Conf42 Cloud Native 2021 - Online

The Cloud: Application and Configuration

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Cloud is now well accepted in development practices. Projects with no cloud element are more the exception than the rule. However, this new paradigm requires adjustment for your workloads. It is not just about containerization, micro-service approach or managed services usage.

Code organisation must also reflect the adoption of this new paradigm.

Configuration is part of it. Traditionally thought as an operational element, it becomes more and more prevalent in an environment where high-scalability, distribution but also failures are the watchwords.

How to guarantee the consistency of our configuration amongst the thousands of instances that represents our applications ? How to automate the entire configuration lifecycle ? Should we even think about live configuration update? Questions that predate the cloud era but that have been exacerbated by the cloud arrival.

In this session we would like to introduce you with a Cloud-Native pattern called “Configuration layer”. Through theory and practice, we would like to demonstrate the elegance of the approach in a Cloud-Native environment to industrialize your configuration management.


  • Welcome to our session about the cloud native configuration. We will introduce a specific pattern that will help you to think your configuration in a cloud native context. Aside from the theory we will introduce you with practical cases and also a demo.
  • At Wescale we are a community of 50 experts on the cloud. We are not bound to any cloud provider nor any tools. We advise clients on the best platform, the best tool that we fit your own needs. We also provide training on those different tools and platforms.
  • In the cloud native application, we in fact deals with hundred if not thousands of instances. The challenge is to get an automatic way to guarantee that each spawn instances for a given version of given application version will spawn with the right application configuration version. The configuration layer pattern is this path.
  • Then our third layer will be the application runtime. For this one we will profit from standard behavior of Lisben config. You can specify a path or a URL for this file as a system properties. If the configuration is not consistent, we won't start.
  • Lightbane is advising us to always have an object model for our configuration. It will make the code using simple objects that are safe without side effects. Once the configuration is load and validated, your application can start without any risk of inconsistency in the configuration.


