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張 旭

GitLab Auto DevOps 深入淺出,自動部署,連設定檔不用?! | 五倍紅寶石・專業程式教育 - 0 views

  • 一個 K8S 的 Cluster,Auto DevOps 將會把網站部署到這個 Cluster
  • 需要有一個 wildcard 的 DNS 讓部署在這個環境的網站有 Domain name
  • 一個可以跑 Docker 的 GitLab Runner,將會為由它來執行 CI / CD 的流程。
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  • 其實 Auto DevOps 就是一份官方寫好的 gitlab-ci.yml,在啟動 Auto DevOps 的專案裡,如果找不到 gitlab-ci.yml 檔,那就會直接用官方 gitlab-ci.yml 去跑 CI / CD 流程。
  • Pod 是 K8S 中可以被部署的最小元件,一個 Pod 是由一到多個 Container 組成,同個 Pod 的不同 Container 之間彼此共享網路資源。
  • 每個 Pod 都會有它的 yaml 檔,用以描述 Pod 會使用的 Image 還有連接的 Port 等資訊。
  • Node 又分成 Worker Node 和 Master Node 兩種
  • Helm 透過參數 (parameter) 跟模板 (template) 的方式,讓我們可以在只修改參數的方式重複利用模板。
  • 為了要有 CI CD 的功能我們會把 .gitlab-ci.yml 放在專案的根目錄裡, GitLab 會依造 .gitlab-ci.yml 的設定產生 CI/CD Pipeline,每個 Pipeline 裡面可能有多個 Job,這時候就會需要有 GitLab Runner 來執行這些 Job 並把執行的結果回傳給 GitLab 讓它知道這個 Job 是否有正常執行。
  • 把專案打包成 Docker Image 這工作又或是 helm 的操作都會在 Container 內執行
  • CI/CD Pipeline 是由 stage 還有 job 組成的,stage 是有順序性的,前面的 stage 完成後才會開始下一個 stage。
  • 每個 stage 裡面包含一到多個 Job
  • Auto Devops 裡也會大量用到這種在指定 Container 內運行的工作。
  • 可以通過 health checks
  • 開 private 的話還要注意使用 Container Registry 的權限問題
  • 申請好的 wildcard 的 DNS
  • Auto Devops 也提供只要設定環境變數就能一定程度客製化的選項
  • 特別注意 namespace 有沒有設定對,不然會找不到資料喔
  • Auto Devops,如果想要進一步的客製化,而且是改 GitLab 環境變數都無法實現的客製化,這時候還是得回到 .gitlab-ci.yml 設定檔
  • 在 Docker in Docker 的環境用 Dockerfile 打包 Image
  • 用 helm upgrade 把 chart 部署到 K8S 上
  • GitLab CI 的環境變數主要有三個來源,優先度高到低依序為Settings > CI/CD 介面定義的變數gitlab_ci.yml 定義環境變數GitLab 預設環境變數
  • 把專案打包成 Docker Image 首先需要在專案下新增一份 Dockerfile
  • Auto Devops 裡面的做法,用 herokuish 提供的 Image 來打包專案
  • 在 Runner 的環境中是沒有 docker 指令可以用的,所以這邊啟動一個 Docker Container 在裡面執行就可以用 docker 指令了。
  • 其中 $CI_COMMIT_SHA $CI_COMMIT_BEFORE_SHA 這兩個都是 GitLab 預設環境變數,代表這次 commit 還有上次 commit 的 SHA 值。
  • dind 則是直接啟動 docker daemon,此外 dind 還會自動產生 TLS certificates
  • 為了在 Docker Container 內運行 Docker,會把 Host 上面的 Docker API 分享給 Container。
  • docker:stable 有執行 docker 需要的執行檔,他裡面也包含要啟動 docker 的程式(docker daemon),但啟動 Container 的 entrypoint 是 sh
  • docker:dind 繼承自 docker:stable,而且它 entrypoint 就是啟動 docker 的腳本,此外還會做完 TLS certificates
  • Container 要去連 Host 上的 Docker API 。但現在連線失敗卻是找 http://docker:2375,現在的 dind 已經不是被當做 services 來用了,而是要直接在裡面跑 Docker,所以他應該是要 unix:///var/run/docker.sock 用這種連線,於是把環境變數 DOCKER_HOST 從 tcp://docker:2375 改成空字串,讓 docker daemon 走預設連線就能成功囉!
  • auto-deploy preparationhelm init 建立 helm 專案設定 tiller 在背景執行設定 cluster 的 namespace
  • auto-deploy deploy使用 helm upgrade 部署 chart 到 K8S 上透過 --set 來設定要注入 template 的參數
  • set -x,這樣就能在執行前,顯示指令內容。
  • 用 helm repo list 看看現在有註冊哪些 Chart Repository
  • helm fetch gitlab/auto-deploy-app --untar
  • nohup 可以讓你在離線或登出系統後,還能夠讓工作繼續進行
  • 在不特別設定 CI_APPLICATION_REPOSITORY 的情況下,image_repository 的值就是預設環境變數 CI_REGISTRY_IMAGE/CI_COMMIT_REF_SLUG
  • A:-B 的意思是如果有 A 就用它,沒有就用 B
  • 研究 Auto Devops 難度最高的地方就是太多工具整合在一起,搞不清楚他們之間的關係,出錯也不知道從何查起
張 旭

