Definition: What Are Containers?
Containers are a standard way to package and ship everything—code, configurations, libraries, and dependencies—needed to run your application in any runtime environment. You can see it as a virtual operating system that runs applications or microservices in a resource-isolated environment.
Container technology offers developers a consistent, lightweight software development and deployment environment capable of running and deploying applications anywhere. This could be your desktop, traditional on-premise IT infrastructure, or the cloud.
Containers vs. Virtual Machines (VMs)
Though they might appear similar, virtual machines (VMs) and containers differ in several ways.
One key difference between containers and virtual machines is how virtualization occurs. By virtualization, we mean the act of creating multiple virtual instances from the hardware elements of a single computer so that multiple OS instances can run on said hardware. Containers virtualize at the OS level while VMs virtualize at the hardware level.
VMs leverage software called hypervisors. Hypervisors allow VMs to virtualize the physical hardware of a physical computer. Each VM must contain a guest OS, the application, its libraries and dependencies, and a virtual hardware copy. For example, the host OS can be Windows while the VM will be Linux. This complexity adds overhead in memory and storage to VMs, whereas containers are lightweight because they handle virtualization differently.
The container virtualizes the host machine’s OS kernel; thus, each cloud container contains just the application, its libraries, and dependencies. The absence of the guest OS makes it faster, more portable, and lightweight.
Computing resources’ utilization is more effective in container environments. This is because whenever an instance of a VM is run, all available resources are immediately assigned to the virtual servers regardless of the current application’s needs. On the other hand, container-based virtualization has a resource-isolated environment approach, which means that the application receives just the amount of resources it needs.
Benefits of Containers: Why Use Them?
The benefits of containers speak for themselves, especially since they offer their users a consistent, standardized way to build, test, and deploy applications across multiple environments.
- Lightweight and improved virtualization: Containers provide a more effective virtualization method because they share the host OS kernel and use the available resources better than VMs.
- Reliable, portable, and platform-independent: Containers can be run within virtual machines and across Windows, Linux, and OS based platforms. Thus, you only need to write and deploy your software without any additional configuration; the runtime environment will remain the same. This brings portability across the development pipeline and removes any complexity.
- Agile application development and high efficiency: At its heart, container technology is a modular software development model that promotes an agile methodology for rapid software development.
- Security: Applications tend to be more secure because containers are developed in a resource-isolated environment from the host system and each other. Containers also run independently and isolate the applications; thus, any malicious code, security breach, or fault in one doesn’t impact the other.
- Technological stack and modern architecture: Containers allow developers to build infrastructure-independent applications. Thus, they’re ideal for DevOps adoption, continuous integration, and continuous delivery (CI/CD) pipeline implementation. The ecosystem also has an array of technologies—Docker, Kubernetes, Istio, and Knative—built around it.
Container Use Cases
Various organizations have adopted containerization to achieve agile software development for their applications. Here are some scenarios when one should use containers:
- Creating and building scalable, cloud-native applications: The cloud-native approach focuses on an application architecture’s modularity while exploiting all the benefits of a cloud computing delivery model. This strategy promotes scalability and dependability while accelerating the software development pipeline across various environments. Thus, containers are the perfect compute vehicle for packaging and running cloud-native applications.
- Working with Microservices: Intrinsic containers’ architecture is ideal for microservices. Containers support microservices architecture by providing each application component with an isolated workload environment that can quickly deploy and scale independently.
- DevOps support: Containers enable DevOps to deploy and scale applications with zero breaks. The cloud ecosystem also comes with various tools and supports the implementation of CI/CD methodologies.
- Distributed cloud deployment: Containers offer flexibility, reliability, and portability when it comes to deployment. Applications can be deployed and run anywhere from on-premises to multi-cloud and hybrid cloud environments without any code rewrite.
- Multi-tenancy: Multiple instances of an application can run and be deployed on different tenants using containers. Containers also effectively manage resources.
- “Lift and Shift” migrations and refactoring of existing applications: Existing applications can be migrated into the cloud to modernize legacy applications. This often requires a lift and shift strategy or refactoring of existing code to containerize it and enjoy the advantages of container-based application architecture.
How Do Containers Work?
Containers are created from a base image. These base images, also called container images, are static files containing executable code, system libraries, and binary components needed to run an application in an isolated computing environment or containerization platform like Docker. Thus, a container means a runnable instance of a base image.
The application is then packaged into the container image and deployed through the IT containerization platform. This process is referred to as containerization. The container platform here is the client-server software and implements the container’s actions through
- A daemon that manages the various objects such as the containers, images, and storage.
- An application programming interface (API) through which applications can interact with the daemon.
- The command line interface (CLI) client issues the scripting commands.
You should know that multiple containers can run on one machine simultaneously, with each instance of the container sharing the operating system (OS) kernel of the host machine. This makes sharing of computing resources at the OS level more efficient.
What Are the Limitations of Containers?