This transcript was autogenerated. To make changes, submit a PR.
Welcome to our session about the cloud native configuration. First of all, we would like to thank comfortitu forks for letting us introduce you with this topic that Joachim and I think often overlooked, and also to thank you for being with us right now. We know it's always very difficult to choose a session amongst these numerous topics. So like we said, in a cloud native context, when we consider application that are massively distributed, that are relying on application that are constantly updating, and also infrastructure that may fail, we need to embrace this uncertainty and we need to apply patterns in order to tackle this uncertainty. And configuration is no exception to that. However, we see that topic overlooked. And when we talk about cloud native patterns, we are more focused on services, the relationship between those services and even the data, but not so much on the configuration. And that's why we would like to introduce you with a specific pattern that will help you to think your configuration in a cloud native context. So first of all, we will introduce the configuration as a definition not only in the cloud native public, but also on premise, because both of which take the same approach, but we have the same definition but not the same approach. And we will see why we need a different approach in a cloud native context. And usually we meet two different approaches, but they come with shortage in term of dealing with the specific challenges on the cloud and we will see what those limits are and how we can overcome them with a specific pattern. That is the central point of this session. Aside from the theory we will introduce you with practical cases we see, we saw on the field and also a demo so that you can yourself think your configuration with inside your own business model. I am Ismail. I am a cloud developer at Wescale. And I am Joe Kim, a cloud native developer and coding architect at Wescale. So at Wescale we are a community of 50 experts on the cloud. More than expert, I would say passionate, passionate about what we do, that is to say, helping clients to think their business in the cloud. To think from day one where we will architect the business inside the cloud, but also to day two when we will help clients already on the cloud to enhance the existing projects in order to be more secure, to be more reliable, to be more available and so on. We are not bound to any cloud provider nor any tools. And we think that there is always context to think on. And usually what we will do is to discuss with the clients so that we can advise the best platform, the best tool that we fit your own needs. And to help this process, we also provide training on those different tools and platforms. Let's go back to our main topic, configuration. What is configuration? Configuration could be simply defined as the thing that will change between deploys that is likely to change, and we distinguish two kind of them. First one that is under our control, called application configuration, will tell how the application behaves. Third party services location. I'll go with them. Configuration feature enabling the second one is more about how the application is called IP address port, and this time we are not able to change it, or usually it's up to the cloud platform to provide it. Why are we considering it? Because in the cloud native application, we in fact deals with hundred if not thousands of instances. Still, we want this application to behave as a single logical entity, meaning we want hidden potency. It does not matter that I call the first instance or the 42nd one. If I have the same input, I should get the same result. We see two obstacles to that. First one is the history of your request, and we know that providing a stateless application will solve this issue. Second one is what if my instance 42 contain a different configuration than the instance one? I won't have the same result for sure. So the challenge is to get an automatic way to guarantee that each spawn instances for a given version of given application version will spawn with the right application configuration version. And this is not possible to consider a semi automatic ways, because unlike the good old days when an operator were connecting inside a virtual machine to upload the right configuration file, now we are dealing with ephemeral instances and even instances that we cannot connect to. So usually we can think two classical approaches. First one, the simplest one, is to embed inside the artifact that we deploy the configuration file with its values. That way we are sure that each instances spawn from this artifact will get the right configuration. Also, from developer point of view, it's easy to think that way because we hard code the values inside the artifact, and also because we have a central model of your configuration. And it's easy to think that way because it gives you a mean to know what your application needs to start. But from the operator point of view, we now have an artifact that is linked to an environment. The artifact meant for the dev environment won't be able to be deployed on the staging environment, so we break any kind of traceability between deployments, and also we break the trust between a developer and an operator, because we will fall into the problem of it works on my machine. So we need to reconciliate those two words and providing a single artifact would help. Twelve factor app with a third factor tells you to store the configuration inside the environment more precisely and how we see it implemented is to store the values of the configuration inside environment variable so that the application code could consume it. Problem is, although we have a single artifact now, we directly consume the environment variable and also we have a code that is spread with get of course, and prevent your developer to have a single logical view of your configuration. We need a third path, and the configuration layer pattern is this path. In this model, we have a single artifact that embeds a configuration model without its values. And values will be stored inside the environment. It would be up to the configuration layer to one, expose the values to the application and two, fetch those value previously to exposing fetch those values from the environment. So this is what we describe here we have the templates that represent our configuration model and whether it is deployed inside the first environment. A second one, it will have different inject, render different values. So now we have an applications that is protected from its environment. We do not consume it directly. We see that the configuration layers act as a single point of truth for the configuration values, and also the configuration layer is in charge to fetch the data that will constitute the configuration values. So we have to keep in mind that the configuration layer is conceptual. It's not a technology, it's more an association of libraries and patterns. If we talk about libraries, we know that we would have different kind of libraries according to the ecosystem we consider. And in this term we know that we can have different kind level of work to produce in order to implement configuration layer. So we saw the theory, now let's see the practice, in particular how to expose values to the configuration layer. We can expose it as always with environment variables, but we can also provide values through web servers that the configuration layer will be in charge to request and get value from. In particular, we have this technology printlock configuration that will expose you a web application, and under the hood it will be able to fetch data from different data store, whether it be a git repository, a blob store and a vault. For instance, note that the application foo here represented by this container, kubernetes pod, won't be able to communicate directly from with this environment