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LLDsystem_design~10 mins

Rating and review system in LLD - Interactive Code Practice

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Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to define a Review class with a rating attribute.

LLD
class Review:
    def __init__(self, rating):
        self.[1] = rating
Drag options to blanks, or click blank then click option'
Arating
Bscore
Cvalue
Drate
Attempts:
3 left
💡 Hint
Common Mistakes
Using unrelated attribute names like 'score' or 'value' which may confuse readers.
2fill in blank
medium

Complete the code to add a method that calculates the average rating from a list of reviews.

LLD
def average_rating(reviews):
    total = 0
    for review in reviews:
        total += review.[1]
    return total / len(reviews) if reviews else 0
Drag options to blanks, or click blank then click option'
Ascore
Brating
Cvalue
Drate
Attempts:
3 left
💡 Hint
Common Mistakes
Using an incorrect attribute name causing attribute errors.
3fill in blank
hard

Fix the error in the code that adds a new review to the system's review list.

LLD
class ReviewSystem:
    def __init__(self):
        self.reviews = []

    def add_review(self, review):
        self.reviews.[1](review)
Drag options to blanks, or click blank then click option'
Ainsert
Badd
Cappend
Dextend
Attempts:
3 left
💡 Hint
Common Mistakes
Using 'add' which is not a list method in Python.
Using 'extend' which expects an iterable, causing errors.
4fill in blank
hard

Fill both blanks to create a dictionary comprehension that maps user IDs to their average rating.

LLD
user_avg_ratings = {user_id: sum(r.rating for r in reviews) [1] len(reviews) for user_id, reviews [2] user_reviews.items()}
Drag options to blanks, or click blank then click option'
A//
B/
Cin
Dof
Attempts:
3 left
💡 Hint
Common Mistakes
Using integer division '//' which truncates decimals.
Using 'of' instead of 'in' causing syntax errors.
5fill in blank
hard

Fill all three blanks to filter reviews with rating above 3 and create a dictionary of user to their filtered reviews count.

LLD
filtered_counts = {user: len([r for r in reviews if r.[1] [2] [3]]) for user, reviews in user_reviews.items()}
Drag options to blanks, or click blank then click option'
Arating
B>
C3
Dscore
Attempts:
3 left
💡 Hint
Common Mistakes
Using 'score' instead of 'rating' causing attribute errors.
Using '<' instead of '>' causing wrong filtering.

Practice

(1/5)
1. What is the primary purpose of a rating and review system in an online store?
easy
A. To process payment transactions
B. To collect user feedback and calculate average product ratings
C. To manage product inventory levels
D. To store user passwords securely

Solution

  1. Step 1: Understand the system's goal

    A rating and review system is designed to gather user opinions and ratings about products.
  2. Step 2: Identify the main function

    It calculates average ratings to help other users make decisions quickly.
  3. Final Answer:

    To collect user feedback and calculate average product ratings -> Option B
  4. Quick Check:

    Rating system = Collect feedback + average rating [OK]
Hint: Focus on feedback and rating calculation [OK]
Common Mistakes:
  • Confusing rating system with payment or inventory systems
  • Thinking it manages user credentials
  • Assuming it handles shipping or delivery
2. Which data structure is best suited to store individual reviews for quick lookup by product ID?
easy
A. Hash map with product ID as key and list of reviews as value
B. Array of reviews without indexing
C. Linked list of all reviews
D. Stack of reviews

Solution

  1. Step 1: Consider lookup efficiency

    Quick lookup by product ID requires a data structure with fast key-based access.
  2. Step 2: Choose appropriate structure

    A hash map (dictionary) allows O(1) average time to find reviews by product ID.
  3. Final Answer:

    Hash map with product ID as key and list of reviews as value -> Option A
  4. Quick Check:

    Fast lookup = Hash map [OK]
Hint: Use hash maps for fast key-based lookup [OK]
Common Mistakes:
  • Using arrays without indexing causes slow searches
  • Linked lists have O(n) lookup time
  • Stacks do not support direct lookup by key
3. Given the following pseudocode for updating average rating after a new review:
current_avg = 4.0
num_reviews = 5
new_rating = 5
new_avg = (current_avg * num_reviews + new_rating) / (num_reviews + 1)

What is the value of new_avg?
medium
A. 4.17
B. 4.16
C. 4.0
D. 4.5

Solution

  1. Step 1: Calculate total rating sum before new review

    Total sum = current_avg * num_reviews = 4.0 * 5 = 20
  2. Step 2: Add new rating and compute new average

    New sum = 20 + 5 = 25
    New average = 25 / (5 + 1) = 25 / 6 ≈ 4.1667
  3. Final Answer:

    4.17 -> Option A
  4. Quick Check:

    Average update formula ≈ 4.17 [OK]
Hint: Multiply avg by count, add new, divide by count+1 [OK]
Common Mistakes:
  • Forgetting to add new rating to total sum
  • Dividing by old count instead of count+1
  • Rounding too early causing wrong average
4. A rating system stores average rating and count per product. After deleting a review, the average becomes incorrect. What is the likely cause?
medium
A. Recalculating average using sum of all reviews
B. Using integer division instead of float division
C. Not updating the count of reviews after deletion
D. Storing reviews in a hash map

Solution

  1. Step 1: Understand average calculation

    Average = sum of ratings / count of reviews. Both must be accurate.
  2. Step 2: Identify deletion impact

    If count is not decreased after deleting a review, average calculation divides by wrong count.
  3. Final Answer:

    Not updating the count of reviews after deletion -> Option C
  4. Quick Check:

    Count mismatch causes wrong average [OK]
Hint: Always update count when reviews change [OK]
Common Mistakes:
  • Ignoring count update after deletion
  • Assuming recalculation always fixes average
  • Confusing data structure choice with calculation error
5. You want to design a scalable rating and review system for millions of products and users. Which approach best balances fast average rating queries and frequent review updates?
hard
A. Store all reviews and compute average on each query
B. Use a single database table without indexes
C. Cache only the latest review per product
D. Maintain precomputed average and count, update incrementally on review changes

Solution

  1. Step 1: Consider query and update load

    Millions of products and users mean many queries and updates.
  2. Step 2: Choose efficient strategy

    Precomputing average and count and updating them incrementally avoids scanning all reviews each time.
  3. Step 3: Evaluate other options

    Computing average on each query is slow; no indexes cause slow lookups; caching only latest review misses full rating info.
  4. Final Answer:

    Maintain precomputed average and count, update incrementally on review changes -> Option D
  5. Quick Check:

    Precompute + incremental update = scalable [OK]
Hint: Precompute averages, update on changes for scale [OK]
Common Mistakes:
  • Recomputing averages on every query
  • Ignoring indexing and caching strategies
  • Caching incomplete data causing stale info