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張 旭

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

  • 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

Using Workflows to Schedule Jobs - CircleCI - 1 views

  • A workflow is a set of rules for defining a collection of jobs and their run order.
  • Schedule workflows for jobs that should only run periodically.
  • run multiple jobs in parallel
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  • rerun just the failed job
  • Builds without workflows require a build job.
  • Refer the YAML Anchors/Aliases documentation for information about how to alias and reuse syntax to keep your .circleci/config.yml file small.
  • workflow orchestration with two parallel jobs
  • jobs run according to configured requirements, each job waiting to start until the required job finishes successfully
  • requires: key
  • fans-out to run a set of acceptance test jobs in parallel, and finally fans-in to run a common deploy job.
  • Holding a Workflow for a Manual Approval
  • Workflows can be configured to wait for manual approval of a job before continuing to the next job
  • add a job to the jobs list with the key type: approval
  • approval is a special job type that is only available to jobs under the workflow key
  • The name of the job to hold is arbitrary - it could be wait or pause, for example, as long as the job has a type: approval key in it.
  • schedule a workflow to run at a certain time for specific branches.
  • The triggers key is only added under your workflows key
  • using cron syntax to represent Coordinated Universal Time (UTC) for specified branches.
  • By default, a workflow is triggered on every git push
  • the commit workflow has no triggers key and will run on every git push
  • The nightly workflow has a triggers key and will run on the specified schedule
  • Cron step syntax (for example, */1, */20) is not supported.
  • use a context to share environment variables
  • use the same shared environment variables when initiated by a user who is part of the organization.
  • CircleCI does not run workflows for tags unless you explicitly specify tag filters.
  • CircleCI branch and tag filters support the Java variant of regex pattern matching.
  • Each workflow has an associated workspace which can be used to transfer files to downstream jobs as the workflow progresses.
  • The workspace is an additive-only store of data.
  • Jobs can persist data to the workspace
  • Downstream jobs can attach the workspace to their container filesystem.
  • Attaching the workspace downloads and unpacks each layer based on the ordering of the upstream jobs in the workflow graph.
  • Workflows that include jobs running on multiple branches may require data to be shared using workspaces
  • To persist data from a job and make it available to other jobs, configure the job to use the persist_to_workspace key.
  • Files and directories named in the paths: property of persist_to_workspace will be uploaded to the workflow’s temporary workspace relative to the directory specified with the root key.
  • Configure a job to get saved data by configuring the attach_workspace key.
  • persist_to_workspace
  • attach_workspace
  • To rerun only a workflow’s failed jobs, click the Workflows icon in the app and select a workflow to see the status of each job, then click the Rerun button and select Rerun from failed.
  • if you do not see your workflows triggering, a configuration error is preventing the workflow from starting.
  • check your Workflows page of the CircleCI app (not the Job page)
  •  
    "A workflow is a set of rules for defining a collection of jobs and their run order."
張 旭

