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RDBMS to Graph Webinar

The document discusses the transition from traditional relational databases (RDBMS) to graph databases like Neo4j, highlighting the advantages such as improved data structuring, speed, and ease of querying complex relationships. It details the challenges of RDBMS, including the complexity of SQL joins and the inefficiency in managing relationships, contrasting this with the intuitive and agile querying capabilities of Neo4j. Various real-world use cases, including fraud detection and master data management, demonstrate the efficacy and growing adoption of graph databases in different industries.

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RDBMS TO GRAPH Live from San Mateo, March 9, 2016 Webinar
Data used to be stored like this: punch tape. Or punch cards. Horrible way to read and understand data. Impossible to index easily, cross-reference, eliminate inconsistencies and cross-reference.
Then we started storing data in tables, and “relational” databases. Sometimes those tables are human-readable. But as soon as you normalize the data to eliminate duplication and inconsistencies, many fields start referencing auto-generated numerical foreign keys. And your data becomes difficult to understand and maintain without complicated JOIN queries.
ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURE LOAN SSN 2 UNSECURE LOAN CREDIT CARD Enter Graph Databases. The future is now. Graph Databases, like Neo4j, store data in a much more logical way. A way that represents the real world, and prioritizes the representation, discoverability and maintainability of data relationships.
Intuitivness Speed Agility
Intuitiveness Speed Agility
Intuitiveness
Intuitivness Speed Agility
Speed “We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” - Volker Pacher, Senior Developer “Minutes to milliseconds” performance Queries up to 1000x faster than RDBMS or other NoSQL
Intuitivness Speed Agility
A Naturally Adaptive Model A Query Language Designed for Connectedness + =Agility
Cypher Typical Complex SQL Join The Same Query using Cypher MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, 
 count(report) AS Total Project Impact Less time writing queries Less time debugging queries Code that’s easier to read Less time writing queries More time understanding the answers Leaving time to ask the next question Less time debugging queries: More time writing the next piece of code Improved quality of overall code base Code that’s easier to read: Faster ramp-up for new project members Improved maintainability & troubleshooting
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
NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Real Time Recommendations VIEWED VIEWED BOUGHT VIEWED BOUGHT BOUGHT BOUGHT BOUGHT Real-Time Recommendations could be about finding the relationsships relevant to make recommend a product or a service…. …which is exactly why Walmart is using Neo4j.
“As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands.” Marcos Wada Software Developer, Walmart NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Master Data Management MANAGES MANAGES LEADS REGION M ANAG ES MANAGES REGION LEADS LEADS COLLABORATES Master Data Management is about bringing together all the entities within an organization and external to the organization. To understand the relationship between each of them.
Neo4j is the heart of Cisco HMP: used for governance and single source of truth and a one-stop shop for all of Cisco’s hierarchies. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations Cisco uses it for this — to power their content management, resources and knowledge-base articles for use by sales teams. It also powers product recommendations to make sure customers are getting the power of their offerings. Although this project is focused on sales teams, another group has used Neo4j to power all of their helpdesk content -
NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Master Data Management Solu%on Support Case Support Case Knowledge Base Ar%cle Message Knowledge Base Ar%cle Knowledge Base Ar%cle Neo4j is the heart of Cisco’s Helpdesk Solution too. Master Data Management is about bringing together all the entities within an organization and external to the organization. To understand the relationship between each of them.
NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Fraud Detection O PENED_ACCO UNT HAS IS_ISSUED HAS LIVES LIVES IS_ISSUED OPENED_ACCOUNT Discovering fraud is another use case that is particularly suitable to graphs, because it’s all about about finding fraudulent patterns. Here we work with the top banks and insurance companies as well as many governments..
“Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.” Gorka Sadowski Cyber Security Expert NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
GRAPH THINKING: Graph Based Search NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations PUBLISH INCLUDE INCLUDE CREATE CAPTURE IN IN SOURCE USES USES IN IN USES SOURCE SOURCE
Uses Neo4j to manage the digital assets inside of its next generation in-flight entertainment system. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations BROWSES CONNECTS BRIDGES ROUTES POWERS ROUTES POWERS POWERS HOSTS QUERIES GRAPH THINKING: Network & IT-Operations Decency analysis Root cause analysis
Uses Neo4j for network topology analysis for big telco service providers NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
GRAPH THINKING: Identity And Access Management NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations TRUSTS TRUSTS ID ID AUTHENTICATES AUTHENTICATES O W NS OWNS CAN_READ Think of organizational hierarchies. No longer is it just a tree.
