Proteus declarative language library for Scalable Data Analysis
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README.md

Emma

Emma is a Scala DSL for scalable data analysis. Our goal is to improve developer productivity by hiding parallelism aspects behind a high-level, declarative API. Emma supports state-of-the-art dataflow engines like Apache Flink and Apache Spark as backend co-processors.

More information about the project is available at http://emma-language.org.

Features

Programs written for distributed execution engines usually suffer from some well-known pitfalls:

  1. Details and subtleties of the target engine execution model must be well understood to write efficient programs.
  2. Due to the number of abstraction leaks, program code can be hard to read.
  3. Opportunities for optimization are missed out due to hard-coded execution strategies or isolated (per-dataflow) compilation.

Emma offers a declarative API for parallel collection processing. Emma programs can benefit from deep linguistic re-use of native Scala features like for-comprehensions, case-classes, and pattern matching. Data analysis algorithms written in Emma are analyzed and optimized holistically for data-parallel execution on a co-processor engine like Flink or Spark in a macro-based compiler pipeline.

Core API

Emma programs require the following import.

import eu.stratosphere.emma.api._

The examples below assume the following domain schema:

case class Person(id: Long, email: String, name: String)
case class Email(id: Long, from: String, to: String, msg: String)

Primitive Type: DataBag[A]

Parallel computation in Emma is represented by expressions over a core type DataBag[A], which modells a collection of elements of type A that do not have particular order and may contain duplicates.

DataBag[A] instances are created directly from a Scala Seq[A] or by reading from disk.

val squares = DataBag(1 to 42 map { x => (x, x * x) })              // DataBag[(Int, Int)]
val emails  = read("hdfs://emails.csv", new CSVInputFormat[Email])  // DataBag[Email]

Conversely, a DataBag[A] can be converted back to a Seq[A] or written to disk.

squares.fetch()                                                     // Seq[(Int, Int)]
write("hdfs://emails.csv", new CSVOutputFormat[(Int, Int)])(emails) // Unit

Primitive Computations: folds

The core processing abstraction provided by DataBag[A] is a generic pattern for parallel collection processing called structural recursion.

Assume for a moment that DataBag[A] instances can be constructed in one of three ways: As the Empty bag, a singleton bag Sng(x), or the union of two existing bags Union(xs, ys). Structural recursion works on bags by

  1. systematically deconstructing the input DataBag[A] instance,
  2. replacing the constructors with corresponding user-defined functions, and
  3. evaluating the resulting expression.

Formally, the above procedure can be specified as the following second-order function called fold.

def fold[B](e: B)(s: A => B, u: (B, B) => B): B = this match {
  case Empty         => e
  case Sng(x)        => s(x)
  case Union(xs, ys) => u(xs.fold(e)(s, u), ys.fold(e)(s, u))
}

In the above signature, e substitutes Empty, s(x) substitutes Sng(x), and u(xs, ys) substitutes Union(xs, ys).

Various collection processing primitives can be specified as a fold.

val min = xs.fold(Int.MaxValue)(identity, Math.min(_,_))
val max = xs.fold(Int.MinValue)(identity, Math.max(_,_))
val sum = xs.fold(0, x => 1, _ + _)

Emma offers pre-defined aliases for common fold operators:

Fold Alias Purpose
min, max, sum, count Aggregation
exists, forall, … Existential Qualifiers

Declarative Dataflows

Joins and cross products in Emma can be declared using for-comprehension syntax in a manner akin to Select-From-Where expressions known from SQL.

for {
  email <- emails
  from  <- people
  to    <- people
  if email.from == from.email  
  if email.to   == to.email
} yield(email, from, to) // DataBag[(Email, Person, Person)]

In addition Emma offers the following bag combinators:

val csx = DataBag(Seq("Meijer", "Beckmann", "Wadler")) // computer scientists
val fbx = DataBag(Seq("Meijer", "Pelé", "Meijer"))     // footballers

// union (with bag semantics)
csx plus fbx
// res: DataBag(Seq("Meijer", "Beckmann", "Pelé", "Wadler", "Meijer", "Meijer"))

// difference (with bag semantics)
fbx minus csx
// res: DataBag(Seq("Pelé", "Meijer"))

// duplicate elimination
fbx distinct
// res: DataBag(Seq("Meijer", "Pelé"))

Nesting

In addition to the collection manipulation primitives presented above, Emma offers a groupBy combinator, which (conceptually) groups bag elements by key and introduces a level of nesting.

