Thinking out loud

Kubernetes has become a must-have container orchestration platform for every company that aims to gain a competitive advantage by delivering high-quality software at a rapid pace. What’s the state of Kubernetes at the beginning of 2020? Is there room for improvement? Here is a list of trends that should shape the first months of the upcoming year.

As a team that provides own Multicloud Enterprise Kubernetes platform and empowers numerous companies in adopting K8s, we follow all the news that helps to prepare for the upcoming trends in using this cloud-native platform. And there are the best places to learn what’s new and what’s coming like KubeCon I CloudNativeCon conferences.

A few weeks ago, San Diego hosted KubeCon + CloudNativeCon North America gathering 12 thousand cloud-native enthusiasts - 50% increase in the number of attendees in comparison to the previous edition shows the scale of the Kubernetes' popularity growth. During the event, we had a chance to listen about new trends and discuss further opportunities with industry experts. Most of the news announced in San Diego will influence the upcoming months in a cloud-native world. Below, we focus on the most important ones.

Kubernetes is a must for the most competitive brands

What makes KubeCon so likable? Access to Kubernetes experts, networking with an amazing community of people gathered around CNCF, chance to learn the trends before they become mainstream? For sure, but what also makes it so special? The answer comes to the hottest brands that join cloud-native nation these days - Pinterest, Home Depot, Walmart, Tinder and many more.

It’s obvious when tech companies present how they build their advantage using the latest technologies, but it becomes more intriguing when you have an opportunity to get to know how companies like Adidas, Nike or Tinder (yes, indeed) are using Kubernetes to provide their customers/users with extraordinary value.

As attached examples show, we live in the software-driven world, where the quality of delivered apps is crucial to stay relevant, regardless of the industry.

Enterprises need container orchestration to sustain their market share

The conference confirmed that Kubernetes is a standard in container orchestration and one of the key elements contributing to the successful implementation of a cloud-first strategy for enterprises.

But why the largest companies should be interested in adopting the newest technologies? Because their industries are being constantly disrupted by fresh startups utilizing agility and cutting-edge tech solutions. The only way to sustain position is by evolving. The way to achieve it comes to adopting a cloud-native strategy and implementing Kubernetes. As Jonathan Smart once said - “You’re never done with improving and learning.”

Automate what can be automated

As more and more teams move Kubernetes to production, a large number of companies is working on solutions that would help streamline and automate certain processes. That drives to the growing market of tools associated with Kubernetes and enriching its usage.

For example, Helm, which has its place in the native cloud toolbox used by administrators as one of the key deployment tools in its latest version, simplifies and improves operation by getting rid of some dependencies, such as Tiller, a server-side component running in the Kubernetes cluster.

Kubernetes-as-a-service and demand for Kubernetes experts

During this year’s KubeCon, many vendors presented a range of domains that have been offering complete solutions for Kubernetes, accelerating container orchestration. At previous events, we met vendors who have been providing storage, networking, and security components for Kubernetes. This evolution expresses the development of the environment built around the platform. Such an extensive offer of solutions allows teams or organizations to migrate to the native cloud to facilitate finding a compromise regarding "building versus buying" concerning components and solutions.

Rancher announced a solution that may be an example of an interesting Kubernetes-as-a-service option. The company collaborated with ARM to design a highly optimized version of Kubernetes for the edge - packaged as a single binary with a small footprint to reduce the dependencies and steps needed to install and run K8s in resource-constrained environments (e.g. IoT or edge devices for ITOps and DevOps teams.) By making K3s (lightweight distribution built for small footprint workloads) available and providing the beta release of Rio, their new application deployment engine for Kubernetes, Rancher delivers integrated deployment experience from operations to the pipeline.

Kubernetes-as-a-service offerings on the market are gaining strength. The huge number of Kubernetes use cases entails another very important trend. Companies are looking for talent in this field more than ever. Many companies have used conferences to meet with experts. Therefore, the number of Kubernetes jobs has also increased. The demand for experts on the subject is huge.

Multicloud is here to stay

Are hybrid solutions becoming a standard? Many cloud providers have claimed to be the best providers for multi-clouds - and we observe the trend that it becomes more popular. Despite some doubts (regarding its complexity, security, regulatory, or performance) enterprises are dealing well with implementing a multicloud strategy.

Top world’s companies are moving to multicloud as this approach empowers them to gain exceptional agility and huge cost savings thanks to the possibility to separate their workloads into different environments and make decisions based on the individual goals and specific requirements.

It is also a good strategy for companies working with private cloud-only. Usually, that’s the case because of storing sensitive data. As numerous case studies show, these businesses can be architected into multicloud solutions, whereas sensitive data is still stored securely on-premise, while other things are moved into the public cloud, which makes them easily scalable and easier to maintain.

Kubernetes is everywhere, even in the car….

During KubeCon, Rafał Kowalski, our colleague from Grape Up shared his presentation about running Kubernetes clusters in the car - "Kubernetes in Your 4x4 - Continuous Deployment Direct to the Car". Rafał showed how to use Kubernetes, KubeEdge, k3s, Jenkins, and RSocket for building continuous deployment pipelines, which ship software directly to the car, deals with rollbacks and connectivity issues. You can watch the entire video here:

https://www.youtube.com/watch?v=zmuOxFp3CAk&feature=youtu.be

…. and can be used in various devices

But these are not all of the possibilities; other devices such as drones or any IoT devices can also utilize containers The need for increased automation of cluster management and the ability to quickly rebuild clusters from scratch were the conclusions breaking through the above-mentioned occurrences.

The environment shows, through the remarkable pattern of the number of companies using Kubernetes and the development of utilities, there are still open needs in terms of simplicity and scalability of tools for operations, e.g. Security, data management, programming tools, and continuing operations in this area should be expected.

“Kubernetes has established itself as the de facto standard for container orchestration,”- these are the most frequently repeated words. It’s good to observe the development of the ecosystem around Kubernetes that strives to provide more reliable and cheaper experiences for enterprises that want to extend their strategic initiatives to the limit.

Have you ever struggled with complex and unreadable redux reducers? If yes, this article will show you how Immutable.js can help you keep reducers easy and clean. It fits perfectly with the redux & react application, so you might try to use it in your app.

Immutable.js is a library that supports an immutable data structure. It means that once created data cannot be changed. It makes maintaining immutable data structures easier and more efficient. The tool supports data structure like: List, Map, Set and also structures that are not implemented in .js by default but can be very useful: OrderedMap, OrderedSet and Record.

Methods such as push, unshift, slice in .js are based on reference and mutate the object directly. In the case of Immutable.js, there are no methods that change the object directly, a new object is always returned.

How using Immutable.js is supposed to help in Redux applications?

Before using Immutable.js, the biggest issue with the Redux library often comes to returning a new object, which is nested in another object. In this case, using the Object.assign and spread operator syntax is not readable and may increase app complexity.

Some may suggest keeping your reducer's state as flat as possible. That could be right, but sometimes, even if your state is flat, you would have to set something in a nested object. So, if you also struggle because of that, the immutable library comes to make your life easier.

How does it look in practice?

Let’s start by showing some examples of how the code looks like with and without using our solution in a reducer. In most of the cases in reducers, you will use method     .set   , which takes two arguments; the first one is a key which you would like to change and the second one is a new value. For setting nested properties, you can use method     .setIn   , which instead of a key as the first argument takes a key path as an array. Worth noting here is that if the key does not exist, a new one will be created. Thanks to this, you don't have to make conditions to handle it.

Here is a very simple reducer:

export const initialState ={

 loaded: false,

 disabled: false

};



export default function bookReducer(state = initialState, { type, payload }) {

 switch (type) {

   case ActionTypes.setLoadedState:

     return {

       ...state,

       loaded: payload

     }

 }



 return state;

}

This is the simplest reducer you can imagine, let's see what it looks like with immutable.js:

export const initialState = from.js({

 loaded: false,

 disabled: false

});



export default function bookReducer(state = initialState, { type, payload }) {

 switch (type) {

   case ActionTypes.setLoadedState:

     return state.set('loaded', payload)

 }



 return state;

}

Here, there is no big difference because the reducer is very simple, but we already can see a small improvement, code becomes more readable.

The second example without our solution:

export const initialState = {

 students: {},

 selectedStudent: null

};



export default function studentReducer(state = initialState, { type, payload }) {

 switch (type) {

   case ActionTypes.setStudentStatus:

     return {

       ...state,

       students: {

         ...state.students,

          [payload.studentId]: {

            ...state.students[payload.studentId],

            status: payload.status

          }

       }

     }

 }



 return state;

}

With Immutable.js:

export const initialState = {

 students: {},

 selectedStudent: null

};



export default function studentReducer(state = initialState, { type, payload }) {

 switch (type) {

   case ActionTypes.setStudentStatus:

     return state.setIn(['students', payload.studentId, 'status'], payload.status)

 }



 return state;

}

In the example above, we can see a huge difference between using and not using the tool:

•  The code is much shorter (10 lines to just 1 line).
•  With the Immutable.js you can easily see at first glance what data in reducer has changed.
•  Without the Immutable.js, it’s not that literal and obvious what’s changed.

In these examples, we provide only 2 methods of using Immutable.js -     .set   and     .setIn   , but there are numerous use cases, not only to set values. Actually, Immutable objects have the same methods which native .js has and a lot more which can speed up your development.

We also recommend checking the     .update   and     .updateIn   methods in the documentation, because, in reducers, they can be invaluable in more complex cases.

Other benefits of using Immutable.js

The main benefits of this library are easy and simple to maintain reducers. Besides this, we also get other advantages:

•  The library provides data structures that are not natively implemented in .js, but it makes your life easier (e.g., Ordered Map, Record).
•  The tool offers numerous ways to simplify your work, for example sortBy, groupBy, reverse, max, maxBy, flatten and many more. You can even stop using the lodash library, as most of the methods from lodash have their equivalents in immutable.js. It is easier to use as we can use chaining by default.
•  Immutable.js does a lot of things under the hood, which improves performance. Immutable data structures usually consume a lot of RAM, because this approach requires creating new copies of objects constantly. Among other things, our solution optimizes this, by sharing the state cleverly.
•  Empty collections are always equal, even if they were created separately. Look at the example below:

Compared to native .js:

There is always the other side of the coin, what are the cons?

Expensive converting to regular JavaScript

To convert Immutable collection to regular .js, you have to use .to.js() on an Immutable Collection. This method is very expensive when it comes to performance and always returns a new reference of an object even if nothing has been changed in the object. It affects PureComponent and React.memo, because these components would detect something has been changed, but actually, nothing has changed.

In most of the cases, you should avoid using to.js() and pass to components Immutable collections. However, sometimes you will have to use to.js, e.g. if you use an external library that requires props.

If you are developing generic components that will be used in other projects, you should avoid using an Immutable Collection in them, because it would force you to use Immutable in all projects that use these components.
There is no destructing operator

If you like getting properties using a destructing operator like this:

const { age, status } = student;

You won’t be happy, because, in Immutable.js, it is impossible to do. The get property from an immutable collection you have to use method .get or getIn, but I think it should not be a bit deal.

Debugging

Immutable collections are difficult to read in the browser console. Fortunately, you can easily solve this problem by using the tool. Object Formatter browser plugin, but it is not available in all browsers.

The above comparison shows what it looks like without and with the plugin. As you can see, the log is completely unreadable without the plugin.

Conclusion

Accordingly to our experiences, the immutable.js library is worth trying out in React applications with Redux applications. Thanks to immutable.js, your  application will be more efficient, easier to develop, maintain and more resistant to errors . Because, as you’ve seen above in a comparison of reducers. It's definitely easier to make a mistake without using Immutable.js. In the long term project, you should definitely consider it.

Kubernetes is currently the most popular container orchestration platform used by enterprises, organizations and individuals to run their workloads . Kubernetes provides software developers with great flexibility in how they can design and architect systems and applications.

Unfortunately, its powerful capabilities come at a price of the platform’s complexity, especially from the developer’s perspective. Kubernetes forces developers to learn and understand its internals fluently in order to deploy workloads, secure them and integrate with other systems.

Why is it so complex?

Kubernetes uses the concept of Objects, which are abstractions representing the state of the cluster. When one wants to perform some operation on the cluster e.g., deploy an application, they basically need to make the cluster create several various Kubernetes Objects with an appropriate configuration. Typically, when you would like to deploy a web application, in the simplest case scenario, you would need to:

• Create a deployment.
• Expose the deployment as a service.
• Configure ingress for the service.

However, before you can create a deployment (i.e. command Kubernetes to run a specific number of containers with your application), you need to start with building a container image that includes all the necessary software components to run your app and of course the app itself. “Well, that’s easy” – you say – “I just need to write a Dockerfile and then build the image using docker build ”. That is all correct, but we are not there yet. Once you have built the image, you need to store it in a container image registry where Kubernetes can pull it from.

You could ask - why is it so complex? As a developer, I just want to write my application code and run it, rather than additionally struggle with Docker images, registries, deployments, services, ingresses, etc., etc. But that is the price for Kubernetes’ flexibility. And that is also what makes Kubernetes so powerful.

Making deployments to Kubernetes easy

What if all the above steps were automated and combined into a single command allowing developers to deploy their app quickly to the cluster? With Cloudboostr’s latest release, that is possible!

What’s new? The Cloudboostr CLI - a new command line tool designed to simplify developer experience when using Kubernetes. To deploy an application to the cluster, you simply execute a single command:

cb push APP_NAME

The concept of “pushing” an application to the cluster has been borrowed from the Cloud Foundry community and its famous cf push command described by cf push haiku:

Here is my source code
Run it on the cloud for me
I do not care how.

When it comes to Cloudboostr , the “push” command automates the app deployment process by:

• Building the container image from application sources.
• Pushing the image to the container registry.
• Deploying the image to Kubernetes cluster.
• Configuring service and ingress for the app.

Looking under the hood

Cloudboostr CLI uses the Cloud Native Buildpacks project to automatically detect the application type and build an OCI-compatible container image with an appropriate embedded application runtime. Cloud Native Buildpacks can autodetect the most popular application languages and frameworks such as Java, .NET, Python, Golang or NodeJS.