UBS was the recipient of the 2014 Graphie Award for “Best Identify And Access Management App” NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
Neo4j Adoption by Selected Verticals SOFTWARE FINANCIAL SERVICES RETAIL MEDIA & BROADCASTING SOCIAL NETWORKS TELECOM HEALTHCARE
AGENDA • Use Cases • SQL Pains • Building a Neo4j Application • Moving from RDBMS -> Graph Models • Walk through an Example • Creating Data in Graphs • Querying Data
I hired this kid for all the handwriting you’ll see throughout the presentation. So, don’t blame me.
SQL Day in the Life of a RDBMS Developer Let’s explore how your SQL developer works today.
They work with data in tables. Here’s a table of people and where they're from, their hair color and the university they attended. This table is fairly natural, but duplicating values across multiple rows. Let’s say you want to change the name of a university or a country, you’d have to update all rows.
So, instead, you’d create a separate table for the country, with an ID that references it. This is your primary key.
This allows you to add additional properties.
Now, you use that ID to reference the country in the people table - a foreign key.
And you’d want to normalize the university table as well.
And use the university ID to reference it. Now your table it a lot less readable.
So, we see this set of 3 tables with arrows indicating references between primary keys and foreign keys, used in JOINs.
SELECT p.name, c.country, c.leader, p.hair, u.name, u.pres, u.state FROM people p LEFT JOIN country c ON c.ID=p.country LEFT JOIN uni u ON p.uni=u.id WHERE u.state=‘CT’ Your SQL looks like this. Only, this is a super simple JOIN across 3 tables. I’ve often had to work with 10+ tables being JOINed.
JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN And your SQL developer? All she’s thinking about is joins
JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN All day long.
JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN And they’re keeping her up at night as well.
Meanwhile, it’s expensive to find data. So we add indexes to make it easier. But when we have to do index lookups for each and every JOIN? And we have a dozen JOINs? That’s expensive.
What’s the solution? Denormalize! But now hard to maintain and have consistent data.
• Complex to model and store relationships • Performance degrades with increases in data • Queries get long and complex • Maintenance is painful SQL Pains
• Easy to model and store relationships • Performance of relationship traversal remains constant with growth in data size • Queries are shortened and more readable • Adding additional properties and relationships can be done on the fly - no migrations Graph Gains
John Resig, who you may know as the creator of jQuery, loves Neo4j because it simplifies life.
What does this Graph look like? So you’ve seen what tables look like. How do graphs make this better?
CYPHER Ann DanLoves The obligatory “Ann Loves Dan” example
Property Graph Model CREATE (:Person { name:“Dan”} ) - [:LOVES]-> (:Person { name:“Ann”} ) LOVES LABEL PROPERTY NODE NODE LABEL PROPERTY The whiteboard model is the physical model.
MATCH (p:Person)-[:WENT_TO]->(u:Uni), (p)-[:LIVES_IN]->(c:Country), (u)-[:LED_BY]->(l:Leader), (u)-[:LOCATED_IN]->(s:State) WHERE s.abbr = ‘CT’ RETURN p.name, c.country, c.leader, p.hair, u.name, l.name, s.abbr
David Meza, chief knowledge architect at NASA, had this to say.
How do you use Neo4j? CREATE MODEL + LOAD DATA QUERY DATA Create your Graph Model Load your data Query your data
How do you use Neo4j? Querying can be done in the Neo4j Browser.
Querying can be done in the Neo4j Browser.
How do you use Neo4j?
Language Drivers javascript, java, ruby, .net, python, php
Language Drivers haskell, go
Native Server-Side Extensions Need to get every last ounce of performance? You can write server-side extensions in Java.
