README.md

Requirements:

📇 docker v20 😸 node v12 or higher

To run locally:

make ip address available to kafka (see why) export HOST_IP=$(ifconfig | grep -E "([0-9]{1,3}\.){3}[0-9]{1,3}" | grep -v 127.0.0.1 | awk '{ print $2 }' | cut -f2 -d: | head -n1)

spin up kafka services through docker. docker-compose up

in a new terminal window, install node packages npm install

run typescript websocket npm run server:dev

in another new terminal window, run consumer app to confirm messages being published npm run consumer:dev

The server will now be available at http://localhost:8080

To send data via websocket, connect to ws://localhost:8080/websocket?session_id=SESSION_ID where SESSION_ID is the key that will be used to retrieve the transformed data.

To retrieve the transformed data, make a GET request to http://localhost:8080/data?session_id=SESSION_ID where SESSION_ID is that session_id value used in the websocket url.

Testing

To test end-to-end, run all the services as described by the steps above. Then, in a new terminal window, run npm run test

Routes

/websocket?session_id={string} create a websocket connection that takes the session_id value as a query parameter

/data?session_id={string} retrieve the transformed data

Solution

I've split the solution into two backend services. One is an express server and the other is consumes and transforms data. The two services communicate using Kafka. I used kafkajs to write kafka services in node.js.

I chose to use a pubsub system with an eye towards future flexibility and scaling. By keeping these services separate, they can be deployed on independent resources and configurations.

There was some risk for me in choosing Kafka over the Google Cloud Platform Pub/Sub system I typically use because this was my first time ver using kafkajs, but I chose to do this instead of being locked in to a vendor. Plus, it's easier to run locally this way.

Speaking of running locally, I didn't completely divorce the two services as I would if they were really being deployed separately, and I did that so that I could keep this as a single package with a single package.json file, for ease of use as an MVP.

Once the consuming service receives data through the kafka topic, that data gets saved to a redis instance. A more typical solution with kafka would probably be to use Faust or a stream processing framework, however I found the options for node to be less developed. Initially I chose to use Redis because it's a fast and light in-memory database, but its limitations meant that the data could not be saved in the transformed schema as defined by the coding instructions.

Therefore, in order to save the data in its transformed state, I replaced Redis with MongoDB for its more powerful features when working with JSON.

What I'd add

• a proper logging client so that logs are searchable by session_id and request_id header
• security & rate limiting
• database access: the server application only needs read permission
• CI/CD
• architecture: no need for this to be a monolithic application. deploying separate resources would improve resilience, scalability, and security.
• replace services from docker-compose with actual deployed instances
• validate event model
• create index on mongodb on session_id
• currently, there is a potential problem if the websocket client tries to send the same data again. There is no uniqueness constraint on each item in the children array so events would be duplicated. Maybe any existing database record should be deleted or archived when a SESSION_START event arrives, or the SaveDataService should use mongo's $unset operator to clear the children array.

why ip address is required