Once the image is ready, it is automatically pushed to the Harbor container registry built into Cloudboostr. By default, Harbor is accessible and serves as a default registry for all Kubernetes clusters deployed within a given Cloudboostr installation. The image stored in the registry is then used to create a deployment in Kubernetes. In the current release only standard Deployment objects are supported, but adding support for StatefulSets is in the roadmap. As the last step, a service object for the application is created and a corresponding ingress object configured with Cloudboostr’s built-in Traefik proxy.

The whole process described above is executed in the cluster. Cloudboostr CLI triggers the creation of a temporary builder container that is responsible for pulling the appropriate buildpack, building the container image and communicating with the registry. The builder container is deleted from the cluster after the build process finishes. Building the image in the cluster eliminates the need to have Docker and pack (Cloud Native Buildpacks command line tool) installed on the local machine.

Cloudboostr CLI uses configuration defined in kubeconfig to access Kubernetes clusters. By default, images are pushed to the Harbor registry in Cloudboostr, but the CLI can also be configured to push images to an external container registry.

Why bother a.k.a. the benefits

While understanding Kubernetes internals is extremely useful, especially for troubleshooting and debugging, it should not be required when you just want to run an app. Many development teams that start working with Kubernetes find it difficult as they would prefer to operate on the application level rather than interact with containers, pods, ingresses, etc. The “cb push” command aims to help those teams and give them a tool to deliver fast and deploy to Kubernetes efficiently.

Cloudboostr was designed to tackle common challenges that software development teams face using Kubernetes. It became clear that we could improve the entire developer experience by providing those teams with a convenient yet effective tool to migrate from Cloud Foundry to Kubernetes. A significant part of that transition came to offer a feature that makes deploying apps to Kubernetes as user-friendly as Cloud Foundry does. That allows developers to work intuitively and with ease.

Cloudboostr CLI significantly simplifies the process of deploying applications to a Kubernetes cluster and takes the burden of handling containers and all Kubernetes-native concepts off of developers’ backs. It boosts the overall software delivery performance and helps teams to release their products to the market faster.

This is the first article of the mini-series that will get you started with a Serverless architecture and the Function-as-a-Service execution model - whose popularity is constantly growing. In this part, you will get answers to some of the most popular questions regarding Serverless, including: what is it, why it’s worth your attention, how does it work under the hood and which cloud provider meets your needs.

No servers at all?

Not really, your code has to be executed somewhere. Okay, so what is it all about then?

Serverless is a cloud computing execution model in which computer resources are dynamically allocated and managed by a cloud provider of your choice. Among serverless databases, storages, analytic tools, and many others, there is also Function-as-a-Service that we will focus on in this article.

FaaS is a serverless backend service that lets you execute and manage your code without bothering about the infrastructure that used to run your apps on it. In simple terms, you can order a function call without caring about how and where it is performed.

Why?

For money, as Serverless is extremely cost-effective in cases described in the next paragraph. In the serverless cloud execution model, you pay only for used resources, you don’t pay a penny when your code is not being executed!

Moreover, neither actual hardware nor public cloud infrastructure costs a company as much as software engineers’ time. Employees are the most cost-consuming resources. Serverless lets developers focus on functionalities instead of server provisioning, hardening and maintaining infrastructure.

Serverless services scale automatically when needed. You can control their performance by toggling memory and throughput. Furthermore, you don’t have to worry about thunderstorms or any other issues! Serverless services come with built-in high availability and fault tolerance features, meaning your function will be executed even if the primary server has blown up.

When to consider serverless?

Whenever you are preparing a proof of concept or prototyping application… Serverless functions do not generate costs at low workloads and are always ready to deal with the situations they increase. Combining this feature with no server management, it significantly accelerates the delivery of MVP.

When it comes to production, a Serverless architecture fits stateless applications like REST / GraphQL APIs very well. It is much easier, faster and cheaper to get such applications up and running. Services with unpredictable load pikes and inactivity periods, as well as cron jobs (running periodically) are also a great use case examples of FaaS.

Imagine the management of an application for ordering lunch. It has very high load peaks around noon, and it is unused for the rest of the day. Why pay for servers hosting such an application 24 hours a day, instead of paying just for the time when it is really used?

A Serverless architecture is often used for data processing, video streaming and handling IoT events. It is also very handy when it comes to integrating multiple SaaS services. Implementing a facade on top of a running application, for the purpose of migrating it or optimization can also be done much easier using this approach. FaaS is like cable ties and insulating tape in a DIY toolbox.

Where’s the catch?

It would be too good if there weren’t any catches. Technically, you could get a facebook-like application up and running using Serverless services, but it would cost a fortune! It turns out that such a solution would cost thousands of times more than hosting it on regular virtual machines or your own infrastructure. Serverless is also a bad choice for applications using sockets to establish a persistent connection with a server described in Rafal’s article about RSocket . Such a connection would need to be reestablished periodically as Lambda stays warmed-up for about 10 minutes after the last call. In this approach, you would be billed for the time of established connection.

Moreover, your whole solution becomes vendor bound. There are situations when a vendor raises prices, or another cloud provider offers new cool features. It is harder to switch between them, once you have your application up and running. The process takes time, money and the other vendor may not offer all the services that you need.

Furthermore, It is harder to troubleshoot your function, and almost every vendor enforces you to use some additional services to monitor logs from the execution - that generate extra costs. There is also a bit less comfortable FaaS feature that we have to take into account - “Cold start”. From time to time, it makes your function work much longer than usual. Depending on the vendor, there are different constraints on function execution time, which might be exceeded because of it. The following paragraph will explain this FaaS behavior in detail.

How does it work?

It is a kind of a mystery what can we find under the hood of FaaS. There are many services and workers that are responsible for orchestrating function invocations, concurrency management, tracking containers busy and idle states, scheduling incoming invocations appropriately, etc. The technology stack differs between vendors, but the general scheme is the same and you can find it below.

Hypervisor which emulates real devices is the first layer of isolation. The second one consists of containers and OS separation that comes with it. Our code is executed on a sandbox container with an appropriate runtime installed on it. A sandbox is being set up (so-called “Cold start” mentioned above) whenever a function is called for the first time after making changes or hasn’t been invoked for 5 - 15 minutes (depending on the vendor). It means that containers persist between calls, which accelerates execution but is also a bit tricky sometimes. For example, if we choose one of the interpreted languages as a runtime, all invocations are being performed on the same interpreter instance as long as the container lives. That means global variables and context are cached in memory between function executions, so keeping there sensitive data like tokens or passwords is a bad idea.

Containers’ load is balanced similarly to CPU resource allocation, which means they are not loaded equally. The workload is concentrated as much as possible, so runtime consumes the maximum capacity of a container. Thanks to that, other containers in the pool are unused and ready to run another function in the meantime.

Which vendor to pick?

Serverless services are offered by many cloud providers like AWS, GCP, Microsoft Azure, and IBM among others. It’s hard to say which one to choose, as it depends on your needs. The main differences between them are: pricing, maximum execution time, supported runtimes and concurrency. Let’s take a brief look at the comparison below.

As of the second half of 2019, you can see that all vendors provide similar pricing except Google. Although Google’s free-tier offer seems promising because of the doubled number of free requests, when we exceed this limit, we have two separate billings for memory and CPU, meaning Google’s pricing model is the most expensive.

Considering execution time IBM and AWS Lambda are the best choices. Although IBM has no time limit for single-function execution, it’s concurrency rate remains unclear. IBM documentation does not guarantee that functions will run concurrently. Google provides 1000 executions at a time per project, while AWS provides the same limit per account. That means you can run multiple Google Cloud Functions with the same concurrency, while on AWS you have to divide this limitation between all your functions.

If you look for a wide variety of supported runtimes, AWS and Azure are the best choices. While AWS supported languages list has not changed much since 2014, Google was providing only JavaScript runtime until June 2019. That means AWS runtimes may be more reliable than Google’s.

In the next article in the series, I will focus on AWS, which has a wide range of services that can be integrated with AWS Lambda for the purpose of building more complete applications. Moreover, AWS has a large community around it, which helps when a problem arises.

Summary

In this article, I tried to address the most common questions regarding Serverless architecture and the Function-as-a-Service execution model. I suggested when to use it, and when not to. We took a brief tour of what lays under the hood of FaaS and compared its vendors.

In the next articles, we will explore AWS. I will guide you through Amazon serverless services and help you create your first serverless application using them.

iOS devices still claim a significant part of the mobile market, taking up to 22 percent of the sales globally. As many devoted clients come back for new Apple products, there is also a great demand for iOS applications. In this article, we are going to look at ensuring the quality of iOS apps striving for the usage of best practices using Appium, Cucumber and Serenity tools.

Structure

The Page Object Model is one of the best approaches to testing that QA engineers can apply to a test automation project. It is such a way of structuring the code in an automation project that improves code quality and readability, test maintenance and on top of that, it is a great way of avoiding chaos. The basic idea behind it comes to keeping all references to mobile elements and methods performing operations on them in one class file for each page or screen of the app (or web page for non-native web applications).

What are the benefits of this approach, you may ask? Firstly, it makes automation really straightforward. Basically, it means finding elements in our iOS app via inspector and then performing operations on them. Another main advantage is the coherent structure of the project that allows anyone to navigate through it quickly.

Let's take an example of an app that contains recipes. It shows the default cookbook with basic recipes on startup, which will be our first page. From there, a user can navigate to any available recipe, thus marking a second page. On top of that, the app also allows to browse other cookbooks or purchase premium ones, making it the third page and consequently - a page object file.

Similarly, we should create corresponding step definition files. This is not an obligatory practice, but keeping all step definitions in one place causes unnecessary chaos.

While creating your pages and step definition class files it is advised to choose names that are related to the page (app screen) which contents you are going to work on. Naming these files after a feature or scenario can seem right at first glance, but as the project expands, you will notice more and more clutter in its structure. Adopting the page naming convention ensures that anyone involved in the project can get familiar with it straight away and start collaboration on it in no time. Such practice also contributes to reusability of code - either step definitions or methods/functions.

Contrary to the mentioned step and step definition files, the Cucumber feature files should be named after a feature they verify. Clever, isn’t it? And again, structuring them into directories named in relation to a particular field of the application under test will make the structure more meaningful.

Serenity’s basic concept is to be a 'living documentation'. Therefore, giving test scenarios and feature files appropriate names helps the team and stakeholders understand reports and the entire project better.

Another ingredient expanding the benefits of the Page Object Model in the test automation project is PageFactory. It is a tool that helps you reduce the coding work and easily put MobileElements locators in code, using @FindBy notation. From there, finding elements for Appium to interact with them in tests is much simpler.

Assertion

Running tests via Appium can be very resource-consuming. To make things easier for your MacOS machine running tests on your iOS device, make sure you are not constantly asserting the visibility of all objects on a page. This practice significantly increases the test execution time, which usually is not the most desirable thing.

What is more, when you do have to check if an element is visible, enabled, clickable, or anything in between - try to avoid locating mobile elements using Xpath. The Appium inspector tip has a valid point! You should do what you can to convince the development team to make an extra effort and assign unique IDs and names to the elements in the app. This will not only make automation testing easier and quicker, consequently making your work as a tester more effective, ultimately resulting in increasing the overall quality of the product. And that is why we are here. Not to mention that the maintenance of the tests (e.g. switching to different locators when necessary) will become much more enjoyable.

Understanding the steps

Another aspect of setting up this kind of project comes down to taking advantage of Cucumber and using Gherkin language.

Gherkin implements a straightforward approach with  Given, When, Then notation with the help of the additional  And and  But which seems fairly easy to use. You could write pretty much anything you want in the test steps of your feature files. Ultimately, the called methods are going to perform actions.

But the reason for using the Behavior Driven Development approach and Cucumber itself is enabling the non-tech people involved in the project to understand what is going on in the tests field. Not only that, writing test scenarios in  Given/When/Then manner can also act in your advantage. Such high-level test descriptions delivered by the client or business analyst will get you coding in no time, provided that they are written properly. Here are some helpful tips:

Test scenarios written in Gherkin should focus on the behavior of the app (hence Behavior Driven Development).
Here's an example of how NOT to write test scenarios in Gherkin, further exploring the theme of cookbook application:

Above example illustrates two bad practices we should avoid: It focuses on the implementation instead of behavior and it uses hard-coded values rather than writing test steps in such a way to enable reusability by changing values within a step.

Therefore, a proper scenario concerning purchasing a cookbook in our example app should look like:

Another example:

Adopting this approach means less work creating and coding the test steps whenever the implementation of a particular feature changes.

Apart from the main notation of  Given/When/Then , Cucumber supports usage of conjunction steps. Using  And and  But step notations will make the test steps more general and reusable, which results in writing less code and maintaining order within the project. Here is a basic example:

Doing so, if you code the above  'Given' step to locate our recipe element by searching its name, you can reuse it many times just changing the string value in the step (provided that you code the step definition properly later on). On top of that, The  'And' step can be a part of any test scenario that involves such action.

Putting it all together

After setting up a project utilizing the practices described above, the most visible parts of using Serenity are the generated test reports. After adopting the @RunWith(CucumberWithSerenity.class) tag in your TestRunner class file, running the test suite will result in Serenity generating an aggregated test results report, which can be useful in evaluating the quality of the app under test and presenting the status of the product to the stakeholders or the development team.

Appium, Cucumber, Serenity - summary

As you can see, the concept of best practices in automation testing can be summarized in three words: reusability, readability, and performance. Reusability means fewer coding, consequently diminishing the time needed to finish the job. Readability improves understanding, which is crucial to ensure that the product does what it needs to do. Finally, performance saves execution time and improves stability. All three contributing not only to the quality of the test automation project but have a significant role in enhancing the overall quality of the delivered app.

Sources:

•     https://gs.statcounter.com/os-market-share/mobile/worldwide  
•     https://cucumber.io/docs/guide  
•     https://serenity-bdd.github.io/theserenitybook/latest/  
•     https://github.com/SeleniumHQ/selenium/wiki/PageFactory  
•     https://www.guru99.com/page-object-model-pom-page-factory-in-selenium-ultimate-guide.html  

Kubernetes has practically become the standard for container orchestration. Enterprises see it as one of the crucial elements contributing to the success of the implementation of a cloud-first strategy. Of course, Kubernetes is not the most important success factor in going cloud-native. But the right tooling is the enabler for achieving DevOps maturity in an enterprise, which builds primarily on cultural change and shift in design thinking. This article highlights the most common challenges an enterprise encounters while adopting Kubernetes and recommendations on how to make Kubernetes adoption smooth and effective in order to drive productivity and business value.

