Benchmark: Immer vs shallow copy

HTML Preparation code:

<script src="https://unpkg.com/immer/dist/immer.umd.js"></script>

Script Preparation code:

var data = { items: {}, count: 0, keys: [] }

for (let index = 0; index < 100; index++) {
  data[index] = { id: index, name: `ITEM-${index}`, value: Math.random() }
  data.count++
  data.keys.push(index)
}

var NEW_ITEM_ID = data.count +1

var produce = immer.default

Tests:

immer
data = produce(data, draft => { draft.items[NEW_ITEM_ID] = { id: NEW_ITEM_ID, name: 'ITEM-NEW', value: 0 } draft.counter++ draft.keys.push(NEW_ITEM_ID) })
shallow copy
data = { ...data, items: { ...data.items, [NEW_ITEM_ID]: { id: NEW_ITEM_ID, name: 'ITEM-NEW', value: 0 } }, count: data.count +1, keys: [ ...data.keys, NEW_ITEM_ID] }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
immer
shallow copy

Fastest: N/A

Slowest: N/A

Latest 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 the provided JSON data and explain what is being tested, compared, and the pros and cons of each approach.

**What is being tested?**

The benchmark measures the performance difference between two approaches:

1. **Shallow copy**: A shallow copy of the `data` object, which means only the top-level properties are copied, while nested objects are referenced directly.
2. **Immer**: The `immer` library is used to create a deep copy of the `data` object, which means all nested properties are also copied.

**Options compared**

The two options being compared are:

1. **Shallow copy**: A simple, lightweight approach that creates a new object with references to the same nested objects as the original data.
2. **Immer**: A library-based approach that creates a deep copy of the data by recursively creating new objects for all nested properties.

**Pros and cons of each approach**

1. **Shallow copy**:
	* Pros:
		+ Lightweight, fast creation
		+ Simple implementation
	* Cons:
		+ Does not preserve nested object references
		+ May cause issues with data consistency if modified later
2. **Immer**:
	* Pros:
		+ Preserves nested object references
		+ Provides a safe and predictable way to modify data
	* Cons:
		+ Requires an external library (immer.js)
		+ Slower creation compared to shallow copy

**Library and its purpose**

The `immer` library provides a simple and efficient way to create immutable, persistent versions of data structures. It is particularly useful when working with complex data that needs to be modified predictably.

In the provided benchmark, `immer` is used to create a deep copy of the `data` object by defining a function that recursively creates new objects for all nested properties.

**Special JS feature or syntax**

There doesn't seem to be any specific JavaScript feature or syntax being tested in this benchmark. The focus is on comparing two different approaches to copying data structures.

**Other alternatives**

If you were to implement your own shallow copy solution without using a library, you could do so by recursively iterating over the object's properties and creating new objects with the same values. For example:
```javascript
function shallowCopy(data) {
  const result = {};
  for (const key in data) {
    if (typeof data[key] === 'object') {
      result[key] = shallowCopy(data[key]);
    } else {
      result[key] = data[key];
    }
  }
  return result;
}
```
However, this approach would require manual implementation and might be less efficient than using a library like `immer`.

Overall, the benchmark is designed to test the performance difference between two approaches to copying data structures: shallow copy versus immer.

Related benchmarks:

Immer vs shallow copy (version: 0)

Comparing performance of: immer vs shallow copy

Created: 7 years ago by: Guest

Jump to the latest result

immer

shallow copy

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):