Benchmark: reduce vs filter+map

Script Preparation code:

var items = [];
for (var i = 0; i < 10000; i++) {
  items[i] = {
    id: i,
    selected: i % 4 == 0
  };
}

Tests:

reduce
items.reduce( ( array, item ) => { if ( item.selected ) { array.push( item.id ); } return array; }, [] );
filter+map
items.filter( x => x.selected ).map( x => x.id );

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
filter+map

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 months ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 YaBrowser/25.8.0.0 Safari/537.36

Browser/OS: Yandex Browser 25 on Linux

View result in a separate tab

Test name	Executions per second
reduce	38391.4 Ops/sec
filter+map	25968.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the benchmark definition and test cases.

**Benchmark Definition:**

The provided JSON represents a JavaScript microbenchmark that tests the performance difference between two approaches:

1. **`items.reduce()`**: This approach uses the `reduce()` method to iterate over an array and accumulate values. In this case, it iterates over the `items` array and pushes the `id` of selected items into a new array.
2. **`filter()` + `map()`**: This approach uses two methods: `filter()` to filter out non-selected items and `map()` to transform the filtered items into their `id` values.

**Options Compared:**

The benchmark compares the performance of these two approaches, with the goal of determining which one is faster.

**Pros and Cons of Each Approach:**

1. **`reduce()`**:
	* Pros:
		+ Can be more memory-efficient for large datasets, as it only creates a single new array.
		+ Can be more expressive and concise in terms of intent.
	* Cons:
		+ Requires the accumulator to keep track of the intermediate results, which can lead to performance overhead.
2. **`filter()` + `map()`**:
	* Pros:
		+ Separates concerns between filtering and mapping, making it easier to understand and maintain.
		+ Can be more parallelizable, as each filtered item can be processed independently.
	* Cons:
		+ Creates two temporary arrays (filtered and mapped), which can lead to memory overhead.

**Library/Functionality Used:**

In this benchmark, the `reduce()` method is used to iterate over the array and accumulate values. The `filter()` and `map()` methods are also used in this approach.

**Special JavaScript Features/Syntax:**

There are no special JavaScript features or syntax used in this benchmark that would require additional explanation.

**Other Considerations:**

* Performance: The benchmark is designed to measure the execution speed of each approach, which may vary depending on the specific use case and dataset.
* Memory usage: The benchmark considers memory usage as a potential factor, although it's not explicitly measured.

**Alternatives:**

If you're looking for alternative approaches or libraries to solve similar problems, consider the following:

1. **`forEach()`**: Similar to `reduce()`, but with fewer features and less control over iteration.
2. **`forEach()` + callback function**: Allows for more flexibility in processing each item, but can lead to more complex code.
3. **Libraries like Lodash or Ramda**: Provide additional functionality for array manipulation and can simplify code, but may introduce performance overhead.

In summary, the benchmark compares two approaches to iterating over an array: `reduce()` and `filter() + map()`. While `reduce()` can be more memory-efficient, `filter()` + `map()` separates concerns and can be more parallelizable. The choice between these approaches depends on the specific use case and performance requirements.

Related benchmarks:

reduce vs filter+map (version: 0)

Tests performance between a single reduce and a filter then map

Comparing performance of: reduce vs filter+map

Created: 4 years ago by: Guest

Jump to the latest result

reduce

filter+map

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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