To compute the average size of email messages by sender, for example, one can simply write the following expression.

for {
  (sender, mails) <- emails.groupBy(_.from)
} yield {
  val sum = mails.map.(_.msg.length).sum()
  val cnt = mails.count()
  sum / cnt
}

DSL Compiler

The presented API is not abstract: The semantics of each operator are given directly by the corresponding DataBag[A]-method definitions. This allows you to incrementally develop, test, and debug data analysis algorithms at small scale locally as a pure Scala programs.

For example, the following code-snippet that counts words runs out-of-the-box.

val words = for {
  line <- read(inPath, new TextInputFormat[String]('\n'))
  word <- DataBag[String](line.toLowerCase.split("\\W+"))
} yield word

// group the words by their identity and count the occurrence of each word
val counts = for {
  group <- words.groupBy[String] { identity }
} yield (group.key, group.values.size)

// write the results into a CSV file
write(outPath, new CSVOutputFormat[(String, Long)])(counts)

Once the algorithm is ready, simply wrap it in the emma.parallelize macro which modifies the code and creates Algorithm object. The returned object can be executed on a co-processor engine like Flink or Spark (for scalable data-parallel execution) or the Native runtime (which will run the quoted code fragment unmodified).

val algorithm = emma.parallelize {
  // word count code from above goes here!
}

// execute the algorithm on a parallel execution engine
algorithm.run(runtime.factory("flink"))

The emma.parallelize macro identifies all DataBag[A] terms in the quoted code fragment and re-writes them jointly in order to maximize the degree of parallelism. For example, the groupBy and the subsequent folds from the nesting example are executed using more efficient target-runtime primitives like reduceByKey. The holistic translation approach also allows us to transparently insert co-processor primitives like cache, broadcast, and partitionBy based on static analysis of the quoted code.

For more information on the Emma compile pipeline see our recent SIGMOD publication Implicit Parallelism through Deep Language Embedding.

Getting Started

Dependencies

  • JDK 7+ (preferably JDK 8)
  • Maven 3

Build using Maven

Add the Emma dependency

<!-- Basic Emma API (required) -->
<dependency>
    <groupId>eu.stratosphere</groupId>
    <artifactId>emma-language</artifactId>
    <version>1.0-SNAPSHOT</version>
    <scope>compile</scope>
</dependency>

Optionally add either the Flink or Spark backend.

<!-- Emma backend for Flink (optional) -->
<dependency>
    <groupId>eu.stratosphere</groupId>
    <artifactId>emma-flink</artifactId>
    <version>1.0-SNAPSHOT</version>
    <scope>runtime</scope>
</dependency>
<!-- Emma backend for Spark (optional) -->
<dependency>
    <groupId>eu.stratosphere</groupId>
    <artifactId>emma-spark</artifactId>
    <version>1.0-SNAPSHOT</version>
    <scope>runtime</scope>
</dependency>

Run

$ mvn clean package -DskipTests

to build Emma without running any tests.

For more advanced build options including integration tests for the target runtimes please see the "Building Emma" section in the Wiki.

Examples of Emma Language

The emma-examples module contains examples from various fields.

Lara

Lara is a deeply embedded language in Scala built on top of Emma. Emma enables authoring scalable programs using two abstract data types (DataBag and Matrix) and control flow constructs. Programs written in Lara are compiled to an intermediate representation (IR) that enables optimizations across linear and relational algebra. The IR is finally used to compile code for different execution engines (currently it is implemented only for Spark). This way, Lara allow programmers to define their machine learning algorithms in one engine-independent language and parallelize automatically. Furthermore, Lara API closely resembles languages such as R, Matlab and libraries such as NumPy (Python). Moreover, it enables joint optimization over both relational and linear algebra.

Core API

Lara programs require the following import.

import eu.stratosphere.emma.api.lara._

Besides Emma's DataBag primitive, Lara has two primitive types, namely, Matrix and Vector. These classes includes a lot of various operations on matrices and vectors, e.g., matrix-scalar, matrix-vector multiplication, matrix-matrix multiplication, filling, extraction, and matrix transformation operations.

One can convert newly introduced types (i.e., Matrix and Vector) to Emma's DataBag and vice verse using the set of transformations in the class Transformations. For instance, the toMatrix transformation converts a bag to a matrix.

Examples of Lara Language

Two examples of using Lara are as follows: - K-Mean Clustering - Linear Regression Using Batch Gradient Descent