly in Java/XML, but this web-centric approach to debugging JSF is the only way that I managed to tackle many requirements that needed some significant component customization, or to be able to fix certain bugs. Let’s start by understanding the inner workings of JSF, so that we can debug it better. The JSF take on MVC The way JSF approaches MVC is that the whole 3 components reside on the server side: The Model is a tree of plain Java objects The View is a server side template defined in XML that is read to build an in-memory view definition The Controller is a Java servlet, that receives each request and processes them through a series of steps The browser is assumed to be simply a rendering engine for the HTML generated at server side. Ajax is achieved by submitting parts of the page for server processing, and requesting a server to ‘repaint’ only portions of the screen, without navigating away from the page. The JSF Lifecycle Once an HTTP request reaches the backend, it gets caught by the JSF Controller that will then process it. The request goes through a series of phases known as the JSF lifecycle, which is essential to understand how JSF works: Design Goals of the JSF Lifecycle The whole point of the lifecycle is to manage MVC 100% on the server side, using the browser as a rendering platform only. The initial idea was to decouple the rendering platform from the server-side UI component model, in order to allow to replace HTML with alternative markup languages by swapping the Render Response phase. This was in the early 2000's when HTML could be soon replaced by XML-based alternatives (that never came to be), and then HTML5 came along. Also browsers where much more qwirkier than what they are today, and the idea of cross-browser Javascript libraries was not widespread. So let’s go through each phase and see how to debug it if needed, starting in the browser. Let's base ourselves in a simple example that uses an Ajax request. A JSF 2 Hello World Example The following is a minimal JSF 2 page, that receives an input text from the user, sends the text via an Ajax request to the backend and refreshes only an output label: JSF 2.2 Hello World Example The page looks like this: Following one Ajax request - to the server and back Let’s click submit in order to trigger the Ajax request, and use the Chrome Dev Tools Network tab (right click and inspect any element on the page).What goes over the wire? This is what we see in the Form Data section of the request: j_idt8:input: Hello World javax.faces.ViewState: -2798727343674530263:954565149304692491 javax.faces.source: j_idt8:j_idt9 javax.faces.partial.event: click javax.faces.partial.execute: j_idt8:j_idt9 j_idt8:input javax.faces.partial.render: j_idt8:output javax.faces.behavior.event: action javax.faces.partial.ajax:true This request says: The new value of the input field is "Hello World", send me a new value for the output field only, and don't navigate away from this page. Let's see how this can be read from the request. As we can see, the new values of the form are submitted to the server, namely the “Hello World” value. This is the meaning of the several entries: javax.faces.ViewState identifies the view from which the request was made. The request is an Ajax request, as indicated by the flag javax.faces.partial.ajax, The request was triggered by a click as defined in javax.faces.partial.event. But what are those j_ strings ? Those are space separated generated identifiers of HTML elements. For example this is how we can see what is the page element corresponding to j_idt8:input, using the Chrome Dev Tools: There are also 3 extra form parameters that use these identifiers, that are linked to UI components: javax.faces.source: The identifier of the HTML element that originated this request, in this case the Id of the submit button. javax.faces.execute: The list of identifiers of the elements whose values are sent to the server for processing, in this case the input text field. javax.faces.render: The list of identifiers of the sections of the page that are to be ‘repainted', in this case the output field only. But what happens when the request hits the server ? JSF lifecycle - Restore View Phase Once the request reaches the server, the JSF controller will inspect the javax.faces.ViewState and identify to which view it refers. It will then build or restore a Java representation of the view, that is somehow similar to the document definition in the browser side. The view will be attached to the request and used throughout. There is usually little need to debug this phase during application development. JSF Lifecycle - Apply Request Values The JSF Controller will then apply to the view widgets the new values received via the request. The values might be invalid at this point. Each JSF component gets a call to it’s decode method in this phase. This method will retrieve the submitted value for the widget in question from the HTTP request and store it on the widget itself. To debug this, let’s put a breakpoint in the decode method of the HtmlInputText class, to see the value “Hello World”: