Neo4j Morpheus: Interweaving Table and Graph Data with SQL and Cypher in Apache Spark

1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2. Morpheus

3. https://db-engines.com/en/ranking_categories

4. Node ● Represents an entity within the graph ● Can have labels Relationship ● Connects a start node with an end node ● Has one type Property ● Describes a node/relationship: e.g. name, age, weight etc ● Key-value pair: String key; typed value (string, number, bool, list, ...)

5. Property graph view of data mirrors conceptual view ○ Entities and relationships, with attributes ○ Nodes and relationships, with properties Graph queries are concise and visual (ASCII Art) MATCH (c:Customer)-[:BOUGHT]-(p:Product) RETURN c.id, p.id Network algorithms run over graphs → Graphs enhance data engineering and science

6. Tables Graphs Transactional PostgreSQL, Oracle, SQLServer Neo4j Data Integration & Analytics Spark SQL Morpheus

7. Spark is an immutable data processing engine ○ Spark graphs are compositions of tables (DFs) ○ Spark graphs can be transformed and combined ○ Functions (including queries) over multiple graphs ○ Cypher query plans mapped to Catalyst Neo4j is a native transactional CRUD database ○ Neo4j graphs use a native graph data representation ○ Neo4j has optimized in-process MT graph algos ○ Morpheus helps move data in and out of Neo4j

8. Graphs and tables are both useful data models ○ Finding paths and subgraphs, and transforming graphs ○ Viewing, aggregating and ordering values The Morpheus project parallels Spark SQL ○ PropertyGraph type (composed of DataFrames) ○ Catalog of graph data sources, named graphs, views, ○ Cypher query language A CypherSession adds graphs to a SparkSession

9. ● Data integration ○ Integrate (non-)graphy data from multiple, heterogeneous data sources into one or more property graphs ● Distributed Cypher execution ○ OLAP-style graph analytics ● Data science ○ Integration with other Spark libraries ○ Feature extraction using Neo4j Graph Algorithms

10. Pathﬁnding & Search Centrality / Importance Community Detection Link Prediction Finds optimal paths or evaluates route availability and quality Determines the importance of distinct nodes in the network Detects group clustering or partition options Evaluates how alike nodes are Estimates the likelihood of nodes forming a future relationship Similarity

11. PROPERTY GRAPH composing DataFrames Hive, DF, JDBC TABLES SUB- GRAPH FS snapshot Morpheus SOURCES

12. DataFrame Table Result Cypher QUERY Property Graph Result Property Graph Cypher QUERY Cypher QUERY Property Graph Result DataFrame Driving Table

13. GRAPH ALGOS ANALYSIS toolsets DataFrame DataFrame Property Graph Property Graph

14. Morpheus STORE SUBGRAPH FS snapshot Property Graph

15. Cypher 9 is the latest full version of openCypher ○ Implemented in Neo4j 3.5 ○ Includes date/time types and functions ○ Implemented in whole/part by six other vendors ○ Several other partial and research implementations ○ Cypher for Gremlin is another openCypher project

16. Cypher is a full CRUD language ← OLTP database ○ RETURNs only tabular results: not composable ○ Results can include graph elements (paths, relationships, nodes) or property values Morpheus implements most of read-only Cypher ○ No MERGE or DELETE ○ Spark immutable data + transformations

17. Cypher 10 proposes Multiple Graph features ○ Multiple Graph CIP: https://git.io/fjmrx Allows for Cypher Query composition ○ Similar to chaining transformations on DataFrames Support Graph Catalog for managing Graphs ○ Analogous to Spark SQL catalog Query support for Graph Construction

18. Input: a property graph Output: a table FROM GRAPH socialNetwork MATCH ({name: 'Dan'})-[:FRIEND*2]->(foaf) RETURN toUpper(foaf.name) AS name ORDER BY name DESC Language features available in Morpheus

19. Input: a property graph Output: a property graph FROM GRAPH socialNetwork MATCH (p:Person)-[:FRIEND*2]->(foaf) WHERE NOT (p)-[:FRIEND]->(foaf) CONSTRUCT CREATE (p)-[:POSSIBLE_FRIEND]->(foaf) RETURN GRAPH Language features available in Morpheus

