Category: Authors

Our voyage to service separation – Part II

We transformed our evolutionary grown IT landscape to a planned setup. Here is what we learned on the way (part 2/2).

Recap of the situation

In the first part of this blog series, we introduced you to our evolutionary grown IT landscape. We had a room full of snow- flaked servers and no overall concept how to use them. We wanted our services to be self-contained and separated. So we chose the approach of virtualization to host one VM per service on a uniform platform. We chose VirtualBox, Vagrant and Ansible to help us along the way.
This blog entry tells you about the way and our experiences and insights.

The migration

In order to migrate every service you use to its own virtual machine (VM), you’ll need a list or map of your services first. We gathered our list, compared it to reality, adjusted it, reiterated everything, added the forgotten services, drew the map, compared again, drew again and even then missed some services that are painfully obvious in hindsight, like DNS or SMTP. We identified more than 15 distinct services and estimated their resource profile. Then we planned the VM layouts and estimated the required computation power to host all of them. Then we bought the servers.

We started with three powerful hosting servers but soon saw that there is a group of “alpha VMs” with elevated requirements on availability and bought a fourth hosting server with emphasis on redundancy. If some seldom used backoffice service goes down, that’s one thing. The most important services of our company should not go down because of a harddisk failure or such.

Four nearly identical hosting servers to run 15+ VMs on required a repeatable process to set things up. This is where the first tip comes into play:

  • Document everything. Document all the details. Have your Wiki ready and write a step-by-step tutorial for every task you perform. It’s really tedious and probably a bit cumbersome at first, but it will pay of sooner and better than you’d imagine.

We started the migration process with the least important services to get a feeling for the required steps. It turned out later that these services were also the most time-consuming ones. The most essential and seemingly complex services took the least time. We essentially experienced the pareto effect but in reverse: We started with the lowest benefit for the highest cost. But we can give two tips from this experience:

  • Go the extra mile. Just forget about the pareto effect and migrate all services. It’s so much more fun to have a clean IT landscape map than one where most things are tidy but there’s an area marked “here be dragons”.
  • Migration effort and service importance aren’t linked. Our most important service was migrated in about half an hour. Our least important service needed nearly three days. It’s all about the system architecture of the service and if it values self-containment.

The migration took place over the course of a few months with frequent address changes of our tools and an awful lot of communication for cutoff dates. If you need to migrate a service, be very open about the process and make sure that the old service address won’t work after the switch. I cannot count the amount of e-mails I wrote with the subject prefix “IMPORTANT!”. But the transition went smooth and without problems, so we probably added some extra caution that might not have been necessary.

After the migration

When we had migrated our last service in its own VM, there were a lot of old servers without any purpose anymore. We switched them off and got rid of them. Now we had nearly two dozen new servers to care for. One insight we had right after the start of our journey is that virtualized servers require the same amount of administration as physical ones. Just using our old approaches for the new IT landscape wouldn’t cut it. So we invested heavily in automation and scripted everything. Want to set up a new CI build slave? Just add its address into Ansible’s inventory and run the script (“playbook”). All servers need security updates? Just one command and a little wait.

Gears by Pete BirkinshawLearning to automate the administrative tasks in the right way had a steep curve, but it’s the only feasible way. We benefit heavily from the simple fact that we forced ourselves to do it by making it impossible to handle the tasks manually. It’s a “burned bridges” approach, but upon reaching the goal, it really pays off. So another tip:

  • Automate everything. Even if you think you’ll perform this task just a few times – that’s exactly the scenario to automate it to never have to bother with the details again. Automation is key if you want to scale your IT landscape to reasonable sizes.

Reaping the profit

We’ve done the migration and have a fully virtualized setup now. This would not be very beneficial in itself, but opens the door for another level of capabilities we simply couldn’t leverage before. Let me just describe two of them:

  • Rethink your backup strategy. With virtual machines, you can now backup your services on an appliance level. If you wanted to perform this with a real server, you would need to buy the exact same hardware, make exact copies of the harddisks and store this “clone machine” somewhere safe. Creating an appliance level backup means to stop the VM, export it and restart it. You’ll have some downtime, but everything else is just a (big) file.
  • Rethink your service maintainance strategy. We often performed test upgrades to newer versions of our important services on test machines. If the upgrade went well, we would perform it again on the live server and hope for the best. With virtual machines and appliance backups, you can try the upgrade on an exact copy of the live server over and over again. And if you are happy with the result, you just swap your copy with the live server and everything’s fine. No need for duplicated procedures, you always work with the real deal – well, an indistinguishable copy of it.

