Approximate Algorithms & Stream Processing
When data is too large for exact algorithms to compute in reasonable time, we need approximate algorithms and streaming algorithms.
Why Approximate Algorithms?
Exact algorithms require storing all data in memory. When you have billions of data points, this becomes impossible or extremely expensive.
Approximate algorithms trade a tiny amount of accuracy for massive memory savings:
| Problem | Exact Solution | Memory | Approximate Solution | Memory | |---------|---------------|:------:|---------------------|:------:| | Find unique IPs in 1TB data | HashSet | 16 GB | HyperLogLog | 16 KB | | Check if email exists | HashSet | 50 MB | Bloom Filter | 1.2 MB | | Count word frequency | HashMap | 5 MB | Count-Min Sketch | 5 KB | | Find similar items | Exact search | O(n^2) | FAISS | O(log n) |
What You Will Learn
| Chapter | Tool | Questions It Answers | |:-------:|:----:|---------------------| | 1 | Bloom Filter | Has this element appeared before? | | 2 | HyperLogLog | How many distinct elements? | | 3 | Count-Min Sketch | How many times has this appeared? | | 4 | FAISS | What is most similar to this? | | 5 | Full Pipeline | How to combine all tools together? |
How Much This Is Worth
- Real-time analytics system: Build UV/trending product stats for e-commerce platforms - worth $10K-$25K per project
- Web crawler dedup: Large-scale crawler projects use Bloom Filter for URL deduplication - $5K-$15K
- Vector search engine: FAISS indexing for RAG and recommendation systems - $15K-$40K
Technologies Used
- Python with datasketch library
- FAISS by Facebook AI
- FastAPI for API deployment
- mmh3 for hashing
Prerequisites
- Basic Python programming
- Understanding of hash functions
- No prior knowledge of probabilistic data structures needed
The Vibe Coding Approach
Describe what you need to AI:
"Build a real-time UV statistics system using HyperLogLog for distinct visitor counting, Bloom Filter for new user detection, and Count-Min Sketch for page view frequency. Wrap as a FastAPI service with /stats and /visit endpoints."
The AI will implement all three probabilistic data structures and package them as a complete API service.
Getting Started
Begin with Chapter 1 (Bloom Filter) to understand the basic concept of trading accuracy for memory efficiency. Each chapter builds on the previous one, culminating in Chapter 5 where all tools are combined into a complete stream processing pipeline.
Let us dive into the world of approximate algorithms!