Challenges in Kubernetes adoption

 Kubernetes is still complex to set up. Correct infrastructure and network setup, installation, and configuration of all Kubernetes components are not that straightforward even though there are tools created with the goal to streamline that part.

 Kubernetes alone is not enough. Kubernetes is not a cloud-native platform by itself, but rather one of the tools needed to build a platform. A lot of additional tooling is needed to create     a manageable platform that improves developers’ experience   and drives productivity. Therefore, it requires a lot of knowledge and expertise to choose the right pieces of the puzzle and connect them in the right way.

 Day 2 operations are not easy. When the initial problems with setup and installation are solved, there comes another challenge: how to productionize the platform, onboard users, and manage Kubernetes clusters at scale. Monitoring, upgrading & patching, securing, maintaining high availability, handling backups – these are just a few operational aspects to consider. And again, it requires a lot of knowledge to operate and manage Kubernetes in production.

 Another aspect is the platform’s complexity from the developer’s perspective. Kubernetes requires developers to understand its internals in order to use it effectively for deploying applications, securing them and integrating them with external services.

Recommendations for a successful Kubernetes adoption

 Choose a turnkey solution – do not build the platform by yourself as the very first step, considering the aforementioned complexity. It is better to pick a production-ready distribution, that allows to set it up quickly and focus on managing the cultural and organizational shift rather than struggling with the technology. Such a solution should offer a right balance between how much is pre-configured and available out-of-the-box, and the flexibility to customize it further down the road. Of course, it is good when the distribution is compatible with the upstream Kubernetes as it allows your engineers and operators to interact with native tools and APIs.

 Start small and grow bigger in time – do not roll out Kubernetes for the whole organization immediately. New processes and tools should be introduced in a small, single team and incrementally spread throughout the organization. Adopting Kubernetes is just one of the steps on the path to cloud-native and you need to be cautious not to slip. Start with a single team or product, learn, gain knowledge and then share it with other teams. These groups being the early adopters, should eventually become facilitators and evangelists of Kubernetes and DevOps approach, and help spread these practices throughout the organization. This is the best way to experience Kubernetes value and understand the operational integration required to deliver software to production in a continuous manner.

 Leverage others’ experiences – usually, it is good to start with the default, pre-defined or templated settings and leverage proven patterns and best practices in the beginning. As you get more mature and knowledgeable about the technology, you can adjust, modify and reconfigure iteratively to make it better suit your needs. At this point, it is good to have a solution which can be customized and gives the operator full control over the configuration of the cluster. Managed and hosted solutions, even though easy to use at the early stage of Kubernetes adoption, usually leave small to no space for custom modifications and cluster finetuning.

 When in need, call for backups – it is good to have cavalry in reserve which can come to the rescue when bad things happen or simply when something is not clear. Secure yourself for the hard times and     find a partner who can help you learn and understand the complexities of Kubernetes and other building blocks of the cloud-native toolset.   Even when your long-term strategy is to build the Kubernetes skills in-house (both from development and operations perspective).

 Do not forget about mindset change – adopting the technology is not enough. Starting to deploy applications to Kubernetes will not instantly transform your organization and speed up software delivery. Kubernetes can become the cornerstone in the new DevOps way the company builds and delivers software but needs to be supported by organizational changes touching many more areas of the company than just tools and technology: the way people think, act and work, the way they communicate and collaborate. And it is essential to educate all stakeholders at all levels throughout the adoption process, to have a common understanding of what DevOps is, what changes it brings and what are the benefits.

Adopting Kubernetes in an Enterprise - conclusion

Even though Kubernetes is not easy, it is definitely worth the attention. It offers a great value in the platform you can build with it and can help transition your organization to the new level. With  Kubernetes as the core technology and DevOps approach to software delivery , the company can accelerate application development, manage its workflows more efficiently and get to the market faster.

If you are familiar with the previous articles of this series (     Introduction   ,     Load balancing & Resumability   ), you have probably noticed that RSocket provides a low-level API. We can operate directly on the methods from the interaction model and without any constraints sends the frames back and forth. It gives us a lot of freedom and control, but it may introduce extra issues, especially related to the contract between microservices. To solve these problems, we can use RSocket through a generic abstraction layer. There are two available solutions out there:  RSocket RPC module and  integration with Spring Framework. In the following sections, we will discuss them briefly.

RPC over RSocket

Keeping the contract between microservices clean and well-defined is one of the crucial concerns of the distributed systems. To assure that applications can exchange the data we can leverage Remote Procedure Calls. Fortunately, RSocket has dedicated     RPC module   which uses Protobuf as a serialization mechanism, so that we can benefit from RSocket performance and keep the contract in check at the same time. By combining generated services and objects with RSocket acceptors we can spin up fully operational RPC server, and just as easily consume it using RPC client.

In the first place, we need the definition of the service and the object. In the example below, we create simple  CustomerService with four endpoints – each of them represents a different method from the interaction model.

syntax = "proto3";

option java_multiple_files = true;

option java_outer_classname = "ServiceProto";



package com.rsocket.rpc;



import "google/protobuf/empty.proto";



message SingleCustomerRequest {

   string id = 1;

}



message MultipleCustomersRequest {

   repeated string ids = 1;

}



message CustomerResponse {

   string id = 1;

   string name = 2;

}



service CustomerService {

   rpc getCustomer(SingleCustomerRequest) returns (CustomerResponse) {} //request-response

   rpc getCustomers(MultipleCustomersRequest) returns (stream CustomerResponse) {} //request-stream

   rpc deleteCustomer(SingleCustomerRequest) returns (google.protobuf.Empty) {} //fire'n'forget

   rpc customerChannel(stream MultipleCustomersRequest) returns (stream CustomerResponse) {} //request-channel

}

In the second step, we have to generate classes out of the proto file presented above. To do that we can create a gradle task as follows:

protobuf {

   protoc {

       artifact = 'com.google.protobuf:protoc:3.6.1'

   }

   generatedFilesBaseDir = "${projectDir}/build/generated-sources/"



   plugins {

       rsocketRpc {

           artifact = 'io.rsocket.rpc:rsocket-rpc-protobuf:0.2.17'

       }

   }

   generateProtoTasks {

       all()*.plugins {

           rsocketRpc {}

       }

   }

}

As a result of  generateProto task, we should obtain service interface, service client and service server classes, in this case,  CustomerService ,  CustomerServiceClient ,  CustomerServiceServer respectively. In the next step, we have to implement the business logic of generated service (CustomerService):

public class DefaultCustomerService implements CustomerService {



       private static final List RANDOM_NAMES = Arrays.asList("Andrew", "Joe", "Matt", "Rachel", "Robin", "Jack");



       @Override

       public Mono getCustomer(SingleCustomerRequest message, ByteBuf metadata) {

           log.info("Received 'getCustomer' request [{}]", message);

           return Mono.just(CustomerResponse.newBuilder()

                   .setId(message.getId())

                   .setName(getRandomName())

                   .build());

       }



       @Override

       public Flux getCustomers(MultipleCustomersRequest message, ByteBuf metadata) {

           return Flux.interval(Duration.ofMillis(1000))

                   .map(time -> CustomerResponse.newBuilder()

                           .setId(UUID.randomUUID().toString())

                           .setName(getRandomName())

                           .build());

       }



       @Override

       public Mono deleteCustomer(SingleCustomerRequest message, ByteBuf metadata) {

           log.info("Received 'deleteCustomer' request [{}]", message);

           return Mono.just(Empty.newBuilder().build());

       }



       @Override

       public Flux customerChannel(Publisher messages, ByteBuf metadata) {

           return Flux.from(messages)

                   .doOnNext(message -> log.info("Received 'customerChannel' request [{}]", message))

                   .map(message -> CustomerResponse.newBuilder()

                           .setId(UUID.randomUUID().toString())

                           .setName(getRandomName())

                           .build());

       }



       private String getRandomName() {

           return RANDOM_NAMES.get(new Random().nextInt(RANDOM_NAMES.size() - 1));

       }

}

Finally, we can expose the service via RSocket. To achieve that, we have to create an instance of a service server (CustomerServiceServer) and inject an implementation of our service (DefaultCustomerService). Then, we are ready to create an RSocket acceptor instance. The API provides  RequestHandlingRSocket which wraps service server instance and does the translation of endpoints defined in the contract to methods available in the RSocket interaction model.

public class Server {



   public static void main(String[] args) throws InterruptedException {



       CustomerServiceServer serviceServer = new CustomerServiceServer(new DefaultCustomerService(), Optional.empty(), Optional.empty());



       RSocketFactory

               .receive()

               .acceptor((setup, sendingSocket) -> Mono.just(

                       new RequestHandlingRSocket(serviceServer)

               ))

               .transport(TcpServerTransport.create(7000))

               .start()

               .block();



       Thread.currentThread().join();

   }

}

On the client-side, the implementation is pretty straightforward. All we need to do is create the RSocket instance and inject it to the service client via the constructor, then we are ready to go.

@Slf4j

public class Client {



   public static void main(String[] args) {

       RSocket rSocket = RSocketFactory

               .connect()

               .transport(TcpClientTransport.create(7000))

               .start()

               .block();

       CustomerServiceClient customerServiceClient = new CustomerServiceClient(rSocket);



       customerServiceClient.deleteCustomer(SingleCustomerRequest.newBuilder()

               .setId(UUID.randomUUID().toString()).build())

               .block();



       customerServiceClient.getCustomer(SingleCustomerRequest.newBuilder()

               .setId(UUID.randomUUID().toString()).build())

               .doOnNext(response -> log.info("Received response for 'getCustomer': [{}]", response))

               .block();



       customerServiceClient.getCustomers(MultipleCustomersRequest.newBuilder()

               .addIds(UUID.randomUUID().toString()).build())

               .doOnNext(response -> log.info("Received response for 'getCustomers': [{}]", response))

               .subscribe();



       customerServiceClient.customerChannel(s -> s.onNext(MultipleCustomersRequest.newBuilder()

               .addIds(UUID.randomUUID().toString())

               .build()))

               .doOnNext(customerResponse -> log.info("Received response for 'customerChannel' [{}]", customerResponse))

               .blockLast();

   }



}

Combining RSocket with RPC approach helps to maintain the contract between microservices and improves day to day developer experience. It is suitable for typical scenarios, where we do not need full control over the frames, but on the other hand, it does not limit the protocol flexibility. We can still expose RPC endpoints as well as plain RSocket acceptors in the same application so that we can easily choose the best communication pattern for the given use case.

In the context of RPC over the RSocket one more fundamental question may arise: is it better than gRPC? There is no easy answer to that question. RSocket is a new technology, and it needs some time to get the same maturity level as gRPC has. On the other hand, it surpasses gRPC in two areas: performance (     benchmarks available here   ) and flexibility - it can be used as a transport layer for RPC or as a plain messaging solution. Before making a decision on which one to use in a production environment, you should determine if RSocket align with your  early adoption strategy and does not put your software at risk. Personally, I would recommend introducing RSocket in less critical areas, and then extend its usage to the rest of the system.

•     Please notice that the examples of RPC over the RSocket are provided here  

Spring boot integration

The second available solution, which provides an abstraction over the RSocket is the  integration with Spring Boot. Here we use RSocket as a reactive messaging solution and leverage spring annotations to link methods with the routes with ease. In the following example, we implement two Spring Boot applications – the requester and the responder. The responder exposes RSocket endpoints through  CustomerController and has a mapping to three routes:  customer ,  customer-stream and  customer-channel . Each of these mappings reflects different method from RSocket interaction model (request-response, request stream, and channel respectively). Customer controller implements simple business logic and returns CustomerResponse object with a random name as shown in the example below:

@Slf4j

@SpringBootApplication

public class RSocketResponderApplication {



   public static void main(String[] args) {

       SpringApplication.run(RSocketResponderApplication.class);

   }



   @Controller

   public class CustomerController {



       private final List RANDOM_NAMES = Arrays.asList("Andrew", "Joe", "Matt", "Rachel", "Robin", "Jack");



       @MessageMapping("customer")

       CustomerResponse getCustomer(CustomerRequest customerRequest) {

           return new CustomerResponse(customerRequest.getId(), getRandomName());

       }



       @MessageMapping("customer-stream")

       Flux getCustomers(MultipleCustomersRequest multipleCustomersRequest) {

           return Flux.range(0, multipleCustomersRequest.getIds().size())

                   .delayElements(Duration.ofMillis(500))

                   .map(i -> new CustomerResponse(multipleCustomersRequest.getIds().get(i), getRandomName()));

       }



       @MessageMapping("customer-channel")

       Flux getCustomersChannel(Flux requests) {

           return Flux.from(requests)

                   .doOnNext(message -> log.info("Received 'customerChannel' request [{}]", message))

                   .map(message -> new CustomerResponse(message.getId(), getRandomName()));

       }



       private String getRandomName() {

           return RANDOM_NAMES.get(new Random().nextInt(RANDOM_NAMES.size() - 1));

       }

   }

}

Please notice that the examples presented below are based on the Spring Boot RSocket starter 2.2.0.M4, which means that it is not an official release yet, and the API may be changed.

It is worth noting that Spring Boot automatically detects the RSocket library on the classpath and spins up the server. All we need to do is specify the port:

spring:

 rsocket:

   server:

     port: 7000

These few lines of code and configuration set up the fully operational responder with message mapping (the code is available     here   )

Let’s take a look on the requester side. Here we implement  CustomerServiceAdapter which is responsible for communication with the responder. It uses RSocketRequester bean that wraps the RSocket instance, mime-type and encoding/decoding details encapsulated inside RSocketStrategies object. The RSocketRequester routes the messages and deals with serialization/deserialization of the data in a reactive manner. All we need to do is provide the route, the data and the way how we would like to consume the messages from the responder – as a single object (Mono) or as a stream (Flux).

