Benchmark: dwfwsdfsdf - MeasureThat.net

Tests:

reduce
['2023-07-04T11:35:19.449Z', 'INFO', '23423423423'].reduce((acc, val) => acc+(`[${val}]`||''), '')
filter + join
['2023-07-04T11:35:19.449Z', 'INFO', '23423423423'].filter(x =>x).map(x => `$[x]`).join()

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
filter + join

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36

Browser/OS: Chrome 112 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
reduce	13153548.0 Ops/sec
filter + join	6546178.5 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the explanation into manageable chunks.

**What is tested on MeasureThat.net?**

MeasureThat.net allows users to create and run JavaScript microbenchmarks. The website provides a platform for developers to compare and analyze the performance of different JavaScript code snippets across various browsers, devices, and operating systems.

**Options compared in the benchmark**

The provided benchmark definition uses two different approaches:

1. `reduce`: This method iterates over an array and accumulates a value by adding each element.
2. `filter + join`: This approach first filters out elements from the array, then maps each remaining element to a string, and finally joins them together into a single string.

**Pros and Cons of different approaches:**

*   `reduce`:
    *   Pros:
        *   Efficient for iterating over arrays.
        *   Less memory-intensive due to not creating intermediate arrays.
    *   Cons:
        *   Can be slower than other methods like filter + join for large arrays, due to the accumulator variable and potential stack overflow issues in older browsers or low-memory devices.
*   `filter + join`:
    *   Pros:
        *   Faster for large arrays because it creates a new array with filtered elements, which can be processed more efficiently by the browser's engine.
        *   Less prone to stack overflow errors compared to reduce.
    *   Cons:
        *   More memory-intensive due to creating an intermediate array.
        *   Can be slower than reduce for small arrays or when dealing with non-array data.

**Library and its purpose:**

The provided benchmark definition uses JavaScript's built-in `Array.prototype.filter()` and `Array.prototype.map()` methods. These methods are part of the ECMAScript standard and do not require any external libraries, making them a good choice for cross-browser compatibility and simplicity.

**Special JS feature or syntax:**

No special features or syntax are used in this benchmark definition. However, it's worth noting that MeasureThat.net does support testing various JavaScript features like async/await, promises, and more. If you're interested in testing specific features, feel free to explore the website for additional information.

**Other alternatives:**

If you'd like to explore other approaches or libraries for your benchmarking needs, here are some alternatives:

1.  **Benchmarking libraries:** There are several third-party libraries available that provide more advanced benchmarking capabilities, such as BenchmarkJS, Fastify, and Benchpress.
2.  **Browser-specific APIs:** You can use browser-specific APIs like Chrome DevTools' `performance` API or Firefox's `performance` API to run benchmarks on specific browsers.
3.  **External benchmarking tools:** Tools like Google's Benchmarking Tool or Microsoft's PerfCT can be used to create and run benchmarks.

These alternatives may offer more features, customization options, and better support for specific use cases, but they might require additional setup and configuration compared to MeasureThat.net.

If you have any further questions about the benchmark definition or want to explore other testing options, feel free to ask!

Related benchmarks:

dwfwsdfsdf (version: 0)

Comparing performance of: reduce vs filter + join

Created: one year ago by: Guest

Jump to the latest result

reduce

filter + join

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):