Benchmark: Array iteration_0001

Tests:

pipe
const array = [Array.from(100000)]; array.filter(i => i % 2 === 0).map(i => i++);
reduce with conditions
const array = [Array.from(100000)]; array.reduce((acc, t) => { return t % 2 === 0? acc.push[t++] : acc; },[]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
pipe
reduce with conditions

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**
The benchmark definition outlines the scenario being tested: pipe chaining vs reduce with if statements (with a new array) compared to creating a new array for iteration.

In this case, the goal is to compare the performance of three different approaches:

1. **Pipe Chaining**: This approach involves chaining multiple methods together on an array, which can be faster due to optimized method calls.
2. **Reduce with Conditions**: This approach uses the `reduce()` method with a callback function that checks conditions for each element in the array and either pushes it to a new accumulator array or returns the current value.
3. **New Array**: Creating a new empty array and pushing elements into it using the `push()` method.

**Pros and Cons of Each Approach**

1. Pipe Chaining:
	* Pros: Fast, optimized method calls, and potentially cache-friendly due to fewer allocations.
	* Cons: May be less readable for complex operations or when working with conditional logic.
2. Reduce with Conditions:
	* Pros: Allows for more flexibility in handling conditions, which can be beneficial in certain scenarios.
	* Cons: May incur additional overhead due to the condition checks and the creation of a new array.
3. New Array:
	* Pros: Provides more control over the iteration process, allowing for conditional logic or custom operations.
	* Cons: Can lead to slower performance due to the allocation of a new array.

**Library Used**
In this benchmark, `Array.from()` is used to create an array from a specified length (in this case, 100,000). `filter()` and `map()` are also used for filtering and transformation operations, respectively.

**Special JavaScript Feature or Syntax**
There's no explicit mention of special features like ES6 syntax (e.g., arrow functions) in the benchmark definition. However, it's worth noting that modern JavaScript environments often support ES6+ features by default.

**Other Alternatives**

For similar benchmarks, you might want to consider alternative approaches, such as:

* Using `for` loops or `forEach()` instead of array methods.
* Implementing custom iteration logic using bitwise operations or other low-level techniques.
* Using parallel processing or multi-threading to take advantage of multiple CPU cores.

Keep in mind that the choice of approach will depend on the specific requirements and performance characteristics of your use case.

In this benchmark, `pipe chaining` seems to be the fastest approach, followed by `reduce with conditions`. The `new array` approach is the slowest.

Related benchmarks:

Array iteration_0001 (version: 0)

pipe chaining vs reduce with if's vs new array

Comparing performance of: pipe vs reduce with conditions

Created: 6 years ago by: Guest

Jump to the latest result

pipe

reduce with conditions

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):