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Deploying FastAPI on AWS, Using PostgreSQL RDS

Synopsis

This project details the deployment of a Python FastAPI project, which uses a PostgreSQL RDS database, to AWS. The AWS technologies used in production are:

Standing on the backs of giants

Major shoutout to iwpnd. I followed his tutorial to have a successful first basic deployment. He also provided sage advice when I got stuck in a few places. Essentially, I catered his project to my specific needs, and added the ability to connect to a PostgreSQL database.

Motivation

I participate in a "fitness challenge" where players log daily points. The format of the collected data is not ideal, so I aim to clean this data, store it indefinitely on AWS in RDS, and make it available via FastAPI so that others can use the data for analysis.

Files

.
├── app
|   ├── __init__.py
│   ├── crud.py
│   ├── database.py
│   ├── main.py
│   ├── models.py
│   ├── routers
│   │   ├── __init__.py
│   │   ├── players.py
│   │   ├── seasons.py
│   │   └── teams.py
│   └── schemas.py
├── .pre-commit.yaml
├── LICENSE
├── README.md
├── requirements.txt
└── template.yml

Setup (linux)

Install and configure AWS CLI and SAM

In order to proceed with set-up and deployment, AWS CLI and SAM need to be installed and configured on your machine.

Create AWS Role

  1. IAM Console >> Roles >> Create
  2. Select AWS service as type, and choose Lambda and use case
  3. Add policies:
    • AWSLambdaBasicExecutionRole: Permission to upload logs to CloudWatch
    • AWSLambdaVPCAccessExecutionRole: Permission to connect our Lambda function to a VPC
  4. Finish creating role, and set name as fastapilambdarole. This name matches role specified in template.yml.

Create S3 Bucket

When we deploy our code with AWS SAM, a zip folder of our code will be uploaded to S3. There are two options for creating an S3 bucket.

(1) In the AWS console
(2) With the AWS CLI

aws s3api create-bucket \
--bucket {your bucket name here} \
--region eu-central-1 \
--create-bucket-configuration LocationConstraint=eu-central-1

Please note that S3 bucket names need to be globally unique. So the name of the bucket you create here will determine the bucket name used in later steps. Also, ensure that you change the region to your local region.

Clone project and test locally

git clone https://github.com/KurtKline/fastapi-postgres-aws-lambda.git
cd fastapi-postgres-aws-lambda
# create and activate a virtual environment
pip install -r requirements.txt
pip install uvicorn

In order to test locally without errors, PostgreSQL needs to be installed on your local machine, and the sample data needs to be loaded into a database table. Installing PostgreSQL on Ubuntu 20.04

From the linux terminal:

psql
postgres=# CREATE DATABASE fitness;
postgres=# CREATE TABLE fit (id serial, player varchar(50), team varchar(50), season varchar(50), data_date date, points float);
postgres=# \copy fit(player, team, season, data_date, points) from 'clean_fit.csv' with DELIMITER ',' CSV HEADER;

Start FastAPI

uvicorn app.main:app --reload
# click the link to open the browser at http://127.0.0.1:8000

Once you click the link, add /docs or /redoc to the URL http://127.0.0.1:8000/docs. You will then see the Swagger UI.

Setting up RDS PostgreSQL Instance

In order to deploy to AWS, our code AND our database needs to live on AWS. Here are some basic guidelines to setting up the RDS PostgreSQL instance.

  1. In RDS instance settings, make sure Public Accessibility is set to Yes
  2. Specify initial database name, which will be used in pg_restore below
  3. Whitelist IP
    • Create new EC2 security group
    • Inbound Rules: Type: All Traffic, Source: My IP
    • Add this security group to RDS instance

How to gain access to RDS instance from Linux terminal:

psql \
   --host=<DB instance endpoint from AWS> \
   --port=<port> \
   --username=<master user name> \
   --password \
   --dbname=<database name>

Once the data is dumped into your RDS PostgreSQL instance, you can set Public Accessibility back to No if you'd like. This just prevents external sources, like your local PC, from accessing your RDS instance.

Loading data into RDS PostgreSQL

Here are two options for loading the data into RDS PostgreSQL

Dump and Restore: if data already exists in local PostgreSQL

  1. Dump (done from terminal line): $ pg_dump -Fc mydb > db.dump
  2. Restore with: pg_restore -v -h [RDS endpoint] -U [master username ("postgres" by default)] -d [RDS database name] [dumpfile].dump
  3. Verify load was successful by connecting with psql block shown above

From .csv file

First connect to RDS through psql as shown above. Depending on your database name, fit=> shown below may be different for you.

fit=> create table fit (id serial, player varchar(50), team varchar(50), season varchar(50), data_date date, points float);
fit=> \copy fit(player, team, season, data_date, points) from 'clean_fit.csv' with DELIMITER ',' CSV HEADER;
COPY 105

More options for loading data into PostgreSQL RDS
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/PostgreSQL.Procedural.Importing.html

Deploying project with AWS SAM

The template.yml file is used for deployment with AWS SAM.

(1) Replace the values in template.yml specified as {replace}.
(2) Uncomment # openapi_prefix="/prod" in app/main.py. This allows proper access of API when deployed.
(3) Run following steps for SAM in linux terminal

sam validate

sam build --debug

sam package --s3-bucket {your bucket name here} --output-template-file out.yml --region eu-central-1

sam deploy --template-file out.yml --stack-name example-stack-name --region eu-central-1 --no-fail-on-empty-changeset --capabilities CAPABILITY_IAM

Bumps along the way

Next Steps