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張 旭

File: README - Documentation by YARD 0.8.7.6 - 0 views

rspec.info/...rspec-core

rails ruby test programming rspec

shared by 張 旭 on 14 Jun 17 - No Cached
  • we can express concepts like a conversation
    • 張 旭
      張 旭 on 14 Jun 17
       
      描述 order 這個東西。 order 就是將登記在它上面的物品價格加總起來。
  • The describe method creates an ExampleGroup.
  • ...15 more annotations...
  • declare examples using the it method
  • an example group is a class in which the block passed to describe is evaluated
  • The blocks passed to it are evaluated in the context of an instance of that class
  • nested groups using the describe or context methods
  • can declare example groups using either describe or context
  • can declare examples within a group using any of it, specify, or example
  • Declare a shared example group using shared_examples, and then include it in any group using include_examples.
  • Nearly anything that can be declared within an example group can be declared within a shared example group.
  • shared_context and include_context.
  • When a class is passed to describe, you can access it from an example using the described_class method
  • rspec-core stores a metadata hash with every example and group
  • Example groups are defined by a describe or context block, which is eagerly evaluated when the spec file is loaded
  • Examples -- typically defined by an it block -- and any other blocks with per-example semantics -- such as a before(:example) hook -- are evaluated in the context of an instance of the example group class to which the example belongs.
  • Examples are not executed when the spec file is loaded
  • run any examples until all spec files have been loaded
張 旭

plataformatec/simple_form - 0 views

github.com/...simple_form

rails gem form html

shared by 張 旭 on 29 Aug 16 - No Cached
  • The basic goal of Simple Form is to not touch your way of defining the layout
  • by default contains label, hints, errors and the input itself
  • Simple Form acts as a DSL and just maps your input type (retrieved from the column definition in the database) to a specific helper method.
  • ...68 more annotations...
  • can overwrite the default label by passing it to the input method
  • configure the html of any of them
  • disable labels, hints or error
  • add a hint, an error, or even a placeholder
  • add an inline label
  • pass any html attribute straight to the input, by using the :input_html option
  • use the :defaults option in simple_form_fo
  • Simple Form generates a wrapper div around your label and input by default, you can pass any html attribute to that wrapper as well using the :wrapper_html option,
  • By default all inputs are required
  • the required property of any input can be overwritten
  • Simple Form will look at the column type in the database and use an appropriate input for the column
  • lets you overwrite the default input type it creates
  • can also render boolean attributes using as: :select to show a dropdown.
  • give the :disabled option to Simple Form, and it'll automatically mark the wrapper as disabled with a CSS class
  • Simple Form accepts same options as their corresponding input type helper in Rails
  • Any extra option passed to these methods will be rendered as html option.
  • use label, hint, input_field, error and full_error helpers
  • to strip away all the div wrappers around the <input> field
  • is to use f.input_field
  • changing boolean_style from default value :nested to :inline
  • overriding the :collection option
  • Collections can be arrays or ranges, and when a :collection is given the :select input will be rendered by default
  • Other types of collection are :radio_buttons and :check_boxes
  • label_method
  • value_method
  • Both of these options also accept lambda/procs
  • define a to_label method on your model as Simple Form will search for and use :to_label as a :label_method first if it is found
  • create grouped collection selects, that will use the html optgroup tags
  • Grouped collection inputs accept the same :label_method and :value_method options
  • group_method
  • group_label_method
  • configured with a default value to be used on the site through the SimpleForm.country_priority and SimpleForm.time_zone_priority helpers.
  • association
  • association
  • render a :select input for choosing the :company, and another :select input with :multiple option for the :roles
  • all options available to :select, :radio_buttons and :check_boxes are also available to association
  • declare different labels and values
  • the association helper is currently only tested with Active Record
  • f.input
  • f.select
  • create a <button> element
  • simple_fields_for
  • Creates a collection of radio inputs with labels associated
  • Creates a collection of checkboxes with labels associated
  • collection_radio_buttons
  • collection_check_boxes
  • associations in your model
  • Role.all
  • the html element you will get for each attribute according to its database definition
  • redefine existing Simple Form inputs by creating a new class with the same name
  • Simple Form uses all power of I18n API to lookup labels, hints, prompts and placeholders
  • specify defaults for all models under the 'defaults' key
  • Simple Form will always look for a default attribute translation under the "defaults" key if no specific is found inside the model key
  • Simple Form will fallback to default human_attribute_name from Rails when no other translation is found for labels.
  • Simple Form will only do the lookup for options if you give a collection composed of symbols only.
  • "Add %{model}"
  • translations for labels, hints and placeholders for a namespaced model, e.g. Admin::User, should be placed under admin_user, not under admin/user
  • This difference exists because Simple Form relies on object_name provided by Rails' FormBuilder to determine the translation path for a given object instead of i18n_key from the object itself.
  • configure how your components will be rendered using the wrappers API
  • optional
  • unless_blank
  • By default, Simple Form will generate input field types and attributes that are supported in HTML5
  • The HTML5 extensions include the new field types such as email, number, search, url, tel, and the new attributes such as required, autofocus, maxlength, min, max, step.
  • If you want to have all other HTML 5 features, such as the new field types, you can disable only the browser validation
  • add novalidate to a specific form by setting the option on the form itself
  • the Simple Form configuration file
  • passing the html5 option
  • as: :date, html5: true
張 旭

