Benchmark: FilterMap perf - MeasureThat.net

Script Preparation code:

var ingredientsPerServing = Array(10_000).fill().map(() => {
  let o = {
    details: Math.random().toString(),
  }
  if (Math.random() > 0.5) o.measurement = {
    amount: Math.random(),
    unit: Math.random().toString(),
  }
  return o
})

Tests:

Filter Map
ingredientsPerServing .filter((ingredient) => { return ingredient.measurement; }) .map((ingredient) => { return { amount: ingredient.measurement.amount, details: ingredient.details, unit: ingredient.measurement.unit, }; }).length
FlatMap
ingredientsPerServing .flatMap((ingredient) => { return !ingredient.measurement ? [] : [{ amount: ingredient.measurement.amount, details: ingredient.details, unit: ingredient.measurement.unit, }]; }).length
Reduce
ingredientsPerServing .reduce((a, ingredient) => { return !ingredient.measurement ? a : [...a, { amount: ingredient.measurement.amount, details: ingredient.details, unit: ingredient.measurement.unit, }]; }, []).length
Push
ingredientsPerServing .reduce((a, ingredient) => { if (ingredient.measurement) a.push({ amount: ingredient.measurement.amount, details: ingredient.details, unit: ingredient.measurement.unit, }); return a; }, []).length

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Filter Map
FlatMap
Reduce
Push

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36

Browser/OS: Chrome 118 on Windows

View result in a separate tab

Test name	Executions per second
Filter Map	3700.3 Ops/sec
FlatMap	692.1 Ops/sec
Reduce	2.4 Ops/sec
Push	5174.6 Ops/sec

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 and benchmark results.

**Benchmark Definition**

The `FilterMap perf` benchmark is testing the performance of different approaches to filter, map, and reduce arrays in JavaScript. The benchmark definition code generates an array of 10,000 objects with random properties, including a measurement property that is present in half of the cases. The benchmark then applies three different operations:

1. **Filter**: Filters out the objects without a measurement property.
2. **FlatMap**: Flattens the filtered array by creating a new array with the measurement amount and other details for each object.
3. **Push**: Accumulates the filtered objects into an array, pushing new objects onto the array as it iterates through the original array.

**Options Compared**

The benchmark compares the performance of three different approaches:

1. `filter()`: A built-in JavaScript method that returns a new array with only the elements that pass the test implemented by the provided function.
2. `flatMap()`: A built-in JavaScript method that returns a new array with the results of applying the provided function to each element of the original array, and then flattening the result.
3. `reduce()`: A built-in JavaScript method that applies a user-defined function to each element of the array (in this case, pushing new objects onto an accumulator array).

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Filter()**:
	* Pros: Efficient, easy to understand, and widely supported.
	* Cons: Creates a new array, which can lead to memory allocation overhead.
2. **FlatMap()**:
	* Pros: Simplifies the filtering process by returning an array with flattened results, reducing code complexity.
	* Cons: May be slower than `filter()` due to additional processing steps.
3. **Reduce()**:
	* Pros: Allows for accumulative computation and can reduce memory allocation overhead compared to creating new arrays.
	* Cons: More complex and less intuitive than `filter()` or `flatMap()`, with a steeper learning curve.

**Library**

None of the benchmark definitions explicitly uses any libraries, but some may rely on external modules (e.g., Lodash) for utility functions. However, since this is a basic JavaScript microbenchmark, the focus is on understanding the built-in methods and their performance characteristics.

**Special JS Feature/Syntax**

This benchmark does not use any special JavaScript features or syntax beyond the standard array methods. The use of `let` instead of `var` for declaring variables is an example of a more modern JavaScript feature, but it is not specific to this benchmark.

**Other Alternatives**

If you were to optimize this benchmark further, you might consider:

1. **Using SIMD instructions**: If performance-critical, using SIMD (Single Instruction, Multiple Data) instructions could provide significant speedups for certain operations.
2. **Caching intermediate results**: If the same data is used repeatedly, caching intermediate results could reduce memory allocation overhead and improve performance.
3. **Parallelizing computation**: With a sufficient number of CPU cores, parallelizing the computation using Web Workers or parallel processing libraries (e.g., ` worker_threads` in Node.js) could further optimize performance.

Keep in mind that these optimizations would depend on the specific requirements and constraints of your use case.

Overall, this benchmark provides a good starting point for understanding the performance characteristics of built-in JavaScript methods like `filter()`, `flatMap()`, and `reduce()`.

Related benchmarks:

FilterMap perf (version: 0)

Comparing performance of: Filter Map vs FlatMap vs Reduce vs Push

Created: one year ago by: Guest

Jump to the latest result

Filter Map

FlatMap

Reduce

Push

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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