Design a rewarding system.

To design a rewarding system with the given functions, we need a data structure that efficiently handles customer insertions and retrieval based on revenue. A combination of a hash map for quick customer lookups by ID and a data structure that supports efficient range queries and retrieval of the lowest K elements based on revenue would be ideal.

Data Structures:

1. Customer Storage: A HashMap<Integer, Customer> to store customer details, where the key is the customerID and Customer is an object holding revenue and referrer information.
2. Revenue Tracking: A data structure that can efficiently store and query customers by their total revenue. A balanced binary search tree (like a TreeMap or TreeSet in Java) or a min-heap could work. For getLowestKCustomersByMinTotalRevenue, a structure that supports ordered retrieval is crucial.

Function Implementations:

• insertNewCustomer(double revenue):

  • Generate a new customerID (auto-increment).
  • Create a new Customer object with the given revenue.
  • Store the customer in the HashMap.
  • Add the customer to the revenue-tracking data structure.
  • Return the customerID.

• insertNewCustomer(double revenue, int referrerID):

  • Generate a new customerID.
  • Create a new Customer object with the given revenue and referrerID.
  • Store the customer in the HashMap.
  • Add the customer to the revenue-tracking data structure.
  • Optionally, update the referrer's total revenue if the system tracks that. (The prompt states 'total revenue consists of the revenue that this customer bring', implying it's per customer, not cumulative across referrers).
  • Return the customerID.

• getLowestKCustomersByMinTotalRevenue(int k, double minTotalRevenue):

  • Iterate through the revenue-tracking data structure.
  • Filter customers whose totalRevenue is greater than or equal to minTotalRevenue.
  • Collect the customerIDs of these filtered customers.
  • Sort these collected IDs based on their total revenue in ascending order.
  • Return the first k customerIDs from the sorted list.

Considerations:

• Efficiency: The choice of the revenue-tracking data structure is critical. A TreeMap or TreeSet keyed by revenue would allow efficient filtering and retrieval of the lowest K elements. If k is small relative to the total number of customers, a min-heap could also be considered, but range queries might be less direct.
• Concurrency: If the system is multi-threaded, appropriate synchronization mechanisms (locks, concurrent data structures) would be necessary.
• Scalability: For very large datasets, consider distributed data stores and more advanced indexing techniques.