Arrow is a set of technologies that enable big data systems to process and transfer data quickly.
Install apache-arrow
from NPM
npm install apache-arrow
or yarn add apache-arrow
(read about how we package apache-arrow below)
Powering Columnar In-Memory Analytics
Apache Arrow is a columnar memory layout specification for encoding vectors and table-like containers of flat and nested data. The Arrow spec aligns columnar data in memory to minimize cache misses and take advantage of the latest SIMD (Single input multiple data) and GPU operations on modern processors.
Apache Arrow is the emerging standard for large in-memory columnar data (Spark, Pandas, Drill, Graphistry, ...). By standardizing on a common binary interchange format, big data systems can reduce the costs and friction associated with cross-system communication.
Get Started
Check out our API documentation to learn more about how to use Apache Arrow's JS implementation. You can also learn by example by checking out some of the following resources:
Cookbook
Get a table from an Arrow file on disk (in IPC format)
import { readFileSync } from 'fs';
import { tableFromIPC } from 'apache-arrow';
const arrow = readFileSync('simple.arrow');
const table = tableFromIPC(arrow);
console.table(table.toArray());
Create a Table when the Arrow file is split across buffers
import { readFileSync } from 'fs';
import { tableFromIPC } from 'apache-arrow';
const table = tableFromIPC([
'latlong/schema.arrow',
'latlong/records.arrow'
].map((file) => readFileSync(file)));
console.table([...table]);
Create a Table from JavaScript arrays
import { tableFromArrays } from 'apache-arrow';
const LENGTH = 2000;
const rainAmounts = Float32Array.from(
{ length: LENGTH },
() => Number((Math.random() * 20).toFixed(1)));
const rainDates = Array.from(
{ length: LENGTH },
(_, i) => new Date(Date.now() - 1000 * 60 * 60 * 24 * i));
const rainfall = tableFromArrays({
precipitation: rainAmounts,
date: rainDates
});
console.table([...rainfall]);
Load data with fetch
import { tableFromIPC } from "apache-arrow";
const table = await tableFromIPC(fetch("/simple.arrow"));
console.table([...table]);
Vectors look like JS Arrays
You can create vector from JavaScript typed arrays with makeVector
and from JavaScript arrays with vectorFromArray
. makeVector
is a lot faster and does not require a copy.
import { makeVector } from "apache-arrow";
const LENGTH = 2000;
const rainAmounts = Float32Array.from(
{ length: LENGTH },
() => Number((Math.random() * 20).toFixed(1)));
const vector = makeVector(rainAmounts);
const typed = vector.toArray()
assert(typed instanceof Float32Array);
for (let i = -1, n = vector.length; ++i < n;) {
assert(vector.get(i) === typed[i]);
}
String vectors
Strings can be encoded as UTF-8 or dictionary encoded UTF-8. Dictionary encoding encodes repeated values more efficiently. You can create a dictionary encoded string conveniently with vectorFromArray
or efficiently with makeVector
.
import { makeVector, vectorFromArray, Dictionary, Uint8, Utf8 } from "apache-arrow";
const utf8Vector = vectorFromArray(['foo', 'bar', 'baz'], new Utf8);
const dictionaryVector1 = vectorFromArray(
['foo', 'bar', 'baz', 'foo', 'bar']
);
const dictionaryVector2 = makeVector({
data: [0, 1, 2, 0, 1],
dictionary: utf8Vector,
type: new Dictionary(new Utf8, new Uint8)
});
Getting involved
See DEVELOP.md
Even if you do not plan to contribute to Apache Arrow itself or Arrow
integrations in other projects, we'd be happy to have you involved:
We prefer to receive contributions in the form of GitHub pull requests.
Please send pull requests against the github.com/apache/arrow repository.
If you are looking for some ideas on what to contribute, check out the GitHub
issues for the Apache Arrow project. Comment on the issue and/or contact
dev@arrow.apache.org
with your questions and ideas.
If you’d like to report a bug but don’t have time to fix it, you can still post
it on GitHub issues, or email the mailing list
dev@arrow.apache.org
Packaging
apache-arrow
is written in TypeScript, but the project is compiled to multiple JS versions and common module formats.
The base apache-arrow
package includes all the compilation targets for convenience, but if you're conscientious about your node_modules
footprint, we got you.
The targets are also published under the @apache-arrow
namespace:
npm install apache-arrow
npm install @apache-arrow/ts
npm install @apache-arrow/es5-cjs
npm install @apache-arrow/es5-esm
npm install @apache-arrow/es5-umd
npm install @apache-arrow/es2015-cjs
npm install @apache-arrow/es2015-esm
npm install @apache-arrow/es2015-umd
npm install @apache-arrow/esnext-cjs
npm install @apache-arrow/esnext-esm
npm install @apache-arrow/esnext-umd
Why we package like this
The JS community is a diverse group with a varied list of target environments and tool chains. Publishing multiple packages accommodates projects of all stripes.
If you think we missed a compilation target and it's a blocker for adoption, please open an issue.
Supported Browsers and Platforms
The bundles we compile support moderns browser released in the last 5 years. This includes supported versions of
Firefox, Chrome, Edge, and Safari. We do not actively support Internet Explorer.
Apache Arrow also works on maintained versions of Node.
People
Full list of broader Apache Arrow committers.
- Brian Hulette, committer
- Paul Taylor, committer
- Dominik Moritz, committer
Powered By Apache Arrow in JS
Full list of broader Apache Arrow projects & organizations.
Open Source Projects
- Apache Arrow -- Parent project for Powering Columnar In-Memory Analytics, including affiliated open source projects
- Perspective -- Perspective is an interactive analytics and data visualization component well-suited for large and/or streaming datasets. Perspective leverages Arrow C++ compiled to WebAssembly.
- Falcon is a visualization tool for linked interactions across multiple aggregate visualizations of millions or billions of records.
- Vega is an ecosystem of tools for interactive visualizations on the web. The Vega team implemented an Arrow loader.
- Arquero is a library for query processing and transformation of array-backed data tables.
- OmniSci is a GPU database. Its JavaScript connector returns Arrow dataframes.
License
Apache 2.0