Notice the conditional breakpoint using the HTML clientId of the field we want. This would allow to quickly debug only the decoding of the component we want, even in a large page with many other similar widgets. Next after decoding is the validation phase. JSF Lifecycle - Process Validations In this phase, validations are applied and if the value is found to be in error (for example a date is invalid), then the request bypasses Invoke Application and goes directly to Render Response phase. To debug this phase, a similar breakpoint can be put on method processValidators, or in the validators themselves if you happen to know which ones or if they are custom. JSF Lifecycle - Update Model In this phase, we know all the submitted values where correct. JSF can now update the view model by applying the new values received in the requests to the plain Java objects in the view model. This phase can be debugged by putting a breakpoint in the processUpdates method of the component in question, eventually using a similar conditional breakpoint to break only on the component needed. JSF Lifecycle - Invoke Application This is the simplest phase to debug. The application now has an updated view model, and some logic can be applied on it. This is where the action listeners defined in the XML view definition (the 'action' properties and the listener tags) are executed. JSF Lifecycle - Render Response This is the phase that I end up debugging the most: why is the value not being displayed as we expect it, etc, it all can be found here. In this phase the view and the new model values will be transformed from Java objects into HTML, CSS and eventually Javascript and sent back over the wire to the browser. This phase can be debugged using breakpoints in the encodeBegin, encodeChildren and encodeEnd methods of the component in question. The components will either render themselves or delegate rendering to aRenderer class. Back in the browser It was a long trip, but we are back where we started! This is how the response generated by JSF looks once received in the browser: -8188482707773604502:6956126859616189525> What the Javascript part of the framework will do is to take the contents of the partial response, update by update. Using the Id of the update, the client side JSF callback will search for a component with that Id, delete it from the document and replace it with the new updated version. In this case, "Hello World" will show up on the label next to the Input text field! And so thats how JSF works under the hood. But what about if we need to debug the Javascript part of the framework? Debugging the JSF Javascript Code The Chrome Dev Tools can help debug the client part. For example let’s say that we want to halt the client when an Ajax request is triggered. We need to go to the sources tab, add an XHR (Ajax) breakpoint and trigger the browser action. The debugger will stop and the call stack can be examined: For some frameworks like Primefaces, the Javascript sources might be minified (non human-readable) because they are optimized for size. To solve this, download the source code of the library and do a non minified build of the jar. There are usually instructions for this, otherwise check the project poms. This will install in your Maven repository a jar with non minified sources for debugging. The UI Debug tag: The ui:debug tag allows to view a lot of debugging information using a keyboard shortcut, see here for further details. Final Thoughts JSF is very popular in the enterprise Java world, and it handles a lot of problems well, specially if the UI designers take into account the possibilities of the widget library being used. The problem is that there are usually feature requests that force us to dig deeper into the widgets internal implementation in order to customize them, and this requires HTML, CSS, Javascript and HTTP plus JSF lifecycle knowledge. Is polyglot an alternative? We can wonder that if developers have to know a fair amount about web technologies in order to be able to debug JSF effectively, then it would be simpler to build enterprise front ends (just the client part) using those technologies directly instead. It's possible that a polyglot approach of a Java backend plus a Javascript-only frontend could be proved effective in a nearby future, specially using some sort of a client side MVC framework like Angular. This would require learning more Javascript, (have a look at Javascript for Java developers post if curious), but this is already often necessary to do custom widget development in JSF anyway. Conclusions and some questions to the reader Thanks for reading, please take a moment to share your thoughts on these matters on the comments bellow: do you believe polyglot development (Java/Javascript) is a viable alternative in general, and in your workplace in particular? Did you find one of the GWT-based frameworks (plain GWT, Vaadin, Errai), or the Play Framework to be easier to use and of better productivity?

Technical debt is pretty bad, and feature toggles are some of the most horrible examples of technical debt.

