Benchmark: Set.prototype.has() vs "in" operator

Script Preparation code:

var set = new Set();
var obj = Object.create(null);

var test = (function (length, size, probability) {
  function random() {
    return Math.random().toString(36).substr(2);
  }
  var i;
  
  var array = [];
  array.length = size;
  for (i = 0; i < size; i++) {
    var str = random();
    set.add(str);
    obj[str] = true;
    array[i] = str;
  }
  
  var test = [];
  test.length = length;
  var c = size / probability;
  for (i = 0; i < length; i++) {
    var index = Math.floor(Math.random() * c);
    test[i] = index < size ? array[index] : random();
  }
  return test;
})(10000, 1000000, 0.5);

Tests:

Set.prototype.has()
var count = 0; for (var i = 0; i < test.length; i++) { if (set.has(test[i])) { count++; } }
"in" operator
var count = 0; for (var i = 0; i < test.length; i++) { if (test[i] in obj) { count++; } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Set.prototype.has()
"in" operator

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

**Benchmark Explanation**

The provided benchmark compares the performance of two approaches: `Set.prototype.has()` and the `"in"` operator.

**`Set.prototype.has()` approach**

This approach uses the `has` method of the `Set` prototype to check if an element is present in the set. The test creates a large set with 1 million unique elements, generates an array of random indices, and then iterates over the array, checking each index against the set using `has`. The number of iterations that result in a hit (i.e., the element is present in the set) is counted.

**`"in"` operator approach**

This approach uses the `"in"` operator to check if an element is present in an object. The test creates an object with 1 million unique elements, generates an array of random indices, and then iterates over the array, checking each index against the object using the `"in"` operator. The number of iterations that result in a hit (i.e., the element is present in the object) is counted.

**Options Compared**

The two approaches are compared with the following options:

* **Randomized Input**: Both approaches use randomly generated input data to test their performance.
* **Set Size**: Both approaches use sets with 1 million unique elements, but the size of the set is fixed for both approaches.
* **Array Length**: The length of the array used as input for both approaches is randomized and can vary between different runs.

**Pros and Cons**

Here are some pros and cons of each approach:

**`Set.prototype.has()`**

Pros:

* This approach is more explicit and uses a standard method of the `Set` prototype, making it easier to understand.
* It's possible that this approach is more optimized for performance because it's a built-in method.

Cons:

* The number of iterations required to generate the random indices can be larger than the set size, leading to slower performance.
* This approach may not work correctly with very large sets or sets containing many duplicate elements.

**`"in"` operator**

Pros:

* This approach is more concise and uses a well-known operator in JavaScript.
* It's possible that this approach is faster because it's a built-in operator.

Cons:

* The `"in"` operator can be slower than `Set.prototype.has()` for large sets because it has to traverse the object to find the element.
* This approach may not work correctly with very large objects or objects containing many duplicate elements.

**Other Considerations**

The benchmark does not consider other factors that could affect performance, such as:

* **Cache effects**: The order in which elements are added to the set or object may affect cache behavior and performance.
* **Garbage collection**: The frequency of garbage collection can impact performance when using large data structures like sets.

**Alternative Approaches**

If you want to explore alternative approaches, here are some options:

* **Use a different data structure**: Instead of using a set, you could use an array or another data structure that's optimized for fast lookups.
* **Use a more explicit optimization technique**: You could try using techniques like memoization or caching to optimize the performance of either approach.
* **Test with different input sizes**: Increase or decrease the size of the sets and arrays used in the benchmark to see how performance changes.

Related benchmarks:

Set.prototype.has() vs "in" operator (version: 0)

Comparing performance of: Set.prototype.has() vs "in" operator

Created: 5 years ago by: Guest

Jump to the latest result

Set.prototype.has()

"in" operator

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):