Benchmark: Object vs Map creation (forEach, reduce)

Script Preparation code:

var data = { ...Array.from(Array(10000).keys()) };

Tests:

Object forEach
const o = {} Object.entries(data).forEach(([k, v]) => { o[k] = v; });
Object reduce
Object.entries(data).reduce((acc, [k, v]) => { acc[k] = v; return acc; }, {});
Map forEach
const o = new Map() Object.entries(data).forEach(([k, v]) => { o.set(k, v); });
Map reduce
Object.entries(data).reduce((acc, [k, v]) => { acc[k] = v; return acc; }, new Map());

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object forEach
Object reduce
Map forEach
Map reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36

Browser/OS: Chrome 99 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Object forEach	3261.5 Ops/sec
Object reduce	3162.8 Ops/sec
Map forEach	2091.0 Ops/sec
Map reduce	2875.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is comparing three approaches for creating an object from data:

1. `Object.entries(data).forEach(([k, v]) => { ... });` (Map-style)
2. `Object.entries(data).reduce((acc, [k, v]) => { ... }, {});` (Reduce-style)
3. `const o = new Map().forEach([k, v]);` (Map forEach)
4. `Object.entries(data).reduce((acc, [k, v]) => { ... }, new Map());` (Map reduce)

**Options Being Compared**

The benchmark is comparing the performance of these four approaches:

* **Map-style**: Using the `forEach` method on an object created from `data`.
* **Reduce-style**: Using the `reduce` method on an object created from `data`.
* **Map forEach**: Using the `forEach` method on a new Map.
* **Map reduce**: Using the `reduce` method on a new Map.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Map-style**:
	* Pros: Simple, concise code. Easy to understand.
	* Cons: May not be as efficient as other approaches, especially for large datasets.
2. **Reduce-style**:
	* Pros: Can be more memory-efficient than the Map-style approach, since it doesn't create a new object.
	* Cons: Requires understanding of the `reduce` method and its callback function.
3. **Map forEach**:
	* Pros: Similar to the Map-style approach, but with better memory management (since Maps are designed for this use case).
	* Cons: May not be as intuitive or easy to understand as other approaches.
4. **Map reduce**:
	* Pros: Combines the efficiency of a Reduce function with the benefits of using a Map.
	* Cons: More complex than the other approaches, requiring understanding of both Maps and Reduce functions.

**Special Considerations**

In this benchmark, no special JavaScript features or syntax are being tested. The focus is on comparing the performance of different object creation approaches.

**Alternatives**

If you're interested in exploring alternative approaches for creating objects from data, here are a few options:

* Using an array of key-value pairs: `const o = Array.from(Array(10000).keys()).map((k) => ({ [k]: ... }));`
* Using a library like Lodash or Underscore.js, which provide utility functions for working with objects and arrays.
* Using a different data structure, such as a plain old JavaScript object (`{}`) created directly from the data.

Keep in mind that these alternatives may have different performance characteristics compared to the approaches tested in this benchmark.

Related benchmarks:

Object vs Map creation (forEach, reduce) (version: 0)

Comparing performance of: Object forEach vs Object reduce vs Map forEach vs Map reduce

Created: 2 years ago by: Guest

Jump to the latest result

Object forEach

Object reduce

Map forEach

Map reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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