Behind every fast query, smart filter, or scalable database lies a powerful data structure. These 20 structures aren’t just theory, they’re the backbone of real-world systems that power search engines, time-series storage, blockchain, and more. 1. Indexing Structures Hash Index, B-Tree, Skiplist, Bitmap Index, Trie These are the go-to structures for fast data access. Whether it’s quick key-value lookups in memory or sorted traversals on disk, these structures form the core of query performance in most databases. 2. Search & Pattern Matching Inverted Index, Suffix Tree, Segment Tree, R-Tree Designed for deep searches — from documents and strings to spatial queries — these structures support full-text search, multi-dimensional lookups, and real-time analytics. 3. Write-Optimized Storage LSM Tree, SSTable, Bloom Filter High-ingestion databases like Cassandra and RocksDB rely on these to optimize write speed while managing data compaction and fast approximate lookups with minimal memory overhead. 4. Spatial & Range Indexing Quad Tree, Z-order Curve, Segment Tree Used in applications like maps, game engines, and time-series systems — these structures help partition and access multi-dimensional or sequential data efficiently. 5. Advanced Use Cases Merkle Tree, Suffix Tree, Bloom Filter From verifying blockchain transactions to bioinformatics and deduplication in distributed systems — these data structures are built for reliability and integrity at scale. Knowing these structures and more importantly, where they’re used - helps you design database systems that are optimized, reliable, and scalable. What will you add to this? Follow me Shalini Goyal for more such insights! | 18 comments on LinkedIn