Vanta·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Vanta software engineering interview with a pretty meaty coding problem about a streaming test logger. The whole thing was one question with three parts, and the complexity ramps up fast by part three.

Questions Asked (1)

Design a streaming logger for test runs with globally strictly increasing timestamps. Implement three operations: log(test_id, timestamp, status) to record a test result, get_min_fix_time(test_id) to return the shortest duration from the start of a failing streak to the next passing status for a given test (or null if no such transition exists), and get_max_concurrent_failure_period(min_tests) to return the longest contiguous time window during which at least min_tests distinct tests are simultaneously failing (or null if none). Describe your data structures, algorithms, and time/space complexities.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

Part one was fine, just storing logs per test.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and assumptions, then propose a design that leverages the globally increasing timestamps to maintain ordered data structures. For each operation, describe the data structures (e.g., balanced BSTs, heaps, interval trees) and algorithms, and analyze time/space complexities. Finally, discuss trade-offs and potential optimizations.

Pro tip: Emphasize how the strictly increasing timestamps simplify the problem: you can process events in order and avoid sorting, which is a key insight that interviewers look for. Also, proactively mention edge cases like multiple failures without a pass, or overlapping failure periods.

1. Clarify requirements and assumptions

Ask questions to confirm the meaning of 'failing streak', 'simultaneously failing', and whether timestamps are unique and strictly increasing. Clarify if test_id and status are from a known set, and if queries are online or offline.

2. Design data structures for log and get_min_fix_time

Propose storing per-test event lists (e.g., in a balanced BST or sorted array) to track status changes. For get_min_fix_time, maintain the minimum duration of a fail-to-pass transition, updating it on each log.

3. Design data structure for get_max_concurrent_failure_period

Use a sweep-line approach with a segment tree or interval tree to track the number of distinct failing tests over time. Maintain the longest interval where the count >= min_tests.

4. Analyze time and space complexities

For each operation, state the complexity: log O(log n) or O(1) amortized, get_min_fix_time O(1) or O(log n), get_max_concurrent_failure_period O(log n) or O(k) where k is number of events. Space O(n).

5. Discuss trade-offs and optimizations

Compare different approaches (e.g., maintaining a global timeline vs. per-test structures). Mention how to handle updates efficiently and potential concurrency issues if the logger is streaming.

Key Points to Mention

Exploit globally increasing timestamps to process events in order without sorting.
Use per-test event logs to efficiently compute fail-to-pass durations.
For concurrent failures, maintain a sweep-line over time with a segment tree or Fenwick tree to track counts of failing tests.
Update the minimum fix time incrementally when a test transitions from fail to pass.
For max concurrent failure period, maintain the longest interval where count >= min_tests, updating on each status change.
Consider edge cases: no failing tests, no passing after failing, multiple tests failing simultaneously, and min_tests=0.

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