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Kubernetes vs Docker

What is Kubernetes?Kubernetes (also known as K8s) is a production-grade container orchestration system. It is an open source cluster management system initially developed by three Google employees during the summer of 2014 and grew exponentially and became the first project to get donated to the Cloud Native Computing Foundation(CNCF).It is basically an open source toolkit for building a fault-tolerant, scalable platform designed to automate and centrally manage containerized applications. With Kubernetes you can manage your containerized application more efficiently.Kubernetes is a HUGE project with a lot of code and functionalities. The primary responsibility of Kubernetes is container orchestration. That means making sure that all the containers that execute various workloads are scheduled to run physical or virtual machines. The containers must be packed efficiently following the constraints of the deployment environment and the cluster configuration. In addition, Kubernetes must keep an eye on all running containers and replace dead, unresponsive, or otherwise unhealthy containers.Kubernetes uses Docker to run images and manage containers. Nevertheless, K8s can use other engines, for example, rkt from the CoreOS. The platform itself can be deployed within almost any infrastructure – in the local network, server cluster, data center, any kind of cloud – public (Google Cloud, Microsoft Azure, AWS, etc.), private, hybrid, or even over the combination of these methods. It is noteworthy that Kubernetes supports the automatic placement and replication of containers over a large number of hosts. It  brings a number of features and which can be thought of as:\ As a container platformAs a microservices platformAs a portable cloud platform and a lot more.Kubernetes considers most of the operational needs for application containers. The top 10 reasons why Kubernetes is so popular are as follow:Largest Open Source project in the worldGreat Community SupportRobust Container deploymentEffective Persistent storageMulti-Cloud Support(Hybrid Cloud)Container health monitoringCompute resource managementAuto-scaling Feature SupportReal-world Use cases AvailableHigh availability by cluster federationBelow is the list of features which Kubernetes provides - Service Discovery and load balancing: Kubernetes has a feature which assigns the containers with their own IP addresses and a unique DNS name, which can be used to balance the load on them.Planning & Placement: Placement of the containers on the node is a crucial feature on which makes the decision based on the resources it requires and other restrictions.Auto Scaling: Based on the CPU usage, the vertical scaling of applications is automatically triggered using the command line.Self Repair: This is a unique feature in the Kubernetes which will restart the container automatically when it fails. If the Node dies, then containers are replaced or re-planned on the other Nodes. You can stop the containers if they don't respond to the health checks.Storage Orchestration: This feature of Kubernetes enables the user to mount the network storage system as a local file system.Batch execution: Kubernetes manages both batch and CI workloads along with replacing containers that fail.Deployments and Automatic Rollbacks: During the configuration changes for the application hosted on the Kubernetes, progressively monitors the health to ensure that it does not terminate all the instances at once, it makes an automatic rollback only in the case of failure.Configuration Management and Secrets: All classifies information like keys and passwords are stored under module called Secrets in Kubernetes. These Secrets are used especially while configuring the application without having to reconstruct the image.What is Docker?Docker is a lightweight containerization technology that has gained widespread popularity in the cloud and application packaging world. It is an open source framework that automates the deployment of applications in lightweight and portable containers. It uses a number of the Linux kernel’s features such as namespaces, cgroups, AppArmor profiles, and so on, to sandbox processes into configurable virtual environments. Though the concept of container virtualization isn’t new, it has been getting attention lately with bigwigs like Red Hat, Microsoft, VMware, SaltStack, IBM, HP, etc, throwing their weight behind newcomer Docker. Start-ups are betting their fortunes on Docker as well. CoreOS, Drone.io, and Shippable are some of the start-ups that are modeled to provide services based upon Docker. Red Hat has already included it as a primary supported container format for Red Hat Enterprise Linux 7.Why is Docker popular?The major factors driving Docker’s popularity are its speed, ease of use and the fact that it is largely free. In performance, it is even said to be comparable with KVM. A container-based approach, in which applications can run in isolation and without relying on a separate operating system, can really save huge amounts of hardware resources. Industry experts have started looking at it as hardware multi-tenancy for applications. Instead of having hundreds of VMs running per server, what if it were possible to have thousands of hardware-isolated applications?Docker is used to running software packages called "containers". A container is a standardized unit of software that packages up a code and all its dependencies so the application runs quickly and reliably from one computing environment to other. Containers are the “fastest growing cloud-enabling technology”* because they speed the delivery of software and cut the cost of operating it. Writing software is faster. Deploying it is easier — in your data center or your preferred cloud. And running it requires less hardware and support.Although container technology has existed for decades, Docker makes it work for the enterprise with core features enterprises require in a container platform and best-practice services to ensure success. And containers work on both legacy applications and new development.Existing, mission-critical applications can be “containerized,” often with little or no change. The result is instant savings in infrastructure, better security, and reduced labor. And new development happens faster because engineers only target a single platform instead of a variety of servers and clouds. Less code to write. Less testing. Faster delivery.Introduction to Docker swarm.Docker Swarm is the native clustering and scheduling tool for Docker.  It allows IT, administrators and developers, to establish and manage a cluster of Docker nodes as a single virtual system.  It is written in Go and released for the first time in November 2015 by Docker, Inc.The cluster management and orchestration features embedded in the Docker Engine are built using swarmkit. Swarmkit is a separate project which implements Docker’s orchestration layer and is used directly within Docker. It is a toolkit for orchestrating distributed systems at any scale. It includes primitives for node discovery, raft-based consensus, task scheduling and more.Its main benefits are:Distributed: SwarmKit uses the Raft Consensus Algorithm in order to coordinate and does not rely on a single point of failure to perform decisions.Secure: Node communication and membership within a Swarm are secure out of the box. SwarmKit uses mutual TLS for node authentication, role authorization, and transport encryption, automating both certificate issuance and rotation.Simple: SwarmKit is operationally simple and minimizes infrastructure dependencies. It does not need an external database to operateCurrent versions of Docker include swarm mode for natively managing a cluster of Docker Engines called a swarm. One can use the Docker CLI to create a swarm, deploy application services to a swarm, and manage swarm behavior. All you need is to initiate it to use the latest features which comes with the Docker Engine. Docker Swarm Mode ArchitectureEvery node in Swarm Mode has a role which can be categorized as a Manager and Worker. Manager node has a responsibility to actually orchestrate the cluster, perform the health-check, running containers serving the API and so on. The worker node just executes the tasks which are actually containers. It cannot decide to schedule the containers on the different machine. It cannot change the desired state. The workers only take work and report back the status. You can enable node promotion or demotion easily through one-liner command.Managers and Workers use two different communication models. Managers have built-in RAFT system that allows them to share information for new leader election. At one time, the only manager is actually performing the scaling and they use a leader-follower model to figure out which one is supposed to be what. No External K-V store is required as a built-in internal distributed state store is available.Workers, on the other side, uses GOSSIP network protocol which is quite fast and consistent. Whenever any new container/tasks get generated in the cluster, the gossip is going to broadcast it to all the other containers in a specific overlay network that this new container has started. Please remember that ONLY the containers which are running in the specific overlay network will be communicated and NOT globally. Gossip is optimized for heavy traffic.How Docker swarm varies with Docker?Today Docker Platform support 3 variants of Swarm:Docker Swarm ( Classic)Swarmkit(a foundation for Docker Swarm Mode)Docker Swarm ModeLet us go through each one of them one by one Docker Swarm 1.0 was introduced for the first time in Docker Engine 1.9 Release during November 2015. It was a separate GITHUB repo and software which needed to be installed for turning a pool of Docker Engines into a single, virtual Engine.. It was announced as the easiest way to run Docker applications at scale on a cluster. You don’t have to worry about where to put containers, or how they talk to each other – it just handles all that for you.In 2016 during Dockercon, Docker Inc. announced Docker Swarm Mode for the first time. Swarm Mode came integrated directly into Docker Engine which means you don’t need to install it separately. All you need is to initiate it using `docker swarm init` command. With an optional “Swarm Mode” feature rightly integrated into core Docker Engine, native management of a cluster of Docker Engines, orchestration, decentralized design, service and application deployment, scaling, desired state reconciliation, multi-host networking, service discovery and routing mesh implementation is just a matter of few liner commands.Said that Docker Swarm mode is fundamentally different from Classic Swarm. The basic difference are listed below:Docker Swarm ModeDocker Classic SwarmDocker Swarm Mode comes integrated into Docker EngineDocker Swarm is a GITHUB repository and comes as a separate project. It is NOT integrated into Docker Engine.Comes with inbuilt Service DiscoveryNeed external KV store based on Consul & etc.Comes with inbuilt feature like: ScalingRolling UpdatesService DiscoveryLoad-Balancing Routing MeshTopological PlacementLack of inbuilt feature like Load Balancing, Scalability, Routing Mesh etc.Secured Control & Data PlaneControl Plane and Data Plane are insecureLet’s talk about Swarmkit a bit.Swarmkit is a plumbing open source project. It is a toolkit for orchestrating distributed systems at any scale. It includes primitives for node discovery, raft-based consensus, task scheduling and more.Its main benefits are:Distributed: SwarmKit uses the Raft Consensus Algorithm in order to coordinate and does not rely on a single point of failure to perform decisions.Secure: Node communication and membership within a Swarm are secure out of the box. SwarmKit uses mutual TLS for node authentication, role authorization, and transport encryption, automating both certificate issuance and rotation.Simple: SwarmKit is operationally simple and minimizes infrastructure dependencies. It does not need an external database to operate.SwarmKit is completely built in Go and leverages a standard project structure to work well with Go tooling. If you want to learn more about Swarmkit, head over to https://github.com/docker/swarmkit/How Docker can be used with Kubernetes?From 30,000 feet, Docker and Kubernetes might appear to be similar