Benchmark: spread vs key insert

Tests:

spread
const arrayA = [{ id: 1, value: 'abc', }, { id: 2, value: 'abc', }, { id: 3, value: 'abc', }, { id: 4, value: 'abc', }]; const reduced = arrayA.reduce((obj, curr) => ({ ...obj, [curr.id]: curr.value }), {});
key insert
const arrayA = [{ id: 1, value: 'abc', }, { id: 2, value: 'abc', }, { id: 3, value: 'abc', }, { id: 4, value: 'abc', }]; const reduced = arrayA.reduce((obj, curr) => { obj[curr.id] = curr.value; return obj; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
spread
key insert

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 dive into the details of this benchmark.

**What is being tested?**

The provided JSON represents two test cases that compare the performance of two different approaches for reducing an array to a single object using the `reduce()` method in JavaScript. The first approach, "spread", uses the spread operator (`...`) to create a new object, while the second approach, "key insert", iterates over the array and inserts each key-value pair into the resulting object.

**Options compared:**

There are two main options being compared:

1. **Spread Operator (`...`)**: This approach creates a new object by spreading the elements of the original array as key-value pairs.
2. **Iterative Approach with Key Insertion**: This approach iterates over the array and inserts each key-value pair into the resulting object using the `obj[curr.id] = curr.value` syntax.

**Pros and Cons:**

1. **Spread Operator (`...`)**:
	* Pros:
		+ More concise and expressive code
		+ Less prone to typos or errors due to its declarative nature
	* Cons:
		+ May be slower due to the creation of a new object
		+ Can lead to increased memory usage if not handled properly
2. **Iterative Approach with Key Insertion**:
	* Pros:
		+ Typically faster and more efficient than the spread operator approach
		+ Less prone to errors or typos, as it's a more explicit process
	* Cons:
		+ More verbose code compared to the spread operator approach
		+ May be less expressive or readable for some developers

**Library usage:**

There is no library explicitly mentioned in the provided JSON. However, the use of `const obj = {}` and the `obj[curr.id] = curr.value` syntax implies that the developer is familiar with JavaScript's built-in object creation and manipulation features.

**Special JS feature or syntax:**

The spread operator (`...`) is a relatively new feature introduced in ECMAScript 2015 (ES6). It allows for concise object creation and can be used to create new objects by spreading existing arrays or objects as key-value pairs. The use of this feature suggests that the developer is familiar with ES6+ syntax.

**Benchmark preparation code:**

The benchmark preparation code appears to be minimal, as only a constant array `arrayA` and a simple reduction function are defined for each test case.

**Other alternatives:**

If the spread operator approach is too slow or inefficient, other alternatives could include:

1. **Using a library like Lodash**: Lodash provides an `_.reduce()` method that can be used to perform reductions in a more concise way.
2. **Implementing a custom reduction function**: A developer could implement their own custom reduction function using a loop or recursive approach.
3. **Using a Just-In-Time (JIT) compiler**: Some browsers, like Google Chrome, have JIT compilers that can optimize JavaScript code for performance.

In summary, this benchmark compares the performance of two approaches to reducing an array to a single object: the spread operator approach and an iterative approach with key insertion. The results indicate that the iterative approach is faster but less concise than the spread operator approach.

Related benchmarks:

spread vs key insert (version: 0)

Comparing performance of: spread vs key insert

Created: 5 years ago by: Guest

Jump to the latest result

spread

key insert

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):