Conclusion

We’ve migrated our IT landscape from evoluationary to a planned virtualized state in just about a year. We’ve invested weeks of work in it, just to have the same services available as before. From a naive viewpoint, nothing much has changed. So – was it worth it?

The answer is short and clear: Absolutely yes. Even in the short time after the migration, the whole setup performs smoother and more in a planned way than just by chance. The layout can be communicated clearer and on different levels. And every virtual machine has its own use case, to the point and dedicated. We now have an IT landscape that obeys our rules and responds to our needs, whereas before we often needed to make hard compromises.

The positive effects of documentation and automation alone are worth the journey, even if they are mere side effects of the main goal. +1, would migrate again.

Simple C++11 – Part I – Unit Structure

C++ has long had the stigma of an overlay complex and unproductive language. Lately, with the advent of C++11, things have brightened a bit, but there are still a lot of misconceptions about the language. I think this is mostly because C++ was taught in a wrong way. This series aims to show my, hopefully somewhat simpler, way of using C++11.

Since it is typically the first thing I do when starting a new project, I will start with how I am setting up a new compile unit, e.g. a header and compile unit pair.

Note that I will try not to focus on a specific C++11 paradigm, such as object-oriented or imperative. This structure seems to work well for all kinds of paradigms. But without much further ado, here’s the header file for my imaginary “MyUnit” unit:

MyUnit.hpp

#pragma once

#include <vector>
#include "MyStuff.hpp"

namespace MyModule { namespace MyUnit {

/** Does something only a good bar could.
*/
std::vector<float> bar(int fooCount);

/** Foo is an integral part of any program.
    Be sure to call it frequently.
*/
void foo(MyStuff::BestType somethingGood);

}}

I prefer the .hpp file ending for headers. While I’m perfectly fine with .h, I think it is helpful to differentiate pure C headers from C++ headers.

#pragma once

I’m using #pragma once here instead of include guards. It is not an official part of the standard, but all the big compilers (Visual C++, g++ and clang) support it, making it a de-facto standard. Unlike include guards, you only have to add only one line, which says exactly what you want to achieve with it. You do not have to find a unique identifier for your include guard that will most certainly break if you rename the file/unit. It’s more readable, more resilient to change and easier to set up.

Namespaces

I like to have all the contents of a unit in a single namespace. The actual structure of the namespaces – i.e. per unit or per module or something else entirely depends on the specifics of the project, but filling more than one namespace is a guarantee for chaos. It’s usually a sign that the unit should be broken up into smaller pieces. An exception to this would be the infamous “detail” namespace, as seen in many of the Boost libraries. In that case, the namespace is not used to structure the API, but to explicitly omit things from the API that have to be visible for technical reasons.

Documentation

Documentation goes into the header, not into the implementation. The header describes the API, not only to the compiler, but also to humans. It is by no means an implementation detail, but part of the seam that isolates it from the rest of the code. Note that this part of the documentation concerns the API contract only, never the implementation. That part goes into the .cpp file.

But now to the implementation file:

MyUnit.cpp

#include "MyUnit.hpp"

#include "CoolFunctionality.hpp"

using namespace MyModule;
using namespace MyUnit;

namespace {

int helperFunction(float rhs)
{
  /* ... */
}

}// namespace

std::vector<float> MyUnit::bar(int fooCount)
{
  /* ... */
}

void MyUnit::foo(MyStuff::BestType somethingGood)
{
  /* ... */
}

Own #include first

The only rule I have for includes is that the unit’s own include is always the first. This is to test whether the header is self-sufficient, i.e. that it will compile without being in the context of other headers or, even worse, code from an implementation file. Some people like to order the rest of their includes according to their “origin”, e.g. sections for system headers or library headers. I think imposing any extra order here is not needed. If anything, I prefer not waste time sorting include directives and just append an include when I need it.

Using namespace

I choose using-directives of my unit’s namespaces over explicitly accessing the namespaces each time. Unlike the headers, the implementation file lives in a locally defined context. Therefore, it is not a problem to use a very specific view onto the unit. In fact, it would be a problem to be overly generic. The same argument also holds for other “local” modules that this unit is only using, as long as there are no collisions. I avoid using namespaces from external libraries to mark the library boundary (such as std, boost etc.).

Unnamed namespace

The unnamed namespace contains all the implementation helpers specific to this unit. It is quite common for this to contain a lot of the “meat” of an actual unit, while the unit’s visible functions merely wrap and canonize the functionality implemented here. I try to keep only one unnamed namespace in each file, to have a clear separation of what is supposed to be visible to the outside – and what is not.

