Marianne Dillard

As many people do when learning how to code, I went straight for the shiny languages, convinced that if I just learned Python or JavaScript, I’d be unstoppable. What I didn’t realize at the time was that I was basically trying to build a house without knowing what a hammer was. I mean, who needs data structures when you can just write some code, right? (Spoiler alert: me, I needed them.) It wasn’t until I found myself tangled up in frustrating bugs and slow code that I realized I should have started with the fundamentals. If I had, the rest of the journey would’ve been a lot smoother.

In this post, we’re breaking down the mystery of data structures, exploring the essential building blocks that will make your coding experience smoother and more efficient. We’ll cover different types of data structures, including when and why you’d want to use them. By the end, you’ll have a solid understanding of how to choose the right structure for your code, helping you write smarter, faster programs with less frustration.

Linear Data Structures

In programming, linear data structures are collections of elements that are stored sequentially in memory, meaning each element has a unique predecessor and successor (except the first and last elements). They are the foundation for many common algorithms and often provide the simplest, most efficient way to store and access data. Linear data structures are the first stepping stones you’ll encounter on your journey into the world of data structures and understanding them will give you a solid base for more complex structures later on.

• Array: An array is a collection of elements, all of the same type (float, integer, string, etc.), stored in contiguous memory locations. You can think of it as a row of boxes, where each box holds a value, and each box has a specific position (or index). Arrays are incredibly efficient for accessing elements by index (ex: getting the 5th element), but they can be inefficient when it comes to inserting or deleting elements, especially in the middle. Arrays are great for storing collections of data that you want to access directly and quickly, like a list of numbers or names. If the size of your data is fixed and doesn’t change often, arrays are perfect.
• List: A list is essentially a more dynamic version of an array. While arrays have a fixed size in many languages, lists are typically dynamic and can grow or shrink as needed. For example, in Python, lists can store different types of data (numbers, strings, objects) in the same list, unlike arrays, which usually store only one type of data. Lists offer flexibility when working with a dynamic amount of data and are often used when the number of elements is unknown or can change over time.
• Matrix: A matrix is a specialized type of array that holds values in two dimensions – essentially a grid of rows and columns. Each row is an array within the main array, making it a 2D array. Matrices are often used in mathematical and scientific applications, such as image processing, machine learning, or any field that requires multi-dimensional data. While not strictly linear in the traditional sense, matrices extend the concept of arrays into a 2D grid structure.
• Stack: A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. Think of it like a stack of plates at a buffet: you add plates to the top, and when you need one, you take it from the top as well. You can only “push” (add) or “pop” (remove) elements from the top. Stacks are useful for scenarios where the most recent item needs to be processed first, like with undo/redo features in software or function calls in recursion. They’re also used in parsing expressions and managing memory in programming languages.
• Queue: A queue works on the First In, First Out (FIFO) principle, just like a line at the grocery store: the first person in line is the first to be served. In a queue, you “enqueue” (add) items at the back and “dequeue” (remove) items from the front. Queues are essential for scheduling tasks, such as managing requests to a server or processing items in a buffer. They’re also used in algorithms like breadth-first search (BFS), where the order of processing matters.
• Set: A set is a collection of unique elements, without any particular order. Unlike arrays, sets don’t allow duplicate values, so if you try to add a duplicate, it simply won’t be added. Sets are great for situations where you need to store unique values and check if an item exists in the collection quickly. They’re used in scenarios like finding unique items in a list, removing duplicates, or performing set operations (union, intersection) in tasks like data filtering or search indexing.

These basic linear data structures are crucial to understanding how data is organized and manipulated in software. As you dive deeper into coding, you’ll see how these structures pave the way for more complex solutions, and how often they’re used in everyday applications!

Linked Data Structures

Linked data structures are different from linear structures in that they don’t store elements in contiguous memory locations. Instead, they use pointers or references to connect elements (nodes) together. This approach allows for more flexible memory allocation and efficient insertion or deletion of elements, but it requires a bit more understanding of how memory and pointers work.

• Singly Linked List: A singly linked list is a collection of nodes where each node contains two parts: a data element and a pointer (or reference) to the next node in the sequence. The last node points to null, signaling the end of the list. This structure is excellent for applications where you need to frequently insert or remove elements from the beginning or middle of the list. However, accessing specific elements can be slower compared to arrays because you need to traverse the list from the head (start) node to the target node. Singly linked lists are often used in situations where memory allocation needs to be flexible, such as implementing queues or managing ordered data with frequent additions or removals.
• Doubly Linked List: A doubly linked list is similar to a singly linked list, but with a key difference: each node contains two pointers – one to the next node and one to the previous node. This allows traversal in both directions, making insertion and deletion at both ends (head and tail) more efficient. However, it uses more memory because each node needs to store an extra pointer. Doubly linked lists are useful in scenarios where you need to frequently add or remove elements from both ends of the list, such as in implementing navigational structures like browsers’ back-and-forward history or in implementing deques (double-ended queues).
• Tree: A tree is a hierarchical data structure that consists of nodes connected by edges. The topmost node is called the root, and each node can have zero or more child nodes. Unlike linked lists, trees are not linear, but they still use pointers or references to link elements. Trees are useful for representing hierarchical relationships, such as directory structures, organizational charts, or classification systems. Trees are used when the data has a natural hierarchy, such as files in a file system, family trees, or when performing searches in a sorted order.
• Binary Tree: A binary tree is a specialized form of a tree in which each node has at most two children: a left child and a right child. This limitation makes binary trees more structured and easier to work with compared to general trees. Binary trees are often used in searching and sorting algorithms, as they allow efficient traversal and searching. Binary trees are commonly used in tasks like binary search trees (BSTs), where searching for values in a sorted dataset is needed, and in algorithms like tree traversal or for implementing priority queues.
• Heap: A heap is a specialized binary tree-based data structure that satisfies the heap property. There are two types of heaps: Max-Heap (where the parent node is greater than or equal to the child nodes) and Min-Heap (where the parent node is less than or equal to the child nodes). Heaps are primarily used to implement priority queues, where elements with the highest or lowest priority are processed first. Heaps are used in algorithms like heapsort or for efficient priority queue operations, where the fastest way to access the highest or lowest value is required, such as in scheduling tasks or finding the shortest path in graphs.

