Why do we need AI in VR and AR, and how did we do it?

“Why?”, or actually "What for?" do we need AI in AR & VR apps? 

AR and VR are both indirect and direct interactions between equipment, software and a person. We recently wrote about the differences between AR and VR technologies in our blog (link to post). Because each person is slightly different and the devices are manufactured on a mass scale, there is a problem with the individual adjustment of interaction between the equipment and the user. Contrary to appearances, this is not that easy as classic algorithms have minimal adaptive skills, not to mention the hardware!. Of course, we do not have to adapt the thickness of the sponge mounted in VR glasses to the shape of the face :-). It's about interacting and working with a complex system on the software level. It would be ideal if the algorithms could adapt to the user themselves; they could learn, taking advantage of observations and demonstrating a lot of tolerance. In the end, each of us can recognise Kozakiewicz's gesture :-) A tip for younger readers: look in Wikipedia for what it means, e.g. here. These are examples of such situations where adaptation is required, and the information is not unambiguous and successfully replaces classic algorithms.

 

The Enthusiasm Stage

While planning another project, we decided to use artificial intelligence elements. As AI and VR integration and AR in one project is still rather rare, we decided to share our solutions, observations, and results.

In the beginning, the task looked rather straightforward: recognizing a user’s dynamic hand gestures. We were only interested in a single hand, and it was not important whether left or right. The key fact was that recognition should be done with minimal delays. Our system would then automatically verify the intentions and correctness of the user-performed actions in the AR or VR world. From our point of view, it was an element necessary in training systems in which users maneuver in virtual space, interacting with, for example, construction or heavy mining machines. Initially, we focused on typical actions, i.e., catching something (rod, switch, handle), turning it left or right, pressing a button, and so on – simple movements. Still, at the same time the most often performed manual interactions with the hardware. Thus, the topic did not look too "daunting" - in the end, many solutions have similar built-in functions, only often in a heavily punctured range.

 

The Curiosity Stage

Because we found that the system must operate with various AR, VR and other devices offering interaction with your hand (starting from Microsoft Hololens and ending with Leap Motion), we looked for a uniform solution. The ideal situation would be to find something like the Hardware Abstraction Layer, which causes us not to prepare solutions for specific hardware and SDK. This is where we found the Mixed Reality Toolkit, from Microsoft, where data on the hand’s position (fingers, joints) is delivered in a unified manner, regardless of whichever equipment we have. Microsoft developed a lesson from drivers for MS-DOS and Windows 95, where developers created several software versions to work with various hardware configurations.

OK - it is true that some devices do not transmit a complete data set, e.g., due to hardware restrictions, but the data that were transferred even by the most "limited" devices turned out to be sufficient. However, the real problem has become not a lack of data but rather their excess, as we mention below.

The MRTK (Mixed Reality Toolkit) transmits data in the form of a position and a rotation of all components of a single hand, and there is a total of 25 joints per hand (you have two). The data on the rotation is transferred as a quaternion. It can be roughly assumed that they correspond to the joints, and therefore the places where the fingers bend. These data are transferred in the world space, and therefore the location and rotation relative to the initial position in the virtual world coordinate system is delivered in the MRTK. More about this solution can be read here: https://docs.microsoft.com/en-us/windows/mixed-reality/mrtk-unity/features/input/hand-tracking.

 

Back to School

Our analysis of gestures is local, and therefore we are not interested in the position of your hand in space but the rotation of the joints. Consequently, we focused on using information about rotations only. However, there was one problem here: how to switch global rotations to local ones while they are present as quaternions? A brief analysis of available scientific and popular literature indicated that this should not be that difficult. Therefore, we prepared a theoretical model. We developed the software with visualisation of the result of the transformation, and ... once we combined theory with practice, it turned out to be not so simple. At first, it appeared that nothing worked, and no one knew why, and the hands, after transforming their joints from global to local rotations, looked like something from a cheap horror movie. Ultimately, however, mathematics started to work for us, and we found the correct transformations.

The stream of data flowing from the MRTK and transformed by our software created something that we call a time series, and this was to be analysed through our mechanism of artificial intelligence. Suppose the concept of a time series is extremely abstract for you - in this case imagine the following movie screens (images) on which the hand performs a movement: here it is the same, but instead of pixels, we use numeric data.

 

The Panic Stage

Diagnosis of the battlefield (read: “scientific articles and the current state of knowledge”) indicated that ... no one before us had even tried it yet! Seriously. Nobody. Everyone plays with gestures exploring the video stream and sometimes depth cameras. Furthermore, it works mainly on computing clusters using advanced graphics cards (GPUs) and powerful CPUs. Meanwhile, we had to do it on mobile devices while utilising quite limited resources. What's more, even after rejecting the location information, our data stream was still far too large for the limited resources of the AR and VR mobile devices (goggles): 25 quaternions. To be clear: a quaternion is a spatial, four-number-long floating-point vector delivered to our algorithm several dozen times per second. Hence, it can bottleneck even an efficient computing system, not to mention VR and AR goggles.

 

The Research and Shortcuts Stage

Fuzzy Logic and Fuzzy Inference Systems are suitable for this class of time series analysis, as they already have a long presence in science. Unfortunately, due to computational complexity and implementation drawbacks, these systems are rarely found in industrial solutions. However, with the recent decade of development in Deep Learning, RNN (Recurrent Neural Networks) these have become more and more popular, particularly their special form - LSTM (Long-Short Term Memory) and their derivatives, so-called Transformers.

Initially, we planned to use just one complex, multi-layer LSTM network in our solution to detect gestures in one step.

Unfortunately, LSTM networks, however, require pretty significant computing resources at the training stage and at the implementation stage. However, these are smaller resources than comparable models in the Fuzzy Logic-based solution. Nevertheless, as you can read below, we had to introduce advanced data optimisation techniques, reduce their dimensionality, and finally, change the approach to the problem. It was essential because the transfer of the one-step LSTM network to the mobile platform (AR and VR goggles) caused an unacceptable latency; thus the "playability" of our solution placed us in the worst tail of the VR solutions ever created.

 

The Euphoria Stage

Without taking into account the analysis complexity, spent time, resources, and energy finding an optimal solution, we can proudly write - yes, it works! And so smoothly. Nevertheless, a hybrid approach was necessary: ​​the time series was analysed in terms of static gestures through a convolutional network (CNN). This is a much faster model and introduces minimal delays because it only uses a single data frame. A similar approach was used, for example, to recognise objects in popular AI models for image recognition, e.g., Inception or Yolo. When a model based on the convolutions will recognise a characteristic hand shape which can potentially start the sequence of a gesture in which we are interested, a second model, using a simplified LSTM network, enters the action. It works on a minimal set of data, mainly due to performance reasons. Such a hybrid works well on AR and VR devices, including Oculus Quest and Hololens 2, with limited computing resources. At the moment, these devices mostly use the CPU for neural network computation (prediction), and there is no GPU support for the majority of the AI mobile frameworks available on the ARM platform.

 

Technical Hints

For both models, both convolutional and LSTM based, machine learning was necessary. 

For this purpose, we planned to use ready frameworks for PCs, including Keras, Pytorch or Caffee. Finally, we decided to use Keras, primarily because of its maturity, many confirmed commercial applications and support for mobile devices (including TensorFlow Lite and the possibility of converting a model to other formats). In addition, after its integration with TensorFlow, Keras seems to be the solution which is most seamlessly supported by NVIDIA CUDA, i.e., calculations using a GPU.

Moving the model from the training platform (PC) to the target solution (AR / VR goggles, ARM-based) in theory is quite simple, but in practice, however, it is not so easy. We had essentially only two possible solutions in our case: to export the model to TFLite (the dedicated format for the TensorFlow Framework for mobiles) or to export to an open model in the ONNX format (Open Neural Network Exchange). The first approach is chosen for platforms for which TensorFlow Lite is available, unfortunately not for all, e.g., there is no TFLite available for Microsoft Hololens. On the other hand, the TensorFlow Lite library has the advantage that it is written using low-level C++. Thus, the AI computing core works efficiently even when creating applications in scripting languages ​​or interpreted ones. However, it also means that binary, precompiled libraries dedicated to each platform are necessary. In the case of exports and subsequent imports for a mobile device in ONNX format, in most cases, we can use the universal library because it is written in C #, Java (JavaScript) or Python and available in the form of a source code. Unfortunately, this second solution is slower. In addition, using the entire development chain you must bear in mind that there are a lot of incompatibility issues between the individual versions of the libraries, both on the "training" side (PC) and the "user" side (mobile devices). For example, training with the TensorFlow version 2.4.0 or a newer library does not allow you to export the model to TFLite (Yes, TFLite!). Without a problem we can export with version 2.4.0 to the ONNX format, but ... without changing the default settings in this "advanced" ONNX features version, it cannot be directly loaded to the software for VR / AR goggles virtually in any available library now. This is because ... and, here we get to the incompatibilities for features versions again. Whilst ONNX is advertised as a universal portable format...

So, as you see, we had to solve a big box of puzzles, and the whole project at this stage more resembled the task of escaping from the “escape room” than classic, solid, development work. Still, this sort of challenge drives our team!

 

The Podium Stage

Finally, we have a solution that allows us to perform efficient training on the PC platform, using GPU units, then transferring it to the mobile VR and AR devices while still working quickly. On the one hand, we have models that provide a high (exceeding 90%) accuracy of gesture recognition. On the other hand, they work so quickly that the user does not even realise the complexity of the mechanisms working behind such advanced analysis of its gestures, the whole works close to real-time, with delays far below 100ms.

A brief overview of what artificial intelligence is.

