Benchmark: Test filter map vs only map

Script Preparation code:

var numbers = [10, 40, 230, 15, 18, 51, 1221]

Tests:

filter + map without filter
numbers.filter(x => false ? x === 40: x).map(x => `number : ${x}`)
filter + map with filter
numbers.filter(x => true ? x === 40: x).map(x => `number : ${x}`)
map without filter
numbers.map(x => { if (false && x === 40) { return `number : ${x}` } if (true){ return `number : ${x}` } })
map with filter
numbers.map(x => { if (true && x === 40) { return `number : ${x}` } if (false){ return `number : ${x}` } })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter + map without filter
filter + map with filter
map without filter
map with filter

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 dive into explaining the benchmark and its various components.

**What is tested?**

MeasureThat.net is testing the performance of three different approaches for filtering and mapping an array of numbers:

1. `filter + map`: This approach uses the `filter` method to remove elements that don't meet a certain condition, followed by the `map` method to transform the remaining elements.
2. `map without filter`: This approach uses the `map` method directly on the original array, without filtering it first.
3. `filter + map without filter`: This approach is similar to the first one, but the `map` method is called without filtering the array first.

**Options compared**

The benchmark compares the performance of these three approaches:

* **Pros and Cons:**
	+ `filter + map`: This approach can be more efficient if the filtering condition is expensive or complex, as it allows for early termination. However, it may involve additional overhead due to the separate call to `filter`.
	+ `map without filter`: This approach can be faster if the transformation is simple and doesn't depend on the original value, as it avoids the need for an extra pass through the array. However, it may not take advantage of filtering if the condition is expensive or complex.
	+ `filter + map without filter`: This approach combines the benefits of both approaches, but may still incur additional overhead due to the separate call to `filter`.
* **Other considerations:**
	+ The performance difference between these approaches can be significant, especially for large datasets. MeasureThat.net provides raw execution counts and frequencies to help compare the results.
	+ The benchmark assumes that the filtering condition is constant across all iterations of the loop. If the condition changes or depends on external factors, a different approach may be needed.

**Libraries used**

There are no specific libraries mentioned in the provided code snippets. However, it's worth noting that the `filter` and `map` methods are built-in JavaScript functions, which means that any JavaScript engine (including those used by MeasureThat.net) will implement these methods internally.

**Special JS features or syntax**

None of the benchmark definitions explicitly use special JavaScript features or syntax beyond what is commonly available. However, the use of template literals (`"number : ${x}"`) in some of the `map` implementations may be considered a minor optimization, as it can reduce the need for string concatenation.

**Benchmark preparation code**

The script preparation code provides an array of numbers (`numbers = [10, 40, 230, 15, 18, 51, 1221]`) that is used throughout all benchmark definitions. The HTML preparation code is empty, which suggests that the benchmark doesn't rely on any external dependencies or DOM manipulation.

**Alternatives**

Some possible alternatives to these approaches could be:

* Using a different filtering library (e.g., Lodash's `filter` function) if the built-in JavaScript implementation is deemed too slow.
* Utilizing SIMD instructions (Single Instruction, Multiple Data) for parallel processing of array elements, especially if the array operations are computationally intensive.
* Exploring the use of just-in-time (JIT) compilers or other optimization techniques to improve performance.
* Implementing a hybrid approach that combines filtering and mapping using different methods depending on the specific requirements.

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36

Browser/OS: Chrome 83 on Mac OS X 10.15.4

View result in a separate tab

Test name	Executions per second
filter + map without filter	4110351.2 Ops/sec
filter + map with filter	7410880.0 Ops/sec
map without filter	5191580.0 Ops/sec
map with filter	8148815.5 Ops/sec

Related benchmarks:

Test filter map vs only map (version: 0)

Comparing performance of: filter + map without filter vs filter + map with filter vs map without filter vs map with filter

Created: 4 years ago by: Guest

Jump to the latest result

filter + map without filter

filter + map with filter

map without filter

map with filter

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):