Presentation of the Gradoop Framework at the Flink & Neo4j Meetup in Berlin (http://www.meetup.com/graphdb-berlin/events/228576494/). The talk is about the extended property graph model, its operators and how they are implemented on top of Apache Flink. The talk also includes some benchmark results on scalability and a demo involving Neo4j, Flink and Gradoop (see www.gradoop.com)

1. GRADOOP: Scalable Graph Analytics
with Apache Flink
Martin Junghanns @kc1s
Apache Flink and Neo4j Meetup Berlin

2. About the speaker and the team
Apache Flink and Neo4j Meetup Berlin 2
André
PhD Student
Martin
PhD Student
Kevin
M.Sc. Student
Niklas
M.Sc. Student
Prof. Dr. Erhard Rahm
Database Chair

3. Apache Flink and Neo4j Meetup Berlin 3
Motivation

4. „Graphs are everywhere“
Apache Flink and Neo4j Meetup Berlin 4
𝑮𝑟𝑎𝑝ℎ = (𝑽𝑒𝑟𝑡𝑖𝑐𝑒𝑠, 𝑬𝑑𝑔𝑒𝑠)

5. „Graphs are everywhere“
Apache Flink and Neo4j Meetup Berlin 5
Alice
Bob
Eve
Dave
Carol
Mallory
Peggy
Trent
𝐺𝑟𝑎𝑝ℎ = (𝐔𝐬𝐞𝐫𝐬, 𝐹𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠)

6. „Graphs are everywhere“
Apache Flink and Neo4j Meetup Berlin 6
𝐺𝑟𝑎𝑝ℎ = (𝐔𝐬𝐞𝐫𝐬, 𝐹𝑟𝑖𝑒𝑛𝑑𝑠ℎ𝑖𝑝𝑠)
Alice
Bob
Eve
Dave
Carol
Mallory
Peggy
Trent

7. Alice
Bob
AC/DC
Dave
Carol
Mallory
Peggy
Metallica
„Graphs are heterogeneous“
Apache Flink and Neo4j Meetup Berlin 7
𝐺𝑟𝑎𝑝ℎ = (𝐔𝐬𝐞𝐫𝐬 ∪ 𝐁𝐚𝐧𝐝𝐬, 𝐹𝑟𝑖𝑒𝑛𝑑𝑠ℎ𝑖𝑝𝑠 ∪ 𝐿𝑖𝑘𝑒𝑠)

8. Alice
Bob
AC/DC
Dave
Carol
Mallory
Peggy
Metallica
„Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 8
𝐺𝑟𝑎𝑝ℎ = (𝐔𝐬𝐞𝐫𝐬 ∪ 𝐁𝐚𝐧𝐝𝐬, 𝐹𝑟𝑖𝑒𝑛𝑑𝑠ℎ𝑖𝑝𝑠 ∪ 𝐿𝑖𝑘𝑒𝑠)

9. 0.2
0.28
0.26
0.33
0.25
0.26
Alice
Bob
AC/DC
Dave
Carol
Mallory
Peggy
Metallica
3.6
2.82
„Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 9
𝐺𝑟𝑎𝑝ℎ = (𝐔𝐬𝐞𝐫𝐬 ∪ 𝐁𝐚𝐧𝐝𝐬, 𝐹𝑟𝑖𝑒𝑛𝑑𝑠ℎ𝑖𝑝𝑠 ∪ 𝐿𝑖𝑘𝑒𝑠)

10. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 10
Assuming a social network

11. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 11
Assuming a social network
1. Determine subgraph

12. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 12
Assuming a social network
1. Determine subgraph

13. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 13
Assuming a social network
1. Determine subgraph
2. Find communities

14. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 14
Assuming a social network
1. Determine subgraph
2. Find communities

15. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 15
Assuming a social network
1. Determine subgraph
2. Find communities
3. Filter communities

16. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 16
Assuming a social network
1. Determine subgraph
2. Find communities
3. Filter communities

17. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 17
Assuming a social network
1. Determine subgraph
2. Find communities
3. Filter communities
4. Find common subgraph

18. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 18
Assuming a social network
1. Determine subgraph
2. Find communities
3. Filter communities
4. Find common subgraph

19. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 19
Assuming a social network
• Heterogeneous data
1. Determine subgraph
• Apply graph transformation
2. Find communities
• Handle collections of graphs
3. Filter communities
• Aggregation, Selection
4. Find common subgraph
• Apply dedicated algorithm

20. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 20
Assuming a social network
• Heterogeneous data
1. Determine subgraph
• Apply graph transformation
2. Find communities
• Handle collections of graphs
3. Filter communities
• Aggregation, Selection
4. Find common subgraph
• Apply dedicated algorithm

21. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 21
Assuming a social network
• Heterogeneous data
1. Determine subgraph
• Apply graph transformation
2. Find communities
• Handle collections of graphs
3. Filter communities
• Aggregation, Selection
4. Find common subgraph
• Apply dedicated algorithm

22. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 22
Assuming a social network
• Heterogeneous data
1. Determine subgraph
• Apply graph transformation
2. Find communities
• Handle collections of graphs
3. Filter communities
• Aggregation, Selection
4. Find common subgraph
• Apply dedicated algorithm

23. „Graphs can be analyzed“
Apache Flink and Neo4j Meetup Berlin 23
Assuming a social network
• Heterogeneous data
1. Determine subgraph
• Apply graph transformation
2. Find communities
• Handle collections of graphs
3. Filter communities
• Aggregation, Selection
4. Find common subgraph
• Apply dedicated algorithm

