from graphs to tables : the design of scalable systems for graph analytics
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From Graphs to Tables : The Design of Scalable Systems for Graph Analytics . Joseph E. Gonzalez Post-doc, UC Berkeley AMPLab [email protected] Co-founder, GraphLab Inc. [email protected] WWW’14 Workshop on Big Graph Mining . - PowerPoint PPT PresentationTRANSCRIPT
From Graphs to Tables: The Design of Scalable Systems for Graph Analytics Joseph E. GonzalezPost-doc, UC Berkeley [email protected], GraphLab [email protected]
WWW’14 Workshop on Big Graph Mining
*These slides are best viewed in PowerPoint with animation.
Graphs are Central to Analytics
Raw Wikipedia
< / >< / >< / >XML
Hyperlinks PageRank Top 20 PagesTitle PRText
TableTitle Body Topic Model
(LDA) Word TopicsWor
d Topic
Editor GraphCommunityDetection
User Community
User Com.
Term-DocGraph
DiscussionTableUser Disc.
CommunityTopic
TopicCom.
Update ranks in parallel Iterate until convergence
Rank of user i Sum of
neighbors
3
PageRank: Identifying Leaders
Ratings Items
Recommending ProductsUsers
Low-Rank Matrix Factorization:
5
r13
r14
r24
r25
f(1)
f(2)
f(3)
f(4)
f(5)User
Fact
ors (
U)
Movie Factors (M
)Us
ers
MoviesNetflix Us
ers≈ x
Movies
f(i)f(j)
Iterate:
Recommending Products
Liberal Conservative
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
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6
Conditional Random FieldBelief Propagation
PostPost
Post
Mean Field Algorithm
Y2
Y1
Y3
X2
X3
X1
Sum over Neighbors
Count triangles passing through each vertex:
Measures “cohesiveness” of local community
More TrianglesStronger Community
Fewer TrianglesWeaker Community
12 3
4
Finding Communities
The Graph-Parallel Pattern
9
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
10
Graph-Parallel Systems
11
Pregel
Expose specialized APIs to simplify
graph programming.
12
The Pregel (Push) Abstraction
Vertex-Programs interact by sending messages.
iPregel_PageRank(i, messages) : // Receive all the messages total = 0 foreach( msg in messages) : total = total + msg
// Update the rank of this vertex R[i] = 0.15 + total
// Send new messages to neighbors foreach(j in out_neighbors[i]) : Send msg(R[i]) to vertex j
Malewicz et al. [PODC’09, SIGMOD’10]
The GraphLab (Pull) AbstractionVertex Programs directly access adjacent
vertices and edgesGraphLab_PageRank(i) // Compute sum over neighbors total = 0 foreach( j in neighbors(i)): total = total + R[j] * wji
// Update the PageRank R[i] = 0.15 + total
13
R[4] * w41
R[3]
* w
31
R[2] * w21
++
4 1
3 2
Data movement is managed by the system and not the user.
BarrierIterative Bulk Synchronous
ExecutionCompute Communicate
Graph-Parallel Systems
15
Pregel
Exploit graph structure to achieve orders-of-magnitude performance
gains over more general data-parallel
systems.
Real-World Graphs
16
Top 1% of vertices are adjacent to50% of the
edges!
More than 108 vertices have one neighbor.
