Shutl delivers with Neo4j by addressing issues with their previous MySQL database including exponential growth of joins, complex unmaintainable code, and slowing API response times. They chose Neo4j as a graph database because relationships are explicitly stored, domain modeling is simplified, and performance remains constant with growth. Queries in Neo4j use the Cypher language which focuses on pattern matching rather than implementation details.

1. Shutl
delivers
with
Neo4j
Tuesday, 30 July 13

2. Volker Pacher
senior developer @shutl
@vpacher
http://github.com/vpacher
Tuesday, 30 July 13

3. Tuesday, 30 July 13

4. Tuesday, 30 July 13

5. Problems?
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6. http://xkcd.com/287/
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7. • exponential growth of joins in mysql with added features
• code base too complex and unmaintanable
• api response time growing too large the more data was added
• our fastest delivery was quicker then our slowest query!
problems with our previous attempt (v1):
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8. The case for graph databases:
• relationships are explicit stored (RDBS lack relationships)
• domain modelling is simplified because adding new ‘subgraphs‘
doesn’t affect the existing structure and queries (additive model)
• white board friendly
• schema-less
• db performance remains relatively constant because queries are
localized to its portion of the graph. O(1) for same query
• traversals of relationships are easy and very fast
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9. What is a graph anyway?
Node 1 Node 2
Node 4
Node 3
a collection of vertices (nodes)
connected by edges (relationships)
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10. a short history: the seven bridges of Königsberg (1735)
Leonard Euler
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11. directed graph
Node 1 Node 2
Node 4
Node 3
each relationship has a direction or
one start node and one end node
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12. property graph:
name:Volker
• nodes contain properties (key, value)
• relationships have a type and are always directed
• relationships can contain properties too
name: Sam
:friends
name: Megan
:knows
since: 2005
name: Paul
:friends
:works_for
:knows
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14. a graph is its own index (constant query performance)
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16. Querying the graph: Cypher
• declarative query language specific to neo4j
• easy to learn and intuitive
• enables the user to specify specific patterns to query for (something that looks like ‘this’)
• inspired partly by SQL (WHERE and ORDER BY) and SPARQL (pattern matching)
• focuses on what to query for and not how to query for it
• switch from a mySQl world is made easier by the use of cypher instead of having to learn
a traversal framework straight away
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17. • START: Starting points in the graph, obtained via index lookups or by element IDs.
• MATCH: The graph pattern to match, bound to the starting points in START.
• WHERE: Filtering criteria.
• RETURN: What to return.
• CREATE: Creates nodes and relationships.
• DELETE: Removes nodes, relationships and properties.
• SET: Set values to properties.
• FOREACH: Performs updating actions once per element in a list.
• WITH: Divides a query into multiple, distinct parts
cypher clauses
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18. an example graph
Node 1
me
Node 2
Steve
Node 3
Sam
Node 4
David
Node 5
Megan
me - [:knows] -> Steve -
[:knows] -> David
me - [:knows] -> Sam -
[:knows] -> Megan
Megan - [:knows] -> David
knows
knowsknows
knows
knows
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19. START me=node(1)
MATCH me-[:knows]->()-[:knows]->fof
RETURN fof
the query
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20. START me=node(1)
MATCH me-[:knows*2..]->fof
WHERE fof.name =~ 'Da.*'
RETURN fof
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21. root (0)
Year: 2013
Month: 05 Month 01
2014
01
05
2013
Year: 2014
Month: 06
06
Day: 24 Day: 25
24
25
Day: 26
26
Event 1 Event 2 Event 3
happens happens happens happens
representing dates/times
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22. find all events on a specific day
START root=node(0)
MATCH root-[:‘2013’]-()-[:’05’]-()-[:’24’]-()-
[:happens]-event
RETURN event
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23. QUESTIONS?
Volker Pacher
volker@shutl.com
www.shutl.com
Tuesday, 30 July 13