Data Structures in Kotlin

12 essential data structures for Kotlin 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

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
Kotlin
// Fixed-size typed array
val nums = intArrayOf(10, 20, 30, 40, 50)
val words = arrayOf("cat", "ant", "bat")

nums[0] = 99                          // O(1) - direct index
val third = nums[2]                   // O(1) - read by index

// Transform and search
val doubled = nums.map { it * 2 }     // O(n) - returns List<Int>
val has = 30 in nums                   // O(n) - linear scan
val idx = nums.indexOf(40)            // O(n) - linear search

nums.sort()                            // O(n log n) - in-place
val found = nums.binarySearch(30)     // O(log n) - requires sorted
val sum = nums.sum()                   // O(n)

Dynamic Array

MutableList (ArrayList)

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
Kotlin
val names = mutableListOf("Alice", "Bob", "Charlie")

names.add("Diana")                    // O(1) amortized
names.add(1, "Eve")                   // O(n) - shifts elements right
names.removeAt(0)                     // O(n) - shifts elements left
names.removeLast()                    // O(1)

val has = "Bob" in names              // O(n) - linear scan
val first = names[0]                  // O(1) - direct index
val idx = names.indexOf("Eve")       // O(n)

names.sort()                          // O(n log n)
names.retainAll { it.length > 3 }    // O(n) - filter in place

// Kotlin idioms
val squares = (1..10).map { it * it } // O(n) - list comprehension

Stack

ArrayDeque (LIFO)

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
Kotlin
// ArrayDeque as stack - push/pop from the end
val stack = ArrayDeque<Int>()

stack.addLast(10)                    // O(1) - push
stack.addLast(20)
stack.addLast(30)

val top = stack.last()               // O(1) - peek -> 30
val popped = stack.removeLast()      // O(1) - pop -> 30

// Interview pattern: valid parentheses
fun isValid(s: String): Boolean {
    val st = ArrayDeque<Char>()
    for (c in s) {
        when (c) {
            '(' -> st.addLast(')')
            '[' -> st.addLast(']')
            '{' -> st.addLast('}')
            else -> if (st.isEmpty() || st.removeLast() != c) return false
        }
    }
    return st.isEmpty()
}

Queue

ArrayDeque (FIFO)

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
Kotlin
// ArrayDeque as queue - FIFO
val queue = ArrayDeque<String>()

queue.addLast("Task A")              // O(1) - enqueue
queue.addLast("Task B")
queue.addLast("Task C")

val front = queue.first()            // O(1) - peek -> "Task A"
val next = queue.removeFirst()       // O(1) - dequeue -> "Task A"

// Interview pattern: BFS level-order
fun bfs(root: TreeNode?): List<List<Int>> {
    val result = mutableListOf<List<Int>>()
    val q = ArrayDeque<TreeNode>()
    root?.let { q.addLast(it) }
    while (q.isNotEmpty()) {
        val level = (0 until q.size).map {
            val node = q.removeFirst()
            node.left?.let { q.addLast(it) }
            node.right?.let { q.addLast(it) }
            node.value
        }
        result.add(level)
    }
    return result
}

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
Kotlin
val map = hashMapOf("apple" to 3, "banana" to 5)

map["apple"] = 10                        // O(1) - update
map["cherry"] = 2                        // O(1) - add
val has = "banana" in map                // O(1) - contains key
map.remove("banana")                     // O(1)

// Safe lookup with default
val count = map.getOrDefault("mango", 0) // O(1) - 0

// Interview pattern: Two Sum
fun twoSum(nums: IntArray, target: Int): IntArray {
    val seen = HashMap<Int, Int>()
    for ((i, num) in nums.withIndex()) {
        val need = target - num
        seen[need]?.let { return intArrayOf(it, i) }
        seen[num] = i                    // O(1) insert
    }
    return intArrayOf()
}

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
Kotlin
val set = hashSetOf(1, 2, 3, 4, 5)

set.add(6)                            // O(1) - true (added)
set.add(3)                            // O(1) - false (duplicate)
set.remove(1)                         // O(1)
val has = 4 in set                    // O(1) - true

// Set operations
val other = hashSetOf(4, 5, 6, 7)
val inter = set intersect other       // {4, 5, 6}
val union = set union other           // {2, 3, 4, 5, 6, 7}
val diff = set subtract other         // {2, 3}

