GraphX: Graph Analytics in Apache Spark (AMPCamp 5, 2014-11-20)
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GraphX is a graph processing framework built into Apache Spark. This talk introduces GraphX, describes key features of its API, and gives an update on its status.
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GraphX: Graph Analytics in Apache Spark (AMPCamp 5, 2014-11-20)
- 1. UC BERKELEY GraphX Graph Analytics in Spark Ankur Dave Graduate Student, UC Berkeley AMPLab Joint work with Joseph Gonzalez, Reynold Xin, Daniel Crankshaw, Michael Franklin, and Ion Stoica
- 4. Web Graphs
- 5. Web Graphs “The political blogosphere and the 2004 U.S. election: divided they blog.” Adamic and Glance, 2005.
- 6. User-Item Graphs ⋆⋆⋆⋆⋆ ⋆ ⋆⋆⋆⋆ ⋆⋆⋆⋆ ⋆⋆⋆⋆⋆
- 8. PageRank
- 10. Collaborative Filtering ⋆⋆⋆⋆⋆ ⋆ ⋆⋆⋆⋆ ⋆⋆⋆⋆ ⋆⋆⋆⋆⋆ ⚙ ♫ ⚙ ♫
- 14. Many Graph-Parallel Algorithms Collaborative Filtering » Alternating Least Squares » Stochastic Gradient Descent » Tensor Factorization Structured Prediction » Loopy Belief Propagation » Max-Product Linear Programs » Gibbs Sampling Semi-supervised ML » Graph SSL » CoEM Community Detection » Triangle-Counting » K-core Decomposition » K-Truss Graph Analytics » PageRank » Personalized PageRank » Shortest Path » Graph Coloring Classification » Neural Networks
- 15. Raw Wikipedia / / / XML Hyperlinks PageRank Top 20 Pages Title PR Link Table Title Link Editor Graph Community Detection User Community User Com. Editor Table Editor Title Top Communities Com. PR.. Modern Analytics
- 16. Tables Raw Wikipedia / / / XML Hyperlinks PageRank Top 20 Pages Title PR Link Table Title Link Editor Graph Community Detection User Community User Com. Top Communities Com. PR.. Editor Table Editor Title
- 17. Editor Table Editor Title Raw Wikipedia / / / XML Hyperlinks PageRank Top 20 Pages Title PR Link Table Title Link Editor Graph Community Detection User Community User Com. Top Communities Com. PR.. Graphs
- 18. The GraphX API
- 19. Property Graphs Vertex Property: • User Profile • Current PageRank Value Edge Property: • Weights • Relationships • Timestamps
- 20. type Creating a Graph (Scala) VertexId = Long Graph val vertices: RDD[(VertexId, String)] = sc.parallelize(List( (1L, “Alice”), (2L, “Bob”), (3L, “Charlie”))) class Edge[ED]( val srcId: VertexId, val dstId: VertexId, val attr: ED) val edges: RDD[Edge[String]] = sc.parallelize(List( Edge(1L, 2L, “coworker”), Edge(2L, 3L, “friend”))) val graph = Graph(vertices, edges) 1 2 3 Alice Bob Charlie coworker friend
- 21. Graph Operations (Scala) class Graph[VD, ED] { // Table Views -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ def vertices: RDD[(VertexId, VD)] def edges: RDD[Edge[ED]] def triplets: RDD[EdgeTriplet[VD, ED]] // Transformations -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ def mapVertices[VD2](f: (VertexId, VD) = VD2): Graph[VD2, ED] def mapEdges[ED2](f: Edge[ED] = ED2): Graph[VD2, ED] def reverse: Graph[VD, ED] def subgraph(epred: EdgeTriplet[VD, ED] = Boolean, vpred: (VertexId, VD) = Boolean): Graph[VD, ED] // Joins -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ def outerJoinVertices[U, VD2] (tbl: RDD[(VertexId, U)]) (f: (VertexId, VD, Option[U]) = VD2): Graph[VD2, ED] // Computation -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ def mapReduceTriplets[A]( sendMsg: EdgeTriplet[VD, ED] = Iterator[(VertexId, A)], mergeMsg: (A, A) = A): RDD[(VertexId, A)]