24. „And let‘s not forget …“
Apache Flink and Neo4j Meetup Berlin 24

25. “…Graphs are large”
Apache Flink and Neo4j Meetup Berlin 25

26. „A framework and research platform for efficient,
distributed and domain independent management
and analytics of heterogeneous graph data.“
Apache Flink and Neo4j Meetup Berlin 26

27. High Level Architecture
Apache Flink and Neo4j Meetup Berlin 27

28. High Level Architecture
Apache Flink and Neo4j Meetup Berlin 27
HDFS/YARN
Cluster

29. High Level Architecture
Apache Flink and Neo4j Meetup Berlin 27
HDFS/YARN
Cluster
Apache HBase Distributed Graph Store

30. High Level Architecture
Apache Flink and Neo4j Meetup Berlin 27
HDFS/YARN
Cluster
Apache HBase Distributed Graph Store
Apache Flink Distributed Operator Execution

31. High Level Architecture
Apache Flink and Neo4j Meetup Berlin 27
HDFS/YARN
Cluster
Apache HBase Distributed Graph Store
Apache Flink Operator Implementation
Apache Flink Distributed Operator Execution
Extended Property Graph Model (EPGM)
Graph Analytical Language (GrALa)  Java 7
 25K (33K) LOC
 GPLv3

32. Apache Flink Third-party library
Apache Flink and Neo4j Meetup Berlin 28
Streaming Dataflow Runtime
DataSet DataStream
HadoopMR
Table
Gelly
ML
Table
Zeppelin
Cascading
MRQL
Dataflow
Storm
Dataflow
SAMOA
GRADOOP
Cluster (e.g. YARN)Local Cloud (e.g. EC2)
Batch Stream
Data Storage (e.g. Files, HDFS, S3, JDBC, Kafka, …)

33. Apache Flink and Neo4j Meetup Berlin 29
Extended Property Graph Model (EPGM)

34. Extended Property Graph Model
• Vertices and directed Edges
Apache Flink and Neo4j Meetup Berlin 30

35. Extended Property Graph Model
• Vertices and directed Edges
• Logical Graphs
Apache Flink and Neo4j Meetup Berlin 31

36. Extended Property Graph Model
• Vertices and directed Edges
• Logical Graphs
• Identifiers
Apache Flink and Neo4j Meetup Berlin 32
1 3
4
5
21 2
3
4
5
1
2

37. Extended Property Graph Model
• Vertices and directed Edges
• Logical Graphs
• Identifiers
• Type Labels
Apache Flink and Neo4j Meetup Berlin 33
1 3
4
5
21 2
3
4
5
Person Band
Person
Person
Band
likes likes
likes
knows
likes
1|Community
2|Community

38. Extended Property Graph Model
• Vertices and directed Edges
• Logical Graphs
• Identifiers
• Type Labels
• Properties
Apache Flink and Neo4j Meetup Berlin 34
1 3
4
5
21 2
3
4
5
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock

39. Apache Flink and Neo4j Meetup Berlin 35
EPGM Operators

40. Basic Binary Operators
Apache Flink and Neo4j Meetup Berlin 36

41. Basic Binary Operators
Apache Flink and Neo4j Meetup Berlin 36
1 3
4
5
2
1
2

42. Basic Binary Operators
Apache Flink and Neo4j Meetup Berlin 36
1 3
4
5
2
1 3
4
5
2
1
2
Combination
3

43. Basic Binary Operators
Apache Flink and Neo4j Meetup Berlin 36
1 3
4
5
2
31 3
4
5
2
1
2
3
Combination
Overlap
3

44. Basic Binary Operators
Apache Flink and Neo4j Meetup Berlin 36
1 3
4
5
2
3
1 2
1 3
4
5
2
1
2
3
3
Combination
Overlap
Exclusion
3

45. Graph Aggregation
Apache Flink and Neo4j Meetup Berlin 37

46. Graph Aggregation
Apache Flink and Neo4j Meetup Berlin 37
1 3
4
5
2
3

47. Graph Aggregation
Apache Flink and Neo4j Meetup Berlin 37
1 3
4
5
2
3
UDF

48. Graph Aggregation
Apache Flink and Neo4j Meetup Berlin 37
1 3
4
5
2
3
1 3
4
5
2
3 | vertexCount: 5
UDF

49. Graph Aggregation
Apache Flink and Neo4j Meetup Berlin 37
1 3
4
5
2
3
1 3
4
5
2
3 | vertexCount: 5
1 3
4
5
2
3
revenue:7000
expense:1000
expense:1000
UDF

50. Graph Aggregation
Apache Flink and Neo4j Meetup Berlin 37
1 3
4
5
2
3
1 3
4
5
2
3 | vertexCount: 5
1 3
4
5
2
3
revenue:7000
expense:1000
expense:1000
UDF
UDF

51. Graph Aggregation
Apache Flink and Neo4j Meetup Berlin 37
1 3
4
5
2
3
1 3
4
5
2
3 | vertexCount: 5
1 3
4
5
2
3
revenue:7000
expense:1000
expense:1000
1 3
4
5
2
3 | profit: 5000
revenue:7000
expense:1000
expense:1000
UDF
UDF

