GraphX: Unifying Data-Parallel and Graph-Parallel Analytics Presented by Joseph Gonzalez Joint work with Reynold Xin, Daniel Crankshaw, Ankur Dave, Michael.

GraphX:Unifying Data-Parallel and Graph-Parallel Analytics Presented by Joseph Gonzalez

Joint work with Reynold Xin, Daniel Crankshaw, Ankur Dave, Michael Franklin, and Ion Stoica

Strata 2014

*These slides are best viewed in PowerPoint with animation.

Graphs are Central to Analytics

Raw Wikipedia

< / >< / >< / >XML

Hyperlinks PageRank Top 20 PagesTitle PR

TextTableTitle Body

Topic Model(LDA) Word Topics

Word

Topic

Editor GraphCommunityDetection

User Community

UserCom

.

Term-DocGraph

DiscussionTableUser Disc.

CommunityTopic

TopicCom

.

Update ranks in parallel

Iterate until convergence

Rank of user i Weighted sum of

neighbors’ ranks

3

PageRank: Identifying Leaders

The Graph-Parallel Pattern

4

Model / Alg. State

Computation depends only on the

neighbors

Many Graph-Parallel Algorithms

• Collaborative Filtering– Alternating Least

Squares– Stochastic Gradient

Descent– Tensor Factorization

• Structured Prediction– Loopy Belief

Propagation– Max-Product Linear

Programs– Gibbs Sampling

• Semi-supervised ML

– Graph SSL – CoEM

• Community Detection– Triangle-Counting– K-core Decomposition– K-Truss

• Graph Analytics– PageRank– Personalized PageRank– Shortest Path– Graph Coloring

• Classification– Neural Networks

5

Graph-Parallel Systems

6

Pregeloogle

Expose specialized APIs to simplify graph programming.

Exploit graph structure to achieve orders-of-magnitude performance

gains over more general data-parallel systems.

PageRank on the Live-Journal Graph

GraphLab

Naïve Spark

Mahout/Hadoop

0 200 400 600 800 1000 1200 1400 1600

22

354

1340

Runtime (in seconds, PageRank for 10 iter-ations)

GraphLab is 60x faster than HadoopGraphLab is 16x faster than Spark

Graphs are Central to Analytics

Raw Wikipedia

< / >< / >< / >XML

Hyperlinks PageRank Top 20 PagesTitle PR

TextTableTitle Body

Topic Model(LDA) Word Topics

Word

Topic

Editor GraphCommunityDetection

User Community

UserCom

.

Term-DocGraph

DiscussionTableUser Disc.

CommunityTopic

TopicCom

.

Separate Systems to Support Each View

Table View Graph View

Dependency Graph

Pregel

Table

Result

Row

Row

Row

Row

Having separate systems for each view is

difficult to use and inefficient

10

Difficult to Program and Use

Users must Learn, Deploy, and Manage multiple systems

Leads to brittle and often complex interfaces

11

Inefficient

12

Extensive data movement and duplication across

the network and file system

< / >< / >< / >XML

HDFS HDFS HDFS HDFS

Limited reuse internal data-structures across stages

Solution: The GraphX Unified Approach

Enabling users to easily and efficiently express the entire graph

analytics pipeline

New APIBlurs the distinction between Tables and

Graphs

New SystemCombines Data-Parallel Graph-

Parallel Systems

Tables and Graphs are composable

views of the same physical data

GraphX Unified

Representation

Graph ViewTable View

Each view has its own operators that

exploit the semantics of the view to achieve efficient execution

View a Graph as a Table

Id

Rxin

Jegonzal

Franklin

Istoica

SrcId DstId

rxin jegonzal

franklin

rxin

istoica franklin

franklin

jegonzal

Property (E)

Friend

Advisor

Coworker

PI

Property (V)

(Stu., Berk.)

(PstDoc, Berk.)