20. Input: property graphs Output: a property graph FROM GRAPH socialNetwork MATCH (p:Person) FROM GRAPH products MATCH (c:Customer) WHERE p.email = c.email CONSTRUCT ON socialNetwork, products CREATE (p)-[:IS]->(c) RETURN GRAPH Language features available in Morpheus

21. Input: property graphs Output: a property graph CATALOG CREATE VIEW youngFriends($inGraph){ FROM GRAPH $inGraph MATCH (p1:Person)-[r]->(p2:Person) WHERE p1.age < 25 AND p2.age < 25 CONSTRUCT CREATE (p1)-[COPY OF r]->(p2) RETURN GRAPH } Language features available in Morpheus

22. Input: property graphs Output: table or graph FROM youngFriends(socialNetwork) MATCH (p:Person)-[r]->(o) RETURN p, r, o // and views over views FROM youngFriends(europe(socialNetwork)) MATCH ... Language features available in Morpheus

23. Morpheus Query EngineProperty Graph Data Sources Property Graph Catalog Scala API SQL JDBC

24. ● Distributed executionSpark Core Spark SQL ● Rule- and Cost-based query optimization via Catalyst MATCH (c:Captain)-[:COMMANDS]->(s:Ship) WHERE c.name = ‘Morpheus’ RETURN c.name, s.name openCypher Frontend ● Parsing, Rewriting, Normalization ● Semantic Analysis (Scoping, Typing, etc.) Morpheus ● Data Import and Export ● Schema and Type handling ● Query translation to Spark operations Relational Planning Logical Planning Spark Backend ● Translation into Logical Operators ● Basic Logical Optimization ● Backend Agnostic Query Representation ● Conversion and typing of Frontend expressions ● Translation into Relational Operations on abstract tables ● Column layout computation Intermediate Language ● Spark-speciﬁc table implementation

25. ● In Morpheus, PropertyGraphs are represented by ○ Node Tables and Relationship Tables ● Tables are represented by DataFrames ○ Require a ﬁxed schema ● Property Graphs have a Graph Type ○ Node and relationship types that occur in the graph ○ Node and relationship properties and their data type Property Graph Node Tables Rel. Tables Graph Type

26. :Captain:Person name: Morpheus :Ship name: Nebuchadnezzar :COMMANDS id name 0 Morpheus id name 1 Nebuchadnezzar id source target 0 0 1 :Captain:Person :Ship :COMMANDS Graph Type { :Captain:Person ( name: STRING ), :Ship ( name: STRING ), :COMMANDS }

27. Property Graph ⋈ ⋈ π MATCH (c:Captain)-[:COMMANDS]->(s:Ship) WHERE c.name = ‘Morpheus’ RETURN c.name, s.name π π Morpheus Relational Planning ...

28. Part 1 From JSON to Graph Create persistent Property Graph from raw Yelp dataset Read Yelp Data from JSON into DataFrames Create Property Graph from DataFrames Store Property Graph using Parquet Part 2 A library of Graphs Create a library of graph projections Read Property Graph from Parquet Create subgraph for a specifc city Project and persist city subgraph Part 3 Federated queries Integrate reviews with social network data Deﬁne Graph Type and Mapping with Graph DDL Load data from Hive and H2 Run analytical query on the integrated graph Part 5 Neo4j Integration II Recommend businesses to users Load graph projections from library Write graphs to Neo4j, run Louvain + Jaccard Run analytical query in Morpheus to ﬁnd recommendations Part 4 Neo4j Integration I Find trending businesses Load graph projections from library Write graphs to Neo4j and run PageRank Combine graphs in Morpheus and select trending businesses https://git.io/fjZ2b

29. ● Yelp is a search service based on crowd-sourced reviews about local businesses ● The Yelp Open Dataset is part of the Yelp Dataset Challenge ○ Yelps’ effort to encourage researchers to explore the dataset ○ ~150K businesses, 10M users, 5M reviews, 35M friendships https://www.yelp.com https://www.yelp.com/dataset https://www.yelp.com/dataset/challenge