What exactly was the point of [ "x$var" = "xval" ]? - Vidar's Blog - 0 views

www.vidarholen.net/...blog

shell programming code

shared by 張 旭 on 21 Apr 21 - No Cached
  • x-hack
  • test "x$arg" = "x-f"
  • the utility used a simple recursive descent parser without backtracking, which gave unary operators precedence over binary operators and ignored trailing arguments.
  • ...3 more annotations...
  • The x-hack is effective because no unary operators can start with x.
  • the x-hack could be used to work around certain bugs all the way up until 2015, seven years after StackOverflow wrote it off as an archaic relic of the past!
  • The Dash issue of [ "(" = ")" ] was originally reported in a form that affected both Bash 3.2.48 and Dash 0.5.4 in 2008. You can still see this on macOS bash today
  • 張 旭 on 21 Apr 21
     
    "x$var"
張 旭

Quick start - 0 views

terragrunt.gruntwork.io/...quick-start

devops terraform

shared by 張 旭 on 15 Jun 21 - No Cached
  • Terragrunt will forward almost all commands, arguments, and options directly to Terraform, but based on the settings in your terragrunt.hcl file
  • the backend configuration does not support variables or expressions of any sort
  • the path_relative_to_include() built-in function,
  • ...9 more annotations...
  • The generate attribute is used to inform Terragrunt to generate the Terraform code for configuring the backend.
  • The find_in_parent_folders() helper will automatically search up the directory tree to find the root terragrunt.hcl and inherit the remote_state configuration from it.
  • Unlike the backend configurations, provider configurations support variables,
  • if you needed to modify the configuration to expose another parameter (e.g session_name), you would have to then go through each of your modules to make this change.
  • instructs Terragrunt to create the file provider.tf in the working directory (where Terragrunt calls terraform) before it calls any of the Terraform commands
  • large modules should be considered harmful.
  • it is a Bad Idea to define all of your environments (dev, stage, prod, etc), or even a large amount of infrastructure (servers, databases, load balancers, DNS, etc), in a single Terraform module.
  • Large modules are slow, insecure, hard to update, hard to code review, hard to test, and brittle (i.e., you have all your eggs in one basket).
  • Terragrunt allows you to define your Terraform code once and to promote a versioned, immutable “artifact” of that exact same code from environment to environment.
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

