Benchmark: reduce vs map and filter

Tests:

reduce
var dogs = [ {name: 'Archie', breed: 'Lurcher', owner: 'Jack'}, {name: 'Charlie', breed: 'Pug', owner: 'John'}, {name: 'Buddy', breed: 'Pug', owner: 'Mike'} ] dogs = dogs.reduce((total, current) => current.breed === "Pug" ? [...total, current.owner] : total, []);
filter and map
var dogs = [ {name: 'Archie', breed: 'Lurcher', owner: 'Jack'}, {name: 'Charlie', breed: 'Pug', owner: 'John'}, {name: 'Buddy', breed: 'Pug', owner: 'Mike'} ] dogs = dogs.filter(dog => dog.breed === 'Pug').map(dog => dog.owner);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
filter and map

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is defined in two test cases: "reduce" and "filter and map". Both tests involve processing an array of objects that represent dogs, with each dog having a name, breed, and owner. The goal of the benchmark is to measure the performance difference between using the `reduce()` method versus chaining multiple methods (`filter()` and `map()`) to achieve a similar result.

**Options Compared**

In the "reduce" test case, only one option is compared: the native JavaScript `reduce()` method.

In the "filter and map" test case, two options are compared:

1. Chaining `filter()` and `map()` methods in sequence.
2. Using a single expression with `filter()`, `map()`, and other functions to achieve the same result.

**Pros and Cons of Each Approach**

**Native Reduce Method (Reduce)**

Pros:

* Native implementation, optimized for performance.
* Simple and concise code.

Cons:

* May require more memory allocations due to the creation of an intermediate array.
* Less readable code compared to chaining methods.

**Filtering and Mapping (Filter & Map)**

Pros:

* More readable code, as each step is clearly defined.
* Can be more efficient in terms of memory allocation, since only one array is created.

Cons:

* Chaining multiple methods can lead to increased function call overhead.
* Code may become longer and harder to maintain.

**Library Used (Lodash)**

In the "filter and map" test case, a popular JavaScript utility library called Lodash is used. Specifically, `_.map()` and `_filter()` functions from Lodash are utilized to simplify the code and reduce boilerplate. The inclusion of Lodash introduces additional dependencies and potential overhead, but also provides concise and readable implementation.

**Special JS Feature or Syntax**

Neither of the test cases uses any special JavaScript features or syntax that would affect performance or readability. However, it's worth noting that some modern browsers may have experimental features or optimizations that could influence the results, such as `Array.prototype.reduce` with a `threshold` parameter for efficient reduction.

**Other Alternatives**

For similar use cases, other approaches could include:

1. Using an accumulator function in `forEach()` instead of chaining methods.
2. Employing a different data structure, like a Map or Set, to process the data.
3. Utilizing parallel processing techniques or concurrent execution to speed up computation.

However, these alternatives might not be directly comparable to the native `reduce()` method and the filtering-mapping approach using Lodash.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36 Edg/108.0.1462.2

Browser/OS: Chrome 108 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	11034570.0 Ops/sec
filter and map	20611062.0 Ops/sec

Related benchmarks:

reduce vs map and filter (version: 0)

Comparing performance of: reduce vs filter and map

Created: 3 years ago by: Registered User

Jump to the latest result

reduce

filter and map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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