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張 旭

Database Profiler - MongoDB Manual - 0 views

docs.mongodb.com/...manage-the-database-profiler

database mongodb

shared by 張 旭 on 16 Apr 21 - No Cached
  • The database profiler collects detailed information about Database Commands executed against a running mongod instance.
  • The profiler writes all the data it collects to the system.profile collection, a capped collection in the admin database.
  • db.setProfilingLevel(2)
  • ...10 more annotations...
  • The slowms and sampleRate profiling settings are global. When set, these settings affect all databases in your process.
  • db.setProfilingLevel(1, { slowms: 20 })
  • db.setProfilingLevel(0, { slowms: 20 })
  • show profile
  • The system.profile collection is a capped collection with a default size of 1 megabyte.
  • By default, sampleRate is set to 1.0, meaning all slow operations are profiled.
  • When logLevel is set to 0, MongoDB records slow operations to the diagnostic log at a rate determined by slowOpSampleRate.
  • The slowms field indicates operation time threshold, in milliseconds, beyond which operations are considered slow.
  • You cannot enable profiling on a mongos instance.
  • profiler logs information about database operations in the system.profile collection.
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

Improving Kubernetes reliability: quicker detection of a Node down | Fatal failure - 0 views

fatalfailure.wordpress.com/...icker-detection-of-a-node-down

kubernetes HA

shared by 張 旭 on 21 Jul 21 - No Cached
  • when a Node gets down, the pods of the broken node are still running for some time and they still get requests, and those requests, will fail.
  • 1- The Kubelet posts its status to the masters using –node-status-update-frequency=10s 2- A node dies 3- The kube controller manager is the one monitoring the nodes, using –-node-monitor-period=5s it checks, in the masters, the node status reported by the Kubelet. 4- Kube controller manager will see the node is unresponsive, and has this grace period –node-monitor-grace-period=40s until it considers the node unhealthy.
  • node-status-update-frequency x (N-1) != node-monitor-grace-period
  • ...2 more annotations...
  • 5- Once the node is marked as unhealthy, the kube controller manager will remove its pods based on –pod-eviction-timeout=5m0s
  • 6- Kube proxy has a watcher over the API, so the very first moment the pods are evicted the proxy will notice and update the iptables of the node, removing the endpoints from the services so the failing pods won’t be accessible anymore.
張 旭

