Benchmark: Object spreading vs mutation in reduce

Tests:

Object spreading
[...new Array(1000)].reduce( (acc, item, index) => ({ ...acc, [index]: {} }), {}, );
Mutation
[...new Array(1000)].reduce( (acc, item, index) => { acc[index] = {}; return acc; }, {}, );

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Object spreading
Mutation

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down the provided benchmark and its test cases.

**Benchmark Definition**

The benchmark is defined as a JavaScript microbenchmark that tests two different approaches to creating an object in a reduce function. The benchmark definition consists of three parts:

* `Script Preparation Code`: This is empty, meaning no code needs to be executed before running the benchmark.
* `Html Preparation Code`: This is also empty, indicating no HTML-related setup is required for this benchmark.
* `Benchmark Definition`: This is where the actual benchmarking logic is defined. It consists of two test cases:

**Test Case 1: Object Spreading**

```javascript
[...new Array(1000)].reduce((acc, item, index) => ({ ...acc, [index]: {} }), {},);
```

This test case uses object spreading (`{ ...acc }`) to create a new object and then spreads an empty object (`{}`) at each index in the resulting object.

**Test Case 2: Mutation**

```javascript
[...new Array(1000)].reduce((acc, item, index) => {
  acc[index] = {};
  return acc;
}, {},);
```

This test case uses mutation by directly assigning an empty object (`{}`) at each index in the `acc` object.

**What's being tested?**

In both test cases, we're interested in measuring the performance difference between these two approaches:

1. **Object Spreading**: This approach creates a new object and then spreads an empty object at each index.
2. **Mutation**: This approach directly assigns an empty object at each index in the `acc` object.

The benchmark measures the execution time of these two approaches to create an object with 1000 properties.

**Pros and Cons**

* **Object Spreading**:
	+ Pros: More explicit, easier to understand, and potentially more efficient due to the use of object literals.
	+ Cons: Creates a new object, which may incur additional memory allocation and garbage collection overhead.
* **Mutation**:
	+ Pros: May be faster since it avoids creating a new object, but can lead to confusion about the original state of `acc`.
	+ Cons: Directly modifies an existing object, which can be error-prone and harder to reason about.

**Other Considerations**

* **Memory Allocation**: Creating a new object using object spreading may incur additional memory allocation overhead due to the dynamic creation of objects. This could impact performance, especially in environments with limited heap size.
* **Garbage Collection**: Frequent object creation and destruction can lead to increased garbage collection overhead, which might slow down the benchmark.

**Libraries**

There are no specific libraries mentioned in this benchmark. However, some JavaScript engines may have built-in optimizations or features that could affect performance (e.g., `Object.assign()`).

**Special JS Features/Syntax**

None of these test cases use any special JavaScript features or syntax beyond what's standard.

**Alternatives**

For more comprehensive benchmarks, you might want to consider additional test cases, such as:

* Using different data types for the object properties (e.g., numbers, strings, booleans).
* Adding additional logic within the `reduce` function.
* Testing performance on other browsers or environments.

Keep in mind that the specific alternatives will depend on your testing goals and requirements.

Related benchmarks:

Object spreading vs mutation in reduce (version: 0)

Comparing performance of: Object spreading vs Mutation

Created: 6 years ago by: Registered User

Jump to the latest result

Object spreading

Mutation

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):