← Databricks Interview Insights
Started with the basic HashMap approach which was fine, but the hit_count requirement tripped me up a little because I initially stored it separately and then had to reconcile that with eviction.
Start by clarifying requirements: expected operations, concurrency needs, and whether capacity/eviction is required. Then design a data structure combining a hash map for O(1) key-value access with a separate hit-count map, and if capacity is needed, integrate a doubly linked list for O(1) LRU eviction. Discuss trade-offs and edge cases before coding.
Pro tip: Mention that hit_count should only increment on successful gets, and consider thread-safety early—Databricks values scalable, concurrent systems, so bringing up locking or concurrent data structures shows production awareness.
Ask about expected load, concurrency, capacity limits, eviction policy, and whether hit counts need to be exact or approximate. Confirm that hit_count only increments on successful gets.
Use a hash map for O(1) key-value storage and a separate hash map for hit counts. If capacity is required, add a doubly linked list to track access order for O(1) LRU eviction.
Define put: insert/update and evict if over capacity. Define get: return value and increment hit count only if key exists. Define hit_count: return count without incrementing.
Discuss thread-safety using locks or concurrent structures. Handle edge cases: null keys/values, capacity zero, updating existing keys, and eviction of least recently used items.
State time and space complexity for each operation. Compare alternative eviction policies (LRU, LFU, FIFO) and explain why LRU is a common choice for caches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like the easy part after the design question.
Start by clarifying the requirements: we need a data structure that supports put, get, and hit_count in O(1) time. Then propose a combination of a hash map for key-value storage and a separate hash map or counter for hit counts, explaining how each operation achieves constant time. Finally, discuss potential trade-offs and edge cases.
Pro tip: Mention that while O(1) is achievable, it often comes with increased memory usage or complexity; showing awareness of this trade-off demonstrates maturity. Also, briefly note that in real systems, factors like concurrency and persistence might affect the choice.
Confirm that put, get, and hit_count must each be O(1) on average, and discuss whether hit_count is per key or global. Clarify if updates to hit_count should happen on get or put.
Suggest using a hash map (e.g., Python dict) for key-value storage to achieve O(1) put and get. For hit_count, propose either a separate hash map mapping keys to counts or an integer counter if global.
Explain that hash map operations are O(1) on average due to constant-time hashing and amortized resizing. For hit_count, if using a separate hash map, lookup and update are also O(1).
Acknowledge that O(1) assumes a good hash function and low collision rate; worst-case is O(n). Mention memory overhead and potential need for synchronization in concurrent environments.
Reiterate that the combination of hash maps meets the O(1) requirement for all operations, and briefly mention alternative approaches like using a single map with value objects storing both value and count.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
LFU felt like the natural fit here since you're already tracking frequency, but I fumbled explaining why LRU might still be preferred in some cases.
Start by defining LRU and LFU eviction policies and their typical use cases, then analyze how each interacts with the requirement to expose accurate per-key hit counts. Compare trade-offs in terms of implementation complexity, memory overhead, and performance, and conclude with a recommendation based on the specific needs of the cache.
Pro tip: Mention that hit counts can be maintained separately from eviction metadata, but if the eviction policy relies on hit counts (like LFU), you must ensure the counts are updated atomically and consistently, which can introduce contention. Also, consider using approximate counting techniques (e.g., count-min sketch) to reduce memory overhead while still providing accurate-enough counts.
Briefly explain LRU (evicts least recently used) and LFU (evicts least frequently used), including their typical data structures (e.g., doubly linked list + hash map for LRU, frequency buckets for LFU).
Discuss how each policy handles per-key hit counts: LRU doesn't need them for eviction but may track them for stats; LFU inherently uses frequency counts, so accuracy is critical. Consider the impact on memory and update overhead.
Evaluate LRU vs. LFU on metrics like implementation complexity, memory usage, hit ratio for different workloads, and how each affects the accuracy and cost of maintaining hit counts.
Address how concurrent access affects hit count accuracy and eviction decisions, and propose techniques like sharding, atomic counters, or approximate counting to balance accuracy and performance.
Conclude with a recommendation: if accurate hit counts are paramount and workload is stable, LFU might be better; if simplicity and recency matter more, LRU with separate hit counters could suffice. Mention hybrid approaches if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.