Before using a data structure, make sure you thoroughly understand how it works, its time and space complexities, and its strengths and weaknesses. This will help you choose the most efficient data structure for your specific needs.

The efficiency of your code can largely depend on your choice of data structures. Understanding the problem requirements and the nature of the data you're working with can guide you in choosing the most suitable data structure.

While data structures can help organize and process data efficiently, unnecessary use of them can lead to complexity and inefficiency in your code. Use them where necessary and keep your code as simple as possible.

Most programming languages have standard libraries that provide implementations of basic data structures. Use these whenever possible, as they are typically highly optimized and reliable.

There is often a trade-off between the time and space efficiency of your code. A data structure that is fast may use more memory, and one that uses less memory may be slower. Understand these trade-offs and choose your data structures accordingly.

Sometimes, combining data structures can lead to more efficient solutions. For instance, a common pattern is to use two data structures, such as a hashmap and a doubly linked list, to implement an LRU Cache.

An array is a collection of elements identified by index or key values.

Arrays

A linked list is a linear data structure where each element is a separate object.

Linked List

A stack is a linear data structure that follows the LIFO principle.

Stack

A queue is a linear data structure that follows the FIFO principle.

Queue

A hash table (or hash map) is a data structure that implements an associative array abstract data type.

Hash Table

A binary tree is a tree-type non-linear data structure with a maximum of two children for each parent.

Binary Tree

A graph is a non-linear data structure consisting of nodes and edges.

Graph

A Heap is a special Tree-based data structure in which the tree is a complete binary tree.

Data Structures

Concepts

Examples

Interview Prep

Best Practices

Learning Resources