Despite its numerous advantages, container technology has some limitations, especially when it comes to container monitoring. This is primarily because of containers’ architecture and how distributed they can get. Thus, getting visibility can be challenging.
Containers are ephemeral, so they get created and destroyed quickly. They’re also stateless entities. This makes it difficult to track changes and store data, particularly logs.
At the same time, managing container logs requires parsing and combining logs from various sources to gather insights. Made up of simple text messages with unstructured and structured logs, these logs have different structures and formats, which makes them difficult to analyze. Moreover, managing your container logs requires you to tell which log event belongs to which container first before you can parse and combine them.
You can’t do any of these by hand, you need a log management solution. Such tools slice and dice log files and send them to a central location where you can easily analyze them and speed up troubleshooting.
Containers share resources among the applications and, by default, will use as much as they need. As great as that sounds, this makes it challenging to monitor resource consumption on the physical host. Also, wrongly restricting resources would lead to poor container performance because they can’t allocate enough resources.
In situations like this, you can set up a threshold using container resource limitation functionalities like the kernel’s OOM management to restrict this. Monitoring container metrics such as the memory failure counters and adjusting the resource limits to meet the container’s demands is also helpful in these situations.
Container monitoring can get complex since your container ecosystem uses various programming languages, applications, and infrastructure. You need a container monitoring tool to help you collect logs from all these systems. However, when working with logs, security issues might arise, such as security breaches that can lead to vulnerabilities of the infrastructure and loss of data or information. Some of these vulnerabilities can open doors to install malware or viruses, specifically if the logs contain plain information – IPs, hostnames and code snippets. From container perspective, this can lead to leak of connections strings and configuration.
To avoid these you need to implement procedures so that the data can be transported securely. This means that the images have to contain only the absolute necessary packets, sign your container images with specific tools, periodically scan the images for vulnerabilities, implementation of transport policies with TLS and private container networks. Another aspect of security is access control by implementing the principle of least privilege.
Containers and Microservices
Microservice is more about the software design pattern. Microservices are an architectural design that involves building your application as an independent, loosely coupled, individually scalable, and deployable service unit. This enables developers to scale, update, and deploy each service as a separate entity without breaking other services in the application.
For example, a FinTech application with a microservice architecture would have the payment, billing, and user onboarding components as independent services. Each service would have its own centralized databases and communicate with one another and other services via an API gateway.
Containers are a great way to package your microservices, especially if you want to build a massively scalable and distributed system that can withstand major disruptions. While they can be utilized alone, combining them delivers agility, flexibility, and portability—essential qualities for the DevOps and multi-cloud ecosystems. Each service in a microservice architecture has a central database that uses a different technology and can be programmed in a different programming language. Containers will allow you to create and deploy your cloud-based applications everywhere without being limited by the software environments.
What Is the Difference Between Docker and Containers?
Container technology emerged decades ago, as far back as 1979 with Unix version 7. Over the years, the container ecosystem has grown and been adopted into technologies like BSD OS, FreeBSD Jails, Solaris Containers, Linux Containers, and AIX Workload Partitions. However, it wasn’t until 2013 that the container ecosystem was rapidly adopted with the birth of Docker.
Though containers and Docker are used interchangeably, they’re different. Containers are technically software that packages your applications and their dependencies. Docker, on the other hand, is a containerization platform that uses cloud technology.
Docker is a run-time platform with tools that allow you to build using an execution file called Docker images and deploy your container-based applications inside containers. There are a lot of alternatives to Docker, like Podman, LXD, and Containerd. However, Docker popularized containers and suddenly became synonymous with containers.
How Kubernetes Relates to Containers
The container environment has various solutions to help manage containers’ complexity, especially when thousands of containers are deployed across different machines in production. Container orchestrators are among them.
Container orchestration is a process that automates the operational actions required to run your containerized workloads, including managing service discovery and automating the scheduling, deployment, networking, scaling, and load balancing.
Kubernetes is one of the most popular container orchestrators available for managing containerized workloads. It does this by unifying clusters of machines into a single pool of computing resources and allocating containers based on their needs and available computing resources. This multi-server cluster is called the Kubernetes cluster. These containers will further be grouped into Kubernetes pods and can scale as much as you desire. Kubernetes also manages and monitors resource allocation and the health of these pods. This is highly useful in real-time production settings.
Container Logging and Monitoring with Sematext
Sematext Cloud is a full stack monitoring solution with container monitoring capabilities, allowing you to correlate and trackcontainer metrics, logs, and events within a single monitoring dashboard. Powerful alerting capabilities and anomaly detection let you know immediately when metric values go above the thresholds set so that you can quickly intervene and limit damages.
Sematext’s service auto-discovery feature automatically detects containers and containerized applications, allowing you to instantly start monitoring the new systems wthout additional configuration.
The platform provides support for a number of container technologies, including the popular Docker and containerd, container orchestrators such as Kubernetes, Docker Swarm, Nomad, Rancher, and many more. It allows you to correlate their performance data with any other part of your infrastructure to give you a full picture of your system’s health.
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