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<h1 class="title">Bagel Programming Guide</h1>
<p><strong>Bagel will soon be superseded by <a href="graphx-programming-guide.html">GraphX</a>; we recommend that new users try GraphX instead.</strong></p>
<p>Bagel is a Spark implementation of Google’s <a href="http://portal.acm.org/citation.cfm?id=1807184">Pregel</a> graph processing framework. Bagel currently supports basic graph computation, combiners, and aggregators.</p>
<p>In the Pregel programming model, jobs run as a sequence of iterations called <em>supersteps</em>. 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 <em>next</em> iteration.</p>
<p>This guide shows the programming model and features of Bagel by walking through an example implementation of PageRank on Bagel.</p>
<h1 id="linking-with-bagel">Linking with Bagel</h1>
<p>To use Bagel in your program, add the following SBT or Maven dependency:</p>
<pre><code>groupId = org.apache.spark
artifactId = spark-bagel_2.10
version = 1.0.0
</code></pre>
<h1 id="programming-model">Programming Model</h1>
<p>Bagel operates on a graph represented as a <a href="programming-guide.html">distributed dataset</a> 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.</p>
<p>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.</p>
<p>We first extend the default <code>Vertex</code> class to store a <code>Double</code>
representing the current PageRank of the vertex, and similarly extend
the <code>Message</code> and <code>Edge</code> classes. Note that these need to be marked <code>@serializable</code> to allow Spark to transfer them across machines. We also import the Bagel types and implicit conversions.</p>
<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.bagel._</span>
<span class="k">import</span> <span class="nn">org.apache.spark.bagel.Bagel._</span>
<span class="nd">@serializable</span> <span class="k">class</span> <span class="nc">PREdge</span><span class="o">(</span><span class="k">val</span> <span class="n">targetId</span><span class="k">:</span> <span class="kt">String</span><span class="o">)</span> <span class="k">extends</span> <span class="nc">Edge</span>
<span class="nd">@serializable</span> <span class="k">class</span> <span class="nc">PRVertex</span><span class="o">(</span>
<span class="k">val</span> <span class="n">id</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="k">val</span> <span class="n">rank</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> <span class="k">val</span> <span class="n">outEdges</span><span class="k">:</span> <span class="kt">Seq</span><span class="o">[</span><span class="kt">Edge</span><span class="o">],</span>
<span class="k">val</span> <span class="n">active</span><span class="k">:</span> <span class="kt">Boolean</span><span class="o">)</span> <span class="k">extends</span> <span class="nc">Vertex</span>
<span class="nd">@serializable</span> <span class="k">class</span> <span class="nc">PRMessage</span><span class="o">(</span>
<span class="k">val</span> <span class="n">targetId</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="k">val</span> <span class="n">rankShare</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">extends</span> <span class="nc">Message</span>
</code></pre></div>
<p>Next, we load a sample graph from a text file as a distributed dataset and package it into <code>PRVertex</code> objects. We also cache the distributed dataset because Bagel will use it multiple times and we’d like to avoid recomputing it.</p>
<div class="highlight"><pre><code class="scala"><span class="k">val</span> <span class="n">input</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"data/pagerank_data.txt"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">numVerts</span> <span class="k">=</span> <span class="n">input</span><span class="o">.</span><span class="n">count</span><span class="o">()</span>
<span class="k">val</span> <span class="n">verts</span> <span class="k">=</span> <span class="n">input</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">line</span> <span class="k">=></span> <span class="o">{</span>
<span class="k">val</span> <span class="n">fields</span> <span class="k">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">'\t'</span><span class="o">)</span>
<span class="k">val</span> <span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="n">linksStr</span><span class="o">)</span> <span class="k">=</span> <span class="o">(</span><span class="n">fields</span><span class="o">(</span><span class="mi">0</span><span class="o">),</span> <span class="n">fields</span><span class="o">(</span><span class="mi">1</span><span class="o">))</span>
<span class="k">val</span> <span class="n">links</span> <span class="k">=</span> <span class="n">linksStr</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">','</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="k">new</span> <span class="nc">PREdge</span><span class="o">(</span><span class="k">_</span><span class="o">))</span>
<span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="k">new</span> <span class="nc">PRVertex</span><span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">numVerts</span><span class="o">,</span> <span class="n">links</span><span class="o">,</span> <span class="kc">true</span><span class="o">))</span>
<span class="o">}).</span><span class="n">cache</span>
</code></pre></div>