Visible implementation

The implementation of the visible API of the module is the most obvious part of the .cpp file. For consistency reasons, the order of the functions should be the same as in the header.

I’d advice against implementing in a file wide open namespace. That means balancing an unnecessary pair of parenthesis over the whole implementation file.  Also, you can not only define functions and types, but also declare them – this leads to a function further down in the implementation to see a different namespace than one before it.

Conclusion

This concludes the first part. I’ve played with the thought of using a 3-piece setup instead, extending the header/implementation with a unit-test file, but have not gathered any sharable experience yet. This setup, however, has worked for me for a long time and with many different projects. Have you had similar – or completely different – setups that worked for you? Do tell!

Synchronizing asynchronous calls in JavaScript

In Node.js all I/O performing operations like HTTP requests, file access etc. are designed to be non-blocking. The functions for these operations usually take a callback function argument as last parameter, which will be called once the operation is finished. The asynchronous nature of these operations often requires special means of synchronization, as exemplified in this post.

Let’s assume we want to download a set of files from a list of URLs:

var urls = [
    'http://example.org/file1',
    'http://example.org/file2',
    'http://example.org/file3',
];

If we were to use a classic, synchronous blocking function for the download operation the implementation might look like this:

urls.forEach(function(url) {
    downloadSync(url);
    console.log('Downloaded ' + url);
});
console.log('Downloads complete.')

Output:

Downloaded http://example.org/file1
Downloaded http://example.org/file2
Downloaded http://example.org/file3
Downloads complete.

The program loops through the list of URLs and downloads one file after the other. Each subsequent download is only started after the previous download has finished. No two downloads are active at the same time. Finally, the program prints a message indicating that all downloads are complete.

Now we want to embrace the asynchronous programming style of Node.js. We have a different function, downloadAsync, which is a non-blocking asynchronous function. The function uses the callback convention of Node.js to let the programmer handle the completion of the asynchronous operation:

urls.forEach(function(url) {
    downloadAsync(url, function() {
        console.log('Downloaded ' + url);
    });
});
console.log('Downloads complete.')

Output:

Downloads complete.
Downloaded http://example.org/file2
Downloaded http://example.org/file3
Downloaded http://example.org/file1

The download operations are now started at the same time and finish in a different order, depending on how long it takes each file to download. The quickest download finishes first, the slowest last. The total time for all downloads to finish is probably shorter than before, because slower connections do not make faster connections wait for them to finish.

However, there is one problem with this code: the message “Downloads complete.” is shown before the downloads are actually completed, which is obviously not the intended behavior. The reason why this happens is because the control flow immediately continues after the forEach loop has started all the downloads.

The ‘async’ module

What we need to correct this behavior is a way to wait for all asynchronous download operations started by the loop to finish before we print the “Downloads complete” message.

The async module for JavaScript provides various synchronization mechanisms for exactly these kinds of situations. In our case we’re going to use async.each:

var async = require('async');

async.each(urls, function(url, callback) {
    downloadAsync(url, function() {
        console.log('Downloaded ' + url);
        callback();
    });
}, function done() {
    console.log('Downloads complete.')
});

The async.each function is similar to JavaScript’s forEach function. However, the function called for each iteration has an additional callback parameter, which must be called when each iteration is considered to be completed. The third parameter to async.each is also a callback function. This function is called after all iterations reported their completion. This is where we place the output of the “Downloads complete” message.

Conclusion

The async module provides a rich toolset for the synchronization of asynchronous operations in JavaScript. Go check it out!

Multi-Page TIFFs with C++

If you are dealing with high-speed cameras or other imaging equipment capable of producing many images in a short time you may find it handy to put many images into a single file. There are several reasons to do so:

  • Dealing with thousands of files in a single directory or spreading them over a directory hierarchy may be slow and cumbersome depending on the tools.
  • Storing many images together may communicate better that they belong together, e.g. to the same scan.
  • Handling and transmitting fewer files is often easier than juggling with many.

TIFF is a wide-spread lossless image format capable of handling many individual images in a single file. Many image viewers and image manipulation tools are able to work with multi-page TIFF files so you are quite flexible in working with such files.

