Dependency free since 2023!
Hyparquet is a lightweight, dependency-free, pure JavaScript library for parsing Apache Parquet files. Apache Parquet is a popular columnar storage format that is widely used in data engineering, data science, and machine learning applications for efficiently storing and processing large datasets.
Hyparquet aims to be the world's most compliant parquet parser. And it runs in the browser.
Try hyparquet online: Drag and drop your parquet file onto hyperparam.app to view it directly in your browser. This service is powered by hyparquet's in-browser capabilities.
- Browser-native: Built to work seamlessly in the browser, opening up new possibilities for web-based data applications and visualizations.
- Performant: Designed to efficiently process large datasets by only loading the required data, making it suitable for big data and machine learning applications.
- TypeScript: Includes TypeScript definitions.
- Dependency-free: Hyparquet has zero dependencies, making it lightweight and easy to use in any JavaScript project. Only 9.7kb min.gz!
- Highly Compliant: Supports all parquet encodings, compression codecs, and can open more parquet files than any other library.
Parquet is widely used in data engineering and data science for its efficient storage and processing of large datasets. What if you could use parquet files directly in the browser, without needing a server or backend infrastructure? That's what hyparquet enables.
Existing JavaScript-based parquet readers (like parquetjs) are no longer actively maintained, may not support streaming or in-browser processing efficiently, and often rely on dependencies that can inflate your bundle size. Hyparquet is actively maintained and designed with modern web usage in mind.
Check out a minimal parquet viewer demo that shows how to integrate hyparquet into a react web application using HighTable.
- Live Demo: https://hyparam.github.io/demos/hyparquet/
- Demo Source Code: https://github.com/hyparam/demos/tree/master/hyparquet
To read the contents of a parquet file in a node.js environment use asyncBufferFromFile:
const { asyncBufferFromFile, parquetReadObjects } = await import('hyparquet')
const file = await asyncBufferFromFile(filename)
const data = await parquetReadObjects({ file })Note: Hyparquet is published as an ES module, so dynamic import() may be required on the command line.
In the browser use asyncBufferFromUrl to wrap a url for reading asynchronously over the network.
It is recommended that you filter by row and column to limit fetch size:
const { asyncBufferFromUrl, parquetReadObjects } = await import('https://cdn.jsdelivr.net/npm/hyparquet/src/hyparquet.min.js')
const url = 'https://hyperparam-public.s3.amazonaws.com/bunnies.parquet'
const file = await asyncBufferFromUrl({ url }) // wrap url for async fetching
const data = await parquetReadObjects({
file,
columns: ['Breed Name', 'Lifespan'],
rowStart: 10,
rowEnd: 20,
})You can read just the metadata, including schema and data statistics using the parquetMetadataAsync function.
To load parquet metadata in the browser from a remote server:
import { parquetMetadataAsync, parquetSchema } from 'hyparquet'
const file = await asyncBufferFromUrl({ url })
const metadata = await parquetMetadataAsync(file)
// Get total number of rows (convert bigint to number)
const numRows = Number(metadata.num_rows)
// Get nested table schema
const schema = parquetSchema(metadata)
// Get top-level column header names
const columnNames = schema.children.map(e => e.element.name)You can also read the metadata synchronously using parquetMetadata if you have an array buffer with the parquet footer:
import { parquetMetadata } from 'hyparquet'
const metadata = parquetMetadata(arrayBuffer)Hyparquet accepts argument file of type AsyncBuffer which is like a js ArrayBuffer but the slice method can return Promise<ArrayBuffer>.
You can pass an ArrayBuffer anywhere that an AsyncBuffer is expected, if you have the entire file in memory.
type Awaitable<T> = T | Promise<T>
interface AsyncBuffer {
byteLength: number
slice(start: number, end?: number): Awaitable<ArrayBuffer>
}You can define your own AsyncBuffer to create a virtual file that can be read asynchronously. In most cases, you should probably use asyncBufferFromUrl or asyncBufferFromFile.
parquetReadObjects is a convenience wrapper around parquetRead that returns the complete rows as Promise<Record<string, any>[]>. This is the simplest way to read parquet files.
parquetReadObjects({ file }): Promise<Record<string, any>[]>parquetRead is the "base" function for reading parquet files.
It returns a Promise<void> that resolves when the file has been read or rejected if an error occurs.
Data is returned via onComplete or onChunk callbacks passed as arguments.
The reason for this design is that parquet is a column-oriented format, and returning data in row-oriented format requires transposing the column data. This is an expensive operation in javascript. If you don't pass in an onComplete argument to parquetRead, hyparquet will skip this transpose step and save memory.
The onChunk callback allows column-oriented data to be streamed back as it is read.
interface ColumnData {
columnName: string
columnData: ArrayLike<any>
rowStart: number
rowEnd: number
}
function onChunk(chunk: ColumnData): void {
console.log(chunk)
}
await parquetRead({ file, onChunk })Pass the requestInit option to asyncBufferFromUrl to provide authentication information to a remote web server. For example:
const requestInit = { headers: { Authorization: 'Bearer my_token' } }
const file = await asyncBufferFromUrl({ url, requestInit })By default, data returned in the onComplete function will be one array of columns per row.
If you would like each row to be an object with each key the name of the column, set the option rowFormat to object.
import { parquetRead } from 'hyparquet'
await parquetRead({
file,
rowFormat: 'object',
onComplete: data => console.log(data),
})The parquet format is known to be a sprawling format which includes options for a wide array of compression schemes, encoding types, and data structures. Hyparquet supports all parquet encodings: plain, dictionary, rle, bit packed, delta, etc.
Hyparquet is the most compliant parquet parser on earth — hyparquet can open more files than pyarrow, rust, and duckdb.
By default, hyparquet supports uncompressed and snappy-compressed parquet files. To support the full range of parquet compression codecs (gzip, brotli, zstd, etc), use the hyparquet-compressors package.
| Codec | hyparquet | with hyparquet-compressors |
|---|---|---|
| Uncompressed | ✅ | ✅ |
| Snappy | ✅ | ✅ |
| GZip | ❌ | ✅ |
| LZO | ❌ | ✅ |
| Brotli | ❌ | ✅ |
| LZ4 | ❌ | ✅ |
| ZSTD | ❌ | ✅ |
| LZ4_RAW | ❌ | ✅ |
For faster snappy decompression, try hysnappy, which uses WASM for a 40% speed boost on large parquet files.
You can include support for ALL parquet compressors plus hysnappy using the hyparquet-compressors package.
import { parquetReadObjects } from 'hyparquet'
import { compressors } from 'hyparquet-compressors'
const file = await asyncBufferFromFile(filename)
const data = await parquetReadObjects({ file, compressors })- https://github.com/apache/parquet-format
- https://github.com/apache/parquet-testing
- https://github.com/apache/thrift
- https://github.com/apache/arrow
- https://github.com/dask/fastparquet
- https://github.com/duckdb/duckdb
- https://github.com/google/snappy
- https://github.com/hyparam/hightable
- https://github.com/hyparam/hysnappy
- https://github.com/hyparam/hyparquet-compressors
- https://github.com/ironSource/parquetjs
- https://github.com/zhipeng-jia/snappyjs
Contributions are welcome! If you have suggestions, bug reports, or feature requests, please open an issue or submit a pull request.
Hyparquet development is supported by an open-source grant from Hugging Face 🤗