Benchmark: Compare multiple filter / map with reduce

Script Preparation code:

a=[];
for(i=0;i<1000;i++) a.push(Number(i)/1000);
var filtering=x=>(x*114514)%1>0.5;
var mapping=x=>x+0.1919;
var reducing=(acc,x)=>{
  var value=mapping(x);
  if(filtering(value)) acc.push(value);
  return acc;
}

Tests:

Filter / map / filter / map
a.filter(filtering).map(mapping).filter(filtering).map(mapping);;
Reduce
a.reduce(reducing,[]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Filter / map / filter / map
Reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous 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 components.

**Benchmark Definition JSON**

The provided JSON represents a JavaScript microbenchmark. It consists of two main parts:

1. **Script Preparation Code**: This section contains the initial setup for the benchmark. In this case, it:
	* Creates an array `a` with 1000 elements, where each element is a number divided by 1000.
	* Defines three functions: `filtering`, `mapping`, and `reducing`. These functions will be used in subsequent benchmarks.
2. **Html Preparation Code**: This section would typically contain HTML code for the benchmark's test environment. However, in this case, it's empty (`null`).

**Individual Test Cases**

The benchmark consists of two individual test cases:

1. **Test Case 1: Filter / map / filter / map**
	* The `Benchmark Definition` string is: `a.filter(filtering).map(mapping).filter(filtering).map(mapping);`
	* This test case applies the filtering and mapping functions twice in sequence.
2. **Test Case 2: Reduce**
	* The `Benchmark Definition` string is: `a.reduce(reducing,[])`
	* This test case uses the reducing function with an initial accumulator value of an empty array (`[]`).

**Options Compared**

The benchmark compares two different approaches to process the array:

1. **Filter / map / filter / map**: This approach applies filtering and mapping functions twice in sequence.
2. **Reduce**: This approach uses a single reducing function to accumulate elements into an array.

**Pros and Cons of Each Approach**

**Filter / Map / Filter / Map:**

Pros:

* Can be more intuitive for developers familiar with functional programming concepts.
* Allows for separate consideration of filtering and mapping operations.

Cons:

* May lead to unnecessary intermediate results, which can impact performance.
* Requires additional function calls, which can increase overhead.

**Reduce:**

Pros:

* Often preferred in JavaScript due to its simplicity and efficiency.
* Can be more efficient by avoiding intermediate results.

Cons:

* May require more complex logic for developers unfamiliar with reducing functions.

**Library Usage**

There is no explicit library usage in the provided benchmark. However, some libraries like Lodash (e.g., `_.filter()`, `_map()`) might be used if the benchmark were extended to include additional functionality.

**Special JavaScript Features/Syntax**

The benchmark does not use any special JavaScript features or syntax beyond what's standard for modern JavaScript engines.

**Other Alternatives**

If you wanted to modify this benchmark, consider exploring other approaches, such as:

1. **Array.prototype.forEach()**: Instead of using `filter()` and `map()`, you could use a single `forEach()` loop.
2. **Closures**: You could define the filtering and mapping functions inside the reducing function, making it a closure.
3. **Native Web APIs**: Depending on your specific requirements, you might want to explore native web APIs like Web Workers or WebAssembly for performance-critical parts of the benchmark.

Keep in mind that modifying the benchmark may require significant changes to its structure and functionality.

Related benchmarks:

Compare multiple filter / map with reduce (version: 0)

Comparing performance of: Filter / map / filter / map vs Reduce

Created: 4 years ago by: Guest

Jump to the latest result

Filter / map / 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):