2008 2009 2010 2011 20120
20406080
100120140160180200
Year
Ratio
of E
dges
to V
erti
ces
Num
ber o
f Ve
rtice
s
AltaVista WebGraph1.4B Vertices, 6.6B Edges
Degree
-Slope = α ≈ 2 Power-Law Degree DistributionEdges >> Vertices
Challenges of High-Degree
Vertices
Touches a largefraction of graph
Sequentially processedges
17
CPU 1 CPU 2
Provably Difficult to Partition
Machine 1 Machine 2
Split High-Degree verticesNew Abstraction Equivalence on Split Vertices
GraphLab(PowerGraph, OSDI’12)
18
Program This
Run on This
Machine 2Machine 1
Machine 4Machine 3
GAS Decomposition
Σ1 Σ2
Σ3 Σ4
+ + +
YYYY
Y’
ΣY’Y’Y’Gather
Apply
Scatter
19
Master
Mirror
MirrorMirror
Minimizing Communication in PowerGraph
YYY
20
Vertex CutCommunication is linear
in the number of machines
each vertex spans.Total communication upper bound:
Shrinking Working Sets
0 10 20 30 40 50 60 701
10100
100010000
1000001000000
10000000100000000
Number of Updates
Num
-Ver
tice
s
51% of vertices run only once!PageRank on Web Graph
The GraphLab (Pull) AbstractionVertex Programs directly access adjacent
vertices and edgesGraphLab_PageRank(i) // Compute sum over neighbors total = 0 foreach( j in neighbors(i)): total = total + R[j] * wji
// Update the PageRank R[i] = 0.15 + total
22
R[4] * w41
R[3]
* w
31
R[2] * w21
++
4 1
3 2
GraphLab_PageRank(i) // Compute sum over neighbors total = 0 foreach( j in neighbors(i)): total = total + R[j] * wji
// Update the PageRank R[i] = 0.15 + total
// Trigger neighbors to run again if R[i] not converged then signal nbrsOf(i) to be recomputedTrigger computation only when
necessary.
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
Counted: 34.8 Billion Triangles
24
Triangle Counting on Twitter
64 Machines15 SecondsGraphLab
1536 Machines423 Minutes
Hadoop[WWW’11]
S. Suri and S. Vassilvitskii, “Counting triangles and the curse of the last reducer,” WWW’11
1000 x Faster
40M Users, 1.4 Billion Links
Graph Analytics Pipeline
Raw Wikipedia
< / >< / >< / >XML
Hyperlinks PageRank Top 20 PagesTitle PRText
TableTitle Body Topic Model
(LDA) Word TopicsWor
d Topic
Editor GraphCommunityDetection
User Community
User Com.
Term-DocGraph
DiscussionTableUser Disc.
CommunityTopic
TopicCom.
TablesRaw
Wikipedia < / >< / >< / >
XML
Hyperlinks PageRank Top 20 PagesTitle PRText
TableTitle Body Topic Model
(LDA) Word TopicsWor
d Topic
Editor GraphCommunityDetection
User Community
User Com.
Term-DocGraph
DiscussionTableUser Disc.
CommunityTopic
TopicCom.
GraphsRaw
Wikipedia < / >< / >< / >
XML
Hyperlinks PageRank Top 20 PagesTitle PRText
TableTitle Body Topic Model
(LDA) Word TopicsWor
d Topic
Editor GraphCommunityDetection
User Community
User Com.
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
Separate systems for each view can bedifficult to use and
inefficient
29
Difficult to Program and UseUsers must Learn, Deploy, and
Manage multiple systems
Leads to brittle and often complex interfaces
30
Inefficient
31
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 TableId
RxinJegonzalFranklinIstoica
SrcId DstIdrxin jegonzal
franklin
rxin
istoica franklinfrankli
njegonzal
Property (E)Friend
AdvisorCoworker
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:
35
mapfiltergroupBysortunionjoinleftOuterJoinrightOuterJoin
reducecountfoldreduceByKeygroupByKeycogroupcrosszip
sampletakefirstpartitionBymapWithpipesave...
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
36
Triplets Join Vertices and Edges
The triplets operator joins vertices and edges:
The mrTriplets operator sums adjacent triplets.SELECT t.dstId, reduce( map(t) ) AS sum FROM triplets AS t GROUPBY t.dstId
TripletsVertices
BA
CD
EdgesA BA CB CC D
A BAB A C
B CC D
SELECT s.Id, d.Id, s.P, e.P, d.PFROM edges AS eJOIN vertices AS s, vertices AS dON e.srcId = s.Id AND e.dstId = d.Id
We express enhanced Pregel and GraphLab
abstractions using the GraphX operators
in less than 50 lines of code!
