Benchmark: Multiple assignments vs One destructuring

Tests:

Multiple assignments
const obj = {a: { e: { i: 6, d: 9 }, f: {g:7} } } const i = obj.a.e.i; const d = obj.a.e.d; const g = obj.a.f.g;
Multiple Destructuring
const obj = {a: { e: { i: 6 }, f: {g:7} } } const {i,d} = obj.a.e; const {g} = obj.a.f;
Single Destructuring
const obj = {a: { e: { i: 6 }, f: {g:7} } } const {a : {e:{i, d}, f:{g}}} = obj;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Multiple assignments
Multiple Destructuring
Single Destructuring

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

**Benchmark Overview**

The benchmark compares three different approaches to assigning values from an object:

1. **Multiple Assignments**: Directly accessing and assigning values to separate variables (`const i = obj.a.e.i; const d = obj.a.e.d;`)
2. **Single Destructuring**: Using a single line of code to destructure multiple values from an object (`const {i, d} = obj.a.e; const g = obj.a.f.g;`)
3. **Multiple Assignments with Object Literal Assignment**: Creating a new object literal and assigning the values using the object assignment syntax (`const {a : {e:{i, d}, f:{g}}} = obj;`)

**Options Compared**

Each option is compared in terms of execution speed.

**Pros and Cons of Each Approach**

1. **Multiple Assignments**:
	* Pros: Simple and straightforward.
	* Cons: Can be less efficient due to the need for multiple assignments.
2. **Single Destructuring**:
	* Pros: More concise and can be more efficient, as it avoids the need for multiple assignments.
	* Cons: Requires knowledge of destructuring syntax and may be less readable for some developers.
3. **Multiple Assignments with Object Literal Assignment**:
	* Pros: Avoids the need for separate variable declarations and can be more efficient.
	* Cons: May require additional memory allocations, as a new object literal is created.

**Library Usage**

There doesn't appear to be any external libraries used in this benchmark.

**Special JS Features/Syntax**

The benchmark uses:

1. **Object Literal Assignment**: Used in the third option to create a new object literal and assign values.
2. **Destructuring Syntax**: Used in options 2 and 3 to extract values from an object.

**Other Considerations**

* The benchmark is likely designed to test the performance of JavaScript engines, specifically Chrome 87.
* The use of specific browser versions (e.g., Chrome 87) ensures that the results are comparable across different browsers and platforms.
* The benchmark's focus on execution speed makes it a good candidate for optimizing JavaScript code.

**Alternative Approaches**

Other possible approaches to assigning values from an object could include:

1. **Using a loop**: Instead of directly accessing or destructuring values, using a loop to iterate over the object's properties could provide alternative performance profiles.
2. **Using function calls**: Creating separate functions to extract specific values from the object could provide additional insights into performance characteristics.
3. **Using different data structures**: Exploring the use of other data structures, such as arrays or trees, to store and retrieve data could provide alternative performance profiles.

Keep in mind that these alternatives might not be relevant to this specific benchmark, but they can help researchers and developers understand the general trade-offs between different approaches when working with JavaScript.

Related benchmarks:

Multiple assignments vs One destructuring (version: 0)

Comparing performance of: Multiple assignments vs Multiple Destructuring vs Single Destructuring

Created: 5 years ago by: Guest

Jump to the latest result

Multiple assignments

Multiple Destructuring

Single Destructuring

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):