Helm | - 0 views

helm.sh/...chart_template_guide

helm kubernetes chart devops development

shared by 張 旭 on 06 Aug 19 - No Cached
  • Templates generate manifest files, which are YAML-formatted resource descriptions that Kubernetes can understand.
  • service.yaml: A basic manifest for creating a service endpoint for your deployment
  • In Kubernetes, a ConfigMap is simply a container for storing configuration data.
  • ...88 more annotations...
  • deployment.yaml: A basic manifest for creating a Kubernetes deployment
  • using the suffix .yaml for YAML files and .tpl for helpers.
  • It is just fine to put a plain YAML file like this in the templates/ directory.
  • helm get manifest
  • The helm get manifest command takes a release name (full-coral) and prints out all of the Kubernetes resources that were uploaded to the server. Each file begins with --- to indicate the start of a YAML document
  • Names should be unique to a release
  • The name: field is limited to 63 characters because of limitations to the DNS system.
  • release names are limited to 53 characters
  • {{ .Release.Name }}
  • A template directive is enclosed in {{ and }} blocks.
  • The values that are passed into a template can be thought of as namespaced objects, where a dot (.) separates each namespaced element.
  • The leading dot before Release indicates that we start with the top-most namespace for this scope
  • The Release object is one of the built-in objects for Helm
  • When you want to test the template rendering, but not actually install anything, you can use helm install ./mychart --debug --dry-run
  • Using --dry-run will make it easier to test your code, but it won’t ensure that Kubernetes itself will accept the templates you generate.
  • Objects are passed into a template from the template engine.
  • create new objects within your templates
  • Objects can be simple, and have just one value. Or they can contain other objects or functions.
  • Release is one of the top-level objects that you can access in your templates.
  • Release.Namespace: The namespace to be released into (if the manifest doesn’t override)
  • Values: Values passed into the template from the values.yaml file and from user-supplied files. By default, Values is empty.
  • Chart: The contents of the Chart.yaml file.
  • Files: This provides access to all non-special files in a chart.
  • Files.Get is a function for getting a file by name
  • Files.GetBytes is a function for getting the contents of a file as an array of bytes instead of as a string. This is useful for things like images.
  • Template: Contains information about the current template that is being executed
  • BasePath: The namespaced path to the templates directory of the current chart
  • The built-in values always begin with a capital letter.
  • Go’s naming convention
  • use only initial lower case letters in order to distinguish local names from those built-in.
  • If this is a subchart, the values.yaml file of a parent chart
  • Individual parameters passed with --set
  • values.yaml is the default, which can be overridden by a parent chart’s values.yaml, which can in turn be overridden by a user-supplied values file, which can in turn be overridden by --set parameters.
  • While structuring data this way is possible, the recommendation is that you keep your values trees shallow, favoring flatness.
  • If you need to delete a key from the default values, you may override the value of the key to be null, in which case Helm will remove the key from the overridden values merge.
  • Kubernetes would then fail because you can not declare more than one livenessProbe handler.
  • When injecting strings from the .Values object into the template, we ought to quote these strings.
  • quote
  • Template functions follow the syntax functionName arg1 arg2...
  • While we talk about the “Helm template language” as if it is Helm-specific, it is actually a combination of the Go template language, some extra functions, and a variety of wrappers to expose certain objects to the templates.
  • Drawing on a concept from UNIX, pipelines are a tool for chaining together a series of template commands to compactly express a series of transformations.
  • pipelines are an efficient way of getting several things done in sequence
  • The repeat function will echo the given string the given number of times
  • default DEFAULT_VALUE GIVEN_VALUE. This function allows you to specify a default value inside of the template, in case the value is omitted.
  • all static default values should live in the values.yaml, and should not be repeated using the default command
  • Operators are implemented as functions that return a boolean value.
  • To use eq, ne, lt, gt, and, or, not etcetera place the operator at the front of the statement followed by its parameters just as you would a function.
  • if and
  • if or
  • with to specify a scope
  • range, which provides a “for each”-style loop
  • block declares a special kind of fillable template area
  • A pipeline is evaluated as false if the value is: a boolean false a numeric zero an empty string a nil (empty or null) an empty collection (map, slice, tuple, dict, array)
  • incorrect YAML because of the whitespacing
  • When the template engine runs, it removes the contents inside of {{ and }}, but it leaves the remaining whitespace exactly as is.
  • {{- (with the dash and space added) indicates that whitespace should be chomped left, while -}} means whitespace to the right should be consumed.
  • Newlines are whitespace!
  • an * at the end of the line indicates a newline character that would be removed
  • Be careful with the chomping modifiers.
  • the indent function
  • Scopes can be changed. with can allow you to set the current scope (.) to a particular object.
  • Inside of the restricted scope, you will not be able to access the other objects from the parent scope.
  • range
  • The range function will “range over” (iterate through) the pizzaToppings list.
  • Just like with sets the scope of ., so does a range operator.
  • The toppings: |- line is declaring a multi-line string.
  • not a YAML list. It’s a big string.
  • the data in ConfigMaps data is composed of key/value pairs, where both the key and the value are simple strings.
  • The |- marker in YAML takes a multi-line string.
  • range can be used to iterate over collections that have a key and a value (like a map or dict).
  • In Helm templates, a variable is a named reference to another object. It follows the form $name
  • Variables are assigned with a special assignment operator: :=
  • {{- $relname := .Release.Name -}}
  • capture both the index and the value
  • the integer index (starting from zero) to $index and the value to $topping
  • For data structures that have both a key and a value, we can use range to get both
  • Variables are normally not “global”. They are scoped to the block in which they are declared.
  • one variable that is always global - $ - this variable will always point to the root context.
  • $.
  • $.
  • Helm template language is its ability to declare multiple templates and use them together.
  • A named template (sometimes called a partial or a subtemplate) is simply a template defined inside of a file, and given a name.
  • when naming templates: template names are global.
  • If you declare two templates with the same name, whichever one is loaded last will be the one used.
  • you should be careful to name your templates with chart-specific names.
  • templates in subcharts are compiled together with top-level templates
  • naming convention is to prefix each defined template with the name of the chart: {{ define "mychart.labels" }}
  • Helm has over 60 available functions.
張 旭