38
39
Enhanced to Pregel in GraphX
Malewicz et al. [PODC’09, SIGMOD’10]
pregelPR(i, messageList ): // Receive all the messagestotal = 0foreach( msg in messageList) : total = total + msg
// Update the rank of this vertexR[i] = 0.15 + total
// Send new messages to neighborsforeach(j in out_neighbors[i]) : Send msg(R[i]/E[i,j]) to vertex
Require Message Combiners
messageSummessageSu
m
Remove Message
Computationfrom the
Vertex Program
sendMsg(ij, R[i], R[j], E[i,j]): // Compute single message return msg(R[i]/E[i,j])
combineMsg(a, b): // Compute sum of two messages return a + b
40
Implementing PageRank in GraphX
// Load and initialize the graphval graph = GraphBuilder.text(“hdfs://web.txt”)val prGraph = graph.joinVertices(graph.outDegrees)
// Implement and Run PageRankval pageRank = prGraph.pregel(initialMessage = 0.0, iter = 10)( (oldV, msgSum) => 0.15 + 0.85 * msgSum, triplet => triplet.src.pr / triplet.src.deg, (msgA, msgB) => msgA + msgB)
We express the Pregel and GraphLab like
abstractions using the GraphX operators
in less than 50 lines of code!
41
By composing these operators we canconstruct entire graph-analytics
pipelines.
Example Analytics Pipeline
// Load raw data tablesval verts = sc.textFile(“hdfs://users.txt”).map(parserV)val edges = sc.textFile(“hdfs://follow.txt”).map(parserE)// Build the graph from tables and restrict to recent linksval graph = new Graph(verts, edges)val recent = graph.subgraph(edge => edge.date > LAST_MONTH)// Run PageRank Algorithmval pr = graph.PageRank(tol = 1.0e-5)// Extract and print the top 25 usersval topUsers = verts.join(pr).top(25).collecttopUsers.foreach(u => println(u.name + ‘\t’ + u.pr))
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
BCD
A
MirrorCache
DEF
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
BCD
A
MirrorCache
DEF
A
Incremental Updates for Iterative mrTriplets
B
C
D
E
A
F
Change AA
Change E
Scan
Vertex Table (RDD)
Edge Table (RDD)
A B
A C
C D
B C
A E
A F
E F
E D
MirrorCache
BCD
A
MirrorCache
DEF
A
Aggregation for Iterative mrTriplets
B
C
D
E
A
F
Change
Change
Scan
Change
Change
Change
Change
LocalAggregate
LocalAggregate
BC
D
F
Reduction in Communication Due to
Cached Updates
0 2 4 6 8 10 12 14 160.1
110
1001000
10000Connected Components on Twitter Graph
Iteration
Net
wor
k Co
mm
. (M
B)
Most vertices are within 8 hopsof all vertices in their comp.
Benefit of Indexing Active Edges
0 2 4 6 8 10 12 14 1605
1015202530Connected Components on Twitter Graph
ScanIndexed
Iteration
Runt
ime
(Sec
onds
)
Scan All Edges
Index of “Active” Edges
Join EliminationIdentify and bypass joins for unused triplet fields
50
0 2 4 6 8 10 12 14 16 18 200
5000
10000
15000PageRank on TwitterThree Way Join
Join Elimination
IterationCom
mun
icat
ion
(MB)
Factor of 2 reduction in communication
sendMsg(ij, R[i], R[j], E[i,j]): // Compute single message return msg(R[i]/E[i,j])
Performance Comparisons
GraphLab
Giraph
Mahout/Hadoop
0 200 400 600 800 1000 1200 1400 160022
68207
3541340
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 Preprocess Spark 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
Conclusion and ObservationsDomain 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
56
Open Source ProjectAlpha release as part of Spark 0.9
Active ResearchStatic Data Dynamic Data
»Apply GraphX unified approach to time evolving data
»Materialized view maintenance for graphs
Serving Graph Structured Data»Allow external systems to interact with
GraphX»Unify distributed graph databases with
relational database technology
58
Collaborators
YuchengLow
AapoKyrola
DannyBickson
AlexSmola
HaijieGu
GraphLab:
CarlosGuestrin
GuyBlelloch
GraphX:
ReynoldXin
AnkurDave
DanielCrankshaw
MichaelFranklin
IonStoica
