What is Docker Swarm?

 

                   Docker Swarm is a simplified way to orchestrate groups of containerized applications into a service.

Docker Swarm provides a simple, straightforward way to orchestrate containers, often used in situations where companies feel their needs are not suitably complex to warrant using Kubernetes. Thousands of organizations use Swarm, today, and Swarm is under active development by Mirantis.

Where do I get Docker Swarm?

Enterprise Swarm is now offered as an alternative orchestration type with Mirantis Kubernetes Engine (MKE). Users can access the Mirantis Kubernetes Engine webUI to switch nodes to Swarm or ‘mixed’ (i.e., Kubernetes+Swarm) mode at will. Open source Docker Engines can also be combined in a swarm, using CLI commands.

What are Swarm services?

Swarm services are application components that work together to create a full application. This may include the application itself, any external components it needs such as databases, and network and storage definitions. We’ll say more about them under Docker Swarm concepts.

Docker Swarm vs Kubernetes

The long-running battle, of course, is between Swarm and Kubernetes. Each has its advantages, of course; Swarm gained a lot of traction to start because it is part of Docker itself, so developers don’t need to add anything else. It’s also simpler to deploy and use than Kubernetes. Kubernetes, however, has long-since surpassed Swarm in usage, and has its own environments and adherents.

That doesn’t however, mean there’s a clear answer as to which is “better”. There are many factors that determine which is better for you, such as existing environment, target environment, application complexity, and so on.

For example, Mirantis Secure Registry (formerly Docker Trusted Registry) runs as a Swarm workload.

There are even utilities that translate Swarm “stacks” into Kubernetes definitions to enable you to move from one to the other. In fact, throughout this page you will see allusions to both, as we provide the Kubernetes “equivalent” for Swarm concepts for those who already understand K8s.

Docker Swarm concepts

To understand which might be right for you, it’s important to understand the concepts that underpin Docker Swarm.

Docker Swarm vs a Docker Swarm

First, let’s tackle a little recursive terminology. Just as Docker the company created Docker the project which oversaw Docker the technology, we should clarify what we mean by Docker Swarm.

Docker Swarm is the technology that enables Docker to orchestrate containers and other components. The collection of servers on which this collection of orchestrated “stuff” runs is referred to as “a Docker Swarm.”

Swarm Services

In Kubernetes, we would consider a “service” to be a network entity that makes it possible to reach individual containers. In Swarm, however, a “service” means something completely different.

A Swarm service is the equivalent of a container and all of the information needed to instantiate and run it.

For example:

HTML pre Tag

version: '3'
services:
 main:
 image: nickchase/rss-php-demo
 environment:
 - site=mirantis
 networks:
 - default
 deploy:
 replicas: 3
networks:
 default:
 external: false
 

There are two types of Swarm services. Replicated services are instantiated as many times as you’ve requested. If you ask for three of a replicated service, you get three. If you ask for 10, you get 10, and so on. Global services, on the other hand, are like Kubernetes DaemonSets, in that you have one instance running on each node.

Nodes

Nodes are the physical or virtual machines on which Swarm runs your workloads. Swarm has two types of nodes: manager nodes and worker nodes.

Manager nodes are just what they sound like; they are the nodes that coordinate everything that goes on within the cluster, from scheduling workloads (that is, running them on a particular machine) to monitoring the swarm so that any failed services can be restarted.

Worker nodes, on the other hand, are where these services actually run.

A single machine can serve as both a manager and worker node, in which case workloads can run on any server in the swarm.

Note that at the end of the day, these are still not just Swarm nodes, but Docker nodes, so individual containers (that is, containers that are not part of Services) can run on any of these nodes.

Swarm Stack

A swarm stack is a collection of services that work together to form a complete application. It also includes other components those services need, such as databases or networks. For example:

HTML pre Tag

version: '3'
services:
 main:
 image: nickchase/rss-php-demo
 environment:
 - DATABASE=db
 networks:
 - public
 - default
 deploy:
 replicas: 3
 db:
 image: mysql
 networks:
 - default
networks:
 default:
 external: false
 public:
 external: true
 

Load balancing and Ingress

As an orchestration engine, Docker Swarm decides where to put workloads, and then manages requests for those workloads. Swarm allows you to use multiple load balancing mechanisms, including Spread, which tries to keep the usage of each node as low as possible, and BinPack, which tries to use as few servers as possible to handle requests from within the cluster.

Internally, Swarm assigns each service its own DNS entry, and when it receives a request for that entry, it load balances requests between different nodes hosting that service.

Docker Swarm at scale

Although Docker is included with environments aimed at developers, such as Docker Desktop, it’s also available for production environments as part of Mirantis Kubernetes Engine (MKE), providing enterprise-level Swarm capabilities.

One of the advantages of working with Docker Swarm is that developers can use the exact same artifacts — including the stack definition — in both their development and production environments.

What, then, is Docker Swarm?

To circle back to the beginning, Docker Swarm is a simplified but powerful container orchestrator that enables you to build complex applications in a robust, scalable way.

What is Kubernetes Orchestration?

 

                   Like a symphony conductor, Kubernetes provides a host of dynamic services for running, connecting, scaling, and managing complex, multi-container workloads: that's orchestration

Containers provide standard ways of packaging applications with dependencies into small, highly-portable units. Increasingly, those units aren’t monolithic. Modern applications are composed from smaller parts, often called “microservices,” that do singular tasks, on demand from one another or driven by higher-order tasks that administer business logic.

Starting such an application up and keeping it running can be pretty complicated to do manually. You have to configure containers so they know which ports to listen on. You have to launch individual containers, finding space on a container runtime for them and managing any persistent storage requirements. You have to make sure all your containers find each other and begin to work together in concert. And you have to handle faults (e.g., a container crashes) and, as required, scaling requests. When new versions of your containers get built, you have to swap these out for old ones, and keep the application healthy. And you need to be responsive to requirements of infrastructure: e.g., stopping your whole application temporarily when it’s time to apply updates to the underlying node, then starting it again.