We live in a time where the phrase "artificial intelligence" (called AI for short) is trendy and appears in the marketing descriptions of many products and services. But what is precisely AI?  Broadly speaking, AI originated as an idea to create artificial "thinking" along the lines of the human brain.    As of today, however, we can only make assumptions about how the human brain works, primarily based on medical research and observation. From a medical point of view, we know that the brain looks like a complex network of connections in which neurons are the main element and that our thoughts, memory, and creativity are a flow of electrical impulses. This knowledge has given hope to construct an analogous brain in an electronic version, either hardware or software, where neurons are replaced by electronics or software. However, since we are not 100% sure exactly how the brain works, all current models in AI are certain mathematical approximations and simplifications, serving only certain specific uses. Nevertheless, we know from observation that it is possible, for example, to create solutions that mimic the mind quite well - they can recognize the writing, images (objects), music, emotions, and even create art based on previously acquired experiences. However, the results of the latter are sometimes controversial.    What else does AI resemble the human brain in?  Well... it has to learn! AI solutions are based on one fundamental difference from classical algorithms: the initial product is a philosophical "tabula rasa", or "pure mind", which must first be taught. In the case of complex living organisms, knowledge emerges with development: the ability to speak, to move independently, to name objects, and in the case of humans and some animal species, there are elements of learning organized in kindergartens, schools, universities, and during work and independent development. Analogously in most artificial intelligence solutions - the AI model must first receive specific knowledge, most often in the form of examples, to be able to later function effectively as an "adult" algorithm. Some of the solutions learn once, while others improve their knowledge while functioning (Online Learning, or Reinforced Learning). It vividly resembles the human community: some people finish their education and work for the rest of their lives in one company doing one task. Others have to train throughout their lives as their work environment changes dynamically.   Is AI already "smarter" than humans? As an interesting aside, we can compare the "computing power" of the brain versus the computing power of computers. It, of course, will be a simplification because the nature of the two is quite different.  First, how many neurons does the average human brain have? It was initially estimated to be around 100 billion neurons. However, according to recent research (https://www.verywellmind.com/how-many-neurons-are-in-the-brain-2794889), the number of neurons in the "average" human brain is "slightly" less, by about 14 billion, or 86 billion neuronal cells. For comparison, the brain of a fruit fly is about 100 thousand neurons, a mouse 75 million neurons, a cat 250 million, a chimpanzee 7 billion. An interesting fact is an elephant’s brain (much larger than a human in terms of size), which has ... 257 billion neurons, which is definitely more than the brain of a human.   From medical research, we know that for each neuron, there are about 1000 connections with neighboring neurons or so-called synapses, so in the case of humans, the total number of connections is around 86 trillion (86 billion neurons * 1000 connections). Therefore, in simplified terms, we can assume that each synapse performs one "operation", analogous to one instruction in the processor.  At what speed does the brain work? In total ... not much. We can determine it based on BCI type interfaces (Brain-Computer Interface), which not so long ago appeared as a result of the development of medical devices for electroencephalography (EEG), such as armbands produced by Emotiv, thanks to which we can control the computer using brain waves. Of course, they do not integrate directly with the cerebral cortex but measure activity by analyzing electrical signals. Based on this, we can say that the brain works at variable speed (analogous to the Turbo mode in the processor), and it is between 0.5Hz for the so-called delta state (complete rest) and about 100Hz for the gamma state (stress, full tension).   Thus, we can estimate the maximum computational power of the brain as 8.6 billion operations (8.6*10^15) or 8.6 Petaflops! Despite the relatively slow performance of the brain, this is a colossal number thanks to the parallelization of operations. From Wikipedia (https://en.wikipedia.org/wiki/Supercomputer), we learn that supercomputers did not break this limit until the first decade of the 21st century. The situation will change with the advent of quantum computers, which inherently work in parallel, just like the human brain. However, as of today, quantum computing technology is still in its infancy. In conclusion, at the moment, AI has not yet overtaken the human brain, but it probably will someday. However, we are only talking about learning speed here, leaving aside the whole issue of creativity, "coming up with" ideas, emotions, etc.   AI and mobile devices Artificial intelligence applications require substantial computational power, especially at the so-called learning stage, and pose a significant challenge in integrating them with AR and VR solutions. Unfortunately, AR and VR devices mostly have very limited resources, as they are effectively ARM processor-based mobile platforms comparable in performance to smartphones. As a result, most artificial intelligence models are so computationally (mathematically) complex that they cannot be trained directly on mobile devices. OK - you can, but it will take an incredibly and unacceptably long time. So in most cases, to learn models, we use powerful PCs (clusters) and GPU gas pedals, mainly Nvidia CUDA. This knowledge is then "exported" into a simplified model "implanted" into AR and VR software or mobile hardware.     In our next blog post, you'll learn how we integrated AI into VR and AR, how we dealt with the limited performance of mobile devices, and what we use AI for in AR and VR.

How do we differentiate between Augmented Reality and Virtual Reality?

Augmented Reality (AR) and Virtual Reality (VR) technologies are something everyone has probably heard of recently. Their popularity, undoubtedly thanks to their spectacular features -while remaining relatively accessible - is certainly not decreasing. These technologies are still being developed and improved by hardware and software manufacturers, new uses are being identified - some for entertainment, others for science and others for business. Although these are two different technologies, they are often mentioned synonymously, and for people who do not work with them every day, the difference may not be so obvious. Therefore, we have decided to answer the question - what is the difference between Augmented Reality and Virtual Reality? In the simplest terms, AR is a technological tool that superimposes virtual elements onto a real image while VR is a reality that is entirely created virtually. Both AR and VR allow you to interact with a created world in real time.   How does AR work? To create AR, you need a device - a phone, tablet, or glasses - with a camera and an appropriate application. Thanks to this application, the camera recognises a pre-developed object or a special marker and superimposes on it a created image (graphics) assigned to it. Such additional graphics - just expanding reality - can show, for example, the interior of the object on which we direct the camera, additional elements that can be attached to it or a completely abstract thing, such as a creature in games for children. What about VR? VR, on the other hand, completely separates us from the real world. To use it, we need special goggles (e.g., Oculus Quest 2), which do not impose an image on a real background, but show us a 100% computer-generated, different reality. Here we have no reference to the surroundings, we find ourselves in a completely different world, unrelated to the real place in which we are physically. Hence the term "immersion" in virtual reality, because we enter it in its entirety, we can look around it, interact with it, for example by touching objects, etc. Now that we know the difference between these two technologies, the question is how we can use them. There are endless possibilities.   How can we apply these tools in everyday practice? Augmented reality is a great marketing tool, it is also perfect for expositions and as a service support function. It allows an extension of the information contained, for example, in advertising brochures by adding static or moving graphics, presentations, charts or simply as an interesting, eye-catching gadget, such as a game. Thanks to the possibility of superimposing an image on an existing object, it will enable, for example, users to illustrate the operation of a historical machine or show the interior of an interesting exhibit. During service works, augmented reality may facilitate work even for an inexperienced employee by providing step-by-step instructions or even a simple, yet clear presentation of all necessary actions. IT Silesia – AR in practice In itSilesia, we have had the opportunity to use AR in many projects. In the Ethnographic Museum in Chorzów you can see how an old-fashioned threshing machine works and…. what a pigsty looked like in the 19th century! AR elements also accompany us throughout the tour in the form of a field game. In the application created for Davis Polska (a fabrics manufacturer), you can check how the selected fabrics look on the finished piece of furniture, and the predictive maintenance application for FAMUR S.A. allows you to track the current parameters of the shearer's components. AR is also a good solution for an educational application for children, which is how it was used in the "Atlas" for the House of Polish-German Cooperation, presenting the history of Silesia. ITSilesia – VR in Practice Virtual reality can be applied wherever we have limited likelihood of contact with a real object - because it is inaccessible (e.g., outer space, a non-existing building, but also very expensive, specialised equipment) or dangerous (e.g., the inside of a volcano, but also places with limited access in production plants), or, on the contrary - very delicate or available in limited quantities - here we mean, for example, not only rare fauna/flora specimens or museum objects, but also the human body. Thanks to the option of generating these objects virtually, we gain the chance to see them and to interact with them - to simulate a medical procedure, the service of a machine, or, for example, a rescue operation in difficult conditions An example of this are the applications created for the Coal Mining Museum in Zabrze which present several types of mining disasters and their causes. As a viewer we find ourselves inside a mine gallery and we can observe in turn - flooding, methane explosion, rock bump and fire. The most developed VR application we have created so far is a virtual presentation of service works for FAMUR SA, furthermore, our team is also working on a platform for training in virtual reality - facilitating full training of employees in a given field - you can read more about this HERE.   Future Reality As you can see, there are already many uses of both the technologies, as described above, and everything indicates that new ones will definitely appear. Their possibilities are practically unlimited – restricted only by our imagination and... the deadlines of our graphic designers and programmers :)     Ready for something more advanced? In our next post, you'll find out how we're integrating VR/ AR technologies and artificial intelligence.

How to debug your Unity application?

Sometimes things happen or do not happen differently than we expect it. That often requires a thorough investigation of the causality and flow of our code. The first move would normally be to throw a Debug.Log somewhere, where we expect our problem to happen. However, what if it is not enough?

Another parameter in Debug.Log

The problem: You got a number of objects with the same name that would be normally hard to distinguish. The solution: The first thing to beef up the log statement is to add an extra parameter to our call. As per documentation:  

public static void Log(object message, Object context);   If you pass a GameObject or Component as the optional context argument, Unity momentarily highlights that object in the Hierarchy window when you click the log message in the Console.

Now what does this mean? Let's say our debug code looks like this: [csharp] public class FindMe : MonoBehaviour { void Start() { Debug.Log("ﾍ(･ω| Where am I?", this); } } [/csharp] Then by simply clicking on the log, the corresponding object will be highlighted. In this case a Component was passed (this refers to the class we're at, which eventually inherits after Component). Similarly, any GameObject could be passed. From the official description, it could seem that only objects that are in the Hierarchy window could be highlighted this way. Though the method header indicates that any UnityEngine.Object could be used. This is exactly the case and we can use it to locate anything that has an instance ID. It includes but is not limited to classes like: Material, ScriptableObject, and Mesh.   Bonus fact: We can use EditorGUIUtility.PingObject to use the highlight functionality without writing logs. Link

Making the logs pop

The problem: You need to log a lot of things and while viewing the data and categorize it quickly at a glance. Using the search box and collapsing the logs do not work as well as the order of messages is important and a value that is searched for is not that obvious. The solution: Spice up your logs visually. Log statements can take in Rich Text tags. Differentiating by color is way faster for eyes than reading each line. In the following example, if a slightly concerning value is shown, then the color of the value changes. This is easily noticed almost immediately.

