Benchmark: filter + map vs reduce in js

Script Preparation code:

var arr = [];
for (var i = 0; i < 12345; i++) {
  arr[i] = i;
}

Tests:

filter + map
arr.filter(a => a % 2 === 0).map(a => a);
reduce
arr.reduce((agg, a) => { if(a % 2 === 0) { agg.push(a); } return agg; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter + map
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.114 Safari/537.36

Browser/OS: Chrome 89 on Linux

View result in a separate tab

Test name	Executions per second
filter + map	2582.2 Ops/sec
reduce	4540.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring JavaScript performance is a crucial task, and MeasureThat.net provides a great platform for benchmarking different approaches.

The provided JSON represents a benchmark that tests two JavaScript methods: `filter` and `map` in combination, versus using the `reduce()` method. The goal of this benchmark is to compare the performance of these three approaches on a large array of numbers.

**Options Compared**

The benchmark compares the following options:

1. **Filter + Map**: This approach uses the `filter()` method to remove odd numbers from the array and then applies the `map()` function to create a new array with only even numbers.
2. **Reduce**: This approach uses the `reduce()` method to accumulate an array of even numbers in a single value.

**Pros and Cons**

* **Filter + Map**:
	+ Pros: Easy to understand, readable code, and can be parallelized more easily.
	+ Cons: Requires two extra iterations over the data, which might lead to higher memory usage and slower performance for large datasets.
* **Reduce**:
	+ Pros: Can be more efficient in terms of memory usage, as it accumulates values directly into an array. Also, some browsers may optimize `reduce` better due to its inherent parallelization capabilities.
	+ Cons: May have a steeper learning curve due to the accumulator pattern and potential performance issues if not implemented correctly.

**Library and Purpose**

There is no specific library mentioned in the benchmark definition or test cases. However, it's essential to note that libraries like Lodash (which includes `filter()` and `map()` functions) can influence performance, especially when used with large datasets. MeasureThat.net might consider adding these factors to its analysis.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark definition.

**Other Considerations**

* The benchmark size (12345 elements) is relatively small, which may not accurately represent performance for larger datasets.
* It would be beneficial to include additional test cases with different dataset sizes or more complex data structures.
* MeasureThat.net might want to consider adding support for other programming languages or frameworks to increase its breadth of coverage.

**Alternatives**

Some alternative approaches or modifications could be explored:

1. **Using `every()` instead of `filter`**: This would eliminate the need for an intermediate array and potentially improve performance.
2. **Using `forEach()` instead of `map`**: This would also avoid creating a new array and might be more suitable for certain use cases.
3. **Parallelizing the benchmark**: MeasureThat.net could explore parallelizing the benchmark across multiple CPU cores or even utilizing multi-threading to improve performance.

By considering these alternatives and incorporating additional test cases, MeasureThat.net can provide a more comprehensive understanding of JavaScript performance and help developers make informed decisions about their code optimizations.

Related benchmarks:

filter + map vs reduce in js (version: 0)

Comparing performance of: filter + map vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

filter + map

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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