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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

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

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

Understanding GitHub Actions - GitHub Docs - 0 views

docs.github.com/...understanding-github-actions

github cicd devops

shared by 張 旭 on 11 Sep 21 - No Cached
  • A job is a set of steps that execute on the same runner. By default, a workflow with multiple jobs will run those jobs in parallel.
  • Workflows are made up of one or more jobs and can be scheduled or triggered by an event
  • An event is a specific activity that triggers a workflow.
  • ...8 more annotations...
  • configure a workflow to run jobs sequentially.
  • A step is an individual task that can run commands in a job. A step can be either an action or a shell command.
  • Each step in a job executes on the same runner, allowing the actions in that job to share data with each other.
  • Actions are standalone commands that are combined into steps to create a job.
  • Actions are the smallest portable building block of a workflow.
  • To use an action in a workflow, you must include it as a step.
  • You can use a runner hosted by GitHub, or you can host your own.
  • GitHub-hosted runners are based on Ubuntu Linux, Microsoft Windows, and macOS, and each job in a workflow runs in a fresh virtual environment.
  • 張 旭 on 11 Sep 21
     
    "A job is a set of steps that execute on the same runner. By default, a workflow with multiple jobs will run those jobs in parallel. "
張 旭

Docker can now run within Docker - Docker Blog - 0 views

www.docker.com/...cker-can-now-run-within-docker

devops docker

shared by 張 旭 on 11 Mar 22 - No Cached
  • Docker 0.6 is the new “privileged” mode for containers. It allows you to run some containers with (almost) all the capabilities of their host machine, regarding kernel features and device access.
  • Among the (many!) possibilities of the “privileged” mode, you can now run Docker within Docker itself.
  • in the new privileged mode.
  • ...8 more annotations...
  • that /var/lib/docker should be a volume. This is important, because the filesystem of a container is an AUFS mountpoint, composed of multiple branches; and those branches have to be “normal” filesystems (i.e. not AUFS mountpoints).
  • /var/lib/docker, the place where Docker stores its containers, cannot be an AUFS filesystem.
  • we use them as a pass-through to the “normal” filesystem of the host machine.
  • The /var/lib/docker directory of the nested Docker will live somewhere in /var/lib/docker/volumes on the host system.
  • since the private Docker instances run in privileged mode, they can easily escalate to the host, and you probably don’t want this! If you really want to run something like this and expose it to the public, you will have to fine-tune the LXC template file, to restrict the capabilities and devices available to the Docker instances.
  • When you are inside a privileged container, you can always nest one more level
  • the LXC tools cannot start nested containers if the devices control group is not in its own hierarchy.
  • if you use AppArmor, you need a special policy to support nested containers.
張 旭

Docker image building on GitLab CI | $AYMDEV() - 0 views

aymdev.io/...er-image-building-on-gitlab-ci

devops cicd

shared by 張 旭 on 11 Mar 22 - No Cached
  • Continuous Integration (or CI) is a practice where you continously test an application to detect errors as soon as possible.
  • Docker is a container technology, many CI tools execute jobs (the tasks of a pipeline) in container to have an isolated environment.
  • Docker in Docker (« DinD » in short) means executing Docker in a Docker container.
  • ...11 more annotations...
  • images are saved in the host registry, we can benefit from Docker layer caching
  • All jobs will share the same environment, if many of them run simultaneously they might get into conflicts.
  • storage management (accumulating images)
  • The Docker socket binding technique means making a volume of /var/run/docker.sock between host and containers.
  • all containers would share the same Docker daemon.
  • Add privileged = true in the [runners.docker] section, the privileged mode is mandatory to use DinD.
  • To avoid that the runner only run one job at a time, change the concurrent value on the first line.
  • To avoid building a Docker image at each job, it can be built in a first job, pushed to the image registry provided by GitLab, and pulled in the next jobs.
  • functional tests depending on a database.
  • Docker Compose allows you to easily start multiple containers, but it has no more feature than Docker itself
  • Docker in Docker works well, but has its drawbacks, like Docker layer caching which needs some more commands to be used.
張 旭

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

aymdev.io/...itlab-ci-with-docker-in-docker

devops docker cache cicd

shared by 張 旭 on 11 Mar 22 - No Cached
  • 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.
張 旭

