Applying core data structures to solve production problems: hash collisions, B-Trees in database engines, LRU cache memory layouts, and graph traversal.
Software engineers frequently view Data Structures and Algorithms (DSA) as abstract interview puzzles. In reality, the foundation of every database index, distributed message broker, memory allocator, and web framework is built on DSA principles.
+---------------------+---------------------------------------------------+
| Data Structure | Production Real-World Application |
+---------------------+---------------------------------------------------+
| Hash Tables / Maps | In-memory caching, indexing, O(1) object lookups |
| B-Trees / LSM Trees | Relational DB indices (Postgres), KV stores (Scylla)|
| Priority Queue / Heap| Job scheduling, rate limiters, timer heaps |
| Ring Buffer | High-performance messaging, Disruptor pattern |
| Trie Prefix Tree | Search autocomplete, IP routing tables |
+---------------------+---------------------------------------------------+
How does a hash map guarantee $O(1)$ lookups? By computing a hash of the key modulo the array capacity. When two distinct keys hash to the same bucket, systems resolve collisions via:
Why do databases like PostgreSQL and MySQL use B-Trees instead of standard AVL or Red-Black Binary Trees?
[ B-Tree Node (4KB Block) ] --> Holds 500 keys in a single disk read!
class LRUCache<K, V> {
private capacity: number;
private map = new Map<K, V>();
constructor(capacity: number) {
this.capacity = capacity;
}
get(key: K): V | undefined {
if (!this.map.has(key)) return undefined;
const val = this.map.get(key)!;
// Re-insert to refresh recency
this.map.delete(key);
this.map.set(key, val);
return val;
}
put(key: K, value: V): void {
if (this.map.has(key)) {
this.map.delete(key);
} else if (this.map.size >= this.capacity) {
// Evict oldest (first key in Map iterator)
const oldestKey = this.map.keys().next().value;
if (oldestKey !== undefined) this.map.delete(oldestKey);
}
this.map.set(key, value);
}
}