Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Meta interview centered around a consecutive logins problem. Not much context to go on but it reads like a coding screen.

Questions Asked (1)

Given a dataset of user login timestamps, find users who have logged in on consecutive days.

Algorithms & Data StructuresProduct Analytics & Metrics

Author's notes

Classic Meta-style SQL or coding problem depending on how they frame it.

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AI HintsAI Generated

Suggested Approach

Clarify the data schema and definition of 'consecutive days' (calendar days vs. 24-hour periods). Then propose an efficient algorithm: sort timestamps per user, deduplicate to unique dates, and check for adjacent dates differing by exactly one day. Discuss time/space complexity and potential optimizations for large-scale data.

Pro tip: Mention handling edge cases like time zones, duplicate logins, and users with sparse data. Also, discuss how to scale the solution using distributed processing (e.g., MapReduce) if the dataset is massive, which is relevant at Meta.

1. Clarify requirements and assumptions

Ask about the data format, definition of consecutive days (calendar vs. 24-hour), time zone handling, and expected scale. Confirm whether the output should be a list of user IDs or counts.

2. Outline a baseline approach

Propose a straightforward method: group timestamps by user, sort each group, extract unique dates, and check for consecutive dates. Mention time complexity O(N log N) due to sorting.

3. Optimize for efficiency

Suggest using a hash set per user to deduplicate dates, then sort the unique dates. Alternatively, use a sliding window or date arithmetic to avoid sorting if dates are bounded.

4. Handle edge cases and scale

Discuss handling time zones, duplicate logins, users with only one login, and large datasets. Mention distributed approaches like MapReduce or Spark if needed.

5. Analyze complexity and trade-offs

Compare time and space complexity of different approaches. Discuss trade-offs between sorting and hashing, and between single-machine and distributed solutions.

Key Points to Mention

Data preprocessing: converting timestamps to dates and handling time zones.
Deduplication of login events per user per day.
Sorting and checking adjacent dates for a difference of exactly one day.
Time complexity: O(N log N) for sorting, O(N) for hashing and checking.
Space complexity: O(N) for storing unique dates per user.
Scalability: using MapReduce or Spark for large datasets.

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