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tessl/maven-org-apache-flink--flink-gelly-scala-2-11

Scala API for Apache Flink's Gelly graph processing library with type-safe functional programming support

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Flink Gelly Scala

Flink Gelly Scala provides idiomatic Scala APIs for Apache Flink's Gelly graph processing library. It offers type-safe, functional programming support for large-scale graph processing with comprehensive operations including graph transformations, iterative algorithms, and graph analytics, all integrated with Flink's distributed processing engine.

Package Information

  • Package Name: flink-gelly-scala_2.11
  • Package Type: maven
  • Language: Scala
  • Installation: 
    <dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-gelly-scala_2.11</artifactId>
  <version>1.14.6</version>
</dependency>

Core Imports

import org.apache.flink.graph.scala._
import org.apache.flink.graph.{Edge, Vertex}
import org.apache.flink.api.scala._

Basic Usage

import org.apache.flink.graph.scala._
import org.apache.flink.graph.{Edge, Vertex}
import org.apache.flink.api.scala._

// Create execution environment
val env = ExecutionEnvironment.getExecutionEnvironment

// Create vertices and edges
val vertices = env.fromCollection(Seq(
  new Vertex(1L, "Alice"),
  new Vertex(2L, "Bob"),
  new Vertex(3L, "Charlie")
))

val edges = env.fromCollection(Seq(
  new Edge(1L, 2L, 0.5),
  new Edge(2L, 3L, 0.3),
  new Edge(1L, 3L, 0.8)
))

// Create graph
val graph = Graph.fromDataSet(vertices, edges, env)

// Perform basic operations
val degrees = graph.getDegrees()
val filteredGraph = graph.filterOnVertices(_.getValue.length > 3)
val mappedGraph = graph.mapVertices(v => v.getValue.toUpperCase)

Architecture

Flink Gelly Scala is built around several key components:

  • Graph Class: Main graph representation with type parameters K (key), VV (vertex value), EV (edge value)
  • Factory Methods: Multiple ways to create graphs from DataSets, Collections, CSV files, and tuples
  • Transformations: Type-safe operations for mapping, filtering, and manipulating graph structure
  • Iterative Algorithms: Support for scatter-gather, gather-sum-apply, and vertex-centric computation models
  • Analytics: Built-in graph metrics and custom reduction operations
  • Integration: Seamless interoperability with Flink DataSets and the broader Flink ecosystem

Capabilities

Graph Creation and Management

Core functionality for creating, modifying, and managing graph structures with various data sources and formats.

// Factory methods in Graph companion object
object Graph {
  def fromDataSet[K, VV, EV](vertices: DataSet[Vertex[K, VV]], edges: DataSet[Edge[K, EV]], 
                             env: ExecutionEnvironment): Graph[K, VV, EV]
  def fromCollection[K, VV, EV](vertices: Seq[Vertex[K, VV]], edges: Seq[Edge[K, EV]], 
                                env: ExecutionEnvironment): Graph[K, VV, EV]
  def fromTupleDataSet[K, VV, EV](vertices: DataSet[(K, VV)], edges: DataSet[(K, K, EV)], 
                                  env: ExecutionEnvironment): Graph[K, VV, EV]
}

// Graph modification methods
class Graph[K, VV, EV] {
  def addVertex(vertex: Vertex[K, VV]): Graph[K, VV, EV]
  def addEdge(source: Vertex[K, VV], target: Vertex[K, VV], edgeValue: EV): Graph[K, VV, EV]
  def removeVertex(vertex: Vertex[K, VV]): Graph[K, VV, EV]
  def union(graph: Graph[K, VV, EV]): Graph[K, VV, EV]
}

Graph Creation and Management

Graph Transformations

Type-safe transformation operations for mapping vertex and edge values, filtering, and structural modifications.

class Graph[K, VV, EV] {
  def mapVertices[NV](fun: Vertex[K, VV] => NV): Graph[K, NV, EV]
  def mapEdges[NV](fun: Edge[K, EV] => NV): Graph[K, VV, NV]
  def subgraph(vertexFilterFun: Vertex[K, VV] => Boolean, 
               edgeFilterFun: Edge[K, EV] => Boolean): Graph[K, VV, EV]
  def filterOnVertices(vertexFilterFun: Vertex[K, VV] => Boolean): Graph[K, VV, EV]
  def translateGraphIds[NEW](fun: (K, NEW) => NEW): Graph[NEW, VV, EV]
}

