Group items tagged

Auto DevOps works with any Kubernetes cluster;

using the Docker or Kubernetes executor, with privileged mode enabled.

Base domain (needed for Auto Review Apps and Auto Deploy)

Kubernetes (needed for Auto Review Apps, Auto Deploy, and Auto Monitoring)

Prometheus (needed for Auto Monitoring)

scrape your Kubernetes cluster.

project level as a variable: KUBE_INGRESS_BASE_DOMAIN

A wildcard DNS A record matching the base domain(s) is required

Once set up, all requests will hit the load balancer, which in turn will route them to the Kubernetes pods that run your application(s).

review/ (every environment starting with review/)

staging

production

need to define a separate KUBE_INGRESS_BASE_DOMAIN variable for all the above based on the environment.

Continuous deployment to production: Enables Auto Deploy with master branch directly deployed to production.

Continuous deployment to production using timed incremental rollout

Automatic deployment to staging, manual deployment to production

Auto Build creates a build of the application using an existing Dockerfile or Heroku buildpacks.

If a project’s repository contains a Dockerfile, Auto Build will use docker build to create a Docker image.

Each buildpack requires certain files to be in your project’s repository for Auto Build to successfully build your application.

Auto Test automatically runs the appropriate tests for your application using Herokuish and Heroku buildpacks by analyzing your project to detect the language and framework.

Auto Code Quality uses the Code Quality image to run static analysis and other code checks on the current code.

Static Application Security Testing (SAST) uses the SAST Docker image to run static analysis on the current code and checks for potential security issues.

Dependency Scanning uses the Dependency Scanning Docker image to run analysis on the project dependencies and checks for potential security issues.

License Management uses the License Management Docker image to search the project dependencies for their license.

Vulnerability Static Analysis for containers uses Clair to run static analysis on a Docker image and checks for potential security issues.

Review Apps are temporary application environments based on the branch’s code so developers, designers, QA, product managers, and other reviewers can actually see and interact with code changes as part of the review process. Auto Review Apps create a Review App for each branch. Auto Review Apps will deploy your app to your Kubernetes cluster only. When no cluster is available, no deployment will occur.

The Review App will have a unique URL based on the project ID, the branch or tag name, and a unique number, combined with the Auto DevOps base domain.

Your apps should not be manipulated outside of Helm (using Kubernetes directly).

Dynamic Application Security Testing (DAST) uses the popular open source tool OWASP ZAProxy to perform an analysis on the current code and checks for potential security issues.

Auto Browser Performance Testing utilizes the Sitespeed.io container to measure the performance of a web page.

add the paths to a file named .gitlab-urls.txt in the root directory, one per line.

After a branch or merge request is merged into the project’s default branch (usually master), Auto Deploy deploys the application to a production environment in the Kubernetes cluster, with a namespace based on the project name and unique project ID

Auto Deploy doesn’t include deployments to staging or canary by default, but the Auto DevOps template contains job definitions for these tasks if you want to enable them.

For internal and private projects a GitLab Deploy Token will be automatically created, when Auto DevOps is enabled and the Auto DevOps settings are saved.

If the GitLab Deploy Token cannot be found, CI_REGISTRY_PASSWORD is used. Note that CI_REGISTRY_PASSWORD is only valid during deployment.

If present, DB_INITIALIZE will be run as a shell command within an application pod as a helm post-install hook.

a post-install hook means that if any deploy succeeds, DB_INITIALIZE will not be processed thereafter.

DB_MIGRATE will be run as a shell command within an application pod as a helm pre-upgrade hook.

Once your application is deployed, Auto Monitoring makes it possible to monitor your application’s server and response metrics right out of the box.

annotate the NGINX Ingress deployment to be scraped by Prometheus using prometheus.io/scrape: "true" and prometheus.io/port: "10254"

While Auto DevOps provides great defaults to get you started, you can customize almost everything to fit your needs; from custom buildpacks, to Dockerfiles, Helm charts, or even copying the complete CI/CD configuration into your project to enable staging and canary deployments, and more.

Auto DevOps uses Helm to deploy your application to Kubernetes.

make use of the HELM_UPGRADE_EXTRA_ARGS environment variable to override the default values in the values.yaml file in the default Helm chart.

specify the use of a custom Helm chart per environment by scoping the environment variable to the desired environment.

