Benchmark: Reduce vs Assign

Script Preparation code:

objectArr = [...Array(10)].map((v, i) => ({[i + 1]: i + 1}))

Tests:

Reduce + assign
objectArr.reduce((carry, current) => (Object.assign(carry, current)), {})
Pure assign
Object.assign({}, ...objectArr)
Reduce + spread
objectArr.reduce((carry, current) => ({ ...carry, ...current }), {})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Reduce + assign
Pure assign
Reduce + spread

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 explain what's being tested.

**Benchmark Definition**

The benchmark defines two different approaches to manipulate an array of objects:

1. `objectArr.reduce((carry, current) => (Object.assign(carry, current)), {})`: This approach uses the `reduce()` method to iterate over the array of objects. The callback function takes two arguments: `carry` and `current`. It uses `Object.assign()` to merge the properties of each object into the accumulator (`carry`). The initial value for the accumulator is an empty object (`{}`).
2. `Object.assign({}, ...objectArr)`: This approach uses the spread operator (`...`) to create a new array of objects, and then applies `Object.assign()` to copy the properties from each object in the array into a single object.

**Options Compared**

The benchmark compares three approaches:

1. **Reduce + Assign**: Uses `reduce()` with `Object.assign()`.
2. **Pure Assign**: Directly applies `Object.assign()` without using `reduce()` or spread operator.
3. **Reduce + Spread**: Uses `reduce()` with the spread operator (`...`) to merge objects.

**Pros and Cons**

Here's a brief overview of each approach:

1. **Reduce + Assign**:
	* Pros: Efficient use of `reduce()`, reduces memory allocation for intermediate results.
	* Cons: May have performance overhead due to repeated calls to `Object.assign()`.
2. **Pure Assign**:
	* Pros: Simple and straightforward, easy to read and maintain.
	* Cons: Can be slower and more memory-intensive due to repeated allocations and copies.
3. **Reduce + Spread**:
	* Pros: Combines efficiency of `reduce()` with the simplicity of spread operator.
	* Cons: May require a recent JavaScript engine that supports the spread operator in this context.

Other considerations:

* The benchmark uses an array of 10 objects (`objectArr`) as input, which may not be representative of real-world scenarios. In practice, arrays can vary significantly in size and complexity.
* The `Script Preparation Code` section initializes the `objectArr` with a specific structure, but it's unclear how this affects the benchmark results.

**Library**

The benchmark uses the built-in JavaScript methods `Object.assign()` and `reduce()`. These are standard library functions provided by most modern JavaScript engines.

**Special JS Features or Syntax**

None of the benchmarked approaches use any special JavaScript features or syntax beyond what's been introduced in recent versions (e.g., arrow functions, spread operator).

**Alternatives**

If you're interested in exploring alternative approaches, here are some options:

* Using `forEach()` instead of `reduce()`: This would iterate over the array and apply each object individually to a target object.
* Using `map()` instead of `reduce()`: This would create a new array with transformed objects, rather than accumulating properties in an accumulator.
* Using a different data structure (e.g., array of arrays) or algorithmic approach altogether.

Keep in mind that the optimal approach will depend on the specific requirements and constraints of your use case.

Related benchmarks:

Reduce vs Assign (version: 5)

Comparing performance of: Reduce + assign vs Pure assign vs Reduce + spread

Created: 3 years ago by: Registered User

Jump to the latest result

Reduce + assign

Pure assign

Reduce + spread

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):