Now imagine needing to do this for dozens of nodes, hundreds of apps, and perhaps thousands of different container images. Clearly, this isn’t scalable without writing lots of scripts and automation, integrating monitoring, and lots of additional work and risk.

Orchestration provides an alternative: instead of building custom scripts, monitoring, and other facilities to manage each application component (and all of them together, and many applications sharing the same server or set of servers)…

• Create a set of standard behaviors and services that work for many kinds of application
• Define standard ways of requesting how those behaviors and services should be applied in particular cases: a configuration
• Make these configurations hierarchical, so you can easily specify how each part and layer of your application works, from bottom to top
• Then also create higher-order services that continually evaluate configurations, monitor the state of all running application components and underlying infrastructure, and actively “converge” the state of components to match configured requirements, based on current conditions. If a container crashes, restart it and hook it back up with peers. If a node fails, restart the containers it was hosting on another node with appropriate resources and relink all connections so the application keeps working.

That’s orchestration: standardized, generalized, abstracted, configurable automation for complex, dynamic applications.

What are orchestration tools?

Many different container orchestration systems and platforms exist. All do some of the same things. The simplest kind of orchestration is built into individual container runtimes like Docker Engine or Mirantis Container Runtime (formerly Docker Engine – Enterprise), and is configured using tools and standard configuration file templates like Docker Compose. Single-engine orchestration is extended to clusters of container runtimes with Swarm orchestration — also configurable with Docker Compose. Features of this kind of orchestration include the ability to define, start, stop, and manage multi-container apps as units, in multiple, isolated application environments; ability to preserve volume data across successive restarts (or replacements) of a container (persistent storage); and the ability to replace only changed containers in a running system, making operations very fast.

Mesos is another orchestrator, created and maintained by an Apache open source project that predates Kubernetes. Mesos turns a collection of container hosts into a cluster, abstracts away physical resources, and provides APIs that applications can consume to manage their own resources, scheduling, and elastic scaling. At this level, Mesos is best-suited for hosting applications that are prepared to take responsibility for their own orchestration: cluster-oriented apps like Hadoop are a good example. Open source projects like Marathon provide another layer of orchestration convenience on top of Mesos, framing additional abstractions and delivering an easier-to-use environment, much like Kubernetes.

Kubernetes orchestration

Today’s most-popular container orchestration environment is Kubernetes, which provides a full suite of general-purpose orchestration methods, services, and agents, and relatively simple standards for configuring them. Kubernetes also lets you define custom orchestration agents, called operators, and custom configurations for them that build on existing functionality.

Developers usually find Kubernetes orchestration a little daunting, at first. But most quickly discover that skills developed learning Docker, Docker Compose, and Swarm are readily applicable in Kubernetes environments. In fact, Mirantis provides an open source extension to the Docker CLI that lets Docker Compose configurations run directly on Kubernetes, in addition to working on Docker Engines and Swarm

What is Enterprise Kubernetes?

 

                   Enterprise Kubernetes delivers what upstream Kubernetes lacks, providing a complete solution for secure enterprise container and cloud computing at scale

Enterprise Kubernetes is open-source, “upstream” Kubernetes, hardened and secured in various ways, and integrated with other software and systems to provide a complete solution that fulfills enterprise requirements for developing and hosting containerized applications securely, at scale.

Kubernetes is incomplete by design 

Kubernetes, by itself, provides a host of features for orchestrating and managing container workloads, and for making them scalable and resilient. But Kubernetes is incomplete by design.

Kubernetes is engineered in a modular way, with standard interfaces for integrating fundamental components like container runtime, networking, storage, and other subsystems and functionality.

Kubernetes promotes choice

This reflects the fact that Kubernetes evolved after, and partly in response to the popularity of foundational technologies like Docker, and was engineered to make those technologies work together seamlessly to support large-scale, complex software projects and answer large organizations’ requirements for resiliency and application lifecycle automation.

You can think of Kubernetes as a “productization” of Google’s original, internal-facing Borg project, realized against an emerging landscape of container-related solutions. Today, Kubernetes lives at the center of a vast Cloud Native Computing Foundation ecosystem of open source and proprietary solutions that provide foundational capabilities, extend Kubernetes functionality for practical use-cases, accelerate software development, and deal with challenges of delivering and operating Kubernetes at large scales, in forms that enterprises find consumable.

Choice entails risk

Because Kubernetes is designed for flexibility, you can’t build a working Kubernetes cluster without making choices. The Kubernetes project has established some baselines and defaults that are widely popular, like use of the open-source containerd runtime which shares DNA with Docker Engine under a Kubernetes-standard Container Runtime Interface (CRI). But they’re not compulsory, and for use-cases beyond “I want to try out Kubernetes,” they may not be completely optimal.

Enterprises often don’t have the in-house technical expertise to feel secure making such choices, and building (and then maintaining) their own, customized enterprise Kubernetes platforms. While many have succeeded in architecting working, small-scale solutions, they’re (sensibly) leery of trying to use these in production.

Enterprise Kubernetes components

Here are some of the choices involved in assembling an enterprise Kubernetes platform:

Upstream hardening without lock-in

Open source software like Kubernetes goes through extensive, ongoing testing by project contributors and maintainers. Each release is updated frequently with bugfixes and limited backports. But the result isn’t guaranteed perfect, and each successive Kubernetes release is deprecated within a fairly short time. Because the ecosystem moves so quickly, users can be challenged to stabilize and maintain reliable clusters over time, free of known security vulnerabilities.

Producers of enterprise Kubernetes distributions can (or should) take on responsibility for aggregating changes, hardening, and supporting the versions of Kubernetes they provide to customers. Important: Ideally, they should do this without adding substantial proprietary code, constraining requirements (e.g., we’ll only support this if you run it on our operating system) or elaborate “improvements” that limit choice and put customers at a remove from upstream innovation.