MySQL cluster vs Galera - How to make the right choice - 0 views

bobcares.com/...mysql-cluster-vs-galera

database mysql cluster

shared by 張 旭 on 27 Sep 20 - No Cached
  • there is no “one size fits all” solution when coming to database clustering.
  • MySQL cluster contains the data nodes that store the cluster data and management node that store the cluster’s configuration.
  • MySQL clients first communicate with the management node and then connect directly to these data nodes.
  • ...13 more annotations...
  • For synchronization of data in the data nodes, MySQL cluster uses a special data engine called NDB (Network Database).
  • it uses automatic shrading aka splitting of a large database into small units.
  • MySQL cluster avoids single point failure and ensures 99.99% availability.
  • MySQL cluster can provide a response time as low as less than 3 ms.
  • Galera Cluster consists of a database server and uses the Galera Replication Plugin to manage replication.
  • a multi-master database cluster that supports synchronous replication.
  • it provides multiple, up-to-date copies of the data.
  • there is a need for instant fail-over.
  • Galera cluster allows the read and write of data in any node.
  • Galera cluster include guaranteed write consistency, automatic node provisioning, etc.
  • Upon restoring the connection, the separated nodes will sync back and rejoin the cluster automatically.
  • there is no need to have management node like MySQL cluster.
  • it gives best results with the InnoDB storage engine.
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

mvn clean install - a short guide to Maven - 0 views

www.marcobehler.com/...install-a-short-guide-to-maven

programming java

shared by 張 旭 on 12 Aug 20 - No Cached
  • An equivalent in other languages would be Javascript’s npm, Ruby’s gems or PHP’s composer.
  • Maven expects a certain directory structure for your Java source code to live in and when you later do a mvn clean install , the whole compilation and packaging work will be done for you.
  • any directory that contains a pom.xml file is also a valid Maven project.
  • ...17 more annotations...
  • A pom.xml file contains everything needed to describe your Java project.
  • Java source code is to be meant to live in the "/src/main/java" folder
  • Maven will put compiled Java classes into the "target/classes" folder
  • Maven will also build a .jar or .war file, depending on your project, that lives in the "target" folder.
  • Maven has the concept of a build lifecycle, which is made up of different phases.
  • clean is not part of Maven’s default lifecycle, you end up with commands like mvn clean install or mvn clean package. Install or package will trigger all preceding phases, but you need to specify clean in addition.
  • Maven will always download your project dependencies into your local maven repository first and then reference them for your build.
  • local repositories (in your user’s home directory: ~/.m2/)
  • clean: deletes the /target folder.
  • mvn clean package
  • mvn clean install
  • package: Converts your .java source code into a .jar/.war file and puts it into the /target folder.
  • install: First, it does a package(!). Then it takes that .jar/.war file and puts it into your local Maven repository, which lives in ~/.m2/repository.
  • calling 'mvn install' would be enough if Maven was smart enough to do reliable, incremental builds.
  • figuring out what Java source files/modules changed and only compile those.
  • developers got it ingrained to always call 'mvn clean install' (even though this increases build time a lot in bigger projects).
  • make sure that Maven always tries to download the latest snapshot dependency versions
張 旭

Services | GitLab - 0 views

docs.gitlab.com/services

gitlab cicd service

shared by 張 旭 on 12 Sep 21 - No Cached
  • The services keyword defines a Docker image that runs during a job linked to the Docker image that the image keyword defines. This allows you to access the service image during build time.
張 旭

