csv

Caution
This is an experimental module.

While we intend to keep experimental modules as stable as possible, we may need to introduce breaking changes. This could happen at future k6 releases until the module becomes fully stable and graduates as a k6 core module. For more information, refer to the extension graduation process.

Experimental modules maintain a high level of stability and follow regular maintenance and security measures. Feel free to open an issue if you have any feedback or suggestions.

The k6-experimental/csv module provides efficient ways to handle CSV files in k6, offering faster parsing and lower memory usage compared to traditional JavaScript-based libraries.

This module includes functionalities for both full-file parsing and streaming, allowing users to choose between performance and memory optimization.

Key features

The csv.parse() function parses a complete CSV file into a SharedArray, leveraging Go-based processing for better performance and reduced memory footprint compared to JavaScript alternatives.
The csv.Parser class is a streaming parser that reads CSV files line-by-line, optimizing memory usage and giving more control over the parsing process through a stream-like API.

Benefits

Faster parsing: The csv.parse() function bypasses the JavaScript runtime, significantly speeding up parsing for large CSV files.
Lower memory usage: Both csv.parse() and csv.Parser support shared memory across virtual users (VUs) when using the fs.open() function.
Flexibility: Users can choose between full-file parsing with csv.parse for speed or line-by-line streaming with csv.Parser for memory efficiency.

Trade-offs

The csv.parse() function parses the entire file during the initialization phase, which might increase startup time and memory usage for large files. Best for scenarios where performance is more important than memory consumption.
The csv.Parser class processes the file line-by-line, making it more memory-efficient but potentially slower due to the overhead of reading each line. Suitable for scenarios where memory usage is critical or more granular control over parsing is needed.

API

Function/Object	Description
csv.parse()	Parses an entire CSV file into a SharedArray for high-performance scenarios.
csv.Parser	A class for streaming CSV parsing, allowing line-by-line reading with minimal memory consumption.

Example

Parsing a full CSV File into a SharedArray

import { open } from 'k6/experimental/fs';
import csv from 'k6/experimental/csv';
import { scenario } from 'k6/execution';

export const options = {
  iterations: 10,
};

const file = await open('data.csv');

// The `csv.parse` function consumes the entire file at once and returns
// the parsed records as a `SharedArray` object.
const csvRecords = await csv.parse(file, { delimiter: ',' });

export default async function () {
  // `csvRecords` is a `SharedArray`. Each element is a record from the CSV file, represented as an array
  // where each element is a field from the CSV record.
  //
  // Thus, `csvRecords[scenario.iterationInTest]` will give us the record for the current iteration.
  console.log(csvRecords[scenario.iterationInTest]);
}

Streaming a CSV file line-by-line

import { open } from 'k6/experimental/fs';
import csv from 'k6/experimental/csv';

export const options = {
  iterations: 10,
};

const file = await open('data.csv');

// The `csv.parse` function consumes the entire file at once and returns
// the parsed records as a `SharedArray` object.
const parser = new csv.Parser(file);

export default async function () {
  // The parser `next` method attempts to read the next row from the CSV file.
  //
  // It returns an iterator-like object with a `done` property that indicates whether
  // there are more rows to read, and a `value` property that contains the row fields
  // as an array.
  const { done, value } = await parser.next();
  if (done) {
    throw new Error('No more rows to read');
  }

  // We expect the `value` property to be an array of strings, where each string is a field
  // from the CSV record.
  console.log(done, value);
}