@severo_tests/hyparquet
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Parquet file parser for JavaScript
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# hyparquet
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Dependency free since 2023!
## What is hyparquet?
**Hyparquet** is a lightweight, dependency-free, pure JavaScript library for parsing [Apache Parquet](https://parquet.apache.org) 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.
## Parquet Viewer
**Try hyparquet online**: Drag and drop your parquet file onto [hyperparam.app](https://hyperparam.app) to view it directly in your browser. This service is powered by hyparquet's in-browser capabilities.
[](https://hyperparam.app/)
## Features
1. **Browser-native**: Built to work seamlessly in the browser, opening up new possibilities for web-based data applications and visualizations.
2. **Performant**: Designed to efficiently process large datasets by only loading the required data, making it suitable for big data and machine learning applications.
3. **TypeScript**: Includes TypeScript definitions.
4. **Dependency-free**: Hyparquet has zero dependencies, making it lightweight and easy to use in any JavaScript project. Only 9.7kb min.gz!
5. **Highly Compliant:** Supports all parquet encodings, compression codecs, and can open more parquet files than any other library.
## Why hyparquet?
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](https://github.com/ironSource/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.
## Demo
Check out a minimal parquet viewer demo that shows how to integrate hyparquet into a react web application using [HighTable](https://github.com/hyparam/hightable).
- **Live Demo**: [https://hyparam.github.io/demos/hyparquet/](https://hyparam.github.io/demos/hyparquet/)
- **Demo Source Code**: [https://github.com/hyparam/demos/tree/master/hyparquet](https://github.com/hyparam/demos/tree/master/hyparquet)
## Quick Start
### Browser Example
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:
```javascript
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,
})
```
### Node.js Example
To read the contents of a local parquet file in a node.js environment use `asyncBufferFromFile`:
```javascript
const { asyncBufferFromFile, parquetReadObjects } = await import('hyparquet')
const file = await asyncBufferFromFile('example.parquet')
const data = await parquetReadObjects({ file })
```
Note: hyparquet is published as an ES module, so dynamic `import()` may be required for old versions of node.
## Parquet Writing
To create parquet files from javascript, check out the [hyparquet-writer](https://github.com/hyparam/hyparquet-writer) package.
## Advanced Usage
### Reading Metadata
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:
```javascript
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:
```javascript
import { parquetMetadata } from 'hyparquet'
const metadata = parquetMetadata(arrayBuffer)
```
### AsyncBuffer
Hyparquet requires an argument `file` of type `AsyncBuffer`. An `AsyncBuffer` is similar to a js `ArrayBuffer` but the `slice` method can return async `Promise<ArrayBuffer>`.
```typescript
type Awaitable<T> = T | Promise<T>
interface AsyncBuffer {
byteLength: number
slice(start: number, end?: number): Awaitable<ArrayBuffer>
}
```
In most cases, you should probably use `asyncBufferFromUrl` or `asyncBufferFromFile` to create an `AsyncBuffer` for hyparquet.
#### asyncBufferFromUrl
If you want to read a parquet file remotely over http, use `asyncBufferFromUrl` to wrap an http url as an `AsyncBuffer` using http range requests.
- Pass `requestInit` option to provide additional fetch headers for authentication (optional)
- Pass `byteLength` if you know the file size to save a round trip HEAD request (optional)
```typescript
const url = 'https://s3.hyperparam.app/wiki_en.parquet'
const requestInit = { headers: { Authorization: 'Bearer my_token' } } // auth header
const byteLength = 415958713 // optional
const file: AsyncBuffer = await asyncBufferFromUrl({ url, requestInit, byteLength })
const data = await parquetReadObjects({ file })
```
#### asyncBufferFromFile
If you are in a node.js environment, use `asyncBufferFromFile` to wrap a local file as an `AsyncBuffer`:
```typescript
import { asyncBufferFromFile, parquetReadObjects } from 'hyparquet'
const file: AsyncBuffer = await asyncBufferFromFile('example.parquet')
const data = await parquetReadObjects({ file })
```
#### ArrayBuffer
You can provide an `ArrayBuffer` anywhere that an `AsyncBuffer` is expected. This is useful if you already have the entire parquet file in memory.
#### Custom AsyncBuffer
You can implement your own `AsyncBuffer` to create a virtual file that can be read asynchronously by hyparquet.
### parquetRead vs parquetReadObjects
#### parquetReadObjects
`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.
```typescript
parquetReadObjects({ file }): Promise<Record<string, any>[]>
```
#### parquetRead
`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` or `onPage` 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.
### Chunk Streaming
The `onChunk` callback returns column-oriented data as it is ready. `onChunk` will always return top-level columns, including structs, assembled as a single column. This may require waiting for multiple sub-columns to all load before assembly can occur.
The `onPage` callback returns column-oriented page data as it is ready. `onPage` will NOT assemble struct columns and will always return individual sub-column data. Note that `onPage` _will_ assemble nested lists.
In some cases, `onPage` can return data sooner than `onChunk`.
```typescript
interface ColumnData {
columnName: string
columnData: ArrayLike<any>
rowStart: number
rowEnd: number
}
await parquetRead({
file,
onChunk(chunk: ColumnData) {
console.log('chunk', chunk)
},
onPage(chunk: ColumnData) {
console.log('page', chunk)
},
})
```
### Returned row format
By default, the `onComplete` function returns an **array** of values for each row: `[value]`. If you would prefer each row to be an **object**: `{ columnName: value }`, set the option `rowFormat` to `'object'`.
```javascript
import { parquetRead } from 'hyparquet'
await parquetRead({
file,
rowFormat: 'object',
onComplete: data => console.log(data),
})
```
The `parquetReadObjects` function defaults to `rowFormat: 'object'`.
### Binary columns
Parquet supports two binary types: `BYTE_ARRAY` and `FIXED_LEN_BYTE_ARRAY`, and the metadata determines how the data should be decoded using an optional [`LogicalType` annotation](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md) (or a deprecated `ConvertedType` annotation).
Hyparquet [respects](https://parquet.apache.org/docs/file-format/implementationstatus/#logical-types) the logical types, but defaults to decoding binary columns as UTF-8 strings (i.e. `LogicalType=STRING` or `ConvertedType=UTF8`) in the frequent case where the annotation is missing.
This behavior can be changed by setting the `utf8` option to `false` in functions such as `parquetRead`. Note that this option only affects `BYTE_ARRAY` columns without an annotation. Columns with a `STRING`, `ENUM` or `UUID` logical type, for example, will be decoded as expected by the specification.
## Supported Parquet Files
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.
## Compression
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](https://github.com/hyparam/hyparquet-compressors) package.
| Codec | hyparquet | with hyparquet-compressors |
|---------------|-----------|----------------------------|
| Uncompressed | ✅ | ✅ |
| Snappy | ✅ | ✅ |
| GZip | ❌ | ✅ |
| LZO | ❌ | ✅ |
| Brotli | ❌ | ✅ |
| LZ4 | ❌ | ✅ |
| ZSTD | ❌ | ✅ |
| LZ4_RAW | ❌ | ✅ |
### hysnappy
For faster snappy decompression, try [hysnappy](https://github.com/hyparam/hysnappy), which uses WASM for a 40% speed boost on large parquet files.
### hyparquet-compressors
You can include support for ALL parquet `compressors` plus hysnappy using the [hyparquet-compressors](https://github.com/hyparam/hyparquet-compressors) package.
```javascript
import { parquetReadObjects } from 'hyparquet'
import { compressors } from 'hyparquet-compressors'
const data = await parquetReadObjects({ file, compressors })
```
## References
- 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
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 :hugs: