In previous post, we introduced this * List* collection and some implementations like

*and*

**LinkedList***.*

**ArrayList**In this post, we are going to compare * ArrayList* and

**LinkedList**performance using

**addition operations**.

**TRY IT YOURSELF**: You can find the source code of this post here.

### Java Collections List Series

- Part 1: Java Collections: List
- Part 2: ArrayList vs LinkedList: Addition (You are here)
- Part 3: ArrayList vs LinkedList: Deletion
- Part 4: ArrayList vs LinkedList: Sort, Get and Iteration

## Environment

- JDK 11.0.3, OpenJDK 64-Bit Server VM, 11.0.3+7-Ubuntu-1ubuntu218.04.1
- Ubuntu 18.04
- Memory 15,6 GiB
- Processor Intel® Core™ i7-7500U CPU @ 2.70GHz × 4
- Graphics Intel® HD Graphics 620 (Kaby Lake GT2)
- OS 64-bit
- Disk 387,9 GB
- Intellj IDE Community 2018.3

## Approach

- We are going to use
**JMH for the micro benchmark**. - We are going to use a list of
**10.000.000 of elements**, generated randomly, and we are going to calculate the**average response time in 10 iterations**. - As JMH
**doesn’t allow sharing data between benchmarks**, we are going to create a file with the list of elements, and read from it by each benchmark. In that way, we guarantee we are measuring the same scenario.

## Adding at the End – `add(type)`

Now, let’s see the code for measuring how * ArrayList* and

*behave when we need to*

**LinkedList****add at the end**:

@Test public void add_end() throws RunnerException, IOException { Utils.populateToFile(); Options opt = new OptionsBuilder() .include( ArrayListVsLinkedListAdditionEndTest.class .getSimpleName()) .detectJvmArgs() .build(); Collection<RunResult> runResults = new Runner(opt) .run(); Result arrayListResult = Utils .find("arrayList", runResults); Result linkedListResult = Utils .find("linkedList", runResults); assertThat(linkedListResult.getScore()) .isLessThan(arrayListResult.getScore()); } @Benchmark @Measurement(iterations = Utils.amountIterations, batchSize = 1, time = 1) @BenchmarkMode(Mode.SingleShotTime) @Fork(1) @Warmup(iterations = 10) @OutputTimeUnit(TimeUnit.MILLISECONDS) public ArrayList<Integer> arrayList( ArrayListExecutionPlan executionPlan) { ArrayList<Integer> list = executionPlan.arrayList; list.add(executionPlan.element); return list; } @Benchmark @Measurement(iterations = Utils.amountIterations, batchSize = 1, time = 1) @BenchmarkMode(Mode.SingleShotTime) @Fork(1) @Warmup(iterations = 10) @OutputTimeUnit(TimeUnit.MILLISECONDS) public LinkedList<Integer> linkedList( LinkedListExecutionPlan executionPlan) { LinkedList<Integer> list = executionPlan.linkedList; list.add(executionPlan.element); return list; }

**TRY IT YOURSELF**: You can find this source code here.

Now, let’s see the results:

That is pretty interesting, ** LinkedList** is pretty fast. Let’s see why they behave like that.

### ArrayList

Well, let’s use this example:

Now, we want to **add a 2 number to the end** of the list. However, this is a pure Java array, it means, we **only have 7 spots for numbers**, nothing more. So, ** ArrayList** will do the following:

1. Creates a new pure Java array with more space:

**NOTE**: ** ArrayList** can choose to increase in different ways,

`list.size()/2`

is the more common.2. Copy the elements from our previous array to the new one:

3. Add the element we requested:

We can conclude:

- In the
**worst case, we need to create a new bigger array**, if we reach the current size, this means, we will reserve more memory than we might not use. - We need to
**copy the whole array in a new one**in the worst case, so, there are**n**operations. - The complexity of this operation is
**O(n)**. - The
**following additions are going to be fast**until we reach the new size. - There is a limit. The max capacity for a
**pure Java array is 2147483639**.