Container runtime – Docker-compatible, Linux or Windows, with built-in security

The container runtime is core software, running on each node, that hosts individual containers. An enterprise Kubernetes needs a runtime that’s entirely compatible with Docker-style container development workflows and binaries, since that’s what enterprises are, with few exceptions, using — plus flexibility to use other runtimes, if they prefer. The default runtime should work comfortably on Linux and Windows hosts to run Docker container binaries built for each platform. It should support host hardware features like GPUs.

But that’s just the beginning. The runtime is the foundation of the Kubernetes system, so an enterprise runtime is ideally situated to provide many security and hardening features, like execution-policy enforcement and content trust (i.e., the ability to prevent unsigned container workloads from executing), FIPS 140-2 encryption (required by regulated industries and government/military), and overall compliance with established multi-criterion security benchmarks, like DISA STIG.

It can also be important that an enterprise runtime be able to support modes of container orchestration besides Kubernetes — for example, Swarm, which provides a simpler orchestration feature set, already familiar to many Docker developers. This (plus support of multiple orchestration types throughout the larger enterprise solution) gives users more choice of how they want to onboard and support existing workloads, and maximizes utility of existing and widespread skills.

Container network

An enterprise Kubernetes needs to provide a battle-tested container networking solution — ideally, one that’s compatible with best-of-breed ingress solutions, known to be able to scale to “carrier grade,” and provides dataplanes for both Linux and Windows, enabling construction of Kubernetes clusters with both worker node types. The ideal solution needs to rigorously embrace Kubernetes-recommended, “principle of least privilege” design, and support end-to-end communications authentication, encryption, and policy management.

Ingress

Routing traffic from the outside world to Kubernetes workloads is the job of Ingress: represented in Kubernetes as a standard resource type, but implemented by one of a wide range of third party proxy solutions. Enterprise-grade ingress controllers often extend the native specification and add new features, such as the ability to manage east/west traffic, more-flexible and conditional ways of specifying routing, rewrite rules, TLS termination, ability to monitor traffic streams, etc. To do this, they may implement sophisticated architectures, with sidecar services on each node; and they may require integration with monitoring solutions like Prometheus, tools like Cert-manager, and other utilities to present complete solutions. An enterprise Kubernetes needs to provide this kind of front-end routing capability, both by implementing a best-of-breed ingress, and making it manageable.

Enterprise Kubernetes and software development

Kubernetes vs. CaaS/PaaS

Full-on enterprise Kubernetes also needs to solve for the challenges of container software development. Some distributions seem to approach this with the attitude that Kubernetes development is inherently too difficult for many developers, and hide Kubernetes beneath a so-called CaaS (Containers as a Service) or PaaS (Platform as a Service) framework. This is a generally quite prescriptive software layer that provides templates for application design patterns, relevant component abstractions, and connection rules (usually provided for a range of programming languages and paradigms), plus software that operationalizes these constructions on the underlying Kubernetes platform.

Such frameworks may help, initially, with legacy application onboarding and certain kinds of early-stage greenfield application development. But developers tend, over time, to find them restrictive: good in some use-cases, but not in others. Using a PaaS extensively, meanwhile, can mean locking applications, automation, and other large investments of time, learning, and effort, into its limited — and perhaps proprietary — framework, rather than Kubernetes’ standardized and portability-minded native substrate. This, in turn, can lock customers into a particular enterprise Kubernetes solution, perhaps with a sub-optimal cost structure.

An important point to remember is that — if users want a CaaS or PaaS (or a serverless computing framework or any other labor-saving abstraction) — many mature open source solutions exist to provide these capabilities on Kubernetes, while keeping investment proportionate to benefit and avoiding lock-in. The best enterprise Kubernetes platforms work hard to ensure compatibility with many solutions, giving their users maximum freedom of choice.

Secure software supply chain

Teams developing applications to run on Kubernetes want and need to work freely with workflow automation, CI/CD, test automation, and other solutions, building pipelines that convey application components from developer desktops, through automated testing, integration testing, QA, staging, and delivery to production Kubernetes environments. Enterprise Kubernetes solutions win with developers by providing maximum freedom of choice, plus easy integration to Kubernetes and related subsystems that work together to enforce policy and facilitate delivery of secure, high-quality code.

Private container registry isn’t part of Kubernetes, per se, but can be a foundational component of a secure software supply chain. An enterprise-grade private registry offers practical features that accelerate development while helping organizations maintain reasonable control. For example, a registry may make it possible to cryptographically sign curated images and layers, permitting only correctly-signed assets to be deployed to (for example) staging or production clusters — a feature called ‘content trust.’ It may be configurable to automatically acquire validated images of popular open source application components, scan these for vulnerabilities, and make them available for use by developers only if no CVEs are found.

Coordinating functionality of this kind requires co-development of registry and other components of the Kubernetes system, notably the container runtime.

Visualization and workflow acceleration

Enterprise Kubernetes also needs to solve for developer and operator experience. “Developer quality of life” features can range widely: from APIs designed to simplify creation of self-service or operational automation, to built-in metrics, to compatibility with popular heads-up Kubernetes dashboards and other tools for speeding interactions with Kubernetes clusters.

Enterprise Kubernetes at scale

Perhaps the most important feature of a complete enterprise Kubernetes solution is that it solves problems of Kubernetes delivery, administration, observability, and lifecycle management at enterprise scales. That means anywhere from “a few small clusters” to hundreds or thousands of clusters, perhaps distributed across a range of host infrastructures, e.g., on-premises “private clouds” like VMware or OpenStack, public clouds like AWS, bare-metal clouds, edge server racks, etc.

