Benchmark: Immer vs Spread Reduce3

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/immer@3.1.3/dist/immer.umd.min.js"></script>

Script Preparation code:

data = [...Array(5).keys()]

Tests:

immer
data.reduce((acc, curr) => { return immer.produce(data, draft => { draft[curr] = curr; }); }, {});
Spread
data.reduce((acc, curr) => ({ ...acc, [curr]: curr, }), {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
immer
Spread

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):

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Let's break down the provided JSON and explore what is being tested in this JavaScript microbenchmark.

**Overview**

The benchmark compares two approaches to update an array: using `immer` library and using the spread operator (`...`) with reduction.

**Test Cases**

There are two test cases:

1. **Immer**: This approach uses the `immer` library to create a draft of the original data, updates it, and then produces the updated data. The benchmark definition code shows how this is done:
   ```javascript
data.reduce((acc, curr) => {
  return immer.produce(data, draft => {
    draft[curr] = curr;
  });
}, {});
```
   This code creates a new array using `immer.produce`, which returns an updated version of the original data with the value at each index updated to the current value.

2. **Spread**: This approach uses the spread operator (`...`) and reduction to update the array. The benchmark definition code shows how this is done:
   ```javascript
data.reduce((acc, curr) => ({ ...acc, [curr]: curr }), {});
```
   This code creates a new object by spreading the accumulator (`acc`) and adding a new key-value pair for each iteration.

**Pros and Cons**

* **Immer**: Pros:
	+ More intuitive and explicit way to update data
	+ Can be more efficient in some cases due to early returns
* Cons:
	+ Requires an external library (`immer`)
	+ May have overhead from importing the library
* **Spread**: Pros:
	+ Does not require any additional libraries or imports
	+ More concise and expressive code
* Cons:
	+ Less intuitive for updating arrays, especially for beginners
	+ May lead to less efficient performance in some cases due to unnecessary object creations

**Library: Immer**

Immer is a popular JavaScript library for working with immutable data. It provides a simple way to create new versions of existing data structures while preserving their original values.

The purpose of Immer is to make it easy to update complex data structures, such as arrays and objects, without having to worry about the performance implications of creating temporary copies or mutating the original data. Instead, Immer creates a new copy of the data structure, which can then be updated and returned.

**Special JavaScript Feature/Syntax**

There is no special JavaScript feature or syntax being tested in this benchmark. The tests focus solely on comparing two different approaches to update an array.

**Alternatives**

If you're interested in exploring other alternatives for updating arrays, here are a few options:

* **Lodash's `mapValues` function**: This function updates the values of a Map (similar to an object) by mapping over its entries.
* **RxJS's `fromArray` operator**: This operator creates an Observable from an array and can be used to update the array in a reactive way.
* **Array methods like `forEach`, `map`, or `reduce()`**: These methods can be used to update arrays, but they may not provide the same level of immutability as Immer or the spread operator.

Keep in mind that each approach has its own trade-offs and use cases. The choice of which one to use depends on the specific requirements of your project and personal preference.

Related benchmarks:

Immer vs Spread Reduce3 (version: 0)

Comparing performance of: immer vs Spread

Created: 6 years ago by: Guest

Jump to the latest result

immer

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):