Faunus: Graph Analytics Engine

FAUNUS

MARKO A. RODRIGUEZ

http://THINKAURELIUS.COM

GRAPH ANALYTICS ENGINE

Faunus is a graph analytics engine built atop the Hadoop distributed computing platform. The graph representation is a distributed adjacency list, whereby a vertex and its incident edges are co-located on the same machine. Querying a Faunus graph is possible with a MapReduce-variant of the Gremlin graph traversal language. A Gremlin expression compiles down to a series of MapReduce-steps that are sequence optimized and then executed by Hadoop. Results are stored as transformations to the input graph (graph derivations) or computational side-effects such as aggregates (graph statistics). Beyond querying, a collection of input/output formats are supported which enable Faunus to load/store graphs in the distributed graph database Titan, various graph formats stored in HDFS, and via arbitrary user-defined functions. This presentation will focus primarily on Faunus, but will also review the satellite technologies that enable it.

ABSTRACT

http://FAUNUS.THINKAURELIUS.COM

SPONSORED BY

ECCO, the Evolution, Complexity and Cognition group, is a multidisciplinary research group, directed by Francis Heylighen. They are localized at the Vrije Universiteit Brussel (VUB), although members are distributed across four continents. Researchers come from a wide variety of backgrounds, from physical science and technology to the social sciences and humanities. The philosophy is intrinsically transdisciplinary, transcending the traditional boundaries between "hard" and "soft" sciences, and between philosophical foundations and practical applications.

The Big-Data Interest Group (BIGDIG) is a focus group at LANL meeting monthly to explore big-data methods and architectures. One goal of the group is to identify early adopters and learn from their experiences. Furthermore, they would like involve scientists that are looking for big-data solutions and foster collaboration with those who might provide the needed technology. The BIGDIG group includes members from all domains: science, security, sensing, computing, library, and more.

The EgoSystem project is creating an integrated social model of the Los Alamos National Laboratory and its surroundings using numerous online services such as Twitter, LinkedIn, MS Academic, Wikipedia, and more. The model is seeded with LANL PostDocs, their created artifacts and continuously grows to encompass their relations to other people and institutions. EgoSystem is a Director sponsored project engineered by the Digital Library Research and Prototyping Team using Big Graph Data technology provided by Aurelius.

VERTEX

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EDGE

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VERTICES + EDGES(ELEMENTS)

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incoming edges outgoing edges

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AN ADJACENCY LIST

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AN ADJACENCY LIST+

CLUSTER0

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A DISTRIBUTED ADJACENCY LIST

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Hadoop is a distributed computing platform composed of two key components:

HDFS: A distributed file system that stores arbitrarily large files within a cluster.

MapReduce: A parallel functional computing model for key/value pair data.

HADOOP

http://hadoop.apache.org

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Structure

Process

Faunus provides graph input/output formats (structure) and a traversal language for graphs (process).

FAUNUS AND HADOOP

127.0.0.2 127.0.0.3 127.0.0.4

PROCESSING GRAPHSWITH FAUNUS

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name:tartarustype:location

name:plutotype:god

lives

brother

name:jupitertype:god 2

brother name:neptunetype:god

pet

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name:cerberustype:monster

lives

father

name:saturntype:titan

brother

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name:seatype:location

lives

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name:skytype:location

lives

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father

battled

name:herculestype:demigod

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name:hydratype:monster

battled

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name:nemeantype:monster

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name:alcmenetype:human

mother

time:1 time:2 time:12

GRAPH OF THE GODS

* Toy graph distributed with Faunus.

faunus$

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time:1 time:2 time:12 127.0.0.2 127.0.0.3 127.0.0.4

faunus$ bin/gremlin.sh

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time:1 time:2 time:12 127.0.0.2 127.0.0.3 127.0.0.4

http://gremlin.tinkerpop.com


\,,,/ (o o)-----oOOo-(_)-oOOo-----gremlin>

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time:1 time:2 time:12 127.0.0.2 127.0.0.3 127.0.0.4


\,,,/ (o o)-----oOOo-(_)-oOOo-----gremlin> hdfs.ls()gremlin>

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time:1 time:2 time:12 127.0.0.2 127.0.0.3 127.0.0.4