What most enterprises seem to want is a seamless, fundamentally simple “cloud” experience for Kubernetes delivery, everywhere: configure a cluster with a few dialog boxes, provision users through integrations with corporate directory, click “deploy,” quickly obtain credentials, and get to work. Monitor and update the cluster non-disruptively through the same interface. Keep the Kubernetes stack secure and fresh with continuous updates.

Being able to do this consistently, across multiple infrastructures, pays off hugely. Developers don’t need to worry about underlying infrastructure mechanics: if they need to scale a cluster or tear it down, the enterprise Kubernetes solution has API features for that. Consistent Kubernetes clusters everywhere mean that applications and their automation can be easily ported and reused. Consistency also minimizes unknown and unmapped attack surfaces, improving security and simplifying policy management and compliance.

What, then, is enterprise Kubernetes?

Expanding on our original definition, enterprise Kubernetes is real Kubernetes, in a flexible, best-of-breed configuration, made “batteries included” with careful choice of default components, and delivered ready for production work, by systems designed to minimize the skill, time, and overhead associated with managing this complex, but valuable group of technologies.

What is Kubernetes multi-cluster?

 

                   Sometimes one Kubernetes cluster just isn't enough to satisfy your needs; a Kubernetes multi-cluster architecture solves myriad problems

What is multi-cluster Kubernetes?

A single Kubernetes cluster can be extremely flexible; it can serve a single developer or application, or you can use it with various techniques to create a multi-tenant Kubernetes environment. At some point, however, you will find that you need a Kubernetes multi-cluster environment.

Multi-cluster Kubernetes is exactly what it sounds like: it’s an environment in which you are using more than one Kubernetes cluster. These clusters may be on the same physical host, on different hosts in the same data center, or even in different clouds in different countries, for a multi-cloud environment.

What makes your environment a Kubernetes multi-cluster environment is not just the fact that more than one cloud is in use. Kubernetes multi-cluster is what happens when an organization takes steps to coordinate delivery and management of multiple Kubernetes environments with appropriate tools and processes.

In this article, we’ll discuss:

• Reasons you’d want to use a Kubernetes multi-cluster environment
• Technical aspects to consider when designing a Kubernetes multi-cluster architecture
• Working with multiple Kubernetes clusters
• Connecting to multiple Kubernetes clusters

Why Multi-Cluster?

Of course, with multiple Kubernetes clusters comes an increase in complexity, so why would you even want to take something like this on? Multi-cluster Kubernetes provides several advantages, including:

• Tenant isolation: Even if you have only one development team, you’re going to need multiple Kubernetes environments to accommodate development, staging, and production, and out of the box, Kubernetes is not a multi-tenant architecture. In some cases, you can achieve this isolation using namespaces within a single cluster, but the Kubernetes security model makes it difficult to isolate these environments from each other, and in any case, you still run into the “noisy neighbor” problem, where one runaway app can affect any environments and applications sharing that hardware. Kubernetes multi-cluster environments enable you to isolate users and/or projects by cluster, simplifying this process.
• Availability and performance: By architecting a Kubernetes multi-cluster environment, you provide the ability to move workloads between clusters, if necessary, which enables you to avoid issues if one cluster should become bogged down or even disappear entirely.
• Simplified management: Remember, just adding additional clusters doesn’t make it a multi-cluster environment. In fact, using multiple isolated clusters, independently managed, can be a management nightmare. Instead, multiple clusters managed from a central point provides consolidating logging and auditing, and helps to prevent runaway spending from “shadow IT.”
• Failover: Having a multi-cluster environment enables you to ensure that workloads don’t experience downtime due to a problem within a single cluster, as you can seamlessly transfer them to another cluster.
• Geographic specificity and Regulatory control: One of the defining characteristics of cloud computing is that you don’t necessarily know (or care) where your workloads are running. In some cases, however, this can be an issue. For example, some countries and regions require that data on their citizens not leave their borders; others have strict regulations on how that data must be treated. By using multiple clusters in multiple locations, you can control the geography of your applications without sacrificing the flexibility of cloud native applications.
• Scaling and bursting of applications: Multi-cluster Kubernetes enables you to provide the ability to scale beyond the limitations of a single cluster by moving parts of your application to additional clusters. This technique is most useful, of course, when utilized in a <a href=””>multi-cloud Kubernetes</a> environment.
• Distributed systems: For geographically distributed organizations, it’s not necessarily practical to run the entire enterprise on a single cluster. In these situations, it can become more feasible to have clusters in each location, but have them centrally managed.
• Edge computing and Internet of Things (IoT): Perhaps the ultimate embodiment of a distributed system is edge computing, in which computing resources are placed as close as possible to the data on which they operate. Edge computing typically involves local clusters that process data and send results to regional clusters, which themselves perform operations and send the results to a central cluster. In an IoT environment, those edge clusters may have “leaf nodes” that consist of small compute devices that act as nodes for the cluster.

What does a Kubernetes multi-cluster architecture look like?

Just as there are many different ways to build an application, there are also multiple ways to architect a multi-cluster Kubernetes environment. In general, they fall into two categories:

cluster-centric and application-centric.

How do I connect to multiple Kubernetes clusters?

With a cluster-central Kubernetes multi-cluster architecture, the focus is on making multiple clusters appear to behave as one, as is the case with Kubernetes federation. In this situation, applications may be unaware of where they are actually running. While this approach can simplify management in some ways (particularly for developers) it does introduce additional complexity for operators, as a fault in one cluster can affect other clusters, leading to overall issues.

With an application-centric approach, individual clusters remain independent (though they may be administered from a single dashboard) and applications themselves are designed to be cluster-independent, migrating as necessary. This migration between clusters may be handled by the application directly, or it may be the purview of a service mesh such as Istio or LinkerD, which sends requests to individual endpoints based on desired criteria.

Whichever approach you choose, it’s important to consider the networking implications. A complete discussion of Kubernetes networking is beyond the scope of this article, but it does provide a great deal of power and flexibility — which means it can be complex to administer.