But how do you produce these files from your programs? I found some solutions with different strengths and weaknesses:

Using Magick++

Magick++ is the C++ API for ImageMagick – a powerful image manipulation library. If you can hold all the images for one file in memory the code is easy and straightforward:

#include &amp;lt;string&amp;gt;
#include &amp;lt;Magick++.h&amp;gt;

class TiffWriter
{
public:
    TiffWriter(std::string filename);
    TiffWriter(const TiffWriter&amp;amp;) = delete;
    TiffWriter&amp;amp; operator=(const TiffWriter&amp;amp;) = delete;
    ~TiffWriter();

    void write(const unsigned char* buffer, int width, int height);

private:
    std::vector&amp;lt;Magick::Image&amp;gt; imageList;
    std::string filename;
};

TiffWriter::TiffWriter(std::string filename) : filename(filename) {}

// for example for a 8 bit gray image buffer
void TiffWriter::write(const unsigned char* buffer, int width, int height)
{
    Magick::Blob gray8Blob(buffer, width * height);
    Magick::Geometry size(width, height);
    Magick::Image gray8Image(gray8Blob, size, 8, "GRAY");
    imageList.push_back(gray8Image);
}

TiffWriter::~TiffWriter()
{
    Magick::writeImages(imageList.begin(), imageList.end(), filename);
}



The caveat is that you need to  hold all your images in memory before writing it to the file on disk. I did not manage to add and persist images on the fly to disk.


In our environment it was absolutely necessary to do so because of the amount of data and the I/O required to persist all image in time. So I had to implement a slightly more low-level solution using libtiff and its C API.


Using libtiff


#include &amp;lt;string&amp;gt;
#include &amp;lt;tiffio.h&amp;gt;

class TiffWriter
{
public:
    TiffWriter(std::string filename, bool multiPage);
    TiffWriter(const TiffWriter&amp;amp;) = delete;
    TiffWriter&amp;amp; operator=(const TiffWriter&amp;amp;) = delete;
    ~TiffWriter();

    void write(const unsigned char* buffer, int width, int height);

private:
    TIFF* tiff;
    bool multiPage;
    unsigned int page;
};

TiffWriter::TiffWriter(std::string filename, bool multiPage) : page(0), multiPage(multiPage)
{
    tiff = TIFFOpen(filename.c_str(), "w");
}

void TiffWriter::write(const unsigned char* buffer, int width, int height)
{
    if (multiPage) {
        /*
         * I seriously don't know if this is supposed to be supported by the format,
         * but it's the only we way can write the page number without knowing the
         * final number of pages in advance.
         */
        TIFFSetField(tiff, TIFFTAG_PAGENUMBER, page, page);
        TIFFSetField(tiff, TIFFTAG_SUBFILETYPE, FILETYPE_PAGE);
    }
    TIFFSetField(tiff, TIFFTAG_PLANARCONFIG, PLANARCONFIG_CONTIG);
    TIFFSetField(tiff, TIFFTAG_IMAGEWIDTH, width);
    TIFFSetField(tiff, TIFFTAG_IMAGELENGTH, height);
    TIFFSetField(tiff, TIFFTAG_SAMPLEFORMAT, SAMPLEFORMAT_UINT);
    TIFFSetField(tiff, TIFFTAG_ROWSPERSTRIP, TIFFDefaultStripSize(tiff, (unsigned int) - 1));

    unsigned int samples_per_pixel = 1;
    unsigned int bits_per_sample = 8;
    TIFFSetField(tiff, TIFFTAG_BITSPERSAMPLE, bits_per_sample);
    TIFFSetField(tiff, TIFFTAG_SAMPLESPERPIXEL, samples_per_pixel);

    std::size_t stride = width;
    for (unsigned int y = 0; y &amp;lt; height; ++y) {
        TIFFWriteScanline(tiff, buffer + y * stride, y, 0);
    }

    TIFFWriteDirectory(tiff);
    page++;
}

TiffWriter::~TiffWriter()
{
    TIFFClose(tiff);
}



Note that line 14 is needed if you do not know the number of images to store in the file in advance!

Learning UX design: where do I start?

Where do I start? A typical question when trying to step into a new field. So many resources, so many definitions, concepts, opinions.

Where do I start? A typical question when trying to step into a new field. So many resources, so many definitions, concepts, opinions. A needle in a haystack. Most of the beginner articles for UX are tailored for design students. Many of the resources for teaching design to developers aim at getting better at visual design or interaction design. But what if your goal is to make life better for users of the software you develop ?
A simple question. ‘It depends’ I hear a lot. I think there are two things everybody can do or learn to do that have a profound effect on the UX of your products.

