This architecture has 6 Components:

1. SQS FIFO Queue
2. Postgres Database
3. Backend Service (written in GO) (Service hosted on AWS ECS: http://15.207.18.247:8080 , use it with appropriate endpoints: look Backend API Documentation)
4. Lmanda Sync Service (written in Python)
5. Elasticsearch Service (Setup on EC2)
6. Search Service (written in GO) (Service hosted on AWS ECS: http://15.207.18.247:8081 , use it with appropriate endpoints: look Search API Documentation)

Create a SQS FIFO Queue from AWS console. Keep the queue_url

Setup a AWS RDS database with postgres engine. Keep the database_url

Clone this fold repo

git clone https://github.com/gagan-gaurav/fold.git

Build docker image (make sure docker daemon is running)

docker build -t backendservice:v1 .

Create an .env file in current folder with following environment variables:

POSTGRES_URL=your-postgres-database-url-from-step-1
AWS_ACCESS_KEY_ID=your-aws-access-key-id
AWS_SECRET_ACCESS_KEY=your-aws-secret-access-key
AWS_DEFAULT_REGION=ap-south-1
SQS_QUEUE_URL=your-sqs-queue-url-from-step-1 

Running docker container

docker run -p 8080:8080 --env-file .env backendservice:v1

The following routes are available for interacting with the API:

Each route is associated with a specific HTTP method and provides functionality related to creating, retrieving, updating, or deleting users, hashtags, and projects.

Make sure to use the appropriate HTTP method and route to perform the desired action on the API.

** Create/Update User Request Body Schema**:

{
  "name": "string",
}

** Create/Update Hashtag Request Body Schema**:

{
  "name": "string",
}

** Create/Update Project Request Body Schema**:

{
  "name": "string",
  "slug": "string",
  "description": "string",
  "user_ids": [],
  "hashtag_ids": [],
}

Create an EC2 instance. SSH into it. (Make sure that your security group has inbound request open for port:22 and port:9200) Install Elasticsearch RPM package -> Follow this instructions for installation. Grab your username(default_username="elastic") and password.

Create a lambda function with python runtime and necessary permissions and set your queue (created in step 1) as trigger. link to lambdahandler code: https://github.com/gagan-gaurav/fold/blob/main/internal/services/lambdahandler.py

Setup environment variables for your lambda through AWS CLI

ELASTICSEARCH_PASSWORD = your-elasticsearch-password
ELASTICSEARCH_USERNAME = your-elasticsearch-username

Clone searchService repo. repoLink

git clone https://github.com/gagan-gaurav/searchService.git

Build docker image (make sure docker daemon is running)

docker build -t searchservice:v1 .

Create an .env file in current folder with following environment variables:

ELASTICSEARCH_USERNAME=your-elasticsearch-username
ELASTICSEARCH_PASSWORD=your-elasticsearch-password
ELASTICSEARCH_URL=https:your-elasticsearch-url

Running docker container

docker run -p 8081:8081 --env-file .env searchservice:v1

The following routes are defined for serarch service:

Each route is associated with a specific HTTP method and provides functionality related to searching users, hashtags, and performing fuzzy searches.

Make sure to use the appropriate HTTP method and route along with the required query parameter to perform the desired search operation.