Java Data Structures

• Data structures in Java are essential for efficient data storage, organization, and manipulation.
• They manage data based on an application's complexity, performance needs, and specific use case.
• Java offers built-in structures via the Java Collections Framework, as well as the ability to create custom implementations.

Types of Data Structures

• Linear data structures arrange data elements sequentially, such as arrays, lists, queues, and stacks.
• Non-linear data structures store data in a hierarchical or interconnected manner, like trees, graphs, HashMaps, and sets.

Built-in Java Data Structures

• Java has predefined data structures available in the Java Collections Framework (JCF).

Arrays

• Arrays are collections of elements of the same data type stored in contiguous memory locations.
• Their size is fixed.
• Arrays allow fast access to elements using an index.
• They are best when the number of elements is known beforehand.
• Arrays can be used for storing fixed-size lists of elements.

ArrayList

• ArrayLists are dynamic arrays that resize themselves when elements are added or removed.
• They provide fast random access, similar to arrays.
• They automatically resize when new elements are added.
• ArrayLists are slower for insertions and deletions compared to LinkedLists.
• They're helpful when the number of elements is unknown or frequently changing.

LinkedList

• LinkedLists are doubly linked lists.
• Each element (node) contains a reference to the next and previous nodes.
• They allow for efficient insertions and deletions.
• They require more memory due to extra references in each node.
• LinkedLists have slower random access than ArrayLists.
• LinkedLists can be useful in implementing queues, stacks, and scenarios where frequent insertions and deletions occur.

Stack

• Stacks are LIFO (Last In, First Out) data structures where elements are added and removed from the top.
• Common stack operations include push() (insert), pop() (remove), and peek() (view top element).
• Stacks are used for recursion, undo/redo functionality, and expression evaluation.

Queue

• Queues are FIFO (First In, First Out) data structures where elements are added at the rear and removed from the front.
• Supported operations are enqueue() (insert at rear) and dequeue() (remove from front).
• Queues are used in scheduling tasks, printer job management, and message queues.

PriorityQueue

• PriorityQueues process elements based on their priority, not insertion order.
• The element with the highest priority is served first.
• PriorityQueues can be implemented using heaps for efficient ordering.
• They're used in event-driven systems and shortest path algorithms (Dijkstra’s algorithm).

HashMap

• HashMaps are key-value based data structures that allow fast retrieval using keys.
• Hashing stores and retrieves data in O(1) average time complexity.
• HashMaps allow null values and one null key.
• They are unordered collections.
• HashMaps are used in database indexing, caching systems, and lookup tables.

HashSet

• HashSets store unique elements in an unordered way.
• No duplicate elements are allowed.
• HashSets use hashing for fast lookup operations.
• HashSets are useful for removing duplicate elements from a dataset and checking membership of elements.

TreeSet

• TreeSets are sorted sets that store elements in ascending order.
• They implement Balanced Binary Search Trees (Red-Black Tree) for fast searching.
• TreeSets guarantee sorted order.
• No duplicate values are allowed.
• TreeSets are useful for storing sorted data and implementing range-based queries.

Graphs

• Graphs are collections of nodes (vertices) connected by edges.
• They can be directed (one-way connections) or undirected (bi-directional connections).
• Graphs can be represented using an adjacency list (efficient memory usage) or adjacency matrix (fast lookup).
• They are used for social networks, shortest path algorithms, and recommendation systems.

Custom Data Structures

• Java allows developers to implement custom data structures based on specific requirements.
• Examples include Trie (Prefix Tree), Binary Search Tree (BST), and Heap.
• Custom implementations can be optimized for memory, speed, or application-specific needs.

Choosing a Data Structure

• Factors to consider include data access speed, insert/delete frequency, ordering requirements, and memory efficiency.
• ArrayList or HashMap are used for fast lookups.
• LinkedList, Stack, or Queue are for frequent modifications.
• TreeSet or PriorityQueue are for sorted data.
• HashSet or Trie are for minimal memory usage.
• Graph are for network-based relationships.
• Each data structure offers specific trade-offs in terms of performance and memory usage.