I knew this problem family but fumbled when they asked me to walk through all three approaches back to back.
Start by clarifying the problem constraints: stream size, memory limits, and whether the stream is static or continuous. Then propose a two-phase approach: count occurrences using a hash map, then select the top K using a min-heap of size K for O(n log K) time. Discuss trade-offs between exact and approximate solutions (e.g., Count-Min Sketch) for large-scale streams.
Pro tip: Mention that for very large streams, a distributed approach like MapReduce can be used: map to (key, 1) pairs, reduce by key to sum counts, then select top K per partition and merge. This shows you think beyond a single machine.
Ask about stream size, memory limits, whether the stream is static or continuous, and if exact counts are required. This determines the appropriate algorithm and data structures.
Use a hash map to count occurrences of each key. For large streams that don't fit in memory, consider approximate counting techniques like Count-Min Sketch or distributed counting.
Use a min-heap of size K to efficiently find the K keys with highest counts. Iterate through the counts, maintaining the heap to keep the top K.
Discuss time and space complexity: O(n) for counting, O(n log K) for heap selection. Compare with alternatives like sorting all counts (O(n log n)) or using a max-heap (O(n + K log n)).
Address edge cases: K larger than unique keys, ties, and memory constraints. For scalability, mention distributed processing (MapReduce) or streaming algorithms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with external sort by key, linear scan to accumulate counts, then top-K on the result.
Start by clarifying the constraints (file size, memory limit, K, and whether exact or approximate counts are acceptable). Then propose an external memory approach: stream the file in chunks, count frequencies per chunk, and merge counts using a hash-based partition or a min-heap for top-K. Finally, discuss trade-offs between exact and approximate methods, and how to handle skew.
Pro tip: Mention that you would first check if the file can be processed in a streaming fashion with a hash map if the number of distinct keys is small enough to fit in memory; if not, use external sorting or partitioning. This shows you optimize for the common case before jumping to complex solutions.
Ask about memory limit, file size, number of distinct keys, K, and whether exact counts are required. This determines the feasible approach.
If distinct keys fit in memory, use a hash map to count frequencies in one pass, then a min-heap of size K to find top-K. Otherwise, use external memory techniques.
Partition the file into chunks that fit in memory, count frequencies per chunk, write intermediate counts to disk, then merge counts across chunks (e.g., using external sort or hash partitioning).
After merging, use a min-heap of size K to track the top-K frequencies, or if data is sorted by key, do a second pass to aggregate and maintain top-K.
Mention approximate algorithms (e.g., Count-Min Sketch, Lossy Counting) for memory-constrained scenarios, and how to handle skew (e.g., hot keys) with combiners or sampling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints (data size, distribution, latency, accuracy) and then outline a distributed architecture that partitions the data, computes local top-K on each machine, and merges results efficiently. Discuss trade-offs between exact and approximate solutions, and how to handle skewed data and fault tolerance.
Pro tip: Emphasize that the merge step is the bottleneck; using a hierarchical merge or a priority queue can reduce network overhead. Also, mention that for very large-scale systems, approximate algorithms like Count-Min Sketch with a heap are often preferred over exact counting.
Ask about data volume, velocity, distribution, required accuracy, latency, and whether the system is batch or streaming. This determines the choice of exact vs. approximate algorithms.
Decide how to partition the data across machines. Common approaches: hash partitioning by key to ensure all occurrences of a key go to the same machine, or range partitioning. Discuss the impact on load balancing and skew.
Each machine computes its local top-K frequencies using a hash map and a min-heap (or similar). This reduces the data that needs to be sent to the reducer.
Collect local top-K results from all machines and merge them to compute the global top-K. Use a priority queue or sort to efficiently find the top-K among the candidates. Consider hierarchical merging to reduce network traffic.
Address data skew (e.g., hot keys) by using techniques like salting or two-phase aggregation. Discuss fault tolerance (replication, checkpointing) and how the system scales with more machines.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.