How do I work with multiple Kubernetes clusters?

In order to configure multi-cluster Kubernetes, we need to look at the ways in which we normally access a single cluster. Typically, you access Kubernetes using a client such as kubectl, which takes its configuration from a KUBECONFIG file. This file typically contains the definition of your cluster, such as:

apiVersion: v1
kind: Config
preferences: {}

clusters:
- cluster:
   certificate-authority-data: LS0tLS1CRUdJTiBDRVJUSUZJQ...
   server: https://172.19.113.9:443
  name: gettingstarted
- cluster:
   certificate-authority-data: LS0tLS1CRUdJTiBDRVJUSUZJQ...
   server: https://172.19.218.42:443
  name: demo

contexts:
- context:
    cluster: gettingstarted
    user: nick
  name: nick@gettingstarted
- context:
    cluster: demo
    user: nick
  name: nick@demo
current-context: nick@gettingstarted

users:
- name: nick
  user:
   auth-provider:
     config:
       client-id: k8s
       id-token: eyJhbGciOiJSUzI1NiIsInR5cC...
       idp-certificate-authority-data: LS0tLS1CRUdJTiBDRVJUSUZJQ0FURS0tLS...
       idp-issuer-url: https://containerauth.int.mirantis.com/auth/realms/iam
       refresh-token: eyJhbGciOiJIUzI1NiIsInR5cCIgOiAiSld...
     name: oidc

In this file, we see two clusters, getting started and demo, and I’m accessing each of them from a single user account.

(So if I were to ask “How many clusters are in Kubernetes?”, the answer is “how many are in your KUBECONFIG?”)

To switch between these clusters, it’s most convenient to use contexts, because they include both the cluster and use information. You can set these contexts by either editing the file by hand, or by using the kubectl config command, as in:

kubectl config --kubeconfig=mykubeconfigfile set-context appcontext --cluster=gettingstarted --namespace=app1 --user=nick

This adds the new context to the KUBECONFIG, as in:

...
contexts:
- context:
    cluster: gettingstarted
    user: nick
  name: nick@gettingstarted
- context:
    cluster: demo
    user: nick
  name: nick@demo
- context:
    cluster: gettingstarted
    namespace: app1
    user: nick
  name: appcontext
current-context: nick@gettingstarted
...

Now to use this new context, we can switch to it using:

kubectl config set current-context appcontext

Now any commands we execute will run against that new context.

What is Kubernetes Support

 

                   Although you can download, install, and use Kubernetes for free, there are times when you are going to need professional support

Once you’ve gotten past the issue of what Kubernetes is and how it works, it’s time to start planning for your future. When and how support enters into the equation depends on where you are in your Kubernetes journey, and where you need to go.

Why do I need Kubernetes support?

Kubernetes support can help you figure out what kinds of Kubernetes clusters you need. Most users leverage Kubernetes for application development. And that, in turn, typically means they need several different kinds of Kubernetes clusters: small clusters (maybe even ‘desktop’ clusters) for individual developers; test clusters for running automated tests on applications, integrations, configurations, lifecycle management tooling, operators and other assets; perhaps staging clusters to evaluate applications in “production-like” environments; and of course, actual scaled-out production clusters: sometimes more than one of these, if you have ambitions to do blue/green deployments or explore similar accelerated release delivery strategies.

Kubernetes support can help you design these cluster types. So you need architectures for all these clusters (which should ideally be self-similar to one another, because building on Brand X, testing on Brand Y, and going to production on Brand Z is a formula for trouble, redundant work, and increased risk). And then you need code and processes for building and delivering and observing and maintaining and scaling and updating all these cluster models dynamically, because development and operations teams grow, and needs change, and projects evolve, and you want to utilize resources efficiently. If you do blue/green deployments, for example, you’ll need to clone your entire production environment, every time you do a release. And nowadays, that could be daily.

Kubernetes support can help you engineer and automate application development processes. You also need to figure out the specifics of your application development process (how you build containers, automate tests, scan and store and secure container images, Helm charts, and other stuff, etc.) and then automate it, using version control, CI/CD, registry, security and other tools, so you can get new features to your customers, quickly and safely. Exactly what you build and how depends very much on projects and priorities. An organization tasked with updating lots of legacy web applications to run in containers may need somewhat different tooling than an organization that’s building complex microservices applications from scratch. Many organizations need to build several software development supply chains, and run them in parallel.

Kubernetes support can help you learn to run all this efficiently, or even run it for you. Once all your systems are designed and built, the ongoing (so-called “Day 2”) job is learning how to operate them efficiently. A mature Kubernetes support organization can help train your folks, and can provide local and remote technical and operational support through each phase of your build-out. Some support organizations can even operate your infrastructure remotely: monitoring for issues, fixing things transparently, organizing to enable regular updates.

Kubernetes support can help you do your most important work. Mature Kubernetes support organizations may also be able to provide a range of professional services to help you get big jobs done with this new infrastructure. For example, some organizations are confronted with the need to update dozens or hundreds of self-similar web apps to run in containers. This kind of work, sometimes called a “brownfield” project, can often be accomplished much more quickly with professional services on hand to do some (or all) of the heavy lifting. In other cases, organizations need help architecting complex, innovative, cloud native solutions. Professional service teams can help with this kind of task (often called a “greenfield” project) as well.

What are Kubernetes support tiers?

Clearly, “one size fits all” is a bad model for Kubernetes support. Some organizations are more ready than others to shoulder parts of the burden of designing and automating around Kubernetes and modern software development and operations. So support organizations generally ‘tier’ their services appropriately for different project phases and customize them to meet specific customer requirements. For example, here at Mirantis, we have four basic support packages:

These packages aim to deliver “just right” support for customers through the phases of a typical cloud journey, which starts by building small “proof of concept” implementations of clusters and development processes, before moving on to create a mature production environment. A top tier of support is also available for customers who don’t want to operate their own production infrastructures, and want things to just work. To learn more, please visit our support page.

