Pretty much a hash map problem once you see it clearly.
Use a hash map to accumulate net balances: for each transaction, subtract the amount from the sender's balance and add it to the receiver's. After processing all transactions, filter the map to include only users with non-zero balances. This approach is O(n) time and O(u) space, where n is the number of transactions and u is the number of unique users.
Pro tip: Clarify edge cases upfront, such as self-transactions (sender equals receiver), zero-amount transactions, and whether balances should be integers or floating-point. Mentioning these shows attention to detail and prevents incorrect assumptions.
Ask about input format, data types (e.g., integer vs. decimal amounts), and edge cases like self-transactions, zero amounts, and duplicate users. Confirm that only non-zero balances should be output.
Select a hash map (dictionary) to store each user's net balance, allowing O(1) average-time updates. Consider whether to use a language-specific structure like HashMap in Java or dict in Python.
Iterate through the list of transactions. For each, subtract the amount from the sender's balance and add it to the receiver's balance, creating entries if they don't exist. Handle self-transactions by skipping or netting to zero.
After processing, iterate through the hash map and collect users whose balance is not zero. Return the result in the required format (e.g., a map or list of tuples).
State the time complexity O(n) and space complexity O(u). Walk through a small example to verify correctness, including edge cases like self-transactions and zero amounts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.