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張 旭

Kubernetes Deployments: The Ultimate Guide - Semaphore - 1 views

semaphoreci.com/...kubernetes-deployment

docker kubernetes devops system

shared by 張 旭 on 21 Oct 19 - No Cached
  • Continuous integration gives you confidence in your code. To extend that confidence to the release process, your deployment operations need to come with a safety belt.
  • these Kubernetes objects ensure that you can progressively deploy, roll back and scale your applications without downtime.
  • A pod is just a group of containers (it can be a group of one container) that run on the same machine, and share a few things together.
  • ...34 more annotations...
  • the containers within a pod can communicate with each other over localhost
  • From a network perspective, all the processes in these containers are local.
  • we can never create a standalone container: the closest we can do is create a pod, with a single container in it.
  • Kubernetes is a declarative system (by opposition to imperative systems).
  • All we can do, is describe what we want to have, and wait for Kubernetes to take action to reconcile what we have, with what we want to have.
  • In other words, we can say, “I would like a 40-feet long blue container with yellow doors“, and Kubernetes will find such a container for us. If it doesn’t exist, it will build it; if there is already one but it’s green with red doors, it will paint it for us; if there is already a container of the right size and color, Kubernetes will do nothing, since what we have already matches what we want.
  • The specification of a replica set looks very much like the specification of a pod, except that it carries a number, indicating how many replicas
  • What happens if we change that definition? Suddenly, there are zero pods matching the new specification.
  • the creation of new pods could happen in a more gradual manner.
  • the specification for a deployment looks very much like the one for a replica set: it features a pod specification, and a number of replicas.
  • Deployments, however, don’t create or delete pods directly.
  • When we update a deployment and adjust the number of replicas, it passes that update down to the replica set.
  • When we update the pod specification, the deployment creates a new replica set with the updated pod specification. That replica set has an initial size of zero. Then, the size of that replica set is progressively increased, while decreasing the size of the other replica set.
  • we are going to fade in (turn up the volume) on the new replica set, while we fade out (turn down the volume) on the old one.
  • During the whole process, requests are sent to pods of both the old and new replica sets, without any downtime for our users.
  • A readiness probe is a test that we add to a container specification.
  • Kubernetes supports three ways of implementing readiness probes:Running a command inside a container;Making an HTTP(S) request against a container; orOpening a TCP socket against a container.
  • When we roll out a new version, Kubernetes will wait for the new pod to mark itself as “ready” before moving on to the next one.
  • If there is no readiness probe, then the container is considered as ready, as long as it could be started.
  • MaxSurge indicates how many extra pods we are willing to run during a rolling update, while MaxUnavailable indicates how many pods we can lose during the rolling update.
  • Setting MaxUnavailable to 0 means, “do not shutdown any old pod before a new one is up and ready to serve traffic“.
  • Setting MaxSurge to 100% means, “immediately start all the new pods“, implying that we have enough spare capacity on our cluster, and that we want to go as fast as possible.
  • kubectl rollout undo deployment web
  • the replica set doesn’t look at the pods’ specifications, but only at their labels.
  • A replica set contains a selector, which is a logical expression that “selects” (just like a SELECT query in SQL) a number of pods.
  • it is absolutely possible to manually create pods with these labels, but running a different image (or with different settings), and fool our replica set.
  • Selectors are also used by services, which act as the load balancers for Kubernetes traffic, internal and external.
  • internal IP address (denoted by the name ClusterIP)
  • during a rollout, the deployment doesn’t reconfigure or inform the load balancer that pods are started and stopped. It happens automatically through the selector of the service associated to the load balancer.
  • a pod is added as a valid endpoint for a service only if all its containers pass their readiness check. In other words, a pod starts receiving traffic only once it’s actually ready for it.
  • In blue/green deployment, we want to instantly switch over all the traffic from the old version to the new, instead of doing it progressively
  • We can achieve blue/green deployment by creating multiple deployments (in the Kubernetes sense), and then switching from one to another by changing the selector of our service
  • kubectl label pods -l app=blue,version=v1.5 status=enabled
  • kubectl label pods -l app=blue,version=v1.4 status-
  • 張 旭 on 21 Oct 19
     
    "Continuous integration gives you confidence in your code. To extend that confidence to the release process, your deployment operations need to come with a safety belt."
張 旭

Intro to deployment strategies: blue-green, canary, and more - DEV Community - 0 views

dev.to/...blue-green-canary-and-more-3a3

devops deploy system micro service

shared by 張 旭 on 23 Nov 18 - No Cached
  • using a service-oriented architecture and microservices approach, developers can design a code base to be modular.
  • Modern applications are often distributed and cloud-based
  • different release cycles for different components
  • ...20 more annotations...
  • the abstraction of the infrastructure layer, which is now considered code. Deployment of a new application may require the deployment of new infrastructure code as well.
  • "big bang" deployments update whole or large parts of an application in one fell swoop.
  • Big bang deployments required the business to conduct extensive development and testing before release, often associated with the "waterfall model" of large sequential releases.
  • Rollbacks are often costly, time-consuming, or even impossible.
  • In a rolling deployment, an application’s new version gradually replaces the old one.
  • new and old versions will coexist without affecting functionality or user experience.
  • Each container is modified to download the latest image from the app vendor’s site.
  • two identical production environments work in parallel.
  • Once the testing results are successful, application traffic is routed from blue to green.
  • In a blue-green deployment, both systems use the same persistence layer or database back end.
  • You can use the primary database by blue for write operations and use the secondary by green for read operations.
  • Blue-green deployments rely on traffic routing.
  • long TTL values can delay these changes.
  • The main challenge of canary deployment is to devise a way to route some users to the new application.
  • Using an application logic to unlock new features to specific users and groups.
  • With CD, the CI-built code artifact is packaged and always ready to be deployed in one or more environments.
  • Use Build Automation tools to automate environment builds
  • Use configuration management tools
  • Enable automated rollbacks for deployments
  • An application performance monitoring (APM) tool can help your team monitor critical performance metrics including server response times after deployments.
張 旭

