LinkedIn·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

LinkedIn SWE coding round with two parts: debugging a broken priority queue and then building a thread-safe concurrent queue for a web crawler scenario. The AI assistant was there but you were expected to talk through your thinking rather than just copy whatever it suggested, which honestly changes the dynamic a lot.

Questions Asked (2)

You're given a buggy priority queue implementation. Find and fix all the bugs, including heap property violations, comparator misuse, off-by-one errors in sift-up and sift-down, and edge cases for empty or single-element heaps.

Algorithms & Data StructuresRoot Cause Analysis

Author's notes

The off-by-one in sift-down got me.

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AI HintsAI Generated

Suggested Approach

First, clarify the expected heap type (min-heap or max-heap) and the comparator semantics, then systematically trace through the code to identify violations of the heap property, off-by-one errors in index calculations, and edge cases. Fix bugs one by one, testing after each fix with small examples to ensure correctness.

Pro tip: Write a few test cases (empty heap, single element, two elements, duplicates) and mentally run the code to catch off-by-one and comparator issues quickly. Also, verify that the comparator is used consistently in both sift-up and sift-down.

1. Understand the expected behavior

Confirm whether it's a min-heap or max-heap and how the comparator defines priority. Check the class interface and any documentation.

2. Inspect heap property and comparator usage

Examine sift-up and sift-down functions to ensure they compare parent and child correctly according to the comparator. Look for reversed comparisons or incorrect index calculations.

3. Check index arithmetic and boundaries

Verify parent/child index formulas (e.g., parent = (i-1)/2, left = 2*i+1, right = 2*i+2) and ensure loops terminate correctly without accessing out-of-bounds indices.

4. Handle edge cases

Test empty heap, single element, and operations that might cause underflow/overflow. Ensure insert and extract handle these gracefully.

5. Validate with test cases

Run through small examples manually or with code to confirm fixes. Check that heap property holds after each operation.

Key Points to Mention

Comparator consistency: ensure the same comparison logic is used in both sift-up and sift-down.
Off-by-one errors: common mistakes include using <= instead of < in loop conditions or incorrect child index calculations.
Heap property: parent must be less than or equal to children (min-heap) or greater than or equal to children (max-heap).
Edge cases: empty heap, single element, duplicate priorities, and resizing if dynamic array is used.
Time complexity: sift-up and sift-down should be O(log n), and operations should maintain this.
Testing: use unit tests with small inputs to catch subtle bugs.

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

Implement a thread-safe concurrent queue to back a web crawler, supporting multiple producer threads enqueuing URLs and multiple consumer threads dequeuing them, with proper blocking behavior, graceful shutdown, and no busy-waiting.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This one is deceptively large.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a design using a thread-safe queue with condition variables for blocking and graceful shutdown. Discuss trade-offs between different synchronization primitives and data structures, and outline how to handle edge cases like spurious wakeups and shutdown signaling.

Pro tip: Mention that you would use a condition variable with a predicate loop to avoid spurious wakeups, and that shutdown should be signaled via a flag and notify_all to wake all waiting threads.

1. Clarify Requirements and Constraints

Ask about expected throughput, latency, queue bounds, shutdown semantics, and whether fairness or priority is needed. This shows you think about the problem context before diving into implementation.

2. Choose Synchronization Primitives

Decide between mutex+condition variables, semaphores, or lock-free structures. For most cases, a mutex-protected std::deque with two condition variables (not_empty, not_full) is simple and efficient.

3. Design Blocking and Shutdown Behavior

Implement blocking enqueue when full and dequeue when empty using condition variables. For shutdown, use an atomic flag and notify_all to wake all threads, ensuring they exit gracefully.

4. Handle Edge Cases and Correctness

Address spurious wakeups with while loops around wait, ensure no busy-waiting, and consider exception safety. Also discuss how to handle multiple producers/consumers without deadlock.

5. Discuss Trade-offs and Alternatives

Compare mutex-based vs lock-free queues, bounded vs unbounded, and the impact on performance and complexity. Mention that lock-free is harder to get right but can reduce contention.

Key Points to Mention

Use of condition variables with predicate loops to avoid spurious wakeups and busy-waiting.
Graceful shutdown via an atomic flag and notify_all to wake all blocked threads.
Bounded queue with blocking on full/empty to provide backpressure and prevent memory exhaustion.
Thread safety through mutex protection of shared state and careful lock granularity.
Trade-offs between mutex-based and lock-free implementations, including performance and complexity.
Consideration of fairness and potential starvation, and how to mitigate (e.g., FIFO ordering).

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