Benchmark: ARRAY VS RAW VALUES

Script Preparation code:

var a = "Here's a string value";
var b = 5; // and a number
var c = false;

var object = {
    a,
    b,
    c
}

var array = [
    a, b, c
];

var passObject = (obj) => {
    return obj.a.length + obj.b * obj.c ? 2 : 1;
}

var passRawValues = (val_a, val_b, val_c) => {
    return val_a.length + val_b * val_c ? 2 : 1;
}

var passArray = (arr) => {
    return arr[0].length + arr[1] * arr[2] ? 2 : 1;
}


var x = 0;

Tests:

pass object
passObject(object)
pass raw
passRawValues(a,b,c)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
pass object
pass raw

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
pass object	12810151.0 Ops/sec
pass raw	7697607.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what's being tested in the provided JSON benchmark.

**Overview**

The benchmark is comparing three different approaches to calculate a value from an object or an array of raw values. The goal is to determine which approach is faster.

**Approaches being compared**

1. **passObject**: This function takes an object as input and returns the sum of the length of the string `a` property, the value of the number `b` multiplied by the boolean `c`, and a conditional expression that adds 2 if the condition is met.
2. **passRawValues**: This function takes three raw values (string, number, and boolean) as input and returns the sum of the length of the string, the product of the number and boolean, and another conditional expression that adds 2 if the condition is met.
3. **passArray**: This function takes an array as input and returns the sum of the length of the first element, the value of the second element multiplied by the third element, and a conditional expression that adds 2 if the condition is met.

**Pros and Cons**

* **passObject**: Pros - this approach works for objects with any property names. Cons - it may require additional parsing or coercion to access properties, which can be slower.
* **passRawValues**: Pros - simple and straightforward. Cons - it requires specific input types (string, number, boolean) and assumes a fixed order of values, which might not always hold true.
* **passArray**: Pros - similar to passObject, works with any array elements. Cons - may require additional parsing or coercion to access elements.

**Libraries**

None explicitly mentioned in the provided JSON.

**Special JS features or syntax**

There are no special JavaScript features or syntax used in this benchmark. The code is straightforward and uses basic operators and functions.

**Considerations**

* When working with objects, consider using `Object.keys()` or `Object.entries()` to get an array of property names or entry points.
* For arrays, using `Array.prototype.map()`, `Array.prototype.reduce()`, or `Array.prototype.forEach()` can simplify calculations.
* If performance is critical, consider using just-in-time (JIT) compilation or optimizing loops.

**Other alternatives**

Some alternative approaches to this benchmark could include:

* Using a library like Lodash or Ramda for functional programming and optimizations
* Implementing a custom parser or compiler for the calculation expressions
* Comparing different data structures, such as arrays vs objects vs sets
* Adding additional test cases with varying input sizes or distributions

Keep in mind that these alternatives may change the scope and focus of the benchmark.

Related benchmarks:

ARRAY VS RAW VALUES (version: 0)

Comparing performance of: pass object vs pass raw

Created: 2 years ago by: Guest

Jump to the latest result

pass object

pass raw

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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