@Slf4j

@SpringBootApplication

public class RSocketRequesterApplication {





   public static void main(String[] args) {

       SpringApplication.run(RSocketRequesterApplication.class);

   }



   @Bean

   RSocket rSocket() {

       return RSocketFactory

               .connect()

               .frameDecoder(PayloadDecoder.ZERO_COPY)

               .dataMimeType(MimeTypeUtils.APPLICATION_JSON_VALUE)

               .transport(TcpClientTransport.create(7000))

               .start()

               .block();

   }



   @Bean

   RSocketRequester rSocketRequester(RSocket rSocket, RSocketStrategies rSocketStrategies) {

       return RSocketRequester.wrap(rSocket, MimeTypeUtils.APPLICATION_JSON,

               rSocketStrategies);

   }



   @Component

   class CustomerServiceAdapter {



       private final RSocketRequester rSocketRequester;



       CustomerServiceAdapter(RSocketRequester rSocketRequester) {

           this.rSocketRequester = rSocketRequester;

       }



       Mono getCustomer(String id) {

           return rSocketRequester

                   .route("customer")

                   .data(new CustomerRequest(id))

                   .retrieveMono(CustomerResponse.class)

                   .doOnNext(customerResponse -> log.info("Received customer as mono [{}]", customerResponse));

       }



       Flux getCustomers(List ids) {

           return rSocketRequester

                   .route("customer-stream")

                   .data(new MultipleCustomersRequest(ids))

                   .retrieveFlux(CustomerResponse.class)

                   .doOnNext(customerResponse -> log.info("Received customer as flux [{}]", customerResponse));

       }



       Flux getCustomerChannel(Flux customerRequestFlux) {

           return rSocketRequester

                   .route("customer-channel")

                   .data(customerRequestFlux, CustomerRequest.class)

                   .retrieveFlux(CustomerResponse.class)

                   .doOnNext(customerResponse -> log.info("Received customer as flux [{}]", customerResponse));

       }

   }



}

Besides the communication with the responder, the requester exposes the RESTful API with three mappings:  /customers/{id} ,  /customers ,  /customers-channel . Here we use spring web-flux and on top of the HTTP2 protocol. Please notice that the last two mappings produce the text event stream, which means that the value will be streamed to the web browser when it becomes available.

@RestController

class CustomerController {



private final CustomerServiceAdapter customerServiceAdapter;





CustomerController(CustomerServiceAdapter customerServiceAdapter) {

    this.customerServiceAdapter = customerServiceAdapter;

}



@GetMapping("/customers/{id}")

Mono getCustomer(@PathVariable String id) {

    return customerServiceAdapter.getCustomer(id);

}



@GetMapping(value = "/customers", produces = MediaType.TEXT_EVENT_STREAM_VALUE)

Publisher getCustomers() {

    return customerServiceAdapter.getCustomers(getRandomIds(10));

}



@GetMapping(value = "/customers-channel", produces = MediaType.TEXT_EVENT_STREAM_VALUE)

Publisher getCustomersChannel() {

    return customerServiceAdapter.getCustomerChannel(Flux.interval(Duration.ofMillis(1000))

            .map(id -> new CustomerRequest(UUID.randomUUID().toString())));

}



private List getRandomIds(int amount) {

    return IntStream.range(0, amount)

            .mapToObj(n -> UUID.randomUUID().toString())

            .collect(toList());

}



}

To play with REST endpoints mentioned above, you can use following curl commands:

curl http://localhost:8080/customers/1

curl http://localhost:8080/customers

curl http://localhost:8080/customers-channel

Please notice that requester application code is available     here  

The integration with Spring Boot and RPC module are complementary solutions on top of the RSocket. The first one is messaging oriented and provides convenient message routing API whereas the RPC module enables the developer to easily control the exposed endpoints and maintain the contract between microservices. Both of these solutions have applications and can be easily combined with RSocket low-level API to fulfill the most sophisticated requirements with consistent manner using a single protocol.

Series summary

This article is the last one of the mini-series related to RSocket – the new binary protocol which may revolutionize service to service communication in the cloud. Its rich     interaction model   , performance and extra features like     client load balancing and resumability   make it a perfect candidate for almost all possible business cases. The usage of the protocol may be simplified by available abstraction layers: Spring Boot integration and RPC module which address most typical day to day scenarios.

Please notice that the protocol is in release candidate version (1.0.0-RC2), therefore it is not recommended to use it in the production environment. Still, you should keep an eye on it, as the growing community and support of the big tech companies (e.g. Netflix, Facebook, Alibaba, Netifi) may turn RSocket as a primary communication protocol in the cloud.

Due to the high entry threshold, it is not that easy to start a journey with Cloud Native. Developing apps focused on reliability and performance, and meeting high SLAs can be challenging. Fortunately, there are tools like Istio which simplify our lives. In this article, we guide you through the steps needed to create CI/CD with Azure Pipelines for deploying microservices using Helm Charts to Kubernetes. This example is a good starting point for preparing your development process. After this tutorial, you should have some basic ideas about how Cloud Native apps should be developed and deployed .

Technology stack

• .NET Core 3.0 (preview)
• Kubernetes
• Helm
• Istio
• Docker
• Azure DevOps

Prerequisites

You need a Kubernetes cluster, free Azure DevOps account, and a docker registry. Also, it would be useful to have kubectl and gcloud CLI installed on your machine. Regarding the Kubernetes cluster, we will be using Google Kubernetes Engine from Google Cloud Platform, but you can use a different cloud provider based on your preferences. On GCP you can create a free account and create a Kubernetes cluster with Istio enabled ( Enable Istio checkbox). We suggest using a machine with 3 standard nodes.

Connecting the cluster with Azure Pipelines

Once we have the cluster ready, we have to use kubectl to prepare service account which is needed for Azure Pipelines to authenticate. First, authenticate yourself by including necessary settings in kubeconfig. All cloud providers will guide you through this step. Then following commands should be run:

kubectl create serviceaccount azure-pipelines-deploy
kubectl create clusterrolebinding azure-pipelines-deploy --clusterrole=cluster-admin --serviceaccount=default:azure-pipelines-deploy
kubectl get secret $(kubectl get secrets -o custom-columns=":metadata.name" | grep azure-pipelines-deploy-token) -o yaml

We are creating a service account, to which a cluster role is assigned. The cluster-admin role will allow us to use Helm without restrictions. If you are interested, you can read more about RBAC on Kubernetes website . The last command is supposed to retrieve secret yaml , which is needed to define connection - save that output yaml somewhere.

Now, in Azure DevOps, go to Project Settings -> Service Connections and add a new Kubernetes service connection. Choose service account for authentication and paste the yaml copied from command executed in the previous step.

One more thing we need in here is the cluster IP. It should be available at cluster settings page, or it can be retrieved via command line. In the example, for GCP command should be similar to this:

gcloud container clusters describe --format=value(endpoint) --zone

Another service connection we have to define is for docker registry. For the sake of simplicity, we will use the Docker hub, where all you need is just to create an account (if you don’t have one). Then just supply whatever is needed in the form, and we can carry on with the application part.

Preparing an application

One of the things we should take into account while implementing apps in the Cloud is the Twelve-Factor methodology. We are not going to describe them one by one since they are explained good enough here but few of them will be mentioned throughout the article.

For tutorial purposes, we’ve prepared a sample ASP.NET Core Web Application containing a single controller and database context. It also contains simple dockerfile and helm charts. You can clone/fork sample project from here . Firstly, push it to a git repository (we will use Azure DevOps), because we will need it for CI. You can now add a new pipeline, choosing any of the available YAML definitions. In here we will define our build pipeline (CI) which looks like that:

trigger:
- master
pool:
vmImage: 'ubuntu-latest'
variables:
buildConfiguration: 'Release'
steps:
- task: Docker@2
inputs:
containerRegistry: 'dockerRegistry'
repository: '$(dockerRegistry)/$(name)'
command: 'buildAndPush'
Dockerfile: '**/Dockerfile'
- task: PublishBuildArtifacts@1
inputs:
PathtoPublish: '$(Build.SourcesDirectory)/charts'
ArtifactName: 'charts'
publishLocation: 'Container'

Such definition is building a docker image and publishing it into predefined docker registry. There are two custom variables used, which are dockerRegistry (for docker hub replace with your username) and name which is just an image name (exampleApp is our case). The second task is used for publishing artifact with helm chart. These two (docker image & helm chart) will be used for the deployment pipeline.

Helm charts

Firstly, take a look at the file structure for our chart. In the main folder, we have Chart.yaml which keeps chart metadata, requirements.yaml with which we can specify dependencies or values.yaml which serves default configuration values. In the templates folder, we can find all Kubernetes objects that will be created along with chart deployment. Then we have nested charts folder, which is a collection of charts added as a dependency in requirements.yaml. All of them will have the same file structure.

Let’s start with a focus on the deployment.yaml - a definition of Deployment controller, which provides declarative updates for Pods and Replica Sets. It is parameterized with helm templates, so you will see a lot of {{ template [...] }} in there. Definition of this Deployment itself is quite default, but we are adding a reference for the secret of SQL Server database password. We are hardcoding ‘-mssql-linux-secret’ part cause at the time of writing this article, helm doesn’t provide a straightforward way to access sub-charts properties.

env:
- name: sa_password
valueFrom:
secretKeyRef:
name: {{ template "exampleapp.name" $root }}-mssql-linux-secret
key: sapassword

As we mentioned previously, we do have SQL Server chart added as a dependency. Definition of that is pretty simple. We have to define the name of the dependency, which will match the folder name in charts subfolder and the version we want to use.

dependencies:
- name: mssql-linux
repository: https://kubernetes-charts.storage.googleapis.com
version: 0.8.0
[...]

For the mssql chart, there is one change that has to be applied in the secret.yaml . Normally, this secret will be created on each deployment ( helm upgrade ), it will generate a new sapassword - which is not what we want. The simplest way to adjust that is by modifying metadata and adding a hook on pre-install. This will guarantee that this secret will be created just once on installing the release.

metadata:
annotations:
"helm.sh/hook": "pre-install"

A deployment pipeline

Let’s focus on deployment now. We will be using Helm to install and upgrade everything that will be needed in Kubernetes. Go to the Releases pipelines on the Azure DevOps, where we will configure continuous delivery. You have to add two artifacts, one for docker image and second for charts artifact. It should look like on the image below.

On the stages part, we could add a few more environments, which would get deployed in a similar manner, but to a different cluster. As you can see, this approach guarantees Deploy DEV stage is simply responsible for running a helm upgrade command. Before that, we need to install helm, kubectl and run helm init command.

For the helm upgrade task, we need to adjust a few things.

• set Chart Path, where you can browse into Helm charts artifact (should look like: “$(System.DefaultWorkingDirectory)/Helm charts/charts”)
• paste that “image.tag=$(Build.BuildNumber)” into Set Values
• and check to Install if release not present or add --install ar argument. This will behave as helm install if release won’t exist (i.e. on a clean cluster)

At this point, we should be able to run the deployment application - you can create a release and run deployment. You should see a green output at this point :).

You can verify if the deployment went fine by running a kubectl get all command.

Making use of basic Istio components

Istio is a great tool, which simplifies services management. It is responsible for handling things like load balancing, traffic behavior, metric & logs, and security. Istio is leveraging Kubernetes sidecar containers, which are added to pods of our applications. You will have to enable this feature by applying an appropriate label on the namespace.

kubectl label namespace default istio-injection=enabled

All pods which will be created now will have an additional container, which is called a sidecar container in Kubernetes terms. That’s a useful feature, cause we don’t have to modify our application.

Two objects that we are using from Istio, which are part of the helm chart, are Gateway and VirtualService . For the first one, we will bring Istio definition, because it’s simple and accurate: “Gateway describes a load balancer operating at the edge of the mesh receiving incoming or outgoing HTTP/TCP connections”. That object is attached to the LoadBalancer object - we will use the one created by Istio by default. After the application is deployed, you will be able to access it using LoadBalancer external IP, which you can retrieve with such command:

kubectl get service/istio-ingressgateway -n istio-system

You can retrieve external IP from the output and verify if http://api/examples url works fine.

Summary

In this article, we have created a basic CI/CD which deploys single service into Kubernetes cluster with the help of Helm. Further adjustments can include different types of deployment, publishing tests coverage from CI or adding more services to mesh and leveraging additional Istio features. We hope you were able to complete the tutorial without any issues. Follow our blog for more in-depth articles around these topics that will be posted in the future.

This article is the second one of the mini-series which will help you to get familiar with RSocket – a new binary protocol which may revolutionize machine to machine communication in distributed systems. In the following paragraphs, we will discuss the load balancing problem in the cloud as well as we will present the resumability feature which helps to deal with network issues, especially in the IoT systems.

• If you are not familiar with RSocket basics, please see the previous article available here
• Please notice that code examples presented in the article are available at GitHub

High availability & load balancing as a crucial part of enterprise-grade systems

Applications availability and reliability are crucial parts of many business areas like banking and insurance. In these demanding industries, the services have to be operational 24/7 even during high traffic, periods of increased network latency or natural disasters. To ensure that the software is always available to the end-users it is usually deployed in redundantly, across the multiple availability zones.

In such a scenario, at least two instances of each microservice are deployed in at least two availability zones. This technique helps our system become resilient and increase its capacity - multiple instances of the microservices are able to handle a significantly higher load. So where is the trick? The redundancy introduces extra complexity. As engineers, we have to ensure that the incoming traffic is spread across all available instances. There are two major techniques which address this problem: server load balancing and client load balancing .

The first approach is based on the assumption that the requester does not know the IP addresses of the responders. Instead of that, the requester communicates with the load balancer, which is responsible for spreading the requests across the microservices connected to it. This design is fairly easy to adopt in the cloud era. IaaS providers usually have built-in, reliable solutions, like Elastic Load Balancer available in Amazon Web Services. Moreover, such a design helps develop routing strategy more sophisticated than plain round ribbon (e.g. adaptive load balancing or chained failover ). The major drawback of this technique is the fact that we have to configure and deploy extra resources, which may be painful if our system consists of hundreds of the microservices. Furthermore, it may affect the latency – each request has extra “network hop” on the load balancer.

