Benchmark: Array updating for angular

Tests:

Array.prototype.concat
var params = [ "hello", true, 7 ]; params = params.concat([ 1]);
spread operator
var params = [ "hello", true, 7 ] params = [ ...params, 1 ]
Push and Map
var params = [ "hello", true, 7 ]; params.push([ 1 ]); params = params.map(x => x);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array.prototype.concat
spread operator
Push and Map

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 and explain what is being tested, compared, and analyzed.

**Benchmark Definition**

The benchmark definition is essentially a script that defines a specific test case for JavaScript performance measurement. In this case, there are three test cases:

1. `Array.prototype.concat`: Tests the concatenation method of the Array prototype.
2. `spread operator`: Tests the spread operator (`...`) used to create new arrays from existing ones.
3. `Push and Map`: Tests a specific sequence of operations involving array push and map methods.

These test cases are designed to measure the performance of JavaScript engines when executing specific scenarios, helping to identify potential optimization opportunities or identifying performance regressions.

**Comparison Options**

The benchmark is comparing three different approaches:

1. **Array.prototype.concat**: Uses the `concat()` method to concatenate an existing array with a new one.
2. **Spread Operator (`...`)**: Uses the spread operator to create a new array from an existing one and append an element to it.
3. **Push and Map**: A specific sequence of operations involving pushing elements onto an array and then mapping over the entire array.

Each approach has its pros and cons:

* **Array.prototype.concat**:
	+ Pros: Simple and efficient, widely supported by JavaScript engines.
	+ Cons: Can be slow for large arrays due to the overhead of creating a new array object.
* **Spread Operator (`...`)**:
	+ Pros: Modern and efficient, can handle large arrays without performance regressions.
	+ Cons: Requires support from modern JavaScript versions (ES6+) and may not work in older environments.
* **Push and Map**:
	+ Pros: Can be optimized for specific use cases where the spread operator is overkill.
	+ Cons: More complex and potentially slower due to the overhead of array push and map operations.

**Library and Special JS Features**

In this benchmark, there are no explicit libraries used. However, the test cases rely on built-in JavaScript features like arrays, prototypes, and operators.

The **spread operator (`...`)** is a modern feature introduced in ES6, which allows creating new arrays from existing ones by spreading elements into an array literal.

**Considerations**

When analyzing these results, consider factors such as:

* The specific use case or application domain for each test case.
* The version of JavaScript engines used to run the benchmark.
* Potential performance implications for different scenarios or optimizations.

**Alternatives**

If you're interested in exploring alternative approaches or testing different JavaScript features, here are some alternatives:

1. Measure the performance of other array methods like `slice()`, `splice()`, or `filter()`.
2. Test the performance of different iteration patterns (e.g., `for` loops, `forEach()`).
3. Compare the performance of different JavaScript engines or versions.
4. Investigate the impact of specific optimizations on JavaScript engine performance.

Feel free to ask if you'd like me to elaborate on any of these points!

Related benchmarks:

Array updating for angular (version: 0)

Comparing performance of: Array.prototype.concat vs spread operator vs Push and Map

Created: 5 years ago by: Guest

Jump to the latest result

Array.prototype.concat

spread operator

Push and Map

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):