Your additions will be merged with the Auto DevOps template using the behaviour described for include

copy and paste the contents of the Auto DevOps template into your project and edit this as needed.

In order to support applications that require a database, PostgreSQL is provisioned by default.

Set up the replica variables using a project variable and scale your application by just redeploying it!

You should not scale your application using Kubernetes directly.

Some applications need to define secret variables that are accessible by the deployed application.

Auto DevOps pipelines will take your application secret variables to populate a Kubernetes secret.

Environment variables are generally considered immutable in a Kubernetes pod.

If STAGING_ENABLED is defined in your project (e.g., set STAGING_ENABLED to 1 as a CI/CD variable), then the application will be automatically deployed to a staging environment, and a production_manual job will be created for you when you’re ready to manually deploy to production.

If CANARY_ENABLED is defined in your project (e.g., set CANARY_ENABLED to 1 as a CI/CD variable) then two manual jobs will be created: canary which will deploy the application to the canary environment production_manual which is to be used by you when you’re ready to manually deploy to production.

If INCREMENTAL_ROLLOUT_MODE is set to manual in your project, then instead of the standard production job, 4 different manual jobs will be created: rollout 10% rollout 25% rollout 50% rollout 100%

To start a job, click on the play icon next to the job’s name.

not all buildpacks support Auto Test yet

When a project has been marked as private, GitLab’s Container Registry requires authentication when downloading containers.

Authentication credentials will be valid while the pipeline is running, allowing for a successful initial deployment.

We strongly advise using GitLab Container Registry with Auto DevOps in order to simplify configuration and prevent any unforeseen issues.

Pod-to-Service communications

External-to-Service communications

sharing machines requires ensuring that two applications do not try to use the same ports.

Dynamic port allocation brings a lot of complications to the system

do not need to explicitly create links between Pods

almost never need to deal with mapping container ports to host ports.

pods on a node can communicate with all pods on all nodes without NAT

agents on a node (e.g. system daemons, kubelet) can communicate with all pods on that node

pods in the host network of a node can communicate with all pods on all nodes without NAT

containers within a Pod share their network namespaces - including their IP address

containers within a Pod must coordinate port usage

“IP-per-pod” model.

request ports on the Node itself which forward to your Pod (called host ports), but this is a very niche operation

The Pod itself is blind to the existence or non-existence of host ports.

AOS is an Intent-Based Networking system that creates and manages complex datacenter environments from a simple integrated platform.

Cisco Application Centric Infrastructure offers an integrated overlay and underlay SDN solution that supports containers, virtual machines, and bare metal servers.

AOS Reference Design currently supports Layer-3 connected hosts that eliminate legacy Layer-2 switching problems.

The AWS VPC CNI offers integrated AWS Virtual Private Cloud (VPC) networking for Kubernetes clusters.

users can apply existing AWS VPC networking and security best practices for building Kubernetes clusters.

Using this CNI plugin allows Kubernetes pods to have the same IP address inside the pod as they do on the VPC network.

The CNI allocates AWS Elastic Networking Interfaces (ENIs) to each Kubernetes node and using the secondary IP range from each ENI for pods on the node.

Big Cloud Fabric is a cloud native networking architecture, designed to run Kubernetes in private cloud/on-premises environments.

Cilium is L7/HTTP aware and can enforce network policies on L3-L7 using an identity based security model that is decoupled from network addressing.

CNI-Genie is a CNI plugin that enables Kubernetes to simultaneously have access to different implementations of the Kubernetes network model in runtime.

CNI-Genie also supports assigning multiple IP addresses to a pod, each from a different CNI plugin.

cni-ipvlan-vpc-k8s contains a set of CNI and IPAM plugins to provide a simple, host-local, low latency, high throughput, and compliant networking stack for Kubernetes within Amazon Virtual Private Cloud (VPC) environments by making use of Amazon Elastic Network Interfaces (ENI) and binding AWS-managed IPs into Pods using the Linux kernel’s IPvlan driver in L2 mode.

to be straightforward to configure and deploy within a VPC

Contiv provides configurable networking

Contrail, based on Tungsten Fabric, is a truly open, multi-cloud network virtualization and policy management platform.