(Prof., Berk)

(Prof., Berk)

R

J

F

I

Property GraphVertex Property Table

Edge Property Table

Table OperatorsTable (RDD) operators are inherited from Spark:

16

map

filter

groupBy

sort

union

join

leftOuterJoin

rightOuterJoin

reduce

count

fold

reduceByKey

groupByKey

cogroup

cross

zip

sample

take

first

partitionBy

mapWith

pipe

save

...

class Graph [ V, E ] { def Graph(vertices: Table[ (Id, V) ], edges: Table[ (Id, Id, E) ])

// Table Views -----------------def vertices: Table[ (Id, V) ]def edges: Table[ (Id, Id, E) ]def triplets: Table [ ((Id, V), (Id, V), E) ]// Transformations ------------------------------def reverse: Graph[V, E]def subgraph(pV: (Id, V) => Boolean,

pE: Edge[V,E] => Boolean): Graph[V,E]def mapV(m: (Id, V) => T ): Graph[T,E] def mapE(m: Edge[V,E] => T ): Graph[V,T]// Joins ----------------------------------------def joinV(tbl: Table [(Id, T)]): Graph[(V, T), E ]def joinE(tbl: Table [(Id, Id, T)]): Graph[V, (E, T)]// Computation ----------------------------------def mrTriplets(mapF: (Edge[V,E]) => List[(Id, T)],

reduceF: (T, T) => T): Graph[T, E]}

Graph Operators

17

Triplets Join Vertices and Edges

The triplets operator joins vertices and edges:

The mrTriplets operator sums adjacent triplets.SELECT t.dstId, reduceUDF( mapUDF(t) ) AS sum

FROM triplets AS t GROUPBY t.dstId

TripletsVertices Edges

B

A

C

D

A B

A C

B C

C D

A BA

B A C

B C

C D

F

E

Map Reduce Triplets

Map-Reduce for each vertex

D

B

A

C

mapF( )A B

mapF( )A C

A1

A2

reduceF( , )A1 A2 A

19

F

E

Example: Oldest Follower

D

B

A

CWhat is the age of the oldest follower for each user?val oldestFollowerAge = graph .mrTriplets( e=> (e.dst.id, e.src.age),//Map (a,b)=> max(a, b) //Reduce ) .vertices

23 42

30

19 75

1620

21

By composing these operators we canconstruct entire graph-analytics

pipelines.

GraphX System Design

Part. 2

Part. 1

Vertex Table (RDD)

B C

A D

F E

A D

Distributed Graphs as Tables (RDDs)

D

Property Graph

B C

D

E

AA

F

Edge Table (RDD)A B

A C

C D

B C

A E

A F

E F

E D

B

C

D

E

A

F

Routing

Table (RDD)

B

C

D

E

A

F

1

2

1 2

1 2

1

2

2D Vertex Cut Heuristic

Vertex Table (RDD)

Caching for Iterative mrTripletsEdge Table

(RDD)

A B

A C

C D

B C

A E

A F

E F

E D

MirrorCache

B

C

D

A

MirrorCache

D

E

F

A

B

C

D

E

A

F

B

C

D

E

A

F

A

D

Vertex Table (RDD)

Edge Table (RDD)

A B

A C

C D

B C

A E

A F

E F

E D

MirrorCache

B

C

D

A

MirrorCache

D

E

F

A

Incremental Updates for Iterative mrTriplets

B

C

D

E

A

F

Change AA

Change E

Sca

n

Vertex Table (RDD)

Edge Table (RDD)

A B

A C

C D

B C

A E

A F

E F

E D

MirrorCache

B

C

D

A

MirrorCache

D

E

F

A

Aggregation for Iterative mrTriplets

B

C

D

E

A

F

Change

Change

Sca

n

Change

Change

Change

Change

LocalAggregate

LocalAggregate

B

C

D

F

Reduction in Communication Due to

Cached Updates

0 2 4 6 8 10 12 14 160.1

1

10

100

1000

10000

Connected Components on Twitter Graph

Iteration

Netw

ork

Com

m.

(MB

)

Most vertices are within 8 hopsof all vertices in their comp.

Benefit of Indexing Active Edges

0 2 4 6 8 10 12 14 160

5

10

15

20

25

30

Connected Components on Twitter Graph

Scan

Indexed

Iteration

Ru

nti

me (

Secon

ds)

Scan All Edges

Index of “Active” Edges

Join EliminationIdentify and bypass joins for unused triplets fields

»Example: PageRank only accesses source attribute

29

0 2 4 6 8 10 12 14 16 18 200

5000

10000

15000

PageRank on TwitterThree Way Join

Join Elimination

IterationCom

mu

nic

ati

on

(M

B)

Factor of 2 reduction in communication

Additional Query Optimizations

Indexing and Bitmaps:»To accelerate joins across graphs»To efficiently construct sub-graphs

Substantial Index and Data Reuse:»Reuse routing tables across graphs and

sub-graphs»Reuse edge adjacency information and

indices30

Performance Comparisons

GraphLab

Giraph

Mahout/Hadoop

0 200 400 600 800 1000 1200 1400 1600

22

68

207

354

1340

Runtime (in seconds, PageRank for 10 iter-ations)

GraphX is roughly 3x slower than GraphLab

Live-Journal: 69 Million Edges

GraphX scales to larger graphs

GraphLab

GraphX

Giraph

0 200 400 600 800

203

451

749

Runtime (in seconds, PageRank for 10 iter-ations)

GraphX is roughly 2x slower than GraphLab»Scala + Java overhead: Lambdas, GC time, …»No shared memory parallelism: 2x increase in comm.

