Benchmark: impact of property digging

Script Preparation code:

var x = {
    a : {
       b : false
    },
    c : false
};

var y = false;

var z = x.a;

Tests:

1
x.a.b = (Math.random() < 0.5) ? false : true;
2
x.c = (Math.random() < 0.5) ? false : true;
3
z.b = (Math.random() < 0.5) ? false : true;
4
y = (Math.random() < 0.5) ? false : true;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1
2
3
4

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmark and provide insights into the different approaches.

**Benchmark Overview**

The provided JSON represents a JavaScript microbenchmark created on MeasureThat.net. The benchmark is designed to measure the impact of property digging in JavaScript. Property digging refers to the process of accessing nested properties using the dot notation (e.g., `x.a.b`).

**Script Preparation Code**

The script preparation code defines two objects, `x` and `y`, with a nested object `a` within `x`. The variable `z` is assigned the value of `x.a`.

```javascript
var x = {
  a: {
    b: false
  },
  c: false
};

var y = false;

var z = x.a;
```

**Html Preparation Code**

The html preparation code is null, indicating that no HTML setup is required for this benchmark.

**Individual Test Cases**

There are four test cases:

1. `x.a.b = (Math.random() < 0.5) ? false : true;`
2. `x.c = (Math.random() < 0.5) ? false : true;`
3. `z.b = (Math.random() < 0.5) ? false : true;`
4. `y = (Math.random() < 0.5) ? false : true;`

Each test case modifies a property of the `x`, `z`, or `y` objects using the dot notation.

**Approaches Compared**

The benchmark compares four different approaches:

1. **Direct Property Access**: `x.a.b` and `z.b`
	* Pros: Fast, as it involves direct access to the property.
	* Cons: May not be optimized for all environments, especially those with strict type checking.
2. **Indirect Property Access**: `x.c = (Math.random() < 0.5) ? false : true;` and `y = (Math.random() < 0.5) ? false : true;`
	* Pros: May be optimized for indirect access, as it allows the engine to evaluate the expression before assigning the value.
	* Cons: Can result in slower performance due to the overhead of evaluating the expression.
3. **Property Digging**: `x.a.b = (Math.random() < 0.5) ? false : true;`
	* Pros: Simulates real-world scenarios where nested properties are accessed using dot notation.
	* Cons: May not be optimized for all environments, as it requires navigating the object's property chain.

**Library and Special JS Features**

There is no library or special JavaScript feature mentioned in the benchmark definition. However, some test cases use built-in functions like `Math.random()`.

**Other Considerations**

To optimize performance in these types of benchmarks:

* Use a consistent syntax and coding style throughout the test suite.
* Minimize the number of allocations (e.g., avoid creating new objects or arrays unnecessarily).
* Avoid using expensive operations, such as DOM manipulations or network requests, in the benchmark.

**Alternatives**

If you're interested in exploring alternative approaches for similar benchmarks, consider:

1. **Micro-benchmarking frameworks**: Libraries like `benchmark` or `js-perf` provide more comprehensive features and options for creating microbenchmarks.
2. **Benchmarking tools**: Tools like `Chrome DevTools` or `Firefox Developer Edition` offer built-in benchmarking capabilities that can help you optimize your code for specific use cases.

Keep in mind that the specific approach and techniques used may vary depending on the requirements of your project and the environment in which it will be executed.

Related benchmarks:

impact of property digging (version: 0)

impact of property digging

Comparing performance of: 1 vs 2 vs 3 vs 4

Created: 9 years ago by: Guest

Jump to the latest result

1

2

3

4

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):