Benchmark: Search: Array to Map and find vs Array.find685000

Script Preparation code:

data = [...Array(685000)].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: 11 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36

Browser/OS: Chrome 120 on Windows

View result in a separate tab

Test name	Executions per second
Array to Map and find	6.5 Ops/sec
Array.find	4287893.5 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**What is being tested?**

The benchmark measures the performance of two different approaches:

1. **Array to Map conversion**: This involves creating a new `Map` data structure from an array, where each element in the array becomes a key-value pair in the map.
2. **Array.find method**: This involves using the built-in `find()` method on an array to find the first element that satisfies a given condition.

**Options compared**

The benchmark compares two different approaches:

1. **Manual implementation (Array to Map)**: The test case uses a manual implementation of creating a map from an array, where each element is converted to a key-value pair.
2. **Built-in Array.find method**: The test case uses the built-in `find()` method on an array to find the first matching element.

**Pros and Cons**

1. **Manual implementation (Array to Map)**:
	* Pros: Can be optimized for specific use cases, can provide more control over data transformation.
	* Cons: May be slower due to the overhead of creating a new map and iterating over the array.
2. **Built-in Array.find method**:
	* Pros: Fast and efficient, often implemented in native code for better performance.
	* Cons: Less control over data transformation, may not work well with specific use cases.

**Other considerations**

The benchmark does not consider other approaches, such as:

1. Using `Array.prototype.filter()` instead of `find()`.
2. Using a library or framework that provides an optimized implementation of these methods.
3. Considering the cost of garbage collection and memory allocation when using manual implementations.

**Library: Map data structure**

A `Map` is a built-in JavaScript data structure that stores key-value pairs. In this benchmark, it's used to store the transformed array data. The purpose of a map is to provide efficient lookup and iteration over its elements.

**Special JS feature or syntax (None)**

There are no special JavaScript features or syntax used in these test cases.

**Benchmark preparation code**

The provided script preparation code creates an array of 685,000 elements, where each element has an `id` property and a random `data` value. This data is then used to create the map and perform the `find()` operation.

**Other alternatives**

If you want to explore other approaches or optimizations for this benchmark, consider the following:

1. Use `Array.prototype.filter()` instead of `find()`.
2. Use a library like Lodash or Ramda that provides optimized implementations of these methods.
3. Profile the code using tools like Chrome DevTools or Node.js Inspector to identify performance bottlenecks.
4. Consider using a just-in-time (JIT) compiler like SpiderMonkey or V8 to optimize the JavaScript engine.

Related benchmarks:

Search: Array to Map and find vs Array.find685000 (version: 0)

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

Created: 11 months ago by: Guest

Jump to the latest result

Array to Map and find

Array.find

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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