Distributed DSA Basics
Explore the algorithms that scale systems across multiple servers, like Consistent Hashing and Bloom Filters.
Explore the algorithms that scale systems across multiple servers, like Consistent Hashing and Bloom Filters. This hands-on tutorial focuses on practical implementation of distributed dsa basics concepts.
Distributed DSA Basics
When data is too large for one machine, we must distribute it across a cluster. This requires specialized algorithms.
1. Consistent Hashing
In a traditional hash table, adding a new server (bucket) causes nearly all keys to be rehashed ($hash(key) \pmod N$). Consistent Hashing minimizes this by placing servers and keys on a Hash Ring.
When a server is added, only a small fraction of keys move.
2. Bloom Filters
A Bloom Filter is a space-efficient probabilistic data structure used to check if an element is in a set.
- Result: Either "Definitely Not" or "Maybe".
- Benefit: Uses much less memory than a hash set.
Comparison: Accuracy vs Efficiency
| Structure | Space | Accuracy | Use Case |
|---|---|---|---|
| Hash Set | High (Stores keys) | 100% Correct | Small datasets |
| Bloom Filter | Very Low (Bits only) | Allows False Positives | Massive Scale |
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Quiz
Quiz
Question 1 of 2What is the main benefit of Consistent Hashing over traditional Modulo hashing?