Auto DevOps | GitLab - 0 views

docs.gitlab.com/...index.html

gitlab auto devops CI deploy

shared by 張 旭 on 02 Aug 19 - No Cached
  • Auto DevOps provides pre-defined CI/CD configuration which allows you to automatically detect, build, test, deploy, and monitor your applications
  • Just push your code and GitLab takes care of everything else.
  • Auto DevOps will be automatically disabled on the first pipeline failure.
  • ...78 more annotations...
  • Your project will continue to use an alternative CI/CD configuration file if one is found
  • 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.
  • Review apps are deployed using the auto-deploy-app chart with Helm, which can be customized.
  • 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.
  • Apps are deployed using the auto-deploy-app chart with Helm.
  • 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.
    • 張 旭
      張 旭 on 02 Aug 19
       
      如果專案類型不同，就要去查 buildpacks 裡面如何叫用該指令，例如 laravel 的 migration
    • 張 旭
      張 旭 on 02 Aug 19
       
      如果是自己的 Dockerfile 建立起來的，看來就不用鳥 buildpacks 的作法
  • 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"
  • If you are also using Auto Review Apps and Auto Deploy and choose to provide your own Dockerfile, make sure you expose your application to port 5000 as this is the port assumed by the default Helm chart.
  • 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.
  • If your project has a Dockerfile in the root of the project repo, Auto DevOps will build a Docker image based on the Dockerfile rather than using buildpacks.
  • Auto DevOps uses Helm to deploy your application to Kubernetes.
  • Bundled chart - If your project has a ./chart directory with a Chart.yaml file in it, Auto DevOps will detect the chart and use it instead of the default one.
  • Create a project variable AUTO_DEVOPS_CHART with the URL of a custom chart to use or create two project variables AUTO_DEVOPS_CHART_REPOSITORY with the URL of a custom chart repository and AUTO_DEVOPS_CHART with the path to the chart.
  • 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.
    • 張 旭
      張 旭 on 02 Aug 19
       
      Auto DevOps 就是一套人家寫好好的傳便便的 .gitlab-ci.yml
  • 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 detects variables where the key starts with K8S_SECRET_ and make these prefixed variables available to the deployed application, as environment variables.
  • 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 you update an application secret without changing any code then manually create a new pipeline, you will find that any running application pods will not have the updated secrets.
  • Variables with multiline values are not currently supported
  • The normal behavior of Auto DevOps is to use Continuous Deployment, pushing automatically to the production environment every time a new pipeline is run on the default branch.
  • 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%
  • The percentage is based on the REPLICAS variable and defines the number of pods you want to have for your deployment.
  • To start a job, click on the play icon next to the job’s name.
  • Once you get to 100%, you cannot scale down, and you’d have to roll back by redeploying the old version using the rollback button in the environment page.
  • With INCREMENTAL_ROLLOUT_MODE set to manual and with STAGING_ENABLED
  • 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.
  • After the pipeline completes, Kubernetes will no longer be able to access the Container Registry.
  • We strongly advise using GitLab Container Registry with Auto DevOps in order to simplify configuration and prevent any unforeseen issues.
張 旭

How to configure a Kubernetes Multi-Pod Deployment - Stack Overflow - 0 views

stackoverflow.com/...ubernetes-multi-pod-deployment

kubernetes deploy

shared by 張 旭 on 02 Oct 21 - No Cached
  • A Deployment is meant to represent a single group of PODs fulfilling a single purpose together.
  • Deployments are meant to contain stateless services. If you need to store a state you need to create StatefulSet instead
  • 張 旭 on 02 Oct 21
     
    "A Deployment is meant to represent a single group of PODs fulfilling a single purpose together."
張 旭

Kubernetes - Traefik - 0 views

docs.traefik.io/...kubernetes

kubernetes networking network ingress traefik

shared by 張 旭 on 04 Jul 19 - No Cached
  • allow fine-grained control of Kubernetes resources and API.
  • authorize Traefik to use the Kubernetes API.
  • namespace-specific RoleBindings
  • ...29 more annotations...
  • a single, global ClusterRoleBinding.
  • RoleBindings per namespace enable to restrict granted permissions to the very namespaces only that Traefik is watching over, thereby following the least-privileges principle.
  • The scalability can be much better when using a Deployment
  • you will have a Single-Pod-per-Node model when using a DaemonSet,
  • DaemonSets automatically scale to new nodes, when the nodes join the cluster
  • DaemonSets ensure that only one replica of pods run on any single node.
  • DaemonSets can be run with the NET_BIND_SERVICE capability, which will allow it to bind to port 80/443/etc on each host. This will allow bypassing the kube-proxy, and reduce traffic hops.
  • start with the Daemonset
  • The Deployment has easier up and down scaling possibilities.
  • The DaemonSet automatically scales to all nodes that meets a specific selector and guarantees to fill nodes one at a time.
  • Rolling updates are fully supported from Kubernetes 1.7 for DaemonSets as well.
  • provide the TLS certificate via a Kubernetes secret in the same namespace as the ingress.
  • If there are any errors while loading the TLS section of an ingress, the whole ingress will be skipped.
  • create secret generic
  • Name-based Routing
  • Path-based Routing
  • Traefik will merge multiple Ingress definitions for the same host/path pair into one definition.
  • specify priority for ingress routes
  • traefik.frontend.priority
  • When specifying an ExternalName, Traefik will forward requests to the given host accordingly and use HTTPS when the Service port matches 443.
  • By default Traefik will pass the incoming Host header to the upstream resource.
  • traefik.frontend.passHostHeader: "false"
  • type: ExternalName
  • By default, Traefik processes every Ingress objects it observes.
  • It is also possible to set the ingressClass option in Traefik to a particular value. Traefik will only process matching Ingress objects.
  • It is possible to split Ingress traffic in a fine-grained manner between multiple deployments using service weights.
  • use case is canary releases where a deployment representing a newer release is to receive an initially small but ever-increasing fraction of the requests over time.
  • annotations: traefik.ingress.kubernetes.io/service-weights: | my-app: 99% my-app-canary: 1%
  • Over time, the ratio may slowly shift towards the canary deployment until it is deemed to replace the previous main application, in steps such as 5%/95%, 10%/90%, 50%/50%, and finally 100%/0%.
張 旭