52. Graph Transformation
Apache Flink and Neo4j Meetup Berlin 38

53. Graph Transformation
Apache Flink and Neo4j Meetup Berlin 38
3 | vertexCount: 5
name:Alice
f_name:Bob1 3
4
5
2

54. Graph Transformation
Apache Flink and Neo4j Meetup Berlin 38
UDF
3 | vertexCount: 5
name:Alice
f_name:Bob1 3
4
5
2
3 | Community| vCount: 5
f_name:Alice
f_name:Bob1 3
4
5
2

55. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39

56. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39
3
1 3
4
5
2

57. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39
3
1 3
4
5
2
UDF

58. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39
3
1 3
4
5
2
3
4
1 2UDF

59. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39
3
1 3
4
5
2
3
4
1 2
UDF
UDF

60. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39
3
1 3
4
5
2
3
4
1 2
3
4
1 2UDF
UDF

61. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39
3
1 3
4
5
2
3
4
1 2
3
4
1 2
UDF
UDF
UDF

62. Subgraph Extraction
Apache Flink and Neo4j Meetup Berlin 39
3
1 3
4
5
2
3
4
1 2
3
4
1 2
4
3
5
2UDF
UDF
UDF

63. Graph Pattern Matching
Apache Flink and Neo4j Meetup Berlin 40

64. Graph Pattern Matching
Apache Flink and Neo4j Meetup Berlin 40
3
1 3
4
5
2

65. Graph Pattern Matching
Apache Flink and Neo4j Meetup Berlin 40
3
1 3
4
5
2 Pattern

66. Graph Pattern Matching
Apache Flink and Neo4j Meetup Berlin 40
3
1 3
4
5
2 Pattern
4 5
1 3
4
2

67. Graph Pattern Matching
Apache Flink and Neo4j Meetup Berlin 40
3
1 3
4
5
2 Pattern
4 5
1 3
4
2
Graph Collection

68. Graph Grouping
Apache Flink and Neo4j Meetup Berlin 41

69. Graph Grouping
Apache Flink and Neo4j Meetup Berlin 41
3
1 3
4
5
2

70. Graph Grouping
Apache Flink and Neo4j Meetup Berlin 41
Keys
3
1 3
4
5
2

71. Graph Grouping
Apache Flink and Neo4j Meetup Berlin 41
Keys
3
1 3
4
5
2
4
6 7

72. Graph Grouping
Apache Flink and Neo4j Meetup Berlin 41
Keys
3
1 3
4
5
2
4
6 7
3
a:23 a:84
a:42
a:12
1 3
4
5
2
a:13
a:21

73. Graph Grouping
Apache Flink and Neo4j Meetup Berlin 41
Keys
3
1 3
4
5
2
4
6 7
+Aggregate
3
a:23 a:84
a:42
a:12
1 3
4
5
2
a:13
a:21

74. Graph Grouping
Apache Flink and Neo4j Meetup Berlin 41
Keys
3
1 3
4
5
2
4
6 7
+Aggregate
3
a:23 a:84
a:42
a:12
1 3
4
5
2
a:13
a:21
4
count:2 count:2
max(a):42
max(a):84
max(a):13 max(a):21
6 7

75. Apply (e.g. Aggregation)
Apache Flink and Neo4j Meetup Berlin 42

76. Apply (e.g. Aggregation)
Apache Flink and Neo4j Meetup Berlin 42
1
2
3
revenue:7000
expense:1000
expense:1000
revenue:2000
revenue:4000
expense:3000
expense:1000
0 2
3
4
1
5 7 86
9 11 1210

77. Apply (e.g. Aggregation)
Apache Flink and Neo4j Meetup Berlin 42
Operator
1
2
3
revenue:7000
expense:1000
expense:1000
revenue:2000
revenue:4000
expense:3000
expense:1000
0 2
3
4
1
5 7 86
9 11 1210

78. Apply (e.g. Aggregation)
Apache Flink and Neo4j Meetup Berlin 42
Operator
1
2
3
revenue:7000
expense:1000
expense:1000
revenue:2000
revenue:4000
expense:3000
expense:1000
0 2
3
4
1
5 7 86
9 11 1210
1 | profit: 5000
2 | profit: -1000
3 | profit: 3000
revenue:7000
expense:1000
expense:1000
revenue:2000
revenue:4000
expense:3000
expense:1000
0 2
3
4
1
5 7 86
9 11 1210

79. Selection
Apache Flink and Neo4j Meetup Berlin 43

80. Selection
Apache Flink and Neo4j Meetup Berlin 43
1 | profit: 5000
2 | profit: -1000
3 | profit: 3000
revenue:7000
expense:1000
expense:1000
revenue:2000
revenue:4000
expense:3000
expense:1000
0 2
3
4
1
5 7 86
9 11 1210

81. Selection
Apache Flink and Neo4j Meetup Berlin 43
UDF
profit > 0
1 | profit: 5000
2 | profit: -1000
3 | profit: 3000
revenue:7000
expense:1000
expense:1000
revenue:2000
revenue:4000
expense:3000
expense:1000
0 2
3
4
1
5 7 86
9 11 1210

82. Selection
Apache Flink and Neo4j Meetup Berlin 43
UDF
profit > 0
1 | profit: 5000
2 | profit: -1000
3 | profit: 3000
revenue:7000
expense:1000
expense:1000
revenue:2000
revenue:4000
expense:3000
expense:1000
0 2
3
4
1
5 7 86
9 11 1210
1 | profit: 5000
3 | profit: 3000
revenue:7000
expense:1000
expense:1000
revenue:4000 expense:1000
0 2
3
4
1
9 11 1210

