Start by defining a hash map as an array of buckets with a hash function mapping keys to indices. Then explain collision resolution (e.g., chaining or open addressing) and the load factor threshold that triggers resizing. Conclude with the trade-offs and why resizing is necessary for performance.
Pro tip: Mention that Apple's Swift Dictionary uses open addressing with linear probing and a load factor of 0.75, and that resizing doubles capacity to maintain O(1) average time complexity.
Describe a hash map as an array of buckets, where each bucket stores key-value pairs. Explain that a hash function converts keys into an integer index.
Detail how the hash code is computed (e.g., using hashCode() in Java or Hashable in Swift) and then compressed to fit the array size, typically via modulo or bitwise AND.
Discuss common strategies: separate chaining (linked lists or trees per bucket) and open addressing (linear/quadratic probing, double hashing). Mention that Java 8+ uses trees for long chains.
Define load factor (entries/buckets) and state that when it exceeds a threshold (e.g., 0.75), the map resizes (usually doubles) and rehashes all entries to maintain performance.
Highlight that resizing is O(n) but amortized O(1), and that collision resolution affects worst-case time (O(n) for chaining, O(n) for probing). Mention alternatives like perfect hashing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things got interesting and not in a good way for me.
Start by acknowledging the problem: resizing causes a latency spike due to O(n) rehashing. Then propose incremental resizing, where you maintain two tables and gradually migrate entries over subsequent operations, ensuring amortized O(1) performance. Discuss trade-offs like increased memory usage and complexity, and mention alternative strategies such as consistent hashing or chunked rehashing.
Pro tip: Emphasize that incremental resizing preserves amortized O(1) but may increase worst-case latency for individual operations; propose a hybrid approach that balances latency and throughput based on workload characteristics.
Explain that traditional resizing causes a latency spike because all entries are rehashed at once, blocking other operations.
Describe maintaining both old and new tables, and migrating a few entries per operation (e.g., on each insert or lookup) until the old table is empty.
Show that each operation does O(1) amortized work, so total time remains O(1) amortized, but individual operations may take slightly longer due to migration.
Mention increased memory usage (two tables) and added complexity in lookups (checking both tables). Compare with alternatives like consistent hashing or chunked rehashing.
Summarize when incremental resizing is appropriate (latency-sensitive systems) and note that it maintains expected constant-time performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.