Auto DevOps | GitLab - 0 views

  • 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.
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  • 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
       
      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
       
      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
       
      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

Run your CI/CD jobs in Docker containers | GitLab - 0 views

  • If you run Docker on your local machine, you can run tests in the container, rather than testing on a dedicated CI/CD server.
  • Run other services, like MySQL, in containers. Do this by specifying services in your .gitlab-ci.yml file.
  • By default, the executor pulls images from Docker Hub
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  • Maps must contain at least the name option, which is the same image name as used for the string setting.
  • When a CI job runs in a Docker container, the before_script, script, and after_script commands run in the /builds/<project-path>/ directory. Your image may have a different default WORKDIR defined. To move to your WORKDIR, save the WORKDIR as an environment variable so you can reference it in the container during the job’s runtime.
  • The runner starts a Docker container using the defined entrypoint. The default from Dockerfile that may be overridden in the .gitlab-ci.yml file.
  • attaches itself to a running container.
  • sends the script to the container’s shell stdin and receives the output.
  • To override the entrypoint of a Docker image, define an empty entrypoint in the .gitlab-ci.yml file, so the runner does not start a useless shell layer. However, that does not work for all Docker versions. For Docker 17.06 and later, the entrypoint can be set to an empty value. For Docker 17.03 and earlier, the entrypoint can be set to /bin/sh -c, /bin/bash -c, or an equivalent shell available in the image.
  • The runner expects that the image has no entrypoint or that the entrypoint is prepared to start a shell command.
  • entrypoint: [""]
  • entrypoint: ["/bin/sh", "-c"]
  • A DOCKER_AUTH_CONFIG CI/CD variable
  •  
    "If you run Docker on your local machine, you can run tests in the container, rather than testing on a dedicated CI/CD server. "
張 旭

Trunk-based Development | Atlassian - 0 views

  • Trunk-based development is a version control management practice where developers merge small, frequent updates to a core “trunk” or main branch.
  • Gitflow and trunk-based development. 
  • Gitflow, which was popularized first, is a stricter development model where only certain individuals can approve changes to the main code. This maintains code quality and minimizes the number of bugs.
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  • Trunk-based development is a more open model since all developers have access to the main code. This enables teams to iterate quickly and implement CI/CD.
  • Developers can create short-lived branches with a few small commits compared to other long-lived feature branching strategies.
  • Gitflow is an alternative Git branching model that uses long-lived feature branches and multiple primary branches.
  • Gitflow also has separate primary branch lines for development, hotfixes, features, and releases.
  • Trunk-based development is far more simplified since it focuses on the main branch as the source of fixes and releases.
  • Trunk-based development eases the friction of code integration.
  • trunk-based development model reduces these conflicts.
  • Adding an automated test suite and code coverage monitoring for this stream of commits enables continuous integration.
  • When new code is merged into the trunk, automated integration and code coverage tests run to validate the code quality.
  • Trunk-based development strives to keep the trunk branch “green”, meaning it's ready to deploy at any commit.
  • With continuous integration, developers perform trunk-based development in conjunction with automated tests that run after each committee to a trunk.
  • If trunk-based development was like music it would be a rapid staccato -- short, succinct notes in rapid succession, with the repository commits being the notes.
  • Instead of creating a feature branch and waiting to build out the complete specification, developers can instead create a trunk commit that introduces the feature flag and pushes new trunk commits that build out the feature specification within the flag.
  • Automated testing is necessary for any modern software project intending to achieve CI/CD.
  • Short running unit and integration tests are executed during development and upon code merge.
  • Automated tests provide a layer of preemptive code review.
  • Once a branch merges, it is best practice to delete it.
  • A repository with a large amount of active branches has some unfortunate side effects
  • Merge branches to the trunk at least once a day
  • The “continuous” in CI/CD implies that updates are constantly flowing.
張 旭

