Benchmark: Reduce vs map/join

Script Preparation code:

var strings = [];
for (var i=0; i<1000; i++) {
  strings[i] = ""+i+i;
}

Tests:

for
var result = ""; for (var i=0; i<strings.length; i++) { result = result+' x-'+strings[i]; }
reduce
var result = strings.reduce(function(string, i) { return string+'x-'+i+' '; }, "");
map
var result = strings.map(function(i) { return 'x-'+i; }).join(' ');
for of
var result = ""; for (var i of strings) { result += ' x-'+i; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for
reduce
map
for of

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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I'll break down the provided benchmark and explain what's being tested, compared, and considered.

**Benchmark Overview**

The benchmark compares four approaches to concatenate an array of strings: `for`, `reduce`, `map`, and `for of`. The input data consists of 1000 strings in the format `"i+i"`, where `i` is a number from 0 to 999.

**Test Cases**

Each test case represents one of the four approaches:

1. **`for`**: A traditional `for` loop that iterates over the array and concatenates each string.
2. **`reduce`**: The `reduce()` method, which applies a callback function to each element in the array and reduces it to a single value (in this case, a concatenated string).
3. **`map`**: The `map()` method, which creates a new array with the results of applying a callback function to each element in the original array.
4. **`for of`**: A newer `for...of` loop that iterates over the array and concatenates each string.

**Comparison**

The benchmark compares these four approaches based on execution speed (measured in executions per second).

**Pros and Cons of Each Approach:**

1. **`for`**:
	* Pros: Simple, widely supported, and well-known.
	* Cons: Can be slow due to the overhead of loop variables and conditionals.
2. **`reduce()`**:
	* Pros: Memory-efficient, as it only uses a single variable to accumulate the result.
	* Cons: Less intuitive for beginners, requires understanding of callback functions.
3. **`map()`**:
	* Pros: Efficient, as it creates a new array without modifying the original.
	* Cons: Requires more memory, as a new array is created.
4. **`for...of`**:
	* Pros: Modern, concise, and easy to read.
	* Cons: Less widely supported in older browsers or environments.

**Library and Syntax**

None of the test cases explicitly use any external libraries beyond the built-in `Array.prototype`. However, if we consider modern JavaScript features, all four approaches could be argued to use a form of functional programming or object-oriented programming.

**Other Considerations**

* **Cache locality**: The order in which strings are concatenated can affect cache performance. In this benchmark, the `for` loop iterates over the array in ascending order, which may lead to better cache locality.
* **Memory allocation and garbage collection**: The `reduce()` and `map()` methods create a single variable or array to accumulate results, which can reduce memory allocation and garbage collection overhead.

**Alternatives**

If you're looking for alternative approaches, consider:

1. **`forEach()`**: Another iteration method that's similar to `for`.
2. **`flatMap()`**: A more modern method from ECMAScript 2017 (ES7) that combines the functionality of `map()` and `reduce()`.
3. **Template literals**: Using template literals (`''`) can simplify string concatenation and reduce memory allocation.

In summary, this benchmark compares four approaches to concatenate an array of strings: traditional `for` loops, functional programming with `reduce()` and `map()`, and modern `for...of` loops. The results provide insight into the performance characteristics of each approach.

Related benchmarks:

Reduce vs map/join (version: 0)

Comparing performance of: for vs reduce vs map vs for of

Created: 8 years ago by: Guest

Jump to the latest result

for

reduce

map

for of

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):