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justify overpaying for ready-made infrastructure and capacities by their extensibility (relevant for startups when the load growth is not predictable). For the implementation of such projects, they generally hire highly qualified specialists of a wide range to implement non-standard solutions, where the infrastructure is already just a tool, and there are simply no specialists in it. The developers are entrusted with the functions of designing the project as a whole, as a whole, and not a program in isolation from the infrastructure. These are mainly foreign companies that are ready to pay well for the labor of valuable employees.

      For deployment, we will use Kubernetes to counter the vender lock, when the project infrastructure is tied to the API of a specific cloud provider and will not allow moving to other or our own clouds without significant changes in the application itself. Kubernetes is supported by Amazon AWS, Google Cloud, Microsoft Azure, on-premises installation of one instance using MiniKube.

      We will use Google Cloud, for the current 2018 it provides free use for one year of limited resources ($ 300), while there are limits that can be viewed in the IAM and Administration -> Quotas menu . It is important to note that cloud providers do not provide tariffs in the modern range, but provide tariffs for the use of certain capacities, that is, the site is visited little – we pay little, it is difficult to process a lot of data – we pay a lot. For this reason, when the company's computing power needs are predictable (not a startup), it may be advisable to use its own capabilities for a constant load, which can be economically feasible, without risking limited computing power.

      And so we go to cloud.google.com, register, bind a debit card with a minimum balance and go to the console.cloud.google.com console, where you can take a tutorial on the interface for general familiarization. In the menu, click the Payment item: I have $ 300 untouched demo money and 356 days left (funds are not debited in real time).

      If you look at it as a basis for Back-End for mobile development (MBasS, Mobile backend as a service), then it is provided by different providers: Google Firebase, AWS Mobile, Azure Mobile

      Google App Engine

      Cluster creation via WEB interface

      Let's first check the restrictions (quotas) Menu -> Products -> IAM and administration -> Quotas, and if you are on a test account, then Static IP addresses will be equal to 1, then the balancer will not be able to create and you will have to delete the cluster. Let's create a cluster in Menu – Resources – Kubernetes Engine in three replicas of the micromachine and the latest version of Kubernetes. In the lower left corner in the Marketplace item, create 2 NGINX instances. After creating the cluster, click on the Services tab and go to the IP address.

      Marketplace: Networking, Free, Kubernetes Applications: NGINX Let's create a custom standard-cluster- NGINX cluster, choosing a minimum of CPU and RAM, 2 nodes instead of 3 and the latest version of Kubernetes (I chose 1.11.3, and my code will be compatible with – at least 1.10). In the Menu – Resources – Kubernetes Engine in the Cluster tab, click the Connect button. Cluster management on the command line is carried out using the cubectl command, you can read about it in the documentation: https://kubernetes.io/docs/reference/kubectl/overview/ and the list at https://gist.github.com/ipedrazas/95391ffd88190bea94ca188d3d2c1cbe …

      Creating a virtual machine:

      You can create a software project, but you can only use it on a paid account:

      NAME_PROJECT = bitrix_12345;

      NAME_CLUSTER = bitrix;

      gcloud projects create $ NAME_CLUSTER –name $ NAME_CLUSTER;

      gcloud config set project $ NAME_CLUSTER;

      gcloud projects list;

      A few subtleties: the –zone key is required and put at the end, the disk should not be less than 10Gb, and the type of machines can be taken from https://cloud.google.com/compute/docs/machine-types. If we have only one replica, then by default a minimal configuration for testing is created:

      gcloud container clusters create $ NAME_CLUSTER –zone europe-north1-a

      You can see it in the admin panel by expanding the drop-down list in the header and opening the All projects tab.

      gcloud projects delete NAME_PROJECT;

      , if more – standard, the parameters of which we will edit:

      $ gcloud container clusters create mycluster \

      –-machine-type = n1-standard-1 –disk-size = 10GB –image-type ubuntu \

      –-scopes compute-rw, gke-default \

      –-machine-type = custom-1-1024 \

      –-cluster-version = 1.11 –enable-autoupgrade \

      –-num-nodes = 1 –enable-autoscaling –min-nodes = 1 –max-nodes = 2 \

      –-zone europe-north1-a

      The –enable-autorepair key starts the work of monitoring the availability of the node, and if it crashes, it will be recreated. The key requires a Kubernetes version of at least 1.11, and at the time of this writing, the default version is 1.10 and therefore you need to set it with a key, for example, –cluster-version = 1.11.4-gke.12 . But you can fix only the major version –cluster-version = 1.11 and set the auto-update version –enable-autoupgrade . We will also set auto-assuring the number of nodes if there are not enough resources: –num-nodes = 1 –min-nodes = 1 –max-nodes = 2 –enable-autoscaling .

      Now let's talk about virtual cores and RAM. By default, the n1-standart-1 machine is raised , which has one virtual core and 3.5Gb of RAM, in triplicate, which together gives three virtual cores and 10.5Gb of RAM. It is important that the cluster has only at least two virtual processor cores, otherwise, formally, according to the limits for Kubernetes system containers, they will not be enough for full operation (containers, for example, system containers, may not rise). I will take two nodes, one core each and the total number of cores will be two. The same situation is with RAM, 1Gb (1024Mb) of RAM was enough for me to raise a container with NGINX, but to raise a container with LAMP (Apache MySQL PHP) is no longer there, the system service kube-dns-548976df6c- mlljx , which is responsible for DNS in the pod. Despite the fact that it is not vitally important and will not be useful to us, the next time it may not rise up a more important one instead. It is important to note that my cluster with 1Gb was normally raised and everything was fine, my total volume of 2Gb turned out to be a borderline value. I set 1080Mb (1.25Gb), taking into account that the minimum level of RAM is 256Mb (0.25Gb) and my volume must be a multiple of it and be at least 1Gb for one core. As a result, the cluster has 2 cores and 2.5Gb instead of 3 cores and 10.5Gb, which is a significant optimization of resources and prices on a paid account.

      Now we need to connect to the server. We already have the key on the server $ {HOME} /. Kube / config and now we just need to log in:

      $ gcloud container clusters get-credentials b –zone europe-north1-a –project essch

      $ kubectl port-forward Nginxlamp-74c8b5b7f-d2rsg 8080: 8080

      Forwarding from 127.0.0.1:8080 -> 8080

      Forwarding from [:: 1]: 8080 -> 8080

      $ google-chrome http: // localhost: 8080 # this won't work in Google Shell

      $ kubectl expose Deployment Nginxlamp –type = "LoadBalancer" –port = 8080

      To use kubectl locally, you need to install gcloud and use it to install kubectl using the gcloud components install kubectl command , but let's not complicate the first steps for now.

      In the Services section of the admin panel, POD will be available not only through the front-end balancer service, but also through the internal balancer Deployment. Although it will be saved after the re-creation, the config is more maintainable and obvious.

      It is also possible to make it possible to adjust the number of nodes in automatic mode depending on the load, for example, the number of containers with established resource requirements, using the keys –enable-autoscaling –min-nodes = 1 –max-nodes = 2 .

      Simple cluster in GCP

      There are two ways to create a cluster: through the Google Cloud Platform graphical interface or through its API with

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