Stopping the flow

The problem: The special case denoted by color is not understood enough once it passed. You need to inspect the scene and/or look at the exact execution in code. The solution: Firstly, the more known approach. Setting up a breakpoint. Unity and Visual Studio work quite well together for this purpose. If on the Visual Studio side for basic debugging, link to the official docs. Getting it working in Unity is quite simple:  

1. Click to the left of the line of code you want the break to happen at.
2. Attach to Unity.
3. Press Play.
4. The breakpoints is hit. From there Visual Studio allows to inspect Local values, investigate the Call Stack and use many other Visual Studio debugging tools.

  When Visual Studio takes over, the Editor becomes unresponsive. This form of breakpoint is not suitable for inspecting the scene. If it is necessary to do so, Debug.Break is your friend. It acts as if the pause button was pressed, except it is exactly at the desired line in code. Then the state of the scene can be fully explored. Alternatively use a debug using  Debug.LogError while enabling the option to pause on error.

Adjusting the console window

The problem: There are a myriad types of data that may need to be displayed. Not every type fits neatly in the limited space provided by the log entry. The solution: Look into the options of the Console window and select as necessary. Additionally, timestamps can be enabled/disabled.

What did I just build?

The problem: Build completed with a result of 'Succeeded' is not enough information about what has just happened. Especially when looking to slim down the size of the app. The solution:  Console window options, then "Open Editor Log". and select as necessary. A text file opens. Starting from section of Build Report there is a detailed breakdown of what assets went in and how much space do they take up, conveniently in descending order.

Logging based on preprocessor directives

The problem: There is a system, that is so vital to the whole app that every time it is being interacted with, you have a set of key debug statements that you want to know about. However it is too many to simply comment and uncomment every time. The solution: Creating a static class that is active with a custom directive will call the corresponding log method with custom prefix. To quickly turn that off have another class with exactly the same name and methods that is active with negation of the directive. Then in Edit > Project Settings > Player > Other Settings > Scripting Define Symbols add the symbol that is used to filter this particular logger. The following example assumes naming conventions for a networking module selective logging: [csharp] using UnityEngine; #if NET_LOGGER && (UNITY_EDITOR || DEVELOPMENT_BUILD) public static class DebugNet { public static void Log(string message) { Debug.Log($"[NET] {message}"); } public static void LogWarning(string message) { Debug.LogWarning($"[NET] {message}"); } public static void LogError(string message) { Debug.LogError($"[NET] {message}"); } } #else public static class DebugNet { public static void Log(string message) { } public static void LogWarning(string message) { } public static void LogError(string message) { } } #endif [/csharp] To ensure that the logs never end up in the final build, display will also be dependent on the build being a development variant or in the editor. To learn more about directives that Unity provides, have a look in the documentation. A potential drawback of this solution is that it does leave debug statements in the main code flow (even if you can hide them in a way that minimizes impact on performance). On the other hand, it is like leaving a comment in code of what is expected to have happened at that particular point in time.

Numbers not enough?

The problem: There is a lot of data. This data does not make much sense when viewed as numbers. Maybe it denotes some direction, or area, or it is a number/string, but it's very important to know at a glance its current value and what object it's attached to. The solution: Depending on the needs, there are many approaches to debug by drawing things on the screen.

Debug class

The quickest way to draw a line is via the Debug class. From documentation (line, ray):

public static void DrawLine(Vector3 start, Vector3 end, Color color = Color.white, float duration = 0.0f, bool depthTest = true); public static void DrawRay(Vector3 start, Vector3 dir, Color color = Color.white, float duration = 0.0f, bool depthTest = true);

Those two methods are especially useful in debugging things like raycast. The simple example would be to see how your object sees the world in front of them. This could highlight many possible issues that arise from improper layer setups or wrongly defined raycast source and direction. [csharp] [SerializeField] float hitDist = 1000; void Update() { Ray ray = new Ray(transform.position, transform.forward); RaycastHit hit; if (Physics.Raycast(ray, out hit, hitDist)) { Debug.DrawLine(ray.origin, hit.point, Color.green); //Do your thing on hit } else { Debug.DrawLine(ray.origin, ray.GetPoint(hitDist), Color.red); } } [/csharp] This snippet shows how to visualize your raycast. Do not that if the point is not hit, DrawLine is used also. This is so that we don't draw a line of infinite length, but the actual distance that is being tested via raycast. The film shows behavior with the aforementioned code:

Gizmos and Handles

If you need more custom shapes to be displayed than simple lines then the Gizmos context is your friend. It can help you draw all sorts of basic shapes, as well as custom meshes. In the following example bounds are being visualized in a similar way a box collider might do it. [csharp] public class BoundingBox : MonoBehaviour { [SerializeField] Bounds bounds; [SerializeField] bool filled; void OnDrawGizmosSelected() { Gizmos.matrix = transform.localToWorldMatrix; if (filled) { Gizmos.DrawCube(bounds.center, bounds.size); } Gizmos.DrawWireCube(bounds.center, bounds.size); } } [/csharp] To ensure the coordinates of the bounds drawn are responsive to the transform component, a matrix that will transform each drawn point from local space to world space is set. There is also a Handles class is generally used to make Editor tools, as it has got mostly methods that return a value if the user modifies something. However, for debugging it has one major advantage that Gizmos doesn't have. It has a handy way to add labels to your objects (documentation).

public static void Label(Vector3 position, string text, GUIStyle style);

[csharp] void OnDrawGizmos() { Handles.Label(transform.position, $"{gameObject.name} {transform.position.ToString()}", EditorStyles.miniButton); } [/csharp] This snippet demonstrates how to draw a temporary label over the object. It is drawn at the position of the object and displays the object name and position. This can be used to display all kinds of data that you need to know the current state of some variable, and to which object they correspond to with a single glance. As the label is white and thin by default, a style was applied to rectify that. For quick setup that will be visible with any background EditorStyles class was used that houses a button type display. Note that Gizmos methods can be called only in functions OnDrawGizmos, OnDrawGizmosSelected and those with proper attribute usage. If you try it in any other place, it will not work. This means that Gizmos are specific to Components. If you need to draw things from an Editor window, then Handles are the only option.

In conclusion...

As a final note sometimes to aid with the debug code some third party tools or scripts are needed. One should always be mindful of adding robust plugins, as it may have many functionalities that are simply not needed for the project, and might even hinder it. That being said, now you have hopefully learned some techniques that you might be considering to use when tackling a new bug or aiming to gain a greater understanding of your system. Keep in mind though that a solution should always be tailored to the problem and using a shiny new technique is usually not the way to go. Always take a step back to consider what is actually needed.  

How NOT to Write Code in React JS

  React, Vue, Angular, Ember, Backbone, Polymer, Aurelia, Meteor, Mithril.js, Preact... There are many super fancy Javascript frameworks nowadays. We can write anything we want, it's comfortable, easy to understand, although difficult to master. After few lines of code, even after writing few small applications you may think that no matter what you write, these frameworks will do the job.     Yeah, what you see above is the iconic series Star Wars and Chewbacca being very skeptical about Han's idea. In the programming universe, you should have this Chewbacca in your mind and this Chewbacca should always be skeptical about your code. It's very important to write your code carefully and thoughtfully no matter what framework you are using. I know it looks that these frameworks do everything for you and you don't have to worry about anything but it's not entirely true. Buckle up, in this article we are going to go through the most common mistakes done in React (and probably other similar frameworks/libraries). I am probably one of the most reliable people to talk about it because I used to make some of these mistakes for a loooong time. And probably I'm still doing some of them.    

New feature? Yeah.. One component is enough.

Nope. Nine times out of ten it won't be enough. Imagine that, in your application, you have a list of board games with some simple filtering controls over the table. You know, choosing a price, an age or a type. At the beginning it looks like it's enough to create a BoardGamesList component and put all of the logic inside. You may think that it doesn't have sense to create a separate BoardGameRow and BoardGamesFilters components. Or even PriceFilter, AgeFilter and BoardGameTypeFilter.   "It's just a few lines! I don't have time to create all these components with so few lines of code."   It's just a few lines for now. But it's very likely that during next year your client will be requiring few more filter options, some fancy icons, 5 ways of ordering the game and 10 ways to display the game row depending on something. Trust me, in my programming life I have experienced too many components which were small at the beginning and after a year it was a massive, uncontrollable piece of sh.. component. Seriously, if you take a few moments and divide it functionally at the beginning, it'll be much easier to work with this component in future. For you. For your colleagues. And even if it stays so small, it'll be easier to find what you need if you rationally divided it into separate React components. Then your work will look like this:  

- Hey, we have some weird bug while filtering board games by age. Can you do something about it? - Yeah, I know exactly where to find it. It's in PriceFilter.js and it's so small that I will need half an hour to fix it! - Great, I think you should get a pay rise!

      It won't necessarily end this way. But there is a better chance if you properly divide your code into components. And look -  I don't even say a word about reusability which is the main reason we use components. But this is a topic for a separate article.    

setState? Pff.. I don't have to read the documentation.