// Interview pattern: contains duplicate
fun containsDuplicate(nums: IntArray): Boolean {
    val seen = HashSet<Int>()
    return nums.any { !seen.add(it) }  // add returns false if dup
}

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
Kotlin
import java.util.LinkedList

val list = LinkedList<Int>()

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

list.add(1, 15)                      // O(n) - traverse to index
val has = 20 in list                 // O(n) - linear scan
list.remove(20)                      // O(n) - find then unlink

val first = list.peekFirst()         // O(1) - 5
val last = list.peekLast()           // O(1) - 15

// Iterate
for (val in list) print("$val ")     // 5 15 10

// Interview: reverse a linked list
fun reverse(head: ListNode?): ListNode? {
    var prev: ListNode? = null; var curr = head
    while (curr != null) {
        val next = curr.next; curr.next = prev; prev = curr; curr = next
    }
    return prev
}

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
Kotlin
import java.util.TreeSet

val sorted = TreeSet(listOf(5, 3, 8, 1, 9))
// Maintains sorted order: {1, 3, 5, 8, 9} (red-black tree)

sorted.add(4)                        // O(log n)
sorted.remove(3)                     // O(log n)
val has = 8 in sorted                // O(log n) - true

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

// Range and navigation queries
val range = sorted.subSet(4, 9)      // [4, 9) -> {4, 5, 8}
val floor = sorted.floor(6)          // 5 - greatest <= 6
val ceil = sorted.ceiling(6)         // 8 - smallest >= 6
val higher = 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
Kotlin
import java.util.TreeMap

val tm = TreeMap<String, Int>()
tm["banana"] = 2                     // O(log n)
tm["apple"] = 5
tm["cherry"] = 1
tm["date"] = 3

val v = tm["apple"]                  // O(log n) -> 5
tm.remove("cherry")                  // O(log n)

// Iterates in sorted key order
for ((key, value) in tm) {
    println("$key: $value")          // apple: 5, banana: 2, date: 3
}

// Navigation methods
val firstKey = tm.firstKey()         // "apple"
val floorKey = tm.floorKey("cat")    // "banana" - greatest <= "cat"
val sub = tm.subMap("apple", "cherry") // {apple=5, banana=2}

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
Kotlin
import java.util.PriorityQueue

// Min-heap by default
val pq = PriorityQueue<Int>()

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

val min = pq.peek()                  // O(1) - 10
val top = pq.poll()                  // O(log n) - 10, removed

// Max-heap using reversed comparator
val maxPQ = PriorityQueue<Int>(compareByDescending { it })

// Interview pattern: K closest points
fun kClosest(points: Array<IntArray>, k: Int): Array<IntArray> {
    val pq = PriorityQueue<IntArray>(
        compareBy { it[0] * it[0] + it[1] * it[1] }
    )
    points.forEach { pq.offer(it) }
    return Array(k) { pq.poll() }
}

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
Kotlin
import java.util.concurrent.ConcurrentHashMap

val cache = ConcurrentHashMap<String, Int>()

// Thread-safe atomic operations
cache["hits"] = 0                               // O(1)
cache.putIfAbsent("sessions", 0)                // O(1)
cache.merge("hits", 1, Int::plus)               // O(1) - atomic increment

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

// Atomic compute
cache.compute("hits") { _, v -> (v ?: 0) + 1 }

// Safe iteration - weakly consistent, no ConcurrentModificationException
cache.forEach { (k, v) -> println("$k: $v") }

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
Kotlin
import java.nio.ByteBuffer

// Heap-allocated buffer
val buf = ByteBuffer.allocate(256)
val direct = ByteBuffer.allocateDirect(256) // off-heap, faster I/O

buf.putInt(42)                       // O(1) - write at position
buf.putDouble(3.14)
buf.flip()                           // switch from write to read mode

val intVal = buf.getInt()            // O(1) - read 42
val dblVal = buf.getDouble()         // O(1) - read 3.14

// Slice creates a shared view (zero-copy, like Span)
buf.position(0).limit(4)
val view = buf.slice()               // O(1) - shared underlying memory

// Kotlin extension: convert to byte array when needed
val bytes = ByteArray(buf.remaining())
buf.get(bytes)                       // O(n) - copy into array

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 Kotlin?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 Kotlin 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 Kotlin?add

It depends on the language runtime. Check the Kotlin standard library documentation for heap or priority queue support.

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