The second technique inverts the relation. Instead of a central point used to connect to responders, the requester knows IP addresses of each and every instance of the given microservice. Having such knowledge, the client can choose the responder instance to which it sends the request or opens the connection with. This strategy does not require any extra resources, but we have to ensure that the requester has the IP addresses of all instances of the responder ( see how to deal with it using service discovery pattern ). The main benefit of the client load balancing pattern is its performance – by reduction of one extra “network hop”, we may significantly decrease the latency. This is one of the key reasons why RSocket implements the client load balancing pattern.

Client load balancing in RSocket

On the code level, the implementation of the client load balancing in RSocket is pretty straightforward. The mechanism relies on the LoadBalancedRSocketMono object which works as a bag of available RSocket instances, provided by RSocket supplier. To access RSockets we have to subscribe to the LoadBalancedRSocketMono which onNext signal emits fully-fledged RSocket instance. Moreover, it calculates statistics for each RSocket, so that it is able to estimate the load of each instance and based on that choose the one with the best performance at the given point of time.

The algorithm takes into account multiple parameters like latency, number of maintained connections as well as a number of pending requests. The health of each RSocket is reflected by the availability parameter – which takes values from 0 to 1, where 0 indicates that the given instance cannot handle any requests and 1 is assigned to fully operational socket. The code snippet below shows the very basic example of the load-balanced RSocket, which connects to three different instances of the responder and executes 100 requests. Each time it picks up RSocket from the LoadBalancedRSocketMono object.

@Slf4j

public class LoadBalancedClient {



static final int[] PORTS = new int[]{7000, 7001, 7002};



public static void main(String[] args) {



List rsocketSuppliers = Arrays.stream(PORTS)

.mapToObj(port -> new RSocketSupplier(() -> RSocketFactory.connect()

.transport(TcpClientTransport.create(HOST, port))

.start()))

.collect(Collectors.toList());



LoadBalancedRSocketMono balancer = LoadBalancedRSocketMono.create((Publisher>) s -> {

s.onNext(rsocketSuppliers);

s.onComplete();

});



Flux.range(0, 100)

.flatMap(i -> balancer)

.doOnNext(rSocket -> rSocket.requestResponse(DefaultPayload.create("test-request")).block())

.blockLast();

}



}

It is worth noting, that client load balancer in RSocket deals with dead connections as well. If any of the RSocket instances registered in the LoadBalancedRSocketMono stop responding, the mechanism will automatically try to reconnect. By default, it will execute 5 attempts, in 25 seconds. If it does not succeed, the given RSocket will be removed from the pool of available connections. Such design combines the advantages of the server-side load balancing with low latency and reduction of “network hops” of the client load balancing.

Dead connections & resumabilty mechanism

The question which may arise in the context of dead connections is: what will happen if I have an only single instance of the responder and the connection drops due to network issues. Is there anything we can do with this? Fortunately, RSocket has built-in resumability mechanism.

To clarify the concept let’s consider the following example. We are building an IoT platform which connects to multiple temperature sensors located in different places. Most of them in the distance to the nearest buildings and internet connection sources. Therefore, the devices connect to cloud services using GPRS. The business requirement for our system is that we need to collect temperature readings every second in the real-time, and we cannot lose any data.

In case of the machine-to-the machine communication within the cloud, streaming data in real-time is not a big deal, but if we consider IoT devices located in areas without access to a stable, reliable internet connection, the problem becomes more complex. We can easily identify two major issues we may face in such a system: the network latency and connection stability . From a software perspective, there is not much we can do with the first one, but we can try to deal with the latter. Let’s tackle the problem with RSocket, starting with picking up the proper interaction model . The most suitable in this case is request stream method, where the microservice deployed in the cloud is the requester and temperature sensor is the responder. After choosing the interaction model we apply resumability mechanism. In RSocket, we do it by method resume() invoked on the RSocketFactory , as shown in the examples below:

@Slf4j

public class ResumableRequester {



private static final int CLIENT_PORT = 7001;



public static void main(String[] args) {

RSocket socket = RSocketFactory.connect()

.resume()

.resumeSessionDuration(RESUME_SESSION_DURATION)

.transport(TcpClientTransport.create(HOST, CLIENT_PORT))

.start()

.block();

socket.requestStream(DefaultPayload.create("dummy"))

.map(payload -> {

log.info("Received data: [{}]", payload.getDataUtf8());

return payload;

})

.blockLast();



}

}

@Slf4j

public class ResumableResponder {



private static final int SERVER_PORT = 7000;

static final String HOST = "localhost";

static final Duration RESUME_SESSION_DURATION = Duration.ofSeconds(60);





public static void main(String[] args) throws InterruptedException {



RSocketFactory.receive()

.resume()

.resumeSessionDuration(RESUME_SESSION_DURATION)

.acceptor((setup, sendingSocket) -> Mono.just(new AbstractRSocket() {

@Override

public Flux requestStream(Payload payload) {

log.info("Received 'requestStream' request with payload: [{}]", payload.getDataUtf8());

return Flux.interval(Duration.ofMillis(1000))

.map(t -> DefaultPayload.create(t.toString()));

}

}))

.transport(TcpServerTransport.create(HOST, SERVER_PORT))

.start()

.subscribe();

log.info("Server running");



Thread.currentThread().join();

}



}

• Please notice that to run provided examples, you need ‘socat’ installed on your machine, please see README file for more details

The mechanism on the requester and responder side works similarly, it is based on a few components. First of all, there is a ResumableFramesStore which works as a buffer for the frames. By default, it stores them in the memory, but we can easily adjust it to our needs by implementing the ResumableFramesStore interface (e.g. store the frames in the distributed cache, like Redis). The store saves the data emitted between keep alive frames, which are sent back and forth periodically and indicates, if the connection between the peers is stable. Moreover, the keep alive frame contains the token, which determines Last received position for the requester and the responder. When the peer wants to resume the connection, it sends the resume frame with an implied position . The implied position is calculated from last received position (is the same value we have seen in the Keep Alive frame) plus the length of the frames received from that moment. This algorithm is applied to both parties of the communication, in the resume frame is it reflected by last received server position and first client available position tokens. The whole flow for resume operation is shown in the diagram below:

By adopting the resumability mechanism built in the RSocket protocol, with the relatively low effort we can reduce the impact of the network issues. Like shown in the example above, the resumability might be extremely useful in the data streaming applications, especially in the case of the device to the cloud communication.

Summary

In this article, we discussed more advanced features of the RSocket protocol, which are helpful in reducing the impact of the network on the system operationality. We covered the implementation of the client load balancing pattern and resumability mechanism. These features, combined with the robust interaction model constitutes the core of the protocol.

In the last article of this mini-series , we will cover available abstraction layers on top of the RSocket.

• Please notice that fully working examples are provided here →

DevOps is moving forward and influences various industries, changing the way companies of all sizes deliver software. Few times a year, the community of DevOps experts and practitioners gathers at a conference to discuss the latest trends, share insights, and exchange best practices. This year’s DevOps Enterprise Summit in London was one of these unique chances to participate in this uplifting movement.

When our team got back after DevOps Enterprise Summit in London, we set an engaging, internal discussion. It’s probably a common attitude for every company valuing knowledge exchange, that once attending some interesting conference, your representatives share insights, their thoughts, and news regarding the topics covered during the event. The discussion arose when members of our team had started sharing their takeaways regarding keynotes, speeches, and ideas presented at the conference.

That opened the stream of news and opinions shared by those of our teammates who also follow the latest trends in the industry by attending various meetups, listening to podcasts, etc. Here is the list of the main topics.

DevOps 2nd day – introduction

DevOps is no longer one of these innovative ideas for early adopters, which everyone has heard about but is not aware of how to start with adopting it. Now, it’s a must-have for every organization that intends to stay relevant in competitive markets. When you ask enterprises about using DevOps in their organizations, their representatives will tell you that they have already implemented this culture or are in the process of doing that. On the other hand, if you ask them if they are already satisfied with the adoption, the answer would be no – there are so many practices and principles, what makes the process demanding and it lasts a while.

Nowadays, discussions from “How to implement DevOps in our organization” have evolved into “How can we improve our DevOps practices.” The truth has been told - tech advanced companies need this agile culture to build a successful business. But simultaneously, once they introduce DevOps to their teams, new challenges occur. It’s a natural way of technology/culture adoption. As a person responsible for the cultural shift, you have to communicate it clearly – DevOps wouldn’t solve all your issues. In some cases, it may seem like a reason for some new struggles. The answer to these concerns is simple, your organization is growing, evolution is never done, and change is a constant way of managing things.

Facing DevOps 2nd Day issues is rather the rich man’s problem – you should be there, and you have to tackle them. All the new challenges appear after making an advanced step forward.

Scaling Up - from a core tech team to the entire organization working globally

Core tech teams are the first to adopt the newest solutions, but they cannot work properly without supportive teams (HR, Sales, Marketing, Accounting, etc.). After going through the successful implementation, the next step is to encourage cooperating teams to this mindset and ways of running projects.

As the enterprises that consist of thousands of employees and hundreds of teams cannot provide their crew with the flexibility in designing their very own working culture, there is a need to encourage all teams to once implemented practices.

For tech leaders, responsible for introducing DevOps in their teams, it means that their job evolves to being a DevOps advocate, who presents its value to the whole organizations and makes it a commonly known and used approach. The larger the company is the more complex the entire change becomes, but it's unavoidable when you intend to get the most out of it.

Along with advocating for expanding DevOps in the entire organization, also the very challenging job is to determine the right tech stack. New tools come and go, being responsible for selecting to the most useful toolset that will be in use for a significant period is tough and requires overall knowledge, strategy, and deep understanding of tech processes. Once determined toolset should be recommended to cooperating teams and that may provoke new issues, but is unavoidable. Should you leverage the same tech stack for all teams? When is the right time to adopt new tools? Should you leave it all to the team? Well, there is no right answer to any of these questions, and it highly depends on the situation.

DevOps is changing various industries and not limit itself to tech companies

Attending DOES in London was a great opportunity to learn more about how DevOps influences the world’s coolest companies, not often associated with technology. Let’s look at the two of the most recognized sportswear retailers - Adidas and Nike. Both these brands are synonyms to heroism, activity, sports achievements. But, as their representatives presented, both companies can overshadow many of tech brands, with their DevOps maturity and advanced approach to using technology in growing their businesses.

Following these business cases, we can agree that the time when cutting-edge technologies and methodologies often paired with them are limited to IT companies is officially over. Nowadays, industry by industry is convincing themselves to the latest solutions as developing software for internal processes is a natural competitive advantage.

Continuous adaptation and life-long learning

The best thing about working in a DevOps culture is that you just cannot say that the process has finished, that a company has transformed, and that a team has mastered the way of delivering software. Taking into account how creative the community gathered around DevOps is, how fast new ideas arise, how often its fundamentals are improved, you have to keep learning about new things.

It would be extremely comfortable if a company could once undergo digital transformation and treat the process as a completed. But if we take a look at the evolution of technology and methodologies designed to take full advantage of its capabilities, it’s obvious that it cannot be finished. Adoption of a DevOps mindset is the beginning of a change and should be conducted as a never-ending evolution.

You can’t be good at everything, which is fine, but you have to know your pain points

Excluding enterprises with enormous budgets, all organizations have limitations that obligate them to focus only on some aspects of conducting business processes. As an expert, a professional who works in a highly competitive market, you have to follow the latest trends, be aware of upcoming solutions, and cutting-edge technologies that are reshaping the business.

Being up to date is extremely important, but almost equally essential is the ability to decide on which things you cannot engage, as your time and resources are not flexible. Being responsible for your company means being aware of pain points and focusing only on the things that matter. Technology is developing extremely fast, you cannot afford to be an early adopter of every promising solution. Your job is to make responsible decisions, based on your deep understanding of the current state of technology development.

If you don’t know what to choose, think about what’s better for your business

DevOps came to being as an efficient solution to the common challenge - how to sync software development and IT operations processes to help companies thrive. Built with business effectiveness in mind, this culture has the right foundations. Choosing approaches that were designed to resolve not only internal issues but also to enable revenue growth is good for your overall success.

Anytime you face a situation when you have to decide between different solutions, always consider your company's long term perspective. When you are focused only on your goals, you may contribute to building siloses. The key to determine which ideas are the right to choose is their overall usability. We all, as professionals in our niches, may tend to prefer idealistic solutions. It’s important that we don’t work in an ideal world and our job is verified by the market.

Adopting new tools and technologies is challenging, but the real quest appears when it comes to change people's habits and company culture

If you want to make your colleagues angry, implement new toolset and new technologies in your team. Apart from tech freaks and beta testers, people are rather skeptical when it comes to learning new features and new UI. Things change when you provide them with solutions that make their work easier and more efficient.

But the real trouble occurs when you are trying to change your company's culture. It’s nothing new that we protect what we know, don’t want to change our habits, or even feel in danger when someone is trying to reshape the way we have been doing our job for ages. Your colleagues defend themselves which is natural, you cannot change it. You have to take this into account and make sure that the process will be smooth enough to help everyone adjust to the new reality. Start with small steps, be the example, discuss the issues, and explain potential opportunities. Shock therapy as a path to the cultural shift is not the way to go.

As a team developing our product -  Cloudboostr - multicloud, enterprise-ready Kubernetes, we help companies adopt a complete cloud-native stack, built with proven patterns and best practices, so they could focus their resources on improving their working culture. The feedback we’re receiving is that our customer’s teams are more open to start using new toolset then to change the approach to software delivery.

Being a DevOps Pprofessional is hot now

DevOps practitioners are much in demand. It’s a great time to master the skills required to be a specialist in DevOps as companies of all sizes are looking for help in modernizing their businesses. There are various ways of approaching it - by building an in-house team, outsourcing processes, collaborating with external consultants. Companies choose preferred manner accordingly to their needs and budget.

No matter if you work in a dedicated team at a huge enterprise, developing startup with your colleagues, or providing consulting services for global brands, being a DevOps expert is a strong competitive advantage on the talent market.

DOES London - sum up

DevOps is moving forward and is great to be among teams that contribute to its evolution. We are willing to  share our expertise , exchange knowledge, and learn from the best in the business, and conferences like DevOps Enterprise Summit are the best platforms to do it.

This article is the first one of the mini-series which will help you to get familiar with RSocket – a new binary protocol which may revolutionize machine-to-machine communication. In the following paragraphs, we discuss the problems of the distributed systems and explain how these issues may be solved with RSocket. We focus on the communication between microservices and the interaction model of RSocket.