### LinkedList

Well, let’s use this example:

Now, we want to **add a 2 number to the end** of the list. This is the process:

1. Create a new node for the new element:

2. Linking the new node to the **Tail**:

3. Moving the **Tail** to the new node:

We can conclude:

- We only need to
**create a new node and linked to the tail**. - The amount of operations is
**1**. - The complexity of this operation is
**O(1)**. - The size
**limit here is defined by your JVM memory**, there is no other restriction.

## Adding to the Middle – `add(index, type)`

Now, let’s see the code for measuring how ** ArrayList** and

**behave when we need to**

*LinkedList***add at the middle**:

@Test public void add_middle() throws RunnerException, IOException { Utils.populateToFile(); Options opt = new OptionsBuilder() .include( ArrayListVsLinkedListAdditionMiddleTest.class .getSimpleName()) .detectJvmArgs() .build(); Collection<RunResult> runResults = new Runner(opt) .run(); Result arrayListResult = Utils .find("arrayList", runResults); Result linkedListResult = Utils .find("linkedList", runResults); assertThat(linkedListResult.getScore()) .isLessThan(arrayListResult.getScore()); } @Benchmark @Measurement(iterations = Utils.amountIterations, batchSize = 1, time = 1) @BenchmarkMode(Mode.SingleShotTime) @Fork(1) @Warmup(iterations = 10) @OutputTimeUnit(TimeUnit.MILLISECONDS) public ArrayList<Integer> arrayList( ArrayListExecutionPlan executionPlan) { ArrayList<Integer> list = executionPlan.arrayList; list.add(list.size() / 2, executionPlan.element); return list; } @Benchmark @Measurement(iterations = Utils.amountIterations, batchSize = 1, time = 1) @BenchmarkMode(Mode.SingleShotTime) @Fork(1) @Warmup(iterations = 10) @OutputTimeUnit(TimeUnit.MILLISECONDS) public LinkedList<Integer> linkedList( LinkedListExecutionPlan executionPlan) { LinkedList<Integer> list = executionPlan.linkedList; list.add(list.size() / 2, executionPlan.element); return list; }

**TRY IT YOURSELF**: You can find this source code here.

Now, let’s see the results:

** LinkedList** is faster. Let’s see why they behave like that.

### ArrayList

Well, let’s use this example:

Now, we want to **add a 2 number to the middle**, before 9 number of the list. So, ** ArrayList** will do the following:

1. Creates a new pure Java array with with more space:

2. Copy the elements from our previous array to the new one:

3. Moves the elements after the index one position:

4. Insert the element to the empty position:

We can conclude:

- In the
**worst case, we need to create a new bigger array**, if we reach the current size, this means, we will reserve more memory than we might not use. - We need to
**copy the whole array in a new one**in the worst case, so, the amount of operations is**n**. - We need to
**move the elements one position to create an empty space (n/2)** - The total amount of operations needed is
**n + n/2**. - The complexity of this operation is
**O(n)**. **Any insertion will require to move the elements one position**.- There is a limit. The max capacity for a pure Java array is 2147483639.

### LinkedList

Well, let’s use this example:

Now, we want to **add a 2 number before the 9 number **of the list. This is the process:

1. Move to the index from the closest reference (Head or Tail), in this case, Head is closest:

2. Creating the new node:

3. Changing references from 5 number and 9 number to the new node:

We can conclude:

- In the
**worst case, we need to move from the head/tail to the index (n/2)**. - The complexity of this operation is
**O(n)**. - We only
**need to create a new node**and linked to the tail. - The size limit here is defined by your JVM memory, there is no other restriction.