83. Call (e.g. Clustering)
Apache Flink and Neo4j Meetup Berlin 44

84. Call (e.g. Clustering)
Apache Flink and Neo4j Meetup Berlin 44
1
0 2
3
4
1
5 7 86
9 11 1210

85. Call (e.g. Clustering)
Apache Flink and Neo4j Meetup Berlin 44
Algorithm
1
0 2
3
4
1
5 7 86
9 11 1210

86. Call (e.g. Clustering)
Apache Flink and Neo4j Meetup Berlin 44
Algorithm
1
0 2
3
4
1
5 7 86
9 11 1210
2
3
4
0 2
3
4
1
5 7 86
9 11 1210

87. Call (e.g. PageRank)
Apache Flink and Neo4j Meetup Berlin 45

88. Call (e.g. PageRank)
Apache Flink and Neo4j Meetup Berlin 45
1
0 2
3
4
1
5 7 86
9 11 1210

89. Call (e.g. PageRank)
Apache Flink and Neo4j Meetup Berlin 45
Algorithm
1
0 2
3
4
1
5 7 86
9 11 1210

90. Call (e.g. PageRank)
Apache Flink and Neo4j Meetup Berlin 45
Algorithm
2
rank:0.11
rank:0.25
rank:0.11
rank:1.29
rank:1.29
rank:1.58rank:0.11rank:5.12
rank:0.11
rank:0.11 rank:0.26 rank:0.11 rank:2.47
0 2
3
4
1
5 7 86
9 11 1210
1
0 2
3
4
1
5 7 86
9 11 1210

91. EPGM Operators Overview
Apache Flink and Neo4j Meetup Berlin 46
Operators
Unary Binary
GraphCollectionLogicalGraph
Algorithms
Aggregation
Pattern Matching
Transformation
Grouping Equality
Call
Combination
Overlap
Exclusion
Equality
Union
Intersection
Difference
Flink Gelly Library
BTG Extraction
Frequent Subgraphs
Limit
Selection
Distinct
Sort
Apply
Reduce
Call
Adaptive Partitioning
Subgraph

92. EPGM Operators Overview
Apache Flink and Neo4j Meetup Berlin 47
Operators
Unary Binary
GraphCollectionLogicalGraph
Algorithms
Aggregation
Pattern Matching
Transformation
Grouping Equality
Call
Combination
Overlap
Exclusion
Equality
Union
Intersection
Difference
Flink Gelly Library
BTG Extraction
Frequent Subgraphs
Limit
Selection
Distinct
Sort
Apply
Reduce
Call
Adaptive Partitioning
Subgraph

93. EPGM Operators Overview
Apache Flink and Neo4j Meetup Berlin 48
Operators
Unary Binary
GraphCollectionLogicalGraph
Algorithms
Aggregation
Pattern Matching
Transformation
Grouping Equality
Call
Combination
Overlap
Exclusion
Equality
Union
Intersection
Difference
Flink Gelly Library
BTG Extraction
Frequent Subgraphs
Limit
Selection
Distinct
Sort
Apply
Reduce
Call
Adaptive Partitioning
Subgraph

94. Apache Flink and Neo4j Meetup Berlin 49
EPGM on Apache Flink

95. Flink DataSet API
Apache Flink and Neo4j Meetup Berlin 50

96. Flink DataSet API
Apache Flink and Neo4j Meetup Berlin 50
• DataSet := Distributed Collection of Data Objects
DataSet
DataSet
DataSet

97. Flink DataSet API
Apache Flink and Neo4j Meetup Berlin 50
• DataSet := Distributed Collection of Data Objects
• Transformation := Operation on DataSets
DataSet
DataSet
DataSet
Transformation
Transformation
DataSet
DataSet

98. Flink DataSet API
Apache Flink and Neo4j Meetup Berlin 50
• DataSet := Distributed Collection of Data Objects
• Transformation := Operation on DataSets
• Flink Programm := Composition of Transformations
DataSet
DataSet
DataSet
Transformation
Transformation
DataSet
DataSet
Transformation DataSet
Flink Program

99. Flink DataSet API
Apache Flink and Neo4j Meetup Berlin 50
DataSetDataSetDataSet
DataSetDataSetDataSet
DataSetDataSetDataSet
DataSetDataSetDataSet
DataSetDataSetDataSet
DataSetDataSetDataSet
• DataSet := Distributed Collection of Data Objects
• Transformation := Operation on DataSets
• Flink Programm := Composition of Transformations
DataSet
DataSet
DataSet
Transformation
Transformation
DataSet
DataSet
Transformation DataSet
Flink Program

100. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51

101. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
EPGMGraphHead
Id Label Properties POJO DataSet<EPGMGraphHead>

102. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
Id Label Properties Graphs
EPGMGraphHead
EPGMVertex
Id Label Properties POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>

103. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
Id Label Properties Graphs
Id Label Properties SourceId TargetId Graphs
EPGMGraphHead
EPGMVertex
EPGMEdge
Id Label Properties POJO
POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>
DataSet<EPGMEdge>

104. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
Id Label Properties Graphs
Id Label Properties SourceId TargetId Graphs
EPGMGraphHead
EPGMVertex
EPGMEdge
Id Label Properties POJO
POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>
DataSet<EPGMEdge>
Id Label Properties Graphs
EPGMVertex

105. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
Id Label Properties Graphs
Id Label Properties SourceId TargetId Graphs
EPGMGraphHead
EPGMVertex
EPGMEdge
Id Label Properties POJO
POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>
DataSet<EPGMEdge>
Id Label Properties Graphs
EPGMVertex
GradoopId := UUID
128-bit

106. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
Id Label Properties Graphs
Id Label Properties SourceId TargetId Graphs
EPGMGraphHead
EPGMVertex
EPGMEdge
Id Label Properties POJO
POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>
DataSet<EPGMEdge>
Id Label Properties Graphs
EPGMVertex
GradoopId := UUID
128-bit
String

107. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
Id Label Properties Graphs
Id Label Properties SourceId TargetId Graphs
EPGMGraphHead
EPGMVertex
EPGMEdge
Id Label Properties POJO
POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>
DataSet<EPGMEdge>
Id Label Properties Graphs
EPGMVertex
GradoopId := UUID
128-bit
String PropertyList := List<Property>
Property := (String, PropertyValue)
PropertyValue := byte[]

108. Graph Representation
Apache Flink and Neo4j Meetup Berlin 51
Id Label Properties Graphs
Id Label Properties SourceId TargetId Graphs
EPGMGraphHead
EPGMVertex
EPGMEdge
Id Label Properties POJO
POJO
POJO
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>
DataSet<EPGMEdge>
Id Label Properties Graphs
EPGMVertex
GradoopId := UUID
128-bit
String PropertyList := List<Property>
Property := (String, PropertyValue)
PropertyValue := byte[]
GradoopIdSet := Set<GradoopId>

109. Graph Representation
Apache Flink and Neo4j Meetup Berlin 52

110. Graph Representation
Apache Flink and Neo4j Meetup Berlin 52
1 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1 2
3
4
5

111. Graph Representation
Apache Flink and Neo4j Meetup Berlin 52
Id Label Properties
1 Community {interest:Heavy Metal}
2 Community {interest:Hard Rock}
1 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1 2
3
4
5
DataSet<EPGMGraphHead>

112. Graph Representation
Apache Flink and Neo4j Meetup Berlin 52
Id Label Properties
1 Community {interest:Heavy Metal}
2 Community {interest:Hard Rock}
Id Label Properties Graphs
1 Person {name:Alice, born:1984} {1}
2 Band {name:Metallica,founded:1981} {1}
3 Person {name:Bob} {1,2}
4 Band {name:AC/DC,founded:1973} {2}
5 Person {name:Eve} {2}
1 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1 2
3
4
5
DataSet<EPGMGraphHead>
DataSet<EPGMVertex>

113. Graph Representation
Apache Flink and Neo4j Meetup Berlin 52
Id Label Properties
1 Community {interest:Heavy Metal}
2 Community {interest:Hard Rock}
Id Label Properties Graphs
1 Person {name:Alice, born:1984} {1}
2 Band {name:Metallica,founded:1981} {1}
3 Person {name:Bob} {1,2}
4 Band {name:AC/DC,founded:1973} {2}
5 Person {name:Eve} {2}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
3 likes 3 4 {since:2015} {2}
4 knows 3 5 {} {2}
5 likes 5 4 {since:2014} {2}
1 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1 2
3
4
5
DataSet<EPGMGraphHead>
DataSet<EPGMVertex> DataSet<EPGMEdge>

114. Flink DataSet Transformations
Apache Flink and Neo4j Meetup Berlin 53

115. Flink DataSet Transformations
Apache Flink and Neo4j Meetup Berlin 53
SQL-like Transformations
• filter
• project
• cross
• union
• distinct
• first-N (limit)
• groupBy
• aggregate
• join
• leftOuterJoin
• rightOuterJoin
• fullOuterJoin

116. Flink DataSet Transformations
Apache Flink and Neo4j Meetup Berlin 53
Hadoop-like Transformations
• map
• flatMap
• mapPartition
• reduce
• reduceGroup
• coGroup
Special Flink Operations
• iterate
• iterateDelta
SQL-like Transformations
• filter
• project
• cross
• union
• distinct
• first-N (limit)
• groupBy
• aggregate
• join
• leftOuterJoin
• rightOuterJoin
• fullOuterJoin

117. Operator Implementation
Apache Flink and Neo4j Meetup Berlin 54
1 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1 2
3
4
5

118. Operator Implementation
Apache Flink and Neo4j Meetup Berlin 54
1 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1 2
3
4
5
Exclusion

119. Operator Implementation
Apache Flink and Neo4j Meetup Berlin 54
1 3
4
5
2
1|Community|interest:Heavy Metal
2|Community|interest:Hard Rock
Person
name : Alice
born : 1984
Band
name : Metallica
founded : 1981
Person
name : Bob
Person
name : Eve
Band
name : AC/DC
founded : 1973
likes
since : 2014
likes
since : 2013
likes
since : 2015
knows
likes
since : 2014
1 2
3
4
5 // input: firstGraph (G[1]), secondGraph (G[2])
1: DataSet<GradoopId> graphId = secondGraph.getGraphHead()
2: .map(new Id<G>());
3:
4: DataSet<V> newVertices = firstGraph.getVertices()
5: .filter(new NotInGraphBroadCast<V>())
6: .withBroadcastSet(graphId, GRAPH_ID);
7:
8: DataSet<E> newEdges = firstGraph.getEdges()
9: .filter(new NotInGraphBroadCast<E>())
10: .withBroadcastSet(graphId, GRAPH_ID)
11: .join(newVertices)
12: .where(new SourceId<E>().equalTo(new Id<V>())
13: .with(new LeftSide<E, V>())
14: .join(newVertices)
15: .where(new TargetId<E>().equalTo(new Id<V>())
16: .with(new LeftSide<E, V>());
Exclusion

120. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55

121. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
graphId = secondGraph.getGraphHead()

122. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
Id Label Properties
2 Community {interest:Hard Rock}
graphId = secondGraph.getGraphHead()

123. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
Id Label Properties
2 Community {interest:Hard Rock}
graphId = secondGraph.getGraphHead()
.map(new Id<G>());

124. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
Id Label Properties
2 Community {interest:Hard Rock}
graphId = secondGraph.getGraphHead()
Id
2
.map(new Id<G>());

125. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
Id Label Properties
2 Community {interest:Hard Rock}
graphId = secondGraph.getGraphHead()
Id
2
newVertices = firstGraph.getVertices()
.map(new Id<G>());

126. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
Id Label Properties
2 Community {interest:Hard Rock}
graphId = secondGraph.getGraphHead()
Id
2
newVertices = firstGraph.getVertices() Id Label Properties Graphs
1 Person {name:Alice} {1}
2 Band {name:Metallica,founded:1981} {1}
3 Person {name:Bob} {1,2}
.map(new Id<G>());

127. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
Id Label Properties
2 Community {interest:Hard Rock}
graphId = secondGraph.getGraphHead()
Id
2
newVertices = firstGraph.getVertices() Id Label Properties Graphs
1 Person {name:Alice} {1}
2 Band {name:Metallica,founded:1981} {1}
3 Person {name:Bob} {1,2}
.map(new Id<G>());
.filter(new NotInGraphBroadCast<V>())
.withBroadcastSet(graphId, GRAPH_ID);

128. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 55
Id Label Properties
2 Community {interest:Hard Rock}
graphId = secondGraph.getGraphHead()
Id
2
newVertices = firstGraph.getVertices() Id Label Properties Graphs
1 Person {name:Alice} {1}
2 Band {name:Metallica,founded:1981} {1}
3 Person {name:Bob} {1,2}
Id Label Properties Graphs
1 Person {name:Alice} {1}
2 Band {name:Metallica,founded:1981} {1}
.map(new Id<G>());
.filter(new NotInGraphBroadCast<V>())
.withBroadcastSet(graphId, GRAPH_ID);

129. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56

130. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges()

131. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}

132. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

133. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

134. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

135. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target … Id Label …
1 likes 1 2 … 1 Person …
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

136. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target … Id Label …
1 likes 1 2 … 1 Person …
.with(new LeftSide<E, V>())
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

137. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target … Id Label …
1 likes 1 2 … 1 Person …
Id Label Source Target …
1 likes 1 2 …
.with(new LeftSide<E, V>())
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

138. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target … Id Label …
1 likes 1 2 … 1 Person …
Id Label Source Target …
1 likes 1 2 …
.join(newVertices)
.where(new TargetId<E>().equalTo(new Id<V>())
.with(new LeftSide<E, V>())
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

139. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target … Id Label …
1 likes 1 2 … 1 Person …
Id Label Source Target …
1 likes 1 2 …
Id Label Source Target … Id Label …
1 likes 1 2 … 2 Band …
.join(newVertices)
.where(new TargetId<E>().equalTo(new Id<V>())
.with(new LeftSide<E, V>())
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

140. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target … Id Label …
1 likes 1 2 … 1 Person …
Id Label Source Target …
1 likes 1 2 …
Id Label Source Target … Id Label …
1 likes 1 2 … 2 Band …
.with(new LeftSide<E, V>());
.join(newVertices)
.where(new TargetId<E>().equalTo(new Id<V>())
.with(new LeftSide<E, V>())
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

141. Operator Implementation – Exclusion
Apache Flink and Neo4j Meetup Berlin 56
newEdges = firstGraph.getEdges() Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target Properties Graphs
1 likes 1 2 {since:2014} {1}
2 likes 3 2 {since:2013} {1}
Id Label Source Target … Id Label …
1 likes 1 2 … 1 Person …
Id Label Source Target …
1 likes 1 2 …
Id Label Source Target … Id Label …
1 likes 1 2 … 2 Band …
Id Label Source Target …
1 likes 1 2 …
.with(new LeftSide<E, V>());
.join(newVertices)
.where(new TargetId<E>().equalTo(new Id<V>())
.with(new LeftSide<E, V>())
.join(newVertices)
.where(new SourceId<E>().equalTo(new Id<V>())
.filter(new NotInGraphBroadCast<E>())
.withBroadcastSet(graphId, GRAPH_ID)

142. GrALa API
Apache Flink and Neo4j Meetup Berlin 57

143. GrALa API
Apache Flink and Neo4j Meetup Berlin 57
class LogicalGraph<G extends EPGMGraphHead,
V extends EPGMVertex,
E extends EPGMEdge> {
fromCollections(...) : LogicalGraph<G, V, E>
fromDataSets(...) : LogicalGraph<G, V, E>
fromGellyGraph(...) : LogicalGraph<G, V, E>
getGraphHead() : DataSet<G>
getVertices() : DataSet<V>
getEdges() : DataSet<E>
aggregate(...) : LogicalGraph<G, V, E>
match(...) : GraphCollection<G, V, E>
groupBy(...) : LogicalGraph<G, V, E>
subgraph(...) : LogicalGraph<G, V, E>
combine(...) : LogicalGraph<G, V, E>
// ...
}

