Benchmark: normalize / trim text

Script Preparation code:

function makeid(length) {
    let result = '';
    const characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 ';
    const charactersLength = characters.length;
    let counter = 0;
    while (counter < length) {
      result += characters.charAt(Math.floor(Math.random() * charactersLength));
      counter += 1;
    }
    return result;
}

var data = Array(10000).fill(Array(40).fill(makeid(1000)))   
var rows = []
var row = {}

Tests:

no
data.forEach((items) => {items.forEach((item,i)=>{row[i] = item}); rows.push(row)})
normalize
data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.normalize()}); rows.push(row)})
trim
data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.trim()}); rows.push(row)})
both 1
data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.normalize().trim()}); rows.push(row)})
both 2
data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.trim().normalize()}); rows.push(row)})

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
no
normalize
trim
both 1
both 2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36

Browser/OS: Chrome 130 on Windows

View result in a separate tab

Test name	Executions per second
no	50.4 Ops/sec
normalize	3.0 Ops/sec
trim	37.0 Ops/sec
both 1	3.0 Ops/sec
both 2	3.0 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 months ago):

LLMs can make mistakes. Check important info.

The benchmark defined in the provided JSON tests the performance of different approaches to normalize and trim text in JavaScript. It evaluates several methods of processing an array of text data containing long strings. Here’s a breakdown of the tests conducted, what they compare, and some implications regarding their performance and usability.

### Test Cases and Comparisons

1. **No Processing**:
   - **Test Name**: "no"
   - **Benchmark Definition**: `data.forEach((items) => {items.forEach((item,i)=>{row[i] = item}); rows.push(row)})`
   - **Description**: This test simply pushes the original strings into an array without any modifications.
   - **Pros/Cons**: 
     - **Pros**: Fastest option as no processing is involved.
     - **Cons**: It does not prepare data for any specific use; the output remains unmodified.

2. **Trimming**:
   - **Test Name**: "trim"
   - **Benchmark Definition**: `data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.trim()}); rows.push(row)})`
   - **Description**: This case utilizes the `String.prototype.trim()` method, which removes whitespace from both ends of a string.
   - **Pros/Cons**:
     - **Pros**: Effective in cleaning data inputs where leading and trailing spaces may be present.
     - **Cons**: There is a performance cost associated with processing, as evidenced by slower execution compared to "no."

3. **Normalizing**:
   - **Test Name**: "normalize"
   - **Benchmark Definition**: `data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.normalize()}); rows.push(row)})`
   - **Description**: Uses the `String.prototype.normalize()` method, which can convert strings to a canonical form, dealing with combined characters or different representations of the same character.
   - **Pros/Cons**:
     - **Pros**: Critical for ensuring text consistency, especially in applications dealing with internationalization or accented characters.
     - **Cons**: Performance is the slowest of all methods tested, indicating a resource-intensive operation.

4. **Both Normalize and Trim (Order 1)**:
   - **Test Name**: "both 1"
   - **Benchmark Definition**: `data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.normalize().trim()}); rows.push(row)})`
   - **Description**: First normalizes the string and then trims it.
   - **Pros/Cons**:
     - **Pros**: Ensures both normalization of the string representation and removal of extraneous whitespace.
     - **Cons**: Performance significantly decreased, making it one of the slowest options due to the order of operations and the overhead of both functions.

5. **Both Normalize and Trim (Order 2)**:
   - **Test Name**: "both 2"
   - **Benchmark Definition**: `data.forEach((items) => {items.forEach((item,i)=>{row[i] = item.trim().normalize()}); rows.push(row)})`
   - **Description**: Trims the string before normalization.
   - **Pros/Cons**:
     - **Pros**: Could be marginally faster than "both 1," depending on the input data, as it may have fewer characters to process in the `normalize()` method.
     - **Cons**: Still slower than the individual trim or normalize due to the operations' cumulative nature.

### JavaScript Features and Libraries

1. **`String.prototype.trim()`**:
   - A built-in JavaScript method that eliminates whitespace from both ends of a string.
   - Useful in data validation, user input cleanup, and preparation of data for comparison or storage.

2. **`String.prototype.normalize()`**:
   - Another built-in JavaScript method, which is particularly useful for text that can have multiple representations.
   - Helpful in applications that need to ensure string equivalence, especially in applications that support multiple cultures.

### Alternatives

Other alternatives for similar string processing might include:

- **Regular Expressions**: Using regex for trimming, e.g., `item.replace(/^\s+|\s+$/g, '')`, although this may be less readable and slower in some cases.
- **Lodash**: A utility library such as Lodash or Underscore.js has its methods for trimming and normalizing strings, offering additional functionalities like handling arrays or chaining.
- **Custom Processing**: For specialized use cases, developers can implement their custom trimming and normalization logic.

### Conclusion

In summary, this benchmark effectively illustrates the trade-offs involved in using different string processing methods to normalize and trim text. Understanding these trade-offs is crucial for software engineers when optimizing applications for performance while ensuring correct data handling.

Related benchmarks:

normalize / trim text (version: 7)

Comparing performance of: no vs normalize vs trim vs both 1 vs both 2

Created: 2 months ago by: Registered User

Jump to the latest result

no

normalize

trim

both 1

both 2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 2 months ago):