If you decided to use React state in your components, you should know that its main function, setState, is asynchronous. What means you can't just put some important code depending on your state just after setState execution. Let's look at this case:   [js] this.setState({isItWorking: true}); console.log(this.state.isItWorking); // returns false, WHY?! [/js]   Method setState is asynchronous what means it needs few moments to properly set the data you passed. How to handle this problem correctly? The most common way is to pass a callback function as a second parameter which will be executed when the data is passed to the state.   [js] this.setState({isItWorking: true}, () => { console.log(this.state.isItWorking); // and now it returns true }); [/js]   Sometimes you have to do some consecutive operations on your state. Because of setState asynchrony, you may receive unexpected results.   [js] // this.state.name = 'STAR WARS: ' this.setState({name: this.state.name + 'THE RISE OF '}); this.setState({url: this.state.name + 'SKYWALKER'}); [/js]   Unfortunately, you won't finish with the real name of episode IX - STAR WARS: THE RISE OF SKYWALKER. Probably you will get partially filled title like STAR WARS: SKYWALKER. It would be a nice title but it's not what we wanted because the second setState has been overwritten by the last one. To fix it you can use one more time the callback technique but there is another way to handle this case. Instead of passing a new object you can pass a function which returns an object. What's the difference? This function's first parameter is the current "version" of state so you will always work on the updated state.   [js] // this.state.name = 'STAR WARS: ' this.setState(state => ({name: state.name + 'THE RISE OF '})); this.setState(state => ({name: state.name + 'SKYWALKER'})); [/js]   If it's not enough for you and you want to know how setState works internally it'll be a smart choice to read an article from Redux co-author, Dan Abramov: How Does setState Know What to Do?    

Hey! Why this.props is undefined?

This mistake is still very common, no matter that arrow functions are one of the main features of ES6 specification.   [js] handleFieldChange() { console.log(this.props); // return undefined } [/js]   Why? I am inside the component, I should have access to my props! Unfortunately not. This function has its own this (different than component's this) and if you want to use the standard function you should consider binding this with .bind(this) or not so beautiful const self = this before the function. Much easier and a simply better option is to use ES6 arrow functions.   [js] handleFieldChange = () => { console.log(this.props); // YEAH! It returns my props! } [/js]   Arrow function uses something what is called lexical scoping. In a simple way - it uses this from the code containing arrow function - in our case, the component. That's it. No more bindings, no more awful selves, no more unexpectedly missing variables. Arrow functions are also very useful if you need to propagate a few functions. For example, you need a setup function which takes some important parameters and then returns the generic handler function.   [js] handleFieldChange = fieldName => value => { this.setState({[fieldName]: value}); // [fieldName] - for your knowledge, it's dynamic key name, it'll take the name you pass in fieldName variable } [/js]   This is a very generic way to create a function that receives field name and then returns the generic handler function for, let's say, an input element. And if you execute it like that..   [js] <Input onChange={this.handleFieldChange('description')} />; [/js]   ..your input will have this classic function assigned to onChange event:   [js] handleFieldChange = value => { this.setState({description: value}); } [/js]   You should also know that you can fully omit curly braces if you have something very short to return.   [js] getParsedValue = value => parseInt(value, 10); [/js]   In my opinion, in most cases you should avoid it because it can be difficult to read it. On the other hand, in simple cases like above, it'll save you a few lines. But you should be careful doing it. Let's say, I have single object to return. I decide to return it in one line because it's a really short line.   [js] getSomeObject = value => {id: value}; [/js]   Oh yeah, you may think that based on the previous code example it should definitely work. But it's not and it's quite easy to explain. In this case, the system thinks that you use standard arrow function and these curly braces are just the beginning and the end of the function. If you really want to return an object in one line you should use this syntax:   [js] getSomeObject = value => ({id: value}); [/js]   In this case, the returned object is contained in the brackets and it works like intended. Personally, I don't like using one line functions but it's a very nice way to pass a short code to functions like map or filter. Clean, easy to read and it's included in one line.   [js] someCrazyArray.map(element => element.value).filter(value => value.active); [/js]   Okaaaaaaay, quite a lot of info about simple arrow functions but I think it's valuable if you didn't know about it. Let's go to the next one of the ReactJS mistakes!    

The browser has crashed.. Why?

I can't even count how many times I or my buddies were struggling with some weird browser crash or inability to do any action in the application. In many cases, the reason is an infinite loop. I suppose there are billions of ways to get the infinite loop but in React the most common is componentWillReceiveProps or any other React lifecycle method. ComponentWillReceiveProps is currently deprecated but I will focus on this one because there are plenty of React applications still using it and for me most of these bugs happened in this very important lifecycle method. I have multiple examples in my mind which can help visualize the problem but for the purposes of this article I will present this use-case based on the board games example:  

Every time a user changes the age, the application should load board games for the chosen age.

  [js] componentWillReceiveProps(nextProps) { if (nextProps.age) { this.loadBoardGames(nextProps.age); } } [/js]   "Right, if there is the age passed, I load board games." If you don't know how this lifecycle works you may end up with the solution similar to above. But this lifecycle method doesn't work exactly like that. First, every time there is some change in component's props, componentWillReceiveProps is executed. It's quite easy to understand. But you may still think: "Okay, so every time the age is changed, it'll load board games. Isn't it okay?". Partially yes, but in most cases there are other props in your component. Props which will also trigger this lifecycle function. Imagine that we have also boardGames prop (where we store currently displayed board games). Let's examine such a situation:  

1. Age prop is changed
2. componentWillReceiveProps is executed (what causes board games' load)
3. Board games prop is changed
4. componentWillReceiveProps is executed (what causes board games' load)
5. Board games prop is changed
6. componentWillReceiveProps is executed (what causes board games' load)
7. INFINITE LOOP!!!

    As you can see, you change age prop only once and board games will be loading forever. Let's change this function a little bit.   [js] componentWillReceiveProps(nextProps) { if (this.props.age !== nextProps.age) { this.loadBoardGames(nextProps.age); } } [/js]   Now we are not simply checking if there is the age but we are checking if there is some change in the age. We check if next "version" of props contains a DIFFERENT age. "Different" is the keyword here. And we are safe, every time boardGames prop (or any other prop) is changed, it won't trigger board games to load because the age prop will still be the same.   Wait.. Are we really safe? Not necessarily. Most Javascript developers prefer strict comparison (=== or !==) because it's more certain than abstract comparison (==). But it can highlight other minor bugs in your application (what is actually good). Sometimes, some prop can theoretically be the same but of a different format. Let's say our age is represented by years. But there is the possibility that this somehow it will be passed in months. For example, the initial age is fetched from the database and initially we forgot to convert it into the year format. In this case, board games will be loaded two times instead of one. Theoretically, there isn't any serious change in the age but there is a change in the age's format and it's enough to trigger loadBoardGames again. The problem appears more often if the given prop is an object. The object can have 100% the same content but it can still be a different object. Generally, I want to indicate that lifecycle methods are powerful but they should be used very, very carefully. If you need to use it, think what you want to achieve and what is the flow of the prop.    

Something else?

No, that's it. There are many different ways to crash or mess your React application but I chose these which I experienced myself. I hope it was a valuable reading for you. Now.. let's code!

Neo4j with Spring Boot

In this article, I will show you the advantages and disadvantages of the neo4j graph database, the technology that is being used by big companies like Google, Facebook or PayPal. I will also show you how to create and populate it with the help of Spring Boot.   Why a graph database…?   The main application of graph databases is to store relationship information as a first-class entity, because, despite the name, relational databases do not describe relationships other than the standard many-to-many, one-to-one and one-to-many.   A huge advantage of graph databases is that the performance is not reduced with the growth of the amount of data.   ...and why neo4j?   Apart from the above points, the neo4j itself has a number of advantages, such as:  

• scalability
• good documentation
• easy to use
• built-in Spring Boot support
• wide user community

