hyparquet

JavaScript parser for Apache Parquet files.

Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval.

Dependency free since 2023!

Features

• Designed to work with huge ML datasets (things like starcoder)
• Can load metadata separately from data
• Data can be filtered by row and column ranges
• Only fetches the data needed
• Written in JavaScript, checked with TypeScript
• Fast data loading for large scale ML applications
• Bring data visualization closer to the user, in the browser

Why make a new parquet parser in javascript? First, existing libraries like parquetjs are officially "inactive". Importantly, they do not support the kind of stream processing needed to make a really performant parser in the browser. And finally, no dependencies means that hyparquet is lean, and easy to package and deploy.

Demo

Online parquet file reader demo available at:

https://hyparam.github.io/hyparquet/

Demo source: index.html

Installation

npm install hyparquet

Usage

If you're in a node.js environment, you can load a parquet file with the following example:

const { parquetMetadata } = await import('hyparquet')
const fs = await import('fs')

const buffer = fs.readFileSync('example.parquet')
const arrayBuffer = new Uint8Array(buffer).buffer
const metadata = parquetMetadata(arrayBuffer)

If you're in a browser environment, you'll probably get parquet file data from either a drag-and-dropped file from the user, or downloaded from the web.

To load parquet data in the browser from a remote server using fetch:

import { parquetMetadata } from 'hyparquet'

const res = await fetch(url)
const arrayBuffer = await res.arrayBuffer()
const metadata = parquetMetadata(arrayBuffer)

To parse parquet files from a user drag-and-drop action, see example in index.html.

Supported Parquet Files

The parquet format supports a number of different compression and encoding types. Hyparquet does not support 100% of all parquet files, and probably never will, since supporting all possible compression types will increase the size of the library, and are rarely used in practice.

Compression:

• Uncompressed
• Snappy
• GZip
• LZO
• Brotli
• LZ4
• ZSTD
• LZ4_RAW

Page Type:

• Data Page
• Index Page
• Dictionary Page
• Data Page V2

Contributions are welcome!

References

• https://github.com/apache/parquet-format
• https://github.com/apache/parquet-testing
• https://github.com/apache/thrift
• https://github.com/dask/fastparquet
• https://github.com/google/snappy
• https://github.com/zhipeng-jia/snappyjs