Common Data Structures for Software Developers

Common Data Structures for Software Developers

Introduction

The significance of data structures lies in their ability to impact the overall performance of an application, so choosing the appropriate data structure for a specific problem can significantly improve the efficiency of algorithms and reduce time complexity. On the other hand, using an inefficient or inappropriate data structure can lead to sluggish execution, excessive memory consumption, and poor scalability.

In this article, I will explain the basics of data structures that every developer should be familiar with. We will touch on each data structure’s characteristics, operations, time complexities, and common use cases.

Types of Data Structures

Many data structures exist in software development to efficiently manage and manipulate data. Each data structure possesses unique characteristics and serves specific purposes. Here are the types of data structures every developer should know:

Array

Arrays are fixed-size collections of elements of the same type stored in contiguous memory locations. It provides a systematic way to organize and access data. Once created, an array’s size remains constant, allowing for efficient memory allocation. Array elements are stored in adjacent memory locations, facilitating efficient element access through indexing. Arrays hold elements of the same data type, ensuring consistency and easily enabling data manipulation.

Arrays support various operations and algorithms for efficient data manipulation. Elements in an array can be accessed individually by their index. Insertion and deletion operations involve adding or removing elements at specific positions, requiring the adjustment of other elements. Searching and sorting algorithms enable finding specific values or organizing elements in a desired order. Arrays are also used for mathematical operations such as summation, averaging, and finding maximum or minimum values.

Linked lists are a data structure consisting of a collection of nodes, where each node holds data and references the next node in the sequence. They provide dynamic memory management, allowing for efficient allocation and deallocation. Linked lists excel in scenarios that require frequent insertions or deletions, as they only need updating the pointers. This makes them versatile and efficient for implementing other data structures like stacks, queues, and graphs.

There are three types of linked lists:

• Singly-linked list: this is a list where each node contains data and a reference/pointer to the next node in the sequence. The list starts with a head node, and each subsequent node connects to the next node, forming a chain-like structure. Singly-linked lists do not have a reference to the previous node. This simplicity allows for efficient memory usage and straightforward traversal.

• Doubly linked list: this refers to a linked list where each node contains data and two pointers: one node pointing to the next node and another pointing to the previous node. This bidirectional connection allows traversal in both forward and backward directions. The doubly linked list provides additional flexibility compared to a singly linked list, enabling efficient insertion and deletion operations at both ends and facilitating easy navigation in both directions.

However, a doubly linked list requires more memory to store the extra pointers. It finds applications in scenarios that require efficient insertion and deletion at both ends, such as implementing deque (double-ended queue) data structures or maintaining a history of actions.

• Circular linked list: this refers to a list where the last node’s pointer points back to the first node, forming a circular structure. Each node in the list contains data and a pointer/reference to the next node. This circular connection allows for continuous traversal of the list without iterating from the beginning. Circular linked lists offer advantages in scenarios that require efficient looping or cyclic operations. They are applied in tasks like managing round-robin scheduling, implementing circular buffers, and creating circular queues. The circular nature of these lists provides a seamless way to access and manipulate data in a cyclic manner.

Stacks

A stack is a linear data structure that aligns with the Last-In-First-Out (LIFO) principle, and it is such that elements are added and removed from the top of the stack. It supports operations like push (adding an element), pop (removing the top element), and peek (viewing the top element).

Stacks are applied in function call management, expression evaluation, undo/redo operations, and backtracking algorithms. They facilitate proper return order, handle operator precedence, enable undo functionality, and assist in recursive or backtracking algorithms. They can be implemented using linked lists or arrays, each with performance, memory, and implementation considerations.

Queues

Queues are linear data structures following the First-In-First-Out (FIFO) principle. They maintain the order of elements, where new elements are added at the rear and existing elements are removed from the front. Queues are applied in process synchronization, task processing, event handling, and resource management. They facilitate inter-process communication, ensure fair task execution, handle real-time events in the order of occurrence, and manage resource allocation effectively.

