Stream Processing with Stop Words Filtering
Last updated: September 4, 2025
Quick Overview
Process a high-throughput token stream, filter stop words, maintain frequency counts, and support sliding window operations.
Perplexity
September 4, 20257
11
2,425 solved
Process a high-throughput token stream, filter stop words, maintain frequency counts, and support sliding window operations.
Perplexity processes millions of tokens in search and LLM pipelines. This tests efficient data structure design for streaming data.
What the Interviewer Expects
- Process tokens in a single pass
- Maintain frequency counts with O(1) updates
- Implement sliding window for recent token analysis
- Filter stop words efficiently using a hash set
- Handle high throughput without memory bloat
Key Topics to Cover
How to Approach This
- Clarify input constraints and edge cases before writing code.
- Walk through your approach verbally and confirm with the interviewer before coding.
- Start with a brute force solution, then optimize. Mention time and space complexity.
- Test your solution with examples, including edge cases like empty input or duplicates.
- Consider common patterns: sliding window, two pointers, hash map, BFS/DFS, dynamic programming.
Possible Follow-up Questions
- How would you extend this to detect trending topics?
- What if the stream is distributed across multiple workers?
- How would you add approximate counting for memory savings?
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Practice DSA ProblemsSample Answer
Implementation
```python from collections import deque, defaultdict class StreamProcessor: def __init__(self, stop_words, window_size): self.stop_words ...
Optimizations
For top-k queries, maintain a min-heap of size k for O(log k) updates instead of sorting the full frequency map. For approximate counting at extreme s...
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