Benchmark: reduce (immutable) vs map + fromEntries

Script Preparation code:

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

Tests:

reduce
Object.entries(data).reduce((acc, [k, v]) => ({ ...acc, [k]: v.toString() }), {});
map + fromEntries
Object.fromEntries(Object.entries(data).map(([k, v]) => ([k, v.toString()])));

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map + fromEntries

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, compared options, pros and cons of each approach, and other considerations.

**What is being tested?**

The test compares two approaches to convert an object into a string:

1. `reduce()`: This approach uses the `Array.prototype.reduce()` method to iterate over the key-value pairs of the object and concatenate the values into a single string.
2. `map + fromEntries`: This approach uses the `Array.prototype.map()` method to create a new array with the value strings, followed by calling `Object.fromEntries()` to convert the array back into an object.

**Options comparison**

The test is comparing these two approaches on a large dataset of 10,000 objects. The options being compared are:

* `reduce()`: A more functional programming approach that iterates over the data and accumulates the results.
* `map + fromEntries`: A more declarative approach that uses an intermediate array to transform the data before converting it back into an object.

**Pros and cons of each approach**

**`reduce()`**

Pros:

* Can be more efficient for large datasets, as it avoids creating an intermediate array.
* More memory-efficient, as it only accumulates a single result.

Cons:

* May have performance issues if the callback function is complex or slow.
* Requires more code and can be less readable for some developers.

**`map + fromEntries`**

Pros:

* Easier to read and write, as it's a more declarative approach.
* Can be faster if the transformation step (converting values to strings) is particularly fast.

Cons:

* Creates an intermediate array, which can consume significant memory for large datasets.
* Requires creating an additional object using `Object.fromEntries()`, which may have performance implications.

**Other considerations**

* The test uses a Linux-based system and Chrome 110 as the browser. This means that the results might not be representative of other operating systems or browsers.
* The test is designed to compare two specific approaches, but it doesn't account for potential variations in JavaScript engine optimizations or caching.
* In a real-world scenario, you'd want to consider additional factors, such as input data distribution, algorithmic complexity, and code organization.

**Library usage**

In this case, the `Array.prototype.reduce()` method is used, which is a built-in Array method. The `Object.fromEntries()` method is also a built-in method in modern JavaScript environments.

**Special JS feature or syntax**

The test uses the `map()` method, which is a standard Array method, but it's worth noting that some older browsers might not support this method natively. Additionally, the use of template literals (e.g., `v.toString()`) for string conversion is a modern JavaScript feature.

**Alternatives**

Other alternatives to these approaches include:

* Using a library like Lodash or Ramda for functional programming.
* Utilizing built-in methods like `JSON.stringify()` to convert objects to strings.
* Implementing custom serialization logic using recursion or iteration.
* Leveraging data processing frameworks like Apache Spark or D3.js for large-scale data transformations.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36

Browser/OS: Chrome 110 on Linux

View result in a separate tab

Test name	Executions per second
reduce	52.5 Ops/sec
map + fromEntries	1089.8 Ops/sec

Related benchmarks:

reduce (immutable) vs map + fromEntries (version: 0)

Comparing performance of: reduce vs map + fromEntries

Created: 4 years ago by: Guest

Jump to the latest result

reduce

map + fromEntries

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):