technologies. They both are an open platform which allows you to run applications within Linux containers. But as you deep-dive little closer, you’ll find that the technologies operate at different layers of the stack, and can even be used together. Let’s talk about Docker first-Docker provides the ability to package and run an application in a loosely isolated environment called a container. At their core, containers are a way of packaging software. The unique feature about container is that when you run a container, you know exactly how it will run - it’s very predictable, repeatable and immutable. You are just left with no unexpected errors when you move it to a new machine, or between environments. All of your application’s code, libraries, and dependencies are packed together in the container as an immutable artifact. You can think of running a container like running a virtual machine, without the overhead of spinning up an entire operating system. Docker CLI provides the mechanism for managing the life cycle of the containers. Whereas the docker image defines the build time framework of runtime containers, CLI commands are there to start, stop, restart and perform lifecycle operations on these containers. Today, containers can be orchestrated and can be made to run on multiple hosts. The questions that need to be answered are how these containers are coordinated and scheduled? And how will the application running in these containers communicate with each other? The answer is Kubernetes.Today, Kubernetes mostly uses Docker to package, instantiate, and run containerized applications. Said that there are various another container runtime available but Docker is the most popular runtime binary used by Kubernetes. Both Kubernetes and Docker build a comprehensive standard for managing the containerized applications intelligently along with providing powerful capabilities. Docker provides a platform for building running and distributing Docker containers. Docker brings up its own clustering tool which can be used for orchestration. But Kubernetes is an orchestration platform for Docker containers which is more extensive than the Docker clustering tool and has the capacity to scale to the production level. Kubernetes is a container orchestration system for Docker containers that is more extensive than Docker Swarm and is meant to coordinate clusters of nodes at scale in production in an efficient manner.  It is a plug and plays architecture for the container orchestration which provides features like high availability among the distributed nodes.For Example ~ Today it is possible to run Kubernetes under Docker EE 2.0 platform. Docker Enterprise Edition (EE) 2.0 is the only platform that manages and secures applications on Kubernetes in multi-Linux, multi-OS, and multi-cloud customer environments. As a complete platform that integrates and scales with your organization, Docker EE 2.0 gives you the most flexibility and choice over the types of applications supported, orchestrators used, and where it’s deployed. It also enables organizations to operationalize Kubernetes more rapidly with streamlined workflows and helps you deliver safer applications through integrated security solutions.Difference between Kubernetes and Dockeri) Kubernetes vs DockerSet up and installationKubernetesDockerIt requires a series of manual steps to setup Kubernetes Master and worker nodes components in a cluster of nodesInstalling Docker is a matter of one-liner command on Linux Platform like Debian, Ubuntu, and CentOS.Kubernetes can run on various platforms: from your laptop, to VMs on a cloud provider, to a rack of bare metal servers. For setting up a single node K8s cluster, one can use Minikube.To install a single-node Docker Swarm or Kubernetes cluster, one can deploy Docker for Mac & Docker for Windows.Kubernetes support for Windows server is under beta phase.Docker has official support for Windows 10 and Windows Server 2016 and 1709.Kubernetes Client and Server packages need to be upgraded manually on all the systems.It’s so easy to upgrade Docker Engine under Docker for Mac & Windows via just 1 click.Working in two systemsKubernetesDockerKubernetes operates at the application level rather than at the hardware level. Kubernetes aims to support an extremely diverse variety of workloads, including stateless, stateful, and data-processing workloads. If an application can run in a container, it should run great on Kubernetes.Kubernetes can run on top of Docker but requires you to know the command line interface (CLI) specifications for both to access your data over the API.There is a kubernetes client called kubectl which talks to kube API which is running on your master node.Unlike Master components that usually run on a single node (unless High Availability Setup is explicitly stated), Node components run on every node.kubelet: agent running on the node to inspect the container health and report to the master as well as listening to new commands from the kube-apiserverkube-proxy: maintains the network rulescontainer runtime: software for running the containers (e.g. Docker, rkt, runc)Docker Platform is available in the form of two editions:Docker Community EditionDocker Enterprise EditionDocker Community comes with community-based support forums whereas Docker Enterprise Edition is offered as enterprise-class support with defined SLAs and private support channels.Docker Community and Enterprise Edition both come by default with Docker Swarm Mode. Additionally, Kubernetes is supported under Docker Enterprise Edition.For Docker Swarm Mode, one can use Docker Compose file and use Docker Stack Deploy CLI to deploy an application across the cluster nodes.The `docker stack` CLI deploys a new stack or update an existing stack. The client and daemon API must both be at least 1.25 to use this command. One can use the docker version command on the client to check your client and daemon API versionsLogging and MonitoringKubernetesDockerLogging:Kubernetes provides no native storage solution for log data, but you can integrate many existing logging solutions into your Kubernetes cluster. Few of popular logging tools are listed below:Fluentd is an open source data collector for a unified logging layer. It’s written in Ruby with a plug-in oriented architecture.It helps to collect, route and store different logs from different sources. While Fluentd is optimized to be easily extended using plugin architecture, fluent-bit is designed for performance. It’s compact and written in C so it can be enabled to minimalistic IOT devices and remain fast enough to transfer a huge quantity of logs. Moreover, it has built-in Kubernetes support. It’s an especially compact tool designed to transport logs from all nodes.Other tools like Stackdriver logging provided by GCP, Logz.io and other 3rd party drivers are available too.Monitoring:There are various open source tools available for Kubernetes application monitoring like:Heapster: Installed as a pod inside of Kubernetes, it gathers data and events from the containers and pods within the cluster.Prometheus: Open source Cloud Native Computing Foundation (CNCF) project that offers powerful querying capabilities, visualization and alerting.Grafana:  Used in conjunction with Heapster for visualizing data within your Kubernetes environment.InfluxDB: A highly-available database platform that stores the data captured by all the Heapster pods.CAdvisor:  focuses on container level performance and resource usage. This comes embedded directly into kubelet and should automatically discover active containers.Logging driver plugins are available in Docker 17.05 and higher. Logging capabilities available in Docker are exposed in the form of drivers, which is very handy since one gets to choose how and where log messages should be shippedDocker includes multiple logging mechanisms to help you get information from running containers and services. These mechanisms are called logging drivers.Each Docker daemon has a default logging driver, which each container uses unless you configure it to use a different logging driver.In addition to using the logging drivers included with Docker, you can also implement and use logging driver plugins.To configure the Docker daemon to default to a specific logging driver, set the value of log-driver to the name of the logging driver in the daemon.json file, which is located in /etc/docker/ on Linux hosts.The following example explicitly sets the default logging driver to syslog:{   "log-driver": "syslog" }When you start a container, you can configure it to use a different logging driver than the Docker daemon default, using the --log-driver flag. If the logging driver has configurable options, you can set them using one or more instances of the --log-opt <NAME>=<VALUE> flag. Even if the container uses the default logging driver, it can use different configurable options.SizeKubernetes DockerAs per official page of Kubernetes documentation K8s v1.12 support clusters with up to 5000 nodes based on the below criteria:No more than 5000 nodesNo more than 150000 total podsNo more than 300000 total containersNo more than 100 pods per node.According to the Docker’s blog post on scaling Swarm clusters published during Nov 2015, Docker Swarm has been scaled and performance tested up to 30,000 containers and 1,000.SpecsDiscovery backend: Consul1,000 nodes30 containers per nodeManager: AWS m4.xlarge (4 CPUs, 16GB RAM)Nodes: AWS t2.micro (1 CPU, 1 GB RAM)Container image: Ubuntu 14.04Results Percentile  API Response Time Scheduling Delay50th     150ms              230ms90th      200ms             250ms99th      360ms             400msii) Building and Deploying Containers with DockerDocker has a capability to builds images automatically by reading the instructions via text file called Dockerfile. It is a simple text file that follows a specific format and instructions set that contains all commands, in order, needed to build a given image. A Docker image consists of read-only layers each of which represents a Dockerfile instruction. The layers are stacked and each one is a delta of the changes from the previous layer. For example, below is a simple Dockerfile which Consider this Dockerfile:FROM nginx:latest COPY wrapper.sh / COPY html /usr/share/nginx/html CMD ["./wrapper.sh"]Each instruction creates one layer:FROM creates a layer from the nginx:latest Docker image.COPY adds files from your Docker client’s current directory.CMD specifies what command to run within the container.When you run an image and generate a container, you add a new writable layer (the “container layer”) on top of the underlying layers. All changes made to the running container, such as writing new files, modifying existing files, and deleting files, are written to this thin writable container layer.Building a Docker Image$docker build -t hellowhaleThe above shown `docker build` command builds an image from a Dockerfile and a context. The build context is the set of files at a specified location PATH or URL. The PATH is a directory on your local filesystem. The URL is a Git repository location.Running the Docker ContainerA running Docker Image is called Docker container and all you need is to run the below command to expose port 80 on host machine from a container and get it up and running:docker run -d -p 80:80 --name hellowhale hellowhaleTagging the Image$docker tag hellowhale userid/hellowhalePushing the Docker Image to DockerHubBefore you push Docker Image to DockerHub, you need to login to DockerHub first using the below command:$docker login $docker push userid/hellowhaleiii) Managing container with KubernetesDocker CLI for a standalone system is used to build, ship and run your Docker containers. But if you want to run multiple containers across multiple machines, you need a robust orchestration tool and Kubernetes is the most popular in the list.Kubernetes is an open source container orchestration platform, allowing large numbers of containers to work together in harmony, reducing operational burden. It helps with things like running containers across many different machines, scaling up or down by adding or removing containers when demand changes, keeping storage consistent with multiple instances of an application, distributing load between the containers and launching new containers on different machines if something fails.Below are the list of comparative CLI used by Docker Vs Kubernetes to manage containers:Docker CLIKubernetes CLIdocker runTo run an nginx container -$ docker run -d --restart=always --name nginx-app -p 80:80 nginxkubectl runTo run an nginx Deployment and expose the Deployment, see kubectl run.