Containerization is a software deployment process that bundles an application’s code with all the files and libraries it needs to run on any infrastructure.
It is a 3 step process that 1, takes a manifest, 2, build and push the image to a container registry, and 3, run the application.
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Manifest: or a Dockerfile is a text file that contains all the commands, in order, to build an application image. Commands such as Pull base images, Install dependencies, copy over source code, and set various configuration parameters.
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Build and push the image to the registry: create the container image based on the manifest (Dockerfile), and then push that image to a container registry for storage and distribution.
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Running the application: Once the image is available in a registry, it can be pulled and run on any system with a container runtime installed(Docker, rocket, containerd, CRI-O, and runc). The container runtime creates a container from the image and executes it.
- Isolation and Fault tolerance Each container runs in its own namespace, with access to only the files and processes it needs. This isolation prevents processes running inside a container from seeing or affecting processes running in another container or the host system. So a single faulty container doesn't affect the other containers. This increases the resilience and availability of the application.
- Portable Containers includes application and their dependencies, they can be run on any system that supports container runtime()
- Scalable Containers are lightweigh software components. can be created, started, and stopped quickly because it doesn't need to boot an operating system, which makes it easy to scale applications up and down based on the demand.
- Agility Containers run in its own namespace, so any change to application code do not interfere with other application services which shorten software release cycles and work on updates quickly.
- Complexity:
- Containers require an orchestration system to run at scale, and orchestration systems like Kubernetes have a steep learning curve.
- Understanding how to write Dockerfiles, manage container registries, handle networking between containers, and debug issues in a containerized environment can be challenging.
- Security:
- Containers share the same host OS, so if an attacker gains access to the host, they could potentially compromise all containers running on it.
- If containers aren't properly isolated from one another, an attacker who gains access to one container might be able to affect others.
- There's a risk associated with using pre-built images from public registries, as they could contain malicious code.
- Performance: Containers add a layer of abstraction that can lead to performance degradation, especially for intensive workloads. Also, the use of a shared OS means that containers could compete for resources.
- Persistent data storage: Containers are ephemeral, which means they can be stopped and deleted and a new one can be created in its place. This is a problem when you need to store data across sessions or share data between containers. Solutions like Docker volumes or network storage can be used to solve this problem, but they add additional complexity.
- Networking: Network configuration can become complex when dealing with containers, especially in multi-host environments. Containers have their own networking namespace, which means they get their own IP address and port space, adding a layer of complexity to application networking.
- Compatibility: While containers can help alleviate "it works on my machine" issues, different container runtimes can sometimes behave in slightly different ways, leading to unexpected behavior.
Kubernetes, also known as K8s, is an open-source platform designed to automate deploying, scaling, and operating application containers. It was originally developed by Google based on their experience running containers at scale and is now maintained by the Cloud Native Computing Foundation.
Here are some of the key features of Kubernetes:
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Service discovery and load balancing: Kubernetes can expose a container using the DNS name or their own IP address. If traffic to a container is high, Kubernetes can load balance and distribute the network traffic to help the deployment stable.
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Storage orchestration: Kubernetes allows you to automatically mount a system of your choice, such as local storages, public cloud providers, and more.
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Automated rollouts and rollbacks: You can describe the desired state for your deployed containers using Kubernetes, and it can change the actual state to the desired state at a controlled rate. For example, you can automate Kubernetes to create new containers for your deployment, remove existing containers and adopt all their resources to the new container.
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Automatic bin packing: You provide Kubernetes with a cluster of nodes that it can use to run containerized tasks. You tell Kubernetes how much CPU and memory (RAM) each container needs. Kubernetes can fit containers onto your nodes to make the best use of your resources.
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Self-healing: Kubernetes can restart containers that fail, replace containers, kill containers that don’t respond to your user-defined health check, and doesn't advertise them to clients until they are ready to serve.
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Secret and configuration management: Kubernetes lets you store and manage sensitive information, such as passwords, OAuth tokens, and SSH keys. You can deploy and update secrets and application configuration without rebuilding your container images, and without exposing secrets in your stack configuration.