<p>We run the Bagel job, passing in <code>verts</code>, an empty distributed dataset of messages, and a custom compute function that runs PageRank for 10 iterations.</p>
<div class="highlight"><pre><code class="scala"><span class="k">val</span> <span class="n">emptyMsgs</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">List</span><span class="o">[(</span><span class="kt">String</span>, <span class="kt">PRMessage</span><span class="o">)]())</span>
<span class="k">def</span> <span class="n">compute</span><span class="o">(</span><span class="n">self</span><span class="k">:</span> <span class="kt">PRVertex</span><span class="o">,</span> <span class="n">msgs</span><span class="k">:</span> <span class="kt">Option</span><span class="o">[</span><span class="kt">Seq</span><span class="o">[</span><span class="kt">PRMessage</span><span class="o">]],</span> <span class="n">superstep</span><span class="k">:</span> <span class="kt">Int</span><span class="o">)</span>
<span class="k">:</span> <span class="o">(</span><span class="kt">PRVertex</span><span class="o">,</span> <span class="kt">Iterable</span><span class="o">[</span><span class="kt">PRMessage</span><span class="o">])</span> <span class="k">=</span> <span class="o">{</span>
<span class="k">val</span> <span class="n">msgSum</span> <span class="k">=</span> <span class="n">msgs</span><span class="o">.</span><span class="n">getOrElse</span><span class="o">(</span><span class="nc">List</span><span class="o">()).</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">rankShare</span><span class="o">).</span><span class="n">sum</span>
<span class="k">val</span> <span class="n">newRank</span> <span class="k">=</span>
<span class="k">if</span> <span class="o">(</span><span class="n">msgSum</span> <span class="o">!=</span> <span class="mi">0</span><span class="o">)</span>
<span class="mf">0.15</span> <span class="o">/</span> <span class="n">numVerts</span> <span class="o">+</span> <span class="mf">0.85</span> <span class="o">*</span> <span class="n">msgSum</span>
<span class="k">else</span>
<span class="n">self</span><span class="o">.</span><span class="n">rank</span>
<span class="k">val</span> <span class="n">halt</span> <span class="k">=</span> <span class="n">superstep</span> <span class="o">>=</span> <span class="mi">10</span>
<span class="k">val</span> <span class="n">msgsOut</span> <span class="k">=</span>
<span class="k">if</span> <span class="o">(!</span><span class="n">halt</span><span class="o">)</span>
<span class="n">self</span><span class="o">.</span><span class="n">outEdges</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">edge</span> <span class="k">=></span>
<span class="k">new</span> <span class="nc">PRMessage</span><span class="o">(</span><span class="n">edge</span><span class="o">.</span><span class="n">targetId</span><span class="o">,</span> <span class="n">newRank</span> <span class="o">/</span> <span class="n">self</span><span class="o">.</span><span class="n">outEdges</span><span class="o">.</span><span class="n">size</span><span class="o">))</span>
<span class="k">else</span>
<span class="nc">List</span><span class="o">()</span>
<span class="o">(</span><span class="k">new</span> <span class="nc">PRVertex</span><span class="o">(</span><span class="n">self</span><span class="o">.</span><span class="n">id</span><span class="o">,</span> <span class="n">newRank</span><span class="o">,</span> <span class="n">self</span><span class="o">.</span><span class="n">outEdges</span><span class="o">,</span> <span class="o">!</span><span class="n">halt</span><span class="o">),</span> <span class="n">msgsOut</span><span class="o">)</span>
<span class="o">}</span>
</code></pre></div>
<p>val result = Bagel.run(sc, verts, emptyMsgs)()(compute)</p>
<p>Finally, we print the results.</p>
<div class="highlight"><pre><code class="scala"><span class="n">println</span><span class="o">(</span><span class="n">result</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">v</span> <span class="k">=></span> <span class="s">"%s\t%s\n"</span><span class="o">.</span><span class="n">format</span><span class="o">(</span><span class="n">v</span><span class="o">.</span><span class="n">id</span><span class="o">,</span> <span class="n">v</span><span class="o">.</span><span class="n">rank</span><span class="o">)).</span><span class="n">collect</span><span class="o">.</span><span class="n">mkString</span><span class="o">)</span>
</code></pre></div>
<h2 id="combiners">Combiners</h2>
<p>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 <em>combiners</em>. 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.</p>
<p>For combiner support, Bagel can optionally take a set of combiner functions that convert messages to their combined form.</p>
<p><em>Example: PageRank with combiners</em></p>
<h2 id="aggregators">Aggregators</h2>
<p>Aggregators perform a reduce across all vertices after each superstep, and provide the result to each vertex in the next superstep.</p>
<p>For aggregator support, Bagel can optionally take an aggregator function that reduces across each vertex.</p>
<p><em>Example</em></p>
<h2 id="operations">Operations</h2>
<p>Here are the actions and types in the Bagel API. See <a href="https://github.com/apache/spark/blob/master/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala">Bagel.scala</a> for details.</p>
<h3 id="actions">Actions</h3>
<div class="highlight"><pre><code class="scala"><span class="cm">/*** Full form ***/</span>