Logstash Alternatives: Pros & Cons of 5 Log Shippers [2019] - Sematext - 0 views

sematext.com/...logstash-alternatives

log monitor system logstash fluentd

shared by 張 旭 on 05 Nov 19 - No Cached
  • In this case, Elasticsearch. And because Elasticsearch can be down or struggling, or the network can be down, the shipper would ideally be able to buffer and retry
  • Logstash is typically used for collecting, parsing, and storing logs for future use as part of log management.
  • Logstash’s biggest con or “Achille’s heel” has always been performance and resource consumption (the default heap size is 1GB).
  • ...37 more annotations...
  • This can be a problem for high traffic deployments, when Logstash servers would need to be comparable with the Elasticsearch ones.
  • Filebeat was made to be that lightweight log shipper that pushes to Logstash or Elasticsearch.
  • differences between Logstash and Filebeat are that Logstash has more functionality, while Filebeat takes less resources.
  • Filebeat is just a tiny binary with no dependencies.
  • For example, how aggressive it should be in searching for new files to tail and when to close file handles when a file didn’t get changes for a while.
  • For example, the apache module will point Filebeat to default access.log and error.log paths
  • Filebeat’s scope is very limited,
  • Initially it could only send logs to Logstash and Elasticsearch, but now it can send to Kafka and Redis, and in 5.x it also gains filtering capabilities.
  • Filebeat can parse JSON
  • you can push directly from Filebeat to Elasticsearch, and have Elasticsearch do both parsing and storing.
  • You shouldn’t need a buffer when tailing files because, just as Logstash, Filebeat remembers where it left off
  • For larger deployments, you’d typically use Kafka as a queue instead, because Filebeat can talk to Kafka as well
  • The default syslog daemon on most Linux distros, rsyslog can do so much more than just picking logs from the syslog socket and writing to /var/log/messages.
  • It can tail files, parse them, buffer (on disk and in memory) and ship to a number of destinations, including Elasticsearch.
  • rsyslog is the fastest shipper
  • Its grammar-based parsing module (mmnormalize) works at constant speed no matter the number of rules (we tested this claim).
  • use it as a simple router/shipper, any decent machine will be limited by network bandwidth
  • It’s also one of the lightest parsers you can find, depending on the configured memory buffers.
  • rsyslog requires more work to get the configuration right
  • the main difference between Logstash and rsyslog is that Logstash is easier to use while rsyslog lighter.
  • rsyslog fits well in scenarios where you either need something very light yet capable (an appliance, a small VM, collecting syslog from within a Docker container).
  • rsyslog also works well when you need that ultimate performance.
  • syslog-ng as an alternative to rsyslog (though historically it was actually the other way around).
  • a modular syslog daemon, that can do much more than just syslog
  • Unlike rsyslog, it features a clear, consistent configuration format and has nice documentation.
  • Similarly to rsyslog, you’d probably want to deploy syslog-ng on boxes where resources are tight, yet you do want to perform potentially complex processing.
  • syslog-ng has an easier, more polished feel than rsyslog, but likely not that ultimate performance
  • Fluentd was built on the idea of logging in JSON wherever possible (which is a practice we totally agree with) so that log shippers down the line don’t have to guess which substring is which field of which type.
  • Fluentd plugins are in Ruby and very easy to write.
  • structured data through Fluentd, it’s not made to have the flexibility of other shippers on this list (Filebeat excluded).
  • Fluent Bit, which is to Fluentd similar to how Filebeat is for Logstash.
  • Fluentd is a good fit when you have diverse or exotic sources and destinations for your logs, because of the number of plugins.
  • Splunk isn’t a log shipper, it’s a commercial logging solution
  • Graylog is another complete logging solution, an open-source alternative to Splunk.
  • everything goes through graylog-server, from authentication to queries.
  • Graylog is nice because you have a complete logging solution, but it’s going to be harder to customize than an ELK stack.
  • it depends
張 旭

How to Benchmark Performance of MySQL & MariaDB Using SysBench | Severalnines - 1 views

severalnines.com/...e-mysql-mariadb-using-sysbench

database mysql performance

shared by 張 旭 on 22 Oct 20 - No Cached
  • SysBench is a C binary which uses LUA scripts to execute benchmarks
  • support for parallelization in the LUA scripts, multiple queries can be executed in parallel
  • by default, benchmarks which cover most of the cases - OLTP workloads, read-only or read-write, primary key lookups and primary key updates.
  • ...21 more annotations...
  • SysBench is not a tool which you can use to tune configurations of your MySQL servers (unless you prepared LUA scripts with custom workload or your workload happen to be very similar to the benchmark workloads that SysBench comes with)
  • it is great for is to compare performance of different hardware.
  • Every new server acquired should go through a warm-up period during which you will stress it to pinpoint potential hardware defects
  • by executing OLTP workload which overloads the server, or you can also use dedicated benchmarks for CPU, disk and memory.
  • bulk_insert.lua. This test can be used to benchmark the ability of MySQL to perform multi-row inserts.
  • All oltp_* scripts share a common table structure. First two of them (oltp_delete.lua and oltp_insert.lua) execute single DELETE and INSERT statements.
  • oltp_point_select, oltp_update_index and oltp_update_non_index. These will execute a subset of queries - primary key-based selects, index-based updates and non-index-based updates.
  • you can run different workload patterns using the same benchmark.
  • Warmup helps to identify “regular” throughput by executing benchmark for a predefined time, allowing to warm up the cache, buffer pools etc.
  • By default SysBench will attempt to execute queries as fast as possible. To simulate slower traffic this option may be used. You can define here how many transactions should be executed per second.
  • SysBench gives you ability to generate different types of data distribution.
  • decide if SysBench should use prepared statements (as long as they are available in the given datastore - for MySQL it means PS will be enabled by default) or not.
  • sysbench ./sysbench/src/lua/oltp_read_write.lua  help
  • By default, SysBench will attempt to execute queries in explicit transaction. This way the dataset will stay consistent and not affected: SysBench will, for example, execute INSERT and DELETE on the same row, making sure the data set will not grow (impacting your ability to reproduce results).
  • specify error codes from MySQL which SysBench should ignore (and not kill the connection).
  • the two most popular benchmarks - OLTP read only and OLTP read/write.
  • 1 million rows will result in ~240 MB of data. Ten tables, 1000 000 rows each equals to 2.4GB
  • by default, SysBench looks for ‘sbtest’ schema which has to exist before you prepare the data set. You may have to create it manually.
  • pass ‘--histogram’ argument to SysBench
  • ~48GB of data (20 tables, 10 000 000 rows each).
  • if you don’t understand why the performance was like it was, you may draw incorrect conclusions out of the benchmarks.
張 旭

