Problem Description

Scenario:
Stripe processes payments for merchants, starting with Authorization Requests, which represent the intent to transfer funds from an end user’s credit card to a merchant. The system processes these requests and determines the Outcome. Currently, the system’s policy is to automatically APPROVE all requests.

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Input and Output Details

Input Format:

Authorization requests are received in the following format:

timestamp_seconds,unique_id,amount,card_number,merchant

Field Descriptions:

• timestamp_seconds: The timestamp of the request in seconds (positive integer).
• unique_id: A unique identifier for the request.
• amount: The authorized amount (decimal value greater than zero).
• card_number: The user’s credit card number.
• merchant: The name of the merchant.

Input Example:

5,R1,5.60,4242424242424242,bobs_burgers  
10,R2,500.00,4242111111111111,a_corp

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Output Format:

The output is a human-readable report with the following fields:

timestamp unique_id amount outcome

• timestamp: The time the request was received.
• unique_id: The unique identifier of the request.
• amount: The authorized amount.
• outcome: The result of the request, which in this case is always APPROVE.

Output Example:

5 R1 5.60 APPROVE  
10 R2 500.00 APPROVE

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Solution Breakdown

1. Input Parsing

Objective:
Parse the input data and extract relevant fields into structured data.

Steps:

1. Split the input string into lines, where each line represents an authorization request.
2. Parse each line by splitting it using commas (',').
3. Map the extracted values to corresponding fields:
   • timestamp: Convert to an integer.
   • unique_id: Retain as a string.
   • amount: Convert to a floating-point number.
   • card_number: Retain as a string.
   • merchant: Retain as a string.
4. Store the parsed data as a list of dictionaries for ease of access.

Example Parsing:

Input Line: 5,R1,5.60,4242424242424242,bobs_burgers  
Parsed Output:  
{
    "timestamp": 5,
    "unique_id": "R1",
    "amount": 5.60,
    "card_number": "4242424242424242",
    "merchant": "bobs_burgers"
}

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2. Sorting the Data

Objective:
Ensure all requests are ordered chronologically by their timestamp.

Steps:

1. Use the sorted() function with the timestamp field as the sorting key.
2. Maintain stable sorting for requests with the same timestamp.

Example Sorted Data:

Before Sorting:
[
    {"timestamp": 10, "unique_id": "R2", "amount": 500.00, ...},
    {"timestamp": 5, "unique_id": "R1", "amount": 5.60, ...}
]

After Sorting:
[
    {"timestamp": 5, "unique_id": "R1", "amount": 5.60, ...},
    {"timestamp": 10, "unique_id": "R2", "amount": 500.00, ...}
]

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3. Formatting the Output

Objective:
Generate a human-readable report with the specified output format.

Steps:

1. For each record in the sorted data, create a formatted string using:"{timestamp} {unique_id} {amount:.2f} APPROVE"
2. Ensure amount is displayed with two decimal places using {:.2f}.
3. Append the formatted strings to a list for the final output.

5 R1 5.60 APPROVE
10 R2 500.00 APPROVE


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Complexity Analysis

Time Complexity:

1. Input Parsing: O(n), where n is the number of input lines.
2. Sorting: O(n log n), for sorting the parsed data by timestamp.
3. Formatting: O(n), for generating output strings.

Overall Time Complexity: O(n log n)

Space Complexity:

• The parsed data requires O(n) space.
• Additional space for storing formatted strings is also O(n).

Overall Space Complexity: O(n)

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Example Walkthrough

Input:

5,R1,5.60,4242424242424242,bobs_burgers  
10,R2,500.00,4242111111111111,a_corp

Steps:

1. Parsing the Input:[ {"timestamp": 5, "unique_id": "R1", "amount": 5.60, ...}, {"timestamp": 10, "unique_id": "R2", "amount": 500.00, ...} ]
2. Sorting by Timestamp:[ {"timestamp": 5, "unique_id": "R1", "amount": 5.60, ...}, {"timestamp": 10, "unique_id": "R2", "amount": 500.00, ...} ]
3. Formatting Output:5 R1 5.60 APPROVE 10 R2 500.00 APPROVE

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CSOAHelp’s Role in the Candidate’s Success

During the interview, CSOAHelp provided the following critical support to the candidate:

1. Step-by-Step Guidance:
   • Helped break down the problem into manageable components: parsing, sorting, and formatting.
   • Suggested optimal data structures like dictionaries and lists for efficient data handling.
2. Performance Optimization:
   • Recommended the use of in-built sorting functions for minimal complexity.
   • Advised on maintaining precision in floating-point operations.
3. Real-Time Debugging:
   • Provided on-the-spot feedback for edge cases like empty input or duplicate timestamps.

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Conclusion

This Stripe interview question evaluates a candidate’s ability to process structured data, perform efficient sorting, and generate formatted output. With CSOAHelp’s assistance, the candidate confidently tackled the problem, demonstrating strong problem-solving and coding skills.

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