tale of two graph frameworks: graph frames and tinkerpop

Artem Aliev and Russell Spitzer, DataStax

A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP

#EUeco3

Pierrot and Harlequin• Artem

• Graph Analytics Expert • Earth

• Russell • Distributed Systems Enthusiast • Earth

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Tinkerpop and GraphFrames provide Complimentary Approaches for Graph Analytics

DataSet Catalyst

GraphFrames

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Graphs are Vertices and Edges

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Vertices are things and edges represent their relations to one another

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Registry: USS Enterprise (NCC-1701-C)Class: AmbassadorService: 2332[11] – 2344 (12 Years)

Registry: USS Enterprise (NCC-1701-D)Class: GalaxyService: 2363–2371 (8 Years)

Registry: USS Enterprise (NCC-1701)Class: Constitution class[6]

Service: 2245–2285 (40 Years)

Registry: USS Enterprise (NCC-1701-A)Class: Enterprise class[8][9]

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https://en.wikipedia.org/wiki/Ambassador_class_starship_(Star_Trek)

https://en.wikipedia.org/wiki/Starship_Enterprise#cite_note-11

https://en.wikipedia.org/wiki/USS_Enterprise_(NCC-1701-D)

https://en.wikipedia.org/wiki/Galaxy_class_starship_(Star_Trek)

https://en.wikipedia.org/wiki/USS_Enterprise_(NCC-1701)

https://en.wikipedia.org/wiki/List_of_Star_Trek_Starfleet_starships_ordered_by_class#Constitution_class

https://en.wikipedia.org/wiki/USS_Enterprise_(NCC-1701-A)

https://en.wikipedia.org/wiki/List_of_Star_Trek_Starfleet_starships_ordered_by_class#Constitution_class_refit

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Vertex

Properties#EUeco3

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succeeded by

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Edge

Edge Labelsucceeded by

succeeded by

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Ship

ShipVertex Label

succeeded by

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Registry: USS Enterprise (NCC-1701)Class: Constitution classService: 2245–2285 (40 Years)

Ship

ShipPosition: Captain Name: Kirk

Position: Captain Name: Picard

Crew

succeeded by

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Registry: USS Enterprise (NCC-1701-A)Class: Enterprise classService: 2286–2293 (7 Years)

Ship

Crew

succeeded by

succeeded byserved onserved on

served on

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Ship

Crew

succeeded by

served on

But why do I want this?

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Graphs let us ask questions about our data based on their relations

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What Captain Served After Kirk?

What Ship was two after the NCC-1701?

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Traversals involve following paths through the Graph

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Ship

Crew

succeeded by

served on

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What Captain was After Kirk?

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Ship

Position: Captain Name: Kirk

Crew

Crewsucceeded by

served on

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What Ship was two after the NCC-1701?

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Ship

succeeded by

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Tinkerpop is a Powerful and Flexible Graph Framework

• Server, Language, Connectors • Graph Framework for

OLAP and OLTP • Node Centric Representations • Fluent API (Gremlin) • Fully Self Contained Framework

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OLTP Examples

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Movie Lens Example Schema

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https://grouplens.org/datasets/movielens/

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https://grouplens.org/datasets/movielens/

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What happens when you have too much data?

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Tinkerpop Spark OLAP Mechanism• Instead of one traversal we traverse starting from all nodes simultaneously

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Distribution Requires Partitioning

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?

Big DataIndependent Chunks

of Data#EUeco3

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Vertex Stored in a PairRDD Id -> StarVertex(Edge and Property Information)

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1

A

C

D

Star Vertex: Adjacency list representation1: "A", "Kirk"A: "C", "Kirk"C: "D", "Picard"D: "Picard" Just Id

Of Connected Vertex

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Vertex Program Runs Initializing Traverser for every Vertex

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1

A

C

D

SparkMemory - Accumulator - Used for GlobalState

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Then we cycle through a message Passing Algorithm

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1

A

C

D

1

A

C

D

1

A

C

D

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1

A

C

D

1

A

C

D

1

A

C

D

Passes messages from one Vertex to another with a join

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1

A

C

D

1

A

C

D

1

A

C

D

Repeat

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1

A

C

D

1

A

C

D

1

A

C

D

All Traversers HaltOr Program Terminates

Result!

