Benchmark: Merge array of objects with an object

Script Preparation code:

var obj = { ...[...Array(10).keys()] };
var arrOfObjs = [];
var obj1 = Array(5).fill(0).map((_, idx) => idx + 5).reduce((acc, val) => {  acc[val] = val;
  return acc;
}, {});
var obj2 = Array(5).fill(0).map((_, idx) => idx + 10).reduce((acc, val) => {acc[val] = val;
  return acc;
}, {});
var obj3 = Array(5).fill(0).map((_, idx) => idx + 15).reduce((acc, val) => {  acc[val] = val;
  return acc;
}, {});
var obj4 = Array(5).fill(0).map((_, idx) => idx + 20).reduce((acc, val) => {  acc[val] = val;
  return acc;
}, {});

Tests:

assign
var result = Object.assign(obj, ...arrOfObjs);
reduce with spread operator
var result = arrOfObjs.reduce((acc, val) => ({ ...acc, ...val }), obj);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
assign
reduce with spread operator

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 benchmark definition and analyze what's being tested, compared options, their pros and cons, and other considerations.

**Benchmark Definition**

The benchmark definition is a JavaScript script that creates an object (`obj`) with keys from 0 to 9. It then defines four object arrays (`arrOfObjs`), each containing objects with incremental keys (5, 10, 15, and 20). The goal of the benchmark is to merge these two types of data into a single object.

**Script Preparation Code**

The script preparation code creates an empty object `obj` and initializes four object arrays (`arrOfObjs`) using array methods:

*   `Array(5).fill(0).map((_, idx) => idx + 5)` creates an array with objects containing keys from 5 to 9.
*   `Array(5).fill(0).map((_, idx) => idx + 10)` creates an array with objects containing keys from 10 to 14.
*   `Array(5).fill(0).map((_, idx) => idx + 15)` creates an array with objects containing keys from 15 to 19.
*   `Array(5).fill(0).map((_, idx) => idx + 20)` creates an array with objects containing keys from 20 to 24.

**Html Preparation Code**

There is no HTML preparation code provided, which means that the benchmark does not require any specific DOM setup or rendering.

**Individual Test Cases**

Two test cases are defined:

*   **assign**: This test case uses the `Object.assign()` method to merge the two data sets into a single object.
*   **reduce with spread operator**: This test case uses the `Array.prototype.reduce()` method and the spread operator (`...`) to achieve the same result as `assign`.

**Comparison of Options**

Both options have their pros and cons:

*   **`Object.assign(obj, ...arrOfObjs)`**: Pros:
    *   Faster execution due to native implementation.
    *   Less overhead compared to using `Array.prototype.reduce()` with spread operator.
*   **`arrOfObjs.reduce((acc, val) => ({ ...acc, ...val }), obj)`**: Pros:
    *   More flexible and dynamic, allowing for more complex data merging scenarios.
    *   Can be used with any array-like data structure.

**Cons of Each Option**

*   **`Object.assign()`**: Cons:
    *   Limited to merging objects only.
    *   Does not support nested object structures or arrays within objects.
*   **`Array.prototype.reduce()` with spread operator**: Cons:
    *   Less efficient due to the overhead of creating an accumulator object and handling edge cases.

**Library Usage**

There is no explicit library usage in the benchmark definition. However, `Object.assign()` uses a native JavaScript method, which is implemented natively by most modern browsers.

**Special JS Features or Syntax**

The benchmark uses the spread operator (`...`) introduced in ECMAScript 2018 (ES2018). This feature allows for more concise and expressive code when merging objects or arrays.

Related benchmarks:

Merge array of objects with an object (version: 0)

Comparing performance of: assign vs reduce with spread operator

Created: 5 years ago by: Guest

Jump to the latest result

assign

reduce with spread operator

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):