An API that determines if a price drop alert needs to be triggered for a product.
The service essentially is responsible for calculating if a price drop alert is required.
Scenario Example
// For productId "01"
lowest price: 100
lowest price: 120
lowest price: 130
lowest price: 100 **PRICE DROP ALERT**
lowest price: 110
lowest price: 130
lowest price: 110
lowest price: 90 **PRICE DROP ALERT**
To achieve this, every time the service gets a valid request that contains retailers, it:
- Calculates the lowest price for that productId (taking retailPrice as well as discountPrice into account)
- Gets the previous lowest price for that productId
- Checks if the latest lowest price for that productId is lower than the previous lowest price by at least £10 (NOTE: this threshold is an environment variable that can be modified without making changes to the service)
- Once it finds if the latest lowest price is lower than the previous, it generates a payload to indicate whether an alert is required
- Upserts the latest lowest price in the database
Note: As this service is only responsible for the lowest price for a given productId, it only ever stores (and upserts) a single record for a given productId. I decided to use this approach over storing historic prices for a given service following the single responsibility principle.
This approach:
- Avoids a scenario where an 'older' record for the lowest price is read
- Avoids concurrency issues: MongoDB uses a process wide write lock, so only one write operation can be performed at a time, ensuring we don't run into concurrency issues while multiple instances are trying to update the same record. The service can use the custom DatabaseError object catch and retry updating a record n number of times in case an update request fails due to a write lock.
To get the app running locally using Docker:
npm run app:docker
The application will run on the client's localhost port 8080 (http://localhost:8080)
To get the app running locally (without Docker):
git clone [email protected]:nkhil/price-drop-alerter.git
cd price-drop-alerter
npm install
npm run develop
NOTE: This requires node V14.0.0 or above as the service likely uses features only available in Node 14 and up (for eg: optional chaining).
- Express
- MongoDB
- Swagger 3.0
- openapi-express-validator
- Docker
- docker-compose
I'm using the Open API spec (v 3.0) to define the API (see ./swagger/swagger.yml) and the openapi-express-validator module to validate requests and responses. This package also allows me to route the request (once validated) to the right controller (see ./src/controllers). I've followed the provided schema for the request and response payloads, and have made the decision to make certain properties (for eg: retailPrice & isInStock) mandatory while leaving certain properties (eg: /discountPrice) as optional.
Docker & Docker-Compose have been used to package up the application for testing purposes, as well as for deployment. This will ensure a predictable, repeatable application when scaling up the service horizontally (i.e. multiple instances).
This endpoint returns a 200 status code with { status: 'OK' }. This can be used by kubernetes to determine if the service is up and ready.
This is the endpoint used to determine if a price drop alert is required.
- If an alert is required, it returns a 200 response with the following payload:
{
"alertRequired": true,
"newPrice": 90
"productId": "ABC123",
"retailerId": "offspring"
}
- If an alert is not required, it returns a 200 response with the following payload:
{
"alertRequired": false,
}
If the request payload is invalid (i.e. it's missing required properties mentioned in the swagger.yml definitions), it returns a 400 status code.
If an error occurs (for eg: a database error), it returns a 500 status code.
You can run all automated tests (unit & integration) using the following command
npm run test:docker
The following scenarios are currently tested and passing at the API level (i.e. hitting the API from the outside)
price-drop-alerter API test
/liveness
✓ can successfully check the liveness of the service (158 ms)
/price-drop-check
✓ returns an expected response when a price drop alert is required (76 ms)
✓ returns a 401 status code when productId is missing in payload (38 ms)
✓ returns a 401 status code when retailers are missing in payload (7 ms)
✓ returns a 401 status code when retailers are missing required properties in payload (6 ms)
The following scenarios have been identified as ones that need to be tested, but have not yet been tested/implemented due to a lack of time.
/price-drop-check
✗ processes a request where none of the retailers have valid stock
✗ returns a correct response when a database error occurs
I've extended Node's Error class to create custom errors that can be thrown (and caught) when errors occur (for eg: during a database operation). The errors can then be caught and an appropriate HTTP response can be returned (see ./src/errors/errors.js). Due to the lack of time, I have not fleshed out all the errors that might occur (for eg: a mongo duplicate error).
I've used pino as its a low overhead logger to log when requests are received and they finish processing to help debug issues in production.
This service uses conventional commits to be able to automatically create documentation / changelogs.
The POST /price-drop-check endpoint is insecure currently. I'd recommend securing it using a client credentials flow by using scoped auth tokens generated by an authorisation server that exchanges a client id and a secret for a scoped jwt auth token that can be used to authorise the service, ensuring that unauthorised agents are not able to access this service.
Requirements for the operations of this service are:
The API will be expected to handle a large number of requests coming from other micro-services hosted within AWS. The volume of this traffic will fluctuate heavily throughout the day.
Firstly, I'd recommend a comprehensive CI/CD pipeline (eg: Github actions) be added to ensure changes made can be integrated and deployed regularly.
As the API gets a highly variable number of requests, it's important that we're able to scale the service up and down based on the number of requests.
I'd recomment the services use Helm along with Amazon EKS (AWS's managed kubernetes service) with a service mesh (eg: Istio) in the clusters. Kubernetes will allow us to declaratively set a threshold, as well as a minimum, maximum and threshold for the service.
For eg: Initially, price-drop-alerter can be deployed with a minimum of 3 pods / instances. We can defer to EKS to load balance and distribute the incoming requests between the pods. We can now set a CPU usage threshold (for instance, 70%) that will auto-scale the services, with maximum pods configured (for eg: initially this can be set to 5 pods). This will ensure that we can scale horizontally, and be able to scale down during less-busy periods.
Note: Before being deployed to prod, we will need to load test the service (for eg: using Locust) to ensure that our k8s config (min 3 pods / max 5 pods) is capable of handling the number of requests we're expecting.