Architectural Options Data Storage and Business Rules Execu5on Data Mining and Aggrega5on Applica'on Graph Database Cluster Neo4j Neo4j Neo4j Ad Hoc Analysis Bulk Analy'c Infrastructure Hadoop, EDW … Data Scien'st End User Databases Rela5onal NoSQL Hadoop
RDBMS to Graph Options MIGRATE ALL DATA MIGRATE SUBSET DUPLICATE SUBSET Non-Graph Queries Graph Queries Graph Queries Non-Graph Queries All Queries Rela3onal Database Graph Database Application Application Application Non Graph Data All Data
FROM RDBMS TO GRAPHS
Northwind
Northwind - the canonical RDBMS Example
( )-[:TO]->(Graph)
( )-[:IS_BETTER_AS]->(Graph)
Starting with the ER Diagram
Locate the Foreign Keys
Drop the Foreign Keys
Find the JOIN Tables
(Simple) JOIN Tables Become Relationships
Attributed JOIN Tables -> Relationships with Properties
Querying a Subset Today
As a Graph
QUERYING THE GRAPH
using openCypher Declarative query language Easy to learn for someone familiar with languages like SQL But optimized for graphs, and quickly readable
Property Graph Model CREATE (:Employee{ firstName:“Steven”} ) -[:REPORTS_TO]-> (:Employee{ firstName:“Andrew”} ) REPORTS_TO Steven Andrew LABEL PROPERTY NODE NODE LABEL PROPERTY
Who do people report to? MATCH (e:Employee)<-[:REPORTS_TO]-(sub:Employee) RETURN *
Who do people report to? Results can be returned as nodes and relationships
Who do people report to? MATCH (e:Employee)<-[:REPORTS_TO]-(sub:Employee) RETURN e.employeeID AS managerID, e.firstName AS managerName, sub.employeeID AS employeeID, sub.firstName AS employeeName; or alternatively as a table.
Who do people report to?
Who does Robert report to? MATCH p=(e:Employee)<-[:REPORTS_TO]-(sub:Employee) WHERE sub.firstName = ‘Robert’ RETURN p
Who does Robert report to?
What is Robert’s reporting chain? MATCH p=(e:Employee)<-[:REPORTS_TO*]-(sub:Employee) WHERE sub.firstName = ‘Robert’ RETURN p But the power of the graph is in the ability to query arbitrary length paths. See the asterisks.
What is Robert’s reporting chain?
Who’s the Big Boss? MATCH (e:Employee) WHERE NOT (e)-[:REPORTS_TO]->() RETURN e.firstName as bigBoss
Who’s the Big Boss?
Product Cross-Selling MATCH (choc:Product {productName: 'Chocolade'}) <-[:INCLUDES]-(:Order)<-[:SOLD]-(employee), (employee)-[:SOLD]->(o2)-[:INCLUDES]->(other:Product) RETURN employee.firstName, other.productName, COUNT(DISTINCT o2) as count ORDER BY count DESC LIMIT 5;
Product Cross-Selling
(ASIDE ON GRAPH COMPUTE) Optimized for OLTP But can be used for Graph Compute Either with built-in functions Or server-side extensions Or via exporting data to spark / graphx for analysis
Shortest Path Between Airports MATCH p = shortestPath( (a:Airport {code:”SFO”})-[*0..2]-> (b:Airport {code: “MSO”})) RETURN p Example using built-in algorithms. Dijkstra also available for weighted paths
(END ASIDE ON GRAPH COMPUTE)
POWERING AN APP
Simple App
Simple App
Simple Python Code
Simple Python Code
Simple Python Code
Simple Python Code
LOADING OUR DATA
CSV
CSV files for Northwind
CSV files for Northwind
3 Steps to Creating the Graph IMPORT NODES CREATE INDEXES IMPORT RELATIONSHIPS
Importing Nodes // Create customers USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/customers.csv" AS row CREATE (:Customer {companyName: row.CompanyName, customerID: row.CustomerID, fax: row.Fax, phone: row.Phone}); // Create products USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/products.csv" AS row CREATE (:Product {productName: row.ProductName, productID: row.ProductID, unitPrice: toFloat(row.UnitPrice)});
Importing Nodes // Create suppliers USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/suppliers.csv" AS row CREATE (:Supplier {companyName: row.CompanyName, supplierID: row.SupplierID}); // Create employees USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/employees.csv" AS row CREATE (:Employee {employeeID:row.EmployeeID, firstName: row.FirstName, lastName: row.LastName, title: row.Title});
Importing Nodes // Create categories USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/categories.csv" AS row CREATE (:Category {categoryID: row.CategoryID, categoryName: row.CategoryName, description: row.Description}); // Create orders USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/orders.csv" AS row MERGE (order:Order {orderID: row.OrderID}) ON CREATE SET order.shipName = row.ShipName;
Creating Indexes CREATE INDEX ON :Product(productID); CREATE INDEX ON :Product(productName); CREATE INDEX ON :Category(categoryID); CREATE INDEX ON :Employee(employeeID); CREATE INDEX ON :Supplier(supplierID); CREATE INDEX ON :Customer(customerID); CREATE INDEX ON :Customer(customerName);
