1. A gentle introduction to graph databases
Michael Green
A gentle introduction to graph databases

2. Michael Green Slides available afterwards DBA.SE chat

3. Plan of Attack
What are graphs Databases - graph versus relational SQL Server’s functionality (demo) Where it might end up

4. What are Graphs
“In a mathematician's terminology, a graph is a collection of points and lines connecting some .. of them.“ [1]
Graph databases have a mathematical basis, as do relational.
Point = nodes = vertex
Line = edge = arc
Points need not be connected. Edges connect exactly two nodes.
[1]

5. Graph Features
A node need not be connected There is no upper limit on how many other nodes a node can connect to An edge connects exactly two nodes

6. Graph Features
Directed / undirected Cyclic / acyclic Property graphs (weighted) Connected / discrete components Simple / multigraph - at most one edge between two nodes Self-connected nodes Labelled
Some terminology
Directed: *I* sent and *to* you – direction
Labels – multiple labels are OK

7. A simple example Michael (Person) SQL Saturday (Event) Presenting at
Is about
Works with
SQL Server
(Product)
This is a directed graph. Edges are directed: there is a “from” and a “to”.
Both nodes and edges can have properties (the name) and labels.
In degree and out degree. A node can be disconnected in = out = zero.
This one is cyclic.

8. Example: Tree
Directed / undirected Cyclic / acyclic Property graphs (weighted) Connected / discrete components Simple / multigraph - at most one edge between two nodes Self-connected nodes
Organisation’s org chart
B-Tree
Query plan
An ERD is a graph but not a Tree

9. Example: Roads
Directed / undirected – one-way streets Cyclic / acyclic Property graphs (weighted) – speed limits, tolls Connected / discrete components (Tasmania?) Simple / multigraph - at most one edge between two nodes Self-connected nodes

10. Directed? Service versus line
Weighted (journey time)
Cyclic mostly a tree except for the loop (the clue is in the name) and a few others
Connected – not interested in stations to which we cannot take a train.

11. The internet in 2003 - http://www.opte.org/the-internet/
Directed, unweighted, cyclic, multigraph, self-connected
The internet in

12. What are Graph Databases
“A database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data.” [1]
If the DBMS presents its interface as nodes & edges it is a graph database.
[1]

13. What are Graph Databases
A node is the “thing” An edge is how things are connected Both can have properties Edges and nodes are “labelled” i.e. enumerable i.e. can have a PK

14. What are Graph Databases
It’s about how the DBMS interfaces with consumers Many internal representations are possible On-disk storage is not a determinant Key-value, relational and graph can solve any given problem
In-memory, columnstore, fixedvar – they’re all relational

15. Graph on the persistence spectrum
Flat file Key – value Column family Relational Graph

16. Thinking in graphs
Files & key-value rows & fields Relational sets, selections & projections Graph sets & paths

17. Graph DB Features
Directed / undirected Cyclic / acyclic Property graphs (weighted) Connected / discrete components Simple / multigraph - at most one edge between two nodes Self-connected nodes
Some terminology
Specifically, DBs are directed

18. Graph versus Relational
Entity type -> table Entity instance -> row Relationship -> FK Normalisation DRI
The model enforces no container that corresponds to a table. Products are moving toward stronger schema.
Labels take the role of defining types.
No limit on which nodes an edge can connect (cf joining on non-FKs e.g. shoe_size <-> house_number <-> description)

19. Graph versus Relational
Entity type -> table Entity instance -> row Relationship -> FK Normalisation DRI
Entity type -> ? (label) Entity instance -> node Relationship -> edge Multi-role permitted Not mandated
The model enforces no container that corresponds to a table. Products are moving toward stronger schema.
Labels take the role of defining types.
Edges can have properties; foreign keys cannot.
No limit on which nodes an edge can connect (cf joining on non-FKs e.g. shoe_size <-> house_number <-> description)

20. Use Cases
Where connectedness is as, or more, important than content Social – friends of friends (of friends of friends …) – especially indeterminate depth Fraud detection – “Is X connected to failed companies?” – “Are the parties in this transaction suspiciously connected?” Network modelling – “If this router goes down what services are lost?” Code dependency analysis – “If I change this data type, where must I re-program?” Crime detection – metadata
Many other examples.

21. SQL Server Demo

22. Alternatives MS GraphEngine MS Azure CosmosDB
Many other vendors – DBEngine, Wikipedia
Neo4j, Cypher query language
DataStax: Graph on Cassandra, Gremlin programming API
NodeXL for MS Excel
Cypher / Gremlin is like SQL / LINQ

23. Neo4j Example

24. Where it might end up
RC1 release blog [1] ALTER existing tables to graph tables Extended to temporary, in-memory etc Transitive closure Pollymorphism Improved syntax, as for joins
[1]

25. Where it might end up
GPUs – node-wise parallel execution R / Python / data science & AI Visualisation in SSMS, SSRS SSAS – OLAP graphs LINQ to SQL Graph Path analytic functions
SSMS, SSRS present geography results differently

26. Some links Graph processing with SQL Server
Graph version of Wide World Importers
Graph Engine
Azure Cosmos DB