Think

Increasingly – since beginning to focus on UX – I get the following feedback by our customers and users: “You surely put much thought into it.” Much thought. What does that even mean? Why do they say that?
Common wisdom says you should think like a user in order to create a better experience for him. In my view you should think for the user. Think about what he wants and what he needs. The goals he want to reach and the information and guidance he needs.
Don’t stop at the what. As developers we often only consider what needs to be done. The user stories. The functional specifications. What functionality needs to be implemented. Fixed scope. We do not need to create exactly what the spec says.
Remember our goal? Make life better for our users. We need to understand what this means. Besides understanding the processes, methods and concepts of our user’s domain, we need to find out what the goals are and why the user wants to reach it.
Nobody wants to use a writing application. Nobody wants to write a letter. We need to dig deeper. Maybe he wants to cancel a magazine subscription or a contract. Or he wants to express his feelings to a loved one. That’s better. But what is his goal? In case of the cancellation letter: To save money? To reduce waste?
The why is important to the user and therefore it should be important to us. Not only our focus is changed from the things we need to implement to the goals the user wants to reach, we also have more freedom and a guiding post at the same time. We can find solutions which are outside of the initial user story or even outside the computer. And on the other hand we can evaluate decisions we need to make against helping our users reach their goals.
Think of our work as a bridge. The user wants to reach the other side of the river. Our bridge should be the most efficient and pleasurable way to get there.

Pleasurable

This is where the feelings from the journey of our user comes in. As a developer we try to find a balance between the goals of the business and the technological constraints. When we consider making the lives of our users better, the goals and needs of the users add a new force to be weighted. This is the primary task of the UX designer. We need to find the underlying goals and intentions on the one side and the needs on the other. All of them need to be balanced with the goals of the business.
So how do I find these intentions and needs?

Listen

Really, really listen to your users. The notion that users do not know what they want is poisonous. They don’t need to tell you directly what they want. You need to listen. You need to observe. Start with a beginner’s mind. Let the user explain. Do not assume. I tend to think ahead, to formulate ideas and solutions while listening. Now I am rather naive. I ask why. Why is it this way. Why after why. Do not settle but also do not stress your users. Get to the goals. Discover the needs from the current office environment of the user or the difficulty of the task. Learn what is important for the user and what not. Learn to listen for specific naming and phrasing. Human needs stem from our basic nature, look at Maslow’s hierarchy of needs.
After you collected all sorts of information you need to resolve conflicts, balance the trade offs, reach consensus. You need to construct a whole from the parts of your listening. Therefore you need to think. Prioritize. Sort out. Reflect. Again: you should not assume. Use your whys.

Start with thinking and listening

UX design is a broad field with a simple goal: making life better for our users. In order to reach this we need to think. We need to listen. We need to care. No tool or method will change that. As developers we like to learn tools, languages and have recipes and methods. We would love to have a top ten resources list. The books to read. The course to learn. But all that does not save us from thinking. UX design is even more so: thinking for the user is the core of UX design.

Our voyage to service separation – Part I

We transformed our evolutionary grown IT landscape to a planned setup. Here is what we learned on the way.

What you need to know

We are a small software development company with a home-grown IT infrastructure. The euphemism for such a state is “evolutionary grown”, denoting a process that was shaped by the most elementary forces, often implicitely. One such implicit force is laziness: If there is a quick way to do things, it will be done this way. Why invest effort if everything works just fine?
During an internal safety review, we identified our IT landscape as a risk factor. It was designed to meet yesterday’s and perhaps today’s demands, but in no way aligned to our strategic vision. We decided to invest in our IT to bring it to a planned state that we are confident will sustain our demands of tomorrow – or be easily adaptable.

Where we started

Our starting point was a room full of servers that were bought at some point to serve a specific need like “be the build box”. Every server started with a good reason to exist and evolved from there. Some gathered more and more services, some were repurposed and some idled along. We identified only two servers that were essential for the company: one was the continuous integration server master and one hosted nearly all mission-critical services at once. The latter server was also our oldest machine in production usage. It was secured against data loss, but not against outages. So everytime this server went down, our company essentially came to a stop because all services were offline. Luckily, it went down very infrequently, but it still identified as a clear single point of failure.

Where we wanted to go

containersIn April 2014, the heartbleed vulnerability was published. We luckily weren’t affected on a large scale, but took it as a wake-up call to review our IT setup and to develop a strategy to mitigate the effects of disasters similar in scope to heartbleed while we still have time. We wanted to have our IT in a condition where we actually choose which risks we take instead of just hoping for the best. So we sat before a whiteboard and outlined the goals: We wanted to separate and self-contain every essential service, so that the compromising or outage of one service doesn’t affect the others. That means one machine (or container) per service. To gain flexibility, we also planned to separate our IT landscape into two layers: The “metal layer” provides the computation power, while the “appliance layer” realizes the services. We wanted to be able to implement the appliance layer nearly independtly to the metal layer, which means to use some sort of virtualization. In modern words, we wanted to have a “cloud platform” to deploy our service applications on. We just don’t wanted it out on the internet but in our computer center. To sum up, we wanted to separate hardware and software and move every service in its own compartment.