$ kubectl run --image=nginx nginx-app --port=80 --env="DOMAIN=cluster"docker psTo list what is currently running, see kubectl get.docker:$ docker ps -akubectl getTo list what is currently running under kubernetes cluster:$ kubectl get po -adocker execTo execute a command in a  Docker container:$ docker psCONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS                NAMES55c103fa1296        nginx               "nginx -g 'daemon of…"   6 minutes ago       Up 6 minutes        0.0.0.0:80->80/tcp   nginx-app$ docker exec 55c103fa1296 cat /etc/hostnamekubectl:To execute a command in a container, see kubectl exec.$ kubectl get poNAME              READY     STATUS    RESTARTS   AGEnginx-app-5jyvm   1/1       Running   0          10m$ kubectl exec nginx-app-5jyvm -- cat /etc/hostnamenginx-app-5jyvmiv) Trends in Docker and KubernetesDocker, Inc has around 550+ enterprise customer who uses Docker in a production environment. Few of non-exhaustive list of companies who actively uses Docker are list below:The New York TimesPayPalBusiness InsiderCornell University (Not a company but still can be considered)SplunkThe Washington PostSwisscomAlm BrandAssa AbloyExpediaJabilMetLifeSociete GeneraleGEGrouponYandexUberEbayShopifySpotifyNew RelicYelpRecently, the Forrester New Wave™: Enterprise Container Platform Software Suites, Q4 2018 report states that Docker leading the pack with a robust container platform well-suited for the enterprise, offering a secure container supply chain from the developer's desktop to production.Lots of organizations are already using Kubernetes in production—like the ones listed on the Kubernetes case studies page, including eBay, Buffer, Pearson, Box, and Wikimedia. But that is not a complete list. Kubernetes is even more versatile than the official case studies page suggests. Below is a list of companies using it:List of Kubernetes Users   Microservices UsageMicroservices help developers break up monolithic applications into smaller components. They can move away from all-at-once massive package deployments and break up apps into smaller, individual units that can be deployed separately. Smaller microservices can give apps more scalability, more resiliency and - most importantly - they can be updated, changed and redeployed faster. Some of the biggest public cloud applications run as microservices already.Containers are a packaging strategy for microservices. Think of them more as process containers than virtual machines. They run as a process inside a shared operating system. A container typically only does one small job - validate a login or return a search result. Docker is a tool that describes those packages in a common format, and helps launch and run them. Linux containers have been around for a while, but their popularity in the public cloud has given rise to an exciting new ecosystem of companies building tools to make them easier to use, cluster and orchestrate them, run them in more places, and manage their life cycles.Over the last two years, many different types of software vendors - from operating system to IT infrastructure companies - have all joined the container ecosystem. There’s already an industry organization - the open container initiative - guiding the market and making sure everyone plays well together. IBM, HP, Microsoft, VMware, Google, Red Hat, CoreOS - these are just some of the major vendors racing to make containers as easy as possible for developers to use, to share, to protect, and to scale.The rising demand for multi-cloud environmentsWith an estimated 85% of today’s enterprise IT organizations employing a multi-cloud strategy, it has become more critical that customers have a ‘single pane of glass’ for managing their entire application portfolio. Most enterprise organizations have a hybrid and multi-cloud strategy. Containers have helped to make applications portable but let us accept the fact that even though containers are portable today but the management of containers is still a nightmare. The reason being –Each Cloud is managed under a separate operational model, duplicating effortsDifferent security and access policies across each platformContent is hard to distribute and trackPoor Infrastructure utilization still remainsThe emergence of Cloud-hosted K8s is exacerbating the challenges with managing containerized applications across multiple CloudsThis time Docker introduced new application management capabilities for Docker Enterprise Edition that will allow organizations to federate applications across Docker Enterprise Edition environments deployed on-premises and in the cloud as well as across cloud-hosted Kubernetes. This includes Azure Kubernetes Service (AKS), AWS Elastic Container Service for Kubernetes (EKS), and Google Kubernetes Engine (GKE). The federated application management feature will automate the management and security of container applications on premises and across Kubernetes-based cloud services. It will provide a single management platform to enterprises so that they can centrally control and secure the software supply chain for all the containerized applications.With this announcement, undoubtedly Docker Enterprise Edition is the only enterprise-ready container platform that can deliver federated application management with a secure supply chain. Not only does Docker give you your choice of Linux distribution or Windows Server, the choice of running in a virtual machine or on bare metal, running traditional or microservices applications with either Swarm or Kubernetes orchestration, it also gives you the flexibility to choose the right cloud for your needs.Talking about Kubernetes Platform, version 1.3 of container management platform KubernetesIntroduced cross-cluster federated services with an ability to span workloads across clusters and, by extension, across multiple clouds. This opens up the possibility for workloads that need to draw resources from multiple clouds. This would also mean that large jobs can be split among clouds. Not only this, this introduced an ability to automatically scale services to match demand. Increasing support for Docker and KubernetesKubernetes has been enjoying widespread adoption among startups, platform vendors, and enterprises. Companies like Amazon, Google, IBM, Red Hat, and Microsoft offer managed Kubernetes under the Containers as a Service (CaaS) model. The open source ecosystem has dozens of players building various tools covering logging, monitoring, automation, storage, and networking aspects of Kubernetes. System integrators have dedicated practices and offerings based on Kubernetes. Global players like Uber, Bloomberg, Blackrock, BlaBlaCar, The New York Times, Lyft, eBay, Buffer, Squarespace, Ancestry, GolfNow, Goldman Sachs and many others are using Kubernetes in production at massive scale. According to Redmonk, a developer-focused research company, 71 percent of the Fortune 100 use containers and more than 50 percent of Fortune 100 companies use Kubernetes as their container orchestration platform.Did you know there are 35 certified Kubernetes distribution, 22 certified Kubernetes hosting platform and 50 certified Kubernetes service provider available? Over the last three years, Kubernetes has been adopted by a vibrant, diverse community of providers. The Cloud Native Computing Foundation® (CNCF®), which sustains and integrates open source technologies like Kubernetes® , today announced the availability of the Certified Kubernetes Conformance Program, which ensures Certified Kubernetes™ products deliver consistency and portability, and that 35 Certified Kubernetes Distributions and Platforms are now available. A Certified Kubernetes product guarantees that the complete Kubernetes API functions as specified, so users can rely on a seamless, stable experience.In the other hand, Docker Enterprise Edition (EE) 2.0 represents a significant leap forward in container platform solutions, delivering the only solution that manages and secures applications on Kubernetes in multi-Linux, multi-OS, and multi-cloud customer environments. One of the most promising features announced with this release includes Kubernetes integration as an optional orchestration solution, running side-by-side with Docker Swarm. Not only this, this release includes Swarm Layer 7 routing improvements, Registry image mirroring, Kubernetes integration to Docker Trusted Registry & Kubernetes integration to Docker EE access controls. With this new release, organizations will be able to deploy applications with either Swarm or fully-conformant Kubernetes while maintaining the consistent developer-to-IT workflow.Docker EE is more than just a container orchestration solution; it is a full lifecycle management solution for the modernization of traditional applications and microservices across a broad set of infrastructure platforms. It is a Containers-as-a-Service(CaaS) platform for IT that manages and secures diverse applications across disparate infrastructure, both on-premises and in the cloud. Docker EE provides an integrated, tested and certified platform for apps running on enterprise Linux or Windows operating systems and Cloud providers. It is tightly integrated into the underlying infrastructure to provide a native, easy to install experience and an optimized Docker environment.V) Kubernetes vs Docker swarmInstallation & Cluster configurationGUIScalabilityAuto-ScalingLoad BalancingRolling Updates & RollbacksData VolumesLogging & MonitoringKubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It was built by Google based on their experience running containers in production using an internal cluster management system called Borg (sometimes referred to as Omega). On the other hand, a Swarm cluster consists of Docker Engine deployed on multiple nodes. Manager nodes perform orchestration and cluster management. Worker nodes receive and execute tasks Below is the major list of differences between Docker Swarm & Kubernetes:Docker SwarmKubernetesApplications are deployed in the form of services (or “microservices”) in a Swarm cluster. Docker Compose is a tool which is majorly used to deploy the app.Applications are deployed in the form of a combination of pods, deployments, and services (or “microservices”).Autoscaling feature is not available either in  Docker Swarm (Classical) or Docker SwarmaAn auto-scaling feature is available under K8s. It uses a simple number-of-pods target which is defined declaratively using deployments. CPU-utilization-per-pod target is available.  Docker Swarm supports rolling updates features. At rollout time, you can apply rolling updates to services. The Swarm manager lets you control the delay between service deployment to different sets of nodes, thereby updating only 1 task at a time.Under kubernetes, the deployment controller supports both “rolling-update” and “recreate” strategies. Rolling updates can specify a maximum number of pods unavailable or maximum number running during the process.Under Docker Swarm Mode, the node joining a Docker Swarm cluster creates an overlay network for services that span all of the hosts in the Swarm and a host-only Docker bridge network for container.By default, nodes in the Swarm cluster encrypt overlay control and management traffic between themselves. Users can choose to encrypt container data traffic when creating an overlay network by themselves.Under K8s, the networking model is a flat network, enabling all pods to communicate with one another. Network policies specify how pods communicate with each other. The flat network is typically implemented as an overlay.Docker Swarm health checks are limited to services. If a container backing the service does not come up (running state), a new container is kicked off.Users can embed health check functionality into their Docker images using the HEALTHCHECK instruction.Under K8s, the health checks are of two kinds: liveness (is app responsive) and readiness (is app responsive, but busy preparing and not yet able to serve)Out-of-the-box K8S provides a basic logging mechanism to pull aggregate logs for a set of containers that make up a pod.
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Kubernetes vs Docker 4275
Kubernetes vs Docker

