Benchmark: reduce concat vs flat vs concat spread

Script Preparation code:

var data = Array.from({length: 1000}, () => Array.from(Array(100).keys()));

Tests:

reduce concat
data.reduce((acc, curr) => acc.concat(curr), []);
flat
data.flat()
concat spread
[].concat(...data)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce concat
flat
concat spread

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 provided benchmark and explain what is being tested, compared options, pros and cons of those approaches, and other considerations.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark test case on MeasureThat.net. The benchmark is designed to measure the performance difference between three approaches:

1. Using `reduce()` with the spread operator (`concat()`) to concatenate arrays.
2. Using `flat()` to flatten an array of arrays.
3. Using the spread operator (`...`) to concatenate arrays.

**Script Preparation Code**

The script preparation code initializes a dataset using `Array.from()` with two nested loops, creating an array of 1000 arrays, each containing 100 elements.

```javascript
var data = Array.from({length: 1000}, () => Array.from(Array(100).keys()));
```

This dataset is used as input for the benchmark test cases.

**Test Cases**

There are three individual test cases:

1. `reduce concat`
	* Benchmark Definition: `data.reduce((acc, curr) => acc.concat(curr), [])`
2. `flat`
	* Benchmark Definition: `data.flat()`
3. `concat spread`
	* Benchmark Definition: `[].concat(...data)`

These test cases compare the performance of each approach in concatenating the dataset.

**Library and Purpose**

None of the test cases use any external libraries. The `Array.from()` method is a built-in JavaScript function used to create an array from an iterable or an array-like object.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax being tested in this benchmark.

**Pros and Cons of Each Approach**

1. **Reduce with Spread Operator (`concat()`)**:
	* Pros: Can handle variable-length arrays, easy to implement.
	* Cons: May have performance overhead due to the spread operator's invocation.
2. **Flat()**:
	* Pros: Simplifies code, less memory allocation required.
	* Cons: May not work well with nested arrays of varying depths, can be slower than the `concat()` approach for shallow datasets.
3. **Concat Spread Operator (`...`)**:
	* Pros: More concise and expressive, suitable for modern JavaScript use cases.
	* Cons: May have performance overhead due to the spread operator's invocation, may not work well with very large datasets.

**Other Considerations**

When choosing between these approaches, consider the following factors:

* Dataset size and structure: For small to medium-sized datasets, `concat()` might be sufficient. For larger or more complex datasets, `flat()` or `concat spread` might be preferred.
* Code readability and maintainability: Choose the approach that best fits your coding style and team's preferences.
* Performance requirements: If performance is critical, consider using profiling tools to identify bottlenecks and optimize accordingly.

**Alternatives**

Other alternatives for concatenating arrays in JavaScript include:

* Using `Array.prototype.push()` or `concat()`: These methods can be slower than the spread operator approach but provide more control over the concatenation process.
* Using `Array.prototype.reduce()` with a custom callback: This method allows for more complex logic and might be suitable for specific use cases.

Keep in mind that the performance difference between these approaches is usually negligible unless dealing with extremely large datasets or high-performance applications.

Related benchmarks:

reduce concat vs flat vs concat spread (version: 1)

Comparing performance of: reduce concat vs flat vs concat spread

Created: 6 years ago by: Registered User

Jump to the latest result

reduce concat

flat

concat spread

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):