7 Data Structures Every Software Engineer Should Know

What is a Data Structure?

Before diving into specific structures, it’s crucial to understand what a data structure is. Essentially, a data structure is a way to store and manage information. For example, if you have a sensor that measures temperature over time, the set of these measurements constitutes a data structure.

To measure the efficiency of a data structure, we use a tool called asymptotic notation. This notation indicates how the time or space required increases as the size of the data grows. A simple example is running a distance: if it takes you one minute to run 100 meters, it will take approximately two minutes to run 200 meters, representing linear growth.

1. Arrays (or Vectors)

Arrays are a data structure where elements are stored contiguously in memory. This allows constant time access to any element by its index, regardless of the size of the array. However, this efficient access comes at a cost: finding a large enough block of contiguous memory can be challenging. Additionally, inserting new elements may require copying the entire array to a new location in memory, which is costly.

2. Linked Lists

Unlike arrays, elements in a linked list are not stored contiguously in memory. Instead, each element (or node) contains a pointer to the next node. This allows for very quick insertion and deletion of elements. However, accessing a specific element requires traversing the list from the beginning, which is less efficient than an array.

3. Hash Tables

Hash tables store elements in key-value pairs. They use a hash function to convert the key into an index, allowing constant time access to elements. However, it’s important to handle collisions, which occur when two different keys hash to the same index.

Common use cases of hash tables

Implementing Dictionaries: Hash tables are commonly used to implement dictionaries, where the keys can be strings and the values can be any data type. This allows for rapid retrieval of values associated with specific keys.

Frequency Counting: When counting the frequency of elements in a collection, hash tables allow for quick updates of counts.

Caching: In algorithms and systems where the same data is frequently accessed, hash tables are useful for caching previously computed results, improving performance.

4. Stacks

Stacks follow the LIFO (Last In, First Out) principle, meaning the last element added is the first to be removed. They are useful in depth-first search algorithms and other applications where the order of elements matters.

5. Queues

Queues, in contrast, follow the FIFO (First In, First Out) principle, meaning the first element added is the first to be removed. They are essential in breadth-first search algorithms and situations where elements need to be processed in the order they arrive.

6. Graphs

Graphs are complex structures that represent relationships between entities, such as cities connected by roads or people in a social network. Within graphs, trees are a special type where there is only one path between any pair of nodes. Trees are used in many applications, such as verifying blocks in blockchains.

Heaps

Heaps are a type of binary tree used to implement priority queues. There are min-heaps and max-heaps, where each node is smaller or larger than its children, respectively. Heaps allow quick extraction of the minimum or maximum element, but insertion and deletion of elements can be more costly due to the need to maintain the heap property.

Conclusion

Knowing these data structures will help you choose the most suitable one to solve a specific problem efficiently. Each structure has its own advantages and disadvantages, and it’s crucial to understand them to make informed decisions in software development. By mastering arrays, linked lists, hash tables, stacks, queues, graphs, and heaps, you will be well-equipped to tackle a wide range of programming challenges.

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