30. :Business name : ACME address : 123 ACME Rd. city : San Jose state : CA :User name : Alice since : 2013 elite : [2014, 2016] :User name : Bob since : 2014 elite : null :REVIEWS stars : 5 date : 2014-02-03 :REVIEWS stars : 4 date : 2014-08-03

31. business.json user.json review.json Create Node and Relationship Tables Create Property Graph Store Property Graph https://git.io/fjZ2N

32. // (:User) val userDataFrame = spark.read.json(...).select(...) val userNodeTable = CAPSEntityTable.create(NodeMappingBuilder.on("id") .withImpliedLabel("User") .withPropertyKey("name") .withPropertyKey("yelping_since") .withPropertyKey("elite") .build, userDataFrame) id name yelping_since elite 0 Alice 2013 [2014, 2016] 1 Bob 2014 null

33. ● Property Graphs are managed within a catalog Cypher Session Property Graph Catalog Property Graph Data Source <namespace> Property Graph <name> QualiﬁedGraphName = <namespace>.<name>

34. ● API to operate with the query engine and the catalog trait CypherSession { def cypher( query: String, parameters: CypherMap = CypherMap.empty, drivingTable: Option[CypherRecords] = None ): Result def catalog: PropertyGraphCatalog }

35. ● API to manage multiple Property Graphs ● Catalog functions can be executed via Cypher or Scala API trait PropertyGraphCatalog { def register(namespace: Namespace, dataSource: PropertyGraphDataSource): Unit def store(qualifiedGraphName: QualifiedGraphName, graph: PropertyGraph): Unit def graph(qualifiedGraphName: QualifiedGraphName): PropertyGraph def drop(qualifiedGraphName: QualifiedGraphName): Unit // additional methods for managing views, listing namespaces and graphs }

36. ● API for loading and saving property graphs trait PropertyGraphDataSource { def hasGraph(name: GraphName): Boolean def graph(name: GraphName): PropertyGraph def schema(name: GraphName): Option[Schema] // additional methods for storing, deleting, listing graphs }

37. PGDS Multiple graphs Read graphs Write graphs File-based Parquet, ORC, CSV HDFS, local, S3 Yes Yes Yes SQL Hive, Jdbc Yes Yes No Neo4j Bolt Yes Yes Yes Neo4j Bulk Import No No Yes

38. Cypher Session Property Graph Catalog Property Graph Data Source <namespace> Property Graph <name> QualiﬁedGraphName = <namespace>.<name>

39. Cypher Session Property Graph Catalog “social-net” (Neo4j PGDS) “US” (Property Graph) FROM social-net.US MATCH (p:Person) RETURN p

40. Cypher Session Property Graph Catalog “social-net” (Neo4j PGDS) “US” “EU” “products” (SQL PGDS) “2018” “2017” FROM social-net.US MATCH (p:Person) FROM products.2018 MATCH (c:Customer) WHERE p.email = c.email RETURN p, c

41. Cypher Session Property Graph Catalog “social-net” (Neo4j PGDS) “US” “EU” “products” (SQL PGDS) “2018” “2017” CATALOG CREATE GRAPH social-net.US_new { FROM social-net.US MATCH (p:Person) FROM products.2018 MATCH (c:Customer) WHERE p.email = c.email CONSTRUCT ON social-net.US CREATE (p)-[:SAME_AS]->(c) RETURN GRAPH }

42. CATALOG CREATE GRAPH social-net.US_new { FROM social-net.US MATCH (p:Person) FROM products.2018 MATCH (c:Customer) WHERE p.email = c.email CONSTRUCT ON social-net.US CREATE (p)-[:SAME_AS]->(c) RETURN GRAPH } Cypher Session Property Graph Catalog “social-net” (Neo4j PGDS) “US” “EU” “products” (SQL PGDS) “2018” “2017” “US_new”

43. Cypher Session Property Graph Catalog “social-net” (Neo4j PGDS) “US” “EU” ... CATALOG CREATE VIEW youngPeople($sn) { FROM $sn MATCH (p:Person)-[r]->(n) WHERE p.age < 21 CONSTRUCT CREATE (p)-[COPY OF r]->(n) RETURN GRAPH } FROM youngPeople(social-net.US) MATCH (p:Person) RETURN p “youngPeople” Views

