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Shutl

Maria do Ceu Morais
Maria do Ceu Morais

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.

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Shutl delivers with Neo4j Tuesday, 30 July 13
Volker Pacher senior developer @shutl @vpacher http://github.com/vpacher Tuesday, 30 July 13
Tuesday, 30 July 13
Tuesday, 30 July 13
Problems? Tuesday, 30 July 13
http://xkcd.com/287/ Tuesday, 30 July 13
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): Tuesday, 30 July 13
The case for graph databases: relationships are explicit stored (RDBS lack relationships) domain modelling is simpli鍖ed because adding new subgraphs doesnt 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 Tuesday, 30 July 13
What is a graph anyway? Node 1 Node 2 Node 4 Node 3 a collection of vertices (nodes) connected by edges (relationships) Tuesday, 30 July 13
a short history: the seven bridges of K旦nigsberg (1735) Leonard Euler Tuesday, 30 July 13
directed graph Node 1 Node 2 Node 4 Node 3 each relationship has a direction or one start node and one end node Tuesday, 30 July 13
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 Tuesday, 30 July 13
Tuesday, 30 July 13
a graph is its own index (constant query performance) Tuesday, 30 July 13
Tuesday, 30 July 13
Querying the graph: Cypher declarative query language speci鍖c to neo4j easy to learn and intuitive enables the user to specify speci鍖c 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 Tuesday, 30 July 13
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 Tuesday, 30 July 13
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 Tuesday, 30 July 13
START me=node(1) MATCH me-[:knows]->()-[:knows]->fof RETURN fof the query Tuesday, 30 July 13
START me=node(1) MATCH me-[:knows*2..]->fof WHERE fof.name =~ 'Da.*' RETURN fof Tuesday, 30 July 13
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 Tuesday, 30 July 13
鍖nd all events on a speci鍖c day START root=node(0) MATCH root-[:2013]-()-[:05]-()-[:24]-()- [:happens]-event RETURN event Tuesday, 30 July 13
QUESTIONS? Volker Pacher volker@shutl.com www.shutl.com Tuesday, 30 July 13

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Shutl

  • 1. Shutl delivers with Neo4j Tuesday, 30 July 13
  • 2. Volker Pacher senior developer @shutl @vpacher http://github.com/vpacher Tuesday, 30 July 13
  • 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): Tuesday, 30 July 13
  • 8. The case for graph databases: relationships are explicit stored (RDBS lack relationships) domain modelling is simpli鍖ed because adding new subgraphs doesnt 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 Tuesday, 30 July 13
  • 9. What is a graph anyway? Node 1 Node 2 Node 4 Node 3 a collection of vertices (nodes) connected by edges (relationships) Tuesday, 30 July 13
  • 10. a short history: the seven bridges of K旦nigsberg (1735) Leonard Euler Tuesday, 30 July 13
  • 11. directed graph Node 1 Node 2 Node 4 Node 3 each relationship has a direction or one start node and one end node Tuesday, 30 July 13
  • 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 Tuesday, 30 July 13
  • 14. a graph is its own index (constant query performance) Tuesday, 30 July 13
  • 16. Querying the graph: Cypher declarative query language speci鍖c to neo4j easy to learn and intuitive enables the user to specify speci鍖c 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 Tuesday, 30 July 13
  • 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 Tuesday, 30 July 13
  • 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 Tuesday, 30 July 13
  • 20. START me=node(1) MATCH me-[:knows*2..]->fof WHERE fof.name =~ 'Da.*' RETURN fof Tuesday, 30 July 13
  • 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 Tuesday, 30 July 13
  • 22. 鍖nd all events on a speci鍖c day START root=node(0) MATCH root-[:2013]-()-[:05]-()-[:24]-()- [:happens]-event RETURN event Tuesday, 30 July 13