Clarify requirements first: the stream is append-only, and we need to return the earliest IP that has appeared exactly once so far. Then propose a data structure that tracks counts and maintains order, such as a doubly linked list of unique IPs combined with a hash map from IP to its node and count, achieving O(1) per operation.
Pro tip: Mention that the solution must handle duplicates correctly: when an IP's count goes from 1 to 2, it should be removed from the unique list, and if it later appears again, it should not be re-added. This shows attention to edge cases and real-world stream processing.
Ask about the definition of 'earliest' (first occurrence time), whether the stream is unbounded, and if we need to handle deletions or updates. Confirm that we only need to support adding hits and querying the earliest unique IP.
Propose a hash map to store IP counts and a doubly linked list to maintain the order of IPs that currently have count 1. Each node in the list represents a unique IP, and the map stores a pointer to the node for O(1) removal.
When a new hit arrives, increment its count in the map. If the count becomes 1, append a new node to the tail of the list. If the count becomes 2, remove the corresponding node from the list and update the map to indicate it's no longer unique.
To return the earliest unique IP, simply return the head of the list (if it exists). This gives O(1) time.
Explain that both operations run in O(1) time and O(n) space, where n is the number of distinct IPs seen. Discuss potential memory concerns for unbounded streams and possible optimizations like approximate counting or time-based eviction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements (e.g., data volume, query patterns, latency SLAs) and then propose a distributed architecture that partitions data across nodes, using approximate data structures like HyperLogLog or Count-Min Sketch to reduce memory footprint. Discuss trade-offs between exact and approximate results in terms of accuracy, memory, and throughput, and how to handle failures and consistency.
Pro tip: Quantify the impact: for example, mention that HyperLogLog can estimate cardinality with 2% error using only 1.5 KB per counter, which is orders of magnitude less than exact sets. This shows you understand the practical implications of design choices.
Ask about data volume (millions vs billions), read/write patterns, latency requirements, and acceptable error margins. This ensures your solution aligns with business needs.
Propose partitioning data across multiple nodes (e.g., sharding by user ID or time) and using a distributed system like Apache Spark or Flink for processing. Discuss replication and fault tolerance.
For exact results, consider distributed hash tables or sorted sets, but highlight memory challenges. For approximate results, introduce probabilistic data structures like HyperLogLog, Count-Min Sketch, or Bloom filters, explaining their memory and accuracy trade-offs.
Estimate memory usage per node and overall, and calculate throughput based on partitioning and parallelism. Compare exact vs approximate in terms of resource consumption and performance.
Summarize trade-offs: exact results offer precision but high memory and lower throughput; approximate results save resources but introduce error. Suggest hybrid approaches or fallback mechanisms if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.