An Overview of Data Structures

Data Structures are a type of data management or storage method. They are typically selected based on the need for efficient access to the data. Listed below are a few examples of data structures. Learning more about data structures will help you choose the best one for your application.

Linear data structures

Linear data structures are collections of data elements that are organized in a sequential order. They are easy to implement because each element is connected to its predecessor or successor. Sequential data structures are similar to the way that computer memory is arranged. Examples of linear data structures are arrays, stacks, queues, and linked lists.

A linear data structure has data elements connected in a hierarchy that can be traversed in a single run. The basic idea behind linear data structures was first proposed by Klaus Samelson and Friedrich L. Bauer in 1955 and patented in 1957. The idea was further developed by Australian Charles Leonard Hamblin, who independently created a linear data structure in 1957.

Hash tables

They are very fast and are used in many tools, databases, and data-fetching libraries. They also have low memory requirements, making them a good choice for distributed storage. This article provides an overview of hash tables, and includes exercises to familiarize you with their use.

A hash table is a data structure in which key-value pairs are stored. It uses a hash function to compute its index. As long as the hash function is good, the hash table is a good option for storing data. When storing data in a hash table, the time it takes to lookup an element is O(1). For example, suppose a string S is given as an index, and a key-value pair is S.


A linked-list is a collection of data elements in a linear order. Its order is not determined by physical memory placement; it is defined by the nodes in the list, each of which points to the next element. Linked-lists are a popular data structure for storing information, and they are useful for many applications.

Linked-lists can be traversed using a ‘prev’ and a ‘next’ field. Linked lists can store any data type. The ‘prev’ and ‘next’ fields are used to perform operations on the lists. For example, when a music player keeps track of selected songs, it stores the next track and the previous song in a loop.

There are two main types of linked-lists. The most common type is a singly-linked list, which contains a single node, an address field, and a reference to the next node. A doubly-linked list, on the other hand, has two pointer storage blocks. The first pointer block holds the address of the previous node, while the second pointer block points to the next node.

Array data structures

Each element is identified by an index or a key. Typically, arrays are stored in a way that the position of each element can be computed using a mathematical formula or index tuple. As an example, if you have a list of three numbers, you can store each one in an array.

Array data structures are most efficient in terms of accessing values because the elements are stored in contiguous memory locations. This makes it easy for the computer to find the information it needs. However, arrays do have a downside. The memory they use is reserved for other operations, so they are not recommended for high-volume usage.

Segment Trees

A Segment Tree is a data structure in which a tree-like structure is built to minimize the number of operations required to return a data set. Each intermediate node of the tree represents a segment of the data set. The root node is the source of all the data in the tree. Each child node represents an interval from the root node. And the leaf nodes represent the elements of a range.

A Segment Tree works by recursing through the children of the tree. The code starts at the root node and updates each child node in the path. It does this by updating the value at index i to index val in the original array. This process can take some time. A Segment Tree is a useful data structure if you need to update a range of data in a data set.

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