Automated Docker-based Rails deployments - 0 views

cocoahunter.com/...docker-3

docker deploy system rails development ruby web

shared by 張 旭 on 23 Oct 15 - No Cached
  • how to automate the whole deployment process with a real world
  • use Unicorn as our webserver
  • 張 旭 on 23 Oct 15
     
    "This is the third post in a series of 3 on how my company moved its infrastructure from PaaS to Docker based deployment."
張 旭

Helm | - 0 views

helm.sh/using_helm

kubernetes helm system

shared by 張 旭 on 17 Jul 19 - No Cached
  • Helm will figure out where to install Tiller by reading your Kubernetes configuration file (usually $HOME/.kube/config). This is the same file that kubectl uses.
  • kubectl cluster-info
  • Role-Based Access Control (RBAC) enabled
  • ...133 more annotations...
  • initialize the local CLI
  • install Tiller into your Kubernetes cluster
  • helm install
  • helm init --upgrade
  • By default, when Tiller is installed, it does not have authentication enabled.
  • helm repo update
  • Without a max history set the history is kept indefinitely, leaving a large number of records for helm and tiller to maintain.
  • helm init --upgrade
  • Whenever you install a chart, a new release is created.
  • one chart can be installed multiple times into the same cluster. And each can be independently managed and upgraded.
  • helm list function will show you a list of all deployed releases.
  • helm delete
  • helm status
  • you can audit a cluster’s history, and even undelete a release (with helm rollback).
  • the Helm server (Tiller).
  • The Helm client (helm)
  • brew install kubernetes-helm
  • Tiller, the server portion of Helm, typically runs inside of your Kubernetes cluster.
  • it can also be run locally, and configured to talk to a remote Kubernetes cluster.
  • Role-Based Access Control - RBAC for short
  • create a service account for Tiller with the right roles and permissions to access resources.
  • run Tiller in an RBAC-enabled Kubernetes cluster.
  • run kubectl get pods --namespace kube-system and see Tiller running.
  • helm inspect
  • Helm will look for Tiller in the kube-system namespace unless --tiller-namespace or TILLER_NAMESPACE is set.
  • For development, it is sometimes easier to work on Tiller locally, and configure it to connect to a remote Kubernetes cluster.
  • even when running locally, Tiller will store release configuration in ConfigMaps inside of Kubernetes.
  • helm version should show you both the client and server version.
  • Tiller stores its data in Kubernetes ConfigMaps, you can safely delete and re-install Tiller without worrying about losing any data.
  • helm reset
  • The --node-selectors flag allows us to specify the node labels required for scheduling the Tiller pod.
  • --override allows you to specify properties of Tiller’s deployment manifest.
  • helm init --override manipulates the specified properties of the final manifest (there is no “values” file).
  • The --output flag allows us skip the installation of Tiller’s deployment manifest and simply output the deployment manifest to stdout in either JSON or YAML format.
  • By default, tiller stores release information in ConfigMaps in the namespace where it is running.
  • switch from the default backend to the secrets backend, you’ll have to do the migration for this on your own.
  • a beta SQL storage backend that stores release information in an SQL database (only postgres has been tested so far).
  • Once you have the Helm Client and Tiller successfully installed, you can move on to using Helm to manage charts.
  • Helm requires that kubelet have access to a copy of the socat program to proxy connections to the Tiller API.
  • A Release is an instance of a chart running in a Kubernetes cluster. One chart can often be installed many times into the same cluster.
  • helm init --client-only
  • helm init --dry-run --debug
  • A panic in Tiller is almost always the result of a failure to negotiate with the Kubernetes API server
  • Tiller and Helm have to negotiate a common version to make sure that they can safely communicate without breaking API assumptions
  • helm delete --purge
  • Helm stores some files in $HELM_HOME, which is located by default in ~/.helm
  • A Chart is a Helm package. It contains all of the resource definitions necessary to run an application, tool, or service inside of a Kubernetes cluster.
  • it like the Kubernetes equivalent of a Homebrew formula, an Apt dpkg, or a Yum RPM file.
  • A Repository is the place where charts can be collected and shared.
  • Set the $HELM_HOME environment variable
  • each time it is installed, a new release is created.
  • Helm installs charts into Kubernetes, creating a new release for each installation. And to find new charts, you can search Helm chart repositories.
  • chart repository is named stable by default
  • helm search shows you all of the available charts
  • helm inspect
  • To install a new package, use the helm install command. At its simplest, it takes only one argument: The name of the chart.
  • If you want to use your own release name, simply use the --name flag on helm install
  • additional configuration steps you can or should take.
  • Helm does not wait until all of the resources are running before it exits. Many charts require Docker images that are over 600M in size, and may take a long time to install into the cluster.
  • helm status
  • helm inspect values
  • helm inspect values stable/mariadb
  • override any of these settings in a YAML formatted file, and then pass that file during installation.
