Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon SWE coding round, one question the whole time. Pretty standard algorithmic problem but they wanted complexity analysis too, which I almost forgot to walk through properly.

Questions Asked (1)

Given an ASCII string, reorder its characters by descending frequency. For ties in frequency, sort those characters in ascending lexicographic order. Return the resulting string, and analyze the time and space complexity of your solution.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew the sorting logic pretty quickly, frequency map then sort, but the tie-breaking part tripped me up for a minute.

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AI HintsAI Generated

Suggested Approach

Clarify assumptions (e.g., ASCII, case sensitivity) and propose a solution using a frequency map and sorting. Explain the sorting criteria: descending frequency, then ascending lexicographic order. Then analyze time and space complexity, discussing trade-offs.

Pro tip: Mention that for a fixed ASCII alphabet, you can achieve O(n) time using counting sort, but for general characters, comparison sort is O(n log n). This shows awareness of constraints and optimization.

1. Clarify requirements

Confirm the character set (e.g., ASCII), case sensitivity, and whether the input can be empty. This ensures the solution meets the expected constraints.

2. Count frequencies

Iterate through the string and build a frequency map (e.g., using a dictionary or array of size 128 for ASCII). This takes O(n) time and O(1) space for fixed alphabet.

3. Sort characters

Extract unique characters and sort them by descending frequency, then ascending lexicographic order. Use a custom comparator or sort by a tuple (-frequency, character).

4. Build result

Construct the output string by repeating each character according to its frequency in the sorted order. This takes O(n) time.

5. Analyze complexity

State time complexity: O(n + k log k) where k is the number of unique characters (≤ n). Space complexity: O(k) for the frequency map and output. Discuss trade-offs and potential optimizations.

Key Points to Mention

Use of a frequency map (hash map or array) to count occurrences.
Sorting with a custom comparator: primary key descending frequency, secondary key ascending character.
Time complexity: O(n + k log k) where k is the number of unique characters; if k is small (e.g., ASCII), it's effectively O(n).
Space complexity: O(k) for the frequency map and O(n) for the output string.
Edge cases: empty string, all characters same frequency, non-ASCII characters.
Potential optimization: counting sort for fixed alphabet to achieve O(n) time.

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