Benchmark: finding item in a structure with map as well

Script Preparation code:

var a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
var b = new Set(a)
var c = {1: true, 2: true, 3: true, 4: true, 5: true, 6: true, 7: true, 8: true, 9: true, 10: true}
var d = new Map(Object.entries(c))

Tests:

includes
var rando = parseInt(Math.random() * 10); a.includes(rando);
has
var rando = parseInt(Math.random() * 10); b.has(rando)
in
var rando = parseInt(Math.random() * 10); rando in c;
map has
var rando = parseInt(Math.random() * 10) + ''; d.has(rando)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
includes
has
in
map has

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36

Browser/OS: Chrome 87 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
includes	2236934.8 Ops/sec
has	2242274.5 Ops/sec
in	2285735.0 Ops/sec
map has	2208535.5 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring performance in JavaScript can be a complex task, as the language's dynamic nature and various execution environments (e.g., browsers) can introduce significant variability.

The provided benchmark definition tests four different approaches for looking up values in an object:

1. **Using `includes()`**: This method checks if a value is present in an array.
2. **Using `has()`** (which is not native to JavaScript, but often implemented by Set objects): This method checks if a value is present in a Set.
3. **Using the `in` operator**: This operator checks if a property name is present in an object.
4. **Using `Map.prototype.has()`**: This method checks if a key is present in a Map.

**Pros and Cons of each approach:**

*   **Inclusive:** Inclusive methods (e.g., `includes()`, `has()`) may be slower for large datasets because they need to scan the entire dataset to check for a value.
    *   **Cons:** They may not provide accurate results if the dataset is modified concurrently, as multiple values might be present during the search.
*   **Exclusive:** Exclusive methods (e.g., `in` operator) directly access an object's property. This can be faster but might have additional overhead due to property lookup.
    *   **Cons:** They may not work correctly in certain scenarios like when using `hasOwnProperty()` or when properties are set dynamically.
*   **Map-based approach**: Using `Map.prototype.has()`, which is a more explicit method, can provide better performance for large datasets and concurrent updates since it uses the underlying hash table to look up values efficiently.

Considerations:

*   **Context and concurrency:** Depending on how your application executes, some approaches may be more suitable than others. For example, in a concurrent environment where dataset modifications are common, `Map.prototype.has()` might offer better performance.
*   **Dataset size:** Larger datasets can favor the exclusive methods (e.g., `in` operator) over inclusive ones (e.g., `includes()`), but this depends on specific hardware and JavaScript engine optimizations.

**Library usage:**

The provided benchmark definition uses:

*   A `Set` object (`var b = new Set(a)`): Sets are implemented in terms of hash tables, providing efficient membership testing for elements.
    *   Purpose: To quickly check if an element is present in the set without iterating through its contents.

**Special JavaScript features and syntax (no special cases mentioned).**

No specific special JavaScript features or syntax are highlighted in this explanation. However, general principles apply when optimizing performance in JavaScript applications:

*   Be mindful of the context in which your code will be executed.
*   Avoid unnecessary overhead by choosing the most suitable approach for a given scenario.
*   Use the built-in methods provided by data structures like arrays (e.g., `includes()`), sets (`has()`), and maps.

**Alternative alternatives:**

Some additional approaches you might consider:

*   **Hash table lookup:** Using the underlying hash table to look up values, as done with `Map.prototype.has()`, can provide better performance for large datasets.
*   **Binary search:** If your dataset is sorted, using binary search (e.g., `binarySearch()` function) could be more efficient than linear searches like `includes()`.
*   **Using a data structure like a trie or prefix tree**: For specific use cases where you're frequently performing substring matching or prefix searching on strings.

Related benchmarks:

finding item in a structure with map as well (version: 0)

looking things up

Comparing performance of: includes vs has vs in vs map has

Created: 4 years ago by: Guest

Jump to the latest result

includes

has

in

map has

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):