GRAPH DATABASE SYSTEMS. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
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1 GRAPH DATABASE SYSTEMS h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
2 Use Case: Route Finding Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
3 Use Case: Logistics Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
4 Use Case: Social Network Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
5 Use Case: Recommendations Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
6 Graph Database: Example Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
7 Graph Database Systems: Definitions A graph database is a database whose data model conforms to some form of graph (...) structure. The graph data model usually consists of nodes (...) and (...) edges (...), where the nodes represent concepts (...) and the edges represent relationships ( ) between these concepts ( ). Encyclopedia of Database Systems (Springer) A graph database management system (henceforth, a graph database) is an online database management system with Create, Read, Update, and delete (CRUD) methods that expose a graph data model Robinson/Webber/Eifrem A graph database is any storage system that provides index-free adjacency. Rodriquez h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
8 Relational vs. Graph (Cont.) Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
9 Relational vs. Graph (Cont.) Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
10 Relational vs. Graph (Cont.) Example: Social Network path exists Performance a sample social graph with ~1,000 persons average 50 friends per person pathexists(a,b) limited to depth 4 caches warmed up to eliminate disk I/O # persons query time Relational DBMS 1,000 2,000 ms Neo4j 1,000 2 ms Neo4j 1.000,000 2ms Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
11 Graph Databases Are good for Highly connected data (social networks) Recommendations (e-commerce) Path Finding (how do I know you?) A* (Least Cost path) If you ve ever Joined more than 7 tables together Modeled a graph in a table Tried to write some crazy stored procedure with multiple recursive self and inner joins you should use a graph database h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
12 Graph Database Management Systems First graph database: R&M (1975) Until 2005: development of different graph models and (research) graph libraries and graph database Since 2005: development of new graph databases (commercial and open source) AllegroGraph (2005) DEX ( Sparksee), Neo4j (2006) Tinkerpop Graph Processing Stack (2007) VertexDB, Pregel (Google)(2008) HyperGraphDB, InfiniteGraph, (sones Graph DB), Filament, Horton (Microsoft), (2009) CloudGraph, Trinity, FlockDB (Twitter), OrientDB, (2010) Titan, ArangoDB, Sqrrl, Giraph (Apache) (2012) h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
13 Graph Database Management Systems (Cont.) Graph Database Management Systems differ strongly in Supported graph data structures Data storing features Operation and manipulation features Query Features Schema and integrity constraints Support for essential graph queries Further reading: Renzo Angles: A Comparison of Current Graph Database Models, 3rd Int. Workshop on Graph Data Management: Techniques and applications (GDM 2012), 5 April, Washington DC, USA h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
14 Graph Database Management Systems (Cont.) Popularity Source: h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
15 Graph Database Systems Topics of Interest Graph Data Models Graph Query Languages (not discussed here) Graph Storage and Indexing Scalability, Availability and Consistency h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
16 Literature Edlich, S., Friedland, A., Hampe, J., Brauer, B., Brückner, M. NoSQL Einstieg in die Welt nichtrelationaler Web 2.0 Datenbanken, Carl Hanser Verlag, 2011 (2 nd ed.) Robinson, I., Webber, J., Eifrem, E. Graph Databases, O Reilly, 2015 (2 nd ed.) free e-book on h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
17 Introduction Graph Database Systems Graph Data Models Graph Storage and Indexing Scalability, Availability and Consistency h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
18 Graph Data Models Orthogonal graph characteristics Directed vs. undirected Simple vs. multi Weighted vs. unweighted Unlabeled vs. edge-labeled vs. vertex-labeled Property Graph Model Most popular graph data model in graph databases today (Tinkerpop, InfiniteGraph, InfoGrid, Neo4j etc.) Directed labeled multigraph Edge properties: key/value pairs h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
19 Property Graph Data Model Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
20 Property Graph Data Model Nodes Entities Relationships Connect entities and structure domain Properties Attributes and metadata (Labels) Group nodes by role h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
21 Property Graph Data Model: Nodes Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
22 Property Graph Data Model: Relationships Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
23 Property Graph Data Model: Relationships Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
