Benchmark: werffddsfc - MeasureThat.net

Script Preparation code:

var a = new Map()
var b = [];

Tests:

Insert
a.insert(Math.random(),Math.random());
arr
b[Math.random()] = Math.random();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Insert
arr

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's dive into the world of MeasureThat.net and understand what's being tested in this benchmark.

**Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided JSON represents a simple benchmark definition, which includes script preparation code and individual test cases. We'll break down each component to understand what's being tested and the pros and cons of different approaches.

**Benchmark Definition**

The benchmark definition is represented by the top-level JSON object:
```json
{
    "Name": "werffddsfc",
    "Description": "ssdfsd",
    "Script Preparation Code": "var a = new Map()\r\nvar b = [];",
    "Html Preparation Code": null
}
```
Here, we have:

* `Name` and `Description`: metadata about the benchmark.
* `Script Preparation Code`: code that is executed before running each test case. In this case, it initializes two variables: an empty Map (`a`) and an empty array (`b`).
* `Html Preparation Code`: not used in this benchmark, likely left for future extensions or customization.

**Individual Test Cases**

The individual test cases are represented by the following JSON array:
```json
[
    {
        "Benchmark Definition": "a.insert(Math.random(),Math.random());",
        "Test Name": "Insert"
    },
    {
        "Benchmark Definition": "b[Math.random()] = Math.random();",
        "Test Name": "arr"
    }
]
```
Here, we have two test cases:

1. `Insert`: This test case measures the performance of inserting a random value into an empty Map (`a`). The `Math.random()` function is used to generate random values.
2. `arr`: This test case measures the performance of accessing and assigning a random value to an element in an empty array (`b`).

**Pros and Cons**

* **Using an empty Map**: Inserting a random value into an empty Map can be slow due to the need to allocate memory and create a new object. However, this approach provides good cache locality, as the `insert()` method modifies the existing object. On the other hand, using an array may incur more overhead due to the need to search for the correct index.
* **Using an empty array**: Accessing and assigning a random value to an element in an empty array can be fast, but it may not provide good cache locality, leading to slower performance compared to inserting into a Map.

**Library: `Map`**

The `Map` data structure is a built-in JavaScript object that stores key-value pairs. It's used here to demonstrate the performance of inserting values into a collection with good cache locality.

**Special JS Feature/Syntax**

None mentioned in this benchmark, but it's worth noting that MeasureThat.net may use other features or syntax, such as async/await or Promises, depending on the specific benchmark and test cases.

**Other Alternatives**

Some alternative approaches to measure performance could include:

* Using a custom data structure, such as an object with array-like properties.
* Measuring the time taken to perform a different operation, such as sorting or searching.
* Using a more complex scenario, such as inserting values into a large dataset or performing multiple operations in sequence.

Keep in mind that these alternatives may not be relevant to this specific benchmark, and MeasureThat.net's primary focus is on evaluating JavaScript performance in various scenarios.

Related benchmarks:

werffddsfc (version: 0)

ssdfsd

Comparing performance of: Insert vs arr

Created: 9 years ago by: Guest

Jump to the latest result

Insert

arr

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):