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https://github.com/ondra-m/ruby-spark

Ruby wrapper for Apache Spark
https://github.com/ondra-m/ruby-spark

distributed rdd ruby ruby-spark spark

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Ruby wrapper for Apache Spark

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README 

        
# Ruby-Spark [![Build Status](https://travis-ci.org/ondra-m/ruby-spark.svg?branch=master)](https://travis-ci.org/ondra-m/ruby-spark)



Apache Spark™ is a fast and general engine for large-scale data processing.



This Gem allows the use Spark functionality on Ruby.



> Word count in Spark's Ruby API



```ruby

file = spark.text_file("hdfs://...")



file.flat_map(:split)

    .map(lambda{|word| [word, 1]})

    .reduce_by_key(lambda{|a, b| a+b})

```



- [Apache Spark](http://spark.apache.org)

- [Wiki](https://github.com/ondra-m/ruby-spark/wiki)

- [Rubydoc](http://www.rubydoc.info/gems/ruby-spark)



## Installation



### Requirments



- Java 7+

- Ruby 2+

- wget or curl

- MRI or JRuby



Add this line to your application's Gemfile:



```ruby

gem 'ruby-spark'

```



And then execute:



```

$ bundle

```



Or install it yourself as:



```

$ gem install ruby-spark

```



Run `rake compile` if you are using gem from local filesystem.



### Build Apache Spark



This command will download Spark and build extensions for this gem ([SBT](ext/spark/build.sbt) is used for compiling). For more informations check [wiki](https://github.com/ondra-m/ruby-spark/wiki/Installation). Jars will be stored at you HOME directory.



```

$ ruby-spark build

```



## Usage



You can use Ruby Spark via interactive shell (Pry is used)



```

$ ruby-spark shell

```



Or on existing project.



If you want configure Spark first. See [configurations](https://github.com/ondra-m/ruby-spark/wiki/Configuration) for more details.



```ruby

require 'ruby-spark'



# Configuration

Spark.config do

   set_app_name "RubySpark"

   set 'spark.ruby.serializer', 'oj'

   set 'spark.ruby.serializer.batch_size', 100

end



# Start Apache Spark

Spark.start



# Context reference

Spark.sc

```



Finally, to stop the cluster. On the shell is Spark stopped automatically when environment exit.



```ruby

Spark.stop

```

After first use, global configuration is created at **~/.ruby-spark.conf**. There can be specified properties for Spark and RubySpark.



## Creating RDD (a new collection)



Single text file:



```ruby

rdd = sc.text_file(FILE, workers_num, serializer=nil)

```



All files on directory:



```ruby

rdd = sc.whole_text_files(DIRECTORY, workers_num, serializer=nil)

```



Direct uploading structures from ruby:



```ruby

rdd = sc.parallelize([1,2,3,4,5], workers_num, serializer=nil)

rdd = sc.parallelize(1..5, workers_num, serializer=nil)

```



There is 2 conditions:

1. choosen serializer must be able to serialize it

2. data must be iterable



If you do not specified serializer -> default is used (defined from spark.ruby.serializer.* options). [Check this](https://github.com/ondra-m/ruby-spark/wiki/Loading-data#custom-serializer) if you want create custom serializer.



## Operations



All operations can be divided into 2 groups:



- **Transformations:** append new operation to current RDD and return new

- **Actions:** add operation and start calculations



More informations:



- [Wiki page](https://github.com/ondra-m/ruby-spark/wiki/RDD)

- [Rubydoc](http://www.rubydoc.info/github/ondra-m/ruby-spark/master/Spark/RDD)

- [rdd.rb](https://github.com/ondra-m/ruby-spark/blob/master/lib/spark/rdd.rb)



You can also check official Spark documentation. First make sure that method is implemented here.



- [Transformations](http://spark.apache.org/docs/latest/programming-guide.html#transformations)

- [Actions](http://spark.apache.org/docs/latest/programming-guide.html#actions)



#### Transformations



          

  
rdd.map(function)


  
Return a new RDD by applying a function to all elements of this RDD.



  
rdd.flat_map(function)


  
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.



  
rdd.map_partitions(function)


  
Return a new RDD by applying a function to each partition of this RDD.



  
rdd.filter(function)


  
Return a new RDD containing only the elements that satisfy a predicate.



  
rdd.cartesian(other)


  
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements `(a, b)` where `a` is in `self` and `b` is in `other`.



  
rdd.intersection(other)


  
Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did.



  
rdd.sample(with_replacement, fraction, seed)


  
Return a sampled subset of this RDD. Operations are base on Poisson and Uniform distributions.



  
rdd.group_by_key(num_partitions)


  
Group the values for each key in the RDD into a single sequence.


  

  
...many more...


  





#### Actions



 

  
rdd.take(count)


  
Take the first num elements of the RDD.



  
rdd.reduce(function)


  
Reduces the elements of this RDD using the specified lambda or method.



  
rdd.aggregate(zero_value, seq_op, comb_op)


  
Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral “zero value”.



  
rdd.histogram(buckets)


  
Compute a histogram using the provided buckets.



  
rdd.collect


  
Return an array that contains all of the elements in this RDD.