What is Kubernetes?

Kubernetes (also known as K8s) is a production-grade container orchestration system. It is an open source cluster management system initially developed by three Google employees during the summer of 2014 and grew exponentially and became the first project to get donated to the Cloud Native Computing Foundation(CNCF).

It is basically an open source toolkit for building a fault-tolerant, scalable platform designed to automate and centrally manage containerized applications. With Kubernetes you can manage your containerized application more efficiently.

Types of kubernetes

Kubernetes is a HUGE project with a lot of code and functionalities. The primary responsibility of Kubernetes is container orchestration. That means making sure that all the containers that execute various workloads are scheduled to run physical or virtual machines. The containers must be packed efficiently following the constraints of the deployment environment and the cluster configuration. In addition, Kubernetes must keep an eye on all running containers and replace dead, unresponsive, or otherwise unhealthy containers.

Kubernetes uses Docker to run images and manage containers. Nevertheless, K8s can use other engines, for example, rkt from the CoreOS. The platform itself can be deployed within almost any infrastructure – in the local network, server cluster, data center, any kind of cloud – public (Google Cloud, Microsoft Azure, AWS, etc.), private, hybrid, or even over the combination of these methods. It is noteworthy that Kubernetes supports the automatic placement and replication of containers over a large number of hosts. It  brings a number of features and which can be thought of as:\ 

  • As a container platform
  • As a microservices platform
  • As a portable cloud platform and a lot more.Kubernetes High level Component Architecture

Kubernetes considers most of the operational needs for application containers. The top 10 reasons why Kubernetes is so popular are as follow:

  • Largest Open Source project in the world
  • Great Community Support
  • Robust Container deployment
  • Effective Persistent storage
  • Multi-Cloud Support(Hybrid Cloud)
  • Container health monitoring
  • Compute resource management
  • Auto-scaling Feature Support
  • Real-world Use cases Available
  • High availability by cluster federation

Below is the list of features which Kubernetes provides - 

  • Service Discovery and load balancingKubernetes has a feature which assigns the containers with their own IP addresses and a unique DNS name, which can be used to balance the load on them.

  • Planning & Placement: Placement of the containers on the node is a crucial feature on which makes the decision based on the resources it requires and other restrictions.

  • Auto ScalingBased on the CPU usage, the vertical scaling of applications is automatically triggered using the command line.

  • Self Repair: This is a unique feature in the Kubernetes which will restart the container automatically when it fails. If the Node dies, then containers are replaced or re-planned on the other Nodes. You can stop the containers if they don't respond to the health checks.

  • Storage Orchestration: This feature of Kubernetes enables the user to mount the network storage system as a local file system.

  • Batch execution: Kubernetes manages both batch and CI workloads along with replacing containers that fail.

  • Deployments and Automatic Rollbacks: During the configuration changes for the application hosted on the Kubernetes, progressively monitors the health to ensure that it does not terminate all the instances at once, it makes an automatic rollback only in the case of failure.

  • Configuration Management and Secrets: All classifies information like keys and passwords are stored under module called Secrets in Kubernetes. These Secrets are used especially while configuring the application without having to reconstruct the image.

What is Docker?

Docker is a lightweight containerization technology that has gained widespread popularity in the cloud and application packaging world. It is an open source framework that automates the deployment of applications in lightweight and portable containers. It uses a number of the Linux kernel’s features such as namespaces, cgroups, AppArmor profiles, and so on, to sandbox processes into configurable virtual environments. Though the concept of container virtualization isn’t new, it has been getting attention lately with bigwigs like Red Hat, Microsoft, VMware, SaltStack, IBM, HP, etc, throwing their weight behind newcomer Docker. Start-ups are betting their fortunes on Docker as well. CoreOS, Drone.io, and Shippable are some of the start-ups that are modeled to provide services based upon Docker. Red Hat has already included it as a primary supported container format for Red Hat Enterprise Linux 7.

Why is Docker popular?

The major factors driving Docker’s popularity are its speed, ease of use and the fact that it is largely free. In performance, it is even said to be comparable with KVM. A container-based approach, in which applications can run in isolation and without relying on a separate operating system, can really save huge amounts of hardware resources. Industry experts have started looking at it as hardware multi-tenancy for applications. Instead of having hundreds of VMs running per server, what if it were possible to have thousands of hardware-isolated applications?

Docker is used to running software packages called "containers". A container is a standardized unit of software that packages up a code and all its dependencies so the application runs quickly and reliably from one computing environment to other. Containers are the “fastest growing cloud-enabling technology”* because they speed the delivery of software and cut the cost of operating it. Writing software is faster. Deploying it is easier — in your data center or your preferred cloud. And running it requires less hardware and support.

Although container technology has existed for decades, Docker makes it work for the enterprise with core features enterprises require in a container platform and best-practice services to ensure success. And containers work on both legacy applications and new development.

Existing, mission-critical applications can be “containerized,” often with little or no change. The result is instant savings in infrastructure, better security, and reduced labor. And new development happens faster because engineers only target a single platform instead of a variety of servers and clouds. Less code to write. Less testing. Faster delivery.
Docker User Applications

Introduction to Docker swarm.

Docker Swarm is the native clustering and scheduling tool for Docker.  It allows IT, administrators and developers, to establish and manage a cluster of Docker nodes as a single virtual system.  It is written in Go and released for the first time in November 2015 by Docker, Inc.
Introduction to Docker swarmThe cluster management and orchestration features embedded in the Docker Engine are built using swarmkit. Swarmkit is a separate project which implements Docker’s orchestration layer and is used directly within Docker. It is a toolkit for orchestrating distributed systems at any scale. It includes primitives for node discovery, raft-based consensus, task scheduling and more.

Its main benefits are:

  • Distributed: SwarmKit uses the Raft Consensus Algorithm in order to coordinate and does not rely on a single point of failure to perform decisions.
  • Secure: Node communication and membership within a Swarm are secure out of the box. SwarmKit uses mutual TLS for node authentication, role authorization, and transport encryption, automating both certificate issuance and rotation.
  • Simple: SwarmKit is operationally simple and minimizes infrastructure dependencies. It does not need an external database to operate

Orchestration ComponentsCurrent versions of Docker include swarm mode for natively managing a cluster of Docker Engines called a swarm. One can use the Docker CLI to create a swarm, deploy application services to a swarm, and manage swarm behavior. All you need is to initiate it to use the latest features which comes with the Docker Engine. 

Docker Swarm Mode Architecture

Every node in Swarm Mode has a role which can be categorized as a Manager and Worker. Manager node has a responsibility to actually orchestrate the cluster, perform the health-check, running containers serving the API and so on. The worker node just executes the tasks which are actually containers. It cannot decide to schedule the containers on the different machine. It cannot change the desired state. The workers only take work and report back the status. You can enable node promotion or demotion easily through one-liner command.
Docker Swarm Communication internalsManagers and Workers use two different communication models. Managers have built-in RAFT system that allows them to share information for new leader election. At one time, the only manager is actually performing the scaling and they use a leader-follower model to figure out which one is supposed to be what. No External K-V store is required as a built-in internal distributed state store is available.
Quorum LayerWorkers, on the other side, uses GOSSIP network protocol which is quite fast and consistent. Whenever any new container/tasks get generated in the cluster, the gossip is going to broadcast it to all the other containers in a specific overlay network that this new container has started. Please remember that ONLY the containers which are running in the specific overlay network will be communicated and NOT globally. Gossip is optimized for heavy traffic.
Worker-to-Worker-Gossip

How Docker swarm varies with Docker?

Today Docker Platform support 3 variants of Swarm:

  • Docker Swarm ( Classic)
  • Swarmkit(a foundation for Docker Swarm Mode)
  • Docker Swarm Mode

Let us go through each one of them one by one 

Docker Swarm 1.0 was introduced for the first time in Docker Engine 1.9 Release during November 2015. It was a separate GITHUB repo and software which needed to be installed for turning a pool of Docker Engines into a single, virtual Engine.. It was announced as the easiest way to run Docker applications at scale on a cluster. You don’t have to worry about where to put containers, or how they talk to each other – it just handles all that for you.

In 2016 during Dockercon, Docker Inc. announced Docker Swarm Mode for the first time. Swarm Mode came integrated directly into Docker Engine which means you don’t need to install it separately. All you need is to initiate it using `docker swarm init` command. With an optional “Swarm Mode” feature rightly integrated into core Docker Engine, native management of a cluster of Docker Engines, orchestration, decentralized design, service and application deployment, scaling, desired state reconciliation, multi-host networking, service discovery and routing mesh implementation is just a matter of few liner commands.

Said that Docker Swarm mode is fundamentally different from Classic Swarm. The basic difference are listed below:

Docker Swarm Mode
Docker Classic Swarm
Docker Swarm Mode comes integrated into Docker Engine
Docker Swarm is a GITHUB repository and comes as a separate project. It is NOT integrated into Docker Engine.
Comes with inbuilt Service Discovery
Need external KV store based on Consul & etc.

Comes with inbuilt feature like:

  • Scaling
  • Rolling Updates
  • Service Discovery
  • Load-Balancing 
  • Routing Mesh
  • Topological Placement

Lack of inbuilt feature like Load Balancing, Scalability, Routing Mesh etc.
Secured Control & Data Plane
Control Plane and Data Plane are insecure


Let’s talk about Swarmkit a bit.

Swarmkit is a plumbing open source project. It is a toolkit for orchestrating distributed systems at any scale. It includes primitives for node discovery, raft-based consensus, task scheduling and more.

