Data structures are the building blocks of efficient programming, organizing data for fast access, manipulation, and storage. From simple lists to complex trees, they solve real-world problems in computing. In the MicroBasement, data structures connect vintage algorithms to modern AI — the invisible frameworks that make software run smoothly. This write-up covers linked lists, trees (binary, red-black, and others), common structures and abstractions, and one of my favorite data structures: hash tables.
A linked list is a linear data structure where elements (nodes) are linked via pointers. Each node contains data and a reference to the next node. Unlike arrays, linked lists allow dynamic size and easy insertions/deletions but slower random access (O(n) time). Types include singly-linked (one direction) and doubly-linked (forward/backward). They’re ideal for stacks, queues, and when memory is fragmented.
Trees are hierarchical data structures with a root node and child nodes, used for efficient searching, sorting, and organization. A binary tree has at most two children per node; balanced trees like AVL or red-black maintain O(log n) operations.
Data structures are abstractions that hide implementation details, focusing on operations:
Hash tables (maps/dictionaries) use a hash function to map keys to array indices for O(1) average-case lookups/insertions/deletions. They power databases, caching, and Python's dict. Collisions are handled by chaining or open addressing. In the MicroBasement, they're essential for efficient data retrieval in vintage and modern software alike.
Data structures are the unsung heroes of programming — efficient ones turn slow code into lightning. In the MicroBasement, they link early algorithms to today's AI, reminding us that good design starts with how you organize your data.