and also it has a useful and pleasant user interface.   Let’s learn!   To start everything off let's create a local server. To do that, navigate to https://neo4j.com/download-center/#releases and download the latest release of the community server, it will be sufficient for this tutorial.   Our next step will be to run the neo4j server. Run command “neo4j console” inside the bin directory in the downloaded package.”. If everything went well, you should see something like this:     Now let's open the localhost link in the browser and we should see the remote UI that I was talking about in the advantages of neo4j. Log in with the default credentials (username: “neo4j" and password: “neo4j”) and then set your new password (do not forget it as we will use it later!).   Creating an application   Now we will build the easy demo application that is going to show Formula 1 results, drivers and also will allow us to retrieve drivers that achieved specific race results.   As I mentioned at the beginning, we will use Spring Boot, so we can create our application, for example, by using the https://start.spring.io/ website. For now, we will need only the neo4j dependency. If you, however, do not want to use the Spring Initializr, here is the dependency you will need:   Maven: [java] <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-neo4j</artifactId> </dependency> [/java] Gradle: [java] compile("org.springframework.boot:spring-boot-starter-data-neo4j") [/java] So now for the actual code… Let’s create a Driver and Race classes [java] @NodeEntity public class Driver { @Id @GeneratedValue private Long id; private String name; private Integer number; public Driver() { } public Driver(final String name, final Integer number, final Team team) { this.name = name; this.number = number; this.team = team; } /* Getters */ } [/java] [java] @NodeEntity public class Race { @Id @GeneratedValue private Long id; private String country; @Relationship(type = "STARTED_IN", direction = Relationship.INCOMING) private Result results; public Race() { } public Race(final String country) { this.country = country; } /* Getters */ } [/java]   We can notice some of the neo4j properties being used in our classes. First of all, they are annotated as @NodeEntity, which obviously means that those classes will represent nodes in our graph. We also need an id that neo4j will use to map our entities (be aware that @Id annotation has to be a neo4j annotation, do not confuse it with JPA @Id!). Next thing we have to be aware of is that the fields are non-final because neo4j needs an empty constructor, which is one of its downsides. Last thing present in the Race class is the @Relationship annotation; as I have mentioned, the main advantage of graph databases is that relationships can have properties. Let’s now create the relationship class: [java] @RelationshipEntity(type = "STARTED_IN") public class Result { @Id @GeneratedValue private Long id; private Integer place; @StartNode private Driver driver; @EndNode private Race race; public Result() { } public Result(final Integer place, final Driver driver, final Race race) { this.place = place; this.driver = driver; this.race = race; } } [/java]   When we are creating a relationship class we must annotate it with @RelationshipEntity and pass the type as an argument; it should be the same type as in the node entities. Relationship entities also need an id and constructors. The last crucial thing is the need to specify @StartNode and @EndNode so that neo4j knows how to connect the nodes.   Our next step will be creating a result repository, if you are familiar with Spring repositories you will feel at home as Neo4jRepository extends PagingAndSortingRepository so all the Spring repository methods (eg. findById, deleteById, save,..) are available: [java] public interface ResultRepository extends Neo4jRepository<Result, Long> { List<Result> findAll(); } [/java]   Alright, so let’s see neo4j in action. For clarity, we will use hard-coded entities in the CommandLineRunner init bean: [java] @SpringBootApplication @EnableNeo4jRepositories public class F1Application { public static void main(String[] args) { SpringApplication.run(F1Application.class, args); } @Bean CommandLineRunner init(ResultRepository resultRepository) { Race italy = new Race("Italy"); Race singapore = new Race("Singapore"); Driver hamilton = new Driver("Lewis Hamilton",44); Driver vettel = new Driver("Sebastian Vettel", 5); return args -> { resultRepository.save(new Result(1,hamilton,italy)); resultRepository.save(new Result(2,vettel,italy)); resultRepository.save(new Result(4,hamilton,singapore)); resultRepository.save(new Result(3,vettel,singapore)); }; } [/java]   For everything to work, you need to enable neo4j repositories by annotating the application class with @EnableNeo4jRepositories. The last thing we will need is to set the username and password in application.properties to the values we have chosen when we were setting up our server: [java] spring.data.neo4j.username=neo4j spring.data.neo4j.password=pass [/java]   If everything went as expected on the remote neo4j interface we should be able to see our populated database:     When we hover over the STARTED_IN relation we will see the places that we have set in the init method.   Custom query and integration testing   Neo4j uses the CYPHER query language and with its use, we can create our own custom query methods, so let's say we want to create a list of drivers that achieved a specific result that is interesting for us. For that in our ResultRepository we will create the getByResult(Integer result) method: [java] @Query("MATCH (r:Race)<-[s:STARTED_IN]-(driver: Driver) WHERE (s.place)={result} RETURN driver") List<Driver> getByResult(@Param("result") Integer result); [/java]   We have created something by ourselves, so it would be nice to test it. Let's add JUnit, neo4j, neo4j ogm test and graphaware dependencies:   Maven: [java] <dependency> <groupId>org.neo4j</groupId> <artifactId>neo4j</artifactId> <version>3.4.8</version> </dependency> <dependency> <groupId>org.neo4j</groupId> <artifactId>neo4j-ogm-test</artifactId> <version>3.1.3</version> <scope>test</scope> </dependency> <dependency> <groupId>com.graphaware.neo4j</groupId> <artifactId>resttest</artifactId> <version>3.4.8.52.18</version> </dependency> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.12</version> <scope>test</scope> </dependency> [/java] Gradle: [java] compile group: 'org.neo4j', name: 'neo4j', version: '3.4.8' testCompile group: 'org.neo4j', name: 'neo4j-ogm-test', version: '3.1.3' testCompile group: 'com.graphaware.neo4j', name: 'tests', version: '3.4.8.52' testCompile group: 'junit', name: 'junit', version: '4.12' [/java]   Our next step will be to create the context. For that let’s create the PersistenceContext class in our test directory: [java] @Configuration @EnableNeo4jRepositories("com.neo4j.f1.repositories") @EnableTransactionManagement @ComponentScan("com.neo4j.f1") public class PersistenceContext { @Bean public org.neo4j.ogm.config.Configuration configuration() { org.neo4j.ogm.config.Configuration.Builder builder = new org.neo4j.ogm.config.Configuration.Builder(); return builder.build(); } @Bean public SessionFactory sessionFactory() { return new SessionFactory(configuration(), "com.neo4j.f1.domain"); } @Bean public PlatformTransactionManager transactionManager() { return new Neo4jTransactionManager(sessionFactory()); } } [/java]   Make sure to provide the valid packages directories where needed!!! Let’s now create a test class and the actual tests: [java] @ContextConfiguration(classes = {PersistenceContext.class}) @RunWith(SpringRunner.class) public class ResultTests { @Autowired ResultRepository resultRepository; @Before public void setup() { Race italy = new Race("Italy"); Race singapore = new Race("Singapore"); Driver hamilton = new Driver("Lewis Hamilton", 44); Driver vettel = new Driver("Sebastian Vettel", 5); Driver alonso = new Driver("Fernando Alonso", 14); resultRepository.save(new Result(1, hamilton, italy)); resultRepository.save(new Result(2, vettel, italy)); resultRepository.save(new Result(13,alonso,italy)); resultRepository.save(new Result(4, hamilton, singapore)); resultRepository.save(new Result(1, vettel, singapore)); resultRepository.save(new Result(12,alonso,singapore)); } @Test public void checkIfAllAreSaved() { List<Result> results = resultRepository.findAll(); Assert.assertEquals(6,results.size()); } @Test public void checkIfCustomQueryHasRightSize() { List<Driver> winners = resultRepository.getByResult(1); Assert.assertEquals(2,winners.size()); } @After public void cleanUp() { resultRepository.deleteAll(); } } [/java]   Conclusions I hope I have shown how nice and easy is to use neo4j. If you would like to broaden your knowledge, more tutorials you can find at https://neo4j.com/docs/developer-manual/3.4/  

6 Tips That Every MySQL User Should Know

Over the last 3 years, I have been working with MySQL almost every day. Even though non-relational databases like MongoDB are gaining more and more popularity every year, traditional SQL solutions are still widely used for many purposes. In this post, I will share some of my tricks I have been using to make my life easier. Note: most of those tips apply only to development machines, for production you should take more care.  

1. Run MySQL in Docker

Seriously, in 2018 there is absolutely no need to run MySQL server natively by installing it, setting users and passwords, performing upgrades etc. You are wasting your client's time if you are still doing it. Just use the sample Docker Compose file as a working starting point: [code] version: '3' services: mysql: image: mysql:5.7 environment: - MYSQL_ROOT_PASSWORD=root - MYSQL_DATABASE=myproject ports: - 127.0.0.1:3306:3306 volumes: - ./varlibmysql:/var/lib/mysql [/code] After docker-compose up, you will have a working server with localhost-only port bound on standard port 3306, one user root/root and a pre-created "myproject" database. Throw in restart: always into Compose file if you want to keep the server running across reboots. That is enough for 95% of software projects, this solution is completely disposable and easy to recreate.   Note: I still have MySQL client installed natively on my development machine. Technically speaking, there is a way of avoiding this too and running the client itself from Docker image, but that is a matter of preference.  

2. Store MySQL data in RAM

In Docker world, the best practice for handling data storage is to store it outside of the container filesystem - in case of a development machine, in some mounted directory on the host. A cool trick, at least on Linux, is to create a RAM-based filesystem called tmpfs and use it as data storage for MySQL. This will, of course, result in data loss after machine restart, but who cares about development data? If it is important, you should have a recent and verified backup anyway, right?   In my case, I am mounting a folder /tmp/varlibmysql into container /var/lib/mysql, since I am using ramdisk for the whole temporary directory to limit SSD wearing. So the relevant part of Compose file is: [code] ... volumes: - /tmp/myproject/varlibmysql:/var/lib/mysql ... [/code] There is a noticeable performance gain with this configuration: I measured the time it takes to run a few hundred Liquibase migrations on application startup and the time it takes to import ~1GB database dump.

• migrations: SSD 0:44, ramdisk 0:07 - huge speedup
• import: SSD 5:23, ramdisk 4:14 - small but noticeable speedup

Of course, this requires a sufficient amount of RAM to be available, in my case an 800 MiB uncompressed dump of application data requires 1.7 GiB of binary MySQL storage. But according to metrics I check sometimes, in a typical day over half of my machine's RAM stays unused, so why not make good use of it?  

3. Manage database dumps like a boss

I personally dislike edge case bugs, which often appear in web applications. One of the ways for a tester to describe such rarely occurring bug is to provide a list of bazillion intricate steps, that have to be carefully performed in order for the bug to appear. It is much easier to create a bug report with a database dump attached, which contains the whole state of the application. As a result, explaining and more importantly reproducing a bug is much easier and faster. However, if every time this happens there is a need to go to StackOverflow to recall mysqldump syntax, no one will want to do this. So let's fix the issue once and for all: [code] $ cat export.sh #! /bin/bash set -e DATABASE=myproject if [ "$#" -ne 1 ]; then echo "Usage: export.sh <filename.sql.bz2>" exit 1 fi echo "Exporting to $1..." mysqldump --protocol tcp -h localhost -u root -proot ${DATABASE} \ | pv \ | bzip2 > "$1" echo "Export finished." [/code]   [code] $ cat import.sh #! /bin/bash set -e DATABASE=myproject if [ "$#" -ne 1 ]; then echo "Usage: import.sh <filename.sql.bz2>" exit 1 fi echo "Importing from $1..." bzcat "$1" \ | pv \ | mysql --protocol tcp -h localhost -u root -proot ${DATABASE} echo "Importing finished." [/code]   [code] $ cat drop.sh #! /bin/bash set -e DATABASE=myproject echo "Dropping and recreating ${DATABASE} ..." mysql --protocol tcp -h localhost -u root -proot ${DATABASE} \ -e "drop database ${DATABASE}; create database ${DATABASE};" echo "Done." [/code] These are 3 scripts I use every day for SQL export, import and one extra for recreating a database for testing purposes. Those scripts use bzip2 compression for minimum file size and pv tool for visualising data flow.  