DANM is a networking solution for telco workloads running in a Kubernetes cluster.

Flannel is a very simple overlay network that satisfies the Kubernetes requirements.

Any traffic bound for that subnet will be routed directly to the VM by the GCE network fabric.

sysctl net.ipv4.ip_forward=1

Jaguar provides overlay network using vxlan and Jaguar CNIPlugin provides one IP address per pod.

Knitter is a network solution which supports multiple networking in Kubernetes.

Kube-OVN is an OVN-based kubernetes network fabric for enterprises.

Kube-router provides a Linux LVS/IPVS-based service proxy, a Linux kernel forwarding-based pod-to-pod networking solution with no overlays, and iptables/ipset-based network policy enforcer.

If you have a “dumb” L2 network, such as a simple switch in a “bare-metal” environment, you should be able to do something similar to the above GCE setup.

Multus is a Multi CNI plugin to support the Multi Networking feature in Kubernetes using CRD based network objects in Kubernetes.

NSX-T can provide network virtualization for a multi-cloud and multi-hypervisor environment and is focused on emerging application frameworks and architectures that have heterogeneous endpoints and technology stacks.

NSX-T Container Plug-in (NCP) provides integration between NSX-T and container orchestrators such as Kubernetes

Nuage uses the open source Open vSwitch for the data plane along with a feature rich SDN Controller built on open standards.

OpenVSwitch is a somewhat more mature but also complicated way to build an overlay network

OVN is an opensource network virtualization solution developed by the Open vSwitch community.

Project Calico is an open source container networking provider and network policy engine.

Calico provides a highly scalable networking and network policy solution for connecting Kubernetes pods based on the same IP networking principles as the internet

Calico can be deployed without encapsulation or overlays to provide high-performance, high-scale data center networking.

Romana is an open source network and security automation solution that lets you deploy Kubernetes without an overlay network

Weave Net runs as a CNI plug-in or stand-alone. In either version, it doesn’t require any configuration or extra code to run, and in both cases, the network provides one IP address per pod - as is standard for Kubernetes.

The network model is implemented by the container runtime on each node.

In-use IP addresses

run one or two control plane instances per failure zone, scaling those instances vertically first and then scaling horizontally after reaching the point of falling returns to (vertical) scale.

Kubernetes nodes do not automatically steer traffic towards control-plane endpoints that are in the same failure zone

store Event objects in a separate dedicated etcd instance.

start and configure additional etcd instance

Kubernetes resource limits help to minimize the impact of memory leaks and other ways that pods and containers can impact on other components.

Addons' default limits are typically based on data collected from experience running each addon on small or medium Kubernetes clusters.

Many addons scale horizontally - you add capacity by running more pods

The VerticalPodAutoscaler can run in recommender mode to provide suggested figures for requests and limits.

Some addons run as one copy per node, controlled by a DaemonSet: for example, a node-level log aggregator.

VerticalPodAutoscaler is a custom resource that you can deploy into your cluster to help you manage resource requests and limits for pods.

The cluster autoscaler integrates with a number of cloud providers to help you run the right number of nodes for the level of resource demand in your cluster.

The addon resizer helps you in resizing the addons automatically as your cluster's scale changes.

checked automatically but requires human intervention to manually and strategically trigger the deployment of the changes.

instead of deploying your application manually, you set it to be deployed automatically.

.gitlab-ci.yml, located in the root path of your repository

all the scripts you add to the configuration file are the same as the commands you run on a terminal in your computer.

GitLab will detect it and run your scripts with the tool called GitLab Runner, which works similarly to your terminal.

declare examples using the it method

an example group is a class in which the block passed to describe is evaluated

The blocks passed to it are evaluated in the context of an instance of that class

nested groups using the describe or context methods

can declare example groups using either describe or context

can declare examples within a group using any of it, specify, or example

Nearly anything that can be declared within an example group can be declared within a shared example group.

shared_context and include_context.

When a class is passed to describe, you can access it from an example using the described_class method

rspec-core stores a metadata hash with every example and group

Example groups are defined by a describe or context block, which is eagerly evaluated when the spec file is loaded

Examples -- typically defined by an it block -- and any other blocks with per-example semantics -- such as a before(:example) hook -- are evaluated in the context of an instance of the example group class to which the example belongs.