Java Academy · Lesson

LinkedList vs ArrayList Trade-offs

Compare insertion, deletion, and random access performance to choose the right list type.

LinkedList vs ArrayList Trade-offs is a free Java Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Java Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

The Core Question

Both ArrayList and LinkedList implement List, so they share the same API. The difference lies in their internal data structures and the operations each performs efficiently.

ArrayList Internals

ArrayList stores elements in a contiguous array. When the array fills up, it is replaced with a new array 1.5× larger and all elements are copied.

import java.util.ArrayList;

ArrayList<String> list = new ArrayList<>(4); // initial capacity 4
list.add("A"); list.add("B"); list.add("C"); list.add("D");
list.add("E"); // triggers resize: new array of capacity 6

System.out.println(list.get(3)); // O(1) — direct index access

LinkedList Internals Revisited

Each element lives in its own Node object with prev/next pointers. No contiguous memory — nodes can be anywhere on the heap.

import java.util.LinkedList;

LinkedList<String> list = new LinkedList<>();
list.add("A"); list.add("B"); list.add("C");

// get(index) must traverse from head or tail
System.out.println(list.get(1)); // O(n) — traverses 1 step from head

Random Access: ArrayList wins

ArrayList.get(i) is O(1) — direct array index. LinkedList.get(i) is O(n) — traverses up to n/2 nodes.

ArrayList<Integer> al = new ArrayList<>();
LinkedList<Integer> ll = new LinkedList<>();
for (int i = 0; i < 100_000; i++) { al.add(i); ll.add(i); }

// Fast:
System.out.println(al.get(99_999)); // O(1)

// Slow — avoid this pattern with LinkedList:
System.out.println(ll.get(99_999)); // O(n)

Head Insertions: LinkedList wins

Adding at index 0 in ArrayList requires shifting all elements — O(n). LinkedList just updates two pointers — O(1).

// ArrayList: O(n) — shifts all elements right
ArrayList<String> al = new ArrayList<>(List.of("B","C","D"));
al.add(0, "A"); // shifts B, C, D

// LinkedList: O(1)
LinkedList<String> ll = new LinkedList<>(List.of("B","C","D"));
ll.addFirst("A"); // updates head pointer only

Tail Insertions: Roughly Equal

Both ArrayList and LinkedList offer amortized O(1) appends at the tail. ArrayList occasionally triggers a resize copy, but amortized it's still O(1). LinkedList allocates a new node — no resize needed.

ArrayList<Integer> al = new ArrayList<>();
LinkedList<Integer> ll = new LinkedList<>();

for (int i = 0; i < 1_000_000; i++) {
    al.add(i); // amortized O(1)
    ll.add(i); // O(1)
}

Memory Usage

ArrayList: ~8 bytes per element (one reference in array). LinkedList: ~48 bytes per element (Node object with data, prev, next, plus object header). For large datasets, ArrayList uses significantly less memory.

Iteration Performance

Sequential iteration (for-each or iterator) is O(n) for both. But ArrayList benefits from CPU cache prefetching — elements are contiguous in memory. LinkedList nodes scatter across the heap, causing cache misses.

// Both O(n), but ArrayList is faster in practice due to cache locality
for (String s : arrayList) { process(s); }
for (String s : linkedList) { process(s); } // more cache misses

Middle Insertion/Deletion

Both require O(n) to find position. Once found, ArrayList shifts elements O(n); LinkedList just unlinks O(1). So for frequent middle mutations when you already hold an iterator, LinkedList wins; otherwise they're similar.

LinkedList<Integer> ll = new LinkedList<>(List.of(1,2,3,4,5));
ListIterator<Integer> it = ll.listIterator();
while (it.hasNext()) {
    int val = it.next();
    if (val == 3) it.remove(); // O(1) unlink via iterator
}
System.out.println(ll); // [1, 2, 4, 5]

Decision Guide

Choose based on your dominant operation:

ArrayList: random access, iteration, tail appends — covers 90% of use cases
LinkedList: frequent head/tail insert/remove, implementing queue/deque/stack
ArrayDeque: if you need a pure queue or stack (better than LinkedList)

Benchmark Summary

Mental model for performance:

get(i): ArrayList O(1) vs LinkedList O(n)
add(0,x): ArrayList O(n) vs LinkedList O(1)
add(x): Both amortized O(1)
iterator remove: Both O(1) once positioned
Memory per element: ArrayList ~8B vs LinkedList ~48B

Quick Check

You are building a task queue where tasks are added to the end and removed from the front millions of times per second. Which data structure is most appropriate?

Recap: LinkedList vs ArrayList

Key takeaways:

ArrayList excels at random access (O(1)) and cache-friendly iteration
LinkedList excels at O(1) head/tail operations
Memory: ArrayList ~8B/element; LinkedList ~48B/element
For queues/stacks prefer ArrayDeque over LinkedList
ArrayList is the right default choice for most scenarios

Frequently asked questions

Is the “LinkedList vs ArrayList Trade-offs” lesson free?

Yes — the full text of “LinkedList vs ArrayList Trade-offs” is free to read here on the web, and the Java Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Java Academy course, upgrade to CoddyKit PRO.

What will I learn in “LinkedList vs ArrayList Trade-offs”?

Compare insertion, deletion, and random access performance to choose the right list type. You practise Java Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Java Academy?

No prior experience is required. Java Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “LinkedList vs ArrayList Trade-offs” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

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