Who supports Kubernetes? Can’t I just use the Kubernetes community for support?

Kubernetes itself is administered by the Cloud Native Computing Foundation (CNCF) and the CNCF interactive community matrix shows more than 190 different companies that are certified to provide Kubernetes services. When choosing a Kubernetes support provider, here are some things to think about:

• Expertise: How much experience does the provider have in Kubernetes support? Are they a single individual freelancing on the side, or a team of many experts with diverse skillsets that can be called upon depending on the problem? How well does the provider know not just Kubernetes, but the specific environment in which you will be running it? For example, if you will be running Kubernetes on top of OpenStack, you will want to choose a provider that has experience in both technologies.
• Distribution: You’ll want to choose a provider who has experience not just with Kubernetes itself, but also with the distribution of Kubernetes you’re using. If you’re using a vendor distribution such as Mirantis Kubernetes Engine, that will usually — but not always — mean the distribution vendor will be best suited to serve you.
• Availability: If your application is in production is someone available to help you if something goes down at 3am? If you’re not in production, does the vendor have experts available during your working hours? Can you reach someone by phone, or do you have to send an email or ticket and cross your fingers until they get back to you?
• Escalation: Anyone who’s been responsible for a production system knows that heart-stopping moment when you realize that something has gone terribly, horribly, wrong. When it does — and it will — are there experts who can jump in immediately to remedy the situation?
• Third party coordination: Cloud native infrastructures involve many different vendors and service providers. It’s easy to get caught up in whether the problem is in your network or operating system or the application itself. You want a vendor who will take responsibility for this coordination, rather than simply telling you, “Sorry, talk to your cloud provider.”

Because using the software itself is free, there’s another option that companies sometimes consider: using the Kubernetes community itself for support. While this seems tempting, it’s not actually practical, especially for production environments.

The Kubernetes community is filled with extremely knowledgeable practitioners, many of whom work for the same companies that contribute to and support Kubernetes, and often, posting a question in the community Slack channel or on StackOverflow will bring you a number of opinions. However, there’s no guarantee you will get an answer quickly — or at all — and none of those people are committed to seeing your issue through to resolution.

When your company’s work is at a standstill because of a problem, you need, as the saying goes, “one throat to choke.”

Mirantis has been providing open source support for more than a decade. Contact us and see what we can do to make your life easier.

Is Kubernetes dropping
Docker support?

Yes and no. Technically speaking, Kubernetes didn’t actually support Docker containers in the first place. Instead, it supports containers based on containerD, the engine inside of Docker containers. Docker support was provided by a component called Dockershim, which provided a translation between the containerD instruction set and the Docker instruction set. It is Dockershim that is being deprecated in Kubernetes 1.23.

For most users, this will be a non-issue; they’re not really using Docker anyway. For those who are, however, Dockershim will still be available, but not “out of the box.” Mirantis and Docker will continue to support and develop Dockershim as a separate package you can add to Kubernetes for Docker container support.

What Is Kubernetes ?

 

                   Kubernetes is software that automatically manages, scales, and maintains multi-container workloads in desired states

Modern software is increasingly run as fleets of containers, sometimes called microservices. A complete application may comprise many containers, all needing to work together in specific ways. Kubernetes is software that turns a collection of physical or virtual hosts (servers) into a platform that:

• Hosts containerized workloads, providing them with compute, storage, and network resources, and
• Automatically manages large numbers of containerized applications — keeping them healthy and available by adapting to changes and challenges

How does Kubernetes work?

1. When developers create a multi-container application, they plan out how all the parts fit and work together, how many of each component should run, and roughly what should happen when challenges (e.g., lots of users logging in at once) are encountered.
2. They store their containerized application components in a container registry (local or remote) and capture this thinking in one or several text files comprising a configuration. To start the application, they “apply” the configuration to Kubernetes.
3. Kubernetes job is to evaluate and implement this configuration and maintain it until told otherwise. It:
   • Analyzes the configuration, aligning its requirements with those of all the other application configurations running on the system
   • Finds resources appropriate for running the new containers (e.g., some containers might need resources like GPUs that aren’t present on every host)
   • Grabs container images from the registry, starts up the new containers, and helps them connect to one another and to system resources (e.g., persistent storage), so the application works as a whole
4. Then Kubernetes monitors everything, and when real events diverge from desired states, Kubernetes tries to fix things and adapt. For example, if a container crashes, Kubernetes restarts it. If an underlying server fails, Kubernetes finds resources elsewhere to run the containers that node was hosting. If traffic to an application suddenly spikes, Kubernetes can scale out containers to handle the additional load, in conformance to rules and limits stated in the configuration.

Why use Kubernetes?

Because it makes building and running complex applications much simpler. Among many other features, Kubernetes provides:

• Standard services like local DNS and basic load-balancing that most applications need, and are easy to use.
• Standard behaviors (e.g., restart this container if it dies) that are easy to invoke, and do most of the work of keeping applications running, available, and performant.
• A standard set of abstract “objects” (called things like “pods,” “replicasets,” and “deployments”) that wrap around containers and make it easy to build configurations around collections of containers.
• A standard API that applications can call to easily enable more sophisticated behaviors, making it much easier to create applications that manage other applications.

All this saves developers and operators a great deal of time and effort, and lets them focus on building features for their applications, instead of figuring out and implementing ways to keep their applications running well, at scale. By keeping applications running despite challenges (e.g., failed servers, crashed containers, traffic spikes, etc.) Kubernetes also reduces business impacts, reduces the need for fire drills to bring broken applications back online, and protects against other liabilities, like the costs of failing to comply with Service Level Agreements (SLAs).

Where can I run Kubernetes?

