Benchmark: Lodash Sum vs ApSum

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.4/lodash.min.js"></script>

Script Preparation code:

function* generate(until) {
  let current = 0
  while(current < until) {
    yield 1 + (5 * current)
    current++
  }
}

function apSum(array) {
  const N = array.length
  const [a] = array
  const L = array[N - 1]
  return (N / 2) * (a + L)
}

function sum(array) {
  return _.sum(array)
}

function naiveSum(array) {
  array.reduce((total, current) => total += current, 0)
}

Tests:

Lodash sum
const data = [...generate(1000 * 1000)] sum(data)
Ap Sum
const data = [...generate(1000 * 1000)] apSum(data)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash sum
Ap Sum

Fastest: N/A

Slowest: N/A

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Autogenerated LLM Summary (model gemma2:9b, generated one year ago):

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This benchmark compares the performance of three different ways to calculate the sum of an array of numbers in JavaScript:

* **Lodash Sum (`sum(data)`)**: This approach utilizes the `_.sum()` function from the Lodash library.  Lodash is a widely used JavaScript utility library that provides a variety of helpful functions for working with data, arrays, and objects. It's known for its performance and conciseness.

* **Arithmetic Mean (`apSum(data)`)**: This method calculates the sum using the arithmetic mean formula: `(N / 2) * (a + L)`, where N is the array length, 'a' is the first element, and 'L' is the last element. This formula leverages the fact that summing consecutive numbers follows a pattern.

* **Reduce (`naiveSum(data)`)**: This method uses the `reduce()` function to iterate through the array and accumulate the sum.  `reduce()` is a powerful functional programming technique in JavaScript for transforming arrays into a single value.

**Pros/Cons:**

* **Lodash Sum:**
    * **Pros:**  Concise, well-tested, potentially optimized.
    * **Cons:**  Adds a dependency on the Lodash library.

* **Arithmetic Mean:**
    * **Pros:** Can be very efficient for large arrays if the data follows a linear pattern.
    * **Cons:** Less versatile than `reduce()`. Not suitable for all types of data or calculations.

* **Reduce:**
    * **Pros:** Very flexible, can handle complex array transformations.
    * **Cons:**  Can be less efficient than specialized methods like `apSum` for simple sums.

**Other Considerations:**

* **Array Size:** The performance difference between these approaches will likely become more noticeable as the size of the array increases.

* **Data Distribution:** The efficiency of `apSum` depends on the data being evenly spaced. 
* **Library vs. Native:** Depending on your project, adding a library like Lodash might not be ideal due to potential bloat or compatibility concerns. Weigh the benefits against these factors.

**Alternatives:**

* You could explore other built-in JavaScript methods like `Array.prototype.reduce()` or `Array.prototype.forEach()`, depending on your specific needs. 
* There are also other specialized libraries for numerical computation in JavaScript, such as NumPy.js, which might offer even more optimized solutions for mathematical tasks.

Related benchmarks:

Lodash Sum vs ApSum (version: 0)

Comparing performance of: Lodash sum vs Ap Sum

Created: 4 years ago by: Guest

Jump to the latest result

Lodash sum

Ap Sum

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model gemma2:9b, generated one year ago):