Benchmark: slice & spread vs reduce

Tests:

slice & spread
function removeTodo(id, list) { const indexToRemove = list.findIndex((item) => item.id === id); return [...list.slice(0, indexToRemove), ...list.slice(indexToRemove + 1)]; } const id = 5; const list = [{id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 5, done: false}] const other = removeTodo(id, list)
reduce
function removeTodo(id, list) { return [...list].reduce( (acc, item) => (item.id === id ? acc : [...acc, item]), [] ); } const id = 5; const list = [{id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 1, done: false}, {id: 5, done: false}] const other = removeTodo(id, list)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice & spread
reduce

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's being tested.

**Benchmark Purpose**

The benchmark is designed to compare two approaches for removing elements from an array in JavaScript: using `slice` and `spread` (also known as the "spread operator") versus using `reduce`.

**Approach 1: `slice` and `spread`**

This approach uses the following code:
```javascript
function removeTodo(id, list) {
  const indexToRemove = list.findIndex((item) => item.id === id);
  return [...list.slice(0, indexToRemove), ...list.slice(indexToRemove + 1)];
}
```
The `slice` method returns a new array that includes the elements from the original array up to the specified index. The `spread` operator (`...`) is then used to concatenate this slice with another slice of the original array, starting from the next index.

**Pros:**

* Easy to read and understand
* Well-established pattern in JavaScript

**Cons:**

* Creates a new array on each execution, which can be memory-intensive for large datasets
* May lead to unnecessary copies of elements if the resulting array is not used immediately

**Approach 2: `reduce`**

This approach uses the following code:
```javascript
function removeTodo(id, list) {
  return [...list].reduce((acc, item) => (item.id === id ? acc : [...acc, item]), []);
}
```
The `reduce` method applies a callback function to each element in the array, accumulating a result. In this case, the callback function checks if the current element matches the specified ID and returns either the accumulator (`acc`) or a new array containing the current element.

**Pros:**

* Only creates one array on execution (although it still uses a temporary variable to store the result)
* Can be more efficient than creating multiple arrays with `slice` and `spread`

**Cons:**

* More complex pattern in JavaScript, potentially harder to read and understand for some developers
* Requires understanding of how `reduce` works

**Library/Functions Used**

None explicitly mentioned in the benchmark definition. However, both approaches rely on built-in JavaScript methods (`slice`, `findIndex`, `reduce`, `spread`) and operators (`...`).

**Special JS Features/Syntax**

None explicitly mentioned in the benchmark definition.

**Alternative Approaches**

Other possible ways to remove elements from an array in JavaScript include:

* Using a for loop: `for (let i = 0; i < list.length; i++) { if (list[i].id === id) list.splice(i, 1); }`
* Using a filter method: `const result = list.filter(item => item.id !== id)`
* Using an array method like `filter` or `map` with a callback function

Keep in mind that these alternative approaches may have different trade-offs in terms of performance and memory usage compared to the two approaches being benchmarked.

Related benchmarks:

slice & spread vs reduce (version: 0)

Comparing performance of: slice & spread vs reduce

Created: 3 years ago by: Registered User

Jump to the latest result

slice & spread

reduce

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):