Choose when to run jobs | GitLab - 0 views

  • Rules are evaluated in order until the first match.
  • no rules match, so the job is not added to any other pipeline.
  • define a set of rules to exclude jobs in a few cases, but run them in all other cases
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  • use all rules keywords, like if, changes, and exists, in the same rule. The rule evaluates to true only when all included keywords evaluate to true.
  • use parentheses with && and || to build more complicated variable expressions.
  • Use workflow to specify which types of pipelines can run.
  • every push to an open merge request’s source branch causes duplicated pipelines.
  • avoid duplicate pipelines by changing the job rules to avoid either push (branch) pipelines or merge request pipelines.
  • do not mix only/except jobs with rules jobs in the same pipeline.
  • For behavior similar to the only/except keywords, you can check the value of the $CI_PIPELINE_SOURCE variable
  • commonly used variables for if clauses
  • rules:changes expressions to determine when to add jobs to a pipeline
  • Use !reference tags to reuse rules in different jobs.
  • Use except to define when a job does not run.
  • only or except used without refs is the same as only:refs / except/refs
  • If you change multiple files, but only one file ends in .md, the build job is still skipped.
  • If you use multiple keywords with only or except, the keywords are evaluated as a single conjoined expression.
  • only includes the job if all of the keys have at least one condition that matches.
  • except excludes the job if any of the keys have at least one condition that matches.
  • With only, individual keys are logically joined by an AND
  • With except, individual keys are logically joined by an OR
  • To specify a job as manual, add when: manual to the job in the .gitlab-ci.yml file.
  • Use protected environments to define a list of users authorized to run a manual job.
  • Use when: delayed to execute scripts after a waiting period, or if you want to avoid jobs immediately entering the pending state.
  • To split a large job into multiple smaller jobs that run in parallel, use the parallel keyword
  • run a trigger job multiple times in parallel in a single pipeline, but with different variable values for each instance of the job.
  • The @ symbol denotes the beginning of a ref’s repository path. To match a ref name that contains the @ character in a regular expression, you must use the hex character code match \x40.
  • Compare a variable to a string
  • Check if a variable is undefined
  • Check if a variable is empty
  • Check if a variable exists
  • Check if a variable is empty
  • Matches are found when using =~.
  • Matches are not found when using !~.
  • Join variable expressions together with && or ||
  •  
    "Rules are evaluated in order until the first match."
張 旭

Reusing Config - CircleCI - 0 views

  • Change the version key to 2.1 in your .circleci/config.yml file and commit the changes to test your build.
  • Reusable commands are invoked with specific parameters as steps inside a job.
  • Commands can use other commands in the scope of execution
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  • Executors define the environment in which the steps of a job will be run.
  • Executor declarations in config outside of jobs can be used by all jobs in the scope of that declaration, allowing you to reuse a single executor definition across multiple jobs.
  • It is also possible to allow an orb to define the executor used by all of its commands.
  • When invoking an executor in a job any keys in the job itself will override those of the executor invoked.
  • Steps are used when you have a job or command that needs to mix predefined and user-defined steps.
  • Use the enum parameter type when you want to enforce that the value must be one from a specific set of string values.
  • Use an executor parameter type to allow the invoker of a job to decide what executor it will run on
  • invoke the same job more than once in the workflows stanza of config.yml, passing any necessary parameters as subkeys to the job.
  • If a job is declared inside an orb it can use commands in that orb or the global commands.
  • To use parameters in executors, define the parameters under the given executor.
  • Parameters are in-scope only within the job or command that defined them.
  • A single configuration may invoke a job multiple times.
  • Every job invocation may optionally accept two special arguments: pre-steps and post-steps.
  • Pre and post steps allow you to execute steps in a given job without modifying the job.
  • conditions are checked before a workflow is actually run
  • you cannot use a condition to check an environment variable.
  • Conditional steps may be located anywhere a regular step could and may only use parameter values as inputs.
  • A conditional step consists of a step with the key when or unless. Under this conditional key are the subkeys steps and condition
  • A condition is a single value that evaluates to true or false at the time the config is processed, so you cannot use environment variables as conditions
張 旭

Understanding GitHub Actions - GitHub Docs - 0 views

  • 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.
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  • 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.
  •  
    "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

Queue Workers: How they work - Diving Laravel - 0 views

  • define workers as a simple PHP process that runs in the background with the purpose of extracting jobs from a storage space and run them with respect to several configuration options.
  • have to manually restart the worker to reflect any code change you made in your application.
  • avoiding booting up the whole app on every job
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  • instruct Laravel to create an instance of your application and start executing jobs, this instance will stay alive indefinitely which means the action of starting your Laravel application happens only once when the command was run & the same instance will be used to execute your jobs
  • This will start an instance of the application, process a single job,
  • and then kill the script.
  • Using queue:listen ensures that a new instance of the app is created for every job, that means you don't have to manually restart the worker in case you made changes to your code, but also means more server resources will be consumed.
  • the queue:listen command runs the WorkCommand inside a loop
  • The connection this worker will be pulling jobs from
  • The queue the worker will use to find jobs
  •  
    "define workers as a simple PHP process that runs in the background with the purpose of extracting jobs from a storage space and run them with respect to several configuration options."
張 旭

