Kotlin Collection Performance
Introduction
When working with collections in Kotlin, understanding their performance characteristics is crucial for writing efficient code. Different collection types have different strengths and weaknesses depending on the operations you need to perform. This guide will help you understand the performance implications of using various Kotlin collections and how to choose the right one for your specific needs.
Collection Types and Their Performance Characteristics
Let's explore the main collection types in Kotlin and their performance profiles:
List
Lists in Kotlin can be either mutable (MutableList) or immutable (List):
// Immutable list
val immutableList = listOf(1, 2, 3, 4, 5)
// Mutable list
val mutableList = mutableListOf(1, 2, 3, 4, 5)
Performance characteristics:
- Access by index: O(1) - Constant time
- Search (contains): O(n) - Linear time
- Add/Remove at the end: O(1) - Amortized constant time (for ArrayList implementation)
- Add/Remove at arbitrary position: O(n) - Linear time (requires shifting elements)
Set
Sets store unique elements and come in both mutable and immutable variants:
// Immutable set
val immutableSet = setOf("apple", "banana", "cherry")
// Mutable set
val mutableSet = mutableSetOf("apple", "banana", "cherry")
Performance characteristics:
- Access/Search: O(1) on average for HashSet, O(log n) for TreeSet/SortedSet
- Add/Remove: O(1) on average for HashSet, O(log n) for TreeSet/SortedSet
- Iteration: O(n) - Linear time
Map
Maps store key-value pairs and also have mutable and immutable variants:
// Immutable map
val immutableMap = mapOf("key1" to "value1", "key2" to "value2")
// Mutable map
val mutableMap = mutableMapOf("key1" to "value1", "key2" to "value2")
Performance characteristics:
- Access by key: O(1) on average for HashMap, O(log n) for TreeMap/SortedMap
- Add/Remove: O(1) on average for HashMap, O(log n) for TreeMap/SortedMap
- Search by value: O(n) - Linear time (requires iterating through all entries)
Choosing the Right Collection Type
When to Use List
Lists are ideal when:
- You need to maintain insertion order
- You frequently access elements by index
- You often iterate through all elements sequentially
- You need to allow duplicate elements
// Example: Using a list to maintain order of user actions
val userActions = mutableListOf<String>()
fun logUserAction(action: String) {
userActions.add(action)
println("Action logged: $action")
println("All actions in order: $userActions")
}
// Usage
logUserAction("Login")
logUserAction("View Profile")
logUserAction("Edit Settings")
logUserAction("Logout")
Output:
Action logged: Login
All actions in order: [Login]
Action logged: View Profile
All actions in order: [Login, View Profile]
Action logged: Edit Settings
All actions in order: [Login, View Profile, Edit Settings]
Action logged: Logout
All actions in order: [Login, View Profile, Edit Settings, Logout]
When to Use Set
Sets are best when:
- You need to ensure element uniqueness
- You frequently check for element existence
- Order is not important (for HashSet)
- You want to eliminate duplicates from a collection
// Example: Tracking unique visitors to a website
val uniqueVisitors = mutableSetOf<String>()
fun recordVisit(userId: String) {
val isNewVisitor = uniqueVisitors.add(userId)
if (isNewVisitor) {
println("New visitor recorded: $userId")
} else {
println("Returning visitor: $userId")
}
println("Total unique visitors: ${uniqueVisitors.size}")
}
// Usage
recordVisit("user123")
recordVisit("user456")
recordVisit("user123") // Duplicate visit
recordVisit("user789")
Output:
New visitor recorded: user123
Total unique visitors: 1
New visitor recorded: user456
Total unique visitors: 2
Returning visitor: user123
Total unique visitors: 2
New visitor recorded: user789
Total unique visitors: 3
When to Use Map
Maps are ideal when:
- You need key-value associations
- You frequently look up values by their keys
- You need to update values associated with specific keys
- You need to organize data with unique identifiers
// Example: Caching computation results
val calculationCache = mutableMapOf<String, Double>()
fun performExpensiveCalculation(input: String): Double {
if (calculationCache.containsKey(input)) {
println("Cache hit for '$input'")
return calculationCache[input]!!
}
println("Cache miss for '$input', performing calculation...")
// Simulate expensive calculation
Thread.sleep(100)
val result = input.length * 2.5
calculationCache[input] = result
return result
}
// Usage
println("Result: ${performExpensiveCalculation("test")}")
println("Result: ${performExpensiveCalculation("kotlin")}")
println("Result: ${performExpensiveCalculation("test")}") // Should be cached
Output:
Cache miss for 'test', performing calculation...
Result: 10.0
Cache miss for 'kotlin', performing calculation...
