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Mastery Task 1 - Filter out the noise

Milestone 1: Update TargetingEvaluator and AddTargetingGroupActivity

While looking over the current service code, you see some instances where we're using a for loop to iterate and process elements in a collection. To help reduce the boilerplate iterative code and make the logic easier to follow, you decide to update the logic to use streams instead!

For this task, you'll be updating logic in two classes to use a stream instead of a for loop to iterate and process elements.

TargetingEvaluator's evaluate method determines if all the TargetingPredicates in a given TargetingGroup are true for the given RequestContext.

  • Update TargetingEvaluator's evaluate method to use a stream instead of a for loop to evaluate the TargetingPredicateResult.

AddTargetingGroupActivity is the activity class for the operation that adds a new targeting group to an existing piece of advertising content.

  • Update AddTargetingGroupActivity's addTargetingGroup method to use a stream when converting the TargetingPredicates from the Coral model to its internal model.

We encourage you to take a look at the Stream Javadoc to see what available methods can help implement the logic.

Milestone 2: Update AdvertisementSelectionLogic

If you look at our service's AdvertisementSelectionLogic, you'll see that we're currently retrieving all advertisements for a marketplace and randomly showing one of those advertisements to customers. To help increase our ad click through rate, our marketing team has created targeting rules that they think will help show ads to customers who are most likely to click on it. They've created ads specifically for each of these targeting groups. So now we want to only show ads that customers are eligible for, based on the customer being a part of an ad's targeting group.

Update AdvertisementSelectionLogic's selectAdvertisement method so that it randomly selects only from ads that the customer is eligible for based on the ad content's TargetingGroup. Your solution should use streams to evaluate the targeting groups (again, take a look at the streams javadoc if you need some guidance!). Use TargetingEvaluator to help filter out the ads that a customer is not eligible for. Then randomly return one of the ads that the customer is eligible for (if any).

If there are no eligible ads for the customer, then return an EmptyGeneratedAdvertisement. Since these ads are being rendered on the retail website, we don't want to return null or throw exceptions if there are no eligible ads for the customer. Instead we return an empty string so that the front-end can handle rendering gracefully.

Exit Checklist:

  • You've updated AdvertisementSelectionLogic's selectAdvertisement method so that it randomly selects an ad that the customer is eligible for
  • You've updated TargetingEvaluator's evaluate method to use a stream instead of a for loop to evaluate the TargetingPredicateResult.
  • You've set both AdvertisementSelectionLogic and TargetingEvaluator‘s IMPLEMENTED_STREAM boolean flag to true.
  • You've updated AddTargetingGroupActivity's addTargetingGroup method to use a stream when building the Map of spend categories to return.
  • Running the gradle command ./gradlew -q clean :test --tests com.tct.mastery.task1.MasteryTaskOneLogicTests passes.
  • Running the gradle command ./gradlew -q clean :test --tests com.tct.introspection.MT1IntrospectionTests passes.