Its main benefits are:

  • Distributed: SwarmKit uses the Raft Consensus Algorithm in order to coordinate and does not rely on a single point of failure to perform decisions.
  • Secure: Node communication and membership within a Swarm are secure out of the box. SwarmKit uses mutual TLS for node authentication, role authorization, and transport encryption, automating both certificate issuance and rotation.
  • Simple: SwarmKit is operationally simple and minimizes infrastructure dependencies. It does not need an external database to operate.

SwarmKit is completely built in Go and leverages a standard project structure to work well with Go tooling. If you want to learn more about Swarmkit, head over to https://github.com/docker/swarmkit/

How Docker can be used with Kubernetes?

From 30,000 feet, Docker and Kubernetes might appear to be similar technologies. They both are an open platform which allows you to run applications within Linux containers. But as you deep-dive little closer, you’ll find that the technologies operate at different layers of the stack, and can even be used together. 

Let’s talk about Docker first-

Docker provides the ability to package and run an application in a loosely isolated environment called a container. At their core, containers are a way of packaging software. The unique feature about container is that when you run a container, you know exactly how it will run - it’s very predictable, repeatable and immutable. You are just left with no unexpected errors when you move it to a new machine, or between environments. All of your application’s code, libraries, and dependencies are packed together in the container as an immutable artifact. You can think of running a container like running a virtual machine, without the overhead of spinning up an entire operating system. 

Docker CLI provides the mechanism for managing the life cycle of the containers. Whereas the docker image defines the build time framework of runtime containers, CLI commands are there to start, stop, restart and perform lifecycle operations on these containers. Today, containers can be orchestrated and can be made to run on multiple hosts. The questions that need to be answered are how these containers are coordinated and scheduled? And how will the application running in these containers communicate with each other? The answer is Kubernetes.

Today, Kubernetes mostly uses Docker to package, instantiate, and run containerized applications. Said that there are various another container runtime available but Docker is the most popular runtime binary used by Kubernetes. Both Kubernetes and Docker build a comprehensive standard for managing the containerized applications intelligently along with providing powerful capabilities. Docker provides a platform for building running and distributing Docker containers. Docker brings up its own clustering tool which can be used for orchestration. But Kubernetes is an orchestration platform for Docker containers which is more extensive than the Docker clustering tool and has the capacity to scale to the production level. Kubernetes is a container orchestration system for Docker containers that is more extensive than Docker Swarm and is meant to coordinate clusters of nodes at scale in production in an efficient manner.  It is a plug and plays architecture for the container orchestration which provides features like high availability among the distributed nodes.

For Example ~ Today it is possible to run Kubernetes under Docker EE 2.0 platform. Docker Enterprise Edition (EE) 2.0 is the only platform that manages and secures applications on Kubernetes in multi-Linux, multi-OS, and multi-cloud customer environments. As a complete platform that integrates and scales with your organization, Docker EE 2.0 gives you the most flexibility and choice over the types of applications supported, orchestrators used, and where it’s deployed. It also enables organizations to operationalize Kubernetes more rapidly with streamlined workflows and helps you deliver safer applications through integrated security solutions.


Difference between Kubernetes and Docker

i) Kubernetes vs Docker

  • Set up and installation

Kubernetes
Docker
It requires a series of manual steps to setup Kubernetes Master and worker nodes components in a cluster of nodes
Installing Docker is a matter of one-liner command on Linux Platform like Debian, Ubuntu, and CentOS.
Kubernetes can run on various platforms: from your laptop, to VMs on a cloud provider, to a rack of bare metal servers. For setting up a single node K8s cluster, one can use Minikube.
To install a single-node Docker Swarm or Kubernetes cluster, one can deploy Docker for Mac & Docker for Windows.
Kubernetes support for Windows server is under beta phase.
Docker has official support for Windows 10 and Windows Server 2016 and 1709.
Kubernetes Client and Server packages need to be upgraded manually on all the systems.
It’s so easy to upgrade Docker Engine under Docker for Mac & Windows via just 1 click.
  • Working in two systems

Kubernetes
Docker
Kubernetes operates at the application level rather than at the hardware level. Kubernetes aims to support an extremely diverse variety of workloads, including stateless, stateful, and data-processing workloads. If an application can run in a container, it should run great on Kubernetes.

Kubernetes can run on top of Docker but requires you to know the command line interface (CLI) specifications for both to access your data over the API.

There is a kubernetes client called kubectl which talks to kube API which is running on your master node.

Unlike Master components that usually run on a single node (unless High Availability Setup is explicitly stated), Node components run on every node.
  • kubelet: agent running on the node to inspect the container health and report to the master as well as listening to new commands from the kube-apiserver
  • kube-proxy: maintains the network rules
  • container runtime: software for running the containers (e.g. Docker, rkt, runc)
Docker Platform is available in the form of two editions:
  • Docker Community Edition
  • Docker Enterprise Edition
Docker Community comes with community-based support forums whereas Docker Enterprise Edition is offered as enterprise-class support with defined SLAs and private support channels.

Docker Community and Enterprise Edition both come by default with Docker Swarm Mode. Additionally, Kubernetes is supported under Docker Enterprise Edition.

For Docker Swarm Mode, one can use Docker Compose file and use Docker Stack Deploy CLI to deploy an application across the cluster nodes.

The `docker stack` CLI deploys a new stack or update an existing stack. The client and daemon API must both be at least 1.25 to use this command. One can use the docker version command on the client to check your client and daemon API versions
  • Logging and Monitoring

Kubernetes
Docker
Logging:


Kubernetes provides no native storage solution for log data, but you can integrate many existing logging solutions into your Kubernetes cluster. Few of popular logging tools are listed below:

Fluentd is an open source data collector for a unified logging layer. It’s written in Ruby with a plug-in oriented architecture.

It helps to collect, route and store different logs from different sources. While Fluentd is optimized to be easily extended using plugin architecture, fluent-bit is designed for performance. 

It’s compact and written in C so it can be enabled to minimalistic IOT devices and remain fast enough to transfer a huge quantity of logs. Moreover, it has built-in Kubernetes support. It’s an especially compact tool designed to transport logs from all nodes.

Other tools like Stackdriver logging provided by GCP, Logz.io and other 3rd party drivers are available too.



Monitoring:

There are various open source tools available for Kubernetes application monitoring like:

Heapster: Installed as a pod inside of Kubernetes, it gathers data and events from the containers and pods within the cluster.

Prometheus: Open source Cloud Native Computing Foundation (CNCF) project that offers powerful querying capabilities, visualization and alerting.

Grafana:  Used in conjunction with Heapster for visualizing data within your Kubernetes environment.

InfluxDB: A highly-available database platform that stores the data captured by all the Heapster pods.

CAdvisor:  focuses on container level performance and resource usage. This comes embedded directly into kubelet and should automatically discover active containers.



Logging driver plugins are available in Docker 17.05 and higher. Logging capabilities available in Docker are exposed in the form of drivers, which is very handy since one gets to choose how and where log messages should be shipped

Docker includes multiple logging mechanisms to help you get information from running containers and services. These mechanisms are called logging drivers.

Each Docker daemon has a default logging driver, which each container uses unless you configure it to use a different logging driver.

In addition to using the logging drivers included with Docker, you can also implement and use logging driver plugins.

To configure the Docker daemon to default to a specific logging driver, set the value of log-driver to the name of the logging driver in the daemon.json file, which is located in /etc/docker/ on Linux hosts.

The following example explicitly sets the default logging driver to syslog:

{
  "log-driver": "syslog"
}

When you start a container, you can configure it to use a different logging driver than the Docker daemon default, using the --log-driver flag. If the logging driver has configurable options, you can set them using one or more instances of the --log-opt <NAME>=<VALUE> flag. Even if the container uses the default logging driver, it can use different configurable options.
  • Size

Kubernetes
 Docker
As per official page of Kubernetes documentation K8s v1.12 support clusters with up to 5000 nodes based on the below criteria:
  • No more than 5000 nodes
  • No more than 150000 total pods
  • No more than 300000 total containers
  • No more than 100 pods per node.

According to the Docker’s blog post on scaling Swarm clusters published during Nov 2015, Docker Swarm has been scaled and performance tested up to 30,000 containers and 1,000.
Specs
  • Discovery backend: Consul
  • 1,000 nodes
  • 30 containers per node
  • Manager: AWS m4.xlarge (4 CPUs, 16GB RAM)
  • Nodes: AWS t2.micro (1 CPU, 1 GB RAM)
  • Container image: Ubuntu 14.04

Results

 Percentile  API Response Time Scheduling Delay

50th     150ms              230ms
90th      200ms             250ms
99th      360ms             400ms


ii) Building and Deploying Containers with Docker

Docker has a capability to builds images automatically by reading the instructions via text file called Dockerfile. It is a simple text file that follows a specific format and instructions set that contains all commands, in order, needed to build a given image. 

A Docker image consists of read-only layers each of which represents a Dockerfile instruction. The layers are stacked and each one is a delta of the changes from the previous layer. For example, below is a simple Dockerfile which 

Consider this Dockerfile:

FROM nginx:latest
COPY wrapper.sh /
COPY html /usr/share/nginx/html
CMD ["./wrapper.sh"]

Each instruction creates one layer:

  • FROM creates a layer from the nginx:latest Docker image.
  • COPY adds files from your Docker client’s current directory.
  • CMD specifies what command to run within the container.

When you run an image and generate a container, you add a new writable layer (the “container layer”) on top of the underlying layers. All changes made to the running container, such as writing new files, modifying existing files, and deleting files, are written to this thin writable container layer.

Building a Docker Image

$docker build -t hellowhale

The above shown `docker build` command builds an image from a Dockerfile and a context. The build context is the set of files at a specified location PATH or URL. The PATH is a directory on your local filesystem. The URL is a Git repository location.

