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-->
# Apache Arrow Ruby
Here are the official Ruby bindings for Apache Arrow.
[Red Arrow](https://github.com/apache/arrow/tree/main/ruby/red-arrow) is the base Apache Arrow bindings.
[Red Arrow CUDA](https://github.com/apache/arrow/tree/main/ruby/red-arrow-cuda) is the Apache Arrow bindings of CUDA part.
[Red Arrow Dataset](https://github.com/apache/arrow/tree/main/ruby/red-arrow-dataset) is the Apache Arrow Dataset bindings.
[Red Gandiva](https://github.com/apache/arrow/tree/main/ruby/red-gandiva) is the Gandiva bindings.
[Red Parquet](https://github.com/apache/arrow/tree/main/ruby/red-parquet) is the Parquet bindings.
## Cookbook
### Getting Started
```shell
gem install red-arrow
gem install red-parquet # for parquet support
gem install red-arrow-dataset # reading from s3 / folders
```
### Create table
#### From file
```ruby
require 'arrow'
require 'parquet'
table = Arrow::Table.load('data.arrow')
table = Arrow::Table.load('data.csv', format: :csv)
table = Arrow::Table.load('data.parquet', format: :parquet)
```
#### From Ruby hash
Types will be detected automatically
```ruby
table = Arrow::Table.new('name' => ['Tom', 'Max'], 'age' => [22, 23])
```
#### From String
Suppose you have your data available via HTTP. Let's connect to demo ClickHouse DB. See https://play.clickhouse.com/ for details
```ruby
require 'net/http'
params = {
query: "SELECT WatchID as watch FROM hits LIMIT 10 FORMAT Arrow",
user: "play",
password: "",
database: "default"
}
uri = URI('https://play.clickhouse.com:443/')
uri.query = URI.encode_www_form(params)
resp = Net::HTTP.get(uri)
table = Arrow::Table.load(Arrow::Buffer.new(resp))
```
#### From S3
```ruby
require 'arrow-dataset'
s3_uri = URI('s3://bucket/public.csv')
Arrow::Table.load(s3_uri)
```
For private access you can pass access_key and secret_key in following way:
```ruby
require 'cgi/util'
s3_uri = URI("s3://#{CGI.escape(access_key)}:#{CGI.escape(secret_key)}@bucket/private.parquet")
Arrow::Table.load(s3_uri)
```
#### From multiple files in folder
```ruby
require 'arrow-dataset'
Arrow::Table.load(URI("file:///your/folder/"), format: :parquet)
```
### Filtering
Uses concept of slicers in Arrow
```ruby
table = Arrow::Table.new(
'name' => ['Tom', 'Max', 'Kate'],
'age' => [22, 23, 19]
)
table.slice { |slicer| slicer['age'] > 19 }
# => #<Arrow::Table:0x7fa38838c448 ptr=0x7fa3ad269f40>
# name age
# 0 Tom 22
# 1 Max 23
table.slice { |slicer| slicer['age'].in?(19..22) }
# => #<Arrow::Table:0x7fa3881cf998 ptr=0x7fa3a4bb5f30>
# name age
# 0 Tom 22
# 1 Kate 19
```
Multiple slice conditions can be joined using and(`&`) / or (`|`) / xor(`^`) logical operations
```ruby
table.slice { |slicer| (slicer['age'] > 19) & (slicer['age'] < 23) }
# => #<Arrow::Table:0x7fa3882cc300 ptr=0x7fa3ad260b00>
# name age
# 0 Tom 22
```
### Operations
Arrow compute functions can be accessed through `Arrow::Function`
```ruby
add = Arrow::Function.find('add')
add.execute([table['age'].data, table['age'].data]).value
# => #<Arrow::ChunkedArray:0x7fa389b87250 ptr=0x7fa3a4bb5c40 [
# [
# 44,
# 46,
# 38
# ]
# ]>
```
### Grouping
```ruby
table = Arrow::Table.new(
'name' => ['Tom', 'Max', 'Kate', 'Tom'],
'amount' => [10, 2, 3, 5]
)
table.group('name').sum('amount')
# => #<Arrow::Table:0x7fa389894ae8 ptr=0x7fa364141a50>
# name amount
# 0 Kate 3
# 1 Max 2
# 2 Tom 15
```
### Joining
```ruby
amounts = Arrow::Table.new(
'name' => ['Tom', 'Max', 'Kate'],
'amount' => [10, 2, 3]
)
levels = Arrow::Table.new(
'name' => ['Max', 'Kate', 'Tom'],
'level' => [1, 9, 5]
)
amounts.join(levels, [:name])
# => #<Arrow::Table:0x55d512ceb1b0 ptr=0x55d51262aa70>
# name amount name level
# 0 Tom 10 Tom 5
# 1 Max 2 Max 1
# 2 Kate 3 Kate 9
```
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