Asana·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

Asana system design round, one meaty question about large-scale file processing. The kind of problem that sounds straightforward until you're twenty minutes in and realizing your first answer doesn't actually work.

Questions Asked (1)

You have a file of IP addresses, one per line, that's too large to fit in memory. How do you find the most frequent IP address(es)?

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the hashmap answer because of course I did.

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AI HintsAI Generated

Suggested Approach

Start by clarifying constraints (e.g., memory limit, file size, whether exact counts are required) and then propose an external sorting or hash-based partitioning approach. Explain how to process the file in chunks, aggregate counts, and merge results to find the most frequent IP(s).

Pro tip: Mention that you can use a two-pass approach: first partition the IPs into buckets by hash, then process each bucket in memory to find local maxima, and finally combine. This shows you understand memory-efficient algorithms and trade-offs.

1. Clarify requirements and constraints

Ask about memory limits, file size, whether exact or approximate counts are acceptable, and if multiple IPs can tie for most frequent.

2. Choose a strategy: external sort or hash partitioning

Decide between sorting the file externally (e.g., merge sort) or partitioning IPs into smaller files by hash, each of which fits in memory.

3. Process each partition to count frequencies

For each partition, load it into memory and use a hash map to count occurrences of each IP, tracking the maximum count and corresponding IP(s).

4. Merge results to find global maximum

Combine the local maxima from each partition to determine the overall most frequent IP(s), handling ties appropriately.

5. Discuss trade-offs and optimizations

Talk about time/space complexity, I/O overhead, and possible optimizations like using a min-heap for top-k or approximate algorithms if exactness isn't required.

Key Points to Mention

External sorting (e.g., merge sort) as an alternative approach
Hash partitioning to split the file into manageable chunks
Using a hash map (dictionary) to count frequencies in memory
Handling ties for most frequent IP(s)
Time and space complexity analysis (e.g., O(N) time, O(1) extra space if using partitioning)
Trade-offs between exact and approximate solutions (e.g., Count-Min Sketch)

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