4. Log executed queries

Recently I have been fixing some performance problems in one of our projects. Our business contact reported that "this specific webpage is slow when there are lots of users present". I started looking around in Chrome Developer Tools and it became clear that the issue is on the backend side, as usual... I could not see any obvious bottlenecks in Java code, so I went a layer down into the database and yep, there was a performance problem there - some innocent SQL query was executed thousands of times for no reason. In order to debug such cases, query logging is a must, otherwise we are shooting in the dark.   You can enable basic query logging using those commands in MySQL console: [code] MySQL [myproject]> SET global general_log = 1; Query OK, 0 rows affected (0.00 sec) MySQL [myproject]> SET global log_output = 'table'; Query OK, 0 rows affected (0.00 sec) [/code] From now on, all queries will be logged in special table mysql.general_log. Fun fact - this table is actually a real physical table, it can be searched, exported etc. - good for documenting bugfixes. Let's create some sample database structure: [code] MySQL [myproject]> create table random_numbers(number float); Query OK, 0 rows affected (0.00 sec) MySQL [myproject]> insert into random_numbers values (rand()), (rand()), (rand()), (rand()); Query OK, 4 rows affected (0.00 sec) Records: 4 Duplicates: 0 Warnings: 0 [/code] And now run a few queries and see if they are captured in the log: [code] MySQL [myproject]> select * from random_numbers where number < 0.1; Empty set (0.00 sec) MySQL [myproject]> select * from random_numbers where number < 0.5; +----------+ | number | +----------+ | 0.254259 | +----------+ 1 row in set (0.00 sec) MySQL [myproject]> select * from random_numbers where number < 0.9; +----------+ | number | +----------+ | 0.777688 | | 0.254259 | +----------+ 2 rows in set (0.00 sec) MySQL [myproject]> select event_time, argument from mysql.general_log; +----------------------------+-------------------------------------------------+ | event_time | argument | +----------------------------+-------------------------------------------------+ | 2018-11-26 12:42:19.784295 | select * from random_numbers where number < 0.1 | | 2018-11-26 12:42:22.400308 | select * from random_numbers where number < 0.5 | | 2018-11-26 12:42:24.184330 | select * from random_numbers where number < 0.9 | | 2018-11-26 12:42:28.768540 | select * from mysql.general_log | +----------------------------+-------------------------------------------------+ 4 rows in set (0.00 sec) [/code] Perfect! Keep in mind that "all queries" means all of all, so if you are using graphical database tools for viewing query logs, those "query querying logs" will be also there.  

5. Use remote servers

MySQL protocol runs over TCP/IP (note to nitpickers: yes, it can also work through UNIX sockets, but the principle is the same). It is perfectly fine to use some remote MySQL server/service for local development instead of a local server. This is useful for working with big databases that would not fit onto tiny laptop SSD, or if we need more performance. The only concern is network latency, which can sometimes diminish any performance gains - but I think it is still a useful trick.   Let's try Amazon RDS. It is pretty expensive for long-term usage but affordable for one-off tasks. Graphical setup wizard in AWS web console is pretty easy to use so I will not cover it here in full, however, keep attention on:

• DB instance class - pick the cheapest one for start, you can always upgrade it later if needed
• Allocated storage - the minimum (20 GiB) is actually a huge amount of space for a typical project, but you can increase it now if you need
• Username and password - pick something secure, because our instance will be publicly visible from the Internet
• Security group - pick/create a security group with full access from the Internet

  After a while, our instance should be running and available. We can look up its details: And finally, find the server hostname to connect to... ...like usual: [code] $ mysql -uroot -pmysqlletmein --protocol tcp \ -h mydbinstance.cizrsk4vjj5m.eu-central-1.rds.amazonaws.com Welcome to the MariaDB monitor. Commands end with ; or \g. Your MySQL connection id is 15 Server version: 5.6.41-log Source distribution Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. MySQL [(none)]> select rand(); +--------------------+ | rand() | +--------------------+ | 0.8418013517202823 | +--------------------+ 1 row in set (0.06 sec) [/code] Now we can configure our backend to use it and it should work exactly like a local server. Amazon RDS gives us some useful development features like real-time monitoring, automatic backups, spinning new database instances from snapshots and much more. Go try it!  

6. Learn some SQL!

Finally, even if you love ORMs and you can't live without Spring Data and sexy dynamic finders (which can sometimes be long enough to wrap on 4k screen), it is very beneficial to learn at least some SQL to understand how everything works underneath, how ORMs are mapping one-to-many and many-to-many relationships with the use of extra join tables, how transactions work (very important in high load systems) and so on. Also, some kinds of performance problems ("N+1 select" being the most common) are completely undiscoverable without knowing how underlying SQL is generated and subsequently executed.   And that is all. Thanks for reaching this point and I hope you've learnt something.

Let’s shake some trees – how to enhance the performance of your application

Nowadays JavaScript applications are getting bigger and bigger. One of the most crucial things while developing is to optimise the page load time by reducing the size of the JavaScript bundle file. JavaScript is an expensive resource when processing and should be compressed when it is about to be sent over the network. One of the most popular techniques to improve the performance of our applications is  code splitting. It is based on splitting the application into chunks and serving only those parts of JavaScript code that are needed at the specified time. However, this article is going to be about another good practice called tree shaking. Tree shaking  is used within the ES2015 import and export syntax and supports the dead-code elimination. Since Webpack 4 released it is possible to provide the hint for a compiler by the “sideEffects” property to point the modules that can be safely pruned from the application tree if unused. Function may be supposed to have  side effects if it modifies something outside its own scope.   Real life example In order to introduce tree shaking concept more precisely, let’s start with creating a new project including the application entry point (index.js) and the output bundle file (main.js). In the next step, a new JavaScript file (utils.js) is added to the src directory... [code] export function foo() { console.log('First testing function') } export function bar() { console.log('Second testing function') } [/code] ...and imported in the index.js. [code] import { foo } from './utils.js' foo() [/code] Webpack 4 introduces the production and development mode. In order to get a not minified output bundle, we are supposed to run a build process in the development mode what can be defined in the package.json file. [code] "scripts": { "dev": "webpack --mode development", "build": "webpack --mode production" } [/code] Now, just get the terminal and run: npm run build script. Despite, only the foo function has been required in the entry point, our output bundle still consists of both foo and bar methods. Bar function is known as a “dead code” since it is unused export that should be dropped. [code] console.log('First testing function');\n\nfunction bar() {\n console.log('Second testing function') [/code] To fix this problem we are about to set the “sideEffects” property in package.json file. [code] { "name": "tree-shaking", "version": "1.0.0", "sideEffects": "false", } [/code] That property just tells the compiler that there are not any side effects files and every unused code can be pruned. It accepts also absolute and relative paths to the files that should not be dropped due having some side effects. [code] { "name": "tree-shaking", "version": "1.0.0", "sideEffects": "./src/file-wth-side-effects.js", } [/code] Minification After we pruned unused ES6 imports and exports, we still need to remove “dead code” from the application bundle. The only thing we have to do is to set the mode configuration to production and execute npm run build. [code] ([function(e,t,n){"use strict";n.r(t),console.log("First testing function")}]); [/code] As we can see, the second testing function is no more included in the bundle minified file. Use three shaking with the ES6 It is crucial to keep in mind that tree shaking pattern can be used only within the ES6 import and export modules. We cannot “shake the tree” while using the CommonJS without the help of special plugins. To solve this issue, setting the  babel-preset-env to leave the ES6 modules on their own should be performed. [code] { "presets": [ ["env", { "modules": false }] ] } [/code]   Exception Removing unused modules does not work while dealing with lodash. Lodash is one of the most popular utility library. If you import a module in the way it is depicted below, it will still pull all the lodash library. [code] import { join } from 'lodash' [/code] To go around that, we need to install lodash-es package and require the modules in the following way: [code] import join from 'lodash-es/join' [/code] Conclusion Let’s prove the statement from the beginning of this article! Let’s take a closer look at the sizes of the bundle file (main.js) before and after the tree shaking and minification process.         As we can see we minimized the output size significantly. When you start using tree shaking in your projects it may seem not taking a big advantage of application performance at all. You will notice how it really boosts up your work while having a more complex application tree.

Kubernetes for poor – how to run your cluster and not go bankrupt

Since the last few years, Kubernetes has proved that it is the best container orchestration software on the market. Today, all 3 biggest cloud providers (Amazon, Google and Azure) are offering a form of managed Kubernetes cluster in form of EKS, GKE and AKS respectively. All of those offerings are production-ready, they are fully integrated with other cloud services and they include commercial support.   There is one problem, though, and it is the price. Typically, cloud offerings like this are targeted for big organizations with a lot of money. For example, Amazon Elastic Kubernetes Service costs 144$/mo for just running cluster management ("control plane"), and all compute nodes and storage are billed separately. Google Kubernetes Engine does not charge anything for the control plane, but instances to run nodes at aren't cheap either - a reasonable 4 GiB machine with only a single core costs 24$/mo. The question is, can we do it cheaper? Since Kubernetes itself is 100% open source, we can install it on our own server of choice. How much we'll be able to save? Big traditional VPS providers (like OVH or Linode for instance) give out decent machines at prices ranging from 5$/mo. Could this work? Well, let's try it out!  

Setting up the server

Hardware

For running our single-master single-node host we don't need anything too fancy. The official requirements for a cluster bootstrapped by kubeadm tool, which we are going to use, are as follows:

• 2 GiB of RAM
• 2 CPU cores
• reasonable disk size for basic host software, Kubernetes native binaries and it's Docker images

For the purpose of this guide I'll use the minimal recommended specs, however, it should be possible to run it on even less powerful hardware. Let's create some small virtual server and log in into it through SSH (I'm using DigitalOcean, which gives out 50$ for free for a year if you register through GitHub Student Program): [code] $ doctl compute droplet create kubernetes-for-poor \ --image ubuntu-16-04-x64 \ --size 2gb \ --region fra1 \ --ssh-keys ee:a4:d1:c3:61:b1:7c:33:ce:49:a0:01:c5:a4:3b:09 (...) $ doctl compute ssh kubernetes-for-poor [/code] In case of further optimization, it's a good idea to turn off unnecessary services, remove unneeded packages, locales and so on. We'll go with the default Ubuntu 16.04 configuration that DigitalOcean gave us.  

