Benchmark: Search: Array to Map and find vs Array.find

Script Preparation code:

data = [...Array(100)].map((_, id) => ({ id, data: Math.random() }))

Tests:

Array to Map and find
const res = new Map(data.map(v => [v.id, v])).get(50)
Array.find
const res = data.find(({ id }) => id === 50)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array to Map and find
Array.find

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36

Browser/OS: Chrome 140 on Windows

View result in a separate tab

Test name	Executions per second
Array to Map and find	570024.1 Ops/sec
Array.find	39848256.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definition and test cases.

**Benchmark Definition:**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The benchmark in question tests two approaches for finding an element in an array:

1. **Array to Map**: This approach creates a Map data structure from the input array, where each key-value pair corresponds to an element's index and value. Then, it uses the `get()` method to retrieve a specific value from the map.
2. **Array.find()**: This is a built-in JavaScript method that allows you to find the first element in an array that satisfies a given condition.

**Options Compared:**

The two approaches being compared are:

* Creating a Map from the input array (`Array to Map`)
* Using the `Array.find()` method

**Pros and Cons of Each Approach:**

### Array to Map:

Pros:

* Can be faster for large arrays, since it avoids the overhead of iterating over each element in the array.
* Can take advantage of JavaScript's built-in map operations.

Cons:

* Requires more memory allocation and copying of data, since a new Map object is created from the input array.
* May have slower initial creation times compared to Array.find(), since it needs to create a map from scratch.

### Array.find():

Pros:

* Fast and lightweight, since it only iterates over each element in the array once.
* Does not require additional memory allocation or copying of data.

Cons:

* Can be slower for very large arrays, since it may need to iterate over a significant portion of the array.
* May have slower performance compared to Array.toMap() for certain use cases.

**Library and Purpose:**

There are no external libraries used in this benchmark. The `Array` class and its methods (such as `map()` and `find()`) are built-in JavaScript classes.

**Special JS Feature or Syntax:**

There is no special JavaScript feature or syntax used in this benchmark. It only relies on standard JavaScript concepts and built-ins.

**Other Alternatives:**

If you're interested in exploring alternative approaches, here are a few examples:

* **Caching**: You could consider caching the result of `Array.find()` for repeated executions, to reduce overhead.
* **Parallel Processing**: If you have multiple CPU cores available, you could use parallel processing techniques (such as Web Workers or async/await) to execute both `Array.toMap()` and `Array.find()` concurrently.
* **Different Data Structures**: You could experiment with using other data structures, such as a Set or a Binary Search Tree, instead of a Map. This might affect the performance characteristics of each approach.

Keep in mind that these alternatives may not necessarily lead to significant improvements, especially for small input sizes. However, they can provide interesting variations on the original benchmark and insights into the performance trade-offs involved.

Related benchmarks:

Search: Array to Map and find vs Array.find (version: 0)

Comparing performance of: Array to Map and find vs Array.find

Created: 6 years ago by: Guest

Jump to the latest result

Array to Map and find

Array.find

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):