Queues support essential operations like enqueue (adding an element to the rear), dequeue (removing the element from the front), peek (retrieving the front element without removal), and checks for emptiness and size. Queue implementations can be based on arrays or linked lists. Array-based queues offer simplicity and efficiency, while linked list-based queues provide dynamic resizing capabilities.

Trees

A tree consists of nodes connected by edges, forming a hierarchical structure. It starts with a root node and branches out into subtrees. Nodes in a tree have parent-child relationships, with each node, except the root, having a parent and zero or more children. The associated terminologies include the root, leaf nodes, internal nodes, siblings, depth, and height. Operations on trees include insertion, deletion, searching, and traversals. Traversals involve exploring all nodes in a specific order. Trees can be viewed in two categories:

Binary trees are trees where each node can have at most two offspring: a left offspring and a right offspring. Types of binary trees include full binary trees, complete binary trees, and perfect binary trees. Binary search trees (BST) are binary trees where the left offspring contains a smaller value, and the right offspring contains a larger value. Operations on BSTs include insertion, deletion, and searching.

Balanced trees are self-balancing binary search trees that maintain balance to ensure efficient operations. AVL trees and red-black trees are examples of balanced trees. B-trees are also balanced search trees. They are designed for efficient disk access and database systems.

Trees find applications in various fields, including file systems for representing directories and files, database management systems for efficient data retrieval, compiler design for syntax tree construction, machine learning for decision trees in classification and regression problems, and network routing algorithms using tree-based protocols.

Graphs

Graphs are powerful data structures used to model relationships between objects. From social networks to computer networks, graphs provide a flexible and intuitive way to represent and analyze complex connections.

A graph is a collection of nodes (vertices) connected by edges. Each edge represents a relationship or connection between two nodes. There are two types of graphs:

• Directed Graphs (Digraphs): this is a type of graph where edges have a specific direction. Each edge represents a one-way relationship between two nodes. In a digraph, the connections between nodes have a clear direction, indicating the flow or dependency between them. This property enables modelling real-world scenarios like network traffic, web page links, and social media followership. Directed graphs are commonly used in algorithms like topological sorting and finding the shortest path in a network with directed edges.

• Undirected Graphs: this is a type of graph where edges have no direction, indicating a bidirectional relationship between nodes. In an undirected graph, the connections between nodes are symmetrical, allowing traversal in both directions. This property makes undirected graphs suitable for modelling relationships without a specific flow, such as friendships in social networks or connections in computer networks. Common operations on undirected graphs include finding connected components, detecting cycles, and implementing algorithms like depth-first search and breadth-first search.

Hash Tables

Hash tables are also known as hash maps. They store data using a hash function to convert keys into indices. This allows for direct access to the corresponding values. Key features of hash tables include their key-value pair storage, use of arrays for efficient memory utilization, collision handling techniques, and dynamic resizing capability.

Hash tables support key-value pair insertion, retrieval, deletion, and updating. Insertion involves calculating the index using the hash function and storing the pair at that index. Retrieval finds the index based on the key and retrieves the associated value. Deletion involves locating the index, removing the pair, and maintaining the structure. Updating modifies the value associated with a key.

Heaps

A heap is a complete binary tree where every parent node has a priority greater than or equal to its offspring (in a max heap) or less than or equal to its offspring (in a min heap). This property, known as the heap property, ensures that the highest or lowest priority element is always at the root. Heaps are represented as complete binary trees, and they provide:

Heaps are extensively used to implement priority queues, where elements are assigned priorities and need to be efficiently accessed in order of importance. They are crucial in Dijkstra’s algorithm for finding the shortest paths in a graph. The heap sort algorithm utilizes heaps for efficient sorting. Other applications include:

• Scheduling tasks based on priority.

• Finding the kth largest or smallest element in a collection.

• Managing event-driven systems.

Conclusion

Well done! The basics of data structure, as you just read, serve as a solid foundation for solving problems as developers.

Understanding those data structures will give you valuable insights into organizing, accessing, and manipulating data effectively in your software projects.

Further Reading

• The Romance Between Data Structures and Software Developers.

• Full Course on Data Structure and Algorithms(free and beginner-friendly).

• Common Operations on Various Data Structures.