Data Structures in Java

12 essential data structures for Java developers. Each one explained with Big O complexity, animated visuals, and real code samples you can copy.

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ArrayDynamic ArrayStackQueueHash MapHash SetLinked ListSorted Set (BST)Sorted Map (BST)Priority Queue (Heap)Concurrent Hash MapMemory View / Slice

Array

T[]

Fixed-size, contiguous block of memory. Elements are stored sequentially and accessed by index in constant time. The foundation of most other data structures.

7
3
9
1
5
[0]
[1]
[2]
[3]
[4]

arr[0] = 7

Complexity

Access by indexO(1)
SearchO(n)
Insert / DeleteO(n)

When to use

  • +You know the exact size at creation time
  • +You need the fastest possible index-based access
  • +Working with fixed-length data like matrices or buffers
Java
// Declaration and initialization
int[] numbers = new int[5];
int[] primes = {2, 3, 5, 7, 11};

// Access and modify
primes[0] = 13;              // O(1)
int third = primes[2];       // O(1) - direct index access

// Iterate
for (int p : primes)
    System.out.print(p + " ");

// Search and sort
Arrays.sort(primes);                        // O(n log n)
int idx = Arrays.binarySearch(primes, 7);   // O(log n)
String[] words = {"cat", "ant", "bat"};
Arrays.sort(words);                          // O(n log n)

Dynamic Array

ArrayList<T>

Resizable array that automatically grows when capacity is exceeded. The most commonly used data structure in most languages. Doubles its internal storage when full, giving amortized O(1) appends.

7
3
9
1
5
[0]
[1]
[2]
[3]
[4]

arr[0] = 7

Complexity

Access by indexO(1)
SearchO(n)
AppendO(1)*
Insert / Remove (middle)O(n)

When to use

  • +You need a resizable collection (most common case)
  • +You frequently access elements by index
  • +You mostly add or remove at the end
Java
List<String> names = new ArrayList<>(List.of("Alice", "Bob"));

names.add("Charlie");          // O(1) amortized
names.add(1, "Diana");         // O(n) - shifts elements right
names.remove("Bob");           // O(n) - shifts elements left
names.remove(0);               // O(n) - shifts elements left

boolean has = names.contains("Charlie"); // O(n)
int idx = names.indexOf("Diana");        // O(n)
String first = names.get(0);             // O(1)

// Sort and iterate
Collections.sort(names);                  // O(n log n)
names.forEach(n -> System.out.print(n + " "));

Stack

ArrayDeque<T>

Last-In-First-Out (LIFO) collection. Only the top element is accessible. Used for tracking state that must be unwound in reverse order.

arrow_downward top
10

Push 10

Complexity

PushO(1)
PopO(1)
PeekO(1)
SearchO(n)

When to use

  • +Undo/redo functionality
  • +Expression parsing and evaluation
  • +DFS traversal of trees and graphs
  • +Matching brackets, parentheses validation
Java
Deque<Integer> stack = new ArrayDeque<>(); // preferred over Stack

stack.push(10);   // O(1)
stack.push(20);
stack.push(30);

int top = stack.peek();   // O(1) - 30, no removal
int val = stack.pop();    // O(1) - 30, removed

System.out.println(stack.size());         // 2
System.out.println(stack.contains(10));   // O(n) - true

// Classic interview pattern: valid parentheses
boolean isValid(String s) {
    Deque<Character> st = new ArrayDeque<>();
    for (char c : s.toCharArray()) {
        if (c == '(') st.push(')');
        else if (st.isEmpty() || st.pop() != c) return false;
    }
    return st.isEmpty();
}

Queue

Queue<T>

First-In-First-Out (FIFO) collection. Elements are added at the back and removed from the front. Fundamental for breadth-first processing.

front
10
back

Enqueue 10

Complexity

EnqueueO(1)
DequeueO(1)
PeekO(1)
SearchO(n)

When to use

  • +BFS traversal of trees and graphs
  • +Task scheduling and job queues
  • +Message passing between components
  • +Rate limiting, buffering
Java
Queue<String> queue = new ArrayDeque<>();

queue.offer("Task A");  // O(1)
queue.offer("Task B");
queue.offer("Task C");

String first = queue.peek();   // O(1) - "Task A"
String next = queue.poll();    // O(1) - "Task A", removed

// Classic interview pattern: BFS level-order traversal
void bfs(TreeNode root) {
    Queue<TreeNode> q = new ArrayDeque<>();
    q.offer(root);
    while (!q.isEmpty()) {
        TreeNode node = q.poll();
        System.out.print(node.val + " ");
        if (node.left != null) q.offer(node.left);
        if (node.right != null) q.offer(node.right);
    }
}

Hash Map

HashMap<K,V>

Maps keys to values using a hash function for near-constant-time lookups. The single most important data structure for coding interviews. Every language has a built-in implementation.

