Benchmark: reduce array to object

Script Preparation code:

var arr = Array.from({length: 10000}, (_, i) => i + '');

Tests:

destruct
arr.reduce((o, k) => ({ ...o, [k]: k }), {});
assign
arr.reduce((o, k) => Object.assign(o, { [k]: k }), {});
property
arr.reduce((o, k) => { o[k] = k; return o; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
destruct
assign
property

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:88.0) Gecko/20100101 Firefox/88.0

Browser/OS: Firefox 88 on Ubuntu

View result in a separate tab

Test name	Executions per second
destruct	0.1 Ops/sec
assign	90.9 Ops/sec
property	3613.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in this JavaScript microbenchmark on MeasureThat.net.

**Benchmark Definition**

The benchmark definition is a single line of code that reduces an array to an object using the `reduce()` method. The reduction function takes two arguments: the accumulator (`o`) and the current element (`k`). It returns the updated accumulator after each iteration, which eventually becomes the final resulting object.

**Options Compared**

Three different options are compared:

1. **Destructuring assignment**: `arr.reduce((o, k) => ({ ...o, [k]: k }), {})`
This approach uses destructuring assignment to create a new object with the current element as its key and value.
2. **Object.assign()**: `arr.reduce((o, k) => Object.assign(o, { [k]: k }), {})`
This approach uses the `Object.assign()` method to merge the accumulator (`o`) with an object containing the current element as a key-value pair.
3. **Simple assignment**: `arr.reduce((o, k) => {\r\n  o[k] = k;\r\n  return o;\r\n}, {})`
This approach simply assigns the current element to the corresponding key in the accumulator (`o`) and returns the updated accumulator.

**Pros and Cons of Each Approach**

1. **Destructuring assignment**:
	* Pros: concise, readable, and efficient.
	* Cons: not all browsers support destructuring assignment (e.g., older versions of Internet Explorer).
2. **Object.assign()**:
	* Pros: widely supported, can handle complex objects with nested properties.
	* Cons: slower than destructuring assignment due to the overhead of creating a new object and merging properties.
3. **Simple assignment**:
	* Pros: simple, fast, and efficient (no additional memory allocation).
	* Cons: less readable and may not be as intuitive for developers familiar with other approaches.

**Library Usage**

There is no library usage in this benchmark definition.

**Special JS Features or Syntax**

No special JavaScript features or syntax are used in this benchmark definition. All code snippets are standard ECMAScript 2015+ syntax.

**Other Considerations**

When writing benchmarks, it's essential to consider the following:

* **Warm-up and preparation**: Ensure that the benchmark script is properly warmed up before running the test cases.
* **Garbage collection**: Some browsers may perform garbage collection during the test, which can impact performance. Mitigation strategies include using `--no-wasm` flag or disabling V8's GC (in Chrome).
* **CPU frequency and scaling**: Test the benchmark on multiple CPU frequencies to ensure accurate results.

**Alternatives**

For reducing an array to an object, you may also consider other approaches such as:

* Using a library like Lodash (`_.reduce()`) or Ramda (`R.reduce()`)
* Utilizing `Array.prototype.forEach()` with a callback function
* Implementing a custom reducer function using `Array.prototype.map()` and `Object.fromEntries()`

However, these alternatives may not be optimized for performance like the benchmarked approaches.

Related benchmarks:

reduce array to object (version: 0)

Comparing performance of: destruct vs assign vs property

Created: 3 years ago by: Guest

Jump to the latest result

destruct

assign

property

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):