Graph Transformations

Graph Algorithms

Iterative computation models for implementing sophisticated graph algorithms using scatter-gather, gather-sum-apply, and vertex-centric paradigms.

class Graph[K, VV, EV] {
  def runScatterGatherIteration[M](scatterFunction: ScatterFunction[K, VV, M, EV],
                                   gatherFunction: GatherFunction[K, VV, M],
                                   maxIterations: Int): Graph[K, VV, EV]
  
  def runGatherSumApplyIteration[M](gatherFunction: GSAGatherFunction[VV, EV, M], 
                                    sumFunction: SumFunction[VV, EV, M], 
                                    applyFunction: ApplyFunction[K, VV, M], 
                                    maxIterations: Int): Graph[K, VV, EV]
  
  def runVertexCentricIteration[M](computeFunction: ComputeFunction[K, VV, EV, M],
                                   combineFunction: MessageCombiner[K, M],
                                   maxIterations: Int): Graph[K, VV, EV]
}

Graph Algorithms

Graph Analytics

Built-in graph metrics, degree calculations, neighborhood operations, and custom reduction functions for graph analysis.

class Graph[K, VV, EV] {
  def getDegrees(): DataSet[(K, LongValue)]
  def inDegrees(): DataSet[(K, LongValue)]
  def outDegrees(): DataSet[(K, LongValue)]
  def numberOfVertices(): Long
  def numberOfEdges(): Long
  def reduceOnNeighbors(reduceNeighborsFunction: ReduceNeighborsFunction[VV], 
                        direction: EdgeDirection): DataSet[(K, VV)]
  def groupReduceOnNeighbors[T](neighborsFunction: NeighborsFunction[K, VV, EV, T],
                                direction: EdgeDirection): DataSet[T]
}

Graph Analytics

User-Defined Functions

Abstract base classes for implementing custom graph processing functions with access to vertex values, edges, and neighborhoods.

abstract class EdgesFunction[K, EV, T] {
  def iterateEdges(edges: Iterable[(K, Edge[K, EV])], out: Collector[T]): Unit
}

abstract class NeighborsFunction[K, VV, EV, T] {
  def iterateNeighbors(neighbors: Iterable[(K, Edge[K, EV], Vertex[K, VV])], 
                       out: Collector[T]): Unit
}

abstract class EdgesFunctionWithVertexValue[K, VV, EV, T] {
  def iterateEdges(v: Vertex[K, VV], edges: Iterable[Edge[K, EV]], out: Collector[T]): Unit
}

User-Defined Functions

Data Integration

Operations for joining graphs with external datasets and converting between different data representations.

class Graph[K, VV, EV] {
  def joinWithVertices[T](inputDataSet: DataSet[(K, T)], 
                          fun: (VV, T) => VV): Graph[K, VV, EV]
  def joinWithEdges[T](inputDataSet: DataSet[(K, K, T)], 
                       fun: (EV, T) => EV): Graph[K, VV, EV]
  def getVerticesAsTuple2(): DataSet[(K, VV)]
  def getEdgesAsTuple3(): DataSet[(K, K, EV)]
  def getTriplets(): DataSet[Triplet[K, VV, EV]]
}

Data Integration

Types

class Graph[K, VV, EV]

// Core graph elements (from Java Gelly)
class Vertex[K, VV](id: K, value: VV)
class Edge[K, EV](source: K, target: K, value: EV) 
class Triplet[K, VV, EV] // Represents (srcVertex, edge, trgVertex)

// Direction enumeration
object EdgeDirection {
  val IN: EdgeDirection
  val OUT: EdgeDirection  
  val ALL: EdgeDirection
}

// Utility mappers
class Tuple2ToVertexMap[K, VV] extends MapFunction[(K, VV), Vertex[K, VV]]
class Tuple3ToEdgeMap[K, EV] extends MapFunction[(K, K, EV), Edge[K, EV]]
class VertexToTuple2Map[K, VV] extends MapFunction[Vertex[K, VV], (K, VV)]
class EdgeToTuple3Map[K, EV] extends MapFunction[Edge[K, EV], (K, K, EV)]
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mavenpkg:maven/org.apache.flink/flink-gelly-scala_2.11@1.14.x
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