0
age:30
1
2
name:Al
3
city:NY

hash("age") = 0

Complexity

InsertO(1)*
LookupO(1)
DeleteO(1)
Contains keyO(1)

When to use

  • +Two Sum and frequency counting patterns
  • +Caching computed results (memoization)
  • +Grouping data by a key
  • +Any problem requiring O(1) lookup by key
Java
Map<String, Integer> map = new HashMap<>();
map.put("apple", 3);              // O(1)
map.put("banana", 5);             // O(1)

map.put("apple", 10);             // O(1) update
boolean has = map.containsKey("banana"); // O(1) - true
map.remove("banana");             // O(1)

// Safe lookup with default
int count = map.getOrDefault("cherry", 0); // O(1) - 0

// Classic interview pattern: Two Sum
int[] twoSum(int[] nums, int target) {
    Map<Integer, Integer> seen = new HashMap<>();
    for (int i = 0; i < nums.length; i++) {
        int need = target - nums[i];
        if (seen.containsKey(need))
            return new int[]{seen.get(need), i};
        seen.put(nums[i], i);
    }
    return new int[]{};
}

Hash Set

HashSet<T>

Unordered collection of unique elements. Uses hashing internally for O(1) membership testing. Supports mathematical set operations like union, intersection, and difference.

0
age:30
1
2
name:Al
3
city:NY

hash("age") = 0

Complexity

AddO(1)*
ContainsO(1)
RemoveO(1)
Union / IntersectO(n)

When to use

  • +Checking if an element exists in O(1)
  • +Removing duplicates from a collection
  • +Set operations: union, intersection, difference
  • +Visited tracking in graph traversal
Java
Set<Integer> set = new HashSet<>(Set.of(1, 2, 3, 4, 5));

set.add(6);             // O(1) - true (added)
set.add(3);             // O(1) - false (duplicate)
set.remove(1);          // O(1)
boolean has = set.contains(4); // O(1) - true

// Set operations
Set<Integer> other = new HashSet<>(Set.of(4, 5, 6, 7));
set.retainAll(other);   // intersection - set = {4, 5, 6}
set.addAll(other);      // union - set = {4, 5, 6, 7}
set.removeAll(other);   // difference - set = {}

// Classic interview pattern: contains duplicate
boolean containsDuplicate(int[] nums) {
    Set<Integer> seen = new HashSet<>();
    for (int n : nums) if (!seen.add(n)) return true;
    return false;
}

Linked List

LinkedList<T>

Sequence of nodes where each node points to the next (singly linked) or both next and previous (doubly linked). Efficient insertion and deletion at any known position, but no index-based access.

5
12
8
20

traversing: 5

Complexity

Insert at head/tailO(1)
Remove (given node)O(1)
SearchO(n)
Access by indexO(n)

When to use

  • +Frequent insertion/deletion in the middle
  • +Implementing LRU cache (with a hash map)
  • +When you need a deque (double-ended queue)
  • +Problems involving pointer manipulation
Java
LinkedList<Integer> list = new LinkedList<>();

list.addLast(10);          // O(1)
list.addLast(20);
list.addFirst(5);          // O(1)

list.add(1, 15);           // O(n) - traverse to index
boolean found = list.contains(20); // O(n)
list.remove(Integer.valueOf(20));   // O(n) - find then unlink

// Deque operations (double-ended queue)
int first = list.peekFirst();  // O(1) - 5
int last = list.peekLast();    // O(1) - 10

// Iterate
for (int val : list)
    System.out.print(val + " ");  // 5 15 10

// LRU Cache pattern: LinkedList + HashMap
// HashMap for O(1) lookup, LinkedList for O(1) reorder

Sorted Set (BST)

TreeSet<T>

Collection of unique elements maintained in sorted order, typically backed by a balanced binary search tree (red-black tree). Supports range queries and O(log n) min/max.