  • helm install -f config.yaml stable/mariadb
  • --values (or -f): Specify a YAML file with overrides.
  • --set (and its variants --set-string and --set-file): Specify overrides on the command line.
  • Values that have been --set can be cleared by running helm upgrade with --reset-values specified.
  • Chart designers are encouraged to consider the --set usage when designing the format of a values.yaml file.
  • --set-file key=filepath is another variant of --set. It reads the file and use its content as a value.
  • inject a multi-line text into values without dealing with indentation in YAML.
  • An unpacked chart directory
  • When a new version of a chart is released, or when you want to change the configuration of your release, you can use the helm upgrade command.
  • Kubernetes charts can be large and complex, Helm tries to perform the least invasive upgrade.
  • It will only update things that have changed since the last release
  • $ helm upgrade -f panda.yaml happy-panda stable/mariadb
  • deployment
  • If both are used, --set values are merged into --values with higher precedence.
  • The helm get command is a useful tool for looking at a release in the cluster.
  • helm rollback
  • A release version is an incremental revision. Every time an install, upgrade, or rollback happens, the revision number is incremented by 1.
  • helm history
  • a release name cannot be re-used.
  • you can rollback a deleted resource, and have it re-activate.
  • helm repo list
  • helm repo add
  • helm repo update
  • The Chart Development Guide explains how to develop your own charts.
  • helm create
  • helm lint
  • helm package
  • Charts that are archived can be loaded into chart repositories.
  • chart repository server
  • Tiller can be installed into any namespace.
  • Limiting Tiller to only be able to install into specific namespaces and/or resource types is controlled by Kubernetes RBAC roles and rolebindings
  • Release names are unique PER TILLER INSTANCE
  • Charts should only contain resources that exist in a single namespace.
  • not recommended to have multiple Tillers configured to manage resources in the same namespace.
  • a client-side Helm plugin. A plugin is a tool that can be accessed through the helm CLI, but which is not part of the built-in Helm codebase.
  • Helm plugins are add-on tools that integrate seamlessly with Helm. They provide a way to extend the core feature set of Helm, but without requiring every new feature to be written in Go and added to the core tool.
  • Helm plugins live in $(helm home)/plugins
  • The Helm plugin model is partially modeled on Git’s plugin model
  • helm referred to as the porcelain layer, with plugins being the plumbing.
  • helm plugin install https://github.com/technosophos/helm-template
  • command is the command that this plugin will execute when it is called.
  • Environment variables are interpolated before the plugin is executed.
  • The command itself is not executed in a shell. So you can’t oneline a shell script.
  • Helm is able to fetch Charts using HTTP/S
  • Variables like KUBECONFIG are set for the plugin if they are set in the outer environment.
  • In Kubernetes, granting a role to an application-specific service account is a best practice to ensure that your application is operating in the scope that you have specified.
  • restrict Tiller’s capabilities to install resources to certain namespaces, or to grant a Helm client running access to a Tiller instance.
  • Service account with cluster-admin role
  • The cluster-admin role is created by default in a Kubernetes cluster
  • Deploy Tiller in a namespace, restricted to deploying resources only in that namespace
  • Deploy Tiller in a namespace, restricted to deploying resources in another namespace
  • When running a Helm client in a pod, in order for the Helm client to talk to a Tiller instance, it will need certain privileges to be granted.
  • SSL Between Helm and Tiller
  • The Tiller authentication model uses client-side SSL certificates.
  • creating an internal CA, and using both the cryptographic and identity functions of SSL.
  • Helm is a powerful and flexible package-management and operations tool for Kubernetes.
  • default installation applies no security configurations
  • with a cluster that is well-secured in a private network with no data-sharing or no other users or teams.
  • With great power comes great responsibility.
  • Choose the Best Practices you should apply to your helm installation
  • Role-based access control, or RBAC
  • Tiller’s gRPC endpoint and its usage by Helm
  • Kubernetes employ a role-based access control (or RBAC) system (as do modern operating systems) to help mitigate the damage that can be done if credentials are misused or bugs exist.
  • In the default installation the gRPC endpoint that Tiller offers is available inside the cluster (not external to the cluster) without authentication configuration applied.
  • Tiller stores its release information in ConfigMaps. We suggest changing the default to Secrets.
  • release information
  • charts
  • charts are a kind of package that not only installs containers you may or may not have validated yourself, but it may also install into more than one namespace.
  • As with all shared software, in a controlled or shared environment you must validate all software you install yourself before you install it.
  • Helm’s provenance tools to ensure the provenance and integrity of charts
  • 張 旭 on 17 Jul 19
     