nor with the web server. It's only the configuration layer that will serve as a single point of truth for the configuration failures, and we can see the configuration layer as a configuration gateway. In that sense, the previous example does not shows you how to fetch those values. It's more about how to store the configuration values. And with the client we implemented in a PHP ecosystem this pattern with the following initial situation. We had an application repository that were deployed through a sync operation on a non premise environment on a given server. Configuration was not embedded inside this repo, it was embedded inside a second repo that contained every configuration of all the applications of the clients applications. And the deployment process was taking into account the two deployments, the first the application code, and the second one the configuration repo, and doing the symbolic link, creating the symbolic link with the right files. So one obvious problem that was that we were exposing too much information. And second, we had no way as a developer to know the model of the configuration unless we have access to this configuration repo. So our first point was to implement a CI CD pipeline to replace the in house CLI tool that was in charge of the synchronization with the server, and also the creation of symbolic link to the configuration file. And what we can notice is that inside a pipeline environment we can do everything we want. In particular we can implement this concept of configuration layer. So first point was to set the template of the configuration inside the source code. This way we can, inside the pipeline environment, render it with the environment values that we provided. How? Thanks to a of file that we fetched from spring cloud config web server. And then a rendering process was in charge to set the values inside the template. And finally we managed these configuration files with the PHP files inside the final artifacts that we deployed on the environment. So we still have the drawbacks to produce packages that are related to a given environment. But we saw that we now have the configuration layer concept inside the pipeline environment, and we think that we can move this logic inside the true platform that in our case was fortunately kubernetes, into which we defined a ninit container into which we implemented a logic of fetching the values from the environment, and also the same spring cloud config server in order to render the template that was exposed inside the configuration volume. Once this staging was done, the application was allowed to start with the right configuration. So we can say that we can see that configuration layer is more about implementing the right tool and associating it with the right platform. And in theory it's accessible to every kind of ecosystem. But we may say that it's too much manual works and maybe it exists some libraries that will do the work anyway. And with Drakim we think that light bend is this kind of library that was not thought at the beginning as a configuration layer technology, but which is very adapted to the cases we just described. So now we will see an example with lightbane configuration about how to implement our three layers of configuration. Lightbane config is Java library that you can use in Java, Scala, Kotlin, etc. All JVM languages. It's a new configuration format at the same time, so it describes the Hokon format that is close to JSON. It is compatible with JSON. If you have a valid JSON, you have a valid hook on. But it comes with a lot of interesting failures, like the order of definitions that matters. Temporal units to make clear temporal values, like 30 seconds it is typed, so it's a real object like JSON configuration. And you may have references and a central entry point for all your configuration. Because once you ask Lightband config to load your configuration, all configuration files, system properties from the JVM and environment variable are visible in the same place in the config object. You can still separate your configuration file to keep the separation of concern and have only one entry point with includes, and we'll see how the standard orchestration of loading and merging will allow us to build three layers of configuration. For our first layer, we will use standard behavior of Laban config, that is the automatic loading of all files that are named reference conf at the root of the class pass. They are concatenated merged. So it's very important to have your own namespace to avoid conflict. But it's very useful because if you have a library, you will use this file to validate your config structure because you will have inside the full configuration with default values. If it's an application, we won't have all the structure, but only default values. Mandatory values without default won't be here, and the application will still access the configuration through the configuration layer that is the library config library here. Then our second layer will have another file,, that will be load programmatically as a fullback configuration for the third layer that we will see later. But because reference conf is always low priority, because it's always default values, the application fixed conf will contain values that can overwrite some default and some values that can complete what is missing in reference. Typically you will put in this file, you will put algorithm parameters, business parameters, everything that is tied to the behavior of one version of your artifact and this file, very important, like the reference, it will be inside your artifact. So it will be shipped with your applications if you want in the docker image, but the preference inside the jar itself because it is tied to a version of your application. All that is inside will not vary depending on the environment, but for more flexibility you can already include references to environment variables. Then our third layer will be the application file, this one. For this one we will profit from standard behavior of Lisben config. You can specify a path or a URL for this file as a system properties. That is, when you launch your JVM, you can specify uppercase d flags that are system properties and one of them will be the path of the configuration file. That will allow us to write a code without a specific reference to this file. And this is very important that we don't have a specific reference to this file because it comes from the environment. In our example it will be on an external, external disk, it will be mount like volume inside our container. But you may put it on a config server and specify it as a URL. And basically we will put inside everything that depends on the environment. You can put hostname ports, sizing parameters, technical parameters, depending on the environment or arguments that you would pass to your application to specialize the instance. Then we'll see how to implement it. So I will show you scalar application. What do we have here? Here we have the reference confile. So because it's an application I will but only default values. And here you can see what you can do with lightband config, especially hocon formats. It's like JSON, but you can use dot syntax to avoid using a cascade of curly braces. For example, this namespace is equivalent in JSON format of config. Demo object inside the scratchpad, object inside the j arrow object inside object. But you can shorten it. You have an application name that is fixed, it's a default value. It's okay. You have an instance id that is defined only if the hostname variable is defined because of the question mark. If the hostname variable is not defined, instance id won't be defined. And for this example we decide to imagine that our applications is consuming and producing messages on the Kafka cluster. So we want to structure our Kafka configuration. We put a default, we have a consumer configuration object with the default session timeout. We have some