What technologies we chose

We thought about fitting technology for a long time but settled for a small-scale, bottom-up approach: Start with just a few metal machines (hosts) and use a familiar virtualization product. In our case, this meant two standard servers, Linux and Oracle’s VirtualBox to run the virtual computers. There sure are more professional and powerful virtualization products out there, but we had years of experience (and sometimes frustration) with VirtualBox and didn’t want to rely on an unknown technology. It’s not exciting, but works well enough for our use case – and we knew that beforehands.
We decided against any fancy cloud or grid software to combine the hosts to a pool and just planned the hosting of the virtual machines (VMs) statically by hand. This might mean that one host gets bored while another host cannot handle the pressure anymore. It will be our responsibility to take that problem into account. This approach primarily achieves one thing: it keeps everything rather simple. Each host has a list of VMs and that’s it. If we want to migrate a VM to another host, we have to do it manually.
To create the VMs, we used Vagrant, which turned out fine for three-quarters of our machines, but proved toxic for the remaining ones. Vagrant is a very handy tool for developers to quickly launch a VM, but it makes a lot of assumptions that might not match your specific requirements. We essentially abandoned Vagrant after the initial phase.
During the migration phase of our services, we adopted another tool to solve the problem of scaling effects in maintainance. It’s another story to maintain 20+ servers instead of the handful we had beforehands. Luckily, Ansible proved useful to automate most of our normal administration tasks. This transition from manual to automated administration wasn’t part of the original plan, but is one of the biggest payoffs. But that’s stuff for the next blog entry.

What’s next?

In this first part of our story to regain control of our IT landscape, we described the starting point, the plan and the tools. In the next part, you’ll hear about the migration and where we ended up. We will also point out our experiences along the way and hopefully give some useful tips if you think of reshaping your services, too: Click here to read part two of the series.

Meet my Expectations!

A while ago I came across a particulary irritating piece of code in a somewhat harmlessly looking mathematical vector class. C++’s rare feature of operator overloading makes it a good fit for multi-dimensional calculations, so vector classes are common and I had already seen quite a few of them in my career. It looked something like this:

template <typename T>
class vec2
{
public:
  /* A few member functions.. */
  bool operator==(vec2 const& rhs) const;

  T x;
  T y;
};

Not many surprises here, except that maybe the operator==() should be a free-function instead. Whether the data members of the class are an array or named individually is often a point of difference between vector implementations. Both certainly have their merits. But I digress…
What really threw me off was the implementation of the operator==(). How would you implement it? Intuitively, I would have expected pretty much this code:

template <typename T>
bool vec2<T>::operator==(vec2 const& rhs) const
{
  return x==rhs.x && y==rhs.y;
}

However, what I found instead was this:

template <typename T>
bool vec2<T>::operator==(vec2 const& rhs) const
{
  if (x!=rhs.x || y!=rhs.y)
  {
    return false;
  }

  return true;
}

What is wrong with this code?

Think about that for a moment! Can you swiftly verify whether this boolean logic is correct? You actually need to apply De Morgan’s laws to get to the expression from the first implementation!
This code was not technically wrong. In fact, for all its technical purposes, it was working fine. And it seems functionally identical to the first version! Still, I think it is wrong on at least two levels.

Different relations

Firstly, it bases its equality on the inequality of its contained type, T. I found this quite surprising, so this already violated the POLA for me. I immediately asked myself: Why did the author choose to implement this based on operator!=(), and not on operator==()? After all, supplying equality for relations is common in templated C++, while inequality is inferred. In a way, this is more intuitive. Inequality already has the negation in its name, while equality is something “original”! Not only that, but why base the equality on a different relation of the contained type instead of the same? This can actually be a problem when the vector is instantiated on a type that supplies operator==(), but not operator!=() – thought that would be equally surprising. It turned out that the vector was only used on built-in types, so those particular concerns were futile. At least, until it is later used with a custom type.

Too many negations

Secondly, there’s the case of immediately returning a boolean after a condition. This alone is often considered a code-smell. It could be argued that this is more readable, but I don’t want to argue in favor of pure brevity. I want to argue in favor of clarity! In this case, that construct is basically used to negate the boolean expression, further obscuring the result of the whole function.
So basically, the function does a double negation (not un-equal) to express a positive concept (equal). And negations are a big source of errors and often lead to confusion.