Twitter Graph: 1.5 Billion Edges

PageRank is just one stage….

What about a pipeline?

HDFSHDFS

ComputeSpark PreprocessSpark Post.

A Small Pipeline in GraphX

Timed end-to-end GraphX is faster than GraphLab

Raw Wikipedia

< / >< / >< / >XML

Hyperlinks PageRank Top 20 Pages

GraphLab + Spark

Giraph + Spark

0 200 400 600 800 1000 1200 1400 1600

342

1492

Total Runtime (in Seconds)

605

375

The GraphX Stack(Lines of Code)

GraphX (3575)

Spark

Pregel (28) + GraphLab (50)

PageRank (5)

Connected Comp. (10)

Shortest Path (10)

ALS(40) LDA

(120)

K-core(51)

Triangle

Count(45)

SVD(40)

StatusAlpha release as part of Spark 0.9

Seeking collaborators and feedback

Conclusion and Observations

Domain specific views: Tables and Graphs

»tables and graphs are first-class composable objects

»specialized operators which exploit view semantics

Single system that efficiently spans the pipeline

»minimize data movement and duplication»eliminates need to learn and manage

multiple systems

Graphs through the lens of database systems

»Graph-Parallel Pattern Triplet joins in relational alg.

»Graph Systems Distributed join optimizations

37

Active Research

Static Data Dynamic Data»Apply GraphX unified approach to time

evolving data»Model and analyze relationships over time

Serving Graph Structured Data»Allow external systems to interact with

GraphX»Unify distributed graph databases with

relational database technology38

Graph Property 1Real-World Graphs

39

Top 1% of vertices are adjacent to50% of the

edges!

Nu

mb

er

of

Vert

ices

AltaVista WebGraph1.4B Vertices, 6.6B Edges

Degree

More than 108 vertices have one neighbor.

-Slope = α ≈

2 2008 2009 2010 2011 20120

20406080

100120140160180200

Facebook

Year

Rati

o o

f Ed

ges

to V

ert

ices

Power-Law Degree DistributionEdges >> Vertices

Graph Property 2Active Vertices

0 10 20 30 40 50 60 701

10

100

1000

10000

100000

1000000

10000000

100000000

Number of Updates

Nu

m-V

ert

ices

51% updated only once!PageRank on Web Graph

Graphs are Essential to Data Mining and Machine Learning

Identify influential people and information

Find communities

Understand people’s shared interests

Model complex data dependencies

Ratings Items

Recommending ProductsUsers

Low-Rank Matrix Factorization:

43

r13

r14

r24

r25

f(1)

f(2)

f(3)

f(4)

f(5)

Use

r Fa

ctors

(U

)

Movie

Facto

rs (M)

Use

rs

MoviesNetflix

Use

rs≈x

Movies

f(i)

f(j)

Iterate:

Recommending Products

LiberalConservati

ve

Post

Post

Post

Post

Post

Post

Post

Post

Predicting User Behavior

Post

Post

Post

Post

Post

Post

Post

Post

Post

Post

Post

Post

Post

Post

??

?

?

??

?

? ??

?

?

??

??

?

?

?

?

?

?

?

?

?

?

?

? ?

?

44

Conditional Random FieldBelief Propagation

Count triangles passing through each vertex:

Measures “cohesiveness” of local community

More TrianglesStronger Community

Fewer TrianglesWeaker Community

1

2 3

4

Finding Communities

Preprocessing Compute Post Proc.

Example Graph Analytics Pipeline

46

< / >< / >< / >XML

RawData

ETL SliceComput

e

Repeat

Subgraph PageRankInitial Graph

Analyze

TopUsers

Thanks!

[email protected]@eecs.berkeley.edu

[email protected]@eecs.berkeley.edu

http://amplab.github.io/graphx/