1 Algorithms & Data Structures
This chapter covers
- Comparing arrays and linked lists for memory and access efficiency
- Using binary heaps for priority queues
- Representing graphs using adjacency matrices and lists
- Ordering dependencies with topological sort
- Solving max flow problems with the Ford-Fulkerson algorithm
Almost every program you write is, underneath, a choice about how to organize data and how to operate on it. The data structure you reach for shapes what your code can do quickly, what it cannot, and what it costs in memory. The algorithm you pick determines whether a problem is tractable at the scale you care about.
This chapter walks through five of the most useful examples, ordered from the simplest to the most expressive. We start with arrays and linked lists — the everyday containers — and look at why their differences matter more in practice than the textbook complexities suggest. From there we move to binary heaps, a specialized structure built around a single, very common access pattern. We then expand outward to graphs, which model relationships rather than sequences, and close with two graph algorithms — topological sort and Ford-Fulkerson — that show how the right algorithm can turn an interconnected mess into a clear answer.
Throughout, the recurring question is the same: what does this structure or algorithm buy you, and what does it cost? Whether you're preparing for an interview or making real decisions in production code, that's the lens that matters.