- 22. // Continued from previous slide def pageRank(tol: Double): Graph[Double, Double] def triangleCount(): Graph[Int, ED] def connectedComponents(): Graph[VertexId, ED] // ...and more: org.apache.spark.graphx.lib } Built-in Algorithms (Scala) PageRank Triangle Count Connected Components
- 23. The triplets view } class EdgeTriplet[VD, ED]( val srcId: VertexId, val dstId: VertexId, val attr: ED, val srcAttr: VD, val dstAttr: VD) RDD class Graph[VD, ED] { def triplets: RDD[EdgeTriplet[VD, ED]] Graph 1 2 3 Alice Bob Charlie coworker friend srcAttr dstAttr attr Alice coworker Bob Bob friend Charlie triplets
- 24. The subgraph transformation class Graph[VD, ED] { def subgraph(epred: EdgeTriplet[VD, ED] = Boolean, vpred: (VertexId, VD) = Boolean): Graph[VD, ED] } graph.subgraph(epred = (edge) = edge.attr != “relative”) subgraph Graph Alice Bob relative Charlie coworker friend relative David Graph Alice Bob Charlie coworker friend David
- 25. The subgraph transformation class Graph[VD, ED] { def subgraph(epred: EdgeTriplet[VD, ED] = Boolean, vpred: (VertexId, VD) = Boolean): Graph[VD, ED] } graph.subgraph(vpred = (id, name) = name != “Bob”) subgraph Graph Alice Bob relative Charlie coworker friend relative David Graph Alice relative Charlie relative David
- 26. Computation with mapReduceTriplets class Graph[VD, ED] { def mapReduceTriplets[A]( upgrade to aggregateMessages sendMsg: EdgeTriplet[VD, ED] = Iterator[(VertexId, A)], mergeMsg: (A, A) = A): RDD[(VertexId, A)] } graph.mapReduceTriplets( edge = Iterator( (edge.srcId, 1), (edge.dstId, 1)), _ + _) Graph Alice Bob relative Charlie friend coworker relative David mapReduceTriplets RDD vertex id degree Alice 2 Bob 2 Charlie 3 David 1 in Spark 1.2.0
- 27. How GraphX Works
- 28. Encoding Property Graphs as RDDs Part. 1 A D Part. 2 Vertex Table (RDD) C A D F E D Property Graph B A Machine 1 Machine 2 Edge Table (RDD) A B A C B C C D A E A F E D E F A B C D E F Routing Table (RDD) A B C D E F 1 2 1 1 1 2 2 2 Vertex Cut
- 29. Graph System Optimizations Specialized Vertex-Cuts Remote Data-Structures Partitioning Caching / Mirroring Message Combiners Active Set Tracking
- 30. 3500 3000 EC2 Cluster of 16 x m2.4xLarge (8 cores) + 1GigE Seconds) 2500 2000 (Runtime 1500 1000 500 0 state-of-the-art graph processing systems. PageRank Benchmark Twitter Graph (42M Vertices,1.5B Edges) UK-Graph (106M Vertices, 3.7B Edges) 9000 8000 7000 6000 5000 7x 18x 4000 3000 2000 1000 0 GraphX performs comparably to
- 31. Future of GraphX 1. Language support a) Java API: PR #3234 b) Python API: collaborating with Intel, SPARK-3789 2. More algorithms a) LDA (topic modeling): PR #2388 b) Correlation clustering c) Your algorithm here? 3. Speculative a) Streaming/time-varying graphs b) Graph database–like queries
- 32. Raw Wikipedia / / / Try GraphX Today XML Hyperlinks PageRank Top 20 Pages Title PR Text Table Title Body Berkeley subgraph
- 33. Thanks! http://spark.apache.org/graphx ankurd@eecs.berkeley.edu jegonzal@eecs.berkeley.edu rxin@eecs.berkeley.edu crankshaw@eecs.berkeley.edu