45. :Business name : ACME address : 123 ACME Rd. city : San Jose state : CA :User name : Alice since : 2013 elite : [2014, 2016] :User name : Bob since : 2014 elite : null :REVIEWS stars : 5 date : 2014-02-03 :REVIEWS stars : 4 date : 2014-08-03

46. 2015 - 2018 https://git.io/fjZ25 Boulder City (:User)-[:CO_REVIEWS]->(:User) (:User)-[:REVIEWS]->(:Business) (:User)-[:CO_REVIEWS]->(:User) Constuct graphs for each year Extract Yelp subgraph for speciﬁc city (:Business)-[:CO_REVIEWED]->(:Business)

47. JDBC Hive Oracle SQL Server Orc Parquet Table/View Table/View Table/View ... ... Graph DDL Graph Instance - Table mappings SQL Tables Property Graphs Property Graph Node Tables Rel. Tables Graph Type SQL Property Graph Data Source Spark SQL Data Sources Graph Type - Element types - Node types - Relationship types

49. :Business name : ACME address : 123 ACME Rd. city : San Jose state : CA :User name : Alice since : 2013 elite : [2014, 2016] email : alice@yelp.com :User name : Bob since : 2014 elite : null email : bob@yelp.com :REVIEWS stars : 5 date : 2014-02-03 :REVIEWS stars : 4 date : 2014-08-03

50. :User email: alice@yelp.com :User email : bob@yelp.com :FRIEND

51. Yelp Reviews Yelp Book Graph DDL + SQL PGDS (:User)-[:REVIEWS]->(:Business) (:User)-[:FRIEND]->(:User) https://git.io/fjZ2p

52. CREATE GRAPH TYPE yelp ( -- Element types (concepts used to describe a graph) User ( name STRING, since DATE ), Business ( name STRING, city STRING ), REVIEWS ( stars INTEGER, date LOCALDATETIME ), FRIEND, -- Node types (User), (Business), -- Relationship types (User)-[REVIEWS]->(Business), (User)-[FRIEND]->(User) )

53. CREATE GRAPH yelp_and_yelpBook OF yelp ( -- Node type mappings (User) FROM HIVE.yelp.user, (Business) FROM HIVE.yelp.business, -- Relationship type mappings (User)-[REVIEWS]->(Business) FROM HIVE.yelp.review e START NODES (User) FROM HIVE.yelp.user n JOIN e.user_email = n.email END NODES (Business) FROM HIVE.yelp.business n JOIN e.business_id = n.business_id, (User)-[FRIEND]->(User) FROM H2.yelpbook.friend e START NODES (User) FROM HIVE.yelp.user n JOIN e.user1_email = n.email END NODES (User) FROM HIVE.yelp.user n JOIN e.user2_email = n.email )

54. ● Morpheus and Neo4j Graph Algorithms ● Spark Graph SPIP sneak peek ● SQL/Cypher/GQL https://theoatmeal.com/comics/sneak_peek

55. PROPERTY GRAPH composing DataFrames Hive, DF, JDBC TABLES SUB- GRAPH FS snapshot Morpheus SOURCES

56. DataFrame Table Result Cypher QUERY Property Graph Result Property Graph Cypher QUERY Cypher QUERY Property Graph Result DataFrame Driving Table

57. GRAPH ALGOS ANALYSIS toolsets DataFrame DataFrame Property Graph Property Graph

58. Morpheus STORE SUBGRAPH FS snapshot Property Graph

59. Coming in Spark 3.0

60. Neo4j Native Graph Database Analytics Integrations Cypher Query Language Wide Range of APOC Procedures Native Graph Algorithms