Creating Relationships USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (customer:Customer {customerID: row.CustomerID}) MERGE (customer)-[:PURCHASED]->(order); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/products.csv" AS row MATCH (product:Product {productID: row.ProductID}) MATCH (supplier:Supplier {supplierID: row.SupplierID}) MERGE (supplier)-[:SUPPLIES]->(product);
Creating Relationships USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j- contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (product:Product {productID: row.ProductID}) MERGE (order)-[pu:INCLUDES]->(product) ON CREATE SET pu.unitPrice = toFloat(row.UnitPrice), pu.quantity = toFloat(row.Quantity); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j- contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (employee:Employee {employeeID: row.EmployeeID}) MERGE (employee)-[:SOLD]->(order);
Creating Relationships USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/ neo4j-contrib/developer-resources/gh-pages/data/northwind/ products.csv" AS row MATCH (product:Product {productID: row.ProductID}) MATCH (category:Category {categoryID: row.CategoryID}) MERGE (product)-[:PART_OF]->(category); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/ neo4j-contrib/developer-resources/gh-pages/data/northwind/ employees.csv" AS row MATCH (employee:Employee {employeeID: row.EmployeeID}) MATCH (manager:Employee {employeeID: row.ReportsTo}) MERGE (employee)-[:REPORTS_TO]->(manager);
High Performance LOADing neo4j-import 4.58 million things and their relationships… Loads in 100 seconds!
WRAPPING UP
“We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10 to 100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” Volker Pacher
 Senior Developer
THANK YOU! Ryan Boyd @ryguyrg ryan@neo4j.com Thank you for listening!

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RDBMS to Graph Webinar

  • 1.
    RDBMS TO GRAPH Livefrom San Mateo, March 9, 2016 Webinar
  • 2.
    Data used tobe stored like this: punch tape. Or punch cards. Horrible way to read and understand data. Impossible to index easily, cross-reference, eliminate inconsistencies and cross-reference.
  • 3.
    Then we startedstoring data in tables, and “relational” databases. Sometimes those tables are human-readable. But as soon as you normalize the data to eliminate duplication and inconsistencies, many fields start referencing auto-generated numerical foreign keys. And your data becomes difficult to understand and maintain without complicated JOIN queries.
  • 4.
    ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURE LOAN SSN 2 UNSECURE LOAN CREDIT CARD Enter Graph Databases. The future is now. Graph Databases, like Neo4j, store data in a much more logical way. A way that represents the real world, and prioritizes the representation, discoverability and maintainability of data relationships.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
    Speed “We found Neo4jto be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” - Volker Pacher, Senior Developer “Minutes to milliseconds” performance Queries up to 1000x faster than RDBMS or other NoSQL
  • 10.
  • 11.
    A Naturally AdaptiveModel A Query Language Designed for Connectedness + =Agility
  • 12.
    Cypher Typical Complex SQLJoin The Same Query using Cypher MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, 
 count(report) AS Total Project Impact Less time writing queries Less time debugging queries Code that’s easier to read Less time writing queries More time understanding the answers Leaving time to ask the next question Less time debugging queries: More time writing the next piece of code Improved quality of overall code base Code that’s easier to read: Faster ramp-up for new project members Improved maintainability & troubleshooting
  • 13.
    ABOUT ME • Developedweb 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
  • 14.
    NEO4j USE CASES RealTime Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  • 15.
    NEO4j USE CASES RealTime Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Real Time Recommendations VIEWED VIEWED BOUGHT VIEWED BOUGHT BOUGHT BOUGHT BOUGHT Real-Time Recommendations could be about finding the relationsships relevant to make recommend a product or a service…. …which is exactly why Walmart is using Neo4j.
  • 16.
    “As the currentmarket leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands.” Marcos Wada Software Developer, Walmart NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  • 17.
    NEO4j USE CASES RealTime Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Master Data Management MANAGES MANAGES LEADS REGION M ANAG ES MANAGES REGION LEADS LEADS COLLABORATES Master Data Management is about bringing together all the entities within an organization and external to the organization. To understand the relationship between each of them.
