Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon SWE interview with a coding question that looked straightforward until you actually thought about it. The problem had a lot of moving parts and I don't think I handled all of them cleanly in the time I had.

Questions Asked (1)

Given a large, unordered log of web access events where each entry is a (user_id, ISO 8601 timestamp) pair, identify which users are returning visitors, meaning they appear on at least two distinct calendar days. Walk through how you'd handle timezone normalization, memory constraints for large logs, algorithmic complexity, and edge cases like events near midnight or duplicate entries.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with the obvious approach: collect all dates per user into a set and check if the set size is >= 2.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a streaming algorithm that normalizes timestamps to a consistent timezone (e.g., UTC) and uses a hash map to track the set of distinct days per user. Discuss memory optimizations like storing only the first two distinct days per user and using approximate data structures if exactness can be relaxed, and analyze time and space complexity.

Pro tip: Mention that you would confirm the definition of 'calendar day' with the interviewer—whether it's based on UTC, a specific timezone, or user-local time—and propose a configurable timezone parameter to handle ambiguity.

1. Clarify requirements and constraints

Ask about timezone expectations, log size, memory limits, and whether approximate results are acceptable. Confirm the definition of 'distinct calendar days' and how to handle duplicates.

2. Design the algorithm

Propose a single-pass streaming approach: parse each event, normalize timestamp to the target timezone, extract the date, and update a per-user set of distinct dates. Stop tracking after two distinct dates to save memory.

3. Address memory constraints

For large logs, use a hash map with user IDs as keys and a small set (max size 2) of dates as values. If memory is still an issue, consider external sorting or approximate counting with Bloom filters, noting trade-offs.

4. Analyze complexity and edge cases

State time complexity O(N) for N events and space O(U) for U users. Discuss edge cases: events near midnight (timezone conversion), duplicate entries (deduplicate by user-date), and malformed timestamps.

5. Summarize and offer optimizations

Recap the solution, mention potential optimizations like parallel processing or using a database for very large logs, and reiterate trade-offs between exactness and resource usage.

Key Points to Mention

Timezone normalization: convert all timestamps to a consistent timezone (e.g., UTC) before extracting the date.
Memory efficiency: store only up to two distinct dates per user; use a hash map with user IDs as keys.
Algorithmic complexity: O(N) time and O(U) space, where N is number of events and U is number of unique users.
Edge cases: events near midnight may fall on different days after timezone conversion; duplicates should be ignored.
Scalability: for logs too large for memory, consider external sorting, streaming with disk-based hash maps, or approximate methods like Bloom filters.
Trade-offs: exact vs. approximate results, timezone configurability, and handling of malformed data.

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