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+---
+layout: global
+title: Bagel Programming Guide
+---
+# Bagel Programming Guide
+
+**Bagel** is a Spark implementation of Google's [Pregel](http://portal.acm.org/citation.cfm?id=1807184) 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 write a Bagel application, you will need to add Spark, its dependencies, and Bagel to your CLASSPATH:
+
+1. Run `sbt/sbt update` to fetch Spark's dependencies, if you haven't already done so.
+2. Run `sbt/sbt assembly` to build Spark and its dependencies into one JAR (`core/target/scala_2.8.1/Spark Core-assembly-0.3-SNAPSHOT.jar`) and Bagel into a second JAR (`bagel/target/scala_2.8.1/Bagel-assembly-0.3-SNAPSHOT.jar`).
+3. Add these two JARs to your CLASSPATH.
+
+## Programming Model
+
+Bagel operates on a graph represented as a [[distributed dataset|Spark Programming Guide]] 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 spark.bagel._
+    import 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("pagerank_data.txt")
+
+    val numVerts = input.count()
+
+    val verts = input.map(line => {
+      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))
+    }).cache
+
+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
+          else
+            self.rank
+        val halt = superstep >= 10
+        val msgsOut =
+          if (!halt)
+            self.outEdges.map(edge =>
+              new PRMessage(edge.targetId, newRank / self.outEdges.size))
+          else
+            List()
+        (new PRVertex(self.id, newRank, self.outEdges, !halt), msgsOut)
+    }
+
+    val result = Bagel.run(sc, verts, emptyMsgs)()(compute)
+
+Finally, we print the results.
+
+    println(result.map(v => "%s\t%s\n".format(v.id, v.rank)).collect.mkString)
+
+### Combiners
+
+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
+
+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.
+
+_Example_
+
+### Operations
+
+Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/mesos/spark/blob/master/bagel/src/main/scala/spark/bagel/Bagel.scala) for details.
+
+#### Actions
+
+    # Full form
+    Bagel.run(sc, 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
+    Bagel.run(sc, vertices, messages, combiner, partitioner, numSplits)(compute)
+        where compute takes (vertex: V, combinedMessages: Option[C], superstep: Int)
+        and returns (newVertex: V, outMessages: Array[M])
+    Bagel.run(sc, vertices, messages, combiner, numSplits)(compute)
+        where compute takes (vertex: V, combinedMessages: Option[C], superstep: Int)
+        and returns (newVertex: V, outMessages: Array[M])
+    Bagel.run(sc, vertices, messages, numSplits)(compute)
+        where compute takes (vertex: V, messages: Option[Array[M]], superstep: Int)
+        and returns (newVertex: V, outMessages: Array[M])
+
+#### Types
+
+    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
+    }
+
+## Where to Go from Here
+
+Two example jobs, PageRank and shortest path, are included in `bagel/src/main/scala/spark/bagel/examples`. You can run them by passing the class name to the `run` script included in Spark -- for example, `./run spark.bagel.examples.ShortestPath`. Each example program prints usage help when run without any arguments.