Let’s take a peek under the hood of high-performance databases 🔍
Behind every lightning-fast search or smooth user experience in apps lies clever use of data structures — particularly for indexing.
When a database needs to find, store, or write data efficiently, it relies on different structures based on:
Whether data is stored in-memory or on-disk
Whether the workload is read-heavy or write-heavy
Whether the data is numeric, text, or geographic
Here’s a breakdown of 8 key data structures that power real-world databases:
✅ Skiplist – Fast and simple for in-memory operations; used in Redis
✅ Hash Index – Perfect for key-value access; the core of most map/dictionary structures
✅ SSTable – Immutable disk-based structure, used in systems like LevelDB
✅ LSM Tree – Combines Skiplists and SSTables for high write performance (used by Cassandra, RocksDB)
✅ B-Tree – Balanced, widely used in traditional RDBMS for efficient range queries
✅ Inverted Index – Optimized for full-text search; critical for search engines like Elasticsearch
✅ Suffix Tree – Ideal for pattern matching in strings
✅ R-Tree – Designed for multi-dimensional spatial searches like maps and location services
🔄 Why it matters:
Choosing the right data structure isn’t just a technical decision — it directly impacts speed, scalability, and user experience. Knowing when and where to use these structures is what separates great system designers from the rest.
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