Deploying Plumber API on AWS EC2 or Alternative Options for Scalability and Reliability

Overview of Plumber API Deployment on AWS EC2 or Alternative Options

As a developer, it’s essential to consider the best practices for deploying a production-ready API on Amazon Web Services (AWS). In this article, we’ll explore how to keep a Plumber API running on an AWS EC2 instance and discuss alternative deployment options.

What is Plumber?

Plumber is an open-source framework for building web APIs in R. It provides a simple way to create RESTful APIs using the R programming language. Plumber allows developers to define functions that can be executed by the API, making it easier to build data-driven applications.

Current Setup and Challenges

The question owner has set up an EC2 instance with Amazon Linux 2 and installed R and all required libraries. The R script for the API and model file (RDS format) are both uploaded to the EC2 instance. The API works perfectly when accessed via the public IP of the EC2 instance using commands like curl or browser requests.

However, running the API in the background using methods like nohup or tmux may not be robust enough for a production-ready solution. To address this concern, we’ll explore alternative deployment options.

Why Lambda is Not Suitable

The question owner mentions that Lambda is not suitable for their use case due to several reasons:

Cold starts: APIs with heavier dependencies like R libraries experience cold starts, which can lead to performance issues.
Timeout limits: Lambda has a timeout limit of 15 minutes maximum execution time, and large R models might exceed memory or runtime limits.
Packaging R with Lambda: Packaging R with Lambda requires custom runtimes.

Scaling the API

To make the API scalable, we can add an Autoscaling group to handle increased traffic. This will ensure that the API can scale up or down based on demand. Additionally, adding a load balancer on top of the EC2 instance will provide a stable endpoint for API calls.

Here’s an example of how you can create an Autoscaling group using the AWS Management Console:

# Create an Autoscaling group

1.  Go to the AWS Management Console and navigate to the Auto Scaling dashboard.
2.  Click "Create autoscaleable group" and select "Application load balancer" as the target.
3.  Choose the EC2 instance running the Plumber API as the source.
4.  Configure the scaling policy to increase or decrease the number of instances based on CPU utilization.
5.  Save the changes.

# Configure a load balancer

1.  Create an Application Load Balancer (ALB) in the AWS Management Console.
2.  Associate the ALB with the EC2 instance running the Plumber API.
3.  Configure the listener to use HTTP/HTTPS ports.
4.  Create a target group and add the EC2 instance as a target.
5.  Save the changes.

# Verify the setup

1.  Use tools like `curl` or Postman to test the API endpoint.
2.  Monitor the load balancer logs to ensure that traffic is being routed correctly.
3.  Verify that the Autoscaling group is scaling the EC2 instances based on CPU utilization.

Using ECS and Fargate

Another option for deploying a scalable Plumber API is using Amazon Elastic Container Service (ECS) and AWS Fargate. This approach provides a serverless experience without requiring manual scaling or patching.

To use ECS and Fargate, you’ll need to:

Create a Docker image for your Plumber API.
Register the image with Amazon ECR.
Create an ECS cluster and task definition for your application.
Deploy the task definition using AWS Fargate.

Here’s an example of how you can create a Docker image for your Plumber API:

# Build a Docker image

1.  Install Docker on your machine.
2.  Create a new directory for your project and navigate to it in the terminal.
3.  Run `docker build -t <image-name> .` to build the Docker image.

# Register the image with Amazon ECR

1.  Go to the AWS Management Console and navigate to the Amazon ECR dashboard.
2.  Click "Create repository" and enter a name for your repository.
3.  Configure the registry policy as required.
4.  Register the Docker image with Amazon ECR using `docker push`.

Conclusion

Deploying a Plumber API on AWS EC2 or alternative options requires careful consideration of scalability, reliability, and cost-effectiveness. By using an Autoscaling group and load balancer, you can create a stable endpoint for API calls while scaling the instance based on traffic demands.

Alternatively, using ECS and Fargate provides a serverless experience without requiring manual scaling or patching. This approach simplifies deployment and reduces costs by leveraging Amazon’s managed services.

When choosing an alternative deployment option, always consider simplicity first and avoid complex services when a simpler setup can effectively meet your requirements. A small, well-optimized setup is often sufficient and easier to manage.

Last modified on 2024-03-13