\,,,/ (o o)-----oOOo-(_)-oOOo-----gremlin> hdfs.ls()gremlin> hdfs.copyFromLocal('graph-of-the-gods.json','graph-of-the-gods.json')==>nullgremlin>

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\,,,/ (o o)-----oOOo-(_)-oOOo-----gremlin> hdfs.ls()gremlin> hdfs.copyFromLocal('graph-of-the-gods.json','graph-of-the-gods.json')==>nullgremlin> hdfs.ls()==>rw-r--r-- marko supergroup 2028 graph-of-the-gods.jsongremlin>

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time:1 time:2 time:12 127.0.0.2 127.0.0.3 127.0.0.4

gremlin> g = FaunusFactory.open('bin/faunus.properties')==>faunusgraph[graphsoninputformat->graphsonoutputformat]gremlin> g.getConf('faunus')==>faunus.graph.input.format =com.thinkaurelius.faunus.formats.graphson.GraphSONInputFormat==>faunus.input.location=graph-of-the-gods.json==>faunus.graph.output.format =com.thinkaurelius.faunus.formats.graphson.GraphSONOutputFormat==>faunus.output.location=output==>faunus.output.location.overwrite=true==>faunus.sideeffect.output.format =org.apache.hadoop.mapreduce.lib.output.TextOutputFormat

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time:1 time:2 time:12 127.0.0.2 127.0.0.3 127.0.0.4

gremlin> g.V13/05/07 12:07:09 INFO mapreduce.FaunusCompiler: Compiled to 1 MapReduce job(s)13/05/07 12:07:09 INFO mapreduce.FaunusCompiler: Executing job 1 out of 1: MapSequence[com.thinkaurelius.faunus.mapreduce.transform.VerticesMap.Map]13/05/07 12:07:09 INFO mapreduce.FaunusCompiler: Job data location: output/job-013/05/07 12:07:10 INFO input.FileInputFormat: Total input paths to process : 113/05/07 12:07:10 INFO mapred.JobClient: Running job: job_201304251105_000413/05/07 12:07:11 INFO mapred.JobClient: map 0% reduce 0%...

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gremlin> g.V.has('type','god')13/05/07 12:08:55 INFO mapreduce.FaunusCompiler: Compiled to 1 MapReduce job(s)13/05/07 12:08:55 INFO mapreduce.FaunusCompiler: Executing job 1 out of 1: MapSequence[com.thinkaurelius.faunus.mapreduce.transform.VerticesMap.Map, com.thinkaurelius.faunus.mapreduce.filter.PropertyFilterMap.Map]13/05/07 12:08:55 INFO mapreduce.FaunusCompiler: Job data location: output/job-013/05/07 12:08:56 INFO input.FileInputFormat: Total input paths to process : 113/05/07 12:08:57 INFO mapred.JobClient: Running job: job_201304251105_000513/05/07 12:08:58 INFO mapred.JobClient: map 0% reduce 0%...

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gremlin> g.V.has('type','god').in('father')13/05/07 12:13:03 INFO mapreduce.FaunusCompiler: Compiled to 1 MapReduce job(s)13/05/07 12:13:03 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.transform.VerticesVerticesMapReduce.Map, com.thinkaurelius.faunus.mapreduce.transform.VerticesVerticesMapReduce.Reduce]13/05/07 12:13:03 INFO mapreduce.FaunusCompiler: Job data location: output/job-013/05/07 12:13:03 INFO input.FileInputFormat: Total input paths to process : 113/05/07 12:13:04 INFO mapred.JobClient: Running job: job_201304251105_000613/05/07 12:13:05 INFO mapred.JobClient: map 0% reduce 0%...

