Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon SWE coding screen, one problem about computing message latency by joining two CSV log files on a shared message ID. Pretty straightforward on the surface but the efficiency constraint made it more interesting than I expected.

Questions Asked (1)

Given two CSV log files, one recording when messages were sent and one recording when they were received, compute the latency for each message that appears in both files. Each row has a message type, a unique integer ID, and a timestamp. Output the message type, ID, and the difference in timestamps, sorted by ID. Aim for near-linear or O(n log n) runtime.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was a nested loop and I almost said it out loud before catching myself.

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AI HintsAI Generated

Suggested Approach

First clarify the problem: we need to join two CSV files on message ID and compute timestamp differences. Propose using a hash map to store sent messages keyed by ID, then iterate through received messages to compute latency, achieving O(n) average time. Discuss trade-offs like memory usage and handling of missing or duplicate IDs.

Pro tip: Mention that you would validate the input files for consistency (e.g., same message type for a given ID) and consider using a streaming approach if files are too large to fit in memory, showing awareness of real-world constraints.

1. Clarify requirements and assumptions

Ask about file sizes, whether IDs are unique per file, and if timestamps are in the same format/timezone. Confirm output format and sorting requirement.

2. Choose data structures and algorithm

Use a hash map to store sent messages (ID -> timestamp) for O(1) lookups. Iterate through received messages, compute latency for matching IDs, and collect results.

3. Handle edge cases and errors

Discuss what to do if an ID appears in only one file, if there are duplicate IDs, or if timestamps are out of order. Decide whether to skip, log, or error.

4. Analyze complexity and trade-offs

Explain that time complexity is O(n) average with hashing, and space is O(n) for the map. Mention alternative approaches like sorting both files and merging (O(n log n)) and when they might be preferable.

5. Outline implementation and testing

Sketch code: read CSV, build map, compute differences, sort by ID, write output. Mention unit tests for edge cases and performance testing with large files.

Key Points to Mention

Hash map for O(1) lookups to achieve near-linear time
Sorting results by ID as required, using O(n log n) sort
Handling missing or duplicate IDs gracefully
Memory considerations and potential streaming approach for large files
Timestamp parsing and timezone consistency
Trade-offs between hash map and sort-merge approaches

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