Benchmark: Lodash sumBy vs forEach vs for vs map reduce

HTML Preparation code:


<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

function getRandomInt(max) {
  return Math.floor(Math.random() * Math.floor(max));
}

var arr = [];
for(var i = 0; i < 100000; i++){
  arr.push({value:getRandomInt(100)});
}

Tests:

_.sumBy
_.sumBy(arr,"value");
foreach
let result = 0; arr.forEach(element => { result += element.value }); return result;
for
let result = 0; for(let i=0;i<arr.length;i++) { result += arr[i].value; }; return result;
map reduce
arr.reduce((prev, next) => prev + next.value, 0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
_.sumBy
foreach
for
map reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested.

**Benchmark Overview**

The benchmark compares four different approaches to summing up values in an array:

1. `_.sumBy` (using Lodash)
2. `forEach`
3. `for` loop
4. `map reduce`

Each test case uses a random array of 100,000 objects with random integer values between 0 and 100.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a lot of functional programming helpers, including `sumBy`. The `sumBy` function takes an array and a function as arguments, where the function returns a value to be summed up. In this case, the function is simply returning the `value` property of each object in the array.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntaxes used in this benchmark. All implementations use standard JavaScript constructs.

**Test Case Comparison**

Here's what's being tested:

* `_.sumBy`: This test case uses Lodash's `sumBy` function to sum up all values in the array.
* `forEach`: This test case uses a traditional `forEach` loop to iterate over the array and add up all values.
* `for`: This test case uses a traditional `for` loop to iterate over the array and add up all values.
* `map reduce`: This test case uses the `reduce` function to sum up all values in the array.

**Options Compared**

The four options are compared in terms of their execution performance. The goal is to determine which approach is the fastest for this specific use case.

**Pros and Cons of Each Approach**

Here's a brief summary:

* `_.sumBy`: Pros: concise, easy to read; Cons: dependent on Lodash library, may have overhead due to function calls.
* `forEach`: Pros: simple, easy to understand; Cons: can be slower than other approaches due to loop overhead.
* `for`: Pros: straightforward, efficient; Cons: can be verbose and hard to read for complex loops.
* `map reduce`: Pros: concise, can be parallelized; Cons: may have higher overhead due to function calls and array manipulation.

**Other Considerations**

* The benchmark uses a random array of values, which may not accurately represent real-world use cases. In reality, the data might be more structured or have specific properties that affect performance.
* Lodash's `sumBy` function has some additional features, such as handling null or undefined values, which are not shown in this benchmark.

**Alternatives**

Other approaches to summing up array values might include:

* Using a built-in JavaScript function like `Array.prototype.reduce()`
* Using a specialized library like NumJS or Mathjs
* Implementing a custom loop using bitwise operations or other optimized techniques

Keep in mind that the best approach depends on the specific use case and performance requirements.

Related benchmarks:

Lodash sumBy vs forEach vs for vs map reduce (version: 0)

Comparing performance of: _.sumBy vs foreach vs for vs map reduce

Created: 3 years ago by: Guest

Jump to the latest result

_.sumBy

foreach

for

map 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):