Conclusion

You need to make sure to make the code as simple and clear as possible and avoids any surprises, especially when dealing with the relatively unconstrained context of C++ templates.  In other words, you need to make sure to meet the expectations of the naive reader as well as possible!

At your service, master!

One of the most important lessons that I had to learn in my job is that you have to be aware of the client. As a service provider, it is my duty to satisfy my client’s needs – and without knowing him, I will not be able to succeed. In this blog post I describe some insights that helped me to gain a better understanding of my clients.

The main connection between the service provider and his client is the communication between them. In an ideal world, the two parties would be able to understand each other perfectly, however, humans and their language are fallible and to me, this seems to be the root of most problems. Of course, both parties are responsible, nevertheless, the service provider should not only deal with his own defeciencies, but also with his client’s, in order to attract and keep clients. Next, I will list five instructions that can reduce or sometimes even prevent the incomprehension in the communication.

Be prepared

This is maybe the clearest rule: Before meeting a client, you should know the basics of the domain he is working in and of the problem he wants to solve. It is not the client’s job to explain his request, but rather the service provider’s job to comprehend it. Besides, if a client feels understood, he will also feel that you can solve his problem – and at that stage this matters more than whether you can actually solve his problem.

Be attentive

You and your client are different persons and, as a result, have a different understanding of the same things. Your client might quickly slur over some little details in a software he wants, so you could assume that they are of no importance – but you will be unpleasantly surprised when it turns out to be a critical aspect of the program. And this is not necessarily a flaw in the customer’s communication: Maybe to a domain expert – and your customer might be one – the importance of these details is totally obvious.

Furthermore, sometimes even language will lead you nowhere. For example, people do not always realize why a system is hard to use or where they make mistakes and hence cannot tell you about it, but by watching them you might find the problems. In such a situation, it is crucial to grasp not only the words the client is saying, but also other signals he is emitting.

Be without bias

As soon as I start listening to a client’s problem, sometimes I can literally watch myself constructing a solution in my head. I create a mental model composed of the components the customer is talking about, think about their relationships – and suddenly, I find myself thrown a curve because the client added a thought that objects my conception.

Of course, a model can improve the understanding of a client’s demands, however, one has to constantly question the validity of the model and – in case it is disproved – one must drop it without hesitation. Do not become attached to a model just because it is so elegant – in most cases, you will be betrayed. In contrast, it will probably become easier to adapt your mental models if you stay open-minded.

And even if your view seems to suit the customer’s requirements perfectly, you should hesitate to present it to him, you should not ask for confirmation early on. In fact, the better the concept seems, the more careful you should be: You might lead your client into thinking that it is a adequate solution, and by focusing on the conformity between the concept and his problem, you and your client may fail to see flaws.

Instead, you should try to ditch your assumptions, try to listen without bias. You still have to prepare yourself before you meet your client, but you should be willing to scrutinize your knowledge and to discard incorrect information.

Be concrete

The human language is a wonderful medium, but unfortunately terribly inaccurate. If, instead of writing, you can talk with your customer, you should usually choose the latter. Even better, if you can meet him in person, do it – there are so many more options to communicate if you are in one room that you will almost surely benefit from it.

For instance, if your client wishes a feature with some user interface, you can sketch it or build a paper prototype; you could even prepare a real prototype consisting only of the user interface. This allows your customer to play with it and facilitates the communication. And do not be abstract, do not fill your widgets with texts as “Lorem ipsum” – it does not matter if the content is made-up, but it should be realistic.

User interface design is a neat example since it is graphical, nevertheless, you can apply this principle to other tasks. It does not matter if you talk about a processes, some architecture, domain models or other structures: Even though most of them have no inherent graphical representation, it is usually easier to describe them graphically then by using text.

Seek for the why, not the what

Often, I tend to ask my clients about the problem they wish to solve – I ask what they wish to solve, not why. Usually, this is sufficient; he knows his situation and is able to express his needs. Unfortunately, it also happens that albeit the customer’s problem is solved according to his description, his wants are not satisfied – and the reason for this is that even he did not know what he actually needed. Even worse, sometimes I get caught by the “how”, that is, I quickly find a nice solution for some parts of a client’s problem, so I stick to it, maybe even implement it – and in the end I realize that it actually prevents me from solving the complete problem.

