Pregel: A System for Large-Scale Graph Processing Presented by Dylan Davis Authors: Grzegorz Malewicz, Matthew H. Austern, Aart J.C. Bik, James C. Dehnert, Ilan Horn, Naty Leiser, Grzegorz Czajkowski (GOOGLE, INC.)

Overview What is a graph? Graph Problems The Purpose of Pregel Model of Computation C++ API Implementation Applications Experiments

What is a graph? G = (V, E) Binary Tree

Graph Problems Network Routing Social Network Connections

The Purpose of Pregel Google was interested in applications that could perform internet-related graph algorithms, such as PageRank, so they designed Pregel to perform these tasks efficiently. It is a scalable, general-purpose system for implementing graph algorithms in a distributed environment. Focus on “Thinking Like a Vertex” and parallelism

Model of Computation

Model of Computation (Vertex) Vertex ID Vertex Value Edge Value Vertex ID Edge Value

Model of Computation (Superstep) Superstep 0Superstep 1Superstep 2 Execution Time Compute()

Model of Computation (Vertex Actions) A vertex can: Vertex ID Vertex Value Modify its values Receive messages from previous superstep Send messages Request topology changes

Model of Computation (State Machine)

C++ API

C++ API (Message Passing) Destination Vertex ID Message Value Message Buffer

C++ API (Combiners & Aggregators) Combiner Aggregator

C++ API (Topology Mutations) V Superstep

C++ API (Input and Output)

Implementation

Implementation (Basic Architecture)

Implementation (Program Execution) Flow: 1.Copy user program – Master copy & worker copies 2.Master assigns graph partitions 3.Master takes user input data, assigns to workers – load vertex data 4.Supersteps (Compute() and send messages) 5.Save output

Implementation (Fault Tolerance) Checkpoint Worker Save() Worker Save() Worker Save() Recover Worker Recompute() Worker Recompute() X

Implementation (Worker) Worker

Implementation (Master) List of Workers Master Partitions

Application s

Applications (Shortest Path)

Experiment s

Experiments (Description) Test the execution times of Pregel running the Single- Source Shortest Path algorithm. Use a cluster of 300 multicore commodity PCs. Run Pregel with Binary Tree graphs, and with a more realistic, randomly-distributed graph. Results do not include initialization, graph generation, and result verification times. Failure Recovery is not included (reduces overhead)

Conclusion Pregel is a model suitable for large-scale graph computing with a production-quality, scalable and fault tolerant implementation. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges. This implementation is flexible enough to express a broad set of algorithms.