Netflix·Software Engineer·Technical Phone Screen·Intermediate

IntermediateNo response

May 2026Remote

Summary

Netflix SWE coding round, one design-heavy implementation problem that went pretty smoothly on my end. Wrote test cases after, all passed, then just... nothing. Classic ghost.

Questions Asked (1)

Implement a multithreaded latency tracker with two methods: setLatency(long timestamp, double latency) to record a data point, and getP99Latency(long windowSize) to return the 99th percentile latency within a sliding time window.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The core challenge isn't the percentile math, it's making both methods thread-safe without killing performance.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: expected throughput, latency distribution, window size, and precision. Then propose a design using a ring buffer of time buckets, each with a thread-safe histogram (e.g., HdrHistogram) for efficient percentile calculation. Discuss trade-offs between accuracy, memory, and performance, and how to handle concurrency with minimal contention.

Pro tip: Emphasize that Netflix cares about real-time performance at scale, so mention using lock-free data structures or fine-grained locking to avoid bottlenecks, and consider using a histogram with configurable precision to balance accuracy and memory.

1. Clarify Requirements

Ask about expected write throughput, read frequency, window size granularity, and acceptable error margin for percentile. This shows you think about practical constraints.

2. Choose Data Structures

Propose a ring buffer of time buckets (e.g., 1-second buckets) covering the window. Each bucket contains a histogram (e.g., HdrHistogram) to store latencies. This allows efficient sliding window and percentile calculation.

3. Handle Concurrency

Use thread-local histograms or striped locks to reduce contention on writes. For reads, aggregate histograms from buckets within the window, possibly using a read-write lock or snapshot to ensure consistency.

4. Implement Percentile Calculation

For getP99Latency, merge histograms from relevant buckets and compute the 99th percentile. Discuss using a histogram's percentile method or maintaining a sorted structure if exact percentile is needed.

5. Discuss Trade-offs and Optimizations

Compare exact vs approximate percentiles, memory usage, and latency. Mention potential optimizations like pre-aggregation, sampling, or using a t-digest for streaming percentiles.

Key Points to Mention

Sliding window implementation using a ring buffer of time buckets
Thread-safety mechanisms: lock-free, striped locks, or thread-local storage
Histogram data structure (e.g., HdrHistogram) for efficient percentile estimation
Trade-offs between accuracy, memory, and performance
Handling out-of-order timestamps and clock skew
Scalability considerations for high-throughput systems

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