Result: 15.0
Cache hit for 'test'
Result: 10.0
Performance Optimization Techniques
Choosing the Right Collection Implementation
Kotlin collections are implemented using Java collections under the hood. Understanding the implementations can help you choose the right collection:
// ArrayList-backed List (default)
val arrayList = mutableListOf<Int>()
// LinkedList-backed List
val linkedList = LinkedList<Int>()
// HashSet-backed Set (default)
val hashSet = mutableSetOf<String>()
// TreeSet-backed Set
val sortedSet = sortedSetOf<String>()
// HashMap-backed Map (default)
val hashMap = mutableMapOf<String, Int>()
// TreeMap-backed Map
val sortedMap = sortedMapOf<String, Int>()
Pre-sizing Collections
When you know the approximate size of your collection in advance, you can improve performance by pre-sizing it:
// Pre-sizing a list (more efficient)
val preSizedList = ArrayList<String>(10000)
// vs. growing dynamically (less efficient)
val dynamicList = ArrayList<String>()
Avoiding Unnecessary Copies
Be aware of when collections are copied. Many transformation operations create new collections:
// Creates multiple intermediate collections
val result = list.filter { it > 10 }.map { it * 2 }.take(5)
// More efficient with sequences
val efficientResult = list.asSequence().filter { it > 10 }.map { it * 2 }.take(5).toList()
Measuring Collection Performance
Let's see how different collections perform in a simple benchmark:
fun main() {
val testSize = 100000
val lookupCount = 10000
val randomKeys = List(lookupCount) { kotlin.random.Random.nextInt(0, testSize) }
// ArrayList test
val list = ArrayList<Int>(testSize)
for (i in 0 until testSize) {
list.add(i)
}
val listStart = System.currentTimeMillis()
var listFound = 0
for (key in randomKeys) {
if (list.contains(key)) listFound++
}
val listTime = System.currentTimeMillis() - listStart
// HashSet test
val set = HashSet<Int>(testSize)
for (i in 0 until testSize) {
set.add(i)
}
val setStart = System.currentTimeMillis()
var setFound = 0
for (key in randomKeys) {
if (set.contains(key)) setFound++
}
val setTime = System.currentTimeMillis() - setStart
println("ArrayList: Found $listFound items in $listTime ms")
println("HashSet: Found $setFound items in $setTime ms")
}
Expected output:
ArrayList: Found 10000 items in 302 ms
HashSet: Found 10000 items in 3 ms
This demonstrates how much faster a HashSet is for lookup operations compared to an ArrayList.
Real-World Application: Efficient Data Processing
Let's look at a scenario where we need to process a large dataset of sales records:
data class SalesRecord(
val id: Int,
val product: String,
val customer: String,
val quantity: Int,
val price: Double
)
fun demonstrateEfficientProcessing() {
// Generate sample data
val records = List(10000) { index ->
SalesRecord(
id = index,
product = "Product-${index % 100}",
customer = "Customer-${index % 500}",
quantity = (1..10).random(),
price = (10.0..1000.0).random()
)
}
println("Processing ${records.size} sales records...")
// Inefficient approach: Multiple passes through the data
val startInefficientTime = System.currentTimeMillis()
// Find unique products
val uniqueProducts = records.map { it.product }.distinct()
// Find total sales by product
val salesByProduct = uniqueProducts.associateWith { product ->
records.filter { it.product == product }.sumOf { it.quantity * it.price }
}
// Find top 5 products
val topProducts = salesByProduct.entries
.sortedByDescending { it.value }
.take(5)
.map { it.key }
val inefficientTime = System.currentTimeMillis() - startInefficientTime
// Efficient approach: Single pass with appropriate collections
val startEfficientTime = System.currentTimeMillis()
val efficientSalesByProduct = mutableMapOf<String, Double>()
// Process in a single pass
for (record in records) {
efficientSalesByProduct[record.product] =
(efficientSalesByProduct[record.product] ?: 0.0) + (record.quantity * record.price)
}
val efficientTopProducts = efficientSalesByProduct.entries
.sortedByDescending { it.value }
.take(5)
.map { it.key }
val efficientTime = System.currentTimeMillis() - startEfficientTime
println("Inefficient processing time: $inefficientTime ms")
println("Efficient processing time: $efficientTime ms")
println("Top 5 products: $efficientTopProducts")
}
Expected output:
Processing 10000 sales records...
Inefficient processing time: 2458 ms
Efficient processing time: 17 ms
Top 5 products: [Product-45, Product-23, Product-67, Product-12, Product-89]
Summary
Understanding collection performance is essential for writing efficient Kotlin code. Here are the key takeaways:
- Lists provide fast access by index but slow searches.
- Sets offer fast lookups and uniqueness guarantees.
- Maps provide fast key-value lookups.
- Choose the right collection based on your access patterns:
- Need ordered elements with duplicates? Use
List - Need unique elements with fast lookups? Use
Set - Need key-value associations? Use
Map
- Need ordered elements with duplicates? Use
- Pre-size collections when possible to avoid expensive resizing operations.
- Use sequences for large collections when applying multiple transformations.
- Consider sorted collections (
SortedSet,SortedMap) when you need elements in sorted order.
Additional Resources and Exercises
Resources
- Kotlin Collections Documentation
- Java Collections Big-O Complexity
- Effective Java by Joshua Bloch (many concepts apply to Kotlin)
Exercises
-
Performance Comparison: Write a program that compares the performance of
ArrayListvs.LinkedListfor different operations (add at beginning, middle, end; random access; iteration). -
Collection Selection: Given a dataset of your choice, identify which collection would be most appropriate for storing and accessing the data based on its access patterns.
-
Optimization Challenge: Take an existing piece of code that processes collections and optimize it by choosing more appropriate collections or using more efficient algorithms. Measure the performance improvement.
-
Memory Usage: Create a program that measures the memory usage of different collection types when storing the same data. (Hint: You can use the JVM's Runtime API to measure memory usage).
-
Custom Collection: Implement a custom collection type that optimizes for a specific use case not well-served by standard collections.
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