Deploy tokens | GitLab - 0 views

  • If a user creates one named gitlab-deploy-token, the username and token of the deploy token is automatically exposed to the CI/CD jobs as CI/CD variables: CI_DEPLOY_USER and CI_DEPLOY_PASSWORD
  • The special handling for the gitlab-deploy-token deploy token is not implemented for group deploy tokens.
張 旭

A Clear, Concise & Comfy Code Review Checklist - DEV Community - 0 views

  • 2 blocks doing similar things might be allowable, but 3 or more is a definitive red cross from me!
  • This would ultimately be integrated into your CI/CD pipelines running on each build/commit/deploy too; stopping any rogue commits getting in.
  • not to say that every code block that is duplicated needs to be refactored
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  • Refactoring is a cyclical process
  • Before accessing variables within objects and collections make sure they are there! PLEASE!
  • If that variable is a constant or won't be changed then use the Const keyword in applicable languages and the CAPITALISATION convention to let users aware of your decisions about them.
  • The name of a method is more important than we give it credit for, when a method changes so should its name.
  • Make sure you are returning the right thing, trying to make it as generic as possible.
  • Void should do something, not change something!
  • Private vs Public, this is a big topic
  • keeping an eye of the access level of a method can stop issues further down the line
  • Gherkin is a Business Readable, Domain Specific Language created especially for behavior descriptions.
  • specify the 3 main points of a test, including what you expect to happen using the following keywords GIVEN,  WHEN / AND , THEN.
  • look at how the code is structured, make sure methods aren't too long, don't have too many branches, and that for and if statements could be simplified.
  • Use your initiative and discuss if a rewrite would benefit maintainability for the future.
  • it's unnecessary to leave commented code when working in and around areas with them.
張 旭

Introduction to CI/CD with GitLab | GitLab - 0 views

  • deploying code changes at every small iteration, reducing the chance of developing new code based on bugged or failed previous versions
  • based on automating the execution of scripts to minimize the chance of introducing errors while developing applications.
  • For every push to the repository, you can create a set of scripts to build and test your application automatically, decreasing the chance of introducing errors to your app.
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  • 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.
  •  
    "deploying code changes at every small iteration, reducing the chance of developing new code based on bugged or failed previous versions"
張 旭

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

  • 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
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  • 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

Pre-Built CircleCI Docker Images - CircleCI - 0 views

  • typically extensions of official Docker images and include tools especially useful for CI/CD.
  • Convenience images are based on the most recently built versions of upstream images, so it is best practice to use the most specific image possible.
  • add -jessie or -stretch to the end of each of those containers to ensure you’re only using that version of the Debian base OS.
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  • language images
  • service images
  • All images add a circleci user as a system user
  • A language image should be listed first under the docker key in your configuration, making it the primary container during execution.
  • For example, if you want to add browsers to the circleci/golang:1.9 image, use the circleci/golang:1.9-browsers image.
  • Service images are convenience images for services like databases
  • should be listed after language images so they become secondary service containers.
  • To speed up builds using RAM volume, add the -ram suffix to the end of a service image tag
  • All convenience images have been extended with additional tools.
  • all images include the following packages, installed via apt-get
  • Most CircleCI convenience images are Debian Jessie- or Stretch-based images, however some extend Ubuntu-based images.
  • The following packages are installed via curl
crazylion lee

Open Source Continuous Delivery and Automation Server | GoCD - 0 views

shared by crazylion lee on 19 Apr 18 - No Cached
  •  
    "SIMPLIFY CONTINUOUS DELIVERY"
張 旭