Full Cycle Developers at Netflix - Operate What You Build - 1 views

netflixtechblog.com/lopers-at-netflix-a08c31f83249

devops system

shared by 張 旭 on 27 May 20 - No Cached
  • Researching issues felt like bouncing a rubber ball between teams, hard to catch the root cause and harder yet to stop from bouncing between one another.
  • In the past, Edge Engineering had ops-focused teams and SRE specialists who owned the deploy+operate+support parts of the software life cycle
  • hearing about those problems second-hand
  • ...17 more annotations...
  • devs could push code themselves when needed, and also were responsible for off-hours production issues and support requests
  • What were we trying to accomplish and why weren’t we being successful?
  • These specialized roles create efficiencies within each segment while potentially creating inefficiencies across the entire life cycle.
  • Grouping differing specialists together into one team can reduce silos, but having different people do each role adds communication overhead, introduces bottlenecks, and inhibits the effectiveness of feedback loops.
  • devops principles
  • develops a system also be responsible for operating and supporting that system
  • Each development team owns deployment issues, performance bugs, capacity planning, alerting gaps, partner support, and so on.
  • Those centralized teams act as force multipliers by turning their specialized knowledge into reusable building blocks.
  • Communication and alignment are the keys to success.
  • Full cycle developers are expected to be knowledgeable and effective in all areas of the software life cycle.
  • ramping up on areas they haven’t focused on before
  • We run dev bootcamps and other forms of ongoing training to impart this knowledge and build up these skills
  • “how can I automate what is needed to operate this system?”
  • “what self-service tool will enable my partners to answer their questions without needing me to be involved?”
  • A full cycle developer thinks and acts like an SWE, SDET, and SRE. At times they create software that solves business problems, at other times they write test cases for that, and still other times they automate operational aspects of that system.
  • the need for continuous delivery pipelines, monitoring/observability, and so on.
  • Tooling and automation help to scale expertise, but no tool will solve every problem in the developer productivity and operations space
張 旭

Deploy Replica Set With Keyfile Authentication - MongoDB Manual - 0 views

docs.mongodb.com/...et-with-keyfile-access-control

database mongodb

shared by 張 旭 on 13 Oct 20 - No Cached
  • Keyfiles are bare-minimum forms of security and are best suited for testing or development environments.
  • With keyfile authentication, each mongod instances in the replica set uses the contents of the keyfile as the shared password for authenticating other members in the deployment.
  • On UNIX systems, the keyfile must not have group or world permissions.
  • ...3 more annotations...
  • Copy the keyfile to each server hosting the replica set members.
  • the user running the mongod instances is the owner of the file and can access the keyfile.
  • For each member in the replica set, start the mongod with either the security.keyFile configuration file setting or the --keyFile command-line option.
張 旭

Helm | Getting Started - 0 views

helm.sh/...getting_started

helm

shared by 張 旭 on 02 Oct 21 - No Cached
  • The templates/ directory is for template files. When Helm evaluates a chart, it will send all of the files in the templates/ directory through the template rendering engine. It then collects the results of those templates and sends them on to Kubernetes.
  • The charts/ directory may contain other charts (which we call subcharts).
  • we recommend using the suffix .yaml for YAML files and .tpl for helpers.
  • ...8 more annotations...
  • 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, and then is followed by an automatically generated comment line that tells us what template file generated this YAML document.
  • name: field is limited to 63 characters because of limitations to the DNS system.
  • The template directive {{ .Release.Name }} injects the release name into the template. 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
  • helm install --debug --dry-run goodly-guppy ./mychart. This will render the templates. But instead of installing the chart, it will return the rendered template to you
  • 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.
  • It's best not to assume that your chart will install just because --dry-run works.
張 旭