Installing Docker and kubeadm

Here we will be basically following the official guide for kubeadm, which is a tool for bootstrapping a cluster on any supported Linux machine.   First thing is Docker - we'll install it through the default Ubuntu repo, but generally, we should pay close attention to Docker version - only specific ones are supported. I've run into many problems creating a cluster with the current newest version (18.06-*), so I think this is worth mentioning. [code] $ apt-get update $ apt-get install -y docker.io [/code] And let's run a sample image to check if the installation was successful: [code] $ docker run --rm -ti hello-world Unable to find image 'hello-world:latest' locally latest: Pulling from library/hello-world 9db2ca6ccae0: Pull complete Digest: sha256:4b8ff392a12ed9ea17784bd3c9a8b1fa32... Status: Downloaded newer image for hello-world:latest Hello from Docker! This message shows that your installation appears to be working correctly. (...) $ [/code] Good. The next step is kubeadm itself - note that the last command (apt-mark hold) will prevent APT from automatically upgrading those packages if a new version appears. This is critical because cluster upgrades are not possible to be done automatically in a self-managed environment. [code] $ apt-get update && apt-get install -y apt-transport-https curl $ curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg \ | apt-key add - $ cat <<EOF >/etc/apt/sources.list.d/kubernetes.list deb http://apt.kubernetes.io/ kubernetes-xenial main EOF $ apt-get update $ apt-get install -y kubelet kubeadm kubectl $ apt-mark hold kubelet kubeadm kubectl [/code]

Setting up a master node

The next step is to actually deploy Kubernetes into our server. Those 2 basic commands below initialize the master node and select Flannel as an inter-container network provider (an out-of-the-box zero-config plugin). People with experience with Docker Swarm will immediately notice the similarity in init command below - here we specify two IP addresses:

• apiserver advertise address is the address at which, well, apiserver will be listening - for single-node clusters we could put 127.0.0.1 there, but specifying external IP of the server here will allow us to add more nodes to the cluster later, if necessary,
• pod network CIDR is a range of IP addresses for pods, this is dictated by the network plugin that we are going to use - for Flannel it must be that way, period.

Also, we copy some configuration files from their default locations to our home directory, so we don't have to use kubectl as root later. [code] $ kubeadm init --apiserver-advertise-address=$PUBLIC_IP \ --pod-network-cidr=10.244.0.0/16 [init] using Kubernetes version: v1.11.2 [preflight] running pre-flight checks (...) Your Kubernetes master has initialized successfully! $ mkdir -p $HOME/.kube $ sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config $ sudo chown $(id -u):$(id -g) $HOME/.kube/config $ kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/v0.10.0/Documentation/kube-flannel.yml [/code] Those commands may take a solid few minutes, depending on CPU power and networking speed. To see the progress, a good trick is to use watch command to repeatedly query for all pods on the entire node. Wait until all pods are in the state Running and no more appear in some time. [code] $ watch kubectl get pods --all-namespaces NAMESPACE NAME READY STATUS kube-system coredns-78fcdf6894-dhfnl 1/1 Running kube-system coredns-78fcdf6894-kf2vl 1/1 Running kube-system etcd-kubernetes-for-cheap 1/1 Running kube-system kube-apiserver-kubernetes-for-cheap 1/1 Running kube-system kube-controller-manager-kuber... 1/1 Running kube-system kube-flannel-ds-lx5ft 1/1 Running kube-system kube-proxy-6nszm 1/1 Running kube-system kube-scheduler-kubernetes-for-cheap 1/1 Running [/code] After a while, they should all be in the state "Running". Congratulations - the cluster is formally up! One tweak remains, though - by default, kubeadm configures our master node in such a way, that no workloads can be run on it, only regular nodes added later would be able to really run anything. This is done mainly for performance reasons, but here we don't care, so we need to remove the appropriate label from our master node. [code] kubectl taint nodes --all node-role.kubernetes.io/master- [/code] And that's it!  

Testing our setup

Testing - pod scheduler

Kubernetes is a really complex piece of container orchestration software, and especially in the context of a self-hosted cluster, we need to make sure that everything has been set up correctly. Let's perform one of the simplest tests, which is to run the same hello-world image, but this time, instead of running it directly through Docker API, we'll tell Kubernetes API to run it for us. [code] $ kubectl run --rm --restart=Never -ti --image=hello-world my-test-pod (...) Hello from Docker! This message shows that your installation appears to be working correctly. (...) pod "my-test-pod" deleted [/code] That's a bit long command. Let's explain in detail what are the parameters and what just happened. When we are using kubectl command, we are talking through the apiserver. This component is responsible for taking our requests (under the hood, HTTP REST requests, but we are using it locally) and communicating our needs to other components. Here we are asking to run an image of name hello-world inside temporary pod named my-test-pod (just a single container), without any restart policy and with automatic removal after exit. After running this command, Kubernetes will find a suitable node for our workload (here we have just one, of course), pull the image, run its entrypoint command and serve us it's console output. After the process finishes, pod is deleted and the command exits.  

Testing - networking

The next test is to check if networking is set up correctly. For this, we will run Apache web server instance - conveniently, it has a default index.html page, which we can use for our test. I'm not going to cover everything in YAML configurations (that would take forever) - please check out pretty decent official documentation. I'm just going to briefly go through concepts. Kubernetes configuration mostly consists of so-called "resources", and here we define two of them, one Deployment (which is responsible for keeping our Apache server always running) and one Service of type NodePort (which will allow us to connect to Apache under assigned random port on the host node). [code] $ cat apache-nodeport.yml --- apiVersion: apps/v1 kind: Deployment metadata: name: apache-nodeport-test spec: selector: matchLabels: app: apache-nodeport-test replicas: 1 template: metadata: labels: app: apache-nodeport-test spec: containers: - name: apache image: httpd ports: - containerPort: 80 --- apiVersion: v1 kind: Service metadata: name: apache-nodeport-test spec: selector: app: apache-nodeport-test type: NodePort ports: - port: 80 $ kubectl apply -f apache-nodeport.yml [/code] It should be up and running in a while: [code] $ kubectl get pods NAME READY STATUS RESTARTS AGE apache-nodeport-test-79c84b9fbb-flc9p 1/1 Running 0 25s $ kubectl get services NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) apache-nodeport-test NodePort 10.108.114.13 <none> 80:31456/TCP kubernetes ClusterIP 10.96.0.1 <none> 443/TCP [/code] Let's try curl-ing our server, using a port that was assigned to our service above: [code] $ curl http://139.59.211.151:31456 <html><body> <h1>It works!</h1> </body></html> [/code] And we are all set!  

How to survive in the Kubernetes world outside a managed cloud

Most DevOps people, when they think "Kubernetes" they are thinking about a managed offering of one of the biggest cloud providers, namely Amazon, Google Cloud Platform or Azure. And it makes sense - such provider-hosted environments are really easy to work with, they provide cloud-expected features like metrics based autoscaling (both on container count and node count level), load balancing or self-healing. Furthermore, they provide well-integrated solutions to (in my opinion) two biggest challenges of containerized environments - networking and storage. Let's tackle the networking problem first.  

Kubernetes networking outside the cloud

How do we access our application running on the server, from the outside Internet? Let's assume we want to run a Spring Boot web hello world. In a typical traditional deployment, we'll have our Java process running on the host, bound to port 8080 listening to traffic, and that's it. In case of containers and especially Kubernetes, things get complicated really quickly.   Every running container is living inside pod, which has its own IP address from pod network CIDR range. This IP is completely private and invisible for clients trying to access the pod from the outside world. The managed cloud solution, in case of Amazon for example, is to create a Service with type LoadBalancer, which will end up creating an ELB instance in your customer account (a whooping 20$/mo minimum), pointing to the appropriate pod. This is, of course, impossible to do in a self-hosted environment, so what can we do? Well, a hacky solution is to use NodePort services everywhere, which will expose our services at high-numbered ports. This is problematic because of those high port numbers, so we slap an Nginx proxy before them, with appropriate virtual hosts and call it a day. This will definitely work, but for such use case Kubernetes provides us something called Ingress Controller, that can be deployed into a self-hosted cluster. Without going into much detail, it's like installing Nginx inside Kubernetes cluster itself, which makes it possible to control domains and virtual hosts easily through the same resources and API. Deploying official Nginx ingress is just a few kubectl apply commands away as usual, however, there is a catch - official YAML-s actually are using a NodePort service for Nginx itself! So that won't really work, because all our applications would be visible on this high port number. The fix to this is to do what Kubernetes explicitly discourages, which is to use fixed hostPort binding for 80 and 443 ports. This way, we'll be able to access our apps from the browser without any complications. Ingress definitions with those changes can be found at my GitHub, but I encourage you to see the official examples and start from there. [code] $ kubectl apply -f . $ (...) $ curl localhost default backend - 404 $ [/code] If you have a domain name for your server already (or you made a static entry in /etc/hosts), you should be able to point your browser at the domain and see "404 - default backend". That means our ingress is running correctly, serving 404 as a default response, since we haven't defined any Ingress resource yet. Let's use our Nginx Ingress to deploy the same Apache server, using domain names instead of random ports. I set up a static DNS entry in hosts file with name kubernetes-for-poor.test here. [code] $ cat apache-ingress.yml --- apiVersion: apps/v1 kind: Deployment metadata: name: ingress-test spec: selector: matchLabels: app: ingress-test replicas: 1 template: metadata: labels: app: ingress-test spec: containers: - name: ingress-test image: httpd ports: - containerPort: 80 --- apiVersion: v1 kind: Service metadata: name: ingress-test spec: selector: app: ingress-test ports: - port: 80 --- apiVersion: extensions/v1beta1 kind: Ingress metadata: name: ingress-test spec: rules: - host: kubernetes-for-poor.test http: paths: - path: '/' backend: serviceName: ingress-test servicePort: 80 $ kubectl apply -f apache-ingress.yml deployment.apps/ingress-test created service/ingress-test created ingress.extensions/ingress-test created $ [/code] And pointing your browser at your domain should yield "It works!" page. Well done! Now you can deploy as many services as you like. An easy trick is to create DNS CNAME record, pointing from *.yourdomain.com to yourdomain.com - this way there is no need to create additional DNS entries for new applications.  

