frontend/node_modules/apache-arrow/README.md

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# [Apache Arrow](https://github.com/apache/arrow) in JS

[![npm version](https://img.shields.io/npm/v/apache-arrow.svg)](https://www.npmjs.com/package/apache-arrow)
[![Build Status](https://travis-ci.org/apache/arrow.svg?branch=master)](https://travis-ci.org/apache/arrow)
[![Coverage Status](https://coveralls.io/repos/github/apache/arrow/badge.svg)](https://coveralls.io/github/apache/arrow)

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](#packaging) below)

# Powering Columnar In-Memory Analytics

[Apache Arrow](https://github.com/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](https://spark.apache.org/), [Pandas](https://wesmckinney.com/blog/pandas-and-apache-arrow/), [Drill](https://drill.apache.org/), [Graphistry](https://www.graphistry.com), ...). 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][7] 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:

* [Observable: Introduction to Apache Arrow][5]
* [Observable: Manipulating flat arrays arrow-style][6]
* [Observable: Rich columnar data tables - Dictionary-encoded strings, 64bit ints, and nested structs][8]
* [/js/test/unit](https://github.com/apache/arrow/tree/master/js/test/unit) - Unit tests for Table and Vector

## Cookbook

### Get a table from an Arrow file on disk (in IPC format)

```js
import { readFileSync } from 'fs';
import { Table } from 'apache-arrow';

const arrow = readFileSync('simple.arrow');
const table = Table.from([arrow]);

console.log(table.toString());

/*
 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

```js
import { readFileSync } from 'fs';
import { Table } from 'apache-arrow';

const table = Table.from([
    'latlong/schema.arrow',
    'latlong/records.arrow'
].map((file) => readFileSync(file)));

console.log(table.toString());

/*
        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

```js
import {
  Table,
  FloatVector,
  DateVector
} 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 = Table.new(
  [FloatVector.from(rainAmounts), DateVector.from(rainDates)],
  ['precipitation', 'date']
);
```

### Load data with `fetch`

```js
import { Table } from "apache-arrow";

const table = await Table.from(fetch(("/simple.arrow")));
console.log(table.toString());

```

### Columns look like JS Arrays

```js
import { readFileSync } from 'fs';
import { Table } from 'apache-arrow';

const table = Table.from([
    'latlong/schema.arrow',
    'latlong/records.arrow'
].map(readFileSync));

const column = table.getColumn('origin_lat');

// Copy the data into a TypedArray
const typed = column.toArray();
assert(typed instanceof Float32Array);

for (let i = -1, n = column.length; ++i < n;) {
    assert(column.get(i) === typed[i]);
}
```

### Usage with MapD Core

```js
import MapD from 'rxjs-mapd';
import { Table } from 'apache-arrow';

const port = 9091;
const host = `localhost`;
const db = `mapd`;
const user = `mapd`;
const password = `HyperInteractive`;

MapD.open(host, port)
  .connect(db, user, password)
  .flatMap((session) =>
    // queryDF returns Arrow buffers
    session.queryDF(`
      SELECT origin_city
      FROM flights
      WHERE dest_city ILIKE 'dallas'
      LIMIT 5`
    ).disconnect()
  )
  .map(([schema, records]) =>
    // Create Arrow Table from results
    Table.from([schema, records]))
  .map((table) =>
    // Stringify the table to CSV with row numbers
    table.toString({ index: true }))
  .subscribe((csvStr) =>
    console.log(csvStr));
/*
Index,   origin_city
    0, Oklahoma City
    1, Oklahoma City
    2, Oklahoma City
    3,   San Antonio
    4,   San Antonio
*/
```

# Getting involved

See [DEVELOP.md](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:

* Join the mailing list: send an email to
  [[email protected]][1]. Share your ideas and use cases for the
  project.
* [Follow our activity on JIRA][3]
* [Learn the format][2]
* Contribute code to one of the reference implementations

We prefer to receive contributions in the form of GitHub pull requests. Please send pull requests against the [github.com/apache/arrow][4] repository.

If you are looking for some ideas on what to contribute, check out the [JIRA
issues][3] for the Apache Arrow project. Comment on the issue and/or contact
[[email protected]](https://mail-archives.apache.org/mod_mbox/arrow-dev/)
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
[[email protected]](https://mail-archives.apache.org/mod_mbox/arrow-dev/)

## 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:

```sh
npm install apache-arrow # <-- combined es5/UMD, es2015/CommonJS/ESModules/UMD, and TypeScript package
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.

# People

Full list of broader Apache Arrow [committers](https://arrow.apache.org/committers/).

* Brian Hulette,  _committer_
* Paul Taylor, Graphistry, Inc.,  _committer_

# Powered By Apache Arrow in JS

Full list of broader Apache Arrow [projects & organizations](https://arrow.apache.org/powered_by/).

## Open Source Projects

* [Apache Arrow](https://arrow.apache.org) -- Parent project for Powering Columnar In-Memory Analytics, including affiliated open source projects
* [rxjs-mapd](https://github.com/graphistry/rxjs-mapd) -- A MapD Core node-driver that returns query results as Arrow columns
* [Perspective](https://github.com/jpmorganchase/perspective) -- Perspective is a streaming data visualization engine by J.P. Morgan for JavaScript for building real-time & user-configurable analytics entirely in the browser.
* [Falcon](https://github.com/uwdata/falcon) is a visualization tool for linked interactions across multiple aggregate visualizations of millions or billions of records.

## Companies & Organizations

* [CCRi](https://www.ccri.com/) -- Commonwealth Computer Research Inc, or CCRi, is a Central Virginia based data science and software engineering company
* [GOAI](https://gpuopenanalytics.com/) -- GPU Open Analytics Initiative standardizes on Arrow as part of creating common data frameworks that enable developers and statistical researchers to accelerate data science on GPUs
* [Graphistry, Inc.](https://www.graphistry.com/) - An end-to-end GPU accelerated visual investigation platform used by teams for security, anti-fraud, and related investigations. Graphistry uses Arrow in its NodeJS GPU backend and client libraries, and is an early contributing member to GOAI and Arrow\[JS\] working to bring these technologies to the enterprise.

# License

[Apache 2.0](https://github.com/apache/arrow/blob/master/LICENSE)

[1]: mailto:[email protected]
[2]: https://github.com/apache/arrow/tree/master/format
[3]: https://issues.apache.org/jira/browse/ARROW
[4]: https://github.com/apache/arrow
[5]: https://beta.observablehq.com/@theneuralbit/introduction-to-apache-arrow
[6]: https://beta.observablehq.com/@lmeyerov/manipulating-flat-arrays-arrow-style
[7]: https://arrow.apache.org/docs/js/
[8]: https://observablehq.com/@lmeyerov/rich-data-types-in-apache-arrow-js-efficient-data-tables-wit