<span class="nc">Bagel</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="n">vertices</span><span class="o">,</span> <span class="n">messages</span><span class="o">,</span> <span class="n">combiner</span><span class="o">,</span> <span class="n">aggregator</span><span class="o">,</span> <span class="n">partitioner</span><span class="o">,</span> <span class="n">numSplits</span><span class="o">)(</span><span class="n">compute</span><span class="o">)</span>
<span class="c1">// where compute takes (vertex: V, combinedMessages: Option[C], aggregated: Option[A], superstep: Int)</span>
<span class="c1">// and returns (newVertex: V, outMessages: Array[M])</span>
<span class="cm">/*** Abbreviated forms ***/</span>
<span class="nc">Bagel</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="n">vertices</span><span class="o">,</span> <span class="n">messages</span><span class="o">,</span> <span class="n">combiner</span><span class="o">,</span> <span class="n">partitioner</span><span class="o">,</span> <span class="n">numSplits</span><span class="o">)(</span><span class="n">compute</span><span class="o">)</span>
<span class="c1">// where compute takes (vertex: V, combinedMessages: Option[C], superstep: Int)</span>
<span class="c1">// and returns (newVertex: V, outMessages: Array[M])</span>
<span class="nc">Bagel</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="n">vertices</span><span class="o">,</span> <span class="n">messages</span><span class="o">,</span> <span class="n">combiner</span><span class="o">,</span> <span class="n">numSplits</span><span class="o">)(</span><span class="n">compute</span><span class="o">)</span>
<span class="c1">// where compute takes (vertex: V, combinedMessages: Option[C], superstep: Int)</span>
<span class="c1">// and returns (newVertex: V, outMessages: Array[M])</span>
<span class="nc">Bagel</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="n">vertices</span><span class="o">,</span> <span class="n">messages</span><span class="o">,</span> <span class="n">numSplits</span><span class="o">)(</span><span class="n">compute</span><span class="o">)</span>
<span class="c1">// where compute takes (vertex: V, messages: Option[Array[M]], superstep: Int)</span>
<span class="c1">// and returns (newVertex: V, outMessages: Array[M])</span>
</code></pre></div>
<h3 id="types">Types</h3>
<div class="highlight"><pre><code class="scala"><span class="k">trait</span> <span class="nc">Combiner</span><span class="o">[</span><span class="kt">M</span>, <span class="kt">C</span><span class="o">]</span> <span class="o">{</span>
<span class="k">def</span> <span class="n">createCombiner</span><span class="o">(</span><span class="n">msg</span><span class="k">:</span> <span class="kt">M</span><span class="o">)</span><span class="k">:</span> <span class="kt">C</span>
<span class="k">def</span> <span class="n">mergeMsg</span><span class="o">(</span><span class="n">combiner</span><span class="k">:</span> <span class="kt">C</span><span class="o">,</span> <span class="n">msg</span><span class="k">:</span> <span class="kt">M</span><span class="o">)</span><span class="k">:</span> <span class="kt">C</span>
<span class="k">def</span> <span class="n">mergeCombiners</span><span class="o">(</span><span class="n">a</span><span class="k">:</span> <span class="kt">C</span><span class="o">,</span> <span class="n">b</span><span class="k">:</span> <span class="kt">C</span><span class="o">)</span><span class="k">:</span> <span class="kt">C</span>
<span class="o">}</span>
<span class="k">trait</span> <span class="nc">Aggregator</span><span class="o">[</span><span class="kt">V</span>, <span class="kt">A</span><span class="o">]</span> <span class="o">{</span>
<span class="k">def</span> <span class="n">createAggregator</span><span class="o">(</span><span class="n">vert</span><span class="k">:</span> <span class="kt">V</span><span class="o">)</span><span class="k">:</span> <span class="kt">A</span>
<span class="k">def</span> <span class="n">mergeAggregators</span><span class="o">(</span><span class="n">a</span><span class="k">:</span> <span class="kt">A</span><span class="o">,</span> <span class="n">b</span><span class="k">:</span> <span class="kt">A</span><span class="o">)</span><span class="k">:</span> <span class="kt">A</span>
<span class="o">}</span>
<span class="k">trait</span> <span class="nc">Vertex</span> <span class="o">{</span>
<span class="k">def</span> <span class="n">active</span><span class="k">:</span> <span class="kt">Boolean</span>
<span class="o">}</span>
<span class="k">trait</span> <span class="nc">Message</span><span class="o">[</span><span class="kt">K</span><span class="o">]</span> <span class="o">{</span>
<span class="k">def</span> <span class="n">targetId</span><span class="k">:</span> <span class="kt">K</span>
<span class="o">}</span>
</code></pre></div>
<h1 id="where-to-go-from-here">Where to Go from Here</h1>
<p>Two example jobs, PageRank and shortest path, are included in <code>examples/src/main/scala/org/apache/spark/examples/bagel</code>. You can run them by passing the class name to the <code>bin/run-example</code> script included in Spark; e.g.:</p>
<pre><code>./bin/run-example org.apache.spark.examples.bagel.WikipediaPageRank
</code></pre>
<p>Each example program prints usage help when run without any arguments.</p>
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