default producer parameters. And so let's say that these default parameters makes the application works nearly everywhere except if you want to override it for specific use case. So these are default values. Then in the same jar we will have So here the example, very simple. I don't have any business example values, but let's imagine that all those variables are tied to the application version, so we want to redefine the application name. So this line overrides the one in the config reference confile, but only if the up name environment variable is defined. We add another environment variable for instances id. So if osname isn't here it will try instance id because the definition are read in order. So the last one will override the first one and then we add the group id for the consumer group and we add a client id for message production. And we decide that for example the consumer group is linked to the application name. Here it's a reference to the upname attribute because all instance of the application will be in the same consumer group, and because each instance of the application will have its own producer. I create client id name from the app name and the instance id. So you may have noticed that the instance id is not always here because for now we define it only if some environment variable are here. That means that if we start our application with no environment variable, this reference won't work and lightband config will throw an exception and so the application won't start. That's exactly what we want. If the configuration is not consistent, we won't start. Another file is outside our application. This is the configuration file we pass at start time. This is application runtime conf. So here you will find some attributes that will be merged inside the Kafka consumer structure. The host names the port number, and for this example we decide to keep the default port. But in a real example you may want to put a reference to another element variable or a fixed number. And here I copy the bootstrap server array from consumer to the producer bootstrap server attribute because I consume and produce on the same cluster. So here we see all features of lightband config and we will see how it is cloud in our application. So it is very simple, it loads the configuration, display it, and then that's all. The first interesting line is this one. So with this line you will cloud reference conf, you will load the application, the applicationfig conf, and everything will be overridden with JVM system properties. Because this is convention in lightband config. The load method will load the reference conf. It's mandatory, it will always be done. And because we don't specify a path for the config file, it will use the system properties that specify the path or the URL of the default file config. That is for us a path that point to a volume that is mounted and with a specific name. Application runtime conf and as we said in slide about layer two, we won't define the application fix conf that is inside the application jar as a fallback. So because reference Comf is always low priority, even with fallback, the reference conf will be used after a while. So here we have that is high priority. Then if we don't find our variable inside this application file, we will look into the and then if you don't find variable inside this file, we will look inside reference. And because JVM system properties are overriding everything, they are very interesting to be used for overriding some values of the configuration at launch time. It's very interesting. So here we display the configuration and we map it then here to configuration to a configuration model. Lightbane is advising us to always have an object model for our configuration and map it to the file. Why? Because it will make the code using simple objects that are safe without side effects. And so once the configuration is load and validated, your application can start without any risk of inconsistency in the configuration. That's very important. So here, because we are in scalar, we are using scalar. The pure config library is mapping automatically the file structure to my model. So we'll see quickly how the model is done. So here you can see that it is like the file with up name and instance id. KFC configuration at the first level. Then inside KfK configuration we have the consumer configuration, the producer configuration. Then we have the bootstrap servers that are inside an array. And if occurrence of the each occurrence of the array is a host per instance, with a host and a port, my model is tightly linked to the file structure and that's very important for validation and for our application. So our application, not only the application is loading the configuration from only one entry point, it is mapped to a model so that the configuration became totally transparent and without any complicated method or inconsistent method like call to get off. Check if the variable is defined, check the type at runtime at the time the variable is used. All those things have been checked just before. So I will show you a run of your application. So I should be in the docker directory. I will show you the docker file quickly to understand how it is done. How here you see that I'm using system property to specify the path. So the config path will point to a config directory. That is the volumes that we will and config file that is application Then I will run my application with a script that will launch docker. And on this line you see that I am using a host directory to mount a configuration volume inside my container. So I can put everything that is depending on the environment in the host directory. And what we are going to get is this. So we have a lot of things here because I activated a kind of debug display so that we can understand how the failures are merged. So for example have here our original namespace. Then we have comments from the lightband config libraries that are telling us that application name is coming from the line two of the reference confile. So no app name of our own variable was here. So the default app name has been kept, the instance id has been defined, so we can deduce that another variable defines it defined it. And in this case this is a hostname variable that is defined to the container id. Then the Kafka structure has been merged from a lot of files you may notice to run application runtime comf, applications conf and the reference conf. And if we go deeper into this structure, we can see that each variable has an origin, some coming from application, some coming from reference conf. So everything is exactly what we expected. We have only one big configuration for all files, even if the application configuration, our artifact, was not complete, and even if we specified the configuration, the last part of the configuration at the last minute, and the result is here we have a display of our configuration model. So I hope it was clear for you. And I write an article about Liband configuration on blog. Ismail wrote about the configuration in the cloud on the same blog. And then I can't let you go without saying that we are hiring. So if you are interested about what we are doing, if you are passionate, you can join us. And the new things is that we are creating a new remote agency. So if you are not near Paris or near Nance, you still can join us from all around France in this new remote agency. That's all folks, thank you for your attention. Thank you for everything. And we hope that this session will inspire you to apply this new pattern or this pattern you already know on your own business context. And we also hope to see you on the Discord channel so that we can further discuss about these topics. See you, see you.

Ismael Hommani

Cloud Native Developer @ WeScale

Ismael Hommani's LinkedIn account

Joachim Rousseau

Coding Architect @ WeScale

Joachim Rousseau's LinkedIn account

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