Tagging AWS resources - AWS General Reference - 0 views

docs.aws.amazon.com/...aws_tagging.html

aws cloud

shared by 張 旭 on 20 Jan 22 - No Cached
  • assign metadata to your AWS resources in the form of tags.
  • a user-defined key and value
  • Tag keys are case sensitive.
  • ...17 more annotations...
  • tag values are case sensitive.
  • Tags are accessible to many AWS services, including billing.
  • personally identifiable information (PII)
  • apply it consistently across all resource types.
  • Use automated tools to help manage resource tags.
  • Use too many tags rather than too few tags.
  • Tag policies let you specify tagging rules that define valid key names and the values that are valid for each key.
  • Name – Identify individual resources
  • Environment – Distinguish between development, test, and production resources
  • Project – Identify projects that the resource supports
  • Owner – Identify who is responsible for the resource
  • Each resource can have a maximum of 50 user created tags.
  • For each resource, each tag key must be unique, and each tag key can have only one value.
  • Tag keys and values are case sensitive.
  • decide on a strategy for capitalizing tags, and consistently implement that strategy across all resource types.
  • AWS Cost Explorer and detailed billing reports let you break down AWS costs by tag.
  • An effective tagging strategy uses standardized tags and applies them consistently and programmatically across AWS resources.
  • 張 旭 on 20 Jan 22
     
    "assign metadata to your AWS resources in the form of tags."
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

Why I Will Never Use Alpine Linux Ever Again | Martin Heinz | Personal Website & Blog - 2 views

martinheinz.dev/92

linux alpine docker image

shared by 張 旭 on 23 Sep 23 - No Cached
  • musl is an implementation of C standard library. It is more lightweight, faster and simpler than glibc used by other Linux distros, such as Ubuntu.
  • Some of it stems from how musl (and therefore also Alpine) handles DNS (it's always DNS), more specifically, musl (by design) doesn't support DNS-over-TCP.
  • By using Alpine, you're getting "free" chaos engineering for you cluster.
  • ...2 more annotations...
  • this DNS issue does not manifest in Docker container. It can only happen in Kubernetes, so if you test locally, everything will work fine, and you will only find out about unfixable issue when you deploy the application to a cluster.
  • if your application requires CGO_ENABLED=1, you will obviously run into issue with Alpine.
  • 張 旭 on 23 Sep 23
     
    "musl is an implementation of C standard library. It is more lightweight, faster and simpler than glibc used by other Linux distros, such as Ubuntu."
張 旭

Java microservices architecture by example - 0 views

www.scnsoft.com/...java-microservices-example

java micro service architecture programming

shared by 張 旭 on 26 Oct 18 - No Cached
baliar liked it
  • A microservices architecture is a particular case of a service-oriented architecture (SOA)
  • What sets microservices apart is the extent to which these modules are interconnected.
  • Every server comprises just one certain business process and never consists of several smaller servers.
  • ...12 more annotations...
  • Microservices also bring a set of additional benefits, such as easier scaling, the possibility to use multiple programming languages and technologies, and others.
  • Java is a frequent choice for building a microservices architecture as it is a mature language tested over decades and has a multitude of microservices-favorable frameworks, such as legendary Spring, Jersey, Play, and others.
  • A monolithic architecture keeps it all simple. An app has just one server and one database.
  • All the connections between units are inside-code calls.
  • split our application into microservices and got a set of units completely independent for deployment and maintenance.
  • Each of microservices responsible for a certain business function communicates either via sync HTTP/REST or async AMQP protocols.
  • ensure seamless communication between newly created distributed components.
  • The gateway became an entry point for all clients’ requests.
  • We also set the Zuul 2 framework for our gateway service so that the application could leverage the benefits of non-blocking HTTP calls.
  • we've implemented the Eureka server as our server discovery that keeps a list of utilized user profile and order servers to help them discover each other.
  • We also have a message broker (RabbitMQ) as an intermediary between the notification server and the rest of the servers to allow async messaging in-between.
  • microservices can definitely help when it comes to creating complex applications that deal with huge loads and need continuous improvement and scaling.
張 旭

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

prinsfrank.nl/...g-gitlab-pipelines-pt-1-basics

devops gitlab cicd proxy performance

shared by 張 旭 on 23 Feb 22 - No Cached
  • 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
  • ...8 more annotations...
  • 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,
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