Detect Recent Duplicate Records with Bounded Memory

Question

This question asks the candidate to devise an efficient algorithm to detect recent duplicate records while adhering to memory constraints.

Codemia · Accepted Answer

To detect recent duplicate records with bounded memory, we need to identify records that appear more than once within a specified time frame. The key constraints include limiting memory usage while ensuring that we accurately track the records. We will assume that each record has a timestamp associated with it.

A suitable approach involves using a sliding window technique combined with a hash map. The hash map will store the records and their timestamps, while the sliding window will help us maintain the recent records within the specified time frame. This allows us to efficiently check for duplicates without exceeding memory limits.

1. Initialize a hash map to store records with their timestamps.
2. As we process each new record, check if it exists in the hash map:
   - If it does, check if the timestamp is within the allowed time frame.
   - If it is, we have found a duplicate.
3. If it does not exist, add it to the hash map with the current timestamp.
4. Periodically remove records from the hash map that are outside the time frame to manage memory.

This approach has a time complexity of O(n) for n records, as each record is processed once. The space complexity is O(k), where k is the number of unique records within the time frame. This design is efficient for large datasets, but care must be taken to manage the size of the hash map to avoid memory overflow.

Consider scenarios such as:
- No records being processed: The algorithm should handle this gracefully without errors.
- All records being duplicates: The algorithm should correctly identify all duplicates.
- Records with the same timestamp: The solution should ensure that duplicates are identified based on the time frame rather than just the record content.

Detect Recent Duplicate Records with Bounded Memory

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