Dropped Requests: Find Time Windows with Unacceptably High Request Drop Rates

Question

## Problem

You have a log of HTTP requests, each with a timestamp (seconds) and a status: `"success"` or `"dropped"`. Find all contiguous time windows of length `W` seconds where the drop rate exceeds threshold `T` (0.0 to 1.0).

Return the start times (inclusive) of all such windows.

```python
from typing import List, Tuple

def high_drop_windows(
    requests: List[Tuple[int, str]],
    W: int,
    T: float
) -> List[int]:
    # requests: [(timestamp, status), ...] sorted by timestamp
    # W: window size in seconds
    # T: drop rate threshold (exclusive)
    # return: sorted list of window start times
    pass
```

**Example:**
```
requests = [(0,"dropped"),(1,"dropped"),(2,"success"),(5,"dropped"),(6,"dropped")]
W=3, T=0.5

Window [0,3): 2 dropped / 3 total = 0.67 > 0.5 -> include start=0
Window [4,7): 2 dropped / 2 total = 1.0  > 0.5 -> include start=4

Output: [0, 4]
```

## Follow-ups

1. What if requests are NOT sorted by timestamp — what preprocessing is needed?
2. How would you adapt this to a live streaming data source using a sliding window?
3. How do you handle windows that contain zero requests (divide-by-zero case)?
4. In an alerting system, how would you avoid duplicate alerts for overlapping windows?

Dropped Requests: Find Time Windows with Unacceptably High Request Drop Rates

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