Notes

In the context of developing a real-time analytics dashboard for an e-commerce platform, the challenge is to implement a Python-based solution that efficiently computes the maximum rectangular area in a histogram representing sales data over time. The histogram is dynamically updated with incoming sales data, necessitating an algorithm that can adapt in real-time to ensure optimal performance and accuracy in visualizing peak sales periods. The solution must leverage a monotonic stack algorithm to maintain computational efficiency as the dataset scales.

### Objectives:

1. **Real-Time Data Handling:** Implement a system that can process a continuous stream of sales data, updating the histogram representation without significant latency.

2. **Monotonic Stack Implementation:** Utilize a monotonic stack to efficiently compute the maximum rectangular area within the histogram. The solution should be capable of handling dynamic changes in the histogram as new data points are added.

3. **Scalability and Performance:** Ensure that the algorithm maintains performance efficiency as the volume of sales data increases, with a focus on minimizing computational overhead.

4. **Accuracy and Visualization:** The system must accurately reflect peak sales periods in the histogram visualization, providing real-time insights into sales trends.

### Constraints:

- The histogram must be updated in real-time as new sales data is received, with a maximum latency of 100 milliseconds per update.
- The algorithm should handle up to 1 million data points efficiently, maintaining a time complexity of O(n) for each update.
- The solution must be implemented in Python, using only standard libraries.
- The system should be capable of running on a single server with limited computational resources (e.g., 2 CPU cores, 4 GB RAM).

### Function Signature:
```python
def compute_max_histogram_area(sales_data_stream: Iterable[int]) -> List[int]:
pass
```

### Sample Input/Output:

#### Input:
- A stream of integers representing sales data over time: [2, 1, 5, 6, 2, 3]

#### Expected Output:
- A list of integers representing the maximum rectangular area for each update: [2, 2, 5, 10, 10, 10]

### Expected Output Format:
The function should return a list of integers, where each integer represents the maximum rectangular area computed for the histogram at each point in time as new data is received.