Running the Docker Container

A running Docker Image is called Docker container and all you need is to run the below command to expose port 80 on host machine from a container and get it up and running:

docker run -d -p 80:80 --name hellowhale hellowhale

Tagging the Image

$docker tag hellowhale userid/hellowhale

Pushing the Docker Image to DockerHub

Before you push Docker Image to DockerHub, you need to login to DockerHub first using the below command:

$docker login
$docker push userid/hellowhale


iii) Managing container with Kubernetes

Docker CLI for a standalone system is used to build, ship and run your Docker containers. But if you want to run multiple containers across multiple machines, you need a robust orchestration tool and Kubernetes is the most popular in the list.

Kubernetes is an open source container orchestration platform, allowing large numbers of containers to work together in harmony, reducing operational burden. It helps with things like running containers across many different machines, scaling up or down by adding or removing containers when demand changes, keeping storage consistent with multiple instances of an application, distributing load between the containers and launching new containers on different machines if something fails.

Below are the list of comparative CLI used by Docker Vs Kubernetes to manage containers:

Docker CLI
Kubernetes CLI
docker run


To run an nginx container -
$ docker run -d --restart=always --name nginx-app -p 80:80 nginx
kubectl run


To run an nginx Deployment and expose the Deployment, see kubectl run.

$ kubectl run --image=nginx nginx-app --port=80 --env="DOMAIN=cluster"

docker ps

To list what is currently running, see kubectl get.

docker:

$ docker ps -a

kubectl get

To list what is currently running under kubernetes cluster:


$ kubectl get po -a

docker exec

To execute a command in a  Docker container:

$ docker ps
CONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS                NAMES
55c103fa1296        nginx               "nginx -g 'daemon of…"   6 minutes ago       Up 6 minutes        0.0.0.0:80->80/tcp   nginx-app

$ docker exec 55c103fa1296 cat /etc/hostname

kubectl:

To execute a command in a container, see kubectl exec.

$ kubectl get po
NAME              READY     STATUS    RESTARTS   AGE
nginx-app-5jyvm   1/1       Running   0          10m

$ kubectl exec nginx-app-5jyvm -- cat /etc/hostname
nginx-app-5jyvm


iv) Trends in Docker and Kubernetes

Docker, Inc has around 550+ enterprise customer who uses Docker in a production environment. Few of non-exhaustive list of companies who actively uses Docker are list below:

  1. The New York Times
  2. PayPal
  3. Business Insider
  4. Cornell University (Not a company but still can be considered)
  5. Splunk
  6. The Washington Post
  7. Swisscom
  8. Alm Brand
  9. Assa Abloy
  10. Expedia
  11. Jabil
  12. MetLife
  13. Societe Generale
  14. GE
  15. Groupon
  16. Yandex
  17. Uber
  18. Ebay
  19. Shopify
  20. Spotify
  21. New Relic
  22. Yelp

Recently, the Forrester New Wave™: Enterprise Container Platform Software Suites, Q4 2018 report states that Docker leading the pack with a robust container platform well-suited for the enterprise, offering a secure container supply chain from the developer's desktop to production.

Lots of organizations are already using Kubernetes in production—like the ones listed on the Kubernetes case studies page, including eBay, Buffer, Pearson, Box, and Wikimedia. But that is not a complete list. Kubernetes is even more versatile than the official case studies page suggests. Below is a list of companies using it:

list of companies using Kubernetes

List of Kubernetes Users


   Microservices Usage

Microservices help developers break up monolithic applications into smaller components. They can move away from all-at-once massive package deployments and break up apps into smaller, individual units that can be deployed separately. Smaller microservices can give apps more scalability, more resiliency and - most importantly - they can be updated, changed and redeployed faster. Some of the biggest public cloud applications run as microservices already.

Containers are a packaging strategy for microservices. Think of them more as process containers than virtual machines. They run as a process inside a shared operating system. A container typically only does one small job - validate a login or return a search result. Docker is a tool that describes those packages in a common format, and helps launch and run them. Linux containers have been around for a while, but their popularity in the public cloud has given rise to an exciting new ecosystem of companies building tools to make them easier to use, cluster and orchestrate them, run them in more places, and manage their life cycles.

Over the last two years, many different types of software vendors - from operating system to IT infrastructure companies - have all joined the container ecosystem. There’s already an industry organization - the open container initiative - guiding the market and making sure everyone plays well together. IBM, HP, Microsoft, VMware, Google, Red Hat, CoreOS - these are just some of the major vendors racing to make containers as easy as possible for developers to use, to share, to protect, and to scale.

The rising demand for multi-cloud environments

With an estimated 85% of today’s enterprise IT organizations employing a multi-cloud strategy, it has become more critical that customers have a ‘single pane of glass’ for managing their entire application portfolio. Most enterprise organizations have a hybrid and multi-cloud strategy. Containers have helped to make applications portable but let us accept the fact that even though containers are portable today but the management of containers is still a nightmare. The reason being –

  • Each Cloud is managed under a separate operational model, duplicating efforts
  • Different security and access policies across each platform
  • Content is hard to distribute and track
  • Poor Infrastructure utilization still remains
  • The emergence of Cloud-hosted K8s is exacerbating the challenges with managing containerized applications across multiple Clouds

This time Docker introduced new application management capabilities for Docker Enterprise Edition that will allow organizations to federate applications across Docker Enterprise Edition environments deployed on-premises and in the cloud as well as across cloud-hosted Kubernetes. This includes Azure Kubernetes Service (AKS), AWS Elastic Container Service for Kubernetes (EKS), and Google Kubernetes Engine (GKE). The federated application management feature will automate the management and security of container applications on premises and across Kubernetes-based cloud services. It will provide a single management platform to enterprises so that they can centrally control and secure the software supply chain for all the containerized applications.

With this announcement, undoubtedly Docker Enterprise Edition is the only enterprise-ready container platform that can deliver federated application management with a secure supply chain. Not only does Docker give you your choice of Linux distribution or Windows Server, the choice of running in a virtual machine or on bare metal, running traditional or microservices applications with either Swarm or Kubernetes orchestration, it also gives you the flexibility to choose the right cloud for your needs.

Talking about Kubernetes Platform, version 1.3 of container management platform Kubernetes

Introduced cross-cluster federated services with an ability to span workloads across clusters and, by extension, across multiple clouds. This opens up the possibility for workloads that need to draw resources from multiple clouds. This would also mean that large jobs can be split among clouds. Not only this, this introduced an ability to automatically scale services to match demand. 

Increasing support for Docker and Kubernetes

Kubernetes has been enjoying widespread adoption among startups, platform vendors, and enterprises. Companies like Amazon, Google, IBM, Red Hat, and Microsoft offer managed Kubernetes under the Containers as a Service (CaaS) model. The open source ecosystem has dozens of players building various tools covering logging, monitoring, automation, storage, and networking aspects of Kubernetes. System integrators have dedicated practices and offerings based on Kubernetes. Global players like Uber, Bloomberg, Blackrock, BlaBlaCar, The New York Times, Lyft, eBay, Buffer, Squarespace, Ancestry, GolfNow, Goldman Sachs and many others are using Kubernetes in production at massive scale. According to Redmonk, a developer-focused research company, 71 percent of the Fortune 100 use containers and more than 50 percent of Fortune 100 companies use Kubernetes as their container orchestration platform.

Did you know there are 35 certified Kubernetes distribution, 22 certified Kubernetes hosting platform and 50 certified Kubernetes service provider available? Over the last three years, Kubernetes has been adopted by a vibrant, diverse community of providers. The Cloud Native Computing Foundation® (CNCF®), which sustains and integrates open source technologies like Kubernetes® , today announced the availability of the Certified Kubernetes Conformance Program, which ensures Certified Kubernetes™ products deliver consistency and portability, and that 35 Certified Kubernetes Distributions and Platforms are now available. A Certified Kubernetes product guarantees that the complete Kubernetes API functions as specified, so users can rely on a seamless, stable experience.

In the other hand, Docker Enterprise Edition (EE) 2.0 represents a significant leap forward in container platform solutions, delivering the only solution that manages and secures applications on Kubernetes in multi-Linux, multi-OS, and multi-cloud customer environments. One of the most promising features announced with this release includes Kubernetes integration as an optional orchestration solution, running side-by-side with Docker Swarm. Not only this, this release includes Swarm Layer 7 routing improvements, Registry image mirroring, Kubernetes integration to Docker Trusted Registry & Kubernetes integration to Docker EE access controls. With this new release, organizations will be able to deploy applications with either Swarm or fully-conformant Kubernetes while maintaining the consistent developer-to-IT workflow.

Enterprise Edition Platform

Docker EE is more than just a container orchestration solution; it is a full lifecycle management solution for the modernization of traditional applications and microservices across a broad set of infrastructure platforms. It is a Containers-as-a-Service(CaaS) platform for IT that manages and secures diverse applications across disparate infrastructure, both on-premises and in the cloud. Docker EE provides an integrated, tested and certified platform for apps running on enterprise Linux or Windows operating systems and Cloud providers. It is tightly integrated into the underlying infrastructure to provide a native, easy to install experience and an optimized Docker environment.

