AI-Augmented Software Engineering - The Series in Practice
Part 9: Production Deployment
The deployment phase is a critical stage in the agile software development process where newly developed features are released to end users. Leveraging large language model (LLM) AI tools in this phase can significantly improve the efficiency and effectiveness of deployments. LLMs can be used to automatically generate release notes, user documentation, and training materials which ensures these important artifacts are created without diverting engineer time. Additionally, LLMs can monitor deployment pipelines and provide managers with real-time progress updates and risk assessments. LLMs can even suggest optimizations to the deployment process by analyzing telemetry data. By integrating LLM AI into the deployment phase, teams can deploy faster with higher quality while freeing up developers to focus on building new features. LLMs are a powerful tool to help streamline the release process while improving transparency and collaboration.
The Deployment Phase
The production deployment phase involves several key stakeholders collaborating to successfully release the software product. The Project Manager plays a vital role, tracking cost, schedule, and performance while securing necessary staff and resources. The Group Lead oversees product quality assurance during deployment. Engineers decide on the best hosting solution, whether internal infrastructure, bare-metal, Kubernetes, or a cloud provider like AWS or Azure. They also configure the production environment settings and secure sensitive data. The main artifact produced is the final software product ready to be released and used by end users. With each stakeholder fulfilling their duties, the team can ensure a smooth transition from development to production deployment.
Intelligent Feedback
We need to determine the best place and way to deploy a small containerized application such as ours. The best way to figure this out would be to use an internet enable tool such as Google Bard, combined with the best pattern for research. In this case, since we don’t have many technical constraints other than little-to-no cost and a small application, we will stick with the persona pattern:
Here is our input prompt:
Act as a professional software and devops engineer. You have a very small containerized Django application you would like to deploy to a production environment. Show me all best options to do this. This must be free or very little cost. Once deployed the application must be internet accessible. Ask me any questions to get information you need for your research.And here would be the response output of that prompt:
Sure, here are a few options for deploying a small containerized Django application to a production environment:
**Option 1: Deploy to a cloud platform**
Both Google Cloud Platform (GCP) and Amazon Web Services (AWS) offer free tiers that include enough compute resources to run a small Django application. These platforms also provide a variety of tools and services that can make it easy to deploy and manage your application, such as container orchestration services like Kubernetes and serverless computing platforms like Google Cloud Run and AWS Lambda.
To deploy your application to GCP or AWS, you will need to create an account and follow their documentation for deploying containerized applications. This will typically involve creating a cloud instance, installing Docker, and then running your container image on the instance.
**Option 2: Deploy to a free hosting provider**
There are a number of free hosting providers that offer limited resources for hosting containerized applications. These providers typically have restrictions on the amount of CPU, memory, and storage that you can use, but they can be a good option for small applications that don't have a lot of traffic.
Some popular free hosting providers for containerized applications include:
* Heroku
* Glitch
* Render
To deploy your application to a free hosting provider, you will need to create an account and follow their documentation for deploying containerized applications. This will typically involve creating a new application, pushing your container image to their registry, and then starting the application.
**Option 3: Deploy to a Raspberry Pi**
If you have a Raspberry Pi, you can use it as a low-cost server for your Django application. This option is a good choice if you want more control over your infrastructure and don't mind managing the server yourself.
To deploy your application to a Raspberry Pi, you will need to install Docker on the Raspberry Pi and then run your container image on the device. You will also need to configure port forwarding so that your application is accessible from the internet.
**Option 4: Deploy to a home server**
If you have a home server, you can use it as a server for your Django application. This option is similar to deploying to a Raspberry Pi, but it may offer more resources and flexibility.
To deploy your application to a home server, you will need to install Docker on the server and then run your container image on the device. You will also need to configure port forwarding so that your application is accessible from the internet.
**Which option is best for you?**
The best option for you will depend on your specific needs and requirements. If you need a scalable and reliable solution, then deploying your application to a cloud platform like GCP or AWS is a good option. If you are on a tight budget, then deploying your application to a free hosting provider or a Raspberry Pi is a good option. If you want more control over your infrastructure, then deploying your application to a home server is a good option.
**Additional considerations**
No matter which option you choose, there are a few additional things to consider when deploying a Django application to production:
* **Security:** You will need to take steps to secure your application, such as using a firewall and HTTPS.
* **Monitoring:** You will need to monitor your application to make sure it is running smoothly and to identify any problems that may arise.
* **Backups:** You will need to back up your application regularly to prevent data loss.
I hope this information is helpful. Please let me know if you have any other questions.Artifacts Created
As mentioned, throughout the process different phases will produce different artifacts and require different intelligent feedback. For the deployment of our application we like Kubernetes, so we will create Kubernetes manifests that can be deployed to a container platform that runs on Kubernetes. This will provide the most flexibility for either local or cloud deployment.
Kubernetes Manifests
Description: A Kubernetes manifest is a YAML or JSON file that defines how an application or service should be deployed and managed within a Kubernetes cluster. It specifies various Kubernetes resources such as pods, services, deployments, and more, detailing their desired states, configurations, and interrelationships.
Best Tool: Since we’re writing YAML that is essentially code, we’re going to use Code Interpreter.
