Benchmark: forEach concat vs reduce push vs forEach push vs reduce concat performance

Tests:

forEach concat
const a = [...Array(1000)].map(() => ({ nested: Array.from({length: 40}, () => Math.floor(Math.random() * 40)) })); let b = []; a.forEach((item) => { b = b.concat(item.nested); });
Reduce push
const a = [...Array(1000)].map(() => ({ nested: Array.from({length: 40}, () => Math.floor(Math.random() * 40)) })); const b = a.reduce((acc, item) => { acc.push(...item.nested); return acc }, []);
Reduce concat
const a = [...Array(1000)].map(() => ({ nested: Array.from({length: 40}, () => Math.floor(Math.random() * 40)) })); const b = a.reduce((acc, item) => { acc.concat(item.nested); return acc }, []);
forEach push
const a = [...Array(1000)].map(() => ({ nested: Array.from({length: 40}, () => Math.floor(Math.random() * 40)) })); let b = []; a.forEach((item) => { b.push(...item.nested); });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
forEach concat
Reduce push
Reduce concat
forEach push

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the benchmark and its individual test cases.

**Benchmark Definition**

The benchmark is designed to compare the performance of different approaches for concatenating arrays in JavaScript. Specifically, it tests four different methods:

1. `forEach concat`
2. `Reduce push`
3. `Reduce concat`
4. `forEach push`

Each method is implemented slightly differently:

* `forEach concat`: uses the `concat` method to concatenate the `nested` array of each object.
* `Reduce push`: uses the `reduce` method with a custom callback function that pushes the elements of `item.nested` onto the accumulator using the spread operator (`...`).
* `Reduce concat`: uses the `reduce` method and concatenates the elements of `item.nested` directly using the `+` operator.
* `forEach push`: uses the `push` method to add each element of `item.nested` to the end of the accumulator array.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

1. **forEach concat**:
	* Pros: simple, easy to read, and understand.
	* Cons: can be slow due to the overhead of calling `concat`.
2. **Reduce push**:
	* Pros: concise, efficient, and scalable.
	* Cons: may require more mental effort to understand the callback function.
3. **Reduce concat**:
	* Pros: concise, efficient, and scalable.
	* Cons: may be slower due to the overhead of concatenation using `+`.
4. **forEach push**:
	* Pros: simple, easy to read, and understand.
	* Cons: can be slow due to the overhead of calling `push` for each element.

**Library and Special JS Features**

None of these approaches rely on any specific libraries or special JavaScript features beyond standard JavaScript syntax.

**Other Considerations**

When choosing an approach, consider the following factors:

1. Readability: if you prioritize understanding your code, `forEach concat` might be a better choice.
2. Performance: if you're looking for the most efficient solution, `Reduce push` or `Reduce concat` might be a better fit.
3. Scalability: if you expect large datasets, `Reduce push` and `Reduce concat` are likely to perform better.

**Alternatives**

Other alternatives for concatenating arrays in JavaScript include:

1. Using the `Array.prototype.push.apply()` method.
2. Creating a new array using `Array.from()` and then pushing elements onto it.
3. Using a library like Lodash, which provides a `concat` function that's optimized for performance.

Keep in mind that these alternatives might not be as concise or readable as the approaches tested in this benchmark.

Related benchmarks:

forEach concat vs reduce push vs forEach push vs reduce concat performance (version: 0)

Comparing performance of: forEach concat vs Reduce push vs Reduce concat vs forEach push

Created: 4 years ago by: Guest

Jump to the latest result

forEach concat

Reduce push

Reduce concat

forEach push

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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