  • 18.
    Neo4j is theheart of Cisco HMP: used for governance and single source of truth and a one-stop shop for all of Cisco’s hierarchies. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations Cisco uses it for this — to power their content management, resources and knowledge-base articles for use by sales teams. It also powers product recommendations to make sure customers are getting the power of their offerings. Although this project is focused on sales teams, another group has used Neo4j to power all of their helpdesk content -
  • 19.
    NEO4j USE CASES RealTime Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Master Data Management Solu%on Support Case Support Case Knowledge Base Ar%cle Message Knowledge Base Ar%cle Knowledge Base Ar%cle Neo4j is the heart of Cisco’s Helpdesk Solution too. Master Data Management is about bringing together all the entities within an organization and external to the organization. To understand the relationship between each of them.
  • 20.
    NEO4j USE CASES RealTime Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Fraud Detection O PENED_ACCO UNT HAS IS_ISSUED HAS LIVES LIVES IS_ISSUED OPENED_ACCOUNT Discovering fraud is another use case that is particularly suitable to graphs, because it’s all about about finding fraudulent patterns. Here we work with the top banks and insurance companies as well as many governments..
  • 21.
    “Graph databases offernew methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.” Gorka Sadowski Cyber Security Expert NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  • 22.
    GRAPH THINKING: Graph BasedSearch NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations PUBLISH INCLUDE INCLUDE CREATE CAPTURE IN IN SOURCE USES USES IN IN USES SOURCE SOURCE
  • 23.
    Uses Neo4j tomanage the digital assets inside of its next generation in-flight entertainment system. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  • 24.
    NEO4j USE CASES RealTime Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations BROWSES CONNECTS BRIDGES ROUTES POWERS ROUTES POWERS POWERS HOSTS QUERIES GRAPH THINKING: Network & IT-Operations Decency analysis Root cause analysis
  • 25.
    Uses Neo4j fornetwork topology analysis for big telco service providers NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  • 26.
    GRAPH THINKING: Identity AndAccess Management NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations TRUSTS TRUSTS ID ID AUTHENTICATES AUTHENTICATES O W NS OWNS CAN_READ Think of organizational hierarchies. No longer is it just a tree.
  • 27.
    UBS was therecipient of the 2014 Graphie Award for “Best Identify And Access Management App” NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
  • 28.
    Neo4j Adoption bySelected Verticals SOFTWARE FINANCIAL SERVICES RETAIL MEDIA & BROADCASTING SOCIAL NETWORKS TELECOM HEALTHCARE
  • 29.
    AGENDA • Use Cases •SQL Pains • Building a Neo4j Application • Moving from RDBMS -> Graph Models • Walk through an Example • Creating Data in Graphs • Querying Data
  • 30.
    I hired thiskid for all the handwriting you’ll see throughout the presentation. So, don’t blame me.
  • 31.
    SQL Day in theLife of a RDBMS Developer Let’s explore how your SQL developer works today.
  • 32.
    They work withdata in tables. Here’s a table of people and where they're from, their hair color and the university they attended. This table is fairly natural, but duplicating values across multiple rows. Let’s say you want to change the name of a university or a country, you’d have to update all rows.
  • 33.
    So, instead, you’dcreate a separate table for the country, with an ID that references it. This is your primary key.
  • 34.
    This allows youto add additional properties.
  • 35.
    Now, you usethat ID to reference the country in the people table - a foreign key.
  • 36.
    And you’d wantto normalize the university table as well.
  • 37.
    And use theuniversity ID to reference it. Now your table it a lot less readable.
  • 38.
    So, we seethis set of 3 tables with arrows indicating references between primary keys and foreign keys, used in JOINs.
  • 39.
    SELECT p.name, c.country, c.leader, p.hair, u.name,u.pres, u.state FROM people p LEFT JOIN country c ON c.ID=p.country LEFT JOIN uni u ON p.uni=u.id WHERE u.state=‘CT’ Your SQL looks like this. Only, this is a super simple JOIN across 3 tables. I’ve often had to work with 10+ tables being JOINed.
  • 40.
  • 41.
  • 42.
  • 43.
    Meanwhile, it’s expensiveto find data. So we add indexes to make it easier. But when we have to do index lookups for each and every JOIN? And we have a dozen JOINs? That’s expensive.
  • 44.