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Example OLAP Traversals

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Tinkerpop Spark OLAP Pros/ConsPros • Every message pass requires only a single shuffle • Edges and edge properties accessible without a step • Very Flexible, Many Provider Specific Shortcuts possible • Internal properties can be any Java type • All in one, Server already ready for multiple clients Cons • Limited in ability to connect to external sources/other spark applications• Flexibility of framework allows for many platform specific shortcuts to be added• Genericness provides difficulty in making some optimizations • Edges co-partitioned with vertices, high degree nodes can cause memory issues

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GraphFrames Background• Third Party Package • https://graphframes.github.io/ • Integrates with Dataset/Dataframe in Spark • Relational under the hood

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GraphFrames are built of two DataFrames

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Row

Column

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id job species

Geordi Chief Engineer

Human

Data Science Officer

Android

Vertex DataFrame

src dst relationship

Geordi Data Friend

Edge DataFrame

Friend

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id job species

Human

Android

Vertex DataFrame

Geordi Data Friend

Edge DataFrame

Friend

Can Only Be Spark Types

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id job species

Human

Android

Vertex DataFrame

Geordi Data Friend

Edge DataFrame

Friend

No Built in Labels

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Catalyst Optimizes any Requests• Simple requests using DataFrame api don't do

anything special • Some methods fall back to GraphX (RDD Based) • Others use pure DataFrame methods

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GraphFrames Motif Matching

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GraphFrame(a)-[e]->(b)

V E

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Vertex (a) Vertices as a UDT "A"V E

A: <VertexRow>

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Vertex (a) Vertices as a UDT "A"

Edge [b] Edges as UDT "E"Join with edges where A.id = E.src

V E

A: <VertexRow>

JoinA: <VertexRow>, E: <EdgeRow>

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[e] Vertices as UDT "B" Join with edges where E.dst = B.id

Edge

Vertex

[b] Edges as UDT "E"Join with edges where A.id = E.src

V E

A: <VertexRow>

A: <VertexRow>, E: <EdgeRow>

Join

JoinA: <VertexRow>, E: <EdgeRow>, B: <VertexRow>

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[e] Vertices as UDT "B" Join with edges where E.dst = B.id

Edge

Vertex

[b] Edges as UDT "E"Join with edges where A.id = E.src

V E

A: <VertexRow>

Join

JoinA: <VertexRow>, E: <EdgeRow>, B: <VertexRow>

THAT'S SO MANY JOINS

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Vertex

Edge

Vertex

A: <VertexRow>

A: <VertexRow>, E: <EdgeRow>, B: <VertexRow>

DataFrames means Optimizations are Automatic

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Vertex

Edge

Vertex

A: <VertexRow>

Select A.ID

Columns Pruned and Predicates Pushed

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Vertex

Edge

Vertex

A: <VertexRow>

Select A.ID

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Vertex

Edge

Vertex

A: <VertexRow>

Select A.ID

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Vertex

Edge

Vertex

A: <VertexRow>

Select A.ID

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All of the normal optimizations happen within this FrameWork

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Vertex

Edge

Vertex

A: <VertexRow>

Broadcast?

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Code Generation and Internal Rows

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Vertex

Edge

Vertex

A: <VertexRow>

Code Generation

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GraphFrames Examples

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GraphFrame Pros ConsPros • Much Faster on basic counts • Powerful optimizations + CodeGen • Easy to connect to other sources Cons • Slower on complex traversals (2 Joins per hop) • Relational Model not as Flexible

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Choosing the Right Framework

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Choose TinkerPop OLAP For Long Paths

• More complicated queries • Traversals that require many hops

• g.V().out.out.out.out

• Avoid for simple counts and aggregations • Avoid if you have very high degree Vertices

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Choose GraphFrames for Interoperability and Short Paths

• General Edge/Vertex stats groupCount, min, max • Connecting to other sources • Short paths • High Degree Vertices

• Avoid • Long path algorithms

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Choosing the Right Framework

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Gremlin on Graphframes

OLTP backed by DSE Graph

Built in Spark

We write it!

Search Built In!

Advanced Security

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Thanks for Listening

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Datastax Academy Graph Course https://academy.datastax.com/resources/ds330-datastax-enterprise-graph

Try out Datastax Enterprise! https://academy.datastax.com/quick-downloadsApache Tinkerpophttp://tinkerpop.apache.org/ GraphFrames Link https://graphframes.github.io/

http://tinkerpop.apache.org/

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