• Please notice that code examples presented in the article are available on GitHub →

Communication problem in distributed systems

Microservices are everywhere, literally everywhere. We went through the long journey from the monolithic applications, which were terrible to deploy and maintain, to the fully distributed, tiny, scalable microservices. Such architecture design has many benefits; however, it also has drawbacks, worth mentioning. Firstly, to deliver value to the end customers, services have to exchange tons of data. In the monolithic application that was not an issue, as the entire communication occurred within a single JVM. In the microservice architecture, where services are deployed in the separate containers and communicate via an internal or external network, networking is a first-class citizen. Things get more complicated if you decide to run your applications in the cloud, where network issues and periods of increased latency is something you cannot fully avoid. Rather than trying to fix network issues, it is better to make your architecture resilient and fully operational even during a turbulent time.

Let’s dive a bit deeper into the concept of the microservices, data, communication and the cloud. As an example, we will discuss the enterprise-grade system which is accessible through a website and mobile app as well as communicates with small, external devices (e.g home heater controller). The system consists of multiple microservices, mostly written in Java and it has a few Python and node.js components. Obviously, all of them are replicated across multiple availability zones to assure that the whole system is highly available.

To be IaaS provider agnostic and improve developer experience the applications are running on top of PaaS. We have a wide range of possibilities here: Cloud Foundry, Kubernetes or both combined in Cloudboostr are suitable. In terms of communication between services, the design is simple. Each component exposes plain REST APIs – as shown in the diagram below.

At first glance, such an architecture does not look bad. Components are separated and run in the cloud – what could go wrong? Actually, there are two major issues – both of them related to communication.

The first problem is the request/response interaction model of HTTP. While it has a lot of use cases, it was not designed for machine to machine communication. It is not uncommon for the microservice to send some data to another component without taking care about the result of the operation (fire and forget) or stream data automatically when it becomes available (data streaming). These communication patterns are hard to achieve in an elegant, efficient way using a request/response interaction model. Even performing simple fire and forget operation has side effects – the server has to send a response back to the client, even if the client is not interested in processing it.

The second problem is the performance. Let’s assume that our system is massively used by the customers, the traffic increases, and we have noticed that we are struggling to handle more than a few hundred requests per second. Thanks to the containers and the cloud, we are able to scale up our services with ease. However, if we track resource consumption a bit more, we will notice that while we are running out of memory, the CPUs of our VMs are almost idle. The issue comes from the thread per request model usually used with HTTP 1.x, where every single request has its own stack memory. In such a scenario, we can leverage the reactive programming model and non-blocking IO. It will significantly cut down memory usage, nevertheless, it will not reduce the latency. HTTP 1.x is a text-based protocol thus size of data that need to be transferred is significantly higher than in the case of binary protocols.

In the machine to machine communication we should not limit ourselves to HTTP (especially 1.x), its request/response interaction model and poor performance. There are many more suitable and robust solutions out there (on the market). Messaging based on the RabbitMQ, gRPC or even HTTP 2 with its support for multiplexing and binarized payloads will do way better in terms of performance and efficiency than plain HTTP 1.x.

Using multiple protocols allow us to link the microservices in the most efficient and suitable way in a given scenario. However, the adoption of multiple protocols forces us to reinvent the wheel again and again. We have to enrich our data with extra information related to security and create multiple adapters which handle translation between protocols. In some cases, transportation requires external resources (brokers, services, etc.) which need to be highly available. Extra resources entail extra costs, even though all we need is simple, message-based fire and forget operation. Besides, a multitude of different protocols may introduce serious problems related to application management, especially if our system consists of hundreds of microservices.

The issues mentioned above are the core reasons why RSocket was invented and why it may revolutionize communication in the cloud. By its reactiveness and built-in robust interaction model, RSocket may be applied in various business scenarios and eventually unify the communication patterns that we use in the distributed systems.

RSocket to the rescue

RSocket is a new, message-driven, binary protocol which standardizes the approach to communication in the cloud. It helps to resolve common application concerns with a consistent manner as well as it has support for multiple languages (e.g java, js, python) and transport layers (TCP, WebSocket, Aeron).
In the following sections, we will dive deeper into protocol internals and discuss the interaction model.

Framed and message-driven

Interaction in RSocket is broken down into frames. Each frame consists of a frame header which contains the stream id, frame type definition and other data specific to the frame type. The frame header is followed by meta-data and payload – these parts carry data specified by the user.

There are multiple types of frames which represent different actions and available methods of the interaction model. We’re not going to cover all of them as they are extensively described in the official documentation (http://rsocket.io/docs/Protocol). Nevertheless, there are few which are worth noting. One of them is the Setup Frame which the client sends to the server at the very beginning of the communication. This frame can be customized so that you can add your own security rules or other information required during connection initialization. It should be noted that RSocket does not distinguish between the client and the server after the connection setup phase. Each side can start sending the data to the other one – it makes the protocol almost entirely symmetrical.

Performance

The frames are sent as a stream of bytes. It makes RSocket way more efficient than typical text-based protocols. From a developer perspective, it is easier to debug a system while JSONs are flying back and forth through the network, but the impact on the performance makes such convenience questionable. The protocol does not impose any specific serialization/deserialization mechanism, it considers the frame as a bag of bits which could be converted to anything. That makes possible to use JSON serialization or more efficient solutions like Protobuf or AVRO.

The second factor, which has a huge impact on RSocket performance is the multiplexing. The protocol creates logical streams (channels) on the top of the single physical connection. Each stream has its unique ID which, to some extent, can be interpreted as a queue we know from messaging systems. Such design deals with major issues known from HTTP 1.x – connection per request model and weak performance of “pipelining”. Moreover, RSocket natively supports transferring of the large payloads. In such a scenario the payload frame is split into several frames with an extra flag – the ordinal number of the given fragment.

Reactiveness & Flow Control

RSocket protocol fully embraces the principles stated in the Reactive Manifesto . Its asynchronous character and thrift in terms of the resources helps decrease the latency experienced by the end users and costs of the infrastructure. Thanks to streaming we don’t need to pull data from one service to another, instead, the data is pushed when it becomes available. It is an extremely powerful mechanism, but it might be risky as well. Let’s consider a simple scenario: in our system, we are streaming events from service A to service B. The action performed on the receiver side is non-trivial and require some computation time. If service A pushes events faster than B is able to process them, eventually, B will run out of resources – the sender will kill the receiver. Since RSocket uses the reactor, it has built-in support for the flow control , which helps to avoid such situations.

We can easily provide the backpressure mechanism implementation, adjusted to our needs. The receiver can specify how much data it would like to consume and will not get more than that until it notifies the sender that it is ready to process more. On the other hand, to limit the number of incoming frames from the requester, RSocket implements a lease mechanism. The responder can specify how many requests requester may send within a defined time frame.

The API

As mentioned in the previous section, RSocket uses Reactor, so that on the API level we are mainly operating on Mono and Flux objects. It has full support for reactive signals as well – we can easily implement “reaction” on different events – onNext, onError, onClose, etc.

The following paragraphs will cover the API and each and every interaction option available in RSocket. The discussion will be backed with the code snippets and the description for all the examples. Before we jump into the interaction model, it is worth describing the API basics, as it will come up in the multiple code examples.

Setting up the connection with RSocketFactory

Setting up the RSocket connection between the peers is fairly easy. The API provides factory (RSocketFactory) with factory methods receive and connect to create RSocket and CloseableChannel instances on the client and the server side respectively. Second common property present in both parties of the communication (the requester and the responder) is a transport. RSocket can use multiple solutions as a transport layer (TCP, WebSocket, Aeron). Whichever you choose the API provides the factory methods which allows you to tweak and tune the connection.

RSocketFactory.receive()

.acceptor(new HelloWorldSocketAcceptor())

.transport(TcpServerTransport.create(HOST, PORT))

.start()

.subscribe();

RSocketFactory.connect()

.transport(TcpClientTransport.create(HOST, PORT))

.start()

.subscribe();

Moreover, in the case of the responder, we have to create a socket acceptor instance. The SocketAcceptor is an interface which provides the contract between the peers. It has a single method accept which accepts the RSocket for sending requests and returns an instance of RSocket that will be used for handling the requests from the peer. Besides providing the contract the SocketAcceptor enables us to access the setup frame content. On the API level, it is reflected by ConnectionSetupPayload object.

public interface SocketAcceptor {

Mono<RSocket> accept(ConnectionSetupPayload setup, RSocket sendingSocket);

}

As shown above, setting up the connection between the peers is relatively easy, especially for those of you who worked with WebSockets previously – in terms of the API both solutions are quite similar.

Interaction model

After setting up the connection we are able to move on to the interaction model. RSocket supports following operations:

The fire and forget , as well as the metadata push , were designed to push the data from the sender to the receiver. In both scenarios the sender does not care about the result of the operation – it is reflected on API level in a return type (Mono). The difference between these actions sits in the frame. In case of fire and forget the fully-fledged frame is sent to the receiver, while for the metadata push action the frame does not have payload – it consists only of the header and the metadata. Such a lightweight message can be useful in sending notifications to the mobile or peer-to-peer communication of IoT devices.

RSocket is also able to mimic HTTP behavior. It has support for request-response semantics, and probably that will be the main type of interaction you are going to use with RSocket. In streams context, such an operation can be represented as a stream which consists of the single object. In this scenario, the client is waiting for the response frame, but it does it in a fully non-blocking manner.

More interesting in the cloud applications are the request stream and the request channel interactions which operate on the streams of data, usually infinite. In case of the request stream operation, the requester sends a single frame to the responder and gets back the stream of data. Such interaction method enables services to switch from the pull data to the push data strategy. Instead of sending periodical requests to the responder requester can subscribe to the stream and react on the incoming data – it will arrive automatically when it becomes available.

Thanks to the multiplexing and the bi-directional data transfer support, we can go a step further using the request channel method. RSocket is able to stream the data from the requester to the responder and the other way around using a single physical connection. Such interaction may be useful when the requester updates the subscription – for example, to change the subscription criteria. Without the bi-directional channel, the client would have to cancel the stream and re-request it with the new parameters.

In the API, all operations of the interaction model are represented by methods of RSocket interface shown below.

public interface RSocket extends Availability, Closeable {



Mono<Void> fireAndForget(Payload payload);



Mono<Payload> requestResponse(Payload payload);



Flux<Payload> requestStream(Payload payload);



Flux<Payload> requestChannel(Publisher<Payload> payloads);



Mono<Void> metadataPush(Payload payload);



}

To improve the developer experience and avoid the necessity of implementing every single method of the RSocket interface, the API provides abstract AbstractRSocket we can extend. By putting the SocketAcceptor and the AbstractRSocket together, we get the server-side implementation, which in the basic scenario may look like this:

@Slf4j

public class HelloWorldSocketAcceptor implements SocketAcceptor {



@Override

public Mono<RSocket> accept(ConnectionSetupPayload setup, RSocket sendingSocket) {

log.info("Received connection with setup payload: [{}] and meta-data: [{}]", setup.getDataUtf8(), setup.getMetadataUtf8());

return Mono.just(new AbstractRSocket() {

@Override

public Mono<Void> fireAndForget(Payload payload) {

log.info("Received 'fire-and-forget' request with payload: [{}]", payload.getDataUtf8());

return Mono.empty();

}



@Override

public Mono<Payload> requestResponse(Payload payload) {

log.info("Received 'request response' request with payload: [{}] ", payload.getDataUtf8());

return Mono.just(DefaultPayload.create("Hello " + payload.getDataUtf8()));

}



@Override

public Flux<Payload> requestStream(Payload payload) {

log.info("Received 'request stream' request with payload: [{}] ", payload.getDataUtf8());

return Flux.interval(Duration.ofMillis(1000))

.map(time -> DefaultPayload.create("Hello " + payload.getDataUtf8() + " @ " + Instant.now()));

}



@Override

public Flux<Payload> requestChannel(Publisher<Payload> payloads) {

return Flux.from(payloads)

.doOnNext(payload -> {

log.info("Received payload: [{}]", payload.getDataUtf8());

})

.map(payload -> DefaultPayload.create("Hello " + payload.getDataUtf8() + " @ " + Instant.now()))

.subscribeOn(Schedulers.parallel());

}



@Override

public Mono<Void> metadataPush(Payload payload) {

log.info("Received 'metadata push' request with metadata: [{}]", payload.getMetadataUtf8());

return Mono.empty();

}

});

}

}

On the sender side using the interaction model is pretty simple, all we need to do is invoke a particular method on the RSocket instance we have created using RSocketFactory, e.g.

socket.fireAndForget(DefaultPayload.create("Hello world!"));

• For more examples of the methods available in the RSocket interaction model please visit GitHub →

More interesting on the sender side is the implementation of the backpressure mechanism. Let’s consider the following example of the requester side implementation:

public class RequestStream {



public static void main(String[] args) {



RSocket socket = RSocketFactory.connect()

.transport(TcpClientTransport.create(HOST, PORT))

.start()

.block();

socket.requestStream(DefaultPayload.create("Jenny", "example-metadata"))

.subscribe(new BackPressureSubscriber());



socket.dispose();

}



@Slf4j

private static class BackPressureSubscriber implements Subscriber<Payload> {



private static final Integer NUMBER_OF_REQUESTED_ITEMS = 5;

private Subscription subscription;

int receivedItems;



@Override

public void onSubscribe(Subscription s) {

this.subscription = s;

subscription.request(NUMBER_OF_REQUESTED_ITEMS);

}



@Override

public void onNext(Payload payload) {

receivedItems++;

if (receivedItems % NUMBER_OF_REQUESTED_ITEMS == 0) {

log.info("Requesting next [{}] elements", NUMBER_OF_REQUESTED_ITEMS);

subscription.request(NUMBER_OF_REQUESTED_ITEMS);

}

}



@Override

public void onError(Throwable t) {

log.error("Stream subscription error [{}]", t);

}



@Override

public void onComplete() {

log.info("Completing subscription");

}

}



}

In this example, we are requesting the stream of data, but to ensure that the incoming frames will not kill the requester we have the backpressure mechanism put in place. To implement this mechanism we use request_n frame which on the API level is reflected by the subscription.request(n) method. At the beginning of the subscription [ onSubscribe(Subscription s) ], we are requesting 5 objects, then we are counting received items in onNext(Payload payload). When all expected frames arrived to the requester, we are requesting the next 5 objects – again using subscription.request(n) method. The flow of this subscriber is shown in the diagram below:

Implementation of the backpressure mechanism presented in this section is very basic. In the production, we should provide a more sophisticated solution based on more accurate metrics e.g. predicted/average time of computation. After all, the backpressure mechanism does not make the problem of an overproducing responder disappear. It shifts the issue to the responder side, where it can be handled better. Further reading about backpressure is available here on Medium and here on GitHub .

