Benchmark: Creating object (Map vs Object literal)

Tests:

Map
const obejct = new Map(); obejct.set('field1', 1); obejct.set('field2', 2); obejct.set('field3', 3); obejct.set('field4', 4);
Object literal
const object = { field1: 1, field2: 3, field3: 3, field4: 4, };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map
Object literal

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark measures the performance difference between creating an object using a `Map` and an object literal in JavaScript.

**Options Compared**

Two options are compared:

1. **Object Literal**: A traditional JavaScript object creation syntax, where you define properties of an object using key-value pairs (e.g., `const object = { field1: 1, field2: 3, field3: 3, field4: 4 };`).
2. **Map**: A built-in JavaScript data structure that stores key-value pairs, similar to objects but with some differences in usage and performance.

**Pros and Cons**

**Object Literal:**

Pros:

* More intuitive syntax for creating simple objects
* Wide browser support

Cons:

* Can lead to slower performance due to the need to create a DOM element (in case of `const object = { ... }`) or a separate object instance (if you assign it to a variable)
* Less efficient than Maps in certain scenarios, such as when dealing with large datasets

**Map:**

Pros:

* More efficient for large datasets and complex data structures
* Native performance optimizations
* Can be used as an iterator, which can be beneficial for certain use cases

Cons:

* Steeper learning curve due to its unique syntax and semantics
* Less intuitive for simple object creation scenarios

**Other Considerations**

When choosing between `Map` and object literal, consider the following factors:

* **Data size**: If you're working with large datasets, Maps might be a better choice. For small to medium-sized objects, either option works well.
* **Complexity**: If your data structure is simple and doesn't require iteration, an object literal might be sufficient. However, if you need to iterate over the data or perform complex operations, a Map can provide better performance.
* **Browser support**: Both `Map` and object literals are widely supported in modern browsers.

**Library and Special JS Feature**

There's no specific library being used in this benchmark. However, note that JavaScript does have built-in support for Maps (since ECMAScript 2015), so you don't need to include any external libraries to use them.

No special JavaScript features or syntax are required for this benchmark.

**Alternatives**

If you're interested in exploring alternative approaches, consider the following:

* **Object.assign()**: This method can be used to create objects from existing ones. However, it's not as efficient as using Maps for large datasets.
* **JSON data structures**: You can use JSON objects or arrays to represent data. While this is a popular choice for client-side storage and serialization, it might not offer the same performance benefits as using native JavaScript data structures like Maps.

Keep in mind that these alternatives are less suitable for most use cases, especially those requiring high-performance data manipulation.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36

Browser/OS: Chrome 122 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Map	10622052.0 Ops/sec
Object literal	2107473408.0 Ops/sec

Related benchmarks:

Creating object (Map vs Object literal) (version: 0)

Comparing performance of: Map vs Object literal

Created: one year ago by: Guest

Jump to the latest result

Map

Object literal

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):