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

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

devops cicd

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

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

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

devops docker cache cicd

shared by 張 旭 on 11 Mar 22 - No Cached
  • optimize our images.
  • When you build an image, it is made of multiple layers: we add a layer per instruction.
  • If we build the same image again without modifying any file, Docker will use existing layers rather than re-executing the instructions.
  • ...21 more annotations...
  • an image is made of multiple layers, and we can accelerate its build by using layers cache from the previous image version.
  • by using Docker-in-Docker, we get a fresh Docker instance per job which local registry is empty.
  • docker build --cache-from "$CI_REGISTRY_IMAGE:latest" -t "$CI_REGISTRY_IMAGE:new-tag"
  • But if you maintain a CHANGELOG in this format, and/or your Git tags are also your Docker tags, you can get the previous version and use cache the this image version.
  • script: - export PREVIOUS_VERSION=$(perl -lne 'print "v${1}" if /^##\s\[(\d\.\d\.\d)\]\s-\s\d{4}(?:-\d{2}){2}\s*$/' CHANGELOG.md | sed -n '2 p') - docker build --cache-from "$CI_REGISTRY_IMAGE:$PREVIOUS_VERSION" -t "$CI_REGISTRY_IMAGE:$CI_COMMIT_TAG" -f ./prod.Dockerfile .
  • « Docker layer caching » is enough to optimize the build time.
  • Cache in CI/CD is about saving directories or files across pipelines.
  • We're building a Docker image, dependencies are installed inside a container.We can't cache a dependencies directory if it doesn't exists in the job workspace.
  • Dependencies will always be installed from a container but will be extracted by the GitLab Runner in the job workspace. Our goal is to send the cached version in the build context.
  • We set the directories to cache in the job settings with a key to share the cache per branch and stage.
  • - docker cp app:/var/www/html/vendor/ ./vendor
  • after_script
  • - docker cp app:/var/www/html/node_modules/ ./node_modules
  • To avoid old dependencies to be mixed with the new ones, at the risk of keeping unused dependencies in cache, which would make cache and images heavier.
  • If you need to cache directories in testing jobs, it's easier: use volumes !
  • version your cache keys !
  • sharing Docker image between jobs
  • In every job, we automatically get artifacts from previous stages.
  • docker save $DOCKER_CI_IMAGE | gzip > app.tar.gz
  • I personally use the « push / pull » technique,
  • we docker push after the build, then we docker pull if needed in the next jobs.
張 旭

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

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

devops

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

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

developer.hashicorp.com/...providers

terraform devops

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

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

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

devops terraform

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

chaifeng/ufw-docker: To fix the Docker and UFW security flaw without disabling iptables - 0 views

github.com/ufw-docker

iptables docker

shared by 張 旭 on 13 Jul 22 - No Cached
  • It requires to disable docker's iptables function first, but this also means that we give up docker's network management function.
  • This causes containers will not be able to access the external network.
  • such as -A POSTROUTING ! -o docker0 -s 172.17.0.0/16 -j MASQUERADE. But this only allows containers that belong to network 172.17.0.0/16 can access outside.
  • ...13 more annotations...
  • Don't need to disable Docker's iptables and let Docker to manage it's network.
  • The public network cannot access ports that published by Docker.
  • In a very convenient way to allow/deny public networks to access container ports without additional software and extra configurations
  • Enable Docker's iptables feature. Remove all changes like --iptables=false , including configuration file /etc/docker/daemon.json
  • Modify the UFW configuration file /etc/ufw/after.rules
  • There may be some unknown reasons cause the UFW rules will not take effect after restart UFW, please reboot servers.
  • If we publish a port by using option -p 8080:80, we should use the container port 80, not the host port 8080
  • allow the private networks to be able to visit each other.
  • The following rules block connection requests initiated by all public networks, but allow internal networks to access external networks.
  • Since the UDP protocol is stateless, it is not possible to block the handshake signal that initiates the connection request as TCP does.
  • For GNU/Linux we can find the local port range in the file /proc/sys/net/ipv4/ip_local_port_range. The default range is 32768 60999
  • It not only exposes ports of containers but also exposes ports of the host.
  • Cannot expose services running on hosts and containers at the same time by the same command.
  • 張 旭 on 13 Jul 22
     
    "It requires to disable docker's iptables function first, but this also means that we give up docker's network management function."
張 旭

Choose when to run jobs | GitLab - 0 views

docs.gitlab.com/...job_control.html

gitlab devops cicd

shared by 張 旭 on 28 Dec 21 - No Cached
  • 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
  • ...32 more annotations...
  • 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 ||
  • 張 旭 on 28 Dec 21
     
    "Rules are evaluated in order until the first match."
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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