Summary

In this article, we discuss the communication issues in the microservice architecture, and how these problems can be solved using RSocket. We covered its API and the interaction model backed with simple “hello world” example and basic backpressure mechanism implementation.

In the next articles of this series, we will cover more advanced features of RSocket including Load Balancing and Resumability as well as we will discuss abstraction over RSocke t – RPC and Spring Reactor.

• Please notice that fully working examples are provided here →

With ever-growing IT projects, automation is nowadays a must-have. From building source code and testing to versioning and deploying, CI/CD tools were always the anonymous team member, who did the job no developer was eager to do. Today, we will take a look at some tips regarding one of the newest tools - Concourse CI. First, we will speed up our Concourse jobs, then we’ll ease the development of the new pipelines for our projects.

Aggregate your steps

By default, Concourse tasks in a job are executed separately. This is perfectly fine for small Concourse jobs that last a minute or two. It also works well at the beginning of the project, as we just want to get the process running. But at some point, it would be nice to optimize our builds.

The simplest way to save time is to start using the aggregate keyword. It runs all the steps declared inside of it in parallel. This leads to time-savings in both - script logic execution and in the overhead that occurs when starting the next task.

Neat, so where can we use it? There are 2 main parts of a job where the aggregation is useful:

1. Resource download and upload.
2. Tests execution.

Get and put statements are ideal targets because download and upload of resources are usually completely independent. Integration tests, contract tests, dependency vulnerabilities tests, and alike are also likely candidates if they don’t interfere with one another. Project build tasks? Probably not, because those are usually sequential and we require their output to proceed.

How much time can aggregating save? Of course, it depends. Assuming we can’t aggregate steps that build and test our code, we do get the advantage of simultaneous upload and download of our resources as well as we get less visible step-to-step overhead. We usually save up to two, maybe even three minutes. The largest saving we got was from over half an hour to below ten minutes. Most of the saved time came from running test-related tasks in parallel.

Use docker images with built-in tools

This improvement is trickier to implement but yields a noticeable build time gains. Each task runs in a container, and the image for that container has a certain set of tools available. At some point in the project comes a time where no available image has the tool required. First thing developers do is they download that tool manually or install it using a package manager as a part of the task execution. This means that the tool is fetched every time the task runs. On top of that, the console output is flooded with tool installation logs.

The solution is to prepare a custom container image that already has everything needed for a task to complete. This requires some knowledge not directly related to Concourse, but for example to Docker. With a short dockerfile and a couple of terminal commands, we get an image with the tools we need.

1. Create dockerfile.
2. Inside of the file, install or copy your tools using RUN or COPY commands.
3. Build the image using docker build.
4. Tag and push the image to the registry.
5. Change image_resource part in your Concourse task to use the new image.

That’s it, no more waiting for tools to install each time! We could even create a pipeline to build and push the image for us.

Create pipelines from a template

Moving from time-saving measures to developer convenience tips, here’s one for bigger projects. Those usually have a certain set of similar build pipelines with the only differences being credentials, service names, etc. - parameters that are not hardcoded in the pipeline script and are injected at execution time from a source like CredHub. This is typical for Cloud Foundry and Kubernetes web projects with microservices. With a little bit of creativity, we could get a bash or python script to generate those pipelines from a single template file.

First, we need to have a template file. Take one of your existing pipeline specifications and substitute parameter names with their pipeline agnostic version. Our script needs to loop over a pipeline names list, substitute generic parameter names with proper pipeline related ones that are available in Credhub and then set the pipeline in Concourse with the fly CLI.

The second part of the equation here is a Concourse job that watches for changes in the template file in a Git repository and starts the pipeline generation script. With this solution, we have to change only one file to get all pipelines updated, and on top of that, a commit to pipeline repository is sufficient to trigger the update.

Log into a task container to debug issues

When debugging Concourse task failures, the main source of information on failure is the console. A quick glance at the output is enough to solve most of the problems. Other issues may require a quick peek into the environment of an unsuccessful task. We can do that with fly intercept command.

Fly intercept allows us to log into a container that executed a specific task in a specific job run. Inside we can see the state of the container when task finished and can try to find the root of failure. There may be an empty environment variable - we forgot to set the proper param in a yml file. The resource has a different structure inside of it - we need to change the task script or the resource structure. When the work is done, don’t forget to log out of the container. Oh, and don’t wait too long! Those containers can be disposed of by Concourse at any time.

Use Visual Studio Code Concourse add-on

The last thing I want to talk about is the Concourse CI Pipeline Editor for Visual Studio Code. It’s a plugin that offers suggestions, documentation popups, and error checking for Concourse yml files. If you use the pipeline template and generation task from the previous tip, then any syntax error in your template will be discovered as late as the update task updating the pipelines from the template. That’s because you won’t run fly set-pipeline yourself. Fixing such issue requires a new commit in the pipeline repository.

With the plugin, any unused resource or a typo in property name will be detected immediately. Add-on will also help you write new pieces of automation code by suggesting keywords and showing available values for Concourse commands. The only action required is to update the files.associations section in the settings. We use separate directories for pipelines and tasks, so we have set it up as follows:

Conclusion

And that’s it! We hope you have found at least one tip useful and will use it in your project. Aggregate is an easy one to implement, and it’s good to have a habit of aggregating steps from the start. Custom images and pipeline templates are beneficial in bigger projects where they help keep CI less clunky. Finally, fly intercept and the VSC add-on are just extra tools to save time during the pipeline development.

A Sprint Review is a meeting that closes and approves a Sprint in Scrum. The value of this meeting comes to inspect the increment and adapt the Product Backlog accordingly to current business conditions. This is a session where the Scrum Team and all interested stakeholders can attend to exchange information regarding progress made during the Sprint. They discuss problems and update the work plan to meet current market needs and approach a once determined product vision in a new way.

This meeting is supposed to enable feedback loop and foster collaboration, especially between the Scrum Team and all Stakeholders. A Sprint Review will be valuable for product development if there is an overall understanding of its purpose and plan. To maximize its value, it’s good to be aware of the impediments Scrum Team could meet. We would like to share our experience with a Sprint Review, the most important problems and facts discovered during numerous projects run by our team from Grape Up . As one of our company values is Drive Change, we are continuously working on enhancing our Scrum Process by investigating problems, resolving them, and implementing proper solutions to our daily work.

“Meetings – coding work balance.“

It’s a hard reality for managers leading projects. A lot of teams have a problem with too many meetings which disorganize daily work and may be an obstacle to deliver increment. Every group consists of both; people who hate meetings and prefer focusing on coding and meetings-lovers who always have thousands of questions and value teamwork over working independently. Internal team meetings or chats with stakeholders are an important part of daily work only when they bring any benefit to the team or product. Scrum prescribes four meetings during a Sprint: Sprint planning, the Daily Scrum, a Sprint Review and a Sprint Retrospective. It all makes collaboration critical to run a successful Scrum Team.

A stereotype developer who sits in his basement and doesn’t go on the daylight, who is introvert doesn’t fit this vision. Communication skills are very important for every scrum developer. Beyond technical skills and the ability to create good quality code, they need to develop their soft skills. Meetings are time-consuming and here comes a real threat that makes many people angry – after a few quarters of discussions, you may see no progress in coding. You need to confront with opinions of your colleagues and many times it’s not easy. Moreover, you need to describe to the managers what are you doing in a different language than JavaScript or Python.

For many professionals, the ideal world would be to sit comfortably in front of a screen and just write good quality code. But wait… Do people really want to do the work that no one wants? Create functionalities no one needs? Feedback exchange may not help to speed up the production process but for sure can boost the quality and usability of an application. The main idea of Scrum is to inspect and adapt continuously, and there is no way to do it without discussion and collaboration. In agile, the team’s information flow and early transformation is the main thing. It saves money and “lives”!

A Sprint Review gives a unique chance to stop for a moment and look at all the work done. It’s an important meeting that helps to keep roadmap and backlog in good health together with the market expectations. This is not only a one-side demo but a discussion of all participating guests. Each presented story should be talked through. Each attendee can ask questions, and the Product Owner or a team member should be able to answer it. It’s good to be as neutral as it’s possible neither praise nor criticize. Focus on evaluating features, not people.

Our team works for external clients from the USA, and our contact with them is constricted by time zones. A Sprint Review helps us to understand better client needs and step into their shoes. The development team can share their ideas and obstacles, and what is most important, enrich the understanding of the business value of their input. Each team member presents own part of a project, describes how it works, and asks the client for feedback. Sometimes we even share a work that's not 100% completed to be sure we are heading into the right direction to adapt on the early phase of implementation.

"Keep calm and work": No timeboxing

A Sprint Review should be open to everyone interested in joining the discussion. Main guests are the Product Owner, the Development Team, and business representation, but other stakeholders are also welcome. Is there a Scrum Master on board? For sure it should be! This may lead to a quite big meeting when many points of view occur and clash with each other. Uncontrolled discussion lasting hours is a real threat.

The Scrum Master’s role is to keep all meetings on track and allow everyone to talk. Parkinson’s Law is an old adage that says "work expands so as to fill the time available for its completion." Every Scrum Master’s golden rule of conducting meetings should be to schedule session blocks for the minimum amount of time needed. Remember that too long gathering may be a waste of time. Even if you are able to finish a meeting in 30 min, but had planned it for 1 hour, it will finally take the entire hour. Don’t forget that long and worthless meetings are boring.

An efficient Sprint Review needs to keep all guests focused and engaged. For a 1-week sprint, it should last about one hour; for a 2-week sprint, two hours. The Scrum Master’s role is to ensure theses time boxes but also to facilitate a meeting and inform when discussion leaves a trace. There should be time to present the summary of the last sprint: what was done and what wasn’t, to demonstrate progress and to answer all questions. Finally, additional time for gathering new ideas that will for sure appear. The second part of the meeting should be focused on discussing and prioritizing current backlog elements to adjust to customer and market needs. It’s a good moment for all team members to listen to the customer and get to know the management perspective and plan the next release content and date. A Sprint Review is an ideal moment for stakeholders to bring new ideas and priorities to the dev team and reorder backlog with Product Owner. During this session, all three Scrum pillars meet: transparency, inspection, and adaptation.

YOLO! : Preparation is for weak

A good plan is an attribute of a well-conducted meeting! No one likes mysterious meetings. An invitation should inform not only about time, place, and date of the meeting but also about the high-level program. Agenda is the first step of a good Sprint Review but not the only one. To be sure that Sprint Review will be satisfying for all participants, everyone needs to be well prepared. Firstly, the Product Owner should decide what work can be presented and create a clear plan of the demo and the entire meeting. The second step is to determine people responsible for each presented feature. Their job is to prepare the environment, interesting scenarios, and all needed materials. But before asking the team for it, make sure they know how to do it. Not all of the people are born presenters, but they can become one with your help.

In our team, we help each other with preparations to the Sprint Review. We work in Poland, but our customers are mostly from the USA. Not only the distance and time difference is a challenge but also language barriers. If there is a need everyone can ask for a rehearsal demo where we can present our work, discuss potential problems, and ask each other for more details. We’re constantly improving our English to make sure everything is clear to our clients. This boosts everyone’s self-confidence and final receipt. Team members know what to do, they’re ready for potential questions and focus on input from stakeholders. This is how we improve not only Sprint Reviews to be better and better but also communication skills.

"Work on your own terms": No DoD

“How ready is your work?”. Imagine a world where each developer decides when s/he finished work on her/his own terms. For one, it’s when the code is just compiled, for others it includes unit tests and it's compatible with coding standards, or even integration and usability tests. Everything works on a private branch but when it comes to integrating… boom! The team is trying to prepare a version to demonstrate but the final product crashes. The Product Owner and stakeholders are confused. A sprint without potentially releasable work is a wasted sprint. This dark scenario shouldn’t have happened to any Scrum Team.

To save time and to avoid misunderstandings, it’s good to speak the same language. All team members should be aware of the meaning of the word “done”. In our team, this keeps us on track with a transparent view of what work really can be found as delivered in a sprint. Definition of Done is like an examination of consistency for a team. Clear, generally achievable, and easy to understand steps to decide how much work is done to create a potentially releasable feature. This provides all stakeholders with clear information and allows the planning of the next business steps.

It is a guide that dictates when teams can legitimately claim that a given user’s story/task is "done" and can be moved to the next level - approval by the Product Owner and release. Basic elements of our DoD are; the code reviewed by other developers, merged to develop branch, and deployed on DEV/QA environment, and finally properly tested by QA and developers with the manual, and automation tests. The last step comes to fixing all defects, verifying if all the Acceptance Criteria are met, and reviewing by the Product Owner. Only when all DoD elements are done, we surely say that this is something we can potentially release when it will be needed.

"Curiosity killed a cat": don't report progress

Functionalities which meet requirements of Definition of Done are the first candidates to be shared on the Sprint Review. In the ideal Agile World, it’s not recommended to demonstrate not finished work. Everything we want to present should be potentially releasable… but wait! Could you agree that an ideal Agile World doesn’t exist? How many times external clients really don’t know how they want to implement something until they see the prototype.

Our company provides not only product development services but also consulting support. We don’t limit this collaboration to performing tasks, in most cases we co-create applications with our clients, advise the best solutions, and tackle their problems. Many times, there’s is no help from a Graphic Designer or a UX team. That happened to us. That’s why we have improved our Sprint Review. Since then we present not only finished work but also this in progress. Advanced features should be divided into smaller stories which can be delivered during one sprint. Final feature will be ready after a few sprints but completed parts of it should be demonstrated. It helps us discuss the vision and potential obstacles. Each meeting starts with work “done” and goes to work "in progress". What is the value? Clients trust us, believe in our ideas but at the same time still, have final word and control. Very quickly we can find out if we’re moving in the right direction or not. It’s better to hear “I imagined it in a different way, let’s change something” after one sprint than live with the falsified view of reality till the feature release.