Persisting Data in Workflows: When to Use Caching, Artifacts, and Workspaces - CircleCI - 0 views

  • Repeatability is also important
  • When a CI process isn’t repeatable you’ll find yourself wasting time re-running jobs to get them to go green.
  • Workspaces persist data between jobs in a single Workflow.
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  • Caching persists data between the same job in different Workflow builds.
  • Artifacts persist data after a Workflow has finished
  • When a Workspace is declared in a job, one or more files or directories can be added. Each addition creates a new layer in the Workspace filesystem. Downstreams jobs can then use this Workspace for its own needs or add more layers on top.
  • Unlike caching, Workspaces are not shared between runs as they no longer exists once a Workflow is complete.
  • Caching lets you reuse the data from expensive fetch operations from previous jobs.
  • A prime example is package dependency managers such as Yarn, Bundler, or Pip.
  • Caches are global within a project, a cache saved on one branch will be used by others so they should only be used for data that is OK to share across Branches
  • Artifacts are used for longer-term storage of the outputs of your build process.
  • If your project needs to be packaged in some form or fashion, say an Android app where the .apk file is uploaded to Google Play, that’s a great example of an artifact.
  •  
    "CircleCI 2.0 provides a number of different ways to move data into and out of jobs, persist data, and with the introduction of Workspaces, move data between jobs"
張 旭

How To Use Bash's Job Control to Manage Foreground and Background Processes | DigitalOcean - 0 views

  • Most processes that you start on a Linux machine will run in the foreground. The command will begin execution, blocking use of the shell for the duration of the process.
  • By default, processes are started in the foreground. Until the program exits or changes state, you will not be able to interact with the shell.
  • stop the process by sending it a signal
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  • Linux terminals are usually configured to send the "SIGINT" signal (typically signal number 2) to current foreground process when the CTRL-C key combination is pressed.
  • Another signal that we can send is the "SIGTSTP" signal (typically signal number 20).
  • A background process is associated with the specific terminal that started it, but does not block access to the shell
  • start a background process by appending an ampersand character ("&") to the end of your commands.
  • type commands at the same time.
  • The [1] represents the command's "job spec" or job number. We can reference this with other job and process control commands, like kill, fg, and bg by preceding the job number with a percentage sign. In this case, we'd reference this job as %1.
  • Once the process is stopped, we can use the bg command to start it again in the background
  • By default, the bg command operates on the most recently stopped process.
  • Whether a process is in the background or in the foreground, it is rather tightly tied with the terminal instance that started it
  • When a terminal closes, it typically sends a SIGHUP signal to all of the processes (foreground, background, or stopped) that are tied to the terminal.
  • a terminal multiplexer
  • start it using the nohup command
  • appending output to ‘nohup.out’
  • pgrep -a
  • The disown command, in its default configuration, removes a job from the jobs queue of a terminal.
  • You can pass the -h flag to the disown process instead in order to mark the process to ignore SIGHUP signals, but to otherwise continue on as a regular job
  • The huponexit shell option controls whether bash will send its child processes the SIGHUP signal when it exits.
張 旭

