1. Custom AI Analytics Dashboards with Grafana behind Nginx Proxy


    To create a custom AI analytics dashboard with Grafana and have it accessible behind an Nginx proxy, we'll need to do several things:

    1. Set up Grafana: Deploy a Grafana instance that will host your dashboards.
    2. Configure Nginx: Set up Nginx as a reverse proxy to Grafana, so that you can access Grafana through the Nginx URL.
    3. Secure Your Setup: You might also want to consider securing your dashboard access using SSL certificates.

    For this setup, you could use various cloud providers. However, in this example, I'll stick to a generic setup that you can adapt depending on where you're deploying your infrastructure.

    Below is a Pulumi program written in Python that demonstrates how to accomplish these steps. This is a simple setup that does not include all the details (like setting up SSL), but it shows the basic structure for getting started.

    import pulumi import pulumi_docker as docker # Replace the following variables with your own values as needed. grafana_image = "grafana/grafana:latest" nginx_image = "nginx:latest" grafana_port = 3000 nginx_port = 80 # Define a network for the Grafana and Nginx containers to communicate. network = docker.Network("network") # Set up the Grafana container. grafana_container = docker.Container("grafana", image=grafana_image, ports=[docker.ContainerPortArgs( internal=grafana_port, external=grafana_port,)], networks_advanced=[docker.ContainerNetworksAdvancedArgs( name=network.name, )], ) # Define the Nginx configuration with Grafana as the reverse proxy target. nginx_config = """ server { listen 80; location / { proxy_pass http://grafana:{}; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; } } """.format(grafana_port) # Create a Docker volume to store the Nginx configuration. config_volume = docker.Volume("config-volume") # Copy the Nginx configuration to the Docker volume. config_volume_container = docker.Container("config-volume-container", image=nginx_image, command=["/bin/sh", "-c", "echo '{}' > /etc/nginx/conf.d/default.conf".format(nginx_config)], volumes=[docker.ContainerVolumeArgs( container_path="/etc/nginx/conf.d", host_path=config_volume.name, read_only=False, )], start=False, ) # Set up the Nginx container. nginx_container = docker.Container("nginx", image=nginx_image, ports=[docker.ContainerPortArgs( internal=nginx_port, external=nginx_port, )], volumes=[docker.ContainerVolumeArgs( container_path="/etc/nginx/conf.d", host_path=config_volume.name, read_only=True, )], networks_advanced=[docker.ContainerNetworksAdvancedArgs( name=network.name, )], depends_on=[config_volume_container], ) # Export the URL of the Nginx reverse proxy. pulumi.export('nginx_url', pulumi.Output.concat("http://", nginx_container.ip_address))

    In the program above:

    • We've defined a Docker network that Grafana and Nginx containers will use to communicate with each other.
    • We've created a Grafana container that will run the Grafana service accessible on the defined port.
    • We've written an Nginx configuration file to set up a reverse proxy pointing to Grafana's port. Since Nginx won't start without a configuration, we first create a Docker volume to hold this configuration.
    • We create a temporary Docker container (config_volume_container) that is responsible for populating the Nginx configuration into the volume.
    • Once the Nginx configuration volume is ready, we launch the Nginx container. It uses the configuration from the volume and sets up the reverse proxy to Grafana.
    • Finally, we export the URL where Nginx is accessible.

    Please note this is a local deployment, not attached to any cloud provider. For a production-grade deployment, you'd likely use cloud services, configure persistent storage, manage domain names, and set up SSL certificates using the relevant Pulumi resources for your chosen cloud provider. You would also need to provide the configuration to the Nginx container in a more secure way, such as using secrets management.