    "Helm will figure out where to install Tiller by reading your Kubernetes configuration file (usually $HOME/.kube/config). This is the same file that kubectl uses."
張 旭

Helm | - 0 views

helm.sh/...chart_template_guide

helm kubernetes chart devops development

shared by 張 旭 on 06 Aug 19 - No Cached
  • Templates generate manifest files, which are YAML-formatted resource descriptions that Kubernetes can understand.
  • service.yaml: A basic manifest for creating a service endpoint for your deployment
  • In Kubernetes, a ConfigMap is simply a container for storing configuration data.
  • ...88 more annotations...
  • deployment.yaml: A basic manifest for creating a Kubernetes deployment
  • using the suffix .yaml for YAML files and .tpl for helpers.
  • It is just fine to put a plain YAML file like this in the templates/ directory.
  • helm get manifest
  • The helm get manifest command takes a release name (full-coral) and prints out all of the Kubernetes resources that were uploaded to the server. Each file begins with --- to indicate the start of a YAML document
  • Names should be unique to a release
  • The name: field is limited to 63 characters because of limitations to the DNS system.
  • release names are limited to 53 characters
  • {{ .Release.Name }}
  • A template directive is enclosed in {{ and }} blocks.
  • The values that are passed into a template can be thought of as namespaced objects, where a dot (.) separates each namespaced element.
  • The leading dot before Release indicates that we start with the top-most namespace for this scope
  • The Release object is one of the built-in objects for Helm
  • When you want to test the template rendering, but not actually install anything, you can use helm install ./mychart --debug --dry-run
  • Using --dry-run will make it easier to test your code, but it won’t ensure that Kubernetes itself will accept the templates you generate.
  • Objects are passed into a template from the template engine.
  • create new objects within your templates
  • Objects can be simple, and have just one value. Or they can contain other objects or functions.
  • Release is one of the top-level objects that you can access in your templates.
  • Release.Namespace: The namespace to be released into (if the manifest doesn’t override)
  • Values: Values passed into the template from the values.yaml file and from user-supplied files. By default, Values is empty.
  • Chart: The contents of the Chart.yaml file.
  • Files: This provides access to all non-special files in a chart.
  • Files.Get is a function for getting a file by name
  • Files.GetBytes is a function for getting the contents of a file as an array of bytes instead of as a string. This is useful for things like images.
  • Template: Contains information about the current template that is being executed
  • BasePath: The namespaced path to the templates directory of the current chart
  • The built-in values always begin with a capital letter.
  • Go’s naming convention
  • use only initial lower case letters in order to distinguish local names from those built-in.
  • If this is a subchart, the values.yaml file of a parent chart
  • Individual parameters passed with --set
  • values.yaml is the default, which can be overridden by a parent chart’s values.yaml, which can in turn be overridden by a user-supplied values file, which can in turn be overridden by --set parameters.
  • While structuring data this way is possible, the recommendation is that you keep your values trees shallow, favoring flatness.
  • If you need to delete a key from the default values, you may override the value of the key to be null, in which case Helm will remove the key from the overridden values merge.
  • Kubernetes would then fail because you can not declare more than one livenessProbe handler.
  • When injecting strings from the .Values object into the template, we ought to quote these strings.
  • quote
  • Template functions follow the syntax functionName arg1 arg2...
  • While we talk about the “Helm template language” as if it is Helm-specific, it is actually a combination of the Go template language, some extra functions, and a variety of wrappers to expose certain objects to the templates.
  • Drawing on a concept from UNIX, pipelines are a tool for chaining together a series of template commands to compactly express a series of transformations.
  • pipelines are an efficient way of getting several things done in sequence
  • The repeat function will echo the given string the given number of times
  • default DEFAULT_VALUE GIVEN_VALUE. This function allows you to specify a default value inside of the template, in case the value is omitted.
  • all static default values should live in the values.yaml, and should not be repeated using the default command
  • Operators are implemented as functions that return a boolean value.
  • To use eq, ne, lt, gt, and, or, not etcetera place the operator at the front of the statement followed by its parameters just as you would a function.
  • if and
  • if or
  • with to specify a scope
  • range, which provides a “for each”-style loop
  • block declares a special kind of fillable template area
  • A pipeline is evaluated as false if the value is: a boolean false a numeric zero an empty string a nil (empty or null) an empty collection (map, slice, tuple, dict, array)
  • incorrect YAML because of the whitespacing
  • When the template engine runs, it removes the contents inside of {{ and }}, but it leaves the remaining whitespace exactly as is.
  • {{- (with the dash and space added) indicates that whitespace should be chomped left, while -}} means whitespace to the right should be consumed.
  • Newlines are whitespace!
  • an * at the end of the line indicates a newline character that would be removed
  • Be careful with the chomping modifiers.
  • the indent function
  • Scopes can be changed. with can allow you to set the current scope (.) to a particular object.
  • Inside of the restricted scope, you will not be able to access the other objects from the parent scope.
  • range
  • The range function will “range over” (iterate through) the pizzaToppings list.
  • Just like with sets the scope of ., so does a range operator.
  • The toppings: |- line is declaring a multi-line string.
  • not a YAML list. It’s a big string.
  • the data in ConfigMaps data is composed of key/value pairs, where both the key and the value are simple strings.
  • The |- marker in YAML takes a multi-line string.
  • range can be used to iterate over collections that have a key and a value (like a map or dict).
  • In Helm templates, a variable is a named reference to another object. It follows the form $name
  • Variables are assigned with a special assignment operator: :=
  • {{- $relname := .Release.Name -}}
  • capture both the index and the value
  • the integer index (starting from zero) to $index and the value to $topping
  • For data structures that have both a key and a value, we can use range to get both
  • Variables are normally not “global”. They are scoped to the block in which they are declared.
  • one variable that is always global - $ - this variable will always point to the root context.
  • $.
  • $.
  • Helm template language is its ability to declare multiple templates and use them together.
  • A named template (sometimes called a partial or a subtemplate) is simply a template defined inside of a file, and given a name.
  • when naming templates: template names are global.
  • If you declare two templates with the same name, whichever one is loaded last will be the one used.
  • you should be careful to name your templates with chart-specific names.
  • templates in subcharts are compiled together with top-level templates
  • naming convention is to prefix each defined template with the name of the chart: {{ define "mychart.labels" }}
  • Helm has over 60 available functions.
張 旭

A visual guide on troubleshooting Kubernetes deployments - 0 views

learnk8s.io/troubleshooting-deployments

kubernetes pod container service HA proxy

shared by 張 旭 on 06 Dec 19 - No Cached
  • Service and Deployment aren't connected at all.
  • the Service points to the Pods directly and skips the Deployment altogether.
張 旭

Running Docker Commands - CircleCI - 0 views

circleci.com/...building-docker-images

circle CI

shared by 張 旭 on 14 Nov 18 - No Cached
  • To build Docker images for deployment, you must use a special setup_remote_docker key which creates a separate environment for each build for security.
  • When setup_remote_docker executes, a remote environment will be created, and your current primary container will be configured to use it.
  • Once setup_remote_docker is called, a new remote environment is created, and your primary container is configured to use it.
  • ...8 more annotations...
  • but building/pushing images and running containers happens in the remote Docker Engine
  • use a primary image that already has Docker (recommended)
  • installs Docker and has Git, use 17.05.0-ce-git
  • The job and remote docker run in separate environments.
  • It is not possible to start a service in remote docker and ping it directly from a primary container or to start a primary container that can ping a service in remote docker.
  • It is not possible to mount a folder from your job space into a container in Remote Docker (and vice versa).
    • 張 旭
      張 旭 on 15 Nov 18
       
      等於是 docker client 跟 docker server 是兩台不同的主機就對了。
  • use https://github.com/outstand/docker-dockup or a similar image for backup and restore to spin up a container
  • 張 旭 on 14 Nov 18
     
    "To build Docker images for deployment, you must use a special setup_remote_docker key which creates a separate environment for each build for security. "
張 旭

