← Databricks Interview Insights
My first instinct was to just store every timestamp in a list and filter on getQPS.
Start by clarifying requirements: whether timestamps are monotonically increasing, if getQPS can be called with arbitrary timestamps, and the expected scale. Then propose a sliding window approach using a queue or circular buffer to store (timestamp, count) pairs, evicting entries older than 5 minutes. For getQPS, sum the counts within the window and divide by 300 seconds, or maintain a running sum for O(1) queries.
Pro tip: Mention that if timestamps are not monotonic, you can use a balanced BST or a time-bucketed approach with a map from second to count, and for high throughput, consider bucketing by second to reduce memory and improve cache efficiency.
Ask about timestamp ordering, query patterns, concurrency, and precision. Confirm whether getQPS is called with the current timestamp or historical ones.
Use a deque (double-ended queue) to store (timestamp, count) pairs for O(1) append and popleft. Alternatively, use a circular buffer or time-bucketed array for fixed memory.
Append the new record to the deque. If the timestamp is out of order, handle by inserting in sorted order or using a different structure. Evict entries older than 5 minutes relative to the new timestamp.
Evict entries older than timestamp - 300 seconds. Sum the counts of remaining entries and divide by 300 to get average QPS. Maintain a running sum to avoid O(n) summation.
State time complexity: O(1) amortized for record and getQPS with running sum. Space O(n) where n is number of records in 5 minutes. Discuss bucketing by second to reduce n and handle high throughput.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and defining the window invariant, then walk through the mechanics of expanding and shrinking the window while maintaining state. Finally, analyze time and space complexity and discuss edge cases.
Pro tip: Emphasize that the sliding window technique is most effective when the problem involves a contiguous subarray or substring and the window's validity can be maintained incrementally. Mention that you would consider using a hash map or frequency array to track window state efficiently.
Ask questions to confirm the input type (array/string), whether the window size is fixed or variable, and what condition defines a valid window. Also check constraints like input size and character set.
Determine what the window represents (e.g., a substring with no repeating characters) and what data structure (e.g., hash map, counter) will track the window's state to check validity in O(1) time.
Describe how the right pointer expands the window and how the left pointer shrinks it when the invariant is violated. Explain how to update the state and the answer (e.g., max length) during these steps.
State that each element is visited at most twice, giving O(n) time and O(k) space where k is the window state size. Discuss edge cases like empty input, all unique elements, or all same elements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the constraints and requirements, then propose approximate data structures like sketches or probabilistic counters that trade accuracy for memory. Explain how these structures work, their trade-offs, and how they maintain reasonable performance for common operations.
Pro tip: Mention that you would first try to understand the required accuracy and query patterns, as that determines the best approach—this shows you think about requirements before jumping to solutions.
Ask about the required accuracy, the types of queries (e.g., count distinct, frequency, percentiles), and the acceptable memory limit. This ensures you choose an appropriate data structure.
Propose structures like Count-Min Sketch for frequency estimation, HyperLogLog for cardinality, or t-digest for quantiles. Explain how they use hashing and probabilistic counting to save memory.
Discuss the trade-off between memory and accuracy, and how parameters (e.g., number of hash functions, bucket count) can be tuned. Mention that these structures provide bounded error guarantees.
Highlight that these structures offer O(1) update and query time, making them suitable for high-throughput systems. Mention that they are often used in stream processing and databases.
If exact results are needed for some queries, suggest a hybrid approach: use sketches for approximate answers and store exact data for a limited time window or for a sample of requests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.