🌐 Deploying a Production-Ready Web App using Docker Swarm on AWS
Table of contents
📍 Introduction
Containerization has revolutionized the way applications are deployed and managed, and Docker Swarm is a powerful tool for orchestrating containers in a production environment. In this step-by-step guide, we will walk you through the process of deploying a web application using Docker Swarm on AWS. By the end of this tutorial, you will have a robust and scalable setup for your web app.
✅ Solving the Problem 🧩
By deploying your web app using Docker Swarm on AWS, you are addressing several key challenges:
Scalability: Docker Swarm enables you to scale your application horizontally by adding or removing nodes seamlessly. This ensures your app can handle increased traffic.
High Availability: With Swarm, your app is distributed across multiple nodes, making it highly available. If one node fails, the others can continue serving the application.
Simplified Management: Docker Swarm provides an easy-to-use interface for managing containerized applications, reducing the complexity of orchestration.
Resource Efficiency: Containers consume fewer resources compared to traditional virtual machines, optimizing resource utilization and reducing costs.
✅ Step 1: Provision AWS Instances 🌐
Start by logging into your AWS portal and create three new EC2 instances, each with unique names:
Swarm-manager
Swarm-worker1
Ensure that you configure the inbound rules for these instances to allow the following traffic:
Custom TCP port 2377 from anywhere (IPv4)
Custom TCP port 8001 from anywhere (IPv4)
✅ Step 2: Install Docker 🐳
SSH into each of the two instances and install Docker with the Docker Engine. You can refer to Docker's official documentation or other resources for detailed instructions on Docker installation.
✅ Step 3: Initialize Swarm on Swarm Manager 🤖
Access the "Swarm Manager" node and initiate the Swarm by running the following command:
sudo docker swarm init
This command initializes an empty Swarm.
✅ Step 4: Add Workers to the Swarm 🏗️
After initializing the Swarm on the "swarm-manager" node, a key will be generated. You need to copy and run this key on the other two servers (Swarm-worker1). This step adds both machines as workers to the Swarm.
✅ Step 5: Check Swarm Node Status 🚥
To verify the status of all nodes in the Swarm, run the following command on the manager node:
docker node ls
This command will display information about all the nodes in the Swarm.
✅ Step 6: Create a Docker Service 🛠️
On the Swarm Manager node, create a Docker service using the following command:
sudo docker service create --name django-app-service --replicas 2 --publish 8001:8001 trainwithshubham/react-django-app:latest
This command creates a service named "django-app-service" with three replicas, publishing port 8001.
✅ Step 7: List Docker Services 📋
To list the Docker services running in the Swarm, use the following command:
sudo docker service ls
This will display a list of services, including the one you just created.
✅ Step 8: Verify Containers 🐋
The service you created will deploy containers on the manager and worker nodes. To check if containers are running on the manager node, execute:
sudo docker ps
You should see containers related to your service.
✅ Step 9: Access the Web App 🌐
Now, your web app service is running on all three nodes. To access it, grab the IP address of any of the nodes followed by port 8001, like this:
http://<Any_IP_of_2_VMs>:8001
http:3.82.60.74:8001
You should be able to access your web application through this URL.
✅ Step 10: Removing Nodes ♻️
If you need to remove a node from the Swarm environment, run the following command on the specific worker node:
sudo docker swarm leave
As a result, the worker node will leave the Swarm, and you can confirm its status by checking the output of:
docker node ls
📍 Conclusion
Congratulations! 🎉 You've successfully deployed a production-ready web app using Docker Swarm on AWS. Docker Swarm's orchestration capabilities make it easier to manage and scale your application, ensuring it runs smoothly in a production environment. Feel free to connect and follow for more informative content on containerization and cloud computing.
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