## Adding to the Start – `add(0, type)`

Now, let’s see the code for measuring how ** ArrayList** and

**behave when we need to**

*LinkedList***add at the start:**

@Test public void add_start() throws RunnerException, IOException { Utils.populateToFile(); Options opt = new OptionsBuilder() .include( ArrayListVsLinkedListAdditionStartTest.class .getSimpleName()) .detectJvmArgs() .build(); Collection<RunResult> runResults = new Runner(opt) .run(); Result arrayListResult = Utils .find("arrayList", runResults); Result linkedListResult = Utils .find("linkedList", runResults); assertThat(linkedListResult.getScore()) .isLessThan(arrayListResult.getScore()); } @Benchmark @Measurement(iterations = Utils.amountIterations, batchSize = 1, time = 1) @BenchmarkMode(Mode.SingleShotTime) @Fork(1) @Warmup(iterations = 10) @OutputTimeUnit(TimeUnit.MILLISECONDS) public ArrayList<Integer> arrayList( ArrayListExecutionPlan executionPlan) { ArrayList<Integer> list = executionPlan.arrayList; list.add(0, executionPlan.element); return list; } @Benchmark @Measurement(iterations = Utils.amountIterations, batchSize = 1, time = 1) @BenchmarkMode(Mode.SingleShotTime) @Fork(1) @Warmup(iterations = 10) @OutputTimeUnit(TimeUnit.MILLISECONDS) public LinkedList<Integer> linkedList( LinkedListExecutionPlan executionPlan) { LinkedList<Integer> list = executionPlan.linkedList; list.add(0, executionPlan.element); return list; }

**TRY IT YOURSELF**: You can find this source code here.

Now, let’s see the results:

** LinkedList** is faster, pretty fast. Let’s see why they behave like that.

### ArrayList

Well, let’s use this example:

Now, we want to **add a 2 number to the start**. So, ** ArrayList** will do the following:

1. Creates a new pure Java array with with more space:

2. Copy the elements from our previous array to the new one:

3. Moves the elements after the index, one position, in this case, it needs to move everything:

4. Insert the element at the start:

We can conclude:

- In the
**worst case, we need to create a new bigger array**, if we reach the current size, this means, we will reserve more memory than we might not use. - We need to
**copy the whole array in a new one**in the worst case (**n**). - We need to
**move every element, one position to create an empty**space at the start (**n**). - The complexity of this operation is
**O(n)**. - There is a limit. The max capacity for a pure Java array is 2147483639.

### LinkedList

Well, let’s use this example:

Now, we want to **add a 2 number at the start** of the list. This is the process:

1. Create a new node for the new element:

2. Linking the new node to the Head:

3. Moving the Head to the new node:

We can conclude:

- We only
**need to create a new node**and linked to the head. - The amount of operations is
**1**. - The complexity of this operation is
**O(1)**. - The size limit here is defined by your JVM memory, there is no other restriction.

## Summary

Now, this is a summary of the** amount of operations** in addition between ** ArrayList** and

**:**

*LinkedList*ArrayList | LinkedList | ||||

Operation | Method | Best | Worst | Best | Worst |

Add At End | add(type) | 1 | n | 1 | 1 |

Add At Middle | add(index, type) | n/2 | n + n/2 | n/2 | n/2 |

Add At Start | add(0, type) | n | n + n | 1 | 1 |

Then, this is a summary of the **Big O complexity** in addition operations between ** ArrayList** and

**:**

*LinkedList*ArrayList | LinkedList | ||||

Operation | Method | Best | Worst | Best | Worst |

Add At End | add(type) | O(1) | O(n) | O(1) | O(1) |

Add At Middle | add(index, type) | O(n) | O(n) | O(n) | O(n) |

Add At Start | add(0, type) | O(n) | O(n) | O(1) | O(1) |

## Final Thought

As we saw, ** LinkedList** is better than

**when we talk about addition operations.**

*ArrayList*This means, if you have a use case were you required a constant performance through the additions, you should choose the ** LinkedList**.

In the following post, we are going to talk about deletion operations.

If you liked this post and are interested in hearing more about my journey as a Software Engineer, you can **follow me on Twitter** and travel together.

[…] Part 2: ArrayList vs LinkedList: Addition […]

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[…] Part 2: ArrayList vs LinkedList: Addition […]

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[…] Part 2: ArrayList vs LinkedList: Addition […]

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Would ArrayList be faster for appending when we know what the amounts of elements to be added is and initialize the ArrayList with such size then creating it (assuming of course that the size is small enough to be created)?

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Yes, you are right, not common to know th size before hand

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