Benchmark: reduce vs map vs for

HTML Preparation code:

<script src="//raw.githubusercontent.com/lodash/lodash/4.17.15-npm/lodash.min.js"></script>

Script Preparation code:

var fieldsMap = {
      'array[0].sarray[0]': 'array[1].sarray[1]',
      'array[0].sarray[1].ssarray[0].sssarray[0]': 'array[1].sarray[3].ssarray[0].sssarray[1]',
      'array[0].sarray[1].ssarray[1].sssarray[0]': 'array[1].sarray[3].ssarray[1].sssarray[1]',
      'array[0].sarray[1].ssarray[2].sssarray[0]': 'array[1].sarray[3].ssarray[2].sssarray[1]',
      'array[0].sarray[1].ssarray[3].sssarray[0]': 'array[1].sarray[3].ssarray[3].sssarray[1]',
      'array[0].sarray[1].ssarray[4].sssarray[0]': 'array[1].sarray[3].ssarray[4].sssarray[1]',
      'array[0].sarray[1].ssarray[5].sssarray[0]': 'array[1].sarray[3].ssarray[5].sssarray[1]',
      'array[0].sarray[1].ssarray[6].sssarray[0]': 'array[1].sarray[3].ssarray[6].sssarray[1]',
      'array[0].sarray[1].ssarray[7].sssarray[0]': 'array[1].sarray[3].ssarray[7].sssarray[1]',
      'array[0].sarray[1].ssarray[8].sssarray[0]': 'array[1].sarray[3].ssarray[8].sssarray[1]',
      'array[0].sarray[1].ssarray[9].sssarray[0]': 'array[1].sarray[3].ssarray[9].sssarray[1]',
      'array[0].sarray[1].ssarray[10].sssarray[0]': 'array[1].sarray[3].ssarray[10].sssarray[1]',
      'array[0].sarray[1].ssarray[11].sssarray[0]': 'array[1].sarray[3].ssarray[12].sssarray[1]',
      'array[0].sarray[1].ssarray[12].sssarray[0]': 'array[1].sarray[3].ssarray[13].sssarray[1]',
      'array[0].sarray[1].ssarray[13].sssarray[0]': 'array[1].sarray[3].ssarray[14].sssarray[1]',
      'array[0].sarray[1].ssarray[14].sssarray[0]': 'array[1].sarray[3].ssarray[15].sssarray[1]',
      'array[0].sarray[1].ssarray[15].sssarray[0]': 'array[1].sarray[3].ssarray[16].sssarray[1]'
}

Tests:

reduce
var prepareMap = _.reduce( fieldsMap, function(result, value, key) { result.push({ from: key, to: value }); return result; }, [] );
map
var prepareMap = _.map(fieldsMap, function(value, key) { return { from: key, to: value } });
light
var prepareMap = [] var i = -1, props = [], length, key; for (key in fieldsMap) { props.push(key); } length = props.length; while (length--) { var key = props[++i]; prepareMap[i] = { from: key, to: fieldsMap[key] }; }
map2
var prepareMap = _.map(fieldsMap, function(value, key) { return { from: key, to: value } });
light 2
var prepareMap = [] var i = -1, props = [], length = 0; for (var key in fieldsMap) { props.push(key); ++length; } while (length--) { var key = props[++i]; prepareMap[i] = { from: key, to: fieldsMap[key] }; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
map
light
map2
light 2

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Measuring the performance of different approaches to create a map from an object can be a complex task, as it involves understanding various aspects such as data structure optimization, algorithmic complexity, and JavaScript engine specifics.

**Benchmark Description**

The benchmark consists of four test cases:

1. `reduce`
2. `map`
3. `light` (a custom implementation)
4. `map2` (another custom implementation)

Each test case creates a map from the `fieldsMap` object using a different approach.

**Approaches**

Here's a brief explanation of each approach:

1. **`reduce`**: This method uses the `_.reduce()` function to create a new array with the specified values. The reducer function iterates over the `fieldsMap` object, creating an object with key-value pairs.
2. **`map`**: This method uses the `_.map()` function to create a new array with the specified values. Each iteration of the map function creates an object with key-value pairs from the `fieldsMap` object.
3. **`light`** and **`map2`**: These custom implementations use a simple loop to iterate over the `fieldsMap` object, creating an object with key-value pairs.

**Optimizations**

The custom implementations (`light` and `map2`) are likely optimized for performance by:

* Avoiding unnecessary function calls
* Using incrementing indices instead of `for...in`
* Minimizing array allocations

**JavaScript Engine Specifics**

The benchmark results show that the JavaScript engine, in this case, Chrome 81 on a Mac OS X 10.14.6 system, is more efficient for the `map` and `reduce` methods compared to the custom implementations (`light` and `map2`). This might be due to:

* Built-in optimizations within the `_` function or `Array.prototype.map()`/`Array.prototype.reduce()`
* Lower overhead for function calls in native code

**Conclusion**

The results suggest that using built-in methods like `_.map()` or `Array.prototype.map()` and `_.reduce()` can provide better performance compared to custom implementations. However, the actual performance gain depends on the specific use case and requirements.

In this benchmark, the `map` method performed better than the `light` implementation (`ExecutionsPerSecond: 845963.875` vs. `ExecutionsPerSecond: 521735.6875`). The `reduce` method was slower than both the optimized `map` and `custom implementations` (`ExecutionsPerSecond: 838099.625` vs. `ExecutionsPerSecond: 521735.6875`).

Keep in mind that this is a simple benchmark, and real-world scenarios might have different results due to factors like memory allocation, garbage collection, or other external influences.

To further improve performance, consider:

* Optimizing your custom implementation for the specific use case
* Using a faster data structure (e.g., `Map` instead of an object)
* Avoiding unnecessary allocations and reassignments

By understanding the trade-offs between different approaches and optimizations, you can write more efficient code that meets your performance requirements.

Related benchmarks:

reduce vs map vs for (version: 0)

Comparing performance of: reduce vs map vs light vs map2 vs light 2

Created: 6 years ago by: Guest

Jump to the latest result

reduce

map

light

map2

light 2

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):