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    8.0.0 • Public • Published

    Apache Arrow in JS

    npm version

    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:


    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);
     foo,  bar,  baz
       1,    1,   aa
    null, null, null
       3, null, null
       4,    4,  bbb
       5,    5, cccc

    Create a Table when the Arrow file is split across buffers

    import { readFileSync } from 'fs';
    import { tableFromIPC } from 'apache-arrow';
    const table = tableFromIPC([
    ].map((file) => readFileSync(file)));
            origin_lat,         origin_lon
    35.393089294433594,  -97.6007308959961
    35.393089294433594,  -97.6007308959961
    35.393089294433594,  -97.6007308959961
    29.533695220947266, -98.46977996826172
    29.533695220947266, -98.46977996826172

    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( - 1000 * 60 * 60 * 24 * i));
    const rainfall = tableFromArrays({
        precipitation: rainAmounts,
        date: rainDates

    Load data with fetch

    import { tableFromIPC } from "apache-arrow";
    const table = await tableFromIPC(fetch("/simple.arrow"));

    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 uft8Vector = vectorFromArray(['foo', 'bar', 'baz'], new Utf8);
    const dictionaryVector1 = vectorFromArray(
        ['foo', 'bar', 'baz', 'foo', 'bar']
    const dictionaryVector2 = makeVector({
        data: [0, 1, 2, 0, 1],  // indexes into the dictionary
        dictionary: uft8Vector,
        type: new Dictionary(new Utf8, new Uint8)

    Getting involved


    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 repository.

    If you are looking for some ideas on what to contribute, check out the JIRA issues for the Apache Arrow project. Comment on the issue and/or contact 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 JIRA, or email the mailing list


    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 # <-- combined es2015/CommonJS/ESModules/UMD + esnext/UMD
    npm install @apache-arrow/ts # standalone TypeScript package
    npm install @apache-arrow/es5-cjs # standalone es5/CommonJS package
    npm install @apache-arrow/es5-esm # standalone es5/ESModules package
    npm install @apache-arrow/es5-umd # standalone es5/UMD package
    npm install @apache-arrow/es2015-cjs # standalone es2015/CommonJS package
    npm install @apache-arrow/es2015-esm # standalone es2015/ESModules package
    npm install @apache-arrow/es2015-umd # standalone es2015/UMD package
    npm install @apache-arrow/esnext-cjs # standalone esNext/CommonJS package
    npm install @apache-arrow/esnext-esm # standalone esNext/ESModules package
    npm install @apache-arrow/esnext-umd # standalone esNext/UMD package

    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.


    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.


    Apache 2.0



    npm i apache-arrow

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