Kubernetes storage outside the cloud

The second biggest challenge in the container world is storage. Containers are by definition ephemeral - when a process is running in a container, it can make changes to the filesystem inside it, but every container recreation will result in loss of this data. Docker solves it by the concept of volumes, which is basically mounting a directory from the host at some mount point in container filesystem, so changes are preserved. Unfortunately, this by definition works only for single-node deployments. Cloud offerings like GKE solve this problem by allowing to mount a networked storage volume (like GCE Persistent Disk) to the container. Since all nodes in the cluster can access PD volumes, storage problem goes away. If we add automatic volume provisioning and lifecycle management provided by PersistentVolumeClaim mechanism (used to request storage from the cloud in an automated manner), we are all set.   But none of the above is possible on a single-node cluster (outside of managing a networked filesystem like NFS or Gluster manually, but that's not fun). The first thing that comes to mind is to go the Docker way and just mount directories from the host. Since we have only 1 node, it shouldn't be a problem, right? Technically true, but then we can't use PersistentVolumeClaims and we have to keep track of those directories, make sure they exist beforehand etc. There is a better way, though. We can use so-called "hostpath provisioner", which uses Docker-like directory mounts under the hood, but exposes everything through PVC mechanism. This way volumes are created when needed and deleted when not used anymore. One of the implementations which I've tested can be found here, it should work out of the box after just another kubectl apply.   Let's test it. For this example, we'll create a single, typical PVC. [code] $ kubectl apply -f . $ (...) $ cat test-pvc.yml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: test-pvc spec: accessModes: - ReadWriteOnce resources: requests: storage: 200Mi $ kubectl apply -f pvc.yml $ kubectl get pvc test-pvc Bound pvc-a5109ca0... 200Mi RWO hostpath 5s [/code] We can see that our claim was fulfilled and the new volume was bound to the claim. Let's write some data to the volume and check if it is present on the host. [code] $ cat test-pvc-pod.yml apiVersion: v1 kind: Pod metadata: name: myapp-pod spec: containers: - name: myapp-container image: ubuntu command: ['bash', '-c', 'while true; do sleep 1 && echo IT_WORKS | tee -a /my-volume/test.txt; done'] volumeMounts: - mountPath: /my-volume name: test volumes: - name: test persistentVolumeClaim: claimName: test-pvc $ kubectl apply -f test-pvc-pod.yml (...) $ ls /var/kubernetes default-test-pvc-pvc-a5109ca0-b289-11e8-bc89-fa163e350fbc $ cat default-test-pvc-pvc-a5109ca0-b289-11e8-bc89-fa163e350fbc/test.txt IT_WORKS IT_WORKS (...) [/code] Great - we have our data persisted outside. If you can keep all your applications running on the cluster, then backups become a piece of cake - all the state is kept in a single folder.  

Let's actually deploy something

We have our single-node cluster up and running, ready to run any Docker image, with external HTTP connectivity and automatic storage handling. As an example, let's deploy something everyone is familiar with, namely Wordpress. We'll use official Google tutorial with just one small change: we want our blog to be visible under our domain name, so we remove "LoadBalancer" from service definition and add an appropriate Ingress definition. [code] $ cat wp.yml --- apiVersion: v1 kind: PersistentVolumeClaim metadata: name: mysql-pv-claim spec: accessModes: - ReadWriteOnce resources: requests: storage: 1Gi --- apiVersion: apps/v1 kind: Deployment metadata: name: wordpress-mysql spec: selector: matchLabels: app: wordpress-mysql strategy: type: Recreate template: metadata: labels: app: wordpress-mysql spec: containers: - image: mysql:5.6 name: mysql env: - name: MYSQL_ROOT_PASSWORD valueFrom: secretKeyRef: name: mysql-pass key: password ports: - containerPort: 3306 name: mysql volumeMounts: - name: mysql-persistent-storage mountPath: /var/lib/mysql volumes: - name: mysql-persistent-storage persistentVolumeClaim: claimName: mysql-pv-claim --- apiVersion: v1 kind: Service metadata: name: wordpress-mysql spec: ports: - port: 3306 selector: app: wordpress-mysql --- apiVersion: v1 kind: Service metadata: name: wordpress-web labels: app: wordpress-web spec: ports: - port: 80 selector: app: wordpress-web --- apiVersion: v1 kind: PersistentVolumeClaim metadata: name: wp-pv-claim labels: app: wordpress spec: accessModes: - ReadWriteOnce resources: requests: storage: 20Gi --- apiVersion: apps/v1 # for versions before 1.9.0 use apps/v1beta2 kind: Deployment metadata: name: wordpress-web spec: selector: matchLabels: app: wordpress-web strategy: type: Recreate template: metadata: labels: app: wordpress-web spec: containers: - image: wordpress:4.8-apache name: wordpress env: - name: WORDPRESS_DB_HOST value: wordpress-mysql - name: WORDPRESS_DB_PASSWORD valueFrom: secretKeyRef: name: mysql-pass key: password ports: - containerPort: 80 name: wordpress volumeMounts: - name: wordpress-persistent-storage mountPath: /var/www/html volumes: - name: wordpress-persistent-storage persistentVolumeClaim: claimName: wp-pv-claim --- apiVersion: extensions/v1beta1 kind: Ingress metadata: name: wordpress-web spec: rules: - host: kubernetes-for-poor.test http: paths: - path: / backend: serviceName: wordpress-web servicePort: 80 $ [/code] The only prerequisite is to create a Secret with the password for MySQL root user. We could easily and safely hardcode 'root/root' in this case (since our database isn't exposed outside the cluster), but we'll do it just to follow the tutorial. [code] $ kubectl create secret generic mysql-pass --from-literal=password=sosecret [/code] And we finally deploy. [code] $ kubectl apply -f wp.yml [/code] After a while, you should see a very familiar Wordpress installation screen at the domain specified in the Ingress definition. Database configuration is handled by dockerized Wordpress startup scripts, so there is no need to do it here like in a traditional install.

Conclusion

And that's it! Fully functional single-node Kubernetes cluster, for all our personal projects. Is this an overkill for a few non-critical websites? Probably yes, but it's the learning process that's important. How would you become a 15k programmer otherwise?

Creating Countinous Integration system with GitLab and Unity

After spending days and nights working in the sweat of our brows and making tons of application builds, we finally decided to take a step forward and make some part of our job automatic.   But...  why ? The main reason of making our Continuous Integration system was shortening amount of time spent on building applications. In our last project it lasted even above half an hour, so we decided to make our life easier. We had a list of features we wanted to achieve:

• builds available online to download
• working on multiple unity versions
• building for iOS, Windows and Android on our Windows device
• builds available on our local server

( Our continuous integration system in few simple steps)   About GitLab CI We chose GitLab CI solution because we had already possessed repositories there. How to configure GitLab and a Runner we described in a separate article:   When we set up our GitLab Runner we had to configure GitLab CI itself by .gitlab-ci.yml Our system works in two stages : build and deploy. Job named “build” is a template for other jobs. It contains a powershell script which run the unity in batchmode and execute script we wrote in C#. To do such a thing we need Unity command lines :  

C:\’Program Files’\Unity\Editor\Unity.exe	call unity executable
-batchmode	execute in batchmode to prevent the editor from opening
-nographics	needed on windows to really make sure there is no GUI
-executeMethod BuildScript.Build	calls the Method “Build()” in the Unity BuildScript
-projectPath %CI_PROJECT_DIR%	sets the unity project path to default GitLab directory
-quit	quit Unity after execution of the build

+ other variables you want to use in unity e.g. -customProjectName %CI_PROJECT_NAME%   GitLab can hands over some variables such as project name, pipeline id, project direction or build platform target. We use them in our unity internal script to build apps for platforms we have chosen [code] .build: &amp;build stage: build variables: tags: - Unity script: - C:\"Program Files"\Unity\Hub\Editor\%UNITY_VERSION%\Editor\Unity.exe -projectPath %CI_PROJECT_DIR% -logfile D:\Logs\%BUILD_TARGET%.log -customBuildPath %BUILD_PATH% -customProjectName %CI_PROJECT_NAME% -customBuildTarget %BUILD_TARGET% -pipelineId %CI_PIPELINE_ID% -batchmode -nographics -executeMethod BuildScript.Build -quit artifacts: name: "%CI_PROJECT_NAME% %BUILD_TARGET% %CI_PIPELINE_ID%" paths: - ./Builds/%CI_PROJECT_NAME%/%CI_PIPELINE_ID%/%BUILD_TARGET% [/code]   Builds on demand  We have four options: Windows, Android, iOS and All, we type them in the “run pipeline” section with a “Target” variable. Unfortunately there is no option (for now) to make a list of a variables, so we need to type them manually. If  we don’t write anything it automatically run a build for all platforms. This is also a reason why job we named “build” is a template, every platform has their own job and is executed only if we write a correct variable. [code] Windows: &gt;&gt;: *build variables: BUILD_TARGET: StandaloneWindows64 BUILD_PATH : ./Builds only: variables: - $Target == "Windows" - $Target == "windows" - $Target == "All" - $Target == null [/code]   Keep it planned All builds can be made on demand, but also after every commit in master branch and everyday at 2 AM. Beside that we are able to configure it in GitLabCI/Schedules.     We are also setting a unity version as a variable on the top of our file to be easily configurable at the start of the project.   Where we can find our builds? We wanted to save builds directly to our local server, but we found out that it was impossible to achieve. We had to workaround it, which we didn't want to use in the first place - copying from a temporary folder on our disc. We were afraid that it will fill the disc too fast with unnecessary data so we had to also clean the folder after completion. That’s what we are doing in the deploy stage. Builds are also available as the artifacts in GitLab.     As fast as possible  To make the whole process faster we decided to start using unity cache server which helps us to save time at changing between different platforms in unity. When it’s working Unity is downloading converted assets from cache server instead of converting them everytime.   Building iOS apps on Windows One of our goals was to build everything on one machine and didn’t involve our mac. The major problem was iOS building, because we thought it was impossible to build an .ipa file on Wndows machine, but we have discovered a plugin that helped us fix the problem. Of course it wasn’t one click configurable, we had to transfer all the certificates from our mac etc. Never the less it was worth the pain. We are also able to use it by command lines so it is perfect for our purposes.   Try it yourself So this is our story of creating continuous integration system with GitLab and Unity. It was harder than it seems to be, but I’m pretty sure that we will keep improving it. The system makes our workflow much more simpler and faster. Don’t wait, go and make yourself your own one!