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Hej! @ryguyrg ABOUT ME Developed web apps for 5 years including - PowerPoint PPT Presentation

Dec 09, 2023 •181 likes •791 views

Hej! @ryguyrg ABOUT ME Developed web apps for 5 years including e-commerce, business workflow, more. Worked at Google for 8 years on Google Apps, Cloud Platform Technologies: Python, Java, BigQuery, Oracle, MySQL, OAuth

  1. Hej! @ryguyrg

  2. ABOUT ME • Developed web apps for 5 years including e-commerce, business workflow, more. • Worked at Google for 8 years on Google Apps, Cloud Platform • Technologies: Python, Java, BigQuery, Oracle, MySQL, OAuth ryan@neo4j.com @ryguyrg

  3. Carpe Diem Data

  8. Why are YOU here today, hopefully

  9. Power of Graph Algorithms to Understand Your Data

  10. Power of Graph Algorithms to Understand Your Data

  11. Graph Algorithms on ACID

  12. Graph Algorithms on ACID Graph Algorithms + ACID-compliant native graph database

  13. IS_OFFICER_OF Acme Inc Person B D E R E T S I G E R LIVES_AT Bank Bahamas NODE Address X RELATIONSHIP LIVES_AT Bank US Person A WITH N T U O C C A _ S A H Account 123

  16. Anti Money Laundering Anti Money Laundering

  17. Product Recommendations

  20. Sports

  21. Literature

  22. Urban Planning

  23. Toxic Waste Management

  24. Historical Tooling

  30. OR OR NumPy

  31. The New World

  34. Twitter 
 Streaming API Tableau MySQL R Scripts 
 Python Tweet MongoDB -Graph Stats Rabbit MQ Collection 
 Neo4j -Community Detection (includes user data) Graph .grap Graph hml Visualization

  35. • Hit a wall with igraph/R • Need to scale graph algorithms

  36. Twitter 
 Streaming API Tableau MySQL R Scripts 
 Python Tweet MongoDB -Graph Stats Rabbit MQ Collection 
 Neo4j -Community Detection (includes user data) Graph .grap Graph hml Visualization

  38. OPTIMIZED FOR

  39. OLTP

  40. GREAT FOR

  41. Subgraph Queries

  42. WORKING ON

  43. Global Queries

  44. IN

  46. Neo4j Graph Algorithms

  47. Neo4j Cypher Query Native Graph Language Database Analytics Integrations Wide Range of APOC Procedures Optimized Graph Algorithms

  48. Finds the optimal path or evaluates route availability and quality Determines the importance of distinct nodes in the network Evaluates how a group is clustered or partitioned

  49. Usage 1.Call as Cypher procedure 2.Pass in specification (Label, Prop, Query) and configuration 3.~.stream variant returns ( a lot ) of results 
 CALL algo.<name>.stream('Label','TYPE',{conf}) 
 YIELD nodeId, score 4.non-stream variant writes results to graph 
 returns statistics 
 CALL algo.<name>('Label','TYPE',{conf})

  50. 
 What about Virtual Graphs? Pass in Cypher statement for node- and relationship-lists. 
 CALL algo.<name>( 
 'MATCH ... RETURN id(n)', 
 'MATCH (n)-->(m) 
 RETURN id(n) as source, 
 id(m) as target', {graph:'cypher'})

  51. Supported Centrality Algos • PageRank (baseline) • Betweeness • Closeness • Degree

  52. 
 Supported Centrality Algos CALL algo.pageRank.stream ('Page', 'LINKS', {iterations:20, dampingFactor:0.85}) YIELD node, score RETURN node, score ORDER BY score DESC LIMIT 20 CALL algo.pageRank('Page', 'LINKS', 
 {iterations:20, dampingFactor:0.85, write: true, writeProperty:"pagerank"}) YIELD nodes, loadMillis, computeMillis, writeMillis

  53. Supported Pathfinding Algos • Single Source Short Path • All-Nodes SSP • Parallel BFS / DFS

  54. Goal: Iterate Quickly • Combine data from sources into one graph • Project to relevant subgraphs • Enrich data with algorithms • Traverse, collect, filter aggregate 
 with queries • Visualize, Explore, Decide, Export • From all APIs and Tools

  55. A note on Performance 500 416 Neo4j is Significantly 375 Faster • 251 Seconds 250 GraphX 152 124 GraphX 125 Neo4j Neo4j 0 Union-Find (Connected Components) PageRank Spark GraphX results publicly available Neo4j Configuration Twitter 2010 Dataset • Amazon EC2 cluster running 64-bit Linux • Physical machine running 64-bit • 1.47 Billion Relationships • 128 CPUs with 68 GB of memory, 2 hard Linux • 41.65 Million Nodes • 128 CPUs with 55 GB RAM, SSDs disks

  56. What’s the Future Look Like?

  57. Improved Performance & Testing

  58. Improved Performance & Testing Scaling via Parallel Processing

  59. Scaling Across the Cluster

  60. THANK YOU! ryan@neo4j.com @ryguyrg

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