In summary, Kubernetes provides a framework to run distributed systems resiliently. It takes care of scaling and failover for your applications, provides deployment patterns, and more.
a Controller is a control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state. Examples of controllers that ship with Kubernetes today are the replication controller, endpoints controller, namespace controller, and others.
Here's a deeper look at the Controller concept:
Control Loop: At the heart of every controller is a control loop. This is a non-terminating loop that regulates the state of a system. In Kubernetes, a controller is a control loop that watches the shared state of the cluster through the apiserver and makes changes attempting to move the current state towards the desired state.
Desired vs. Actual State: Kubernetes controllers continuously compare the desired state of cluster objects (how many replicas a deployment should have) to the actual cluster state (how many replicas are currently running). When the actual state doesn't match the desired state, the controller takes action to fix the discrepancy.
Reconciliation: The process of bringing the actual state closer to the desired state is called reconciliation. For example, if a Pod is deleted, the controller notices the change in state and creates a new Pod to maintain the desired state of having a specific number of Pods running.
Here are some examples of controllers in Kubernetes:
ReplicaSet Controller: Ensures the correct number of pod replicas are running at any given time.
Deployment Controller: Provides declarative updates for Pods and ReplicaSets.
DaemonSet Controller: Ensures that a copy of a Pod runs on all (or some) nodes in a cluster.
Job Controller: Responsible for running tasks to completion, like batch jobs.
StatefulSet Controller: Used for workloads that require stable network identifiers and stable, persistent storage.
Controllers are a key part of the Kubernetes system, and the model of using controllers and control loops allows Kubernetes to handle a wide range of workload types, from stateless web servers to stateful databases, and provides the self-healing and auto-scaling capabilities that make Kubernetes powerful for automating operational tasks.
Operators are software extensions to Kubernetes that make use of Custom resources to manage applications and their components.
- software
- custom controller
- Encapsulation of the human operational knowledge of managing an application
- extensions to Kubernetes that make use of Custom Resources
- Custom Resource Definition
- An Operator defines a custom resource using a CRD
- Custom controller watches for changes to these custom resources.
- The domain-specific knowledge is embedded in the controller, specifically within the reconciliation logic.
- The controller watches for changes to its associated Custom Resource(s) (CRs) and reacts to these changes in the reconciliation loop. The reconciliation loop is where the desired state, as defined by a Custom Resource, is compared to the current state of the system. If the current state does not match the desired state, the reconciler will take action to make them match.
- The "domain knowledge" — the understanding of how an application should be deployed, configured, scaled, and managed — is encoded in this reconciliation process. For example, if you have a database Operator, the controller's reconciliation loop would contain the logic for deploying the database, handling failover, managing backups, and so forth.
- Custom Resource Definitions (CRDs)
- Custom controllers.
A Custom Resource Definition (CRD) is a way to extend the Kubernetes API with a new resource type. This new resource is treated by Kubernetes the same way built-in resource types (like Pods, Deployments, and Services) are. You can create, delete, and manage instances of this new resource type using kubectl, just as with built-in types.
A custom controller is a controller that users can deploy to a Kubernetes cluster to watch and respond to events related to custom resources. Custom controllers can be written to take almost any action when a custom resource changes. L5-operator
Operators in Kubernetes are incredibly useful for several reasons:
- Deep application-specific operational knowledge: Operators are designed to encode the deep operational knowledge that typically only human operators possess, such as how to deploy, scale, configure, or upgrade a complex software system. This knowledge can be automated and shared, reducing the operational burden on humans and increasing the consistency and reliability of the operations.
- Automated operations: Operators automate routine tasks like software installation, configuration updates, software upgrades, backups, recovery, and failure handling. This reduces the risk of human error and frees up time for developers and operators to focus on higher-value work.
- Custom behavior: Because you can define your own resources and controllers with Operators, you can implement custom behavior that isn't possible with Kubernetes' built-in resources. For example, an Operator could automate the process of deploying a database cluster, managing backups, and handling failover.