Kubernetes also runs almost anywhere, on a wide range of Linux operating systems (worker nodes can also run on Windows Server). A single Kubernetes cluster can span hundreds of bare-metal or virtual machines in a datacenter, private, or any public cloud. Kubernetes can also run on developer desktops, edge servers, microservers like Raspberry Pis, or very small mobile and IoT devices and appliances. With some forethought (and the right product and architectural choices) Kubernetes can even provide a functionally-consistent platform across all these infrastructures. This means that applications and configurations composed and initially tested on a desktop Kubernetes can move seamlessly and quickly to more-formal testing, large-scale production, edge, or IoT deployments. In principle, this means that enterprises and organizations can build “hybrid” and “multi-clouds” across a range of platforms, quickly and economically solving capacity problems without lock-in.

What is a Kubernetes cluster?

The K8s architecture is relatively simple. You never interact directly with the nodes hosting your application, but only with the control plane, which presents an API and is in charge of scheduling and replicating groups of containers named Pods. Kubectl is the command line interface that allows you to interact with the API to share the desired application state or gather detailed information on the infrastructure’s current state. Let’s look at the various pieces.

Nodes

Each node that hosts part of your distributed application does so by leveraging Docker or a similar container technology, such as Rocket from CoreOS. The nodes also run two additional pieces of software: kube-proxy, which gives access to your running app, and kubelet, which receives commands from the k8s control plane. Nodes can also run flannel, an etcd backed network fabric for containers.

Master

The control plane itself runs the API server (kube-apiserver), the scheduler (kube-scheduler), the controller manager (kube-controller-manager) and etcd, a highly available key-value store for shared configuration and service discovery implementing the Raft consensus Algorithm.

What is “enterprise Kubernetes?”

Kubernetes, by itself, provides a core software framework for container and resource management, default services, plus an API. It’s engineered to be extensible via standard interfaces to provide important capabilities like:

• Running containers – a container runtime or ‘engine’
• Letting containers communicate – a container network
• Providing persistent storage – a container storage solution
• Routing inbound traffic to containers in a secure and orderly way – an ingress solution
• Full-featured load balancing – distributing inbound traffic evenly to container workloads – via integration with an external load-balancing solution

… and many other components essential for efficient use and operations at scale. To make Kubernetes work at all — you or someone else needs to choose and integrate solutions to fill these critical slots. Kubernetes alternatives made available free of charge typically select from among open source alternatives to provide these capabilities. These are often very good solutions for learning and small-scale use. Organizations that want to use Kubernetes to run production software at scale need more, and more-mature functionality:

• They need Kubernetes that’s feature-complete, hardened and secure, and easily integrated with centralized IT resources like directory services, monitoring and observability, notifications and ticketing, and so on.
• They need Kubernetes that can be deployed, scaled, managed, and updated in consistent ways, perhaps across many different kinds of infrastructure.
• They need all the different parts of Kubernetes to be validated together, and supported by a single vendor.

“Enterprise Kubernetes” refers to products and suites of products that answer these needs: that fill all of Kubernetes’ feature slots with best-of-breed solutions, solve problems of Kubernetes management across multiple infrastructures, enable consistency, and provide complete support.

How do I start using Kubernetes?

Mirantis makes several Kubernetes solutions, appropriate for different uses. Our open source products can be used free of charge, with community support. Our flagship products can be trialed free of charge and are available with tiered support up to fully-managed services. Mirantis Container Cloud (formerly Docker Enterprise Container Cloud) is a solution for deploying, observing, managing, and non-disruptively updating Kubernetes (plus other applications that run on top of Kubernetes, like containerized OpenStack) on any infrastructure — ideal if you need to run Kubernetes reliably at scale with security, simplicity, and freedom of choice. (Download Mirantis Container Cloud) Mirantis Kubernetes Engine (formerly Docker Enterprise/UCP) is fully-baked Enterprise Kubernetes for development, testing, and production. It includes the Universal Control Plane webUI for easy management, Mirantis Secure Registry (formerly Docker Trusted Registry) for private container image storage and security scanning, and runs on Mirantis Container Runtime (formerly Docker Engine – Enterprise) — a hardened container runtime with optional FIPS 140-2 encryption and other security and reliability features. (Download Mirantis Kubernetes Engine) K0S – (pronounced “K-zeroes”) is zero-friction, open source Kubernetes that starts with a single command and runs on almost any Linux at almost any scale, from Raspberry Pis to giant datacenters. It’s our best choice for learners. (Download k0s – zero friction Kubernetes) Finally, Lens – the open source Kubernetes IDE, accelerates Kubernetes learning and development. Lens lets you manage and interact with multiple Kubernetes clusters easily using a context-aware terminal, visualize object hierarchies inside them, view container logs, log directly into container command shells, and more. (Download Lens – the Kubernetes IDE)  

What is Kubernetes Ingress?

 

                   Ingress routes and manages traffic from the outside world to workloads running on Kubernetes

Out of the box, a minimal Kubernetes cluster provides several abstractions for letting applications receive requests from other apps (i.e., inside the cluster) and from the outside world. When developers want to expose an application to traffic (e.g., for testing), they typically define one of these basic services as a starting point:

• ClusterIP – Assigns a port within a known range. Lets a workload receive requests from other applications and entities inside the cluster.
• NodePort – Lets a workload receive requests from the outside world on a specific port, exposed on all (or a subset of) cluster node IP addresses.
• LoadBalancer – Assigns an external load balancer (and optionally, DNS name) to the workload’s NodePort. The load balancer, which must be provided by surrounding infrastructure marshaled by a specific infrastructure provider running on the cluster (for example, a Kubernetes cluster running on AWS might integrate with Elastic Load Balancer — another AWS service, via an AWS provider), can then balance requests across nodes exposing the workload.

These primitive service types, however, don’t support all features production applications need. They can’t terminate SSL connections. They can’t support rewrite rules. They can’t (at least not easily) support conditional routing schemes — for example, sending requests to myapp/dothis to one workload, and requests to myapp/dothat to another.