This is the fourth in a series of posts on the new JBoss Red Deer automated test framework. This post describes features in Red Deer that make it easier to debug test failures. Writing any automated UI tests can be a challenging task. Your tests have to be able to manipulate the UI, deal with platform-dependent display characteristics, and timing issues. But, what about when things go wrong and you have to debug a failing test? Are you looking at a bug in the program that you’re testing? Or, perish the thought, what if the bug is not in the program you’re testing, but it’s in your test program? The primary goal of Red Deer is to make this task easier by providing a open source solution that is reliable and extensible. Red Deer also makes your life easier when things go wrong by providing your with multiple debugging features. This post describes these features and illustrates how you can enable/disable Red Deer’s debugging features, and how you can use them to debug test failures. Maven and Eclipse Debugging is Still Usable Before discussing Red Deer’s debugging features, it’s important to remember that in no way does Red Deer prevent you from performing the types of debugging that you can use with Maven or Eclipse. You can use the Eclipse debugger to set breakpoints in Red Deer test programs in the same manner as any other programs. Likewise, to debug a Red Deer test program with maven, all you have to do is to run maven with -DdebugPort=, and in Eclipse, select the test class that you want to execute, select "Debug As," and create a new debug configuration. In the debug configuration, specify that you want to run the test class as a Remote Java Application, and the port number. Starting the debugger makes the waiting test execution run and then stop on the first breakpoint: Debugging Features Added by Red Deer The Red Deer debugging features that we’ll discuss in this post are: Red Deer Debug Logging Automatic Screenshots for Failed Tests Pausing Failing Tests Recording Screencasts Let’s start by looking at the simplest of Red Deer’s debugging tools; logging. Red Deer Debug Logging By default, Red Deer creates a DEBUG level log whenever a test is run. The log is written to stdout when you run tests from a shell and to the console view when you run tests in Eclipse. Some example output of a Red Deer log looks like this: INFO [thread][class] Log message-N INFO [thread][class] Log message-N+1 ERROR [thread][class] Hey! Something failed here DEBUG [thread][class] And, here’s some additional debug information INFO [thread][class] Log message-N+2 To disable debug logging, you set the “logDebug” JVM argument to false. For example: -DlogDebug=false In addition, you can also filter the log contents with the “logMessageFilter” JVM argument. The supported filter values are: debug error warn info trace none all You can set multiple filters by specifying filter values in a single string using vertical pipe separators. For example: -DlogMessageFilter=error|warn Note that the filter values are case insensitive. Automatic Screenshots for Failed Tests As is the case with debug logging, screenshot saving for failed tests is enabled by default by Red Deer. What happens is that when a Red Deer test fails, at the point of failure a screenshot is taken and stored for you. The screenshot illustrates the state of the UI when the test failure occured. By default, Red Deer saves the screenshot files in the “target/screenshot” directory. You can select the directory into which Red Deer will save the screenshot files by setting this JVM argument: relativeScreenshotDirectory For example: -DrelativeScreenshotDirectory=/home/jsmith/screenshots Let’s take a look at this in action. The following screenshot was generated when a test failed. The server address in question is valid. The error was due to a temporary network connectivity issue. What happens under the hood here is that when a test fails, the Red Deer watchdog process takes over, and invokes an extension of the org.eclipse.swt.graphics.ImageLoader class to create a screenshot, before it terminates the test. Having a screenshot is helpful, as it shows the state of the UI Red Deer determined that something went very wrong, but it's limited in that after the failure, Red Deer immediately stops the test and exits. In some cases having Red Deer take a screenshot and then exit may be adequate as the source of the failure is obvious. In other cases, however, you might prefer that Red Deer enable you to investigate the cause of the failure before the test is stopped and Red Deer exits. Pausing Failing Tests In some ways, an automated test failure is like an automobile crash. Something unexpected happens, and before you can react, the test goes off the road, rolls over and finishes in a ditch. Wouldn't life be easier if you could press a pause button to avoid bad things like this from happening? Likewise, it would be helpful if Red Deer, instead of terminating the test immediately could enable you to press a "pause" button and freeze the test so that you could examine the state of the accident so that you could understand its causes. Luckily Red Deer is a built-in feature to enable you to pause failing tests. Unlike the screenshot feature, having Red Deer pause when a test fails is not enabled by default. To enable the feature, all you have to do is to set the "pauseFailedTest" JVM argument to “true.” For example: -DpauseFailedTest=true With this argument set to “true,” when a test fails, it connects to the Red Deer watchdog process and test execution is paused. You can cause the test execution to continue by pressing the Enter key. Note that in the current version of Red Deer, pauseFailedTest only works when your test extends the RedDeerTest class. In a future release of Red Deer, it will work on all test types. Recording Screencasts Up to now, the Red Deer debugging features that we’ve discussed have all had one characteristic in common; they all require that you run the test and manually observe the results. This means that for an error that is subtle and difficult to track, you have may have to rerun the test several times in order to be able to see the error. It would be more helpful if you had an easy way to pause the execution of the test at the point where it fails and “rewind” the execution to a point before the failure, without having to take the time, and occupy system resources, to rerun the test from its beginning. Red Deer solves this problem by enabling you to save a screencast of all failed tests. Red Deer performs the screencast captures through an extension of the org.monte.screenrecorder.ScreenRecorder (http://www.randelshofer.ch/monte/) class As is the case with pausing tests, Red Deer’s screencast feature is disabled by default. To enable the the recording of screenshots, you set the recordScreenCast JVM argument to “true.” For example: -DrecordScreenCast=true The screencast files are stored in a subdirectory named, appropriately enough, “screencast.” References https://github.com/jboss-reddeer/reddeer/wiki/Debugging-RedDeer Acknowledgements Many thanks to Jirka Peterka and Vlado Pakan for their input to this post!