61. • Parallel Breadth First Search* • Parallel Depth First Search • Shortest Path* • Single-Source Shortest Path • All Pairs Shortest Path • Minimum Spanning Tree • A* Shortest Path • Yen’s K Shortest Path • K-Spanning Tree (MST) • Random Walk • Degree Centrality • Closeness Centrality • CC Variations: Harmonic, Dangalchev, Wasserman & Faust • Betweenness Centrality • Approximate Betweenness Centrality • PageRank* • Personalized PageRank • ArticleRank • Eigenvector Centrality • Triangle Count* • Clustering Coefficients • Connected Components (Union Find)* • Strongly Connected Components* • Label Propagation* • Louvain Modularity – 1 Step & Multi-Step • Balanced Triad (identification) • Euclidean Distance • Cosine Similarity • Jaccard Similarity • Overlap Similarity • Pearson Similarity Pathfinding & Search Centrality / Importance Community Detection Similarity neo4j.com/docs/ graph-algorithms/current/ Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors* Available in GraphFrames

62. Free O’Reilly Book neo4j.com/ graph-algorithms-book • Spark & Neo4j Examples • Machine Learning Chapter

64. ● Use when ○ Anytime you’re looking for broad inﬂuence over a network ○ Many domain speciﬁc variations for differing analysis, e.g. Personalized PageRank for personalized recommendations ● Examples: ○ Twitter Recommendations ○ Fraud Detection

65. 2017 to 2018 call algo.pagerank 2017 2018 trendRank = pageRank_2018 - pageRank_2017 ⋈ (:Business) -[:CO_REVIEWED]-> (:Business) https://git.io/fjZ2j

67. ● Use when ○ Community Detection in large networks ○ Uncover hierarchical structures in data ● Examples ○ Money Laundering ○ Protein-Protein-Interactions

68. ● Use when ○ Computing pair-wise similarities ○ Accommodates vectors of different lengths ● Examples ○ Recommendations ○ Disambiguation

69. call algo.louvain (:User)-[:REVIEWS]->(:Business) (:User)-[:CO_REVIEWS]->(:User) call algo.jaccard Recommend businesses similar users have reviewed 2017 Compute similarity based on overlapping reviewed businesses Compute communities based on co-reviews for each community :IS_SIMILAR https://git.io/fjZaU

70. Coming in Spark 3.0

71. ● SPARK-25994 Spark Graph for Apache Spark 3.0 ○ Property Graphs, Cypher Queries, and Algorithms ● Deﬁnes a Cypher-compatible Property Graph type based on DataFrames ● Replaces GraphFrames querying with Cypher ● Reimplements GraphFrames/GraphX algos on the Property Graph type

72. ● “Spark Cypher” ○ Run a Cypher 9 query on a Property Graph returning a tabular result ● Migrate GraphFrames to Spark Graph ● Implementation is based on Spark SQL ○ Property Graphs are composed of one or more DFs ● Provide Scala, Python and Java APIs

73. ● Addresses the Cypher Property Graph Model ○ Does not deal with variants of that model (e.g. RDF) ● No Cypher 10 multiple graph features ○ API is ﬂexible to support this in future iterations ● No Property Graph Catalog ○ Also no Property Graph Data Sources

74. [SPARK-27299][GRAPH][WIP] Spark Graph API design proposal (GraphExamplesSuite.scala) test("create PropertyGraph from Node- and RelationshipFrames") { val nodeData: DataFrame = spark.createDataFrame(Seq(0 -> "Alice", 1 -> "Bob")).toDF("id", "name") val relationshipData: DataFrame = spark.createDataFrame(Seq((0, 0, 1))).toDF("id", "source", "target") val nodeFrame: NodeFrame = NodeFrame(nodeData, "id", Set("Person")) val relationshipFrame: RelationshipFrame = RelationshipFrame(relationshipData, "id", "source", "target", "KNOWS") val graph: PropertyGraph = cypherSession.createGraph(Seq(nodeFrame), Seq(relationshipFrame)) val result: CypherResult = graph.cypher( """ |MATCH (a:Person)-[r:KNOWS]->(:Person) |RETURN a, r""".stripMargin) result.df.show() } https://git.io/fjqp6

75. spark-graph-api spark-cypher spark-sql okapi morpheus spark-sql openCypherSPIP Cypher to relational operators compiler openCypher

76. Spark SQL and “Spark GQL” Two models, two languages A common core of datatypes and expressions GQL as the focal point of graph programming Graph languages with a shared graph type system

77. Q&A Thanks for listening

Neo4j Morpheus: Interweaving Table and Graph Data with SQL and Cypher in Apache Spark

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