Benchmark: object members - MeasureThat.net

Script Preparation code:

var a = { start: { end: 42 } };
var b = { start: { end: 42 } };

var c = { start: 42 }
var d = { start: 42 }

Tests:

check 2 levels
a.start.end === b.start.end
check 1 level
c.start === d.start

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
check 2 levels
check 1 level

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 8 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.113 Safari/537.36

Browser/OS: Chrome 53 on Mac OS X 10.11.5

View result in a separate tab

Test name	Executions per second
check 2 levels	4243551.5 Ops/sec
check 1 level	4335183.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to help you understand the JavaScript microbenchmark provided by MeasureThat.net.

**What is being tested?**

The benchmark tests the performance of accessing object properties using different methods: nested dot notation (e.g., `a.start.end`) versus single-level dot notation (e.g., `c.start`). The test checks how fast each approach can access a property in an object.

**Options compared:**

There are two main options being compared:

1. **Nested dot notation**: `a.start.end === b.start.end` and `c.start === d.start`. This involves accessing properties through multiple levels of nesting (i.e., `start` > `end`).
2. **Single-level dot notation**: `c.start === d.start`. This involves accessing a property without any nesting.

**Pros and cons:**

* **Nested dot notation**:
	+ Pros: Can be more readable, especially for nested object structures.
	+ Cons: Can lead to slower performance due to the additional levels of access.
* **Single-level dot notation**:
	+ Pros: Typically faster since it requires fewer accesses to resolve the property path.
	+ Cons: May require more boilerplate code or explicit type definitions.

In general, when working with nested object structures, the benefits of readability often outweigh the potential performance costs. However, in situations where every millisecond counts (e.g., high-performance web applications), using single-level dot notation might be preferred to squeeze out every last bit of performance.

**Library and purpose:**

There are no libraries mentioned in this specific benchmark. However, when working with JavaScript, you may encounter libraries like Lodash or Underscore.js that provide utility functions for accessing object properties.

For example, the `_.get()` function from Lodash can be used to access nested properties:
```javascript
var obj = { a: { b: { c: 'value' } } };
console.log(_.get(obj, ['a', 'b', 'c'])); // Output: "value"
```
**Special JS feature or syntax:**

None of the provided benchmark tests utilize any special JavaScript features or syntax that are not standard.

**Alternatives:**

If you're interested in exploring alternative approaches to accessing object properties, here are a few:

1. **Array methods**: Instead of using dot notation, you can use array methods like `indexOf()` or `findIndex()` to access nested properties.
2. **Path-to-Object**: The `path-to-object` library provides a convenient way to traverse and access nested objects using a path-based syntax.
3. **Query-Style Accessors**: Some frameworks, like React or Angular, use query-style accessor patterns (e.g., `.find()`, `.get()`) to access data in application components.

Keep in mind that these alternatives might introduce additional overhead or require changes to your codebase, so it's essential to consider the trade-offs and performance implications before switching.

Related benchmarks:

object members (version: 0)

Comparing performance of: check 2 levels vs check 1 level

Created: 8 years ago by: Registered User

Jump to the latest result

check 2 levels

check 1 level

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):