Distributed Graph Computing with Gremlin

The script-step in Faunus’ Gremlin allows for the arbitrary execution of a Gremlin script against all vertices in the graph (or those which currently exist in Faunus’ computational pipeline). This simple idea has interesting ramifications for Gremlin-based distributed graph computing.

• Global graph mutations: update a Titan cluster in parallel given some arbitrary computation.
• Global graph algorithms: propagate information to arbitrary depths in the graph in order to compute some algorithm in a parallel fashion.

The script-step requires that a Gremlin script exist in HDFS and has the following method definitions:

• setup(String... args): called once per mapper during Map.setup()
• map(FaunusVertex v, String... args): called for each key/value of Map.map() with v being a FaunusVertex
• cleanup(String... args): called once per mapper during Map.cleanup()

Finally, the script-step has a method signature of: script(String scriptUri, String... args).

Global Graph Mutations

One way to do global graph mutations with Faunus is to use an InputFormat that reads a graph from a graph database (e.g. Titan and/or Rexster) and then mutate the Faunus representation of that graph in HDFS over various Gremlin/Faunus steps. Finally, delete the original graph in the database and bulk load the new mutated Faunus graph. The problem with this method is that it requires the graph database to be cleared and re-loaded which, for production 24×7 systems, is not a reasonable requirement.

Another way to parallel update the graph is to use script-step in order to allow for real-time, distributed bulk updates of the original graph in the graph database itself. A simple example explains the idea. Assume the Graph of the Gods dataset (see visualization) in Titan/Cassandra (or Titan/HBase) co-located with Hadoop data nodes and task trackers.

Assume the following Gremlin/Groovy script called FathersNames.groovy (distributed with Faunus at data/FathersNames.groovy). This script will add a new property to each vertex that is the name of the vertex’s father (if the vertex has a father).

// FathersName.groovy

def g

def setup(args) {
    conf = new org.apache.commons.configuration.BaseConfiguration()
    conf.setProperty('storage.backend', args[0])
    conf.setProperty('storage.hostname', 'localhost')
    g = com.thinkaurelius.titan.core.TitanFactory.open(conf)
}

def map(v, args) {
    u = g.v(v.id) // the Faunus vertex id is the same as the original Titan vertex id
    pipe = u.out('father').name
    if (pipe.hasNext()) u.fathersName = pipe.next();
    u.name + "'s father's name is " + u.fathersName
}

def cleanup(args) {
    g.shutdown()
}

Place this FathersName.groovy file into HDFS using Gremlin.

gremlin> hdfs.copyFromLocal('data/FathersNames.groovy', 'FathersNames.groovy')
==>null

With this file in HDFS, it is possible to execute the following Gremlin/Faunus traversal. For all demigod and god vertices in the graph, add the vertex’s father’s name to the vertex if it exists.

g.V.has('type','demigod','god').script('FathersName.groovy','cassandrathrift')

The Graph of the Gods in Titan serves as the input to the Faunus job. NoOpOutputFormat is used as there no need to write the graph as the original Titan representation of the graph is being mutated.

gremlin> g = FaunusFactory.open('bin/titan-cassandra-input.properties')                
==>faunusgraph[titancassandrainputformat->graphsonoutputformat]
gremlin> g.setGraphOutputFormat(NoOpOutputFormat.class)                                
==>null
gremlin> g.V.has('type','demigod','god').script('FathersName.groovy','cassandrathrift')
13/03/06 18:21:43 INFO mapreduce.FaunusCompiler: Compiled to 1 MapReduce job(s)
13/03/06 18:21:43 INFO mapreduce.FaunusCompiler: Executing job 1 out of 1: MapSequence[com.thinkaurelius.faunus.mapreduce.transform.VerticesMap.Map, com.thinkaurelius.faunus.mapreduce.filter.PropertyFilterMap.Map, com.thinkaurelius.faunus.mapreduce.util.ScriptMap.Map]
13/03/06 18:21:43 INFO mapreduce.FaunusCompiler: Job data location: output/job-0
13/03/06 18:21:43 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
13/03/06 18:21:44 INFO mapred.JobClient: Running job: job_201303061253_0077
13/03/06 18:21:45 INFO mapred.JobClient:  map 0% reduce 0%
13/03/06 18:21:53 INFO mapred.JobClient:  map 50% reduce 0%
...
==>hercules's father's name is jupiter
==>pluto's father's name is null
==>jupiter's father's name is saturn
==>neptune's father's name is null

Looking at the original graph in Titan, those vertices that have fathers, have a new fathersName property.

gremlin> g.V.transform("{it.name + ' ' + it.fathersName}")             
...
13/03/06 18:25:40 INFO mapred.JobClient: Running job: job_201303061253_0078
13/03/06 18:25:41 INFO mapred.JobClient:  map 0% reduce 0%
...
==>tartarus null
==>alcmene null
==>sea null
==>hydra null
==>hercules jupiter
==>cerberus null
==>pluto null
==>saturn null
==>sky null
==>jupiter saturn
==>neptune null
==>nemean null

One of the important ideas to take away from this example is that while Faunus is vertex-centric (a step can only operate on the vertex and its incident edges), with script and a Titan connection it is possible to do arbitrary walks of arbitrary depth and thus, allows for more expressive bulk/global computations over Titan.

Finally, note that localhost is used as the storage.hostname for Titan. If the Hadoop cluster is colocated with the Cassandra (or HBase) cluster, then localhost is the location of both the FaunusVertex and the TitanVertex. Thus, the computation exists along side the data within the cluster.

Global Graph Algorithms

More to come…