Benchmark: Spread vs object assign

Script Preparation code:

var arr = [
  { key: 'one',       val: 1 },
  { key: 'two',       val: 2 },
  { key: 'three',     val: 3 },
  { key: 'four',      val: 4 },
  { key: 'five',      val: 5 },
  { key: 'six',       val: 6 },
  { key: 'seven',     val: 7 },
  { key: 'eight',     val: 8 },
  { key: 'nine',      val: 9 },
  { key: 'ten',       val: 10 },
  { key: 'eleven',    val: 11 },
  { key: 'twelve',    val: 12 },
  { key: 'thirteen',  val: 13 },
  { key: 'fourteen',  val: 14 },
  { key: 'fifteen',   val: 15 },
  { key: 'sixteen',   val: 16 },
  { key: 'seventeen', val: 17 },
  { key: 'eighteen',  val: 18 },
  { key: 'nineteen',  val: 19 },
  { key: 'twenty',    val: 20 }
];

Tests:

Immutable reducer via spread operator
const reducer = (obj, pair) => ({...obj, [pair.key]: pair.val }); arr.reduce(reducer, {});
Mutable reducer via property assign
const reducer = (obj, pair) => { obj[pair.key] = pair.val; return Object.assign({}, obj); }; arr.reduce(reducer, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Immutable reducer via spread operator
Mutable reducer via property assign

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 benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark is comparing two approaches to reduce an array of objects using the `reduce()` method:

1. **Immutable reducer via spread operator**: This approach uses the spread operator (`{...obj, [pair.key]: pair.val}`) to create a new object with the updated properties.
2. **Mutable reducer via property assign**: This approach uses property assignment (`obj[pair.key] = pair.val`) to update the existing object.

**Pros and Cons**

1. **Immutable reducer via spread operator**:
	* Pros: Creates a new object, avoids mutating the original array, can be more efficient for large datasets.
	* Cons: Can be slower due to the overhead of creating a new object, may require additional memory allocation.
2. **Mutable reducer via property assign**:
	* Pros: Faster and more lightweight than creating a new object, no memory allocation required.
	* Cons: Mutates the original array, can lead to unexpected side effects if not handled carefully.

**Library Used**

There is no specific library used in this benchmark. The `reduce()` method is a built-in JavaScript method that is supported by most modern browsers and Node.js environments.

**Special JS Feature or Syntax**

The benchmark uses the spread operator (`{...obj, [pair.key]: pair.val}`) which is a feature introduced in ECMAScript 2018 (ES2018). This syntax allows for more concise object creation and merging. The `Object.assign()` method used in the second approach is also a built-in JavaScript method that has been around since ES5.

**Other Considerations**

When working with large datasets or performance-critical code, it's essential to consider the trade-offs between immutability and mutability. In general:

* Immutable data structures can provide better predictability and thread-safety, but may incur a performance overhead.
* Mutable data structures can be more efficient, but may introduce unexpected side effects if not handled carefully.

**Alternatives**

If you're looking for alternative approaches to reduce an array of objects, consider the following options:

1. **Lodash's `reduce()` method**: Lodash is a popular utility library that provides a robust implementation of the `reduce()` method.
2. **Ramda's `mapReduce()` function**: Ramda is another functional programming library that provides a concise implementation of the `mapReduce()` function, which can be used to reduce an array of objects.
3. **Custom implementation using `forEach()` and `Object.assign()`**: You can also implement the reduction logic using `forEach()` and `Object.assign()` methods, although this approach may be less efficient than using the built-in `reduce()` method.

In summary, the benchmark is testing two approaches to reduce an array of objects using the `reduce()` method. The immutable reducer via spread operator provides better predictability but incurs a performance overhead, while the mutable reducer via property assign is faster but may lead to unexpected side effects if not handled carefully.

Related benchmarks:

Spread vs object assign (version: 0)

Comparing performance of: Immutable reducer via spread operator vs Mutable reducer via property assign

Created: 8 years ago by: Registered User

Jump to the latest result

Immutable reducer via spread operator

Mutable reducer via property assign

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):