Benchmark: Map vs Reduce - MeasureThat.net

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min.js"></script>

Script Preparation code:

var data = [],
    numRows = 1000,
    numCols = 1000;

for (var y = 0; y < numRows; y++) {
  var lineData = [];
  for (var x = 0; x < numCols; x++) {
    lineData[x] = [131840, ' ', 1];
  }
  data[y] = lineData; 
}

Tests:

Map + Array.Join
_.map(data, function(line) { return _.map(line, function(char) { return char[1]; }).join(""); }).join("\n");
Reduce + String.Concat
_.reduce(data, function(memo, line) { return memo + _.reduce(line, function(lineMemo, char) { return lineMemo + char; }, "") + "\n" }, "");

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map + Array.Join
Reduce + String.Concat

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's dive into the world of JavaScript microbenchmarks!

**Benchmark Overview**

The provided JSON represents a benchmark test case called "Map vs Reduce" on the MeasureThat.net website. The goal is to compare the performance of two approaches: using `map` and `reduce` functions from the Underscore.js library, along with string concatenation.

**What's being tested?**

In this benchmark, we have an array of arrays (`data`) containing a 2D grid of characters. We want to extract the second character from each inner array and join them into strings using newline characters as separators.

The two test cases use different approaches:

1. **Map + Array.Join**: Uses `map` on the outer array, mapping each line to an array of characters mapped by `map` again, and then joins these arrays into a single string.
2. **Reduce + String.Concat**: Uses `reduce` on the outer array, reducing each line to a string by concatenating characters using `reduce`, and then joins these strings together.

**Options compared**

The benchmark compares two approaches:

* `Map + Array.Join`: uses the `map` function to transform arrays
* `Reduce + String.Concat`: uses the `reduce` function to concatenate strings

**Pros and Cons of each approach:**

1. **Map + Array.Join**
	* Pros:
		+ More explicit and readable code for transforming arrays
		+ Can be faster for large datasets due to the optimized `map` implementation in modern browsers
	* Cons:
		+ Requires two separate operations (mapping and joining)
		+ May lead to slower performance due to unnecessary intermediate results
2. **Reduce + String.Concat**
	* Pros:
		+ More efficient, as it only requires a single pass over the data
		+ Can be faster for large datasets due to optimized `reduce` implementation in modern browsers
	* Cons:
		+ Code can become more complex and harder to read due to the reduced function's multiple steps
		+ May lead to slower performance if not implemented correctly

**Underscore.js Library**

The benchmark uses Underscore.js, a popular utility library for functional programming in JavaScript. In this case, it provides:

* `_map`: applies a function to each element of an array and returns a new array with the results
* `_reduce`: applies a function to each element of an array and reduces it to a single value

**Special JS Feature**

There is no special JavaScript feature or syntax being used in this benchmark.

**Alternatives**

Other alternatives for achieving similar results could be:

* Using `forEach` instead of `map`
* Using a custom implementation with loops
* Using a different library or framework that provides optimized string concatenation and array manipulation functions

Keep in mind that the choice of approach depends on the specific requirements and constraints of your project, such as performance, readability, and maintainability.

Related benchmarks:

Map vs Reduce (version: 0)

Comparing performance of: Map + Array.Join vs Reduce + String.Concat

Created: 9 years ago by: Guest

Jump to the latest result

Map + Array.Join

Reduce + String.Concat

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):