Welcome to today’s lesson! We’ll explore the fascinating world of using Kotlin for managing product reviews and putting data aggregation into practice. We’ll begin with a straightforward Starter Task to establish our foundation, then gradually progress to a more advanced solution involving data aggregation. Let’s get started!

For our starter task, we will lay the foundation by implementing basic operations for managing product reviews. These are the methods we will need to implement:

• addReview(productId: String, reviewId: String, reviewText: String, rating: Int): Boolean — adds a review to the product specified by productId. If a review with reviewId already exists, it updates the existing review. Returns true if the review was added or updated successfully, false otherwise.

• getReview(productId: String, reviewId: String): Map<String, Any>? — returns the review details (reviewText, rating, and flagged fields) for the review specified by reviewId under the given productId. If the review or product does not exist, returns null.

• deleteReview(productId: String, reviewId: String): Boolean — deletes the review specified by reviewId under the given productId. Returns true if the review was deleted, false otherwise.

Let's look at the code that implements these functionalities:

This code establishes the foundational methods needed for managing product reviews within a ReviewManager class. The addReview method allows for adding a new review or updating an existing one, ensuring each review contains valid rating values between 1 and 5. It utilizes the getOrPut method to retrieve or initialize a map of reviews for a given product, ensuring smooth addition or updates without requiring separate checks for initialization. The getReview method retrieves the review details for a specific product, including the review text and rating, returning null if the product or review doesn't exist. The deleteReview method facilitates the removal of a specific review, and if no reviews are left for a product, the product itself is removed from the product list. Together, these methods form the basic operations required to manage a collection of product reviews efficiently.

Now, let's extend this with new features.

With our basic review management system in place, we will now introduce new methods to handle more complex operations, such as flagging inappropriate reviews and aggregating review data for a specific product.

Here are the new methods we will add:

• flagReview(productId: String, reviewId: String): Boolean — This method flags a specific review as inappropriate for a given product. Returns true if the review was successfully flagged, false otherwise.

• aggregateReviews(productId: String): Map<String, Any>? — This method aggregates review data for a given product, providing statistics such as the total number of reviews, the number of flagged reviews, average rating, and review texts excluding flagged ones. If the product does not have any reviews or does not exist, returns null.

First, let's add functionality to flag a review:

In this step, we are adding the flagReview method to our ReviewManager class. This method enables users to mark a specific review as inappropriate. It checks whether the product and review exist in the dataset, and if they do, it creates a mutable copy of the review using toMutableMap() with null-safety operators (?. and ?:), then sets the flagged attribute to true. The use of a mutable copy is necessary because our original review map is immutable, and creating a copy allows us to modify the review's properties safely. If at any point the product or review doesn't exist, or if the conversion to a mutable map fails, the method returns false. This flagging mechanism is crucial for maintaining the quality and appropriateness of the reviews in the system.

Next, we will implement the method to aggregate reviews:

In this step, the aggregateReviews method is added to the ReviewManager class. This method aggregates review data for a given product by calculating various statistics, such as the total number of reviews, the number of flagged reviews, the average rating excluding flagged reviews, and a list of review texts that are not flagged. The method first ensures the product exists and contains reviews. It then iterates through the reviews to collect the necessary data, considering only non-flagged reviews for the average rating and review texts. If all reviews are flagged, the average rating defaults to zero. This aggregation provides a comprehensive overview of a product's review status, useful for both users and administrators.

Great job! Today, you have learned how to manage product reviews and apply data aggregation in practice. We started with basic operations for adding, retrieving, and deleting reviews. Then, we extended our functionality to include flagging reviews and aggregating review data. This gradual build-up demonstrates how to enhance features incrementally and handle more complex data aggregation tasks.

Feel free to practice solving similar challenges to further strengthen your skills. Keep coding, and see you in the next lesson!