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gremlin> g.V.has('type','god').in('father').out('mother').name13/05/07 12:25:18 INFO mapreduce.FaunusCompiler: Compiled to 3 MapReduce job(s)13/05/07 12:25:18 INFO mapreduce.FaunusCompiler: Executing job 1 out of 3: MapSequence[com.thinkaurelius.faunus.mapreduce.transform.VerticesMap.Map, com.thinkaurelius.faunus.mapreduce.filter.PropertyFilterMap.Map, com.thinkaurelius.faunus.mapreduce.transform.VerticesVerticesMapReduce.Map, com.thinkaurelius.faunus.mapreduce.transform.VerticesVerticesMapReduce.Reduce]13/05/07 12:25:18 INFO mapreduce.FaunusCompiler: Job data location: output/job-013/05/07 12:25:18 INFO input.FileInputFormat: Total input paths to process : 113/05/07 12:25:18 INFO mapred.JobClient: Running job: job_201305071220_0007...==>alcmenegremlin>

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k1:v1

vertex edge

incoming edges

4

edge edge

outgoing edges

edge

TRAVERSAL DATA

1. A long counter denoting how many traversers exist at the element.

-OR-

2. A list of lists denoting path history of individual traversers at the element.

coun

ter =

chea

p

enum

erativ

e =

expe

nsive

* Each element in a row maintains traversal data as well.

k1:v1 k1:v1 k1:v1

gremlin> g.V.has('type','god').in('father').out('mother').path13/05/07 14:37:59 WARN mapreduce.FaunusCompiler: Path calculations are enabled for this Faunus job (space and time expensive)13/05/07 14:37:59 INFO mapreduce.FaunusCompiler: Compiled to 3 MapReduce job(s)13/05/07 14:37:59 INFO mapreduce.FaunusCompiler: Executing job 1 out of 3: MapSequence[com.thinkaurelius.faunus.mapreduce.transform.VerticesMap.Map, com.thinkaurelius.faunus.mapreduce.filter.PropertyFilterMap.Map, com.thinkaurelius.faunus.mapreduce.transform.VerticesVerticesMapReduce.Map, com.thinkaurelius.faunus.mapreduce.transform.VerticesVerticesMapReduce.Reduce]13/05/07 14:38:00 INFO mapred.JobClient: Running job: job_201305071220_0005...==>[v[1], v[7], v[8]]gremlin>

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[1,7,8]

GREMLIN

GRAPH TRAVERSAL LANGUAGE

TRANSFORM FILTER SIDE-EFFECT BRANCHt : (V [ E)! P(V [ E) f : (V [ E) ! (V [ E [ ;) s : (V [ E)/!(V [ E)

f1 � f2 � f3 � · · · � f4

transform{}VidlabeloutinoutEinEinVmaporder...

filter{}hashasNot[0..10]randomsimplePathback...

sideEffect{}groupCountgroupByaggregatetablestorelinkInlinkOutcount...

loopcopySplitfairMergeexhaustMerge...

Gremlin is a functional graph language where traversals are defined using function composition. A set of useful predefined functions are provided with the language and generic lambdas/closures are possible for arbitrary mappings.

http://gremlin.tinkerpop.com

EXAMPLE TRAVERSALS

g.V.has('type','person').out('attends') .has('type','academy').name.groupCount

g.V.out.out.out.simplePath.count()

"How many people attend each academy?"

g.V.sideEffect{it.degree = it.inE('friend').count()} .degree.groupCount

"What is the in-degree distribution of the friendship subgraph?"

"How many 3-step acyclic paths exist in the graph?"

* The only memory structure is the graph, thus all data must be in the graph.

g.V.as('x').out('father').out('father') .linkIn('grandfather','x')

"Derive all implicit grandfather relations in the graph."

g.V.count()

"How many vertices are in the graph?"

* Mutates the graph.

hdfs://user/ubuntu/output/job-0/output/job-1/output/job-2/ { graph*

sideeffect*

g.V.out .out .count()

<NullWritable, FaunusVertex> <NullWritable, FaunusVertex>

<NullWritable, FaunusVertex> <LongWritable, Holder<FaunusElement>>

<LongWritable, Iterable<Holder<FaunusElement>>> <NullWritable, FaunusVertex>

MAP ONLY STEPS (NO REDUCE NEEDED)

MAP/REDUCE STEPS

map

map

reduce

FAUNUS DATA FLOW

valuekey

GREMLIN IN MAP/REDUCE

map(null, vertex, context) { key = context.getConf().get('provided.key') value = context.getConf().get('provided.value') if(!vertex.getProperty(key).equals(value)) { vertex.clearPaths(); } context.write(vertex);}

