Benchmark: filter + reduce vs reduce

Tests:

filter + reduce
const data = []; for (let i = 0; i < 1000000; i++) { data[i] = {price: i % 4 == 0 ? i : ''} } // const filtered = data.filter(d => d.price !== '') Object.values(filtered).reduce((a, d) => a + parseFloat(d.price), 0)
reduce
const data = []; for (let i = 0; i < 1000000; i++) { data[i] = {price: i % 4 == 0 ? i : ''} } // Object.values(data).reduce((a, d) => a + (parseFloat(d.price) || 0), 0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
filter + reduce
reduce

Fastest: N/A

Slowest: N/A

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

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Let's dive into the benchmark.

The provided JSON represents two microbenchmarks: `filter + reduce` and `reduce`. These benchmarks are designed to measure the performance of JavaScript engines when executing specific code paths.

**Benchmark Definitions**

The first benchmark, `filter + reduce`, tests a scenario where an array is filtered using the `filter()` method, and then the filtered values are reduced using the `reduce()` method. The filter condition uses a conditional expression that checks if the price is not empty (`''`).

In contrast, the second benchmark, `reduce`, tests only the `reduce()` method without filtering. It creates an array with 1 million elements, each containing a `price` property, and then applies the `reduce()` method to sum up the prices.

**Options Compared**

The two benchmarks compare different approaches to executing the same code path:

*   **Filtering before reducing**: This approach involves filtering the array first using `filter()`, which creates a new filtered array, and then applying `reduce()` to that filtered array.
*   **Reducing without filtering**: In this case, the `reduce()` method is applied directly to the original array, skipping the filtering step.

**Pros and Cons**

Here's a brief analysis of each approach:

### Filter + Reduce Approach

Pros:

*   This approach allows for more control over the filtering process, as you can define your own filter condition.
*   It might be beneficial when working with specific filtering requirements or complex conditions.

Cons:

*   Filtering an array creates a new array object, which can lead to increased memory usage and potentially slower performance compared to reducing the original array directly.
*   This approach requires two separate operations: filtering and reducing, which may increase overhead due to function call overhead.

### Reduce Only Approach

Pros:

*   Reducing the original array directly eliminates the need for creating a new filtered array, which can improve memory efficiency and potentially reduce overhead.
*   This approach might be beneficial when working with large datasets or high-performance applications where every optimization counts.

Cons:

*   By skipping filtering, this approach may not handle edge cases or complex conditions as effectively as the filter + reduce approach.
*   It requires a more specialized understanding of how `reduce()` works and its potential limitations.

**Library Usage**

Neither benchmark explicitly uses any external libraries, but it's worth noting that the `filter()` and `reduce()` methods are built-in JavaScript functions that operate on arrays. These methods are essential for array manipulation and processing in JavaScript.

**Special JS Feature or Syntax**

There are no special JavaScript features or syntax used in these benchmarks beyond the standard language constructs mentioned earlier (e.g., loops, conditional expressions).

**Other Alternatives**

For microbenchmarking JavaScript code, you can consider using other tools and frameworks, such as:

*   [Benchmark.js](https://www.benchmarkjs.com/): A popular JavaScript benchmarking library that allows for easy creation of benchmarks and provides features like automatic benchmarking and visualization.
*   [JSPerf](http://jsperf.com/): A classic JavaScript benchmarking tool that enables users to create, run, and compare benchmarks.

Related benchmarks:

filter + reduce vs reduce (version: 0)

Comparing performance of: filter + reduce vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

filter + reduce

reduce

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):