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張 旭

Configuration Blocks and Attributes - 0 views

terragrunt.gruntwork.io/...config-blocks-and-attributes

terraform devops

shared by 張 旭 on 16 Jun 21 - No Cached
  • The generate block can be used to arbitrarily generate a file in the terragrunt working directory (where terraform is called).
  • This can be used to generate common terraform configurations that are shared across multiple terraform modules.
  • 張 旭 on 16 Jun 21
     
    "The generate block can be used to arbitrarily generate a file in the terragrunt working directory (where terraform is called). "
張 旭

Kubernetes Components | Kubernetes - 0 views

kubernetes.io/...components

kubernetes system

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

鳥哥的 Linux 私房菜 -- 第一章、Linux是什麼與如何學習 - 0 views

linux.vbird.org/...0110whatislinux.php

system linux

shared by 張 旭 on 22 Jun 21 - Cached
  • Linux就是核心與系統呼叫介面那兩層
  • 核心與硬體的關係非常的強烈
  • Linux提供了一個完整的作業系統當中最底層的硬體控制與資源管理的完整架構， 這個架構是沿襲Unix良好的傳統來的，所以相當的穩定而功能強大
  • ...31 more annotations...
  • Linux的核心是由Linus Torvalds在1991年的時候給他開發出來的， 並且丟到網路上提供大家下載，後來大家覺得這個小東西(Linux Kernel)相當的小而精巧， 所以慢慢的就有相當多的朋友投入這個小東西的研究領域裡面去
  • 1960年代初期麻省理工學院(MIT)發展了所謂的： 『相容分時系統(Compatible Time-Sharing System, CTSS)』， 它可以讓大型主機透過提供數個終端機(terminal)以連線進入主機，來利用主機的資源進行運算工作
  • 為了更加強化大型主機的功能，以讓主機的資源可以提供更多使用者來利用，所以在1965年前後， 由貝爾實驗室(Bell)、麻省理工學院(MIT)及奇異公司(GE, 或稱為通用電器)共同發起了Multics的計畫
  • 以組合語言(Assembler)寫出了一組核心程式，同時包括一些核心工具程式， 以及一個小小的檔案系統。那個系統就是Unix的原型！ 當時Thompson將Multics龐大的複雜系統簡化了不少，於是同實驗室的朋友都戲稱這個系統為：Unics。(當時尚未有Unix的名稱)
  • 所有的程式或系統裝置都是檔案
  • 不管建構編輯器還是附屬檔案，所寫的程式只有一個目的，且要有效的完成目標。
  • Dennis Ritchie (註3) 將B語言重新改寫成C語言，再以C語言重新改寫與編譯Unics的核心， 最後正名與發行出Unix的正式版本！
  • 由於Unix是以較高階的C語言寫的，相對於組合語言需要與硬體有密切的配合， 高階的C語言與硬體的相關性就沒有這麼大了！所以，這個改變也使得Unix很容易被移植到不同的機器上面喔！
  • AT&T此時對於Unix是採取較開放的態度，此外，Unix是以高階的C語言寫成的， 理論上是具有可移植性的！亦即只要取得Unix的原始碼，並且針對大型主機的特性加以修訂原有的原始碼(Source Code)， 就可能將Unix移植到另一部不同的主機上頭了。
  • 柏克萊大學的Bill Joy (註4)在取得了Unix的核心原始碼後，著手修改成適合自己機器的版本， 並且同時增加了很多工具軟體與編譯程式，最終將它命名為Berkeley Software Distribution (BSD)。
  • 每一家公司自己出的Unix雖然在架構上面大同小異，但是卻真的僅能支援自身的硬體， 所以囉，早先的Unix只能與伺服器(Server)或者是大型工作站(Workstation)劃上等號！
  • AT&T在1979年發行的第七版Unix中，特別提到了 『不可對學生提供原始碼』的嚴格限制！
  • 純種的Unix指的就是System V以及BSD
  • AT&T自家的System V
  • 既然1979年的Unix第七版可以在Intel的x86架構上面進行移植， 那麼是否意味著可以將Unix改寫並移植到x86上面了呢？在這個想法上， 譚寧邦教授於是乎自己動手寫了Minix這個Unix Like的核心程式！
  • 『既然作業系統太複雜，我就先寫可以在Unix上面運行的小程式，這總可以了吧？』
  • 如果能夠寫出一個不錯的編譯器，那不就是大家都需要的軟體了嗎？ 因此他便開始撰寫C語言的編譯器，那就是現在相當有名的GNU C Compiler(gcc)！
  • 他還撰寫了更多可以被呼叫的C函式庫(GNU C library)，以及可以被使用來操作作業系統的基本介面BASH shell！ 這些都在1990年左右完成了！
  • 有鑑於圖形使用者介面(Graphical User Interface, GUI) 的需求日益加重，在1984年由MIT與其他協力廠商首次發表了X Window System ，並且更在1988年成立了非營利性質的XFree86這個組織。所謂的XFree86其實是 X Window System + Free + x86的整合名稱呢！
  • 譚寧邦教授為了教育需要而撰寫的Minix系統！ 他在購買了最新的Intel 386的個人電腦後，就立即安裝了Minix這個作業系統。 另外，上個小節當中也談到，Minix這個作業系統是有附上原始碼的， 所以托瓦茲也經由這個原始碼學習到了很多的核心程式設計的設計概念喔！
  • 托瓦茲自己也說：『我始終是個性能癖』^_^。 為了徹底發揮386的效能，於是托瓦茲花了不少時間在測試386機器上！ 他的重要測試就是在測試386的多功性能。首先，他寫了三個小程式，一個程式會持續輸出A、一個會持續輸出B， 最後一個會將兩個程式進行切換。他將三個程式同時執行，結果，他看到螢幕上很順利的一直出現ABABAB...... 他知道，他成功了！ ^_^
  • 為了讓所有的軟體都可以在Linux上執行，於是托瓦茲開始參考標準的POSIX規範。
  • POSIX是可攜式作業系統介面(Portable Operating System Interface)的縮寫，重點在規範核心與應用程式之間的介面， 這是由美國電器與電子工程師學會(IEEE)所發佈的一項標準喔
  • 因為托瓦茲放置核心的那個FTP網站的目錄為：Linux， 從此，大家便稱這個核心為Linux了。(請注意，此時的Linux就是那個kernel喔！ 另外，托瓦茲所丟到該目錄下的第一個核心版本為0.02呢！)
  • Linux其實就是一個作業系統最底層的核心及其提供的核心工具。 他是GNU GPL授權模式，所以，任何人均可取得原始碼與可執行這個核心程式，並且可以修改。
  • Linux參考POSIX設計規範，於是相容於Unix作業系統，故亦可稱之為Unix Like的一種
  • 為了讓使用者能夠接觸到Linux，於是很多的商業公司或非營利團體， 就將Linux Kernel(含tools)與可運行的軟體整合起來，加上自己具有創意的工具程式， 這個工具程式可以讓使用者以光碟/DVD或者透過網路直接安裝/管理Linux系統。 這個『Kernel + Softwares + Tools + 可完整安裝程序』的咚咚，我們稱之為Linux distribution， 一般中文翻譯成可完整安裝套件，或者Linux發佈商套件等。
  • 在1994年終於完成的Linux的核心正式版！version 1.0。 這一版同時還加入了X Window System的支援呢！且於1996年完成了2.0版、2011 年釋出 3.0 版，更於 2015 年 4 月釋出了 4.0 版哩！ 發展相當迅速喔！此外，托瓦茲指明了企鵝為Linux的吉祥物。
  • Linux本身就是個最陽春的作業系統，其開發網站設立在http://www.kernel.org，我們亦稱Linux作業系統最底層的資料為『核心(Kernel)』。
  • 常見的 Linux distributions 分類有『商業、社群』分類法，或『RPM、DPKG』分類法
  • 事實上鳥哥認為distributions主要分為兩大系統，一種是使用RPM方式安裝軟體的系統，包括Red Hat, Fedora, SuSE等都是這類； 一種則是使用Debian的dpkg方式安裝軟體的系統，包括Debian, Ubuntu, B2D等等。
張 旭

