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

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

Dec 09, 2023 181 likes •792 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

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Hej!

@ryguyrg
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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
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Carpe Diem Data

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Why are YOU here today, hopefully

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Power of Graph Algorithms to Understand Your Data

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Power of Graph Algorithms to Understand Your Data

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Graph Algorithms on ACID

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Graph Algorithms on ACID

Graph Algorithms +

ACID-compliant native graph database

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

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Product Recommendations

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Sports

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Literature

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Urban Planning

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Toxic Waste Management

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Historical Tooling

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NumPy

OR

OR

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The New World

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SLIDE 34 Twitter 
 Streaming API Python Tweet Collection 
 (includes user data) Rabbit MQ MongoDB Neo4j R Scripts

  • Graph Stats
  • Community Detection
MySQL Graph .grap hml Tableau Graph Visualization
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  • Hit a wall with igraph/R
  • Need to scale graph algorithms
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SLIDE 36 Twitter 
 Streaming API Python Tweet Collection 
 (includes user data) Rabbit MQ MongoDB Neo4j R Scripts

  • Graph Stats
  • Community Detection
MySQL Graph .grap hml Tableau Graph Visualization
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OPTIMIZED FOR

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OLTP

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GREAT FOR

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Subgraph Queries

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WORKING ON

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Global Queries

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IN

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Neo4j Graph Algorithms

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SLIDE 47 Neo4j Native Graph Database Analytics Integrations Cypher Query Language Wide Range of APOC Procedures Optimized Graph Algorithms
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SLIDE 48 Finds the optimal path
  • r evaluates route
availability and quality Evaluates how a group is clustered
  • r partitioned
Determines the importance of distinct nodes in the network
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SLIDE 49 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})

Usage

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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'})
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Supported Centrality Algos

  • PageRank (baseline)
  • Betweeness
  • Closeness
  • Degree
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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
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Supported Pathfinding Algos

  • Single Source Short Path
  • All-Nodes SSP
  • Parallel BFS / DFS
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  • 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

Goal: Iterate Quickly

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A note on Performance

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

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Improved Performance & Testing

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Improved Performance & Testing Scaling via Parallel Processing

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Scaling Across the Cluster

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THANK YOU!

ryan@neo4j.com @ryguyrg