V) Kubernetes vs Docker swarm

  • Installation & Cluster configuration
  • GUI
  • Scalability
  • Auto-Scaling
  • Load Balancing
  • Rolling Updates & Rollbacks
  • Data Volumes
  • Logging & Monitoring

Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It was built by Google based on their experience running containers in production using an internal cluster management system called Borg (sometimes referred to as Omega). On the other hand, a Swarm cluster consists of Docker Engine deployed on multiple nodes. Manager nodes perform orchestration and cluster management. Worker nodes receive and execute tasks 

Below is the major list of differences between Docker Swarm & Kubernetes:

Docker Swarm
Kubernetes
Applications are deployed in the form of services (or “microservices”) in a Swarm cluster. Docker Compose is a tool which is majorly used to deploy the app.
Applications are deployed in the form of a combination of pods, deployments, and services (or “microservices”).
Autoscaling feature is not available either in  Docker Swarm (Classical) or Docker Swarma
An auto-scaling feature is available under K8s. It uses a simple number-of-pods target which is defined declaratively using deployments. CPU-utilization-per-pod target is available.  
Docker Swarm supports rolling updates features. At rollout time, you can apply rolling updates to services. The Swarm manager lets you control the delay between service deployment to different sets of nodes, thereby updating only 1 task at a time.
Under kubernetes, the deployment controller supports both “rolling-update” and “recreate” strategies. Rolling updates can specify a maximum number of pods unavailable or maximum number running during the process.
Under Docker Swarm Mode, the node joining a Docker Swarm cluster creates an overlay network for services that span all of the hosts in the Swarm and a host-only Docker bridge network for container.

By default, nodes in the Swarm cluster encrypt overlay control and management traffic between themselves. Users can choose to encrypt container data traffic when creating an overlay network by themselves.
Under K8s, the networking model is a flat network, enabling all pods to communicate with one another. Network policies specify how pods communicate with each other. The flat network is typically implemented as an overlay.
Docker Swarm health checks are limited to services. If a container backing the service does not come up (running state), a new container is kicked off.Users can embed health check functionality into their Docker images using the HEALTHCHECK instruction.
Under K8s, the health checks are of two kinds: liveness (is app responsive) and readiness (is app responsive, but busy preparing and not yet able to serve)
Out-of-the-box K8S provides a basic logging mechanism to pull aggregate logs for a set of containers that make up a pod.


Ajeet Singh

Ajeet Singh Raina

Blog Author

Ajeet Singh Raina is a Docker Captain & {code} Catalysts by DellEMC. He is currently working as Technical Lead Engineer in Enterprise Solution Group @ Dell R&D. He has over 10+ years of solid understanding of a diverse range of IT infrastructure, systems management, systems integration and quality assurance.  He is a frequent blogger at www.collabnix.com and have 150+ blogs contributed on new upcoming Docker releases and features. His personal blog attracts roughly thousands of visitors and tons of page-views every month. His areas of interest includes Docker on Swarm Mode, IoTs, and Legacy Applications & Cloud. 

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Why Stop Inventing New DevOps Combinations?

DevOps - What's in a name?The term DevOps is well known by now. It was initially introduced by Patrick Dubois a Belgian IT consultant who organized an agile oriented event in October 2009 and named it DevOpsDays, targeting not only developers but also systems administrators, managers, and toolsmiths from all over the world. After the conference, the conversations continued on Twitter with the hashtag #DevOps.If you want to know more about the origin of the DevOps, you can check the video given below which gives you a lot of background about the reason why Patrick Dubois initially started this DevOpsDays conference:DevOps and the rise of the combinations and derivatives With the increasing popularity of DevOps, more people start to give their definition of DevOps. The different definitions of DevOps that go around can differ, depending on what aspect(s) of DevOps you want to focus.In a previous article, I wrote about how to explain DevOps in 5 letters - CALMS or CALMR i.e CALMS framework for DevOpsSome other definitions tend to focus primarily on the automation aspect, omitting the Agile foundation. As a consequence, you get the first combination of DevOps, named BizDevOps or BusDevOps. There are different interpretations about what BizDevOps actually means. “BizDevOps, also known as DevOps 2.0, is an approach to software development that encourages developers, operations staff and business teams to work together so the organization can develop software more quickly, be more responsive to user demand and ultimately maximize revenue.”At the same time, it is the most disputable definition. This definition assumes that DevOps is mainly a technology-driven initiative that hardly involves business people. But as mentioned in my previous article, the foundation of DevOps is culture, which goes back to the agile principles. And we all know that agile without business is only symptomatic. So DevOps without business is as symptomatic as agile without business.According to the Dzone article, DevOps is focusing on a single application or system whereas BizDevOps is focusing on the entire enterprise with all its complex processes and the mixture of applications and systems that support these complex processes.According to this article, BizDevOps provides an answer to dealing with:OK, fair point, but these aspects could as well be tackled by defining proper value streams and Agile Release Trains to deal with all the links and dependencies between these systems and applications. I don't see the need to come up with a different term.I guess you understand by now that I am not a big fan of the BizDevOps term and the confusion it creates. But it can get worse. It was some likely clever tool vendors that came up with the term DevSecOps. And if it is not the tool vendors that invented it, at least they were so clever to jump on the wagon to support the need for more security awareness in DevOps.Nowadays, large tool vendors using of the term DevSecOps instead of DevOps.Here's my opinion on this: security should be an integral part of DevOps. It should be a part of the culture:Don't only think about what something functionally should do, but also what can go wrong (think Abuse or Misuse cases). It is also a part of the automation. All security related tests should be automated as much as possible. Think about scanning vulnerabilities in your own source code, vulnerabilities in external libraries that you use, scanning your container images for vulnerabilities, or even - up to some extent - automated penetration testing. It is also a part of Lean principles: when a security test in your build pipeline fails (e.g. scanning your source code discovers a critical vulnerability), you stop the line.So again, the is no reason why the term DevSecOps should exist at all.Now that we have business and security covered, we can go on and see who else could feel denied or at least ignored? Maybe DBA's? Or any other person involved in data management? Maybe, that is the reason why we also have DevDataOps nowadays.I could go on for a while like this. But you get the point by now: it is uselessMaybe the DAD is right!I recently got to read an interesting article on disciplined agile delivery, the information portal from Mark Lines and Scott Ambler of their Disciplined Agile Delivery, or short DAD. DAD is not - as they call it - an agile methodology, but a process selection framework. DAD is the kernel of a layered model, like an onion, that they call Disciplined Agile and that consists of the following layers:Let’s explore each aspect in Disciplined Agile Framework mentioned in the diagram.1. Disciplined Agile Delivery (DAD)Disciplined Agile Delivery (DAD) aspect consists of initial modeling and planning, forming the team, securing funding, continuous architecture, continuous testing, continuous development, and governance all the way through the lifecycle. The Disciplined Agile Delivery (DAD) framework supports multiple delivery life cycles, basic/Agile lifecycle based on Scrum, a lean lifecycle based on Kanban, and a modern Agile lifecycle for continuous delivery. This aspect is responsible for addressing all the aspects of solution delivery.2. Disciplined DevOpsDisciplined DevOps streamlines the IT solution development and IT operations activities, and supports organization-IT activities, to benefit more effective outcomes to the organizations.3. Disciplined Agile IT (DAIT)DAIT aspect helps to understand how to apply Agile and Lean strategies to IT organizations. This aspect comprises of all IT-level activities such as enterprise architecture, data management, portfolio management, IT governance, and other capabilities.4.Disciplined Agile Enterprise (DAE)DAE can predict and respond quickly to the changes in the marketplace by facilitating a change through an organizational culture and structure. This aspect can be applied to organizations having the learning mindset in the mainstream business and underlying lean and agile processes to drive innovation.The second one, Disciplined DevOps principles deal exactly with what I mentioned before: the different derivatives and combinations of DevOps. They start by giving an answer to the question of why it is so difficult to come to a common definition of DevOps:Specialized IT practitionersMany IT professionals still tend to specialize, choose a focus, like DBA, enterprise architect, operations engineer, or whatever. Each discipline will focus on its own aspect of DevOps.Agilists are focused on continuous deliveryBecause of their focus on releasing daily or even several times a day, a lot of discussions deal with bringing new features faster and more frequently to production and not paying attention to all aspects of DevOpsOperations professionals are often frustratedSystems administrators are crunched between the push of the development teams to deliver faster and more frequently and the typical stringent service management processes they have to deal with, that are not yet adapted to the need for more frequent changesTool vendors have limited offeringsA fool with a tool is still a fool… DevOps tool vendors only focus on these DevOps-aspects that their tools coverService vendors have limited offeringsSimilarly to tool vendors, service vendors will only focus on these DevOps aspects that their  services can currently coverTool vendors treat DevOps as a marketing buzzwordSurfing the waves of the hypes, vendors might be persuaded to rebrand their existing toolset to something DevOps-ish, because it sounds better in a sales pitch. Sounds like window dressing…The DevOps = Cloud visionApparently, some people think that implementing DevOps in your organization can only succeed if you move to a cloud-based platform. Although cloud-native development practices are a facilitator for implementing DevOps, it not a requirement. And moving to a cloud platform definitely isn’t a requirement.All these reasons make that person come up with DevOps combinations that give an answer to only part of the problem.Disciplined DevOps mentions the following visions:1. BizDevOpsBizDevOps is a basic DevOps vision that explicitly brings the customers into the picture. BizDevOps is also called BusDevOps. DevOps is not just for teams, but it can be potentially applicable to any team supporting an incremental delivery lifecycle. The BizDevOps workflow consists of Business Operations, activities of delivering of products and services to the organizations. BusDevOps seeks to streamline the entire value stream, not just the IT portion of it. Its workflow is depicted in the diagram below.2.   DevSecOpsAnother common improvement over the basic DevOps vision is something called DevSecOps. The aim behind this vision is to ensure data security by getting the various security issues, adopting the latest security practices, and finding out and addressing the highest priority security gaps [DevSecOps]. This vision includes collaborative security engineers, exploit testing, real-time security monitoring, and building “rugged software” that has built-in security controls. The workflow of DevSecOps is shown in the figure.  3. DevDataOpsThe aim behind DevDataOps is to maintain a balance between the current needs of data management consists of providing timely and accurate information to the organization and DevOps to respond to the marketplace. Supporting data management activities include the definition, support, and evolution of data and information standards and guidelines; the creation, support, evolution, and operation of data sources of record within your organization; and the creation, support, evolution, and operation of  data warehouse (DW)/business intelligence (BI) solutions. The following figure depicting the workflow of DevDataOps.Or should we just stick to the term DevOps?Even though the message of Scott Ambler and Mark Lines is perfectly reasonable, not everybody might the term Disciplined DevOps. It fits their framework like a glove: everything boils down to Disciplined. If you don’t want to be framed into the Disciplined Agile/DevOps framework (pun intended), you may as well stick to the term DevOps and make sure that you cover all the aspects, which include business, security, data, release management and support.
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Why Stop Inventing New DevOps Combinations?