"As you make your bed, so you must lie in it": don't inform about obstacle

Finally, even the best and ideal team can turn into the worst bunch of co-workers if they are not honest. We all know that customer satisfaction is a priority but not at all costs. It is not a shame to talk about problems the team met during the Sprint or obstacles we face with advanced features. Stakeholders need to know clearly the state of product development to confront it with the business teams involved in a project; marketing, sales or colleagues responsible for the project budget. When tasks take much more time than predicted, it’s better to show a delay in production and explain their reasons. Putting lipstick on a pig does not work. Transparency, which is so important is Scrum, allows all the people involved in the project to make good decisions for further product development. A Product Owner, as someone who defines the product vision, evaluates product progress and anticipates client needs, is obligated to look at the entire project from a general perspective, and monitor its health.

One more time, a stay-cool rule is up to date. Don’t panic and share a clear message based on facts.

We all want to implement the best practices and visions that make our life and work more productive and fruitful. Scrum helps with its values, pillars, and rules . But there is a long way from unconscious incompetence to conscious competence. It’s good to be aware of the problems we can meet and how to manage with them. “Rome wasn't built in a day”. If your team doesn’t use a Sprint Review but only a demo at the end of the sprint, just try to change it. As a team member, Scrum Master or Product Owner, observe, analyze and adapt continuously not only with your product but also with your team and processes.

Pro Tips:

• Adjust the time of a Sprint Review to accurate needs. For a 1-week Sprint, it’s one hour, for a 2-week Sprint give it two hours. It is a recommendation based on our experience.
• Have a plan. Create stable agenda and a brief summary of a Sprint and share it before meeting with everyone invited to a Sprint Review.
• Prepare yourself and a team. Coach your team members and discuss potential questions that can be asked by stakeholders.
• Facilitate meeting to keep all stakeholders interested and give everyone possibility to share feedback.
• Create a clear Definition of Done that is understandable by all team members and stakeholders.
• Be honest. Talk about problems and obstacles, show work in progress if you see there is value in it. Engage and co-create the product with your client.
• Try to be as neutral as it’s possible nor to praise or criticize. Focus on facts and substantive information.

We know that adjusting to the new working environment may be tough. It’s even more challenging when you have to collaborate with people located in different offices around the world. We both experienced such a demanding situation and want to describe a few problems and suggest some ways to tackle them. We hope to help fellow professionals who are at the beginning of this ambitious career path. In this article, we want to elaborate on working in multicultural, half-remote teams and main challenges related to this. To dispel doubts, by “half remote team” we mean a situation in which part of the group works together on-site when other part/parts of the crew work in other places, single or in a larger group/groups. We've gathered our experiences during our works in this kind of teams in Europe and the USA.

It’s nothing new that some seemingly harmless things can nearly destroy whole relations in a team and can start an internal tension. One of us worked in a team where six people worked together in one office and the rest of the team (three people) in the second office. We didn’t know why, but something wrong started to happen, these two groups of people started calling themselves “we” and “them”. One team, divided into two mutually opposing groups of people.

Those groups started to defy each other, gossip about yourself, and disturb yourself at work. What's more, there was not a person who tried to fix it, conflicts were growing, and teamwork was impossible. The project was closed. One year later, we started to observe this in our another project. In that project situation was different. There were 3 groups of people, a larger group with 4 people, and 2 smaller with 2 people each. We had delved into the state of things, and we discovered the reasons for this situation.

Information exchange

One of the reasons was the information exchange. The biggest team was located together, and they often discuss things related to work. Often the discussion turned into planning and decision-making, as you can guess, the rest of the team did not have the opportunity to take part in them. The large team made the decision without consulting it, and it really annoyed the rest…

What was done wrong, how can you avoid it in your project? The team should be team, even if its members don’t work in the same location. Everyone should take an active part in making decisions. Despite the fact that it is difficult to achieve, all team members must be aware of this, they must treat the rest of the team in the same way, as if they were sitting next to them.

Firstly, if an idea is developed during the discussion, a group of people must grab it and present it to the rest of the team for further analysis. You should avoid situations where the idea is known only to a local group of people. It reduces the knowledge about the project and increases the anger of other developers. They do not feel part of the team, they do not feel that they have an impact on its development. What's more, if the idea does not please the rest, they begin to treat authors hostile which create conflicts and leads to a situation where people start to say “we” and “them”. Part of the team should not make important decisions, it should be taken by the whole team, if a smaller group has something to talk about everyone should know about it and have a chance to join them (even remotely!).

Secondly, if a group notices they are discussing things which other people may be interested in, they should postpone local discussion and create remote room when discussion can be continued. Anyone can join it as if sitting next to them.

Thirdly, if it was not possible to include others in the conversation the conversation summary should be saved and made available to all team members.

Team integration

The second reason We found was an integration of the parts of the team. The natural thing was that people sitting together knew each other better, thus, a natural division into groups within the team was formed. Sadly, this can not be avoided… but we can reduce the impact of this factor.

Firstly, if possible, we should ensure the integration of the parts of the team. They have to meet at least once, and preferably in regular meetings not related to work, so-called integration trips.

Secondly, mutual trust among the team should be built. The team should talk about difficult situations in full composition, not in local groups over coffee. And if a local conversation took place, the problem should be presented to the whole team. Everyone should be able to speak honestly and feel comfortable in the team, it is very important!

Language and insufficient communication

Another obstacle is a different culture or language. If there are people who speak different languages in the team, they will usually use English which will not be a native language for a part of the team… Different team members may have different levels of English speaking skills, less skilled team members may not understand intricate phrases.

It is very important to make sure everyone understands the given statement. If you know that you have some people in your team whose English is not so fluent, you can ask and make sure they understood everything. Confidence should be built inside the team, everyone should feel that they can ask for an explanation of the statement in the simplest words without taunting and consistency. We have seen such a problem many times in teams especially multicultural. A lack of understanding leads to misunderstandings and the collapse of the project. Each of the team members should learn and improve their skills, the team should support colleagues with lower language skills, politely correcting them and communicating that they use some language form incorrectly. We recommend doing it in private unless the confidence in the team is so large that it can be done in a group.

Communication can also lead to misunderstandings, at the beginning of our careers our language skills were not the best. Our statements were very simple and crude. As a result, sometimes our messages were perceived as aggressive… We did not realize it until We started to notice the tension between us and some of the team members. It is very difficult to remedy this, after all, we do not know what others think. Therefore, small advice from us - talk to each other, seriously and try to build a culture of open feedback in the team, address even uncomfortable topics. Even if you have a language problem it is sometimes better to try to describe something in 100 simple sentences than not to speak at all...

Time difference

Let’s focus on one more challenging difficulty that may cause a lot of troubles while working in half-remote teams. While working in teams distributed over a larger area of the world, the time difference between team member’s locations might cause an issue that is very hard to overcome. We have been working in a team where team members were located in the USA (around both eastern and western coasts), Australia and Poland. As per our experience, it is nearly impossible to gather all team members together because of working hours in those locations. We have observed some common issues that such a situation may cause.

Team members working in different time zones have limited capabilities of teamwork. There is often not enough time for team activities like technical or non-work-related discussions over a cup of coffee that build team spirit and good relations between members. It is impossible to integrate distributed teams without cyclic meetings in one place. We have seen how such odds and ends lead to team divisions on “we” and “they” mentioned before. It is also a blocker when it comes to applying good programming practices in the project like pair programming and knowledge sharing.

Distributed teams are more difficult to manage, and some of the Agile work methodologies are not applicable at all, as it often requires the participation of all team members. In the case of our team, Scrum methodology did not work at all, because we could not organize successful planning sessions, sprint reviews, retrospectives and demos on which everyone’s input matters. It was a common situation where after planning team members did not know what they are supposed to do next, and at first, they needed to discuss something with absent teammates.

If we take a look at distributed team performance, it will usually seem to be lower than in the case of local teams. That is mainly because of inevitable delays when some team member needs assistance from another. Imagine that you start working and after an hour you encounter a problem that requires your teammate’s help, but s/he will wake up no sooner than in 7 hours. You have to postpone task you were working on, and focus on some other - what usually slows your job down. Of course, it is a sunny day scenario, because there might be more serious issues where you cannot do anything else in the meantime (i.e. you have broken all environments including production, backup was “nice to have” on planning - and your mate from Australia is the only one who can restore it). It also takes more time to exchange information, process code reviews and share knowledge about a project if we cannot reach other team members immediately when they are needed.

On the other hand, distributed teams have some advantages. There are many projects or professions that require client support for 24/7 - and in this case, it is much easier for such time coverage. It can save a team from on-calls and other inconveniences.

We have learned that there is no cure for all the problems that distributed teams struggle with, but the impact of some of them can be reduced. Companies that are aware of how time difference impacts team performance often offer possibilities to work remotely from home in fully flexible hours. In some cases, it works and it is faster to get things done, but it does not solve all problems on a daily basis, because everyone wants to live their private life as well, meet friends on the evening or grab a beer and watch TV series rather than work late at night. Moreover, team integration and cooperation issue could be solved by frequent travels but it is expensive and the majority of people do not have the possibility to leave home for a longer period of time.

Summary

To sum it up, multicultural half-remote teams are really challenging to manage. Distributed teams struggle with a lot of troubles such as information exchange, teamwork, communication, and integration - which may be caused by cultural differences, remote communication and the time difference between team members. Without all this, there is just a bunch of individuals that cannot act as a team. Despite the above tips to solve some of the problems, it is hard to avoid the lack of partnership among team members, that may lead to divisions, misunderstandings and team collapse.

And while the struggles described above are real, we can't forget why we do it. Building a distributed team allows a company to acquire talent often not available on the local market. By creating an international environment, the same company can gain a wider perspective and better understand different markets. Diversification of the workforce can be a lifesaver when it comes to some emergency issue that may be a danger for a company that the entire team works in one location. We at Grape Up share different experiences, and thanks to knowledge exchange, our team members are prepared to work is such a demanding environment.

Working with cloud-native applications entails continuously tackling and implementing solutions to cross-cutting concerns. One of these concerns that every project is bound to run into comes to deploying highly scalable, available logging, and monitoring solutions.

You might ask, “how do we do that? Is it possible to find "one size fits all" solution for such a complex and volatile problem?” You need to look no further!

Taking into account our experience based on  working with production-grade environments , we propose a generic architecture, built totally from open source components, that certainly provide you with the highly performant and maintainable workload. To put this into concrete terms, this platform is characterized by its:

•  High availability - every component is available 24/7 providing users with constant service even in the case of a system failure.
•  Resiliency -  crucial data are safe thanks to redundancy and/or backups.
•  Scalability - every component is able to be replicated on demand accordingly to the current load.
•  Performance - ability to be used in any and all environments.
•  Compatibility - easily integrated into any workflows.
•  Open source - every component is accessible to anyone with no restrictions.

To build an environment that enables users to achieve outcomes described above, we decided to look at Elastic Stack, fully open source logging solution, structured in a modular way.

Elastic stack

Each component has a specific function, allowing it to be scaled in and out as needed. Elastic stack is composed of:

•  Elasticsearch - RESTful, distributed search and analytics engine built on Apache Lucene able to index copious amount of data.
•  Logstash - server-side data processing pipeline, able to transform, filter and enrich events on the fly.
•  Kibana – a feature-rich visualization tool, able to perform advanced analysis on your data.

While all this looks perfect, you still need to be cautious while deploying your Elastic Stack cluster. Any downtime or data loss caused by incorrect capacity planning can be detrimental to your business value. This is extremely important, especially when it comes to production environments. Everything has to be carefully planned, including worst-case scenarios. Concerns that may weigh on the successful Elastic stack configuration and deployment are described below.

High availability

When planning any reliable, fault-tolerant systems, we have to distribute its critical parts across multiple, physically separated network infrastructures. It will provide redundancy and eliminate single points of failure.

Scalability

ELK architecture allows you to scale out quickly. Having good monitoring tools setup makes it easy to predict and react to any changes in the system's performance. This makes it resilient and helps you optimize the cost of maintaining the solution.

Monitoring and alerts

A monitoring tool along with a detailed set of alerting rules will save you a lot of time. It lets you easily maintain the cluster, plan many different activities in advance, and react immediately if anything bad happens to your software.

Resource optimization

In order to maximize the stack performance, you need to plan the hardware (or virtualized hardware) allocation carefully. While data nodes need efficient storage, ingesting nodes will need more computing power and memory. While planning this take into consideration the number of events you want to process and amount of data that has to be stored to avoid many problems in the future.

Proper component distribution

Make sure the components are properly distributed across the VMs. Improper setup may cause high CPU and memory usage, can introduce bottlenecks in the system and will definitely result in lower performance. Let's take Kibana and ingesting node as an example. Placing them on one VM will cause poor user experience since UI performance will be affected when more ingesting power is needed and vice-versa.

Data replication

Storing crucial data requires easy access to your data nodes. Ideally, your data should be replicated across multiple availability zones which will guarantee redundancy in case of any issues.

Architecture

Our proposed architecture consists of five types of virtual machines - Routers, elastic masters, elastic data, ingestors, and Kibana instances. This toolset simplifies scaling of components while separating their responsibilities. Each of them has a different function:

•  Elasticsearch_master - controls indexes and Elasticsearch master. Responsible for creating new indexes, rolling updates and monitoring clusters health.
•  Elasticsearch_data - stores data and retrieves it as needed. Can be run both as hot and warm storage, as well as provides redundancy on data.
•  Ingestor - exposes input endpoints for events both while transforming and enriching data stored in Elasticsearch.
•  Kibana - provides users with visualizations by querying Elasticsearch data.
•  Router - serves as a single point of entry, both for users and services producing data events.

Architecting your Elastic Stack deployment in this way allows for the simple upgrade procedure. Thanks to using a single point of entry, switching to a new version of Elastic Stack is as simple as pointing HAProxy to an upgraded cluster.

Using a clustered structure also allows for freely adding data nodes as needed when your traffic inevitably grows.