Amazon·Software Engineer·Online Assessment (OA)·Intermediate

IntermediatePrefer not to say

Jul 2026Remote

Summary

Amazon SWE coding round, one question the whole time about log processing. Pretty standard heap problem but they pushed on the parallelization angle which I wasn't fully ready for.

Questions Asked (1)

Given a log file with page names and their click counts, find the top K pages by click count. How would you approach this efficiently, and how would you handle a very large log file?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

Got the min-heap solution down fine, K-size heap, iterate through logs, swap when you find something bigger.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., log size, memory limits, K value) and then propose a two-phase approach: first aggregate click counts using a hash map, then use a min-heap of size K to find the top K pages. For very large logs, discuss distributed processing (e.g., MapReduce) or external sorting, and consider memory-efficient techniques like streaming and partitioning.

Pro tip: Demonstrate awareness of real-world constraints by mentioning that in practice, you'd leverage existing tools like Hadoop or Spark for large-scale log processing, and always validate assumptions about data distribution and skew.

1. Clarify Requirements and Constraints

Ask about log size, memory availability, K value, and whether the log fits in memory. This shows you consider practical limitations before diving into solutions.

2. Design In-Memory Solution

For moderate logs, use a hash map to count clicks per page, then a min-heap of size K to efficiently extract top K pages in O(N log K) time.

3. Scale to Large Logs

If the log is too large for memory, propose partitioning the log by page name (e.g., using hashing) and processing each partition separately, or use a distributed framework like MapReduce.

4. Handle Data Skew and Optimizations

Discuss handling hot pages that may cause skew in distributed processing, and consider approximate algorithms (e.g., Count-Min Sketch) if exact counts are not required.

5. Summarize Trade-offs

Compare approaches in terms of time/space complexity, scalability, and accuracy, and recommend a solution based on the constraints.

Key Points to Mention

Time and space complexity of hash map + heap approach (O(N log K) time, O(N) space for counts).
Use of a min-heap to maintain top K elements efficiently.
Distributed processing with MapReduce: map phase emits (page, 1), reduce phase aggregates counts, then a second pass for top K.
Partitioning strategies to handle logs larger than memory (e.g., hash partitioning by page name).
Handling data skew (e.g., a single page with millions of clicks) via combiners or two-level aggregation.
Approximate algorithms like Count-Min Sketch for memory-efficient counting when exactness is not critical.

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