Billing Status Replay (OOD)

Problem Overview

We accidentally dropped the database that stored the current billing state for advertisers. The old transaction logs still exist, so the task is to replay those transactions and rebuild one.

Problem Statement

Design a system to replay billing transactions to restore the billing status for advertisers. The system should be able to process a large volume of transactions efficiently and accurately. The transactions are stored in old logs and need to be replayed to rebuild the current billing state.

Constraints

1. The transaction logs are stored in a file system.
2. The logs contain a sequence of transactions for each advertiser.
3. Each transaction has a timestamp and a status update (e.g., payment received, payment failed).
4. The system should handle concurrent access to the billing state.
5. The system should be able to scale to handle a large number of advertisers and transactions.

Examples

1. Input: A log file with the following transactions: Advertiser A: 2022-01-01 12:00:00, Payment Received Advertiser B: 2022-01-01 12:05:00, Payment Failed Advertiser A: 2022-01-01 12:10:00, Payment Received Output: The billing status for Advertiser A is "Payment Received" and for Advertiser B is "Payment Failed".

2. Input: A log file with the following transactions: Advertiser C: 2022-01-02 13:00:00, Payment Received Advertiser D: 2022-01-02 13:05:00, Payment Failed Advertiser C: 2022-01-02 13:10:00, Payment Failed Output: The billing status for Advertiser C is "Payment Failed" and for Advertiser D is "Payment Failed".

Hints

1. Consider using a concurrent data structure to store the billing status for each advertiser.
2. Process the transaction logs in a streaming fashion to avoid loading the entire log into memory.
3. Use a priority queue to process transactions in chronological order.

Solution

To solve this problem, you can follow these steps:

1. Create a concurrent data structure (e.g., ConcurrentHashMap in Java) to store the billing status for each advertiser.
2. Read the transaction logs in a streaming fashion, processing each transaction one by one.
3. For each transaction, update the billing status for the corresponding advertiser in the concurrent data structure.
4. Use a priority queue to process transactions in chronological order, ensuring that transactions are applied in the correct order.
5. After processing all transactions, the concurrent data structure will contain the final billing status for each advertiser.

Here's a high-level implementation outline in Java:

`java import java.util.concurrent.ConcurrentHashMap; import java.util.PriorityQueue;

public class BillingStatusReplay { private ConcurrentHashMap<String, String> billingStatus; private PriorityQueue<Transaction> transactionQueue;

public BillingStatusReplay() {
    billingStatus = new ConcurrentHashMap<>();
    transactionQueue = new PriorityQueue<>(Comparator.comparing(Transaction::getTimestamp));
}

public void processTransaction(Transaction transaction) {
    transactionQueue.add(transaction);
}

public void replayTransactions() {
    while (!transactionQueue.isEmpty()) {
        Transaction transaction = transactionQueue.poll();
        String advertiserId = transaction.getAdvertiserId();
        String status = transaction.getStatus();

        billingStatus.put(advertiserId, status);
    }
}

public String getBillingStatus(String advertiserId) {
    return billingStatus.get(advertiserId);
}

}

class Transaction { private String advertiserId; private long timestamp; private String status;

// Constructor, getters, and setters

} `

This solution provides a scalable and efficient way to replay billing transactions and restore the billing status for advertisers.