Glossary - CircleCI - 0 views

  • User authentication may use LDAP for an instance of the CircleCI application that is installed on your private server or cloud
  • The first user to log into a private installation of CircleCI
  • Contexts provide a mechanism for securing and sharing environment variables across projects.
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  • The environment variables are defined as name/value pairs and are injected at runtime.
  • The CircleCI Docker Layer Caching feature allows builds to reuse Docker image layers
  • from previous builds.
  • Image layers are stored in separate volumes in the cloud and are not shared between projects.
  • Layers may only be used by builds from the same project.
  • Environment variables store customer data that is used by a project.
  • Defines the underlying technology to run a job.
  • machine to run your job inside a full virtual machine.
  • docker to run your job inside a Docker container with a specified image
  • A job is a collection of steps.
  • The first image listed in config.yml
  • A CircleCI project shares the name of the code repository for which it automates workflows, tests, and deployment.
  • must be added with the Add Project button
  • Following a project enables a user to subscribe to email notifications for the project build status and adds the project to their CircleCI dashboard.
  • A step is a collection of executable commands
  • Users must be added to a GitHub or Bitbucket org to view or follow associated CircleCI projects.
  • Users may not view project data that is stored in environment variables.  
  • A Workflow is a set of rules for defining a collection of jobs and their run order.
  • Workflows are implemented as a directed acyclic graph (DAG) of jobs for greatest flexibility.
  • referred to as Pipelines
  • A workspace is a workflows-aware storage mechanism.
  • A workspace stores data unique to the job, which may be needed in downstream jobs.
張 旭

Orbs, Jobs, Steps, and Workflows - CircleCI - 0 views

  • Orbs are packages of config that you either import by name or configure inline to simplify your config, share, and reuse config within and across projects.
  • Jobs are a collection of Steps.
  • All of the steps in the job are executed in a single unit which consumes a CircleCI container from your plan while it’s running.
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  • Workspaces persist data between jobs in a single Workflow.
  • Caching persists data between the same job in different Workflow builds.
  • Artifacts persist data after a Workflow has finished.
  • run using the machine executor which enables reuse of recently used machine executor runs,
  • docker executor which can compose Docker containers to run your tests and any services they require
  • macos executor
  • Steps are a collection of executable commands which are run during a job
  • In addition to the run: key, keys for save_cache:, restore_cache:, deploy:, store_artifacts:, store_test_results: and add_ssh_keys are nested under Steps.
  • checkout: key is required to checkout your code
  • run: enables addition of arbitrary, multi-line shell command scripting
  • orchestrating job runs with parallel, sequential, and manual approval workflows.
張 旭

Using Orbs - CircleCI - 0 views

  • Orbs enable you to share, standardize, and simplify config across your projects.
  • Jobs are comprised of two parts: a set of steps, and the environment they should be executed within.
  • Executors define the environment in which the steps of a job will be run.
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  • Commands are reusable sets of steps that you can invoke with specific parameters within an existing job.
  • you can pass my-executor as the value of a name key under executor. This method is primarily employed when passing parameters to executor invocations.
  • Development orbs are mutable and expire after 90 days.
  • Production Orbs are immutable and durable.
  • CircleCI allows development orbs that have versions that start with dev:
  • Production orbs are immutable
  • Each installation of CircleCI, including circleci.com, has only one registry where orbs can be kept.
  • Organization Admins publish production orbs.
  • Organization members publish development orbs
  • You must invoke jobs in the workflow stanza of config.yml file, making sure to pass any necessary parameters as subkeys to the job.
  • When you declare an executor in a configuration outside of jobs, you can use these declarations for all jobs in the scope of that declaration, enabling you to reuse a single executor definition across multiple jobs.
  • Orbs are transparent - If you can execute an orb, you and anyone else can view the source of that orb.
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.
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  • 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

Optimizing Gitlab pipelines - Basics (1) | PrinsFrank.nl - 0 views

  • When you use specific docker image, make sure you have the Dependency Proxy enabled so the image doesn’t have to be downloaded again for every job.
  • stages are used to group items that can run at the same time.
  • Instead of waiting for all jobs to finish, you can mark jobs as interruptible which signals a job to cancel when a new pipeline starts for the same branch
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  • mark all jobs as interruptible as it doesn’t make sense to wait for builds and tests based on old information.
  • Deployment jobs are the main exception as they should probably finish.
  • only running it when specific files have changed
  • To prevent the ‘vendor’ and ‘node_modules’ folder from being regenerated in every job, we can configure a build job for composer and npm assets.
  • To share assets between multiple stages, Gitlab has caches and artifacts. For dependencies we should use caches.
  • The pull-push policy is the default, but specified here for clarity.
  • All consecutive runs for the build step with the same ‘composer.lock’ file don’t update the cache.
  • composer prevents this by caching packages in a global package cache,
張 旭