- Native Kubernetes experience: Operators extend the Kubernetes API, so they can be managed using standard Kubernetes tools (
kubectl, dashboard). This means you don't need to introduce new tools or workflows for managing applications on Kubernetes. - Consistency: Operators provide a consistent way of managing applications across different Kubernetes environments. Whether you're running in a public cloud, private cloud, or on-premise, if you're using Operators, the process for managing your applications remains the same.
- Leverage the Kubernetes ecosystem: Because Operators are built on top of Kubernetes, they can take advantage of Kubernetes features and integrations, including role-based access control, auditing, service discovery, secrets management, and more.
In summary, Operators make it easier to manage complex, stateful applications on Kubernetes by automating much of the manual work involved in deploying, scaling, and managing these applications.
Kubernetes Operators can significantly ease the workflow and management of applications in several ways:
- Automating Routine Tasks: Operators can automate many of the routine tasks associated with managing an application. This includes tasks like setting up database schemas, handling upgrades, and managing backups and restores. Automating these tasks not only saves time but also ensures they're performed consistently and correctly, reducing the chance of human error.
- Day-2 Operations: Operators shine in handling "Day-2" operations - tasks that come up after an application has been initially deployed. These might include tasks like scaling an application to handle increased load, reconfiguring an application to use a new security certificate, or rolling out an update to the application across your environment. Operators can automate these tasks, making them easier and more reliable.
- Self-Healing: Operators can implement self-healing logic specific to the application they manage. If an application crashes or otherwise enters an error state, the Operator can take corrective action automatically - such as restarting a failed pod, creating a new replica, or even alerting a human operator to intervene.
- Deep Application Insights: Since operators have deep knowledge about the specific application they are designed to handle, they can make intelligent decisions based on the application's state. For instance, an operator could monitor the load on a database and automatically spin up additional replicas when the load gets too high.
- Custom resources are native to Kubernetes: This means you can use familiar tools like
kubectland the Kubernetes dashboard to manage your application. - Consistent Environment: Operators help ensure consistency across your environment. Regardless of where your Kubernetes cluster is running - on-premises, in the public cloud, or in a hybrid environment - the Operator ensures that the application behaves consistently.
Kubernetes Operators are designed to be used by various roles involved in deploying and managing applications, including:
- DevOps Engineers: These are likely the primary users of Operators. They are responsible for setting up and managing the infrastructure that applications run on. Operators can help DevOps engineers by automating many of the routine tasks involved in deploying, scaling, and managing applications.
- Platform Engineers: Those who are responsible for managing and maintaining the Kubernetes platform itself will find Operators useful for extending the functionality of Kubernetes and managing complex, stateful applications.
- Software Developers: Developers who are building applications to run on Kubernetes can use Operators to handle the operational aspects of running their applications. This allows them to focus on developing the business logic of their applications.
- SREs (Site Reliability Engineers): SREs are responsible for the reliability and performance of an application. They can use Operators to automate tasks that are important for maintaining the reliability of an application, such as failover and backup.
- System Administrators: Sysadmins who are managing a Kubernetes environment can use Operators to automate many of the tasks involved in managing applications.
Operators can also be beneficial for software vendors and open-source projects. By providing an Operator for their software, they can make it easier for their users to run their software on Kubernetes. Users get the benefits of automated operations and deep application-specific knowledge, while the vendor or project can ensure their software is run in a way that adheres to best practices.
- Charmed Operator Framework
- Java Operator SDK
- Kopf (Kubernetes Operator Pythonic Framework)
- kube-rs (Rust)
- kubebuilder
- KubeOps (.NET operator SDK)
- KUDO (Kubernetes Universal Declarative Operator)
- Mast
- Metacontroller along with WebHooks that you implement yourself
- Operator Framework
- shell-operator
While Kubernetes Operators offer many benefits, they also come with their own set of challenges and potential setbacks:
- Complexity: Writing an Operator requires a deep understanding of both the application being managed and Kubernetes itself. This can make developing an Operator a complex task, especially for those new to Kubernetes.
- Maturity: The Operator pattern is still relatively new, and many Operators are in the early stages of development. As a result, they may not have all the features you need, or they may have bugs that need to be addressed.