In conventional web environments, features like these are provided by the webserver/proxy (e.g., nginx), by adaptations (e.g., certificates) on the host supporting the webserver, by helper functions like .htaccess, by front-end proxies, etc., or even by applications themselves.

Kubernetes, however, is designed to encourage:

Decoupling of workloads from housekeeping – Simpler workloads are easier to maintain, improve, and assemble dynamically to achieve operational goals. Ideally, each container or service should just do its assigned job, robustly and statelessly, making as few assumptions about its environment as possible. Routing should be managed outside of applications. 

Aggregation and simplification of configuration – Ideally, it should be possible to collect configuration information defining something as potentially-complicated as traffic routing for a complex application in one or as few places as possible, and represent configuration in standard ways, rather than trying to produce desired effects by coordinating diverse configs for many different entities.

Enterprise Kubernetes ingress

Ingress is a Kubernetes service type designed to solve these problems. It provides a standard way of describing routing, termination, URL-rewriting and other rules in a YAML configuration file, plus standards for building applications/services to read and implement these configurations.

Kubernetes itself doesn’t implement an Ingress solution — most simple cluster models (k0s being a good example) don’t support it, unless users choose and run an ingress controller on their cluster, and integrate with it (nginX is a frequent default choice).

Enterprise Kubernetes solutions are more frequently provided with an ingress controller pre-integrated. An enterprise-grade ingress controller often provides features in excess of those defined by Kubernetes standard ingress, and will provide ways for configuring these additional features alongside basic features, in otherwise-standard ingress configuration files. For example, the well-known Istio ingress controller provides means for blacklisting IP address ranges (perhaps because these IP addresses are recognized as a source of denial-of-service attacks).

apiVersion: "config.istio.io/v1alpha2"
kind: handler
metadata:
 name: blacklisthandler
 namespace: istio-system
spec:
 compiledAdapter: listchecker
 params:
 overrides:
 - 37.72.166.13
 - <IP/CIDR TO BE BLACKLISTED>
 blacklist: true
 entryType: IP_ADDRESSES
 refresh_interval: 1s
 ttl: 1s
 caching_interval: 1s
---
apiVersion: "config.istio.io/v1alpha2"
kind: instance
metadata:
 name: blacklistinstance
 namespace: istio-system
spec:
 compiledTemplate: listentry
 params:
 value: ip(request.headers["x-forwarded-for"]) || ip("0.0.0.0")
---
apiVersion: "config.istio.io/v1alpha2"
kind: rule
metadata:
 name: blaclistcidrblock
 namespace: istio-system
spec:
 match: (source.labels["istio"] | "") == "ingressgateway"
 actions:
 - handler: blacklisthandler
 instances:
 - blacklistinstance

Is ingress a load balancer?

Ingress is similar to Kubernetes load balancing, in that ingress functionality is specified by Kubernetes, but implemented by a third-party solution. An enterprise Kubernetes solution will typically implement both integrations, so that Ingress (which manages routing) can work behind external load balancing (which terminates traffic on FQDNs and distributes it across nodes running instances of an application).

Ingress can be hard to integrate

An enterprise-grade ingress solution like Istio can be fairly challenging to manually integrate with a Kubernetes cluster. A full implementation may require clustered implementation of the controller, plus sidecars on each node, as well as integration with other cluster services (e.g., Cert-manager, for SSL certificate management) and metrics solutions like Prometheus (for enabling dynamic routing schemes based on changes in traffic, for example).

Ingress delivers big benefits

Fully integrated in an enterprise Kubernetes solution, however, ingress becomes easy for operators and developers to use, and confers huge benefits. In the most basic sense, ingress provides the routing functionality needed to weave together the components of microservices applications. Ingress helps applications keep processing traffic seamlessly while Kubernetes helps them self-repair and scale according to conditions. It provides fundamental security services, like enabling https, and more advanced services, like the ability to quickly apply blacklists and make apps more resilient against concerted attacks.

Ingress can also play an important role in accelerating delivery of new features to end-users. For example, a paradigm now growing in popularity among developers is to implement so-called “canary deployments” of new application releases. In a canary deployment, a new release gets deployed alongside a stable release, and configured to receive traffic from only a known subset of end-users. Developers can then watch what happens, evaluate, and if needed, roll back the new release to fix problems — all without causing disruption for the whole customer base. Ingress is typically the way modern Kubernetes apps manage this trick: an ingress configuration is created to identify traffic from the target customer pool, and route it to the new release, while letting most traffic continue to the stable release

How to install the JENKINS on Linux

 

INSTALL JENKINS
	Go to https://pkg.jenkins.io/redhat-stable/
	and copy these commands
	# sudo wget -O /etc/yum.repos.d/jenkins.repo https://pkg.jenkins.io/redhat-stable/jenkins.repo
	# sudo rpm --import https://pkg.jenkins.io/redhat-stable/jenkins.io.key yum install java -y
	# yum install jenkins
	# systemctl start jenkins
	# systemctl status jenkins
	# clear
	# cat /var/lib/jenkins/secrets/initialAdminPassword
	Click on install plugins
	Give username and password details
	Jenkisn is ready to use

How to install the Maven on Linux

 

MAVEN INSTALLATION
	****************************************
	# yum install maven
	# mvn --version
	# vim /etc/profile
	i
	export MAVEN_HOME=/usr/share/maven
	ESC :wq!
	# source /etc/profile
	# echo $MAVEN_HOME

How to install the JDK on Linux

 

JAVA Installation
	SEE THAT YOU ARE IN ROOT DIRECTORY
	# yum install java-1.8.0-openjdk-devel
	# alternatives --config java
	# vim /etc/profile
	i
	export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.265.b01-1.amzn2.0.1.x86_64
	export PATH=$JAVA_HOME/bin:$PATH
	ESC :wq!
	# source /etc/profile