Choosing an Executor Type - CircleCI - 0 views

  • Containers are an instance of the Docker Image you specify and the first image listed in your configuration is the primary container image in which all steps run.
  • In this example, all steps run in the container created by the first image listed under the build job
  • If you experience increases in your run times due to installing additional tools during execution, it is best practice to use the Building Custom Docker Images Documentation to create a custom image with tools that are pre-loaded in the container to meet the job requirements.
  • ...9 more annotations...
  • The machine option runs your jobs in a dedicated, ephemeral VM
  • Using the machine executor gives your application full access to OS resources and provides you with full control over the job environment.
  • Using machine may require additional fees in a future pricing update.
  • Using the macos executor allows you to run your job in a macOS environment on a VM.
  • In a multi-image configuration job, all steps are executed in the container created by the first image listed.
  • All containers run in a common network and every exposed port will be available on localhost from a primary container.
  • If you want to work with private images/registries, please refer to Using Private Images.
  • Docker also has built-in image caching and enables you to build, run, and publish Docker images via Remote Docker.
  • if you require low-level access to the network or need to mount external volumes consider using machine
張 旭

Helm | - 0 views

  • A chart is a collection of files that describe a related set of Kubernetes resources.
  • A single chart might be used to deploy something simple, like a memcached pod, or something complex, like a full web app stack with HTTP servers, databases, caches, and so on.
  • Charts are created as files laid out in a particular directory tree, then they can be packaged into versioned archives to be deployed.
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  • A chart is organized as a collection of files inside of a directory.
  • values.yaml # The default configuration values for this chart
  • charts/ # A directory containing any charts upon which this chart depends.
  • templates/ # A directory of templates that, when combined with values, # will generate valid Kubernetes manifest files.
  • version: A SemVer 2 version (required)
  • apiVersion: The chart API version, always "v1" (required)
  • Every chart must have a version number. A version must follow the SemVer 2 standard.
  • non-SemVer names are explicitly disallowed by the system.
  • When generating a package, the helm package command will use the version that it finds in the Chart.yaml as a token in the package name.
  • the appVersion field is not related to the version field. It is a way of specifying the version of the application.
  • appVersion: The version of the app that this contains (optional). This needn't be SemVer.
  • If the latest version of a chart in the repository is marked as deprecated, then the chart as a whole is considered to be deprecated.
  • deprecated: Whether this chart is deprecated (optional, boolean)
  • one chart may depend on any number of other charts.
  • dependencies can be dynamically linked through the requirements.yaml file or brought in to the charts/ directory and managed manually.
  • the preferred method of declaring dependencies is by using a requirements.yaml file inside of your chart.
  • A requirements.yaml file is a simple file for listing your dependencies.
  • The repository field is the full URL to the chart repository.
  • you must also use helm repo add to add that repo locally.
  • helm dependency update and it will use your dependency file to download all the specified charts into your charts/ directory for you.
  • When helm dependency update retrieves charts, it will store them as chart archives in the charts/ directory.
  • Managing charts with requirements.yaml is a good way to easily keep charts updated, and also share requirements information throughout a team.
  • All charts are loaded by default.
  • The condition field holds one or more YAML paths (delimited by commas). If this path exists in the top parent’s values and resolves to a boolean value, the chart will be enabled or disabled based on that boolean value.
  • The tags field is a YAML list of labels to associate with this chart.
  • all charts with tags can be enabled or disabled by specifying the tag and a boolean value.
  • The --set parameter can be used as usual to alter tag and condition values.
  • Conditions (when set in values) always override tags.
  • The first condition path that exists wins and subsequent ones for that chart are ignored.
  • The keys containing the values to be imported can be specified in the parent chart’s requirements.yaml file using a YAML list. Each item in the list is a key which is imported from the child chart’s exports field.
