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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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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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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