If you have recently come across the world of containers, it’s probably not a bad idea to understand the underlying elements that work together to offer containerisation benefits. But before that, there’s a question that you may ask. What problem do containers solve?
After building an application in a typical development lifecycle, the developer sends it to the tester for testing purposes. However, since the development and testing environments are different, the code fails to work.
Now, predominantly, there are two solutions to this – either you use a Virtual Machine or a containerised environment such as Docker. In the good old times, organisations used to deploy VMs for running multiple applications.
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So, why did they started adopting containerisation over VMs? In this article, we will provide detailed explanations of all such questions.
Behind this fantastic tool, there has to be equally well-thought architecture. Before knowing about the Docker architecture components, let’s understand Docker containers and how they are superior to VMs.
Docker is an open-source project which provides the ability to create, package, and run applications in loosely isolated and contained environments called containers.
With all the isolation and security provided by the Docker platform, it allows you to run many containers simultaneously on a particular host.
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Reasons Why Docker Containers Are Widely Adopted Includes
- It allows developers to write code locally and share the work with their team using Containers.
- They can push their applications into the test environments, which are the containers and execute automated tests.
- When bugs are found, they can be fixed within the development environment and then redeploy.
- Getting a fix is as simple as pushing an updated image to the production environment.
Before diving deep into the topic, we must differentiate the traditional virtualisation practices from the new-generation containerisation.
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Virtual Machines Vs Docker Containers
Before we used containerisation for our DevOps practices, Virtual Machines were on top of the deck. We used to create VMs for each application.
While VMs fulfilled almost all the necessities, the down-side of using VMs was cumbersome and allocated all the required memory and hardware resources from the underlying host machines.
However, it was easily avoided with containerisation because containers provide OS-level virtualisation and usually require less memory. Thus, it became popular and was eventually adopted by the DevOps community.
The diagram above describes how the VMs and Containers’ architectures differ and why Containers have now surpassed VMs for everyday development processes. Unlike VMs, the Containers sit on top of the Container Engines to provide OS-level Virtualization, thus saving many resources.
Before discussing the different architectural components of Docker, it’s essential to understand the workflow of Docker. Let’s take a look at the Docker Engine and its several parts, which will give us an idea of how the Docker system works. The Docker Engine is primarily a typical client-server application with three principal components.
The Docker daemon is a continuous process that runs in the background and manages all the Docker objects. It listens to the Docker API requests put forward by the client and processes them continuously.
It is the interface that the Docker clients use to interact with the Docker daemon. The clients can talk to the daemon through the API and can provide instructions to it.
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The Docker Client is a Command Line Interface (CLI) that can interact with the daemon. It simplifies the entire process of container management.
The Docker Client (which can be an HTTP client such as a CLI) talks to the daemon, which performs the heavy task of creating, running, and sharing containers. The Client and the daemon can either run on the same machine or connect a client to a remote daemon. The Client and the daemon communicate with each other using a Rest API over sockets or network interface. The Docker Client helps users to manage Docker objects such as containers, images, volumes, etc. Learn more about Docker Projects
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As discussed earlier, Docker uses a Client-Server architecture, where the Client talks to the daemon over a Rest API. The Docker architecture consists of several components, as discussed below.
It listens for the API requests initiated by the clients and manages Docker objects, including containers, images, volumes, and networks. It can also communicate with other daemons to manage Docker services, especially to manage large Docker networks.
The Docker users can communicate with the daemon using the Docker Client. The users execute commands such as “Docker run …” using a client such as CLI which then forwards these commands to Docker (daemon), ultimately carrying them out. The Docker client communicates with multiple daemons.
It stores Docker Images and can be public or private. Docker is configured to look for Images by default on Docker Hub. When the client issue a pull or run command, the images are pulled from the repositories.
When working with Docker, we interact with several objects such as containers, images, volumes, networks, etc.
Some of these objects are
It is a read-only template and contains instructions for creating containers. It also contains metadata describing the container capabilities. The users can pull images from the Docker registry and create writable image layers on top of them to create customized images to suit their application’s requirements. Some popular ideas include Ubuntu, Nginx, MySQL, etc. These ideas one can share across teams which helps them to work collaboratively on an application.
Containers are instances of images that provide isolated environments for the applications. They only have access to resources that are defined by the images used to build them.
Docker Networks allows isolated containers on the same network to communicate and share resources. Some networks provided by Docker include bridge, host, overlay, Macvlan, etc.
Docker allows you to store data within the writable container layer with the help of drivers. Docker allows four options for persistent storage – Docker Volumes, Volume Containers, Directory Mounts, and Storage Plugins.
The most widely used storage option is the volumes. They are placed on the host file system and allow several containers to share and write data inside these volumes.
Docker uses a set of underlying state-of-the-art technologies to provide efficient containerization services to its users. No doubt, in recent years, Docker has started gaining traction among the developer community and will continue to do so in the upcoming years.
Due to the wide range of benefits provided by containers such as resource efficiency, scalability, etc., it rightly secures its position on top of the deck.
In this article, we have discussed some of Docker’s most essential concepts such as the Docker workflow, its architecture, and the underlying technologies, the several Docker objects such as containers, images, registries, networks, etc.
You are now right on track to dive deep into the beautiful world of Docker Containers. You should now better understand how different Docker resources work together to provide you with a bunch of features that would allow you to build, deploy, and share your applications seamlessly.
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1. What is the Prometheus architecture?
The Prometheus architecture is a network monitoring system that collects and stores metrics from network nodes in a time-series database. It collects metrics from nodes using a pull paradigm, allowing it to scale to large numbers of nodes. Grafana, a visualisation tool included with Prometheus, can be used to create graphs and dashboards from the collected data. It's free software distributed under the Apache 2.0 licence. Prometheus Plus, a commercial version with additional features including alerts, long-term storage, and support, is also available. You can use Prometheus to keep track of CPU load, memory utilisation, storage space, and network traffic, among other things. Similarly, you can use Grafana to create graphs and dashboards for a variety of data sources, including Prometheus, InfluxDB, and OpenTSDB.
2. What are the drawbacks of Prometheus architecture?
The architecture of Prometheus has a few flaws. Prometheus is not, first and foremost, a replacement for a traditional monitoring system. It's a time-series database that lets you save data for later examination. Second, Prometheus isn't a one-size-fits-all monitoring solution. It works particularly well for monitoring cloud-based applications, although it may not be appropriate for monitoring traditional systems. It has a stiff learning curve as well. Similarly, learning the Prometheus Query Language (PromQL) isn't always simple. To learn how to use it, you may either utilise the built-in help or read the documentation.
3. What is continuous integration?
Continuous integration is a software engineering method that involves integrating isolated changes as frequently as possible into a shared mainline. It is a software development method in which developers are required to integrate their work into a shared repository numerous times each day. A developer submits a change to the shared repository once it is complete. The process then repeats again as the other developers incorporate the update into their own code. This aids in the early detection of conflicts, preventing them from escalating into larger issues. Continuous delivery, which refers to the process of automatically distributing updates to users after they have been certified as safe, is frequently paired with continuous integration. This allows developers to get feedback on their work as quickly as possible, which can improve the quality of the software.