docker-containers

Running Containers

To start a new container, we use the docker run command followed by the image name. The basic syntax is as follows:

docker run [options] IMAGE [COMMAND] [ARG...]

For example, to run the official Nginx image, we would use:

docker run -d -p 8080:80 nginx

This starts a new container and maps the host’s port 8080 to the container’s port 80.

Listing Containers

To list all running containers, use the docker container ls command. To view all containers (including those that have stopped), use the -a flag:

docker container ls -a

Accessing Containers

To access a running container’s shell, use the docker exec command:

docker exec -it CONTAINER_ID bash

Replace CONTAINER_ID with the ID or name of your desired container. You can find this in the output of docker container ls.

Stopping Containers

To stop a running container, use the docker stop command followed by the container ID or name:

docker container stop CONTAINER_ID

Removing Containers

Once a container is stopped, we can remove it using the docker rm command followed by the container ID or name:

docker container rm CONTAINER_ID

To automatically remove containers when they exit, add the --rm flag when running a container:

docker run --rm IMAGE

Deploying Containers

Deploying containers is a crucial step in using Docker and containerization to manage applications more efficiently, easily scale, and ensure consistent performance across environments. This topic will give you an overview of how to deploy Docker containers to create and run your applications.

Overview

Docker containers are lightweight, portable, and self-sufficient environments that can run applications and their dependencies. Deploying containers involves starting, managing, and scaling these isolated environments in order to run your applications smoothly.

Benefits of Container Deployment

Consistency: Containers enable your application to run in the same way across various environments, avoiding the common “it works on my machine” issue.
Isolation: Each container runs in an isolated environment, avoiding conflicts with other applications and ensuring that each service can be independently managed.
Scalability: Containers make it easy to scale applications by running multiple instances and distributing the workload among them.
Version Control: Deploying containers helps you manage different versions of your application, allowing you to easily roll back to previous versions if needed.

Key Concepts

Image: A Docker image is a lightweight, standalone, executable package that contains everything needed to run a piece of software, including the code, runtime, system tools, libraries, and settings.
Container: A Docker container is a running instance of a Docker image. You can deploy multiple containers from the same image, each running independently.
Docker Registry: A place where Docker images are stored and retrieved. Docker Hub is the default registry used by Docker, but you can use your own private registry if desired.

Steps to Deploy Containers

Create a Dockerfile: A Dockerfile is a script with instructions to build a Docker image. It should specify the base image, application code, dependencies, and configurations needed to run your application.
Build the Docker Image: Using the Docker client, you can build a new image by running docker build and specifying the path to your Dockerfile. This will create a new Docker image based on the instructions in your Dockerfile.
Push the Docker Image: After building the image, you must push it to a registry (e.g., Docker Hub) so that it can be easily retrieved when deploying containers. Use the docker push command followed by the image name and tag.
Deploy the Container: To deploy a new container from the Docker image, use the docker run command followed by the image name and tag. This will start a new container and execute the required application.
Manage the Container: Deployment involves ensuring the container is running properly and managing scaling, updates, and other key aspects. Use Docker commands like docker ps (to list running containers), docker stop (to stop a container), and docker rm (to remove a container) to manage your deployed containers.
Monitor and Log: Collect logs and monitor the performance of your deployed containers to ensure they are running optimally. Use commands like docker logs (to view logs) and docker stats (to see container statistics) as needed.

Conclusion

Deploying containers with Docker allows you to improve application consistency, security, and scalability while simplifying management and reducing the overhead typically associated with deployment. By understanding the concepts and steps outlined in this guide, you’ll be well-equipped to deploy your applications using Docker containers.

Comparative table between Nomad, Kubernetes, and Docker Swarm

Category	Nomad	Kubernetes	Docker Swarm
Orchestration Type	General-purpose orchestrator (supports containers, VMs, and non-containerized workloads)	Container orchestration platform	Native Docker container orchestration
Ease of Setup	Simple to set up with minimal dependencies	Complex setup, requires extensive configuration	Easy to set up and integrates with Docker
Supported Workloads	Containers, VMs, Java apps, binaries, batch jobs	Containerized workloads (mainly Docker)	Docker containers only
Scheduler	Optimized for high flexibility and multi-cloud environments	Advanced, highly customizable, with support for different scheduling policies	Simple scheduling with Docker engine
Scalability	Highly scalable with multi-datacenter support	Extremely scalable (used by large enterprises)	Scales to small to medium-sized clusters
Networking	Requires manual setup for advanced networking options	Advanced, supports service discovery, load balancing, and network policies	Simplified networking with built-in service discovery
State Management	Single binary with client/server architecture, minimal overhead	Requires etcd for state persistence and control plane	Built-in state management via Raft consensus algorithm
Service Discovery	Simple, integrated DNS-based discovery	Robust service discovery and DNS integration	Simple service discovery with built-in DNS
Storage Support	Basic volume management	Advanced storage management with CSI support	Limited, but supports Docker volumes
Deployment Complexity	Lower complexity, can run with minimal infrastructure	High complexity, needs multiple components (API server, etcd, scheduler)	Lower complexity, deeply integrated into Docker
Extensibility	Can be extended via plugins, integrates with Consul, Vault	Highly extensible with many APIs and plugins	Less extensible, focused on simplicity
Learning Curve	Low to medium	Steep learning curve due to the complexity and ecosystem	Low learning curve, very Docker-native
Community & Ecosystem	Smaller community, mainly supported by HashiCorp	Large, active open-source community with many tools and integrations	Smaller community, mostly Docker-focused
Multi-cloud Support	Built-in multi-cloud support, works across different infrastructures	Good support for multi-cloud, but more complex to manage	Limited multi-cloud support
Security	Integrates with HashiCorp Vault for secrets management	Robust security model with Role-Based Access Control (RBAC), secrets, and policies	Basic security, with simpler RBAC
Use Cases	Mixed workloads, multi-datacenter, hybrid cloud	Large-scale, complex, and enterprise container orchestration	Simple container deployments for small to medium-sized clusters
Popular Use Cases	Batch processing, multi-cloud, dynamic infrastructure	Microservices, large-scale app deployments, hybrid and cloud-native apps	Small to medium-sized Docker-only environments

Summary

Nomad is a flexible, general-purpose orchestrator with simplicity, multi-datacenter, and mixed workload support.
Kubernetes is the most robust and complex orchestrator, suited for large-scale production deployments and advanced use cases.
Docker Swarm is Docker’s native solution, best for simpler, smaller-scale environments where Docker integration is key.

Each orchestrator has different strengths depending on the complexity, scalability, and type of workloads being deployed.