Asked in: IBM
Image of the Question
All Testcases Passed ✔
Solution
#!/bin/python3
import math
import os
// ... rest of solution available after purchaseExplanation
```
To solve the getQueryAnswers problem, start by carefully analyzing the requirements and the structure of the input data. You are given a list of cached prediction entries, each entry containing three fields: timestamp, modelId, and predictionValue (as a string that represents an integer). Then, you receive multiple queries where each query specifies a modelId and a timestamp, and your task is to find the predictionValue that matches exactly the modelId and timestamp for each query.
The key challenge is to efficiently locate the predictionValue for each query. Since there can be many cache entries and many queries, a naive approach that scans through the entire cache for each query would be very inefficient, resulting in a time complexity of O(n * q), which could be prohibitively slow for large inputs.
Instead, think about organizing the cache data in a way that allows for fast lookups by modelId and timestamp. One natural approach is to use a nested mapping (dictionary or hash map) structure, where the outer key is the modelId, and the inner key is the timestamp. This would allow you to store prediction values indexed first by modelId, then by timestamp. Using this approach, queries can be answered in near constant time by directly accessing the relevant prediction value in the nested map.
Step-by-step, your approach should be:
1. Preprocess the cache entries to build the nested map. Iterate over each cache entry:
- Extract modelId, timestamp, and predictionValue.
- Convert predictionValue from string to integer since the output expects integers.
- Insert predictionValue into the nested map using modelId as the first key and timestamp as the second key.
This preprocessing step will cost O(n), where n is the number of cache entries.
2. For each query:
- Extract the modelId and timestamp.
- Access the nested map using these keys.
- If an exact match is found, retrieve the predictionValue.
- If no match exists for the given modelId and timestamp, decide how to handle it (the problem statement doesn't explicitly specify this, but typically, returning some default value or an indication of "not found" might be appropriate).
3. Collect the results for all queries into a result list and return it.
This structure ensures that each query is answered in O(1) time after preprocessing, making the overall time complexity O(n + q), which is efficient for large datasets.
Additional considerations:
- Data validation: Ensure that the input strings for predictionValue can be safely converted to integers.
- Memory usage: The nested map might consume extra memory, but given that this is the only efficient way to achieve fast lookups, it is a worthwhile tradeoff.
- If the dataset is extremely large and memory is a concern, consider alternative approaches like sorting the cache entries by modelId and timestamp and using binary search to answer queries. However, this would increase the query time to O(log n), which might be acceptable depending on constraints.
In summary, the problem boils down to efficient data indexing and retrieval. Preprocessing cacheEntries into a dictionary keyed by modelId and timestamp enables constant-time query responses, satisfying both correctness and performance requirements.
```