The per-camera part wasn't bad once I remembered to group consecutive active readings into intervals rather than treating each reading independently.
First, clarify the input format and edge cases, then propose an efficient algorithm that converts each camera's readings into active intervals and finds their intersection. Discuss trade-offs between different approaches (e.g., sweep line vs. interval intersection) and analyze time/space complexity.
Pro tip: Mention that you would handle unsorted timestamps and duplicate timestamps, and consider using a sweep line with a counter to avoid explicitly generating all intervals, which can be more memory-efficient for large N.
Ask about input format (sorted timestamps? multiple readings per timestamp?), definition of 'consecutive' (time gap threshold?), and output format (list of intervals).
For each camera, scan readings and group consecutive readings above threshold into intervals. Handle edge cases like single reading, all above/below threshold, and unsorted data.
Use a sweep line algorithm: collect all interval start/end events, sort them, and track the number of active cameras. When count equals N, record the start of a common interval; when it drops below N, record the end.
Discuss time complexity O(M log M) where M is total number of intervals, and space O(M). Compare with alternative approaches like iteratively intersecting intervals pairwise.
Walk through a small example, including cases with no common intervals, overlapping intervals, and boundary conditions (e.g., intervals touching at endpoints).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly stating the time and space complexity of your current solution, then identify the bottleneck (e.g., per-camera processing, storage, or query latency). Propose a redesign that leverages streaming for real-time ingestion, pre-aggregation for efficient queries, and interval indexing for fast time-range lookups, discussing trade-offs.
Pro tip: Quantify the impact of your redesign: e.g., 'Streaming reduces latency from minutes to seconds, and pre-aggregation cuts query time by 90%.' This shows you think in terms of measurable improvements.
Clearly specify the time and space complexity of your existing solution, using Big-O notation and explaining what N represents (e.g., number of cameras, events, or data volume).
Analyze which part of the system limits scalability—e.g., CPU-bound processing, I/O, network bandwidth, or database queries—and explain why it becomes problematic at scale.
Describe how to ingest data via a streaming platform (e.g., Kafka, Kinesis) to handle high throughput and enable real-time processing, reducing latency and decoupling producers from consumers.
Explain how to pre-compute and store aggregates (e.g., counts, averages) at ingestion time or in a materialized view, so queries don't scan raw data, improving read performance.
Discuss using interval trees or time-series databases with interval indexing to efficiently query time ranges, enabling fast lookups and reducing the need for full scans.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.