  • specifying the key data in our import list, Helm looks in the exports field of the child chart for data key and imports its contents.
  • the parent key data is not contained in the parent’s final values. If you need to specify the parent key, use the ‘child-parent’ format.
  • To access values that are not contained in the exports key of the child chart’s values, you will need to specify the source key of the values to be imported (child) and the destination path in the parent chart’s values (parent).
  • To drop a dependency into your charts/ directory, use the helm fetch command
  • A dependency can be either a chart archive (foo-1.2.3.tgz) or an unpacked chart directory.
  • name cannot start with _ or .. Such files are ignored by the chart loader.
  • a single release is created with all the objects for the chart and its dependencies.
  • Helm Chart templates are written in the Go template language, with the addition of 50 or so add-on template functions from the Sprig library and a few other specialized functions
  • When Helm renders the charts, it will pass every file in that directory through the template engine.
  • Chart developers may supply a file called values.yaml inside of a chart. This file can contain default values.
  • Chart users may supply a YAML file that contains values. This can be provided on the command line with helm install.
  • When a user supplies custom values, these values will override the values in the chart’s values.yaml file.
  • Template files follow the standard conventions for writing Go templates
  • {{default "minio" .Values.storage}}
  • Values that are supplied via a values.yaml file (or via the --set flag) are accessible from the .Values object in a template.
  • pre-defined, are available to every template, and cannot be overridden
  • the names are case sensitive
  • Release.Name: The name of the release (not the chart)
  • Release.IsUpgrade: This is set to true if the current operation is an upgrade or rollback.
  • Release.Revision: The revision number. It begins at 1, and increments with each helm upgrade
  • Chart: The contents of the Chart.yaml
  • Files: A map-like object containing all non-special files in the chart.
  • Files can be accessed using {{index .Files "file.name"}} or using the {{.Files.Get name}} or {{.Files.GetString name}} functions.
  • .helmignore
  • access the contents of the file as []byte using {{.Files.GetBytes}}
  • Any unknown Chart.yaml fields will be dropped
  • Chart.yaml cannot be used to pass arbitrarily structured data into the template.
  • A values file is formatted in YAML.
  • A chart may include a default values.yaml file
  • be merged into the default values file.
  • The default values file included inside of a chart must be named values.yaml
  • accessible inside of templates using the .Values object
  • Values files can declare values for the top-level chart, as well as for any of the charts that are included in that chart’s charts/ directory.
  • Charts at a higher level have access to all of the variables defined beneath.
  • lower level charts cannot access things in parent charts
  • Values are namespaced, but namespaces are pruned.
  • the scope of the values has been reduced and the namespace prefix removed
  • Helm supports special “global” value.
  • a way of sharing one top-level variable with all subcharts, which is useful for things like setting metadata properties like labels.
  • If a subchart declares a global variable, that global will be passed downward (to the subchart’s subcharts), but not upward to the parent chart.
  • global variables of parent charts take precedence over the global variables from subcharts.
  • helm lint
  • A chart repository is an HTTP server that houses one or more packaged charts
  • Any HTTP server that can serve YAML files and tar files and can answer GET requests can be used as a repository server.
  • Helm does not provide tools for uploading charts to remote repository servers.
  • the only way to add a chart to $HELM_HOME/starters is to manually copy it there.
  • Helm provides a hook mechanism to allow chart developers to intervene at certain points in a release’s life cycle.
  • Execute a Job to back up a database before installing a new chart, and then execute a second job after the upgrade in order to restore data.
  • Hooks are declared as an annotation in the metadata section of a manifest
  • Hooks work like regular templates, but they have special annotations
  • pre-install
  • post-install: Executes after all resources are loaded into Kubernetes
  • pre-delete
  • post-delete: Executes on a deletion request after all of the release’s resources have been deleted.
  • pre-upgrade
  • post-upgrade
  • pre-rollback
  • post-rollback: Executes on a rollback request after all resources have been modified.
