LinkedIn·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

LinkedIn Data Scientist interview that was basically one meaty coding problem about reservoir sampling. The kind of question where you either know the algorithm or you're fumbling through probability logic on the spot.

Questions Asked (1)

You have an unbounded stream of items that can't all fit in memory. Write Python code to maintain a uniform random sample of size k from the stream, where the total number of items is unknown in advance. Explain the algorithm, its time and space complexity, and any edge cases.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is reservoir sampling and I'd seen it before, which saved me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and then describe reservoir sampling as the optimal solution. Walk through the algorithm step-by-step, provide Python code, and analyze time and space complexity. Finally, discuss edge cases and potential optimizations.

Pro tip: Mention that reservoir sampling is a classic algorithm used in systems like LinkedIn's feed sampling, and highlight that it guarantees uniformity without knowing the stream length. Also, note that for weighted streams, a variant exists, showing depth.

1. Clarify Requirements

Confirm that the stream is unbounded, items arrive sequentially, and we need a uniform random sample of size k. Ensure that each item has an equal probability of being in the final sample.

2. Explain Reservoir Sampling

Describe the algorithm: initialize a reservoir of size k with the first k items. For each subsequent item (i-th item, i > k), generate a random integer j between 1 and i. If j <= k, replace the j-th item in the reservoir with the current item.

3. Provide Python Implementation

Write clean Python code using a list for the reservoir and random.randint for sampling. Include a generator-based stream simulation for testing.

4. Analyze Complexity

State that time complexity is O(n) for n items, as each item is processed once. Space complexity is O(k) for the reservoir. Emphasize that it's a single-pass algorithm.

5. Discuss Edge Cases and Extensions

Cover cases like k=0, k > stream length, and empty stream. Mention extensions like weighted reservoir sampling or distributed sampling if relevant.

Key Points to Mention

Uniformity proof: each item has probability k/n of being in the final sample.
Time complexity O(n) and space complexity O(k).
Single-pass algorithm suitable for streaming data.
Edge cases: k=0, k > n, empty stream, and handling of random number generation.
Python implementation details: using random.randint and list operations.
Potential optimizations: using random.random() for efficiency, or skipping items when k is large.

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