Benchmark: map and join vs reduce

Script Preparation code:

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

Tests:

map join
strings.map(s => s.name).join('');
reduce
strings.reduce((a,n) => a+n.name, '');

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
map join
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/137.0.0.0 Safari/537.36

Browser/OS: Chrome 137 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
map join	98946.3 Ops/sec
reduce	258536.2 Ops/sec

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The benchmark measures the performance difference between two approaches: using `Array.prototype.map()` and then concatenating the results with `String.prototype.join()`, versus using `Array.prototype.reduce()` to concatenate the strings.

**Options Compared**

There are two main options compared:

1. **`map` followed by `join"`:
	* This approach uses the `map()` method to create a new array with the mapped values, and then concatenates them into a single string using `join()`.
	* Pros: Easy to understand and implement, can be used for any type of data.
	* Cons: Can lead to inefficient memory allocation if the resulting string is very large, since a new string object needs to be created and concatenated with each iteration.
2. **`reduce()`**:
	* This approach uses the `reduce()` method to iterate over the array and accumulate the values into a single string.
	* Pros: Can be more efficient than `map` followed by `join`, especially for large datasets, since it avoids creating multiple intermediate arrays.
	* Cons: Can be less intuitive for developers without experience with `reduce()`, may lead to performance issues if not implemented correctly.

**Library Used**

There is no specific library used in this benchmark. The functions being compared (`map()`, `join()`, and `reduce()`) are built-in methods of the JavaScript Array prototype.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax mentioned in the benchmark definition or test cases.

**Other Considerations**

When choosing between these two approaches, consider the following:

* Memory efficiency: If you're working with large datasets and need to minimize memory allocation, `reduce()` might be a better choice.
* Code readability: If code readability is more important than performance, `map` followed by `join` might be easier to understand.

**Alternatives**

Other approaches for concatenating strings in JavaScript include:

1. Using template literals (introduced in ECMAScript 2015): This approach allows you to embed expressions inside string literals and can be more efficient than concatenation methods.
2. Using the `+=` operator: This approach involves using the `+` operator to concatenate strings, but it can lead to performance issues if not implemented correctly.
3. Using a library like Lodash's `join()` function: While not strictly necessary for this benchmark, using a third-party library can provide additional features and optimizations.

For this specific benchmark, the choice between `map` followed by `join`, or `reduce()`, depends on the specific use case and performance requirements.

Related benchmarks:

map and join vs reduce (version: 0)

Comparing performance of: map join vs reduce

Created: 3 years ago by: Guest

Jump to the latest result

map join

reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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