144. GrALa API
Apache Flink and Neo4j Meetup Berlin 57
class LogicalGraph<G extends EPGMGraphHead,
V extends EPGMVertex,
E extends EPGMEdge> {
fromCollections(...) : LogicalGraph<G, V, E>
fromDataSets(...) : LogicalGraph<G, V, E>
fromGellyGraph(...) : LogicalGraph<G, V, E>
getGraphHead() : DataSet<G>
getVertices() : DataSet<V>
getEdges() : DataSet<E>
aggregate(...) : LogicalGraph<G, V, E>
match(...) : GraphCollection<G, V, E>
groupBy(...) : LogicalGraph<G, V, E>
subgraph(...) : LogicalGraph<G, V, E>
combine(...) : LogicalGraph<G, V, E>
// ...
}
class GraphCollection<G extends EPGMGraphHead,
V extends EPGMVertex,
E extends EPGMEdge > {
fromCollections(...) : GraphCollection<G, V, E>
fromDataSets(...) : GraphCollection<G, V, E>
getGraphHeads() : DataSet<G>
getVertices() : DataSet<V>
getEdges() : DataSet<E>
select(...) : GraphCollection<G, V, E>
distinct( ) : GraphCollection<G, V, E>
sortBy(...) : GraphCollection<G, V, E>
union(...) : GraphCollection<G, V, E>
difference(...) : GraphCollection<G, V, E>
// ...
}

145. GrALa API
Apache Flink and Neo4j Meetup Berlin 58
class EPGMDatabase<G extends EPGMGraphHead,
V extends EPGMVertex,
E extends EPGMEdge> {
fromCollections(...) : EPGMDatabase<G, V, E>
fromDataSets(...) : EPGMDatabase<G, V, E>
fromHBase(...) : EPGMDatabase<G, V, E>
fromJSON(...) : EPGMDatabase<G, V, E>
fromExternalGraph(...) : EPGMDatabase<G, V, E>
writeAsJSON(...) : void
writeToHBase(...) : void
getDatabaseGraph( ) : LogicalGraph<G, V, E>
getGraphById(...) : LogicalGraph<G, V, E>
getGraphsById(...) : GraphCollection<G, V, E>
// ...
}

146. GrALa API
Apache Flink and Neo4j Meetup Berlin 59
class EPGMDatabase<G extends EPGMGraphHead,
V extends EPGMVertex,
E extends EPGMEdge> {
fromCollections(...) : EPGMDatabase<G, V, E>
fromDataSets(...) : EPGMDatabase<G, V, E>
fromHBase(...) : EPGMDatabase<G, V, E>
fromJSON(...) : EPGMDatabase<G, V, E>
fromExternalGraph(...) : EPGMDatabase<G, V, E>
writeAsJSON(...) : void
writeToHBase(...) : void
getDatabaseGraph( ) : LogicalGraph<G, V, E>
getGraphById(...) : LogicalGraph<G, V, E>
getGraphsById(...) : GraphCollection<G, V, E>
// ...
}

147. Apache Flink and Neo4j Meetup Berlin 60
Performance

148. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61

149. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
http://www.ldbcouncil.org/

150. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
http://www.ldbcouncil.org/

151. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
http://www.ldbcouncil.org/

152. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
http://www.ldbcouncil.org/

153. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
http://www.ldbcouncil.org/

154. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
5. Select communities with more than 50K users
http://www.ldbcouncil.org/

155. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
5. Select communities with more than 50K users
6. Combine large communities to a single graph
http://www.ldbcouncil.org/

156. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
5. Select communities with more than 50K users
6. Combine large communities to a single graph
7. Group graph by Persons location and gender
http://www.ldbcouncil.org/

157. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 61
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
5. Select communities with more than 50K users
6. Combine large communities to a single graph
7. Group graph by Persons location and gender
8. Aggregate vertex and edge count of grouped graph
http://www.ldbcouncil.org/

158. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 62
1. Extract subgraph containing only Persons and knows relations
2. Transform Persons to necessary information
3. Find communities using Label Propagation
4. Aggregate vertex count for each community
5. Select communities with more than 50K users
6. Combine large communities to a single graph
7. Group graph by Persons location and gender
8. Aggregate vertex and edge count of grouped graph
https://git.io/vgozj

159. Social Network Benchmark
Apache Flink and Neo4j Meetup Berlin 63
Dataset # Vertices # Edges Disk size
Graphalytics.1 61,613 2,026,082 570 MB
Graphalytics.10 260,613 16,600,778 4.5 GB
Graphalytics.100 1,695,613 147,437,275 40.2 GB
Graphalytics.1000 12,775,613 1,363,747,260 372 GB
Graphalytics.10000 90,025,613 10,872,109,028 2.9 TB
• 16x Intel(R) Xeon(R) 2.50GHz 6 (12)
• 16x 48 GB RAM
• 1 Gigabit Ethernet
• Hadoop 2.6.0
• Flink 1.0-SNAPSHOT
• slots (per worker) 12
• jobmanager.heap.mb 2048
• taskmanager.heap.mb 40960

