Benchmark: Test Test 3123213

Script Preparation code:

const participants = [
{
  name: 'Nichole',
  email: 'nichole@bla.com',
 },
 {
     name: 'Edgar',
  email: 'edgar@bla.com',
 },
  {
     name: 'Pablo',
  email: 'pablo@bla.com',
 },

 ];

const EXCLUDE = 'pablo@bla.com';

Tests:

filter.map
const participants = [ { name: 'Nichole', email: 'nichole@bla.com', }, { name: 'Edgar', email: 'edgar@bla.com', }, { name: 'Pablo', email: 'pablo@bla.com', }, ]; const EXCLUDE = 'pablo@bla.com'; participants.filter( d => d.email !== EXCLUDE ).map((p, i) => ({ ...p, signingOrder: i + 1, }))
reduce
const participants = [ { name: 'Nichole', email: 'nichole@bla.com', }, { name: 'Edgar', email: 'edgar@bla.com', }, { name: 'Pablo', email: 'pablo@bla.com', }, ]; const EXCLUDE = 'pablo@bla.com'; participants.reduce((accum, item) => { if (item.email !== EXCLUDE) { accum.push({ ...item, signingOrder: accum.length + 1, }); } return accum; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter.map
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):

LLMs can make mistakes. Check important info.

I'll break down the provided JSON and explain what's being tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark definition is represented by two JSON objects: one for each individual test case (`filter.map` and `reduce`). These definitions specify how to prepare the JavaScript code to be executed, including:

1. Script Preparation Code: This code snippet defines an array of participant objects, `participants`, and a constant `EXCLUDE`. The purpose of this code is to create a dataset to be processed.
2. Html Preparation Code: There is no HTML preparation code provided for these benchmarks.

**Individual Test Cases**

Each test case has the following components:

1. **Benchmark Definition**: This defines how to prepare the JavaScript code, as mentioned earlier.
2. **Test Name**: A descriptive name for each benchmark, indicating what operation will be performed on the `participants` array: either filtering (`filter.map`) or reducing (`reduce`).

**Operations being compared**

The two test cases compare two different approaches to process the `participants` array:

1. **Filtering (`filter.map`)**:
	* Filters out participants who match the excluded email address (`pablo@bla.com`)
	* Then maps each remaining participant object to a new object with an additional `signingOrder` property
2. **Reducing (`reduce`)**:
	* Iterates over the participants array and accumulates objects in an array, excluding those that match the excluded email address
	* Assigns a unique `signingOrder` value to each accumulated object

**Pros and Cons**

Here are some pros and cons of each approach:

**Filtering (`filter.map`) Pros:**

* More efficient for large datasets, as it avoids creating an intermediate array
* Can be more intuitive for developers who are familiar with filtering and mapping operations

**Filtering (`filter.map`) Cons:**

* May lead to performance issues if the filter condition is computationally expensive
* Requires additional memory allocation for the mapped objects

**Reducing (`reduce`)** Pros:

* More efficient for small datasets, as it avoids creating an intermediate array
* Can be more suitable for scenarios where accumulation of objects is necessary

**Reducing (`reduce`)** Cons:

* May lead to performance issues if the reduce operation is computationally expensive
* Requires more memory allocation for the accumulated objects

**Library and Special JS Features**

In both test cases, a JavaScript library or standard feature is used: `Array.prototype.filter()` and `Array.prototype.map()`, respectively. These methods are part of the ECMAScript Standard Library.

There are no special JavaScript features or syntax mentioned in these benchmark definitions.

**Alternatives**

If you want to explore alternative approaches, here are some options:

* Using other filtering libraries (e.g., Lodash's `filterBy`)
* Implementing a custom filtering and mapping algorithm
* Using a different data structure, such as a linked list, instead of an array

Keep in mind that the choice of approach depends on the specific use case, performance requirements, and developer preferences.

Related benchmarks:

Test Test 3123213 (version: 0)

Comparing performance of: filter.map vs reduce

Created: 5 years ago by: Guest

Jump to the latest result

filter.map

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):