  • crd-install
  • test-success: Executes when running helm test and expects the pod to return successfully (return code == 0).
  • test-failure: Executes when running helm test and expects the pod to fail (return code != 0).
  • Hooks allow you, the chart developer, an opportunity to perform operations at strategic points in a release lifecycle
  • Tiller then loads the hook with the lowest weight first (negative to positive)
  • Tiller returns the release name (and other data) to the client
  • If the resources is a Job kind, Tiller will wait until the job successfully runs to completion.
  • if the job fails, the release will fail. This is a blocking operation, so the Helm client will pause while the Job is run.
  • If they have hook weights (see below), they are executed in weighted order. Otherwise, ordering is not guaranteed.
  • good practice to add a hook weight, and set it to 0 if weight is not important.
  • The resources that a hook creates are not tracked or managed as part of the release.
  • leave the hook resource alone.
  • To destroy such resources, you need to either write code to perform this operation in a pre-delete or post-delete hook or add "helm.sh/hook-delete-policy" annotation to the hook template file.
  • Hooks are just Kubernetes manifest files with special annotations in the metadata section
  • One resource can implement multiple hooks
  • no limit to the number of different resources that may implement a given hook.
  • When subcharts declare hooks, those are also evaluated. There is no way for a top-level chart to disable the hooks declared by subcharts.
  • Hook weights can be positive or negative numbers but must be represented as strings.
  • sort those hooks in ascending order.
  • Hook deletion policies
  • "before-hook-creation" specifies Tiller should delete the previous hook before the new hook is launched.
  • By default Tiller will wait for 60 seconds for a deleted hook to no longer exist in the API server before timing out.
  • Custom Resource Definitions (CRDs) are a special kind in Kubernetes.
  • The crd-install hook is executed very early during an installation, before the rest of the manifests are verified.
  • A common reason why the hook resource might already exist is that it was not deleted following use on a previous install/upgrade.
  • Helm uses Go templates for templating your resource files.
  • two special template functions: include and required
  • include function allows you to bring in another template, and then pass the results to other template functions.
  • The required function allows you to declare a particular values entry as required for template rendering.
  • If the value is empty, the template rendering will fail with a user submitted error message.
  • When you are working with string data, you are always safer quoting the strings than leaving them as bare words
  • Quote Strings, Don’t Quote Integers
  • when working with integers do not quote the values
  • env variables values which are expected to be string
  • to include a template, and then perform an operation on that template’s output, Helm has a special include function
  • The above includes a template called toYaml, passes it $value, and then passes the output of that template to the nindent function.
  • Go provides a way for setting template options to control behavior when a map is indexed with a key that’s not present in the map
  • The required function gives developers the ability to declare a value entry as required for template rendering.
  • The tpl function allows developers to evaluate strings as templates inside a template.
  • Rendering a external configuration file
  • (.Files.Get "conf/app.conf")
  • Image pull secrets are essentially a combination of registry, username, and password.
  • Automatically Roll Deployments When ConfigMaps or Secrets change
  • configmaps or secrets are injected as configuration files in containers
  • a restart may be required should those be updated with a subsequent helm upgrade
  • The sha256sum function can be used to ensure a deployment’s annotation section is updated if another file changes
  • checksum/config: {{ include (print $.Template.BasePath "/configmap.yaml") . | sha256sum }}
  • helm upgrade --recreate-pods
  • "helm.sh/resource-policy": keep
  • resources that should not be deleted when Helm runs a helm delete
  • this resource becomes orphaned. Helm will no longer manage it in any way.
  • create some reusable parts in your chart
  • In the templates/ directory, any file that begins with an underscore(_) is not expected to output a Kubernetes manifest file.
  • by convention, helper templates and partials are placed in a _helpers.tpl file.
  • The current best practice for composing a complex application from discrete parts is to create a top-level umbrella chart that exposes the global configurations, and then use the charts/ subdirectory to embed each of the components.
