Benchmark: Object VS new Sets

Script Preparation code:

var a = [];
var b = {}
var set = new Set();

for (var i = 0; i < 1000000; i++) {
    a.push(i);
    b[i] = `${i}`;
    set.add(i);
}

Tests:

Object
b[114500]
Set
set.has(114500)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object
Set

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.

**What is being tested?**

The provided JSON represents a benchmark test on JavaScript, specifically comparing the performance of accessing elements in an object versus using a Set data structure.

In more detail, the test creates two variables: `a` (an array) and `b` (an object). It then populates both `a` and `b` with 1 million iterations, where each iteration adds a unique value to the corresponding data structure. Additionally, it initializes a Set (`set`) and adds the same values to it.

The test then measures the performance of accessing elements in both data structures:

*   For `Object`, the benchmark definition is `"b[114500]"`, which means accessing the 114,500th element in the object `b`.
*   For `Set`, the benchmark definition is `"set.has(114500)"`, which checks if the value 114,500 exists in the Set.

**Options compared:**

The options being compared are:

1.  **Object**: Accessing elements in an object using bracket notation (`[index]`).
2.  **Set**: Checking membership in a Set using the `has()` method.

**Pros and Cons of each approach:**

### Object

Pros:

*   Generally faster than checking membership in a Set for small to medium-sized datasets.
*   Can be more convenient for frequent access or modification.

Cons:

*   Searching for an element in an object can be slower for very large datasets due to the need to traverse the entire object.
*   May incur additional overhead due to object lookup and potential hash collisions.

### Set

Pros:

*   Much faster than checking membership in an object for large datasets, as Sets use a data structure optimized for fast lookups (hash tables).
*   More memory-efficient, especially for sparse or frequently updated datasets.

Cons:

*   Can be slower for small to medium-sized datasets due to the overhead of creating and maintaining a Set.
*   May require additional library support if not using built-in Set functionality.

**Library usage:**

In this benchmark test, the `Set` data structure is used in its native form, relying on JavaScript's built-in Set implementation. This is a common approach for testing performance differences between various data structures.

**Special JS feature or syntax:**

None of the tested code snippets employ any special JavaScript features or syntax that would affect the outcome of the benchmarking test.

**Alternative alternatives:**

Other alternatives to consider when comparing object access and Set lookups include:

*   **Array**: Similar to objects, accessing elements in an array using index notation (`arr[index]`) could be compared.
*   **Map**: A Map data structure, which is a variant of Object that provides key-value pairs, could also be used for comparison.
*   **WeakSet** or **WeakMap**: These Set-like structures are designed to work efficiently with weak references, but may have different performance characteristics.

In summary, the provided benchmark test measures the performance difference between accessing elements in an object versus using a Set data structure. By comparing these two approaches, developers can gain insights into the trade-offs of each method and choose the most suitable solution for their specific use cases.

Related benchmarks:

Object VS new Sets (version: 0)

Comparing performance of: Object vs Set

Created: 3 years ago by: Guest

Jump to the latest result

Object

Set

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):