How To Create a Kubernetes Cluster Using Kubeadm on Ubuntu 18.04 | DigitalOcean - 0 views

www.digitalocean.com/...-using-kubeadm-on-ubuntu-18-04

cluster ubuntu kubernetes system

shared by 張 旭 on 27 Mar 19 - No Cached
  • A pod is an atomic unit that runs one or more containers.
  • Pods are the basic unit of scheduling in Kubernetes: all containers in a pod are guaranteed to run on the same node that the pod is scheduled on.
  • Each pod has its own IP address, and a pod on one node should be able to access a pod on another node using the pod's IP.
  • ...12 more annotations...
  • Communication between pods is more complicated, however, and requires a separate networking component that can transparently route traffic from a pod on one node to a pod on another.
  • pod network plugins. For this cluster, you will use Flannel, a stable and performant option.
  • Passing the argument --pod-network-cidr=10.244.0.0/16 specifies the private subnet that the pod IPs will be assigned from.
  • kubectl apply -f descriptor.[yml|json] is the syntax for telling kubectl to create the objects described in the descriptor.[yml|json] file.
  • deploy Nginx using Deployments and Services
  • A deployment is a type of Kubernetes object that ensures there's always a specified number of pods running based on a defined template, even if the pod crashes during the cluster's lifetime.
  • NodePort, a scheme that will make the pod accessible through an arbitrary port opened on each node of the cluster
  • Services are another type of Kubernetes object that expose cluster internal services to clients, both internal and external.
  • load balancing requests to multiple pods
  • Pods are ubiquitous in Kubernetes, so understanding them will facilitate your work
  • how controllers such as deployments work since they are used frequently in stateless applications for scaling and the automated healing of unhealthy applications.
  • Understanding the types of services and the options they have is essential for running both stateless and stateful applications.
張 旭

Queues - Laravel - The PHP Framework For Web Artisans - 0 views

laravel.com/queues

php programming laravel queue worker framework

shared by 張 旭 on 02 Mar 19 - No Cached
  • Laravel queues provide a unified API across a variety of different queue backends, such as Beanstalk, Amazon SQS, Redis, or even a relational database.
  • The queue configuration file is stored in config/queue.php
  • a synchronous driver that will execute jobs immediately (for local use)
  • ...56 more annotations...
  • A null queue driver is also included which discards queued jobs.
  • In your config/queue.php configuration file, there is a connections configuration option.
  • any given queue connection may have multiple "queues" which may be thought of as different stacks or piles of queued jobs.
  • each connection configuration example in the queue configuration file contains a queue attribute.
  • if you dispatch a job without explicitly defining which queue it should be dispatched to, the job will be placed on the queue that is defined in the queue attribute of the connection configuration
  • pushing jobs to multiple queues can be especially useful for applications that wish to prioritize or segment how jobs are processed
  • specify which queues it should process by priority.
  • If your Redis queue connection uses a Redis Cluster, your queue names must contain a key hash tag.
  • ensure all of the Redis keys for a given queue are placed into the same hash slot
  • all of the queueable jobs for your application are stored in the app/Jobs directory.
  • Job classes are very simple, normally containing only a handle method which is called when the job is processed by the queue.
  • we were able to pass an Eloquent model directly into the queued job's constructor. Because of the SerializesModels trait that the job is using, Eloquent models will be gracefully serialized and unserialized when the job is processing.
  • When the job is actually handled, the queue system will automatically re-retrieve the full model instance from the database.
  • The handle method is called when the job is processed by the queue
  • The arguments passed to the dispatch method will be given to the job's constructor
  • delay the execution of a queued job, you may use the delay method when dispatching a job.
  • dispatch a job immediately (synchronously), you may use the dispatchNow method.
  • When using this method, the job will not be queued and will be run immediately within the current process
  • specify a list of queued jobs that should be run in sequence.
  • Deleting jobs using the $this->delete() method will not prevent chained jobs from being processed. The chain will only stop executing if a job in the chain fails.
  • this does not push jobs to different queue "connections" as defined by your queue configuration file, but only to specific queues within a single connection.
  • To specify the queue, use the onQueue method when dispatching the job
  • To specify the connection, use the onConnection method when dispatching the job
  • defining the maximum number of attempts on the job class itself.
  • to defining how many times a job may be attempted before it fails, you may define a time at which the job should timeout.
  • using the funnel method, you may limit jobs of a given type to only be processed by one worker at a time
  • using the throttle method, you may throttle a given type of job to only run 10 times every 60 seconds.
  • If an exception is thrown while the job is being processed, the job will automatically be released back onto the queue so it may be attempted again.
  • dispatch a Closure. This is great for quick, simple tasks that need to be executed outside of the current request cycle
  • When dispatching Closures to the queue, the Closure's code contents is cryptographically signed so it can not be modified in transit.
  • Laravel includes a queue worker that will process new jobs as they are pushed onto the queue.
  • once the queue:work command has started, it will continue to run until it is manually stopped or you close your terminal
  • queue workers are long-lived processes and store the booted application state in memory.
  • they will not notice changes in your code base after they have been started.
  • during your deployment process, be sure to restart your queue workers.
  • customize your queue worker even further by only processing particular queues for a given connection
  • The --once option may be used to instruct the worker to only process a single job from the queue
  • The --stop-when-empty option may be used to instruct the worker to process all jobs and then exit gracefully.
  • Daemon queue workers do not "reboot" the framework before processing each job.
  • you should free any heavy resources after each job completes.
  • Since queue workers are long-lived processes, they will not pick up changes to your code without being restarted.
  • restart the workers during your deployment process.
  • php artisan queue:restart
  • The queue uses the cache to store restart signals
  • the queue workers will die when the queue:restart command is executed, you should be running a process manager such as Supervisor to automatically restart the queue workers.
  • each queue connection defines a retry_after option. This option specifies how many seconds the queue connection should wait before retrying a job that is being processed.
  • The --timeout option specifies how long the Laravel queue master process will wait before killing off a child queue worker that is processing a job.
  • When jobs are available on the queue, the worker will keep processing jobs with no delay in between them.
  • While sleeping, the worker will not process any new jobs - the jobs will be processed after the worker wakes up again
  • the numprocs directive will instruct Supervisor to run 8 queue:work processes and monitor all of them, automatically restarting them if they fail.
  • Laravel includes a convenient way to specify the maximum number of times a job should be attempted.
  • define a failed method directly on your job class, allowing you to perform job specific clean-up when a failure occurs.
  • a great opportunity to notify your team via email or Slack.
  • php artisan queue:retry all
  • php artisan queue:flush
  • When injecting an Eloquent model into a job, it is automatically serialized before being placed on the queue and restored when the job is processed
張 旭