The Squeaky Blog | Why we don't use a staging environment - 0 views

squeaky.ai/...dont-use-a-staging-environment

devops

shared by 張 旭 on 15 Apr 22 - No Cached
  • Pre-live environments are never at parity with production
  • multiple people use staging to validate their changes before release.
  • Branches are then constantly out of sync with each other, and problems often surface when you merge, rebase, and backfill hotfixes.
  • ...10 more annotations...
  • Big Bang releases
  • there is a lengthy suite of tests and checks that run before it is deployed to staging. During this period, which could end up being hours, engineers will likely pick up another task. I’ve seen people merge, and then forget that their changes are on staging, more times than I can count.
  • only merge code that is ready to go live
  • written sufficient tests and have validated our changes in development.
  • All branches are cut from main, and all changes get merged back into main.
  • If we ever have an issue in production, we always roll forward.
  • Feature flags can be enabled on a per-user basis so we can monitor performance and gather feedback
  • Experimental features can be enabled by users in their account settings.
  • we have monitoring, logging, and alarms around all of our services. We also blue/green deploy, by draining and replacing a percentage of containers.
  • Dropping your staging environment in favour of true continuous integration and deployment can create a different mindset for shipping software.
  • 張 旭 on 15 Apr 22
     
    "Pre-live environments are never at parity with production "
張 旭

Override Files - Configuration Language | Terraform | HashiCorp Developer - 0 views

developer.hashicorp.com/...override

devops terraform

shared by 張 旭 on 21 Jan 23 - No Cached
  • the overriding effect is compounded, with later blocks taking precedence over earlier blocks.
  • Terraform has special handling of any configuration file whose name ends in _override.tf or _override.tf.json. This special handling also applies to a file named literally override.tf or override.tf.json.Terraform initially skips these override files when loading configuration, and then afterwards processes each one in turn (in lexicographical order).
  • If the original block defines a default value and an override block changes the variable's type, Terraform attempts to convert the default value to the overridden type, producing an error if this conversion is not possible.
  • ...1 more annotation...
  • Each locals block defines a number of named values.
  • 張 旭 on 21 Jan 23
     
    "the overriding effect is compounded, with later blocks taking precedence over earlier blocks."
張 旭

Syntax - Configuration Language | Terraform | HashiCorp Developer - 0 views

developer.hashicorp.com/...configuration

devops terraform

shared by 張 旭 on 22 Jan 23 - No Cached
  • the native syntax of the Terraform language, which is a rich language designed to be relatively easy for humans to read and write.
  • Terraform's configuration language is based on a more general language called HCL, and HCL's documentation usually uses the word "attribute" instead of "argument."
  • A particular block type may have any number of required labels, or it may require none
  • ...34 more annotations...
  • After the block type keyword and any labels, the block body is delimited by the { and } characters
  • Identifiers can contain letters, digits, underscores (_), and hyphens (-). The first character of an identifier must not be a digit, to avoid ambiguity with literal numbers.
  • The # single-line comment style is the default comment style and should be used in most cases.
  • he idiomatic style is to use the Unix convention
  • Indent two spaces for each nesting level.
  • align their equals signs
  • Use empty lines to separate logical groups of arguments within a block.
  • Use one blank line to separate the arguments from the blocks.
  • "meta-arguments" (as defined by the Terraform language semantics)
  • Avoid separating multiple blocks of the same type with other blocks of a different type, unless the block types are defined by semantics to form a family.
  • Resource names must start with a letter or underscore, and may contain only letters, digits, underscores, and dashes.
  • Each resource is associated with a single resource type, which determines the kind of infrastructure object it manages and what arguments and other attributes the resource supports.
  • Each resource type is implemented by a provider, which is a plugin for Terraform that offers a collection of resource types.
  • By convention, resource type names start with their provider's preferred local name.
  • Most publicly available providers are distributed on the Terraform Registry, which also hosts their documentation.
  • The Terraform language defines several meta-arguments, which can be used with any resource type to change the behavior of resources.
  • use precondition and postcondition blocks to specify assumptions and guarantees about how the resource operates.
  • Some resource types provide a special timeouts nested block argument that allows you to customize how long certain operations are allowed to take before being considered to have failed.
  • Timeouts are handled entirely by the resource type implementation in the provider
  • Most resource types do not support the timeouts block at all.
  • A resource block declares that you want a particular infrastructure object to exist with the given settings.
  • Destroy resources that exist in the state but no longer exist in the configuration.
  • Destroy and re-create resources whose arguments have changed but which cannot be updated in-place due to remote API limitations.
  • Expressions within a Terraform module can access information about resources in the same module, and you can use that information to help configure other resources. Use the <RESOURCE TYPE>.<NAME>.<ATTRIBUTE> syntax to reference a resource attribute in an expression.
  • resources often provide read-only attributes with information obtained from the remote API; this often includes things that can't be known until the resource is created, like the resource's unique random ID.
  • data sources, which are a special type of resource used only for looking up information.
  • some dependencies cannot be recognized implicitly in configuration.
  • local-only resource types exist for generating private keys, issuing self-signed TLS certificates, and even generating random ids.
  • The behavior of local-only resources is the same as all other resources, but their result data exists only within the Terraform state.
  • The count meta-argument accepts a whole number, and creates that many instances of the resource or module.
  • count.index — The distinct index number (starting with 0) corresponding to this instance.
  • the count value must be known before Terraform performs any remote resource actions. This means count can't refer to any resource attributes that aren't known until after a configuration is applied
  • Within nested provisioner or connection blocks, the special self object refers to the current resource instance, not the resource block as a whole.
  • This was fragile, because the resource instances were still identified by their index instead of the string values in the list.
  • 張 旭 on 22 Jan 23
     
