Benchmark: dna-transcription

Tests:

Map
const map = { G: 'C', C: 'G', T: 'A', A: 'U' } function transcribe(str) { return str.split("") .map(char => map[char]) .join("") } console.log(transcribe("ACGTGGTCTTAAACGTGGTCTTAAACGTGGTCTTAA"))
Reduce
const map = { G: 'C', C: 'G', T: 'A', A: 'U' } function transcribe(str) { return str.split("") .reduce((a,char) => `${a}${map[char]}`, '') } console.log(transcribe("ACGTGGTCTTAAACGTGGTCTTAAACGTGGTCTTAA"))
Replacement
const map = { G: 'C', C: 'G', T: 'A', A: 'U' } function transcribe(str) { return str.replace(/./g, char => map[char]) } console.log(transcribe("ACGTGGTCTTAAACGTGGTCTTAAACGTGGTCTTAA"))

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map
Reduce
Replacement

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 dive into the world of JavaScript microbenchmarks!

**Benchmark Definition**

The benchmark is designed to test the efficiency of three different approaches for editing single character values in a DNA string:

1. **Map**: Uses the `map()` function to create a new array with modified characters, and then joins them back together into a string.
2. **Reduce**: Uses the `reduce()` function to iterate over the characters in the string, applying a transformation to each one using an accumulator.
3. **Replacement**: Uses the `replace()` function with a regular expression to replace each character in the string.

**Options Compared**

The benchmark compares the performance of these three approaches across different JavaScript engines and browsers.

**Pros and Cons of Each Approach**

1. **Map**:
	* Pros: Simple, easy to understand, and efficient for small datasets.
	* Cons: May be slower for large datasets due to array creation and iteration.
2. **Reduce**:
	* Pros: Can be faster for large datasets since it avoids creating intermediate arrays, but its behavior can be less predictable than `map()`.
	* Cons: More complex and harder to understand, with the added complexity of accumulator management.
3. **Replacement**:
	* Pros: Efficient for large datasets since it modifies the original string in place without creating intermediate arrays.
	* Cons: May have performance issues if the regular expression is too complex or if the engine doesn't optimize it well.

**Library and Special JS Feature**

There is no library explicitly mentioned, but `map()` and `reduce()` are built-in JavaScript functions. The `replace()` function uses a regular expression to match characters in the string.

No special JS features are used beyond standard syntax.

**Other Alternatives**

For DNA transcription tasks, other approaches might include:

1. **String iteration with a loop**: A simple, low-overhead approach that iterates over each character in the string.
2. **Using a library for string manipulation**: Libraries like `lodash` or `string-persist` provide optimized functions for string operations.

However, these alternatives are not directly related to the benchmarked approaches (Map, Reduce, Replacement).

**Understanding Benchmark Results**

The latest benchmark results show:

* Firefox 61 performs best for the "Map" approach with an execution rate of approximately 25,000 executions per second.
* Firefox 61 also performs well for the "Reduce" approach but is slower than the "Map" approach by about 20%.
* The "Replacement" approach has the lowest performance, likely due to the overhead of regular expressions and the engine's optimization challenges.

These results provide valuable insights into the relative efficiency of different string manipulation approaches in JavaScript.

Related benchmarks:

dna-transcription (version: 0)

Tests the efficiency between using .replace(), .split().map().join(), and .split().reduce() to edit single character values in a DNA string.

Comparing performance of: Map vs Reduce vs Replacement

Created: 7 years ago by: Guest

Jump to the latest result

Map

Reduce

Replacement

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):