Query Unique and Most-Frequent Keys in a Recent Event Window

Question

This question asks the candidate to devise a solution for querying unique and most-frequent keys from a dataset within a specified recent event window.

Codemia · Accepted Answer

To solve the problem of querying unique and most-frequent keys in a recent event window, we need to efficiently process a stream of events and maintain a count of occurrences for each key. The recent event window can be defined by a specific time frame, and our goal is to extract both unique keys and the keys that appear most frequently within that frame.

We can utilize a sliding window technique combined with a hash map to keep track of the frequency of each key. As we iterate through the events, we will maintain a count of occurrences for each key within the current window. When the window shifts, we will update our counts accordingly. This allows us to efficiently query the unique keys and determine the most frequent ones.

Here is a Python implementation of the solution:

```python
from collections import defaultdict, Counter

def query_unique_and_frequent_keys(events, window_size):
    frequency_map = defaultdict(int)
    result = []
    for i in range(len(events)):
        frequency_map[events[i]] += 1
        # Remove keys that are out of the window
        if i >= window_size:
            frequency_map[events[i - window_size]] -= 1
            if frequency_map[events[i - window_size]] == 0:
                del frequency_map[events[i - window_size]]
        # Collect unique keys and most frequent keys
        unique_keys = list(frequency_map.keys())
        most_frequent_keys = Counter(frequency_map).most_common(1)
        result.append((unique_keys, most_frequent_keys))
    return result
```
This code maintains a frequency map of keys and updates it as the window shifts.

The time complexity of this solution is O(n) where n is the number of events processed, as each event is processed once. The space complexity is O(k) where k is the number of unique keys in the current window, which is manageable given that we only store keys present in the window.

One potential pitfall is not handling the case where the event window size exceeds the number of events. Additionally, we should ensure that our implementation correctly handles cases where keys may have a frequency of zero after removal from the window. Properly managing these edge cases will ensure the robustness of our solution.

Query Unique and Most-Frequent Keys in a Recent Event Window

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