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

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

docker container devops

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

Kubernetes 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.
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  • 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

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.
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  • 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

podman/rootless.md at master · containers/podman - 0 views

github.com/...rootless.md

docker kubernetes

shared by 張 旭 on 03 Aug 20 - No Cached
  • Podman can not create containers that bind to ports < 1024
  • If /etc/subuid and /etc/subgid are not setup for a user, then podman commands can easily fail
  • Fedora 31 defaults to cgroup V2, which has full support of rootless cgroup management.
  • ...6 more annotations...
  • Some system unit configuration options do not work in the rootless container
  • it's better to create an override.conf drop-in that sets PrivateNetwork=no
  • Difficult to use additional stores for sharing content
  • Can not use overlayfs driver, but does support fuse-overlayfs
  • No CNI Support
  • Making device nodes within a container fails, even when running --privileged.
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

Understanding GitHub Actions - GitHub Docs - 0 views

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

github cicd devops

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

Introducing the MinIO Operator and Operator Console - 0 views

blog.min.io/minio-kubernetes-operator

s3 storage minio backup

shared by 張 旭 on 20 Sep 21 - No Cached
  • Object-storage-as-a-service is a game changer for IT.
  • provision multi-tenant object storage as a service.
  • have the skill set to create, deploy, tune, scale and manage modern, application oriented object storage using Kubernetes
  • ...12 more annotations...
  • MinIO is purpose-built to take full advantage of the Kubernetes architecture.
  • MinIO and Kubernetes work together to simplify infrastructure management, providing a way to manage object storage infrastructure within the Kubernetes toolset.  
  • The operator pattern extends Kubernetes's familiar declarative API model with custom resource definitions (CRDs) to perform common operations like resource orchestration, non-disruptive upgrades, cluster expansion and to maintain high-availability
  • The Operator uses the command set kubectl that the Kubernetes community was already familiar with and adds the kubectl minio plugin . The MinIO Operator and the MinIO kubectl plugin facilitate the deployment and management of MinIO Object Storage on Kubernetes - which is how multi-tenant object storage as a service is delivered.
  • choosing a leader for a distributed application without an internal member election process
  • The Operator Console makes Kubernetes object storage easier still. In this graphical user interface, MinIO created something so simple that anyone in the organization can create, deploy and manage object storage as a service.
  • The primary unit of managing MinIO on Kubernetes is the tenant.
  • The MinIO Operator can allocate multiple tenants within the same Kubernetes cluster.
  • Each tenant, in turn, can have different capacity (i.e: a small 500GB tenant vs a 100TB tenant), resources (1000m CPU and 4Gi RAM vs 4000m CPU and 16Gi RAM) and servers (4 pods vs 16 pods), as well a separate configurations regarding Identity Providers, Encryption and versions.
  • each tenant is a cluster of server pools (independent sets of nodes with their own compute, network, and storage resources), that, while sharing the same physical infrastructure, are fully isolated from each other in their own namespaces.
  • Each tenant runs their own MinIO cluster, fully isolated from other tenants
  • Each tenant scales independently by federating clusters across geographies.
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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