Uber is doing their part to disrupt an industry that hasn’t seen much innovation in decades. And for cities whose public transportation is only moderately effective, the change cannot come fast enough. But what is Uber actually providing? And based on that, what are they competing for? With any ride service, the temptation is to think of the business primarily as shuttling people to and fro. Accordingly, people building out this type of business have focused on adding ride capacity. For cab companies, this meant adding additional vehicles. Cab drivers are basically renting the cabs from the owners. For cab companies, the business more closely approximates property rental than anything else. And given the wages for drivers and the general way they are treated, it is probably not a surprise that cab owners have a reputation not unlike slumlords. So you take an industry that is generally reviled in many major cities (New York stands out as an exception here) and you fail to really evolve the business model for decades, and you end up with something that is ripe for disruption. Enter Uber. But what is Uber really doing? They aren’t going out and buying a fleet of cars like the cab companies and black car services that have cropped up. Uber is really not about transportation. What they have done is create a clever way to identify available capacity in the system, and then deploy that capacity as needed. If you look at where Uber is most successful, it is in cities where cab service is dreadful. By dreadful, I mean that it is difficult to get a cab in a timely fashion. Take San Francisco, for instance. Getting a cab in SF can be nigh impossible. Even when you call central dispatchers, wait times can be atrocious. And if you are trying to use a cab on a high-volume night, you are better off packing some comfortable shoes and hoofing it around the city. For San Francisco, Uber represents a painless way to get just-in-time delivery of a ride service. Because Uber’s business is around discovery and redeployment of capacity, the real competition for Uber is not for fares. To scale their business, they need access to more fluid ride capacity. The more capacity they have in the system, the better their deployment service. They can extend their reach and shorten the time-to-wait for a ride service. This means that the real fight Uber needs to win is the one for drivers. It’s not the cab companies so much as the other ride sharing services (like Lyft) that threaten to cap Uber’s ability to add additional capacity. So what does this have to do with IT infrastructure? IT infrastructure generally (and data centers in particular) are about providing resources to satisfy application or tenant workload requirements. The capacity required takes three general forms: compute, storage, and networking. The objective is not merely providing some aggregate capacity but rather pairing that capacity with a specific demand. And as cloud continues to grow, it is increasingly about providing just-in-time delivery of that capacity. On the compute and storage side, we have solved a big part of this challenge. Virtualization essentially frees up compute resources, which means that application workloads can be satisfied as-needed through application portability. If you need additional horsepower, you launch a new application instance on a VM that resides on some server with capacity to give. In this context, the dispatching of available capacity is moving the application workload to a server. And tools like DRS allow for the definition of resource pools that can then be allocated as needed. But what about the networking side? To date, the networking world has evolved in much the same way as the cab companies. The game has always been about adding addition cabs to the fleet (more capacity to the network). And while we can use monitoring tools to determine where capacity is not being fully utilized, there is no simple means of dispatching that capacity to where it is needed. Additionally, even the tools we have to shape paths are not particularly well-suited for providing just-in-time capacity. There is an opportunity in the networking space to move in this direction. SDN as a movement provides a couple of tools that are architecturally necessary if this is to become a reality. A central controller is a logical way to locate available capacity. With a global view of the network as a resource, the controller is in a unique position to see how the physical transport is actually being used. But imagine using Uber if it only told you where the available cars were but could not dispatch them to you. Without performing both actions – locating and dispatching – the service is incomplete. So it is in networking. Knowing where capacity resides is interesting but not terribly useful. The network needs the ability to dispatch that capacity to suit the applications. And one final point, whether dispatching occurs in the moment or at a scheduled time is dependent on the needs of the customers (applications or tenants, in this case). Ultimately, what Uber is doing is actually quite impressive. But there is subtlety in the strategy and the innovation. The whole of IT might be able to learn a bit from Uber’s creativity.