These linked data structures introduce more complex relationships between elements, enabling more flexible and efficient data management in various scenarios. Understanding when and how to use them is key to becoming proficient in data structure design and algorithm optimization.

Hash-Based Data Structures

Hash-based data structures use a technique called hashing to store and retrieve data efficiently. These structures rely on a hash function, which converts a given key into an index in an array (or “bucket”). This allows for fast lookups, insertions, and deletions because instead of searching through all the elements, the hash function maps the key directly to a location in the data structure.

• Hash Table (Hash Map): A hash table, or hash map, is a data structure that stores key-value pairs. Each key is passed through a hash function that determines where the associated value is stored in the table. Hash tables are ideal for situations where fast access to data using a unique key is necessary. The primary advantage of hash tables is that they are very efficient in terms of performance. Hash tables are widely used in databases, caching systems, and any application where quick lookups are essential, such as building an index for search engines or storing configurations and settings. They are also commonly used in implementing dictionaries and associative arrays in many programming languages.
• Hash Set: A hash set is a variation of a hash table that stores only the keys, with no associated values. Like hash tables, it uses a hash function to determine where to store each key, ensuring that all elements are unique (duplicate values are not allowed). Hash sets are useful for scenarios where you need to track unique elements, such as in finding unique words in a document, checking for duplicates in a dataset, or performing set operations like unions and intersections. They are also commonly used in algorithms that need to quickly check if an element has already been encountered, such as in finding unique elements or checking for cycles in graphs.
• Hash Map vs. Hash Set: While both hash maps and hash sets rely on hashing to store data efficiently, the key difference is that hash maps store key-value pairs, whereas hash sets only store keys. If you need to store data associated with a specific key, such as an ID with a user’s details, you would use a hash map. If you only need to check for membership or store unique items, you would use a hash set.

Hash-based data structures provide fast, efficient ways to store and retrieve data based on keys, making them invaluable tools for many software applications. The choice between a hash table and a hash set depends on whether you need to store associated values or just track unique elements. Both options allow for highly efficient operations, making them ideal for performance-critical applications.

Advanced Data Structures

As we venture into more complex data structures, we start to see structures that provide unique solutions to specific challenges. These advanced data structures are often used in specialized areas like searching algorithms, network routing, and artificial intelligence. Though they’re a bit more difficult to grasp initially, understanding them opens up a whole new world of possibilities in software development.

• Trie (Prefix Tree): A trie (pronounced “try”) is a tree-like data structure used to store a dynamic set of strings, where each node represents a common prefix of some string. Tries are particularly efficient for scenarios where you need to perform tasks like autocomplete, spell checking, or prefix matching. By storing strings in a trie, you can quickly search for a prefix or word. Tries are often used in search engines, autocomplete systems, and text analysis applications where you need to quickly search, insert, or delete strings based on their prefixes. For example, a search bar in an application that suggests possible completions for a partially typed word might be implemented with a trie.
• Graph: A graph is a collection of nodes (also called vertices) connected by edges. Graphs can be directed (where the edges have a direction) or undirected (where edges don’t have a direction). They can also be weighted (where edges have a weight or cost) or unweighted. Graphs are used to model networks, such as computer networks, social media connections, or transportation systems. Graphs are powerful tools for representing relationships between objects, and they support algorithms such as shortest path, depth-first search (DFS), and breadth-first search (BFS), which are useful for traversing and analyzing connected data.  Graphs are commonly used in social networks, routing algorithms (like GPS systems), and network protocols. For example, social networks use graphs to represent relationships between users, where each user is a node, and each connection is an edge between them.

You Survived Data Structures

Congratulations! You’ve just survived learning about linear data structures, linked data structures, hash-based structures, and advanced structures. We’ve covered everything from the simplicity of arrays to the complexity of graphs and tries. It might feel like a lot to take in (trust me, I definitely still get confused sometimes), but understanding these building blocks will make coding feel like less of a mystery. Whether you’re traversing a linked list or trying to match a prefix in a trie, these structures will be your trusty sidekicks. And remember, just like real trees don’t grow overnight, mastering data structures takes time and patience!

But don’t stop here! There’s so much more ahead in this series. Next, we’ll be tackling algorithms (I can feel your excitement!), and future posts will go into everything from version control and testing to debugging and documentation. So check back often as we continue exploring the essential tools and techniques that will make you a coding pro. See you in the next post!