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RegEx vs Trim
(version: 2)
Compare splitting CSV columns using regex includes spaces or split and trim
Comparing performance of:
regex vs trim
Created:
4 years ago
by:
Registered User
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Script Preparation code:
line = "Date/Time, DynamX_SHKX, DynamX_SHKY, DynamX_SHKZ, DynamX_SHKLAT, DynamX_SHKLATAVG, DynamX_SHKRSK, DynamX_SHK_AXL_TOT, DynamX_SHKTY, DynamX_SHKTZ, DynamX_SHK_LAT_TOT, DynamX_ACCMINLAT, DynamX_SHK_AXL_MAX, DynamX_ACCYMAX, DynamX_ACCZMAX, DynamX_SHK_LAT_MAX, DynamX_ACCXAVG, DynamX_ACCYAVG, DynamX_ACCZAVG, DynamX_ACCLATAVG, DynamX_CRPM_MIN, DynamX_CRPM_MAX, DynamX_CPRM_AVG, DynamX_RPM_RAW, DynamX_STUCKEVT, DynamX_STUCKCNT, DynamX_NEGEVT, DynamX_NEGCNT, DynamX_STKSLP_RM, DynamX_STKSLP_Level, DynamX_STICK_RT,AXIAL_SHKRSK,SHKRSK_2019,Vibe_Axl_Max,Vibe_Lat_Max"
Tests:
regex
line.split(/,\s?/);
trim
line.split(",").map(s=>s.trim());
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
Previous results
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Test case name
Result
regex
trim
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the provided benchmark and explain what's being tested. **Benchmark Definition** The benchmark is designed to compare two approaches for splitting a CSV string: using regular expressions (RegEx) versus trimming whitespace from each column. The script preparation code provides a sample CSV string `line` with 10 columns, each containing a mix of numeric and string values. The description mentions that the test aims to measure which approach is faster. **Options Compared** Two options are being compared: 1. **RegEx**: Using the `split()` function with a RegEx pattern to split the CSV columns. This involves creating a regular expression pattern to match commas (`\`) followed by optional whitespace characters (`\\s?`). 2. **Trim**: Using the `trim()` method on each column after splitting the string using `.split()`. This removes leading and trailing whitespace from each column. **Pros and Cons of Each Approach** * **RegEx**: + Pros: Can handle more complex CSV structures with quotes or escaped characters. + Cons: May be slower due to the overhead of regular expression compilation and matching. * **Trim**: + Pros: Generally faster, as it only requires iterating through each column once to remove whitespace. + Cons: May not handle all possible edge cases (e.g., quoted values with multiple consecutive spaces). **Library Usage** In this benchmark, there is no specific library being used beyond the built-in JavaScript functions (`split()` and `trim()`). However, it's essential to note that if you were using a third-party CSV parsing library, its implementation would likely affect the results. **Special JS Feature or Syntax** There are no special features or syntaxes being tested in this benchmark. The focus is solely on comparing two standard JavaScript approaches for splitting CSV strings. **Other Alternatives** If you wanted to explore alternative approaches for handling CSV data, some options might include: * Using a dedicated CSV parsing library (e.g., Papa.js, csv-parser) * Implementing your own custom CSV parser using loops and string manipulation * Utilizing streaming or asynchronous processing techniques to improve performance Keep in mind that the choice of approach depends on the specific requirements and constraints of your project. **Benchmark Preparation Code** The provided script preparation code creates a sample CSV string `line` with 10 columns, which is then used as input for both test cases. The HTML preparation code is null, indicating that no additional setup or rendering is necessary before running the benchmark. Overall, this benchmark provides a straightforward comparison between two standard JavaScript approaches for splitting CSV strings, allowing users to evaluate their performance and choose the best option for their specific use case.
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