831215

search(8)

Complexity

AddO(log n)
ContainsO(log n)
RemoveO(log n)
Min / MaxO(log n)

When to use

  • +Maintaining a sorted collection of unique items
  • +Range queries (all elements between X and Y)
  • +Sliding window problems needing sorted order
  • +Leaderboards, ranking systems
Java
TreeSet<Integer> sorted = new TreeSet<>(List.of(5, 3, 8, 1, 9));
// Internal order: 1, 3, 5, 8, 9 (red-black tree)

sorted.add(4);           // O(log n)
sorted.remove(3);        // O(log n)
boolean has = sorted.contains(8); // O(log n) - true

int min = sorted.first();  // O(log n) - 1
int max = sorted.last();   // O(log n) - 9

// Range queries
SortedSet<Integer> range = sorted.subSet(4, 9); // [4, 9)
System.out.println(sorted.floor(6));    // 5 - greatest <= 6
System.out.println(sorted.ceiling(6));  // 8 - smallest >= 6
System.out.println(sorted.higher(5));   // 8 - strictly greater

Sorted Map (BST)

TreeMap<K,V>

Key-value pairs maintained in sorted key order, typically backed by a balanced BST. Enables ordered iteration and range lookups that hash maps cannot provide.

831215

search(8)

Complexity

InsertO(log n)
LookupO(log n)
RemoveO(log n)
Iterate (sorted)O(n)

When to use

  • +You need sorted key-value pairs
  • +Ordered iteration over entries
  • +Range lookups by key
  • +When insertion order or sorted order matters
Java
TreeMap<String, Integer> tm = new TreeMap<>();
tm.put("banana", 2);    // O(log n)
tm.put("apple", 5);
tm.put("cherry", 1);

tm.put("date", 3);      // O(log n)

// Iterates in sorted key order
for (var entry : tm.entrySet())
    System.out.println(entry.getKey() + ": " + entry.getValue());
// apple: 5, banana: 2, cherry: 1, date: 3

// Navigation methods
String firstKey = tm.firstKey();           // "apple"
String floorKey = tm.floorKey("cat");      // "banana" (greatest <= "cat")
Map.Entry<String, Integer> low = tm.firstEntry(); // apple=5
SortedMap<String, Integer> sub = tm.subMap("apple", "cherry");

Priority Queue (Heap)

PriorityQueue<T>

Collection where elements are dequeued by priority rather than insertion order. Typically implemented as a binary heap. Essential for shortest-path algorithms and top-K problems.

13579

min-heap

Complexity

InsertO(log n)
Extract min/maxO(log n)
PeekO(1)
SearchO(n)

When to use

  • +Dijkstra's shortest path algorithm
  • +Merge K sorted lists/streams
  • +Top-K / Kth largest element problems
  • +Event-driven simulation, scheduling
Java
// Min-heap by default
PriorityQueue<Integer> pq = new PriorityQueue<>();

pq.offer(30);     // O(log n)
pq.offer(10);
pq.offer(20);

int min = pq.peek();    // O(1) - 10
int next = pq.poll();   // O(log n) - 10, removed

// Max-heap using reversed comparator
PriorityQueue<Integer> maxPQ = new PriorityQueue<>(
    Comparator.reverseOrder());

// Classic interview pattern: K closest points
int[][] kClosest(int[][] points, int k) {
    PriorityQueue<int[]> pq = new PriorityQueue<>(
        (a, b) -> (a[0]*a[0]+a[1]*a[1]) - (b[0]*b[0]+b[1]*b[1]));
    for (int[] p : points) pq.offer(p);
    int[][] res = new int[k][];
    for (int i = 0; i < k; i++) res[i] = pq.poll();
    return res;
}

Concurrent Hash Map

ConcurrentHashMap<K,V>

Thread-safe hash map designed for concurrent read/write access from multiple threads. Uses fine-grained locking or lock-free techniques instead of a single global lock.