24 Property Graph Data Model: Labels Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
25 Property Graph Data Model Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
26 Graph Data Modeling Easy to design and model direct representation of the model Whiteboard Friendlyness Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
27 Graph Data Modeling (Cont.) Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
28 Graph Data Modeling (Cont.) Source: Neo Technology, Inc. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
29 Graph Data Modeling: Best Practice Nodes for Things Relationships for Structure Represent Complex value types as nodes Iterative and incremental development Test-driven data model development h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
30 Graph Data Modelling: Cross-Domain Models Source: Robinson/Webber/Eifrem: 2013 h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
31 Graph Data Modelling: Cross-Domain Models Source: Robinson/Webber/Eifrem: 2013 h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
32 Graph Data Modelling: Cross-Domain Models theatrical domain literary domain geospatial domain Source: Robinson/Webber/Eifrem: 2013 h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
33 Graph Databases and Schema Most graph databases are schema-less However, first graph databases started to introduce schema constructs Example: Neo4j 2.0 Necessary requirement: Labels Unique Constraints: Unique constraints do not mean that all nodes have to have a unique value for the properties nodes without the property are not subject to this rule. CREATE CONSTRAINT / DROP CONSTRAINT CREATE CONSTRAINT ON (book:book) ASSERT book.isbn IS UNIQUE h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
34 Graph Data Models Further graph data model approaches Hypergraphs with hyperedges (e.g. HyperGraphDB, sonesgraphdb) Related approaches RDF Triples (subject, predicate, object) Standardized (W3C) Optimized for reasoning However, e.g. AllegroGraph: RDF store and graph database Further reading for graph data models: Edlich, S., Friedland, A., Hampe, J., Brauer, B., Brückner, M. NoSQL Einstieg in die Welt nichtrelationaler Web 2.0 Datenbanken, Carl Hanser Verlag, 2011 (2nd ed.) h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
35 Graph Databases Introduction Graph Data Models Graph Storage and Indexing Scalability, Availability and Consistency h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
36 Graph Storage Adjacency Matrix vs. Adjacency List Advantages? Disadvantages? Source: Edlich et al.:2011 h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
37 Graph Storage: Example Concrete Example: Neo4j Architecture Source: Robinson/Webber/Eifrem: 2015 h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
38 Graph Storage: Example Concrete Example: Neo4j Physical Storage of a Graph Source: Robinson/Webber/Eifrem: 2015 h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
39 Graph Storage: Example (Cont.) Concrete Example: Neo4j File Record Structure Source: Robinson/Webber/Eifrem: 2015 h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
40 Graph Indexing Do we really need indexes in graph databases? Graphs are their own indexes! But sometimes we want short-cuts to well-known nodes Indexes for efficient lookup of specific properties of nodes or relationships AND: Indexes for uniqueness constraints! (properties of nodes or relationships) h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
41 Index data structures B-Trees Hash Indexes Quadtrees R-Trees Graph Indexing (Cont.) Some graph databases use search engines like Apache Lucene as index backend Supports exact and regex-based matching Supports scoring Number of hits in the index for a given item Great for recommendations! h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
42 Graph Databases Introduction Graph Data Models Graph Storage and Indexing Scalability, Availability and Consistency h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
43 Graph Databases: Scalability, Availability and Consistency Example: Neo4J Transactions ACID Write-Ahead logging Replication Master-Slave Replication (asynchronous!) Neo4j also supports writing through slaves the slave first ensures that it is consistent with the master thereafter, the write is synchronously transacted across both instances. h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
44 Graph Database Systems: Conclusion Graph Database Systems: Pros & Cons Strengths Fast, for connected data Whiteboard friendly, agile development Weaknesses Global Queries / Number Crunching Binary Data / Blobs Requires conceptual shift No standardization yet h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
45 Graph Database Systems: Outlook Standardization?! Integration of graph database features and native graph storage in RDBMS? RDF already supported in IBM DB2 and Oracle What will be next? Polyglot Persistence?! h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
46 Big Data Technologies Introduction NoSQL Database Systems Column Store Database Systems In-Memory Database Systems Graph Database Systems Conclusion & Outlook h_da Prof. Dr. Uta Störl Big Data Technologies: Graph Database Systems - SoSe
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