Bagel Programming Guide

Bagel is deprecated, and superseded by GraphX.

Bagel is a Spark implementation of Google’s Pregel graph processing framework. Bagel currently supports basic graph computation, combiners, and aggregators.

In the Pregel programming model, jobs run as a sequence of iterations called supersteps. In each superstep, each vertex in the graph runs a user-specified function that can update state associated with the vertex and send messages to other vertices for use in the next iteration.

This guide shows the programming model and features of Bagel by walking through an example implementation of PageRank on Bagel.

Linking with Bagel

To use Bagel in your program, add the following SBT or Maven dependency:

groupId = org.apache.spark
artifactId = spark-bagel_2.10
version = 1.6.0

Programming Model

Bagel operates on a graph represented as a distributed dataset of (K, V) pairs, where keys are vertex IDs and values are vertices plus their associated state. In each superstep, Bagel runs a user-specified compute function on each vertex that takes as input the current vertex state and a list of messages sent to that vertex during the previous superstep, and returns the new vertex state and a list of outgoing messages.

For example, we can use Bagel to implement PageRank. Here, vertices represent pages, edges represent links between pages, and messages represent shares of PageRank sent to the pages that a particular page links to.

We first extend the default Vertex class to store a Double representing the current PageRank of the vertex, and similarly extend the Message and Edge classes. Note that these need to be marked @serializable to allow Spark to transfer them across machines. We also import the Bagel types and implicit conversions.

import org.apache.spark.bagel._
import org.apache.spark.bagel.Bagel._

@serializable class PREdge(val targetId: String) extends Edge

@serializable class PRVertex(
  val id: String, val rank: Double, val outEdges: Seq[Edge],
  val active: Boolean) extends Vertex

@serializable class PRMessage(
  val targetId: String, val rankShare: Double) extends Message

Next, we load a sample graph from a text file as a distributed dataset and package it into PRVertex objects. We also cache the distributed dataset because Bagel will use it multiple times and we’d like to avoid recomputing it.

val input = sc.textFile("data/mllib/pagerank_data.txt")

val numVerts = input.count()

val verts = => {
  val fields = line.split('\t')
  val (id, linksStr) = (fields(0), fields(1))
    val links = linksStr.split(',').map(new PREdge(_))
  (id, new PRVertex(id, 1.0 / numVerts, links, true))

We run the Bagel job, passing in verts, an empty distributed dataset of messages, and a custom compute function that runs PageRank for 10 iterations.

val emptyMsgs = sc.parallelize(List[(String, PRMessage)]())

def compute(self: PRVertex, msgs: Option[Seq[PRMessage]], superstep: Int)
: (PRVertex, Iterable[PRMessage]) = {
  val msgSum = msgs.getOrElse(List()).map(_.rankShare).sum
    val newRank =
      if (msgSum != 0)
        0.15 / numVerts + 0.85 * msgSum
    val halt = superstep >= 10
    val msgsOut =
      if (!halt) =>
          new PRMessage(edge.targetId, newRank / self.outEdges.size))
    (new PRVertex(, newRank, self.outEdges, !halt), msgsOut)

val result =, verts, emptyMsgs)()(compute)

Finally, we print the results.

println( => "%s\t%s\n".format(, v.rank)).collect.mkString)


Sending a message to another vertex generally involves expensive communication over the network. For certain algorithms, it’s possible to reduce the amount of communication using combiners. For example, if the compute function receives integer messages and only uses their sum, it’s possible for Bagel to combine multiple messages to the same vertex by summing them.

For combiner support, Bagel can optionally take a set of combiner functions that convert messages to their combined form.

Example: PageRank with combiners


Aggregators perform a reduce across all vertices after each superstep, and provide the result to each vertex in the next superstep.

For aggregator support, Bagel can optionally take an aggregator function that reduces across each vertex.



Here are the actions and types in the Bagel API. See Bagel.scala for details.


/*** Full form ***/, vertices, messages, combiner, aggregator, partitioner, numSplits)(compute)
// where compute takes (vertex: V, combinedMessages: Option[C], aggregated: Option[A], superstep: Int)
// and returns (newVertex: V, outMessages: Array[M])

/*** Abbreviated forms ***/, vertices, messages, combiner, partitioner, numSplits)(compute)
// where compute takes (vertex: V, combinedMessages: Option[C], superstep: Int)
// and returns (newVertex: V, outMessages: Array[M]), vertices, messages, combiner, numSplits)(compute)
// where compute takes (vertex: V, combinedMessages: Option[C], superstep: Int)
// and returns (newVertex: V, outMessages: Array[M]), vertices, messages, numSplits)(compute)
// where compute takes (vertex: V, messages: Option[Array[M]], superstep: Int)
// and returns (newVertex: V, outMessages: Array[M])


trait Combiner[M, C] {
  def createCombiner(msg: M): C
  def mergeMsg(combiner: C, msg: M): C
  def mergeCombiners(a: C, b: C): C

trait Aggregator[V, A] {
  def createAggregator(vert: V): A
  def mergeAggregators(a: A, b: A): A

trait Vertex {
  def active: Boolean

trait Message[K] {
  def targetId: K