    "the native syntax of the Terraform language, which is a rich language designed to be relatively easy for humans to read and write. "
張 旭

The for_each Meta-Argument - Configuration Language | Terraform | HashiCorp Developer - 0 views

developer.hashicorp.com/...for_each

devops terraform

shared by 張 旭 on 23 Jan 23 - No Cached
  • A given resource or module block cannot use both count and for_each
  • The for_each meta-argument accepts a map or a set of strings, and creates an instance for each item in that map or set
  • each.key — The map key (or set member) corresponding to this instance.
  • ...10 more annotations...
  • each.value — The map value corresponding to this instance. (If a set was provided, this is the same as each.key.)
  • for_each keys cannot be the result (or rely on the result of) of impure functions, including uuid, bcrypt, or timestamp, as their evaluation is deferred during the main evaluation step.
  • The value used in for_each is used to identify the resource instance and will always be disclosed in UI output, which is why sensitive values are not allowed.
  • if you would like to call keys(local.map), where local.map is an object with sensitive values (but non-sensitive keys), you can create a value to pass to for_each with toset([for k,v in local.map : k]).
  • for_each can't refer to any resource attributes that aren't known until after a configuration is applied (such as a unique ID generated by the remote API when an object is created).
  • he for_each argument does not implicitly convert lists or tuples to sets.
  • Transform a multi-level nested structure into a flat list by using nested for expressions with the flatten function.
  • Instances are identified by a map key (or set member) from the value provided to for_each
  • Within nested provisioner or connection blocks, the special self object refers to the current resource instance, not the resource block as a whole.
  • Conversion from list to set discards the ordering of the items in the list and removes any duplicate elements.
張 旭

Provisioners Without a Resource | Terraform | HashiCorp Developer - 0 views

developer.hashicorp.com/...null_resource

devops terraform

shared by 張 旭 on 23 Jan 23 - No Cached
  • triggers - A map of values which should cause this set of provisioners to re-run. Values are meant to be interpolated references to variables or attributes of other resources.
  • 張 旭 on 23 Jan 23
     
    "triggers - A map of values which should cause this set of provisioners to re-run. Values are meant to be interpolated references to variables or attributes of other resources. "
張 旭

