Benchmark: log benchmark list

Tests:

log string
console.log('444444')
log number
console.log(444444)
log number list
console.log([444444])
log stringlist
console.log(["444444"])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
log string
log number
log number list
log stringlist

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36

Browser/OS: Chrome 90 on Windows

View result in a separate tab

Test name	Executions per second
log string	216320.3 Ops/sec
log number	212953.1 Ops/sec
log number list	205188.1 Ops/sec
log stringlist	201923.7 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 2 months ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**What is tested?**

The provided benchmark tests the execution time of four different `console.log` statements with varying data types:

1. A string literal: `'444444'`
2. A number literal: `444444`
3. An array containing a single number: `[444444]`
4. An array containing a single string value: `["444444"]`

These tests measure the performance of each approach in printing the output to the console.

**Options compared**

The benchmark compares four different options:

1. **String interpolation**: Using template literals or concatenation (`+`) to concatenate strings with numbers.
2. **Array indexing**: Accessing elements within an array using bracket notation `[ ]`.
3. **String encoding**: Converting a number to a string using `toString()` or `String()`.
4. **No conversion**: Simply printing the output without any additional processing.

**Pros and cons of each approach**

Here's a brief summary:

1. **String interpolation**:
	* Pros: Fast, readable, and concise.
	* Cons: May not work as expected in certain cases (e.g., when using non-string data types).
2. **Array indexing**:
	* Pros: Can be used with arrays, which can improve performance for large datasets.
	* Cons: Requires additional setup and may lead to slower execution times due to the array lookup.
3. **String encoding**:
	* Pros: Works well with all data types (numbers, strings, booleans).
	* Cons: May introduce unnecessary overhead due to string conversion.
4. **No conversion**:
	* Pros: Simple and straightforward approach.
	* Cons: May not work as expected in certain cases (e.g., when printing binary numbers).

**Special JS features or syntax**

None of the benchmarked expressions use any special JavaScript features or syntax, such as async/await, callbacks, or ES6+ features.

**Library usage**

The benchmark does not appear to use any external libraries. The tests only rely on built-in JavaScript functions and data types.

**Other alternatives**

If you're looking for alternative approaches to microbenchmarking JavaScript performance, consider the following options:

1. **Benchmarking frameworks**: Utilize dedicated benchmarking frameworks like Benchmark.js or JS-Benchmark.
2. **WebAssembly benchmarks**: Use WebAssembly (WASM) as a target platform for benchmarking JavaScript performance.
3. **Microbenchmarking libraries**: Leverage libraries like micro-benchmark or benchmarked to create and run your own benchmarks.

In conclusion, the MeasureThat.net benchmark provides a straightforward example of how to compare the execution times of different `console.log` statements with varying data types. Understanding the pros and cons of each approach will help you make informed decisions when optimizing performance in JavaScript applications.

Related benchmarks:

log benchmark list (version: 0)

Comparing performance of: log string vs log number vs log number list vs log stringlist

Created: 3 years ago by: Guest

Jump to the latest result

log string

log number

log number list

log stringlist

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 2 months ago):