Temperature Stream Analyzer

Data Structures Algorithm Stream

You are given a stream of temperature readings collected during a live event (for example, a summer festival). Each reading is a pair:

  • timestamp: an integer representing the time in seconds (assume the readings are recorded in non-decreasing order of timestamp).
  • temperature: a floating-point number representing the temperature at that timestamp.

Your task is to design and implement a class, for example, called TemperatureMonitor, that processes these readings and efficiently supports the following two operations:

  1. record(timestamp, temperature): Record a temperature reading at the given timestamp.

  2. query(timestamp): Return the maximum temperature recorded in the most recent window of W seconds (i.e., considering readings in the interval [timestamp - W + 1, timestamp]). If there are no readings in that window, return a value (for example, None or an equivalent) indicating that the query could not be satisfied.

Requirements:

  • The class should be initialized with the window size W (in seconds).
  • Both operations should be optimized for scenarios where there can be a large number of readings (think of real-time streaming data).
  • Consider space optimization so that old readings outside the window need not be stored, yet the query operation remains fast.

Example usage:

monitor = TemperatureMonitor(W=300)  # 300-second window

monitor.record(100, 22.5)
monitor.record(150, 23.0)
monitor.record(400, 21.0)

# query at timestamp 450 should only consider readings in the interval [151, 450]
result = monitor.query(450)
print(result)  # Expected output would be the maximum temperature in that window

Implement the TemperatureMonitor class in the programming language of your choice. Make sure your implementation can handle a continuous stream of data and frequent queries efficiently.