Introduction to Data Structures and Algorithms: Basics, Sorting, and Advanced Structures

The author announces a new series on data structures and algorithms, outlining three parts: basic data structures (including hash tables, trees, and heaps), sorting algorithms (bubble, selection, insertion, merge, quicksort, heap sort), and advanced structures (B+ trees, red‑black trees, cuckoo hashing).

Emphasizing the practical importance of these concepts, the article explains how hash tables enable O(1) lookups (e.g., Redis, TiDB) and how B+ tree indexes provide O(log N) range queries in relational databases.

Big O notation is introduced as a step‑count based measure of algorithmic time complexity, with examples of O(1), O(N), O(log N), and O(N²) and a brief discussion of its limitations.

Arrays are described as contiguous memory containers offering O(1) random access but costly insertions/deletions; a Java public class DiyList { ... } implementation demonstrates a dynamic array with automatic resizing via a grow() method.

Linked lists are presented as structures with non‑contiguous nodes, enabling O(1) insertions/deletions at the head; both singly and doubly linked list Java implementations are shown using inner Node classes and maintaining first / last references.

Queues are defined as FIFO structures, with examples of bounded (array‑based circular buffer) and unbounded (linked‑list‑based) implementations, highlighting enqueue/dequeue operations and their O(1) complexity.

Stacks are introduced as LIFO structures, typically built on arrays; the article notes Java's Stack class and its design drawbacks, and suggests implementing a simple array‑based stack.

The conclusion reviews the four core structures—arrays, linked lists, queues, and stacks—summarizing their performance characteristics and offering guidance on choosing the appropriate container based on workload, while previewing the next topic: tree data structures.