Understanding the GitHub flow · GitHub Guides - 0 views

guides.github.com/...flow

git programming github flow

shared by 張 旭 on 14 Mar 20 - No Cached
  • anything in the master branch is always deployable.
  • Your branch name should be descriptive
  • Commits also create a transparent history of your work that others can follow to understand what you've done and why.
  • ...9 more annotations...
  • each commit is considered a separate unit of change.
  • By writing clear commit messages, you can make it easier for other people to follow along and provide feedback.
  • Pull Requests initiate discussion about your commits.
  • If you're using a Fork & Pull Model, Pull Requests provide a way to notify project maintainers about the changes you'd like them to consider.
  • Pull Requests are designed to encourage and capture this type of conversation.
  • You can also continue to push to your branch in light of discussion and feedback about your commits.
  • If your branch causes issues, you can roll it back by deploying the existing master into production.
  • With GitHub, you can deploy from a branch for final testing in production before merging to master.
  • your changes have been verified in production, it is time to merge your code into the master branch.
  • 張 旭 on 14 Mar 20
     
    "anything in the master branch is always deployable."
crazylion lee

GitHub - checkr/codeflow: Open-source deployment pipeline from the future - 0 views

github.com/codeflow

deploy

shared by crazylion lee on 16 Apr 17 - No Cached
  • crazylion lee on 16 Apr 17
     
    "Open-source deployment pipeline from the future"
張 旭

Kubernetes Components | Kubernetes - 0 views

kubernetes.io/...components

kubernetes system

shared by 張 旭 on 17 May 21 - No Cached
  • A Kubernetes cluster consists of a set of worker machines, called nodes, that run containerized applications
  • Every cluster has at least one worker node.
  • The control plane manages the worker nodes and the Pods in the cluster.
  • ...29 more annotations...
  • The control plane's components make global decisions about the cluster
  • Control plane components can be run on any machine in the cluster.
  • for simplicity, set up scripts typically start all control plane components on the same machine, and do not run user containers on this machine
  • The API server is the front end for the Kubernetes control plane.
  • kube-apiserver is designed to scale horizontally—that is, it scales by deploying more instances. You can run several instances of kube-apiserver and balance traffic between those instances.
  • Kubernetes cluster uses etcd as its backing store, make sure you have a back up plan for those data.
  • watches for newly created Pods with no assigned node, and selects a node for them to run on.
  • Factors taken into account for scheduling decisions include: individual and collective resource requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, and deadlines.
  • each controller is a separate process, but to reduce complexity, they are all compiled into a single binary and run in a single process.
  • Node controller
  • Job controller
  • Endpoints controller
  • Service Account & Token controllers
  • The cloud controller manager lets you link your cluster into your cloud provider's API, and separates out the components that interact with that cloud platform from components that only interact with your cluster.
  • If you are running Kubernetes on your own premises, or in a learning environment inside your own PC, the cluster does not have a cloud controller manager.
  • An agent that runs on each node in the cluster. It makes sure that containers are running in a Pod.
  • The kubelet takes a set of PodSpecs that are provided through various mechanisms and ensures that the containers described in those PodSpecs are running and healthy.
  • The kubelet doesn't manage containers which were not created by Kubernetes.
  • kube-proxy is a network proxy that runs on each node in your cluster, implementing part of the Kubernetes Service concept.
  • kube-proxy maintains network rules on nodes. These network rules allow network communication to your Pods from network sessions inside or outside of your cluster.
  • kube-proxy uses the operating system packet filtering layer if there is one and it's available.
  • Kubernetes supports several container runtimes: Docker, containerd, CRI-O, and any implementation of the Kubernetes CRI (Container Runtime Interface).
  • Addons use Kubernetes resources (DaemonSet, Deployment, etc) to implement cluster features
  • namespaced resources for addons belong within the kube-system namespace.
  • all Kubernetes clusters should have cluster DNS,
  • Cluster DNS is a DNS server, in addition to the other DNS server(s) in your environment, which serves DNS records for Kubernetes services.
  • Containers started by Kubernetes automatically include this DNS server in their DNS searches.
  • Container Resource Monitoring records generic time-series metrics about containers in a central database, and provides a UI for browsing that data.
  • A cluster-level logging mechanism is responsible for saving container logs to a central log store with search/browsing interface.
張 旭