160. Social Network Benchmark – Runtime
Apache Flink and Neo4j Meetup Berlin 64
Dataset # Vertices # Edges Disk size
Graphalytics.1 61,613 2,026,082 570 MB
Graphalytics.10 260,613 16,600,778 4.5 GB
Graphalytics.100 1,695,613 147,437,275 40.2 GB
Graphalytics.1000 12,775,613 1,363,747,260 372 GB
Graphalytics.10000 90,025,613 10,872,109,028 2.9 TB
• 16x Intel(R) Xeon(R) 2.50GHz 6 (12)
• 16x 48 GB RAM
• 1 Gigabit Ethernet
• Hadoop 2.6.0
• Flink 1.0-SNAPSHOT
• slots (per worker) 12
• jobmanager.heap.mb 2048
• taskmanager.heap.mb 40960
0
200
400
600
800
1000
1200
1 2 4 8 16
Runtime[s]
Number of workers
Graphalytics.100

161. 1
2
4
8
16
1 2 4 8 16
Speedup
Number of workers
Graphalytics.100 Linear
Social Network Benchmark – Speedup
Apache Flink and Neo4j Meetup Berlin 65
Dataset # Vertices # Edges Disk size
Graphalytics.1 61,613 2,026,082 570 MB
Graphalytics.10 260,613 16,600,778 4.5 GB
Graphalytics.100 1,695,613 147,437,275 40.2 GB
Graphalytics.1000 12,775,613 1,363,747,260 372 GB
Graphalytics.10000 90,025,613 10,872,109,028 2.9 TB
• 16x Intel(R) Xeon(R) 2.50GHz 6 (12)
• 16x 48 GB RAM
• 1 Gigabit Ethernet
• Hadoop 2.6.0
• Flink 1.0-SNAPSHOT
• slots (per worker) 12
• jobmanager.heap.mb 2048
• taskmanager.heap.mb 40960

162. 1
10
100
1000
10000
Runtime[s]
Social Network Benchmark – Datasets
Apache Flink and Neo4j Meetup Berlin 66
Dataset # Vertices # Edges Disk size
Graphalytics.1 61,613 2,026,082 570 MB
Graphalytics.10 260,613 16,600,778 4.5 GB
Graphalytics.100 1,695,613 147,437,275 40.2 GB
Graphalytics.1000 12,775,613 1,363,747,260 372 GB
Graphalytics.10000 90,025,613 10,872,109,028 2.9 TB
• 16x Intel(R) Xeon(R) 2.50GHz 6 (12)
• 16x 48 GB RAM
• 1 Gigabit Ethernet
• Hadoop 2.6.0
• Flink 1.0-SNAPSHOT
• slots (per worker) 12
• jobmanager.heap.mb 2048
• taskmanager.heap.mb 40960

163. Apache Flink and Neo4j Meetup Berlin 67
Demo
https://github.com/s1ck/neo4j-gradoop-demos

164. Apache Flink and Neo4j Meetup Berlin 68
Current State and Future Work

165. Current State – Operator Implementations
Apache Flink and Neo4j Meetup Berlin 69
Operators
Unary Binary
GraphCollectionLogicalGraph
Algorithms
Aggregation
Pattern Matching
Transformation
Grouping Equality
Call
Combination
Overlap
Exclusion
Equality
Union
Intersection
Difference
Flink Gelly Library
BTG Extraction
Frequent Subgraphs
Limit
Selection
Distinct
Sort
Apply
Reduce
Call
Adaptive Partitioning
Subgraph

166. Release History
Apache Flink and Neo4j Meetup Berlin 70
• 0.0.1 First Prototype (May 2015)
– Hadoop MapReduce and Giraph for operator implementations
– Too much complexity
– Performance loss through serialization in HDFS/HBase
• 0.0.2 Using Flink as execution layer (June 2015)
– Basic operators
• 0.1 December 2015
– System-side identifiers (UUID)
– Improved property handling
– More operator implementations (e.g., Equality, Bool operators)
– Code refactoring
• 0.2-SNAPSHOT
– Graph Pattern Matching
– Frequent Subgraph Mining
– Memory optimization (96-bit ID, Dictionary Encoding, …)
– Tuple Implementation

167. Contributions to Flink
Apache Flink and Neo4j Meetup Berlin 71
• FLINK-2411 Add basic graph summarization algorithm
• FLINK-2590 DataSetUtils.zipWithUniqueID creates duplicate Ids
• FLINK-2905 Add intersect method to Graph class
• FLINK-2910 Combine tests for binary graph operators
• FLINK-2941 Implement a neo4j - Flink/Gelly connector
• FLINK-2981 Update README for building docs
• FLINK-3064 Missing size check in GroupReduceOperatorBase leads to NPE
• FLINK-3118 Check if MessageFunction implements ResultTypeQueryable
• FLINK-3122 Generalize value type in LabelPropagation
• FLINK-3272 Generalize vertex value type in ConnectedComponents
• Flink Forward (October 2015)
• Meetup Big Data Usergroup Saxony (December 2015)
• FOSDEM (January 2016)

168. Contributions Welcome
Apache Flink and Neo4j Meetup Berlin 72
• Code
– Operator implementations / improvement
– Performance Tuning
• People
– Bachelor / Master Thesis
– Open PhD positions in Leipzig, Germany
• Use Cases and (Big) Data!

169. Apache Flink and Neo4j Meetup Berlin 73
Thank you!
www.gradoop.com
http://flink.apache.org
http://neo4j.com
http://ldbcouncil.org
https://github.com/s1ck/neo4j-gradoop-demos
https://github.com/s1ck/flink-neo4j
https://github.com/s1ck/ldbc-flink-import
https://github.com/s1ck/gdl