- Standardization: There isn't a clear standard for how to write Operators, which can lead to inconsistencies between different Operators. For instance, how an Operator handles upgrades, backups, or error handling may vary widely.
- Debugging and Troubleshooting: Debugging Operators can be challenging. Since Operators extend the Kubernetes API, standard debugging tools may not provide all the information needed to troubleshoot issues.
- Security: Operators have the ability to make significant changes to the state of a Kubernetes cluster, which can pose security risks if not managed carefully. For example, an Operator could potentially delete resources unintentionally or grant permissions that expose the cluster to security threats.
- Resource Management: Operators can consume significant resources, including CPU, memory, and storage, particularly if they are poorly written or not optimized.
- Version compatibility: Keeping Operators compatible with different versions of Kubernetes and different distributions of Kubernetes can be a challenge. It can also be difficult to keep Operators up to date with changes in the applications they manage.
Despite these potential challenges, it's important to remember that Operators can provide significant benefits in terms of automating complex operational tasks. As the ecosystem matures and best practices continue to evolve, many of these challenges will likely be addressed.
Absolutely, there is always room for improvement. As Kubernetes Operators continue to evolve and mature, several areas could be enhanced:
- Best Practices and Standards: The Kubernetes community can continue to develop best practices and standards for writing and using Operators. This would help reduce the variation in how Operators are implemented and make them easier to use and understand.
- Tooling: Improved tooling could make it easier to develop, test, and debug Operators. This might include better support in IDEs, testing frameworks tailored to Operators, and improved logging and monitoring tools.
- Documentation: More comprehensive and accessible documentation could make it easier for developers to learn how to write Operators and for users to understand how to use them effectively.
- Security: Continued focus on security is crucial as Operators have the potential to impact a Kubernetes cluster significantly. This could include better tools and practices for auditing and securing Operators.
- Integration with Existing Tools: Better integration with existing Kubernetes tools and APIs would make Operators feel more like a natural extension of Kubernetes.
- Performance Optimization: As with any software, performance can often be improved. Optimizing the performance of Operators could reduce the resources they consume and make them more efficient.
- Operator Lifecycle Management: There are existing tools like the Operator Framework's Operator Lifecycle Manager (OLM) that help in the management of operators on a Kubernetes cluster. These tools can be continually improved to simplify the process of installing, upgrading, and managing operators.
In conclusion, while Kubernetes Operators have already brought significant benefits to managing complex applications on Kubernetes, there is still a lot of potential for future improvements. The Kubernetes community continues to actively develop and refine the concept of Operators, and we can expect to see these and other improvements in the future.
The future of Kubernetes Operators looks very promising. As more organizations adopt Kubernetes and cloud-native technologies, the use of Operators is expected to grow. Operators help manage the complexity of running stateful and distributed systems on Kubernetes, making them an essential part of many Kubernetes deployments.
Here are a few areas where we might see development and progress:
- Increased Adoption: As the benefits of Operators become more widely recognized, we'll likely see more organizations adopting them. This includes not only tech companies but also organizations in other sectors that are adopting Kubernetes and cloud-native technologies.
- More Sophisticated Operators: As the Operator pattern matures, we'll likely see more sophisticated Operators capable of managing increasingly complex systems. This includes Operators that can handle complex tasks like automatic scaling based on application-level metrics, automated disaster recovery, and so on.
- Standardization: As more Operators are developed, we'll likely see increased standardization. This could include best practices for developing Operators, standardized ways to package and distribute Operators, and possibly even standardized APIs for common operational tasks.
- Better Tooling: We'll likely see improvements in the tooling around Operators. This could include better ways to test Operators, improved logging and monitoring for Operators, and better integration with existing Kubernetes tools and APIs.
- Integration with CI/CD Pipelines: We may see tighter integration of Operators with CI/CD pipelines to automate deployment and management of applications on Kubernetes as part of the deployment process.
The ultimate goal of Operators is to automate as much operational knowledge as possible. This allows human operators to focus on higher-level concerns while the Operator takes care of routine tasks and ensures that the application is running correctly. As the technology matures, we'll likely see Operators taking on more responsibilities and becoming an even more integral part of managing applications on Kubernetes.