Best Prompt Pattern: The PQD pattern
Our Pattern Input:
You are an expert python programmer, software engineer, and devops engineer. You are working on containerizing a small simple application that can be deployed anywhere Kubernetes runs.
Here is the online location of the repository code:
https://github.com/adamhoke/ai-software-engineering
Please create all the manifests required to deploy this application to Kubernetes. It is a python web application and has a Postgres database backend. Look at the docker compose yaml files in the repository for reference. The application has the minimum requirements:
- Fast enough and scalable enough to support 10 users at a time
- Restart on crash
- Be web accessible
- Store secrets in configuration secrets
- Store environment variables in configMaps
- Have storage volumes for files
- Anything necessary to also host a postgres database
Please create the manifests files in .yaml format. Please document each line clearly. Please create them in a way that they are agnostic to any specific hosting provider.Our Response Output:
Creating Kubernetes manifests for deploying a Python web application with a PostgreSQL database involves several steps. Since I can't access external URLs like GitHub, I'll provide a general guide and example YAML files based on common practices.
1. Namespace
First, create a namespace to logically isolate your application within the Kubernetes cluster.# namespace.yaml
apiVersion: v1
kind: Namespace
metadata:
name: myapp-namespace2. ConfigMap
ConfigMaps are used to store non-confidential data in key-value pairs. This can include environment variables.# configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: myapp-config
namespace: myapp-namespace
data:
APP_SETTING: "some-value" # Replace with actual environment variables
3. Secret
Secrets are used to store sensitive information, such as database passwords.# secret.yaml
apiVersion: v1
kind: Secret
metadata:
name: myapp-secret
namespace: myapp-namespace
type: Opaque
data:
DATABASE_PASSWORD: <base64-encoded-password> # Use base64 to encode your real password4. Persistent Volume and Persistent Volume Claim
Persistent Volumes (PVs) and Persistent Volume Claims (PVCs) are used for managing storage in Kubernetes.# persistentvolumeclaim.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: postgres-pvc
namespace: myapp-namespace
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi # Adjust size as needed5. Deployment for the Python Application
Deployments manage the creation and scaling of pods.# app-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-deployment
namespace: myapp-namespace
spec:
replicas: 2 # Adjust based on your scaling needs
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: <your-python-app-image> # Replace with your Docker image
ports:
- containerPort: 80 # Adjust if your app uses a different port
envFrom:
- configMapRef:
name: myapp-config
- secretRef:
name: myapp-secret6. Deployment for PostgreSQL
Similarly, create a deployment for PostgreSQL.# postgres-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: postgres-deployment
namespace: myapp-namespace
spec:
replicas: 1
selector:
matchLabels:
app: postgres
template:
metadata:
labels:
app: postgres
spec:
containers:
- name: postgres
image: postgres:latest
ports:
- containerPort: 5432
envFrom:
- secretRef:
name: myapp-secret
volumeMounts:
- mountPath: /var/lib/postgresql/data
name: postgres-storage
volumes:
- name: postgres-storage
persistentVolumeClaim:
claimName: postgres-pvc
7. Service
Services are used to expose applications running on a set of Pods.# app-service.yaml
apiVersion: v1
kind: Service
metadata:
name: myapp-service
namespace: myapp-namespace
spec:
selector:
app: myapp
ports:
- protocol: TCP
port: 80
targetPort: 80 # Adjust if your app uses a different port
type: LoadBalancer # Use LoadBalancer for cloud environments or NodePort for on-premises8. Ingress (Optional)
If you need to expose your service outside of your cluster, you can use an Ingress. This is optional and depends on your environment.# ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: myapp-ingress
namespace: myapp-namespace
spec:
rules:
- http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: myapp-service
port:
number: 80Deployment Steps
1. Apply the namespace: kubectl apply -f namespace.yaml
2. Apply the ConfigMap and Secret: kubectl apply -f configmap.yaml -f secret.yaml
3. Apply the PersistentVolumeClaim: kubectl apply -f persistentvolumeclaim.yaml
4. Deploy the application and database: kubectl apply -f app-deployment.yaml -f postgres-deployment.yaml
5. Expose the application: kubectl apply -f app-service.yaml
(Optional) Apply the Ingress: kubectl apply -f ingress.yaml
Notes:
- Replace placeholders like <your-python-app-image> and <base64-encoded-password> with your actual data.
- Adjust resource requests and limits based on your application's needs.
- The number of replicas in the deployment can be adjusted for scalability.
- Ensure your Kubernetes cluster supports the LoadBalancer service type; otherwise, use NodePort.
- The Ingress configuration might vary based on your cloud provider or Ingress controller.
This setup should provide a good starting point for deploying your Python web application with a PostgreSQL backend on Kubernetes.Conclusion
In conclusion, integrating large language model AI into the deployment phase of agile development can lead to tangible benefits for engineering teams. By automating the creation of release documentation and providing intelligent insights into the deployment process, LLMs enable developers to focus their efforts on building high-quality features. The result is faster, more robust production deployments and greater transparency for managers overseeing the release. As LLMs continue to advance in capabilities, they will become an indispensable assistant that speeds up deployments, reduces risks, and improves the overall development lifecycle. Organizations that leverage AI for deployments gain a real competitive edge in their ability to release software faster and more efficiently.