Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon SWE coding round, one problem the whole session. CSV parsing with a hashmap join to compute message latency. Pretty focused and not too flashy, but the edge cases are where they really dig in.

Questions Asked (1)

Given two CSV log files (one for sent messages, one for received), compute and print the latency for every message that appears in both files. Latency is the received timestamp minus the sent timestamp. Messages that were sent but never received, or received but never sent, should be omitted from the output.

Algorithms & Data StructuresSystem Design

Author's notes

The core of the problem is just a hashmap join keyed on (message_type, id).

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AI HintsAI Generated

Suggested Approach

Clarify the CSV schema and assumptions (e.g., message ID, timestamps, file sizes), then propose a hash-based join: load the smaller file into a hash map keyed by message ID, stream the larger file to compute latencies for matches. Discuss time/space complexity and potential optimizations for large files.

Pro tip: Mention that you would validate timestamps (e.g., received >= sent) and handle clock skew or timezone issues, showing attention to real-world data quality. Also, suggest using a streaming approach with a database or external sort if the files are too large to fit in memory.

1. Clarify requirements and assumptions

Ask about CSV format (columns, headers), timestamp format, uniqueness of message IDs, and file sizes. Confirm that latency is computed only for messages present in both files.

2. Design the algorithm

Propose a hash join: read the smaller file into a dictionary mapping message ID to timestamp, then iterate over the larger file, look up each ID, and compute latency if found.

3. Analyze complexity and edge cases

State time complexity O(N+M) and space O(min(N,M)). Discuss edge cases: duplicate IDs, missing timestamps, invalid data, and large files that don't fit in memory.

4. Discuss scalability and optimizations

If files are huge, suggest external sorting, partitioning, or using a database. Mention parallel processing or streaming with a bounded memory footprint.

5. Outline implementation and testing

Sketch code structure (e.g., using Python's csv module), and describe how to test with sample data and verify correctness.

Key Points to Mention

Hash map (dictionary) for efficient lookup by message ID
Time complexity O(N+M) and space complexity O(min(N,M))
Handling large files: streaming, external sort, or database join
Data validation: timestamp format, timezone, clock skew, received >= sent
Edge cases: duplicate message IDs, missing fields, malformed CSV
Output format: print latency for each matching message, possibly sorted by message ID or timestamp

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.