    What’s the solution? Denormalize! But now hard to maintain and have consistent data.
  • 45.
    • Complex tomodel and store relationships • Performance degrades with increases in data • Queries get long and complex • Maintenance is painful SQL Pains
  • 46.
    • Easy tomodel and store relationships • Performance of relationship traversal remains constant with growth in data size • Queries are shortened and more readable • Adding additional properties and relationships can be done on the fly - no migrations Graph Gains
  • 47.
    John Resig, whoyou may know as the creator of jQuery, loves Neo4j because it simplifies life.
  • 48.
    What does thisGraph look like? So you’ve seen what tables look like. How do graphs make this better?
  • 49.
    CYPHER Ann DanLoves The obligatory“Ann Loves Dan” example
  • 50.
    Property Graph Model CREATE(:Person { name:“Dan”} ) - [:LOVES]-> (:Person { name:“Ann”} ) LOVES LABEL PROPERTY NODE NODE LABEL PROPERTY The whiteboard model is the physical model.
  • 52.
  • 53.
    David Meza, chiefknowledge architect at NASA, had this to say.
  • 54.
    How do youuse Neo4j? CREATE MODEL + LOAD DATA QUERY DATA Create your Graph Model Load your data Query your data
  • 55.
    How do youuse Neo4j? Querying can be done in the Neo4j Browser.
  • 56.
    Querying can bedone in the Neo4j Browser.
  • 57.
    How do youuse Neo4j?
  • 58.
    Language Drivers javascript, java,ruby, .net, python, php
  • 59.
  • 60.
    Native Server-Side Extensions Needto get every last ounce of performance? You can write server-side extensions in Java.
  • 62.
    Architectural Options Data Storage and Business Rules Execu5on Data Mining and Aggrega5on Applica'on Graph Database Cluster Neo4j Neo4j Neo4j Ad Hoc Analysis Bulk Analy'c Infrastructure Hadoop, EDW … Data Scien'st End User Databases Rela5onal NoSQL Hadoop
  • 63.
    RDBMS to GraphOptions MIGRATE ALL DATA MIGRATE SUBSET DUPLICATE SUBSET Non-Graph Queries Graph Queries Graph Queries Non-Graph Queries All Queries Rela3onal Database Graph Database Application Application Application Non Graph Data All Data
  • 64.
  • 67.
  • 68.
    Northwind - thecanonical RDBMS Example
  • 69.
  • 71.
  • 72.
  • 73.
  • 74.
  • 75.
  • 76.
    (Simple) JOIN TablesBecome Relationships
  • 77.
    Attributed JOIN Tables-> Relationships with Properties
  • 78.
  • 79.
  • 80.
  • 81.
    using openCypher Declarative querylanguage Easy to learn for someone familiar with languages like SQL But optimized for graphs, and quickly readable
  • 82.
  • 83.
    Who do peoplereport to? MATCH (e:Employee)<-[:REPORTS_TO]-(sub:Employee) RETURN *
  • 84.
    Who do peoplereport to? Results can be returned as nodes and relationships
  • 85.
    Who do peoplereport to? MATCH (e:Employee)<-[:REPORTS_TO]-(sub:Employee) RETURN e.employeeID AS managerID, e.firstName AS managerName, sub.employeeID AS employeeID, sub.firstName AS employeeName; or alternatively as a table.
  • 86.
    Who do peoplereport to?
  • 87.
    Who does Robertreport to? MATCH p=(e:Employee)<-[:REPORTS_TO]-(sub:Employee) WHERE sub.firstName = ‘Robert’ RETURN p
  • 88.
    Who does Robertreport to?
  • 89.
    What is Robert’sreporting chain? MATCH p=(e:Employee)<-[:REPORTS_TO*]-(sub:Employee) WHERE sub.firstName = ‘Robert’ RETURN p But the power of the graph is in the ability to query arbitrary length paths. See the asterisks.
  • 90.
    What is Robert’sreporting chain?
  • 91.
    Who’s the BigBoss? MATCH (e:Employee) WHERE NOT (e)-[:REPORTS_TO]->() RETURN e.firstName as bigBoss
  • 92.
  • 93.
    Product Cross-Selling MATCH (choc:Product {productName:'Chocolade'}) <-[:INCLUDES]-(:Order)<-[:SOLD]-(employee), (employee)-[:SOLD]->(o2)-[:INCLUDES]->(other:Product) RETURN employee.firstName, other.productName, COUNT(DISTINCT o2) as count ORDER BY count DESC LIMIT 5;
  • 94.