FILTER

f : (V [ E) ! (V [ E [ ;)

g.V.ha

s('typ

e','go

d')

* Most filters are map-only steps. If the predicate returns false, then all the path metadata is cleared from the element.

f(v)

'type''god'

map(null, vertex, context) { for(e : vertex.getEdges(OUT)) { context.write(e.getVertex(IN).id, holder('p',vertex.pathsOnly())) } context.write(vertex.id, holder('v',vertex))}

reduce(long, iterable<holder> holders, context) { vertex = new FaunusVertex(long) for(h : holders) { if(h.getTag() == 'v')) vertex.addAll(h.getVertex()) else vertex.addPaths(h.getVertex()) } context.write(null, vertex)}

127.0.0.4

127.0.0.3

127.0.0.2

GREMLIN IN MAP/REDUCE

t : (V [ E)! P(V [ E)

TRANSFORM

g.V.out

* Traversals implement a reduce-side join.

map(null, vertex, context) { key = context.getConf().get('provided.key') context.write('graph',null,vertex) context.write('sideeffect', vertex.getProperty(key),vertex.getPathCount())}

reduce(object, iterable<long> longs, context) { sum = 0 for(l : longs) { sum += l } context.write('sideeffect',object,sum)}

GREMLIN IN MAP/REDUCESIDE-EFFECT

s : (V [ E)/!(V [ E)

g.V.ty

pe.gro

upCoun

t()

s(v)

'type'

* Leverages MultipleInputs/Outputs

STRUCTURING GRAPHS

WITH FAUNUS

INPUT/OUTPUT FORMATS

SequenceFileOutputFormat

A list of serialized vertex objects in a compressed binary format. <NullWritable,FaunusVertex>

The intermediate data format between MapReduce jobs within a Faunus pipeline.

Fastest available format for both reading and writing.

Compressed using variable-width and prefix encodings.

gremlin> g==>faunusgraph[graphsoninputformat->graphsonoutputformat]gremlin> g.setGraphOutputFormat(SequenceFileOutputFormat)==>nullgremlin> g==>faunusgraph[graphsoninputformat->sequencefileoutputformat]gremlin>

SequenceFileInputFormat


GraphSONOutputFormat

A verbose JSON-based text-format. Each vertex is a single JSON document.

Easy for developers to generate. Useful for testing and examples.

Limited to JSON supported datatypes for element property values.

{"name":"saturn","type":"titan","_id":0,"_inE":[{"_label":"father","_id":12,"_outV":1}]}

{"name":"jupiter","type":"god","_id":1,"_outE":[{"_label":"lives","_id":13,"_inV":4},

{"_label":"brother","_id":16,"_inV":3},{"_label":"brother","_id":14,"_inV":2},

{"_label":"father","_id":12,"_inV":0}],"_inE":[{"_label":"brother","_id":17,"_outV":3},

{"_label":"brother","_id":15,"_outV":2},{"_label":"father","_id":24,"_outV":7}]}

{"name":"neptune","type":"god","_id":2,"_outE":[{"_label":"lives","_id":20,"_inV":5},

{"_label":"brother","_id":19,"_inV":3},{"_label":"brother","_id":15,"_inV":1}],"_inE":

[{"_label":"brother","_id":18,"_outV":3},{"_label":"brother","_id":14,"_outV":1}]}

...

GraphSONInputFormat

* JSON specification is available at http://json.org


faunus.graph.input.format=

com.thinkaurelius.faunus.formats.edgelist.rdf.RDFInputFormat

faunus.input.location=graph-example-1.ntriple

faunus.graph.input.rdf.format=n-triples

faunus.graph.input.rdf.as-properties=http://www.w3.org/1999/02/22-rdf-syntax-ns#type

faunus.graph.input.rdf.use-localname=true

faunus.graph.input.rdf.literal-as-property=true

RDFInputFormat

Maps popular RDF text formats to a property graph.

Configurations allow for different mappings of RDF to the property graph model.

Utilizes a MapReduce step to convert an edge-list into an adjacency list.

