Benchmark: Immer@4 vs Spread Reduce

HTML Preparation code:

<script src="https://cdn.jsdelivr.net/npm/immer@4.0.0/dist/immer.umd.min.js"></script>
<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

data = _.range(100)

Tests:

immer
const reducer = immer.produce((draft, curr) => { draft[0] = 0; }); const answer = data.reduce(reducer, {})
Mutate
const reducer = (draft, curr) => { draft[0] = 0; return draft; }; const answer = data.reduce(reducer, {})
Spread
const answer = data.reduce((acc, curr) => ({ ...acc, 0: 0, }), {});
immer pull up
const answer = immer.produce({}, draft => { data.reduce((acc, curr) => { acc[0] = 0; return acc }, draft) });

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
immer
Mutate
Spread
immer pull up

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 benchmark definition and test cases.

**Benchmark Overview**

The benchmark is designed to compare the performance of three approaches for performing aggregation on an array of numbers:

1. Using Immer (a library for efficient and functional data updates)
2. Mutating the accumulator directly
3. Using the spread operator (`{...}`) to create a new object

**Immer (immer@4)**

The first test case uses Immer's `produce` function, which returns an immutable version of the original array using a reducer function. The reducer takes two arguments: `draft` (the original array) and `curr` (an optional value used to update the draft). In this test, `draft[0]` is updated to 0, and then the `data.reduce` method is called with the reducer as an argument.

**Immer (immer pull up)**

The second test case uses Immer's `produce` function in a slightly different way. Instead of passing `curr` explicitly, it passes a callback function that updates the draft directly. This approach is called "pull-up" because it avoids creating an intermediate immutable version of the array.

**Mutate**

The third test case uses a simple reducer function to update the accumulator (`acc`) directly. The difference between this and Immer's approach is that the accumulator is not wrapped in an immutable object.

**Spread**

The fourth test case uses the spread operator to create a new object with the aggregated value. This approach avoids mutating the original array but requires creating a new object.

**Pros and Cons of Each Approach:**

1. **Immer (immer@4)**:
	* Pros: Efficient, concise code, and easy to reason about.
	* Cons: Can be slower than other approaches due to overhead from Immer's immutable data structures.
2. **Mutate**:
	* Pros: Simple, fast execution, and flexible for complex aggregations.
	* Cons: May lead to unintended side effects or mutations of the original array.
3. **Spread**:
	* Pros: Memory-efficient, easy to implement, and suitable for most use cases.
	* Cons: Can be slower than Immer's approach due to object creation overhead.

**Other Considerations:**

1. **Library Usage**: The benchmark uses Lodash (a popular utility library) and Immer (a functional data update library). Other libraries or approaches may have varying performance characteristics.
2. **Browser vs. Node.js**: The benchmark results are reported for a specific browser, Firefox 82. Running the same benchmark in different browsers or environments can yield varying results due to differences in JavaScript engine optimizations and memory management.

**Alternative Approaches:**

1. **Array.prototype.reduce()** with explicit accumulator updates (similar to Mutate).
2. **Using `map()` and `reduce()` separately**, which might be more efficient than Immer's approach for smaller arrays.
3. **Using a custom data structure** that blends elements of each approach, potentially offering a balance between performance and code readability.

When choosing an approach, consider factors like:

* Performance requirements
* Code complexity and maintainability
* Memory constraints (e.g., when dealing with large datasets)
* Library dependencies (if applicable)

Keep in mind that the optimal approach may vary depending on your specific use case.

Related benchmarks:

Immer@4 vs Spread Reduce (version: 0)

Comparing performance of: immer vs Mutate vs Spread vs immer pull up

Created: 6 years ago by: Guest

Jump to the latest result

immer

Mutate

Spread

immer pull up

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):