  • 95.
    (ASIDE ON GRAPHCOMPUTE) Optimized for OLTP But can be used for Graph Compute Either with built-in functions Or server-side extensions Or via exporting data to spark / graphx for analysis
  • 96.
    Shortest Path BetweenAirports MATCH p = shortestPath( (a:Airport {code:”SFO”})-[*0..2]-> (b:Airport {code: “MSO”})) RETURN p Example using built-in algorithms. Dijkstra also available for weighted paths
  • 97.
    (END ASIDE ONGRAPH COMPUTE)
  • 98.
  • 99.
  • 100.
  • 101.
  • 102.
  • 103.
  • 104.
  • 105.
  • 106.
  • 107.
    CSV files forNorthwind
  • 108.
    CSV files forNorthwind
  • 109.
    3 Steps toCreating the Graph IMPORT NODES CREATE INDEXES IMPORT RELATIONSHIPS
  • 110.
    Importing Nodes // Createcustomers USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/customers.csv" AS row CREATE (:Customer {companyName: row.CompanyName, customerID: row.CustomerID, fax: row.Fax, phone: row.Phone}); // Create products USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/products.csv" AS row CREATE (:Product {productName: row.ProductName, productID: row.ProductID, unitPrice: toFloat(row.UnitPrice)});
  • 111.
    Importing Nodes // Createsuppliers USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/suppliers.csv" AS row CREATE (:Supplier {companyName: row.CompanyName, supplierID: row.SupplierID}); // Create employees USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/employees.csv" AS row CREATE (:Employee {employeeID:row.EmployeeID, firstName: row.FirstName, lastName: row.LastName, title: row.Title});
  • 112.
    Importing Nodes // Createcategories USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/categories.csv" AS row CREATE (:Category {categoryID: row.CategoryID, categoryName: row.CategoryName, description: row.Description}); // Create orders USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/orders.csv" AS row MERGE (order:Order {orderID: row.OrderID}) ON CREATE SET order.shipName = row.ShipName;
  • 113.
    Creating Indexes CREATE INDEXON :Product(productID); CREATE INDEX ON :Product(productName); CREATE INDEX ON :Category(categoryID); CREATE INDEX ON :Employee(employeeID); CREATE INDEX ON :Supplier(supplierID); CREATE INDEX ON :Customer(customerID); CREATE INDEX ON :Customer(customerName);
  • 114.
    Creating Relationships USING PERIODICCOMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (customer:Customer {customerID: row.CustomerID}) MERGE (customer)-[:PURCHASED]->(order); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/products.csv" AS row MATCH (product:Product {productID: row.ProductID}) MATCH (supplier:Supplier {supplierID: row.SupplierID}) MERGE (supplier)-[:SUPPLIES]->(product);
  • 115.
    Creating Relationships USING PERIODICCOMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j- contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (product:Product {productID: row.ProductID}) MERGE (order)-[pu:INCLUDES]->(product) ON CREATE SET pu.unitPrice = toFloat(row.UnitPrice), pu.quantity = toFloat(row.Quantity); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j- contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (employee:Employee {employeeID: row.EmployeeID}) MERGE (employee)-[:SOLD]->(order);
  • 116.
    Creating Relationships USING PERIODICCOMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/ neo4j-contrib/developer-resources/gh-pages/data/northwind/ products.csv" AS row MATCH (product:Product {productID: row.ProductID}) MATCH (category:Category {categoryID: row.CategoryID}) MERGE (product)-[:PART_OF]->(category); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/ neo4j-contrib/developer-resources/gh-pages/data/northwind/ employees.csv" AS row MATCH (employee:Employee {employeeID: row.EmployeeID}) MATCH (manager:Employee {employeeID: row.ReportsTo}) MERGE (employee)-[:REPORTS_TO]->(manager);
  • 117.
    High Performance LOADing neo4j-import 4.58million things and their relationships… Loads in 100 seconds!
  • 118.
  • 119.
    “We found Neo4jto be literally thousands of times faster than our prior MySQL solution, with queries that require 10 to 100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” Volker Pacher
 Senior Developer
  • 120.
    THANK YOU! Ryan Boyd @ryguyrgryan@neo4j.com Thank you for listening!