  • SAP’s Converged charts: These charts install SAP Converged Cloud a full OpenStack IaaS on Kubernetes. All of the charts are collected together in one GitHub repository, except for a few submodules.
  • Deis’s Workflow: This chart exposes the entire Deis PaaS system with one chart. But it’s different from the SAP chart in that this umbrella chart is built from each component, and each component is tracked in a different Git repository.
  • YAML is a superset of JSON
  • any valid JSON structure ought to be valid in YAML.
  • As a best practice, templates should follow a YAML-like syntax unless the JSON syntax substantially reduces the risk of a formatting issue.
  • There are functions in Helm that allow you to generate random data, cryptographic keys, and so on.
  • a chart repository is a location where packaged charts can be stored and shared.
  • A chart repository is an HTTP server that houses an index.yaml file and optionally some packaged charts.
  • Because a chart repository can be any HTTP server that can serve YAML and tar files and can answer GET requests, you have a plethora of options when it comes down to hosting your own chart repository.
  • It is not required that a chart package be located on the same server as the index.yaml file.
  • A valid chart repository must have an index file. The index file contains information about each chart in the chart repository.
  • The Helm project provides an open-source Helm repository server called ChartMuseum that you can host yourself.
  • $ helm repo index fantastic-charts --url https://fantastic-charts.storage.googleapis.com
  • A repository will not be added if it does not contain a valid index.yaml
  • add the repository to their helm client via the helm repo add [NAME] [URL] command with any name they would like to use to reference the repository.
  • Helm has provenance tools which help chart users verify the integrity and origin of a package.
  • Integrity is established by comparing a chart to a provenance record
  • The provenance file contains a chart’s YAML file plus several pieces of verification information
  • Chart repositories serve as a centralized collection of Helm charts.
  • Chart repositories must make it possible to serve provenance files over HTTP via a specific request, and must make them available at the same URI path as the chart.
  • We don’t want to be “the certificate authority” for all chart signers. Instead, we strongly favor a decentralized model, which is part of the reason we chose OpenPGP as our foundational technology.
  • The Keybase platform provides a public centralized repository for trust information.
  • A chart contains a number of Kubernetes resources and components that work together.
  • A test in a helm chart lives under the templates/ directory and is a pod definition that specifies a container with a given command to run.
  • The pod definition must contain one of the helm test hook annotations: helm.sh/hook: test-success or helm.sh/hook: test-failure
  • helm test
  • nest your test suite under a tests/ directory like <chart-name>/templates/tests/
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.
  • ...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
       
      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

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

  • 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

Best Practices · mperham/sidekiq Wiki - 0 views

  • Don't save state to Sidekiq, save simple identifiers.
  • Look up the objects once you actually need them in your perform method.
  • The Sidekiq client API uses JSON.dump to send the data to Redis
  • ...6 more annotations...
  • The Sidekiq server pulls that JSON data from Redis and uses JSON.load to convert the data back into Ruby types to pass to your perform method
  • Idempotency means that your job can safely execute multiple times
  • use a database transaction to ensure data changes are rolled back if there is an error
  • Sidekiq will execute your job at least once.
  • Sidekiq is designed for parallel execution so design your jobs so you can run lots of them in parallel
  • Sidekiq will not provide features which hack around a lack of concurrency in your jobs.
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。
  • ...37 more annotations...
  • 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

Which Ruby background job framework is right for you? - 0 views

  • A background or asynchronous job (or task) is one that is processed outside of the usual request/response workflow that is part of any modern web framework
  • Asynchronous tasks on the other hand, are those that may be started from a normal web request, but require a longer time to complete than the normal request.
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