DevOps - What's in a name?The term DevOps is well ... Read More

Best Practices For Successful Implementation Of DevOps

What is DevOps?DevOps is nothing but the combination of process and philosophies which contains four basic component culture, collaboration, tools, and practices. In return, this gives a good automated system and infrastructure which helps an organisation to deliver a quality and reliable build. The beauty of this culture is it enables a quality for organizations to better serve their customers and compete more effectively in the market and also add some promised benefits which include confidence and trust, faster software releases, ability to solve critical issues quickly, and better manage unplanned work.                                       “DevOps is not a goal, but a never-ending process of continual improvement.”                                                                           Jez Humble Here are the key DevOps best practices that can help you for successful implementation of DevOps.1. Understand Your Infrastructure need: Before building the infrastructure, spend some good time to understand the application and then align your goals to design your Infrastructure and this implementation of DevOps should be business-driven. While understanding infra, make sure you are capturing below components:Cycle Time : Your software cycle needs to be defined in a generic way where you need to know the limitations, ability and if there is any down time then the exact time need to be noted.Versioning Environments: While planning DevOps, always be ready for an alternative solution and versioning your environments helps you to roll out/back your plan. If you are having multiple module and tightly coupled then it requires a clean and neat plan to identify each and every patch and release.Infra as a code: When we say infra as a code it means a solution to addressing both needs – minimizing cycle time and versioning environments can be addressed by capturing and managing your Infrastructure as code. What you built should scalable for a long run.2. Don’t jump start : There is no need to automate the complete cycle in one shot, always take a small entity and apply your philosophy and get this validated. Once you feel your POC is justified, start scaling up now and create a complete pipeline and define a process so anytime you can go back and check what all need to improve and where. All these small success will help you to get confidence internally in your team and builds a trust to stakeholder and your customers.                                                        “DevOps isn’t magic, and transformations never happen overnight”3. Continuous Integration and Continuous Deployment: If  your team is not planning to implement this continuous integration and continuous delivery, then it is not fair with DevOps. Even I’ll say the beauty of DevOps is how frequently your team can deliver without disturbance and how much you are automated in this process. Let’s take a use case: You and your team members are working in an Agile team. In fact, there are multiple teams and multiple modules which are tightly coupled  in which you are also involved. Every day you work on your stories and at the end of the day, you push your ‘private build’ of your work to verify if it builds and ‘deliver’ it to a team build server and same applies to other individuals. Which indicates you all ‘integrate’ your work in the common build area and do an ‘Integration Build’. Doing these integrations and builds to verify them on a regular, preferably daily basis, is what is known as Continuous Integration.Continuous Deployment doesn’t mean every change is deployed to production as soon as possible. It means every change is proven to be deployable at any time.  What it takes is your all validated feature and build from CI and deploys them into the production environment. And here we can follow some of the practices. a) Maintain a Staging Environment that Emulates Production b) Always deploy in staging then move to production c) Automate Testing of Features and Nonfunctional Requirements d) Automatically fetch version-controlled development artifacts.4. Define Performance and do benchmarking : Always do some performance testing and get a collective benchmarking report for the latest build shared by your team because this will only justify the quality of your build and the required infra as well.For example : We have done one performance testing a few days back and got good results, explaining in details. So we did some benchmarking for our CFM machines because we are having a global footprint and at the same time, for us, latency matters and we need CFM in the nearest region. We have verified with our current build how many requests we can handle and we found we are firing more than 200 RPS (request per second). So we planned to check our build capability and fired a good number of requests and noted the number where our build got crashed and noted the RPS and then we did autoscaling of CFM. We might have upgraded our CFM but we planned for auto scaling because the number of requests is an assumption and we don’t want to spend amount for that but at the same time we are ready to consume the experimented traffic. And then we found 7 out 2 CFM are only consuming exact or little less number configuration and request (181 to 191 RPS). So we shared a report to the business team to focus on other regions where we were having very less traffic because we were paying the same amount.Conclusion: We verified our build which has given good confidence to our dev team and we shared the report to the business team which helped them to plan their marketing strategies, meanwhile we completed auto scaling the process as well.  5. Communicate and Collaborate : Collaboration and communication are the X-factors to help organisation grow and assess for DevOps. Collaboration with business and development team helps DevOps team to understand to design & define a culture. This helps to speed up the development, operations, and even other teams like marketing or sales, allowing all parts of the organization to align more closely with goals and projects.6. Start Documenting : Document everything (All your work done) which you are spreading across the process and infrastructure and specially the reports, RCA’s (Root cause Analysis), change management. This helps you to go back and see if all issues we faced can be automated in the next cycle or other ways to handle them smoothly without interrupting your production environment.7. Keep your eyes on cost burning: It has been experienced many a time that if we don’t keep an eye on cloud bills it will keep increasing and will tend to be proportional to the growth of your business till the time you don’t look for optimization. Always do an audit in 2 months and evaluate your cloud computation to optimize. Do some experiment with infra because you should not spend not more than 5  to 10 % of cost for cloud infra if you are completely dependent. Tools you can try : Reoptimize, Cloudyn, Orbitera etc.                                                                                 “If you are DevOps you should account the no’s.”8. Secure your infra : If your team follows certain compliances from day 1 then there is very less chance to compromise with your data and this can be easily enabled by providing a setup where you can verify your vulnerabilities. Before moving your build to the production team you may need to follow the standard at an early stage of development by using configured tools like: SonarQube, VeraCode, Codacy, CodeClimate etc.9. Tool Selection : Always select tools which all are compatible with rest of the toolchain you are planning to use. Why you should have to be so careful is because you have to capture each and every request capture. Once you are done with the tool selection, draft a tools metrics you are willing to capture or will be going to help you to debug. Start logging and monitoring them and have some clear definition for those logs so you can justify and determine that your processes are working as expected. Tools you can have a look : Nagios, Grafana, Pingdom, Monit, OpsGenie, Observium, Logstash etc.                                                                                                        Tool chain for DevOps process:                                                                             “If you are not monitoring,  you are not in the production”Conclusion:An organization that follows all the above best practices creates the right culture, which finally gets the ending it deserves i.e DevOps organization. "A good DevOps organization will free up developers to focus on doing what they do best: write software," says Rob Steward, Vice President of product development at Progress Software. "DevOps should take away the work and worry involved in deploying, securing and running the software once it is written."
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Best Practices For Successful Implementation Of De...

What is DevOps?DevOps is nothing but the combinati... Read More

DevOps & Automation- Advantages Of DevOps

Automation is the key to realizing the philosophy of DevOps and in ensuring that it delivers. The underlying building blocks of DevOps are to ensure that the engineering platform is in place to facilitate continuous delivery, integration, and improvement.  Consider the following processes that have traditionally been carried out manually- Creating development, testing and production infrastructure and configuring networks Harnessing security and data protection Setting up, configuring and deploying software Testing and validation of data – data generated from the application and about the usage of the application Supporting infrastructure and the applications running on it – maintenance, upgrades and transitions In a traditional development scenario, each new environment has to be created from scratch and include all of the above processes, making it a very tedious and lengthy process. However, in a DevOps environment, releases are more frequent and time for testing and quality assurance is therefore much shorter. Performing all these tasks manually therefore severely undermines the efficacy of the DevOps approach. However, it is not just about making DevOps possible, it also has its own advantages. Unexpected errors in production still occur in manual builds as it is difficult to exactly replicate each environment. This in turn, increases the risk of errors occurring in production after testing has been carried out on non-identical pre-production systems. In today’s software world, it is all about productizing and replicating solutions. A product needs to be customized and deployed at a new client site within a really short period of time. Once deployed, the operations support team must be able to support with issues, bug fixes and day-to-day activities in a smooth manner. Similarly, a product deployed for one business domain must easily be configured and utilized by another industry. Such is the flexibility expected from software today. What’s more, in the traditional development cycle, each member of the team has a local copy of the code. When a developer implements a new feature or fixes a bug locally, once complete, the new code is committed back to a central repository. But in a team of developers and system operators, more than one individual can be following this process at the same time, unintentionally breaking the code or affecting another developer’s code. The rule of thumb is that the greater the human intervention, the more testing that will be required. Pre-production or development environments become non-standard which makes processes like testing or releasing new software versions more difficult to repeat and more prone to error. In worst-case scenarios, developers are left to re-invent the wheel each time they need to make changes in response to new business demands. The advantage of DevOps By creating a more responsive development environment that is closely aligned to business requirements and which removes human error from the project lifecycle, DevOps enables organizations to:  Reduce the implementation time of new services from months to minutes  Increase productivity of business and IT teams  Save costs on maintenance and upgrades, and eliminate unnecessary capital expenditure  Standardize processes for easy replication and faster delivery  Improve quality, reliability and reusability of all system components  Increase the rate of success for digitalization strategies and transformation projects  Ensure that money invested in cloud infrastructure, analytics and data management are not wasted Since it focuses on delivering value much earlier in a project lifecycle, DevOps can be seen as an ideal approach to national and government IT projects, as well as massive scale projects for the private sector. It helps accelerate new services through continuous improvement and operational flexibility, providing innovative and cost-effective ways for delivering value through new ways of development and operations. Devops course helps in automation of organization & more benefits
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DevOps & Automation- Advantages Of DevOps

Automation is the key to realizing the philosophy ... Read More