Benchmark: Array,sort+Array.map vs Array.reduce

Script Preparation code:

var namespaces = [];
for (var i = 0; i < 10000; i++) {
  let predicate = Math.random();
  let level = '';
  if (predicate < 0.25) {
    level = ':error';
  } else if (predicate < 0.5) {
    level = ':warn';
  } else if (predicate < 0.75) {
    level = ':verbose';
  }
  const off = Math.random() < 0.5;
  namespaces.push(`${off}LH:${Math.floor(Math.random() * 1000)}${level}`);
}

Tests:

sort-map
const rules = namespaces .sort() .map((namespace) => { let enabled = true; if ('-' === namespace[0]) { namespace = namespace.slice(1); enabled = false; } return {text: namespace, pattern: new RegExp(`^${namespace}$`), enabled}; });
reduce
const rules = namespaces .reduce((rules, namespace) => { let enabled = true; if ('-' === namespace[0]) { namespace = namespace.slice(1); enabled = false; } const rule = {text: namespace, pattern: new RegExp(`^${namespace}$`), enabled}; const nextIndex = rules.findIndex((rule) => namespace.length < rule.text.length); const index = nextIndex > -1 ? nextIndex : rules.length; rules.splice(index, 0, rule); return rules; }, []);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
sort-map
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36

Browser/OS: Chrome 112 on Windows

View result in a separate tab

Test name	Executions per second
sort-map	216.3 Ops/sec
reduce	9.3 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and benchmarking code to understand what is being tested.

**Benchmark Definition**

The benchmark definition describes two different approaches to process an array of strings:

1. `Array, sort+Array.map`
2. `Array.reduce`

Both approaches aim to filter out rules based on a specific pattern in each string, while also determining whether the rule should be enabled or not.

**Options compared**

In this benchmark, the following options are being compared:

* Sorting the array before applying the filtering process (`sort+Array.map`)
* Using the `reduce` method to apply the filtering process (without sorting)

**Pros and Cons of each approach**

1. **Sorting + Array.map**
	* Pros:
		+ Can be faster for large datasets, as it sorts the array first and then applies the filtering process.
	* Cons:
		+ Requires extra memory to store the sorted array.
		+ May have a higher overhead due to sorting.
2. **Array.reduce**
	* Pros:
		+ More efficient in terms of memory usage, as it only processes one element at a time.
		+ Less overhead compared to sorting and mapping.
	* Cons:
		+ Can be slower for large datasets, as the filtering process is applied sequentially.

**Library and its purpose**

In both benchmark definitions, the `namespace` array is filtered using regular expressions. The `RegExp` object is used to create patterns that match specific strings in the `namespace` array. The `pattern` property of each rule object contains this regex pattern.

**Special JavaScript feature or syntax**

There is no special JavaScript feature or syntax mentioned in these benchmark definitions. They use standard JavaScript features, such as arrays, loops, and regular expressions.

**Other alternatives**

If you were to consider alternative approaches, some options could be:

* Using `Array.prototype.filter()` instead of sorting and mapping
* Implementing a custom filtering algorithm using bitwise operations or other optimizations
* Utilizing WebAssembly or other low-level languages for performance-critical parts of the benchmark

However, these alternatives would require significant changes to the benchmark code and might not provide noticeable performance improvements.

Keep in mind that this is just an analysis based on the provided information. Without more context or details about the specific use case or requirements, it's difficult to suggest more tailored optimizations or approaches.

Related benchmarks:

Array,sort+Array.map vs Array.reduce (version: 0)

Comparing performance of: sort-map vs reduce

Created: one year ago by: Guest

Jump to the latest result

sort-map

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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