0
age:30
1
2
name:Al
3
city:NY

hash("age") = 0

Complexity

InsertO(1)*
LookupO(1)
DeleteO(1)
Atomic updateO(1)*

When to use

  • +Multi-threaded caching
  • +Shared state across threads or async tasks
  • +Producer-consumer patterns with keyed data
  • +When you need concurrent reads and writes
Java
ConcurrentHashMap<String, Integer> cache = new ConcurrentHashMap<>();

// Thread-safe atomic operations
cache.put("hits", 0);                       // O(1)
cache.putIfAbsent("sessions", 0);           // O(1) - only if absent
cache.merge("hits", 1, Integer::sum);       // O(1) - atomic increment

// Compute if absent - factory called once even under contention
int val = cache.computeIfAbsent("users", key -> {
    return expensiveComputation(key);       // called only if key missing
});

// Safe bulk operations
cache.forEach(1, (k, v) ->
    System.out.println(k + ": " + v));

// Atomic compute
cache.compute("hits", (k, v) -> v == null ? 1 : v + 1);

Memory View / Slice

ByteBuffer

Zero-copy view over a contiguous region of memory. Lets you reference a portion of an array or buffer without allocating new memory. Critical for performance-sensitive parsing and processing.

1
2
3
4
5
6

Span[0..3] = [1, 2, 3]

Complexity

Create sliceO(1)
Access by indexO(1)
SearchO(n)
CopyO(n)

When to use

  • +Parsing strings or binary data without copies
  • +Processing sub-arrays without allocation
  • +High-performance, zero-allocation code paths
  • +Interop with native or unmanaged memory
Java
// Arrays.copyOfRange - creates a new sub-array (copies data)
int[] data = {1, 2, 3, 4, 5};
int[] slice = Arrays.copyOfRange(data, 1, 4); // O(k) - [2, 3, 4]

// ByteBuffer - closest to Span, a view over byte memory
ByteBuffer buf = ByteBuffer.allocate(256);    // heap buffer
ByteBuffer direct = ByteBuffer.allocateDirect(256); // off-heap
buf.putInt(42);          // O(1) - write at position
buf.flip();              // switch from write to read mode
int val = buf.getInt();  // O(1) - read 42

// Slice creates a shared view (no copy, like Span)
ByteBuffer view = buf.slice();  // O(1) - shared underlying memory

// Java has no stack-allocated Span equivalent
// Use ByteBuffer for zero-copy views of byte data
// Use List.subList() for zero-copy list views

Big O Comparison

Average-case time complexity. * = amortized.

StructureAccessSearchInsertDelete
ArrayO(1)O(n)O(n)O(n)
Dynamic ArrayO(1)O(n)O(1)*O(n)
StackO(n)O(n)O(1)*O(1)
QueueO(n)O(n)O(1)*O(1)
Hash MapO(1)O(1)O(1)*O(1)
Hash SetN/AO(1)O(1)*O(1)
Linked ListO(n)O(n)O(1)O(1)
Sorted SetO(n)O(log n)O(log n)O(log n)
Sorted MapO(log n)O(log n)O(log n)O(log n)
Priority QueueO(n)O(n)O(log n)O(log n)
Concurrent MapO(1)O(1)O(1)*O(1)
Memory ViewO(1)O(n)N/AN/A

Which collection should I use?

I need to...Use
Store items by index, resize dynamicallyList / Dynamic Array
Map keys to values with O(1) lookupHashMap / Dictionary
Track unique items, check existence in O(1)HashSet / Set
Last-in-first-out (undo, DFS, brackets)Stack
First-in-first-out (BFS, task queues)Queue
Keep elements sorted at all timesSortedSet / TreeSet
Process items by priority (Dijkstra, top-K)PriorityQueue / Heap
Insert/delete at a known position in O(1)LinkedList
Sorted key-value pairsSortedDictionary / TreeMap
Thread-safe shared cacheConcurrentDictionary
Slice arrays/strings without copyingSpan / Slice / memoryview

Frequently Asked Questions

What are the most important data structures in Java?add

The most commonly used are dynamic arrays (List/ArrayList/vector), hash maps (Dictionary/HashMap/dict), and hash sets. For interviews, also know stacks, queues, trees, and priority queues. These cover 90%+ of coding interview problems.

Which Java data structure should I learn first?add

Start with the dynamic array and hash map. Together they solve the majority of interview problems. Then learn stacks (for DFS, bracket matching) and queues (for BFS). After that, tackle trees, heaps, and graphs.

Does Big O complexity change between languages?add

No. Big O measures algorithmic complexity, not language-specific performance. A hash map lookup is O(1) whether you use Python dict, Java HashMap, or C# Dictionary. Constant factors differ (C++ is faster than Python in wall-clock time), but Big O is the same.

Is there a built-in priority queue in Java?add

Yes. java.util.PriorityQueue is a min-heap. For a max-heap, pass Collections.reverseOrder() as the comparator.

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