Benchmark: Reducer with object spread vs direct mutation

Tests:

With object spread
const toReduce = [{key: 'a', value: 1}, {key: 'b', value: 2}, {key: 'c', value: 3}]; const reduced = toReduce.reduce((acc, cur) => { return { ...acc, [cur.key]: cur.value, }; }, {});
With parameter mutation
const toReduce = [{key: 'a', value: 1}, {key: 'b', value: 2}, {key: 'c', value: 3}]; const reduced = toReduce.reduce((acc, cur) => { acc[cur.key] = cur.value; return acc; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
With object spread
With parameter mutation

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 provided JSON and explain what is being tested in the benchmark.

**Benchmark Definition**

The benchmark is testing two approaches to reducing an array of objects:

1. **Object Spread Syntax**: This approach uses the spread operator (`...`) to create a new object that includes all properties from the original object (`acc`) plus the new key-value pair added by each iteration.
2. **Direct Parameter Mutation**: This approach directly modifies the `acc` object by assigning the new key-value pair to an existing property, without creating a new object.

**Options Compared**

The two options being compared are:

* **Object Spread Syntax**: uses the spread operator (`...`) to create a new object and update its properties
* **Direct Parameter Mutation**: directly modifies the `acc` object by assigning the new key-value pair to an existing property

**Pros and Cons of Each Approach**

### Object Spread Syntax

Pros:

* More concise and expressive code
* Less prone to errors caused by incorrect property names or types
* Can be more efficient for large datasets, as it avoids unnecessary copies

Cons:

* May incur additional overhead due to the spread operator's syntax and parsing
* Can lead to unexpected behavior if not properly used (e.g., with nested objects)

### Direct Parameter Mutation

Pros:

* Often faster and more lightweight than object spread syntax
* Easier to understand for those familiar with traditional array manipulation

Cons:

* More prone to errors due to incorrect property names, types, or overwrite of existing properties
* Can lead to unexpected behavior if not properly used (e.g., with large datasets)

**Library**

There is no explicit library mentioned in the benchmark definition. However, it's worth noting that some modern JavaScript engines and browsers may have optimized implementations for array reduction, which could affect the performance difference between these two approaches.

**Special JS Feature or Syntax**

The benchmark uses a feature that is commonly used in modern JavaScript: the spread operator (`...`). This syntax was introduced in ECMAScript 2018 (ES9) and has since become widely adopted. It allows for more concise code and easier manipulation of objects, but can also lead to performance differences depending on the engine or browser.

**Alternative Approaches**

Other approaches to reducing an array of objects might include:

* Using a library like Lodash or Ramda, which provide optimized implementations for common array operations
* Implementing a custom reducer function with specific optimizations (e.g., caching intermediate results)
* Using a different data structure, such as a linked list or a tree, to represent the array of objects

Keep in mind that these alternatives might not be relevant to this specific benchmark, which seems to focus on comparing two specific approaches using object spread syntax and direct parameter mutation.

Related benchmarks:

Reducer with object spread vs direct mutation (version: 0)

Benchmark test to compare a reducer that uses object spread syntax vs direct parameter mutation

Comparing performance of: With object spread vs With parameter mutation

Created: 6 years ago by: Registered User

Jump to the latest result

With object spread

With parameter mutation

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):