This project benchmarks four popular CSV parsing libraries in Node.js to determine their performance under different dataset sizes. The libraries compared are:
CSV parsing is a common task in many applications. This benchmark compares the speed of these parsers to help developers choose the best tool for their use case, based on performance across varying dataset sizes from 1k to 1 million records.
| Size | csv-parser (ms) | fast-csv (ms) | papaparse (ms) | csv-parse (ms) |
|---|---|---|---|---|
| 1k | 4.68 | 14.46 | 4.98 | 7.74 |
| 2k | 9.55 | 29.24 | 9.87 | 15.70 |
| 3k | 13.99 | 42.13 | 14.76 | 22.71 |
| 4k | 18.39 | 58.90 | 19.28 | 31.40 |
| 5k | 23.49 | 70.57 | 23.80 | 38.64 |
| 10k | 47.33 | 138.50 | 45.81 | 80.28 |
| 50k | 234.47 | 684.22 | 220.50 | 394.24 |
| 100k | 473.84 | 1663.71 | 438.93 | 828.91 |
| 500k | 2371.66 | 7317.74 | 2404.32 | 3810.30 |
| 1000k | 4842.03 | 13724.31 | 4896.55 | 7899.10 |
Below is a graphical representation of the benchmark results:
A lightweight, fast CSV parser for Node.js that works great with streaming data.
A robust CSV parsing library from the same maintainers of the csv-stringify package. It supports various data formats and options.
A powerful CSV parser that works both in the browser and in Node.js. It is well-suited for large datasets and includes many advanced features.
A comprehensive library for handling CSV and formatting data, which includes parsing, formatting, and validation capabilities.
- Clone the repository.
- Install dependencies using
npm install. - Run the benchmark using
npm run start.
This project is licensed under the MIT License.