  
...many more...


  





## Examples



##### Basic methods



```ruby

# Every batch will be serialized by Marshal and will have size 10

ser = Spark::Serializer.build('batched(marshal, 10)')



# Range 0..100, 2 workers, custom serializer

rdd = Spark.sc.parallelize(0..100, 2, ser)



# Take first 5 items

rdd.take(5)

# => [0, 1, 2, 3, 4]



# Numbers reducing

rdd.reduce(lambda{|sum, x| sum+x})

rdd.reduce(:+)

rdd.sum

# => 5050



# Reducing with zero items

seq = lambda{|x,y| x+y}

com = lambda{|x,y| x*y}

rdd.aggregate(1, seq, com)

# 1. Every workers adds numbers

#    => [1226, 3826]

# 2. Results are multiplied

#    => 4690676



# Statistic method

rdd.stats

# => StatCounter: (count, mean, max, min, variance,

#                  sample_variance, stdev, sample_stdev)



# Compute a histogram using the provided buckets.

rdd.histogram(2)

# => [[0.0, 50.0, 100], [50, 51]]



# Mapping

rdd.map(lambda {|x| x*2}).collect

# => [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, ...]

rdd.map(:to_f).collect

# => [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ...]



# Mapping the whole collection

rdd.map_partitions(lambda{|part| part.reduce(:+)}).collect

# => [1225, 3825]



# Selecting

rdd.filter(lambda{|x| x.even?}).collect

# => [0, 2, 4, 6, 8, 10, 12, 14, 16, ...]



# Sampling

rdd.sample(true, 10).collect

# => [3, 36, 40, 54, 58, 82, 86, 95, 98]



# Sampling X items

rdd.take_sample(true, 10)

# => [53, 87, 71, 74, 18, 75, 55, 94, 46, 32]



# Using external process

rdd.pipe('cat', "awk '{print $1*10}'")

# => ["0", "10", "20", "30", "40", "50", ...]

```



##### Words count using methods



```ruby

# Content:

# "first line"

# "second line"

rdd = sc.text_file(PATH)



# ["first", "line", "second", "line"]

rdd = rdd.flat_map(lambda{|line| line.split})



# [["first", 1], ["line", 1], ["second", 1], ["line", 1]]

rdd = rdd.map(lambda{|word| [word, 1]})



# [["first", 1], ["line", 2], ["second", 1]]

rdd = rdd.reduce_by_key(lambda{|a, b| a+b})



# {"first"=>1, "line"=>2, "second"=>1}

rdd.collect_as_hash

```



##### Estimating PI with a custom serializer



```ruby

slices = 3

n = 100000 * slices



def map(_)

  x = rand * 2 - 1

  y = rand * 2 - 1



  if x**2 + y**2 < 1

    return 1

  else

    return 0

  end

end



rdd = Spark.context.parallelize(1..n, slices, serializer: 'oj')

rdd = rdd.map(method(:map))



puts 'Pi is roughly %f' % (4.0 * rdd.sum / n)

```



##### Estimating PI



```ruby

rdd = sc.parallelize([10_000], 1)

rdd = rdd.add_library('bigdecimal/math')

rdd = rdd.map(lambda{|x| BigMath.PI(x)})

rdd.collect # => #

```



### Mllib (Machine Learning Library)



Mllib functions are using Spark's Machine Learning Library. Ruby objects are serialized and deserialized in Java so you cannot use custom classes. Supported are primitive types such as string or integers.



All supported methods/models:



- [Rubydoc / Mllib](http://www.rubydoc.info/github/ondra-m/ruby-spark/Spark/Mllib)

- [Github / Mllib](https://github.com/ondra-m/ruby-spark/tree/master/lib/spark/mllib)



##### Linear regression



```ruby

# Import Mllib classes into Object

# Otherwise are accessible via Spark::Mllib::LinearRegressionWithSGD

Spark::Mllib.import(Object)



# Training data

data = [

  LabeledPoint.new(0.0, [0.0]),

  LabeledPoint.new(1.0, [1.0]),

  LabeledPoint.new(3.0, [2.0]),

  LabeledPoint.new(2.0, [3.0])

]



# Train a model

lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initial_weights: [1.0])



lrm.predict([0.0])

```



##### K-Mean



```ruby

Spark::Mllib.import



# Dense vectors

data = [

  DenseVector.new([0.0,0.0]),

  DenseVector.new([1.0,1.0]),

  DenseVector.new([9.0,8.0]),

  DenseVector.new([8.0,9.0])

]



model = KMeans.train(sc.parallelize(data), 2)



model.predict([0.0, 0.0]) == model.predict([1.0, 1.0])

# => true

model.predict([8.0, 9.0]) == model.predict([9.0, 8.0])

# => true

```



## Benchmarks