README.md

akvokolekta

Akvokolekta is an extension of the akka streams library. It adds additional (memory-bounded) functionality for processing the stream that is currently not found in the akka streams library.

Features

• Deduplicate stream
• Count distinct elements
• Count union elements
• Count intersection elements

Installation

Make sure your build.sbt contains the Sonatype snapshot resolver.

resolvers +=
  "Sonatype OSS Snapshots" at "https://oss.sonatype.org/content/repositories/snapshots"

Then just add a library dependency.

libraryDependencies += "com.janschulte" %% "akvokolekta" % "0.1.0-SNAPSHOT"

Usage

To use akvokolekta, add the following import:

import com.janschulte.akvokolekta.StreamAdditions._

Deduplication

Deduplicate the stream using memory-bounded bloom filter.

Using scala dsl

val source = Source(List(1, 2, 3, 4, 1, 2, 3, 4))

// deduplicate a source
val deduplicated = source.deduplicate()
// prints 1 2 3 4 
deduplicated.runForeach(println)

// deduplicate a flow
val deduplicator = Flow[Int].deduplicate()
// prints 1 2 3 4
source.via(deduplicator).runForeach(println)

Using graph api

import com.janschulte.akvokolekta.graph.Deduplicator
import GraphDSL.Implicits._

val partial = GraphDSL.create() { implicit builder =>
  val source = builder.add(Broadcast[Int](1))
  val dedup = builder.add(Deduplicator[Int]())
  val sink = builder.add(Merge[Int](1))

  source ~> dedup ~> sink

  FlowShape(source.in, sink.out)
}

Count distinct elements

Count the distinct elements of the stream using memory-bounded data sketches.

Using scala dsl

// count distinct elements from a source
val distincts = Source(List(1, 2, 1, 3, 2, 1, 3, 4)).countDistinct()

// prints 1.0 2.0 2.0 3.0 3.0 3.0 3.0 4.0
distincts.runForeach(println)

// count distinct elements from a flow
val source = Source(List(1, 2, 1, 3, 2, 1, 3, 4))
val distincts = Flow[Int].countDistinct()

// prints 1.0 2.0 2.0 3.0 3.0 3.0 3.0 4.0
source.via(distincts).runForeach(println)

Using graph api

import com.janschulte.akvokolekta.graph.Deduplicator
import GraphDSL.Implicits._

val partial = GraphDSL.create() { implicit builder =>
  val source = builder.add(Broadcast[Int](1))
  val counter = builder.add(DistinctCounter[Int]())
  val sink = builder.add(Merge[Double](1))

  source ~> counter ~> sink

  FlowShape(source.in, sink.out)
}

Count union elements

Count the union of two streams using memory-bounded data sketches, i.e. the cardinality of the union of two streams |S1 ∪ S2|.

Using scala dsl

// estimate cardinality of union of two sources
val left = Source((0 to 10).toList)
val right = Source((5 to 14).toList)

// prints ... 15.0
left.countUnion(right).runForeach(println)

// estimate cardinality of union of source and flow
val union = Flow[Int].countUnion(right)

// prints ... 15.0
left.via(union).runForeach(println)

Count intersecting elements

Count the intersection of two streams using memory-bounded data sketches, i.e. the cardinality of the intersection of two streams |S1 ∩ S2|.

Using scala dsl

// estimate cardinality of intersection of two sources
val left = Source((0 to 10).toList)
val right = Source((5 to 14).toList)

// prints ... 5.0
left.countIntersection(right).runForeach(println)

// estimate cardinality of intersection of source and flow
val intersection = Flow[Int].countIntersection(right)

// prints ... 5.0
left.via(intersection).runForeach(println)

The giants

Like many other projects akvokolekta stands on the shoulders of giants:

• Yahoo Data Sketches
• ScalaNLP

Contributing

If you like to contribute, please create an issue and send a pull request. For more information on pull requests see the Github pull request tutorial.