Introducing Infrastructure as Code | Linode - 0 views

  • Infrastructure as Code (IaC) is a technique for deploying and managing infrastructure using software, configuration files, and automated tools.
  • With the older methods, technicians must configure a device manually, perhaps with the aid of an interactive tool. Information is added to configuration files by hand or through the use of ad-hoc scripts. Configuration wizards and similar utilities are helpful, but they still require hands-on management. A small group of experts owns the expertise, the process is typically poorly defined, and errors are common.
  • The development of the continuous integration and continuous delivery (CI/CD) pipeline made the idea of treating infrastructure as software much more attractive.
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  • Infrastructure as Code takes advantage of the software development process, making use of quality assurance and test automation techniques.
  • Consistency/Standardization
  • Each node in the network becomes what is known as a snowflake, with its own unique settings. This leads to a system state that cannot easily be reproduced and is difficult to debug.
  • With standard configuration files and software-based configuration, there is greater consistency between all equipment of the same type. A key IaC concept is idempotence.
  • Idempotence makes it easy to troubleshoot, test, stabilize, and upgrade all the equipment.
  • Infrastructure as Code is central to the culture of DevOps, which is a mix of development and operations
  • edits are always made to the source configuration files, never on the target.
  • A declarative approach describes the final state of a device, but does not mandate how it should get there. The specific IaC tool makes all the procedural decisions. The end state is typically defined through a configuration file, a JSON specification, or a similar encoding.
  • An imperative approach defines specific functions or procedures that must be used to configure the device. It focuses on what must happen, but does not necessarily describe the final state. Imperative techniques typically use scripts for the implementation.
  • With a push configuration, the central server pushes the configuration to the destination device.
  • If a device is mutable, its configuration can be changed while it is active
  • Immutable devices cannot be changed. They must be decommissioned or rebooted and then completely rebuilt.
  • an immutable approach ensures consistency and avoids drift. However, it usually takes more time to remove or rebuild a configuration than it does to change it.
  • System administrators should consider security issues as part of the development process.
  • Ansible is a very popular open source IaC application from Red Hat
  • Ansible is often used in conjunction with Kubernetes and Docker.
  • Linode offers a collection of several Ansible guides for a more comprehensive overview.
  • Pulumi permits the use of a variety of programming languages to deploy and manage infrastructure within a cloud environment.
  • Terraform allows users to provision data center infrastructure using either JSON or Terraform’s own declarative language.
  • Terraform manages resources through the use of providers, which are similar to APIs.
張 旭

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

  • 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.
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  • 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

Using cache in GitLab CI with Docker-in-Docker | $AYMDEV() - 0 views

  • optimize our images.
  • When you build an image, it is made of multiple layers: we add a layer per instruction.
  • If we build the same image again without modifying any file, Docker will use existing layers rather than re-executing the instructions.
  • ...21 more annotations...
  • an image is made of multiple layers, and we can accelerate its build by using layers cache from the previous image version.
  • by using Docker-in-Docker, we get a fresh Docker instance per job which local registry is empty.
  • docker build --cache-from "$CI_REGISTRY_IMAGE:latest" -t "$CI_REGISTRY_IMAGE:new-tag"
  • But if you maintain a CHANGELOG in this format, and/or your Git tags are also your Docker tags, you can get the previous version and use cache the this image version.
  • script: - export PREVIOUS_VERSION=$(perl -lne 'print "v${1}" if /^##\s\[(\d\.\d\.\d)\]\s-\s\d{4}(?:-\d{2}){2}\s*$/' CHANGELOG.md | sed -n '2 p') - docker build --cache-from "$CI_REGISTRY_IMAGE:$PREVIOUS_VERSION" -t "$CI_REGISTRY_IMAGE:$CI_COMMIT_TAG" -f ./prod.Dockerfile .
  • « Docker layer caching » is enough to optimize the build time.
  • Cache in CI/CD is about saving directories or files across pipelines.
  • We're building a Docker image, dependencies are installed inside a container.We can't cache a dependencies directory if it doesn't exists in the job workspace.
  • Dependencies will always be installed from a container but will be extracted by the GitLab Runner in the job workspace. Our goal is to send the cached version in the build context.
  • We set the directories to cache in the job settings with a key to share the cache per branch and stage.
  • - docker cp app:/var/www/html/vendor/ ./vendor
  • after_script
  • - docker cp app:/var/www/html/node_modules/ ./node_modules
  • To avoid old dependencies to be mixed with the new ones, at the risk of keeping unused dependencies in cache, which would make cache and images heavier.
  • If you need to cache directories in testing jobs, it's easier: use volumes !
  • version your cache keys !
  • sharing Docker image between jobs
  • In every job, we automatically get artifacts from previous stages.
  • docker save $DOCKER_CI_IMAGE | gzip > app.tar.gz
  • I personally use the « push / pull » technique,
  • we docker push after the build, then we docker pull if needed in the next jobs.
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