Benchmark: Object mapping - MeasureThat.net

HTML Preparation code:

<script src="//cdn.jsdelivr.net/npm/ramda@latest/dist/ramda.min.js"></script>

Script Preparation code:

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

Tests:

Object.fromEntries
const x = Object.fromEntries(Object.entries(data).map((key, value) => [key, value*2])); console.log(x)
Reduce (reuse object)
const x = Object.entries(data).reduce((acc, [k, v]) => { acc[k] = v*2 return acc; }, {}); console.log(x)
R.mapObjIndexed
const x = R.mapObjIndexed((value, key) => value * 2, data) console.log(x)

Historical Performance Data aggregated from 7 community runs

Desktop Results:

Chrome 7 runs

R.mapObjIndexed

5K/s

Reduce (reuse object)

3K/s

Object.fromEntries

249/s

Fastest: R.mapObjIndexed (22.0x faster)

View exact numbers

✓ R.mapObjIndexed: 5,470 ops/sec
• Reduce (reuse object): 2,778 ops/sec
• Object.fromEntries: 249 ops/sec

Results show median performance across all community submissions. Run this benchmark yourself to verify on your device.

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object.fromEntries
Reduce (reuse object)
R.mapObjIndexed

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36

Browser/OS: Chrome 125 on Windows

View result in a separate tab

R.mapObjIndexed

6K/s

Reduce (reuse object)

3K/s

Object.fromEntries

268/s

View exact numbers

Test name	Executions per second
✓ R.mapObjIndexed	6,094 Ops/sec
Reduce (reuse object)	2,778 Ops/sec
Object.fromEntries	268 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 and its test cases to understand what's being tested.

**Benchmark Overview**

The benchmark measures performance differences between three approaches for mapping over an object in JavaScript:

1. `Object.fromEntries`
2. `Reduce` (with reusing an existing object)
3. `R.mapObjIndexed` (from the Ramda library)

**Script Preparation Code**

The script preparation code creates a large data array with 10,000 elements, each with a unique key and value.

**Html Preparation Code**

The HTML preparation code includes a script tag that loads the latest version of the Ramda library, which is used by two of the test cases (`R.mapObjIndexed`).

**Test Cases**

There are three individual test cases:

1. **Object.fromEntries**: This test case uses `Object.fromEntries` to create a new object from an array of key-value pairs.
2. **Reduce (reuse object)**: This test case uses the `Reduce` method with a callback function to iterate over the data array, reusing an existing object to store intermediate results.
3. **R.mapObjIndexed**: This test case uses the `mapObjIndexed` function from the Ramda library to create a new object from the data array.

**Comparison**

The three approaches are compared in terms of their performance, measured by the number of executions per second (ExecutionsPerSecond).

**Pros and Cons**

Here's a brief summary of each approach:

* **Object.fromEntries**: Pros: concise and easy to read. Cons: may incur additional overhead due to object creation.
* **Reduce**: Pros: allows reusing an existing object, reducing memory allocation. Cons: can be more complex and harder to read due to the callback function.
* **R.mapObjIndexed**: Pros: provides a more functional programming style, leveraging Ramda's optimized implementation. Cons: requires loading an additional library.

**Other Considerations**

* The benchmark assumes that `Object.fromEntries` is used for creating new objects, while `Reduce` might be used for other purposes, such as aggregating values or sorting.
* The use of Ramda's `mapObjIndexed` function introduces an additional dependency and potential performance overhead due to library loading.

**Alternatives**

If you're looking for alternative approaches, consider:

* Using a library like Lodash, which provides similar functionality to Ramda but with more extensive feature sets.
* Implementing your own mapping functions using built-in JavaScript methods, such as `forEach`, `map`, and `reduce`.
* Leveraging modern JavaScript features like template literals or object destructuring for concise and expressive code.

Keep in mind that benchmark results may vary depending on specific use cases, hardware, and software configurations. It's essential to consider these factors when choosing an approach for your project.

Object mapping (version: 0) Benchmark.js

Comparing performance of: `Object.fromEntries vs Reduce (reuse object) vs R.mapObjIndexed`

Created: 3 years ago by: Guest

Go to the latest result

Object.fromEntries

Reduce (reuse object)

R.mapObjIndexed

Historical Performance Data aggregated from 7 community runs

Desktop Results:

Chrome 7 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Object mapping (version: 0) Benchmark.js

Comparing performance of: Object.fromEntries vs Reduce (reuse object) vs R.mapObjIndexed

Created: 3 years ago by: Guest

Go to the latest result

Object.fromEntries

Reduce (reuse object)

R.mapObjIndexed

Historical Performance Data aggregated from 7 community runs

Desktop Results:

Chrome 7 runs

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

Comparing performance of: `Object.fromEntries vs Reduce (reuse object) vs R.mapObjIndexed`