Data Sources - Configuration Language | Terraform | HashiCorp Developer - 0 views

developer.hashicorp.com/...data-sources

devops terraform

shared by 張 旭 on 23 Jan 23 - No Cached
  • Each provider may offer data sources alongside its set of resource types.
  • When distinguishing from data resources, the primary kind of resource (as declared by a resource block) is known as a managed resource.
  • Each data resource is associated with a single data source, which determines the kind of object (or objects) it reads and what query constraint arguments are available.
  • ...4 more annotations...
  • Terraform reads data resources during the planning phase when possible, but announces in the plan when it must defer reading resources until the apply phase to preserve the order of operations.
  • local-only data sources exist for rendering templates, reading local files, and rendering AWS IAM policies.
  • As with managed resources, when count or for_each is present it is important to distinguish the resource itself from the multiple resource instances it creates. Each instance will separately read from its data source with its own variant of the constraint arguments, producing an indexed result.
  • Data instance arguments may refer to computed values, in which case the attributes of the instance itself cannot be resolved until all of its arguments are defined. I
張 旭

Providers - Configuration Language | Terraform | HashiCorp Developer - 0 views

developer.hashicorp.com/...providers

terraform devops

shared by 張 旭 on 23 Jan 23 - No Cached
  • Terraform relies on plugins called providers to interact with cloud providers, SaaS providers, and other APIs.
  • Terraform configurations must declare which providers they require so that Terraform can install and use them.
  • Each provider adds a set of resource types and/or data sources that Terraform can manage.
  • ...6 more annotations...
  • Every resource type is implemented by a provider; without providers, Terraform can't manage any kind of infrastructure.
  • The Terraform Registry is the main directory of publicly available Terraform providers, and hosts providers for most major infrastructure platforms.
  • Dependency Lock File documents an additional HCL file that can be included with a configuration, which tells Terraform to always use a specific set of provider versions.
  • Terraform CLI finds and installs providers when initializing a working directory. It can automatically download providers from a Terraform registry, or load them from a local mirror or cache.
  • To save time and bandwidth, Terraform CLI supports an optional plugin cache. You can enable the cache using the plugin_cache_dir setting in the CLI configuration file.
  • you can use Terraform CLI to create a dependency lock file and commit it to version control along with your configuration.
張 旭

Dependency Lock File (.terraform.lock.hcl) - Configuration Language | Terraform | Hashi... - 0 views

developer.hashicorp.com/...dependency-lock

devops terraform

shared by 張 旭 on 21 Jan 23 - No Cached
  • Version constraints within the configuration itself determine which versions of dependencies are potentially compatible, but after selecting a specific version of each dependency Terraform remembers the decisions it made in a dependency lock file
  • At present, the dependency lock file tracks only provider dependencies.
  • Terraform does not remember version selections for remote modules, and so Terraform will always select the newest available module version that meets the specified version constraints.
  • ...14 more annotations...
  • The lock file is always named .terraform.lock.hcl, and this name is intended to signify that it is a lock file for various items that Terraform caches in the .terraform
  • Terraform automatically creates or updates the dependency lock file each time you run the terraform init command.
  • You should include this file in your version control repository
  • If a particular provider has no existing recorded selection, Terraform will select the newest available version that matches the given version constraint, and then update the lock file to include that selection.
  • the "trust on first use" model
  • you can pre-populate checksums for a variety of different platforms in your lock file using the terraform providers lock command, which will then allow future calls to terraform init to verify that the packages available in your chosen mirror match the official packages from the provider's origin registry.
  • The h1: and zh: prefixes on these values represent different hashing schemes, each of which represents calculating a checksum using a different algorithm.
  • zh:: a mnemonic for "zip hash"
  • h1:: a mnemonic for "hash scheme 1", which is the current preferred hashing scheme.
  • To determine whether there still exists a dependency on a given provider, Terraform uses two sources of truth: the configuration itself, and the state.
  • Version constraints within the configuration itself determine which versions of dependencies are potentially compatible, but after selecting a specific version of each dependency Terraform remembers the decisions it made in a dependency lock file so that it can (by default) make the same decisions again in future.
  • At present, the dependency lock file tracks only provider dependencies.
  • Terraform will always select the newest available module version that meets the specified version constraints.
  • The lock file is always named .terraform.lock.hcl
  • 張 旭 on 21 Jan 23
     
    "the overriding effect is compounded, with later blocks taking precedence over earlier blocks."
張 旭

