The following is an example of a Deployment. It creates a ReplicaSet to bring up three nginx Pods:
apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx-deployment
labels:
app: nginx
spec:
replicas: 3
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.14.2
ports:
- containerPort: 80
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.
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.
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.
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.
To understand which might be right for you, it’s important to understand the concepts that underpin 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.”
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 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.
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
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.
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.
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.
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)…
That’s orchestration: standardized, generalized, abstracted, configurable automation for complex, dynamic applications.
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.
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
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, 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.
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.
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.
Here are some of the choices involved in assembling an enterprise Kubernetes platform:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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:
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.
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.
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.
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.
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.
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.
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:
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.
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.
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:
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:
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.
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
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).
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).
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
| 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 |
| MAVEN INSTALLATION | |
| **************************************** | |
| # yum install maven | |
| # mvn --version | |
| # vim /etc/profile | |
| i | |
| export MAVEN_HOME=/usr/share/maven | |
| ESC :wq! | |
| # source /etc/profile | |
| # echo $MAVEN_HOME |
| JAVA Installation | |
| SEE THAT YOU ARE IN ROOT DIRECTORY | |
| # yum install java-1.8.0-openjdk-devel | |
| # alternatives --config java | |
| # vim /etc/profile | |
| i | |
| export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.265.b01-1.amzn2.0.1.x86_64 | |
| export PATH=$JAVA_HOME/bin:$PATH | |
| ESC :wq! | |
| # source /etc/profile |