LXC vs Docker: Why Docker is Better | UpGuard - 0 views

www.upguard.com/...docker-vs-lxc

docker container devops

shared by 張 旭 on 28 Jun 21 - No Cached
  • LXC (LinuX Containers) is a OS-level virtualization technology that allows creation and running of multiple isolated Linux virtual environments (VE) on a single control host.
  • Docker, previously called dotCloud, was started as a side project and only open-sourced in 2013. It is really an extension of LXC’s capabilities.
  • run processes in isolation.
  • ...35 more annotations...
  • Docker is developed in the Go language and utilizes LXC, cgroups, and the Linux kernel itself. Since it’s based on LXC, a Docker container does not include a separate operating system; instead it relies on the operating system’s own functionality as provided by the underlying infrastructure.
  • Docker acts as a portable container engine, packaging the application and all its dependencies in a virtual container that can run on any Linux server.
  • a VE there is no preloaded emulation manager software as in a VM.
  • In a VE, the application (or OS) is spawned in a container and runs with no added overhead, except for a usually minuscule VE initialization process.
  • LXC will boast bare metal performance characteristics because it only packages the needed applications.
  • the OS is also just another application that can be packaged too.
  • a VM, which packages the entire OS and machine setup, including hard drive, virtual processors and network interfaces. The resulting bloated mass usually takes a long time to boot and consumes a lot of CPU and RAM.
  • don’t offer some other neat features of VM’s such as IaaS setups and live migration.
  • LXC as supercharged chroot on Linux. It allows you to not only isolate applications, but even the entire OS.
  • Libvirt, which allows the use of containers through the LXC driver by connecting to 'lxc:///'.
  • 'LXC', is not compatible with libvirt, but is more flexible with more userspace tools.
  • Portable deployment across machines
  • Versioning: Docker includes git-like capabilities for tracking successive versions of a container
  • Component reuse: Docker allows building or stacking of already created packages.
  • Shared libraries: There is already a public registry (http://index.docker.io/ ) where thousands have already uploaded the useful containers they have created.
  • Docker taking the devops world by storm since its launch back in 2013.
  • LXC, while older, has not been as popular with developers as Docker has proven to be
  • LXC having a focus on sys admins that’s similar to what solutions like the Solaris operating system, with its Solaris Zones, Linux OpenVZ, and FreeBSD, with its BSD Jails virtualization system
  • it started out being built on top of LXC, Docker later moved beyond LXC containers to its own execution environment called libcontainer.
  • Unlike LXC, which launches an operating system init for each container, Docker provides one OS environment, supplied by the Docker Engine
  • LXC tooling sticks close to what system administrators running bare metal servers are used to
  • The LXC command line provides essential commands that cover routine management tasks, including the creation, launch, and deletion of LXC containers.
  • Docker containers aim to be even lighter weight in order to support the fast, highly scalable, deployment of applications with microservice architecture.
  • With backing from Canonical, LXC and LXD have an ecosystem tightly bound to the rest of the open source Linux community.
  • Docker Swarm
  • Docker Trusted Registry
  • Docker Compose
  • Docker Machine
  • Kubernetes facilitates the deployment of containers in your data center by representing a cluster of servers as a single system.
  • Swarm is Docker’s clustering, scheduling and orchestration tool for managing a cluster of Docker hosts. 
  • rkt is a security minded container engine that uses KVM for VM-based isolation and packs other enhanced security features. 
  • Apache Mesos can run different kinds of distributed jobs, including containers. 
  • Elastic Container Service is Amazon’s service for running and orchestrating containerized applications on AWS
  • LXC offers the advantages of a VE on Linux, mainly the ability to isolate your own private workloads from one another. It is a cheaper and faster solution to implement than a VM, but doing so requires a bit of extra learning and expertise.
  • Docker is a significant improvement of LXC’s capabilities.
張 旭

Kubernetes 基本概念 · Kubernetes指南 - 0 views

feisky.gitbooks.io/...concepts.html

kubernetes volume namespace system devops

shared by 張 旭 on 30 May 19 - No Cached
  • Container（容器）是一种便携式、轻量级的操作系统级虚拟化技术。它使用 namespace 隔离不同的软件运行环境，并通过镜像自包含软件的运行环境，从而使得容器可以很方便的在任何地方运行。
  • 每个应用程序用容器封装，管理容器部署就等同于管理应用程序部署。+
  • Pod 是一组紧密关联的容器集合，它们共享 PID、IPC、Network 和 UTS namespace，是 Kubernetes 调度的基本单位。
  • ...9 more annotations...
  • 进程间通信和文件共享
  • 在 Kubernetes 中，所有对象都使用 manifest（yaml 或 json）来定义
  • Node 是 Pod 真正运行的主机，可以是物理机，也可以是虚拟机。
  • 每个 Node 节点上至少要运行 container runtime（比如 docker 或者 rkt）、kubelet 和 kube-proxy 服务。
  • 常见的 pods, services, replication controllers 和 deployments 等都是属于某一个 namespace 的（默认是 default）
  • node, persistentVolumes 等则不属于任何 namespace
  • Service 是应用服务的抽象，通过 labels 为应用提供负载均衡和服务发现。
  • 匹配 labels 的 Pod IP 和端口列表组成 endpoints，由 kube-proxy 负责将服务 IP 负载均衡到这些 endpoints 上。
  • 每个 Service 都会自动分配一个 cluster IP（仅在集群内部可访问的虚拟地址）和 DNS 名
  • 張 旭 on 30 May 19
     
    "常见的 pods, services, replication controllers 和 deployments 等都是属于某一个 namespace 的（默认是 default），而 node, persistentVolumes 等则不属于任何 namespace。"
張 旭

Ephemeral Containers | Kubernetes - 0 views

kubernetes.io/...ephemeral-containers

kubernetes docker

shared by 張 旭 on 19 Aug 22 - No Cached
  • a special type of container that runs temporarily in an existing Pod to accomplish user-initiated actions such as troubleshooting.
  • you cannot add a container to a Pod once it has been created. Instead, you usually delete and replace Pods in a controlled fashion using deployments.
  • you can run an ephemeral container in an existing Pod to inspect its state and run arbitrary commands.
  • ...4 more annotations...
  • Ephemeral containers differ from other containers in that they lack guarantees for resources or execution, and they will never be automatically restarted, so they are not appropriate for building applications.
  • Ephemeral containers are created using a special ephemeralcontainers handler in the API rather than by adding them directly to pod.spec, so it's not possible to add an ephemeral container using kubectl edit
  • distroless images enable you to deploy minimal container images that reduce attack surface and exposure to bugs and vulnerabilities.
  • enable process namespace sharing so you can view processes in other containers.
  • 張 旭 on 19 Aug 22
     
    "a special type of container that runs temporarily in an existing Pod to accomplish user-initiated actions such as troubleshooting. "
crazylion lee

The Pragmatic Bookshelf | DevOps in Practice - 0 views

pragprog.com/...devops-in-practice

devops

shared by crazylion lee on 08 Jun 16 - No Cached
  • crazylion lee on 08 Jun 16
     
    "Delivering production software can often be a painful task. Long test periods and the integration between operations and development can ruin or delay a promising delivery. That's what DevOps can fix. DevOps is a cultural change that aims to smoothly integrate development and operations procedures, breaking the barriers between them and focusing on automation, collaboration, and sharing of knowledge and tools. This book shows you how to implement DevOps and Continuous Delivery practices to raise your system's deployment frequency, increasing your production application's stability and robustness."
crazylion lee

Deploy together. Fearlessly. - 0 views

dockbit.com

deploy devops

shared by crazylion lee on 03 Apr 16 - No Cached
  • crazylion lee on 03 Apr 16
     
    "Dockbit turns your software deployments into repeatable, manageable workflows. Bring your team together and ship better."
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