Production environment | Kubernetes - 0 views

kubernetes.io/...production-environment

devops kubernetes

shared by 張 旭 on 25 Jan 23 - No Cached
  • to promote an existing cluster for production use
  • Separating the control plane from the worker nodes.
  • Having enough worker nodes available
  • ...22 more annotations...
  • You can use role-based access control (RBAC) and other security mechanisms to make sure that users and workloads can get access to the resources they need, while keeping workloads, and the cluster itself, secure. You can set limits on the resources that users and workloads can access by managing policies and container resources.
  • you need to plan how to scale to relieve increased pressure from more requests to the control plane and worker nodes or scale down to reduce unused resources.
  • Managed control plane: Let the provider manage the scale and availability of the cluster's control plane, as well as handle patches and upgrades.
  • The simplest Kubernetes cluster has the entire control plane and worker node services running on the same machine.
  • You can deploy a control plane using tools such as kubeadm, kops, and kubespray.
  • Secure communications between control plane services are implemented using certificates.
  • Certificates are automatically generated during deployment or you can generate them using your own certificate authority.
  • Separate and backup etcd service: The etcd services can either run on the same machines as other control plane services or run on separate machines
  • Create multiple control plane systems: For high availability, the control plane should not be limited to a single machine
  • Some deployment tools set up Raft consensus algorithm to do leader election of Kubernetes services. If the primary goes away, another service elects itself and take over.
  • Groups of zones are referred to as regions.
  • if you installed with kubeadm, there are instructions to help you with Certificate Management and Upgrading kubeadm clusters.
  • Production-quality workloads need to be resilient and anything they rely on needs to be resilient (such as CoreDNS).
  • Add nodes to the cluster: If you are managing your own cluster you can add nodes by setting up your own machines and either adding them manually or having them register themselves to the cluster’s apiserver.
  • Set up node health checks: For important workloads, you want to make sure that the nodes and pods running on those nodes are healthy.
  • Authentication: The apiserver can authenticate users using client certificates, bearer tokens, an authenticating proxy, or HTTP basic auth.
  • Authorization: When you set out to authorize your regular users, you will probably choose between RBAC and ABAC authorization.
  • Role-based access control (RBAC): Lets you assign access to your cluster by allowing specific sets of permissions to authenticated users. Permissions can be assigned for a specific namespace (Role) or across the entire cluster (ClusterRole).
  • Attribute-based access control (ABAC): Lets you create policies based on resource attributes in the cluster and will allow or deny access based on those attributes.
  • Set limits on workload resources
  • Set namespace limits: Set per-namespace quotas on things like memory and CPU
  • Prepare for DNS demand: If you expect workloads to massively scale up, your DNS service must be ready to scale up as well.
張 旭

Container Runtimes | Kubernetes - 0 views

kubernetes.io/...container-runtimes

kubernetes container docker devops

shared by 張 旭 on 25 Jan 23 - No Cached
  • Kubernetes releases before v1.24 included a direct integration with Docker Engine, using a component named dockershim. That special direct integration is no longer part of Kubernetes
  • You need to install a container runtime into each node in the cluster so that Pods can run there.
  • Kubernetes 1.26 requires that you use a runtime that conforms with the Container Runtime Interface (CRI).
  • ...9 more annotations...
  • On Linux, control groups are used to constrain resources that are allocated to processes.
  • Both kubelet and the underlying container runtime need to interface with control groups to enforce resource management for pods and containers and set resources such as cpu/memory requests and limits.
  • When the cgroupfs driver is used, the kubelet and the container runtime directly interface with the cgroup filesystem to configure cgroups.
  • The cgroupfs driver is not recommended when systemd is the init system
  • When systemd is chosen as the init system for a Linux distribution, the init process generates and consumes a root control group (cgroup) and acts as a cgroup manager.
  • Two cgroup managers result in two views of the available and in-use resources in the system.
  • Changing the cgroup driver of a Node that has joined a cluster is a sensitive operation. If the kubelet has created Pods using the semantics of one cgroup driver, changing the container runtime to another cgroup driver can cause errors when trying to re-create the Pod sandbox for such existing Pods. Restarting the kubelet may not solve such errors.
  • The approach to mitigate this instability is to use systemd as the cgroup driver for the kubelet and the container runtime when systemd is the selected init system.
  • Kubernetes 1.26 defaults to using v1 of the CRI API. If a container runtime does not support the v1 API, the kubelet falls back to using the (deprecated) v1alpha2 API instead.
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