33^^xsd:intex:markofoaf:age 0

uri:ex:markoage:33

* RDF parsers provided by http://openrdf.org


RexsterInputFormat

Rexster

{ "results": { "_type":"vertex", "_id":1, "name":"tiberius", "age":29 }, "queryTime":0.123 }

HTTP REXPRO

http://.../vertices/1

g.v(1).out('mother') .out('mother').name

==>aurelia

Rexster is a graph server that is accessed via: REST and a Gremlin binary protocol.

Rexster supports any Blueprints-enabled graph database.

http://rexster.tinkerpop.com


A Gremlin script stored in HDFS (distributed cache) allows for an arbitrary parse.

def boolean read(FaunusVertex v, String line) { parts = line.split(':'); v.reuse(Long.valueOf(parts[0])) parts[1].split(',').each { v.addEdge(OUT, 'linkedTo', Long.valueOf(it)); } return true;}

ScriptInputFormat

0:1,2,3,41:2,32:0,3,5,63:1,2...

def void write(FaunusVertex vertex, DataOutput output) { output.writeUTF(vertex.getId().toString() + ':'); Iterator<Edge> itty = vertex.getEdges(OUT).iterator() while (itty.hasNext()) { output.writeUTF( itty.next().getVertex(IN).getId() + ','); } output.writeUTF('\n');}

ScriptOutputFormat

0:1,2,3,41:2,32:0,3,5,63:1,2...

Adam Jacobs. 2009. The Pathologies of Big Data. Communications of the ACM 52, 8 (August 2009), 36-44. doi:10.1145/1536616.1536632 http://doi.acm.org/10.1145/1536616.1536632

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Serial Key/Value Data Structure Indexed Key/Indexed Value Data Structure

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GLOBAL VS. LOCALGRAPH ANALYSIS

TITAN

DISTRIBUTED GRAPH DATABASE

Application Servers Reading/Writing Graph Data

Titan Cluster Processing Gremlin Traversals and Writes

The biggest known Titan/Cassandra cluster to date:

~120 billion edge graph stored in a 16 hi1.4xlarge machine cluster. Ego-centric graph traversals are requested by 80 m1.large machines. The cluster serves ~10,000 transactions a second w/ ~200ms return times.

http://titan.thinkaurelius.com

http://thinkaurelius.com/2013/05/13/educating-the-planet-with-pearson/

http://thinkaurelius.com/2013/05/13/educating-the-planet-with-pearson/

FAUNUS AND TITAN

SUPPORTED TITAN INPUT/OUTPUT FORMATS

TitanCassandraInputFormatTitanCassandraOutputFormat

TitanHBaseInputFormatTitanHBaseOutputFormat

FAUNUS AND TITAN

Faunus/HadoopTitan/Cassandra

INTRA-CLUSTER CONFIGURATION

Data is processed on the machine where it is located. Limited network communication.

FAUNUS AND TITAN

INTER-CLUSTER CONFIGURATION

Graph data is offloaded to another cluster.Repeated analysis does not interfere with production graph database.

Graph glong counter = 0

def setup(args) { g = TitanFactory.open('cassandra:localhost')}

def map(vertex, args) { g.v(vertex.id).as('x').out('father') .out('father').linkIn('grandfather','x') if(counter++ % 1000 == 0) g.commit()}

FAUNUS AND TITANVERTEX-CENTRIC COMPUTING WITH GREMLIN

A Gremlin script is stored in HDFS (distributed cache).Vertex long ids are pulled out of Titan (FaunusVertex with id only).The Gremlin script is evaluated concurrently for every vertex long id.Guaranteed co-location of Gremlin script JVM and Titan vertex.

* Provided by the Gremlin script()-step

CREDITSPRESENTED BY

MARKO A. RODRIGUEZ

SUPPORTED BYLOS ALAMOS NATIONAL LABORATORY

LANL RESEARCH LIBRARYVRIJE UNIVERSITEIT BRUSSEL

MANY THANKS TOMATTHIAS BRöCHELER

STEPHEN MALLETTEPAVEL YASKEVICHDAN LAROCQUE

AURELIUS COMMUNITYTINKERPOP COMMUNITY

KETRINA YIM

Faunus: Graph Analytics Engine

Technology

Transcript of Faunus: Graph Analytics Engine