Benchmark: Reduce vs map/join with bound function

Script Preparation code:

var strings = [];
var vars = [];

for (var i=0; i<1000; i++) {
  strings[i] = ""+i+i;
  vars[i] = ""+i+i;
}

var reducer = function reducer(acc, v, i) {
  return acc + ( i > 0 ? ', ' : '' ) + this[i]+"="+v;
}

var mapper = function(v, i) {
  return this[i]+"="+v;
}

Tests:

for
var result = vars[0]+"="+strings[0]; for (var i=1; i<strings.length; i++) { result = result + ', ' + vars[i] + "=" + strings[i]; }
reduce
var result = strings.reduce(reducer.bind(vars), ", ");
map
var result = strings.map(mapper, vars).join(', ');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for
reduce
map

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36

Browser/OS: Chrome 93 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
for	5097.5 Ops/sec
reduce	28390.6 Ops/sec
map	16525.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmarking test cases.

**Benchmark Overview**

The test case compares the performance of three different approaches: `map`, `reduce`, and a simple loop (`for`).

The script preparation code creates two arrays, `strings` and `vars`, with 1000 elements each. The `reducer` function is used as both a `reduce` function and a bound function in another context.

**Approaches Compared**

1. **map**: This approach uses the `map()` method to apply the `mapper` function to each element of the `strings` array, returning an array with the results.
2. **reduce**: This approach uses the `reduce()` method with the `reducer` function as both the accumulator and a bound function in another context.
3. **for loop**: This approach uses a traditional `for` loop to iterate over the `strings` array and calculate the result.

**Pros and Cons of Each Approach**

1. **map**:
	* Pros: Concise and expressive, eliminates explicit loops.
	* Cons: May have performance overhead due to function call overhead, can be less efficient for large datasets.
2. **reduce**:
	* Pros: Can be more efficient than map for large datasets, provides a concise way to chain operations.
	* Cons: Requires understanding of the reduce() method and its callback function.
3. **for loop**:
	* Pros: Simple, intuitive, and well-supported by most browsers.
	* Cons: May have performance overhead due to explicit loops.

**Library/Utility Functions**

1. `map()` and `reduce()` are built-in JavaScript methods for array operations.

**Special JS Feature/Syntax**

There is no special JavaScript feature or syntax used in this benchmarking test case.

**Other Alternatives**

For similar benchmarks, you might want to explore other approaches:

* Using `forEach` instead of `map`
* Using `filter` and `concat` instead of `reduce`
* Using template literals or string concatenation with a loop

It's worth noting that the choice of approach often depends on the specific use case and personal preference. This benchmarking test case provides a clear comparison between three different approaches, making it easier to understand which one might be the most efficient for a particular scenario.

Related benchmarks:

Reduce vs map/join with bound function (version: 0)

Comparing performance of: for vs reduce vs map

Created: 3 years ago by: Guest

Jump to the latest result

for

reduce

map

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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