Hence, it is not only important to find out what your client wants to achieve, but also why he wants to achieve it, you have to understand his motivation. This can enable you to correct your client’s mistakes and to lead him to the question he actually wants to answer. Furthermore, this is a great handle to control the effort of a project: It becomes easier to identify indispensable core functionality and to find features whose usefulness is questionable, and hence, one can communicate with the client if some of the latters might be dropped. Simon Sinek gave an interesting TED talk to a similar topic found here.

Conclusion

Understanding your customers is difficult, but not impossible. I think that actively directing the attention at your counterpart, being open for input and questioning your assumptions and knowledge can strongly improve the communication with your clients.

Transferring commits via Git bundles

Sometimes you want to send (e.g. by e-mail) a set of new Git commits to someone else who has the same repository at an older state, without transferring the whole repository and without sharing a common remote repository.

One feature that might come to your mind are Git patches. Patches, however, don’t work when there are branches and merge commits in the commit history: git format-patch creates patches for the commits across the various branches in the order of their commit times and doesn’t create patches for merge commits.

Git bundles

The solution to the problem are Git bundles. Git bundles contain a partial excerpt of a Git repository in a single file.

This is how to create a bundle, including branches, merge commits and tags:

$ git bundle create my.bundle <base commit>..HEAD --branches --tags

<base commit> must be replaced with the last commit (i.e. commit hash or tag), which was included in the old state of the repository.

A Git bundle can be imported into a repository via git pull:

$ git pull /path/to/my.bundle

Get it up and running

West Side-project story
West Side-project story

I have seen quite a few projects that spent a ton of calendar/developer time and bugdet in building components and frameworks instead of getting something running. Running in this sense does not mean being able to show an application started from a developers IDE on her development notebook. With running I mean versioned artifacts deployed on some sort of staging infrastructure the client/customer has access to. Let me elaborate on that:

Walking skeleton

I really like the notion of the walking skeleton coined by Steve Freeman and Nat Pryce in “Growing Object-Oriented Software, Guided by Tests“. While I do not want to emphasize TDD and/or automated end-to-end testing I see great benefits in producing a walking skeleton that touches all important parts of a system and is working with a minimal set of functionality. For me that means that all of the following elements are in place, albeit in a primitve way which can be refined on the way:

  • The code is hosted in a source code repository accessible to the project members
  • A build system is chosen and able to produce a runnable artifact
  • A continuous integration server (CI) is triggered on changes in the repository and produces the runnable artifact
  • The artifact is easily installable on the target machines and/or installed on a staging system that resembles the target system as closely as reasonable
  • If there are components they talk to another using minimal requests and stubbed replies that can be refined over time

I usually aim for that walking skeleton in the first few hours into a project after the base technologies and requirements allow starting with coding. That may take up to several days when the system is more complex but it should not take weeks. Connect different parts of the system as early as possible even if responses are minimal, hardcoded or “wrong”. It shows that the parts are able to communicate and mismatches will become visible either someway along the build process or at least when running application. Why should you choose such an approach?

Benefits

  • Most people I know are better at evolving and improving existing stuff than at creating new stuff in empty space in a focused and efficient way. If you have a skeleton of the system it is much easier to talk about the interfaces and responsibilites of the different parts of the system.
  • You get to define APIs which you can evolve over time instead of specifing the complete API and experience implementation and mismatch problems much later when the components need to be integrated.
  • Similarily it is much more difficult to package a complex system after it is finished than to package a simple minimal system and evolve all aspects – building, implemenation and packaging/deployment, maintainance – when necessary.
  • You see if things may work like expected or if there is some inherent problem in the whole design much earlier. Essentially you evolve your proof-of-concept into something with real value for your clients.
  • As soon as your system provides some value you can deploy the working stuff long before the whole project is finished.
  • It is much easier to discuss a working system than to reason about a system not yet existing.
  • You are eating your own dogfood from early on and can address pain points in development, user experience, deployment and running the application.
  • You have something to show more or less right from the beginning and progress will be visible throughout the project.
  • No “Works on my machine!” syndrome.

Potential Problems

Of course there is the challenge of continuously refactoring and extending existing stuff. Later on data migration or migration of configuration may be additional tasks. But hey, you are skilled, agile developers that embrace the idea of changing requirements and the ability to move fast. You have to pay attention not to accumulate too much technical debt as it will slow you down and hurt you in the long run.

Summary

Running systems providing value are what your clients often care about the most. If you can something like that early on communication with your stakeholders tends to be more relaxed as they have better possibilities of steering in the right direction and they steadily see progress. Running software should be the primary goal.