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Relational to Big Graph

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Relational databases power most applications, but new use-cases have requirements that they are not well suited for.

That's why new approaches like graph databases are used to handle join-heavy, highly-connected and realtime aspects of your applications.

This talk compares relational and graph databases, show similarities and important differences.

We do a hands-on, deep-dive into ease of data modeling and structural evolution, massive data import and high performance querying with Neo4j, the most popular graph database.

I demonstrate a useful tool which makes data import from existing relational databases with a non-denormalized ER-model a "one click"-experience.

Which leaves biggest challenge for people coming from a relational background is to adapt some of their existing database experience to new ways of thinking.

Published in: Technology
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Relational to Big Graph

  1. 1. Rela%onal  to  (Big)  Graph   Harnessing  the  Power  of  the  Graph   Michael  Hunger   JAX  Mainz  2015  
  2. 2. Agenda   • History  of  Neo4j   • Rela1onal  Pains  –  Graph  Pleasure   • Rela1onal  to  Graph   • Model  -­‐>  Import  -­‐>  Query  -­‐>  Build  -­‐>  Integrate   • Demo   • Q&A  
  3. 3. History  of  Neo4j   A  Story  of  Rela%onal  Pain  
  4. 4. History  of  Neo4j  -­‐  Problem   •  Digital  Asset  Management  System  in  2000   •  SaaS  many  users  in  many  countries   •  Two  hard  use-­‐cases   •  Mul1  language  keyword  search   •  Including  synonyms  /  word  hierarchies   •  Access  Management  to  Assets  for  SaaS  Scale  
  5. 5. History  of  Neo4j  –  Rela%onal  ABempt   •  Tried  with  many  rela1onal  DBs   •  JOIN  Performance  Problems   •  Hierarchies,  Networks,  Graphs   •  Modeling  Problems   •  Data  Model  evolu1on   •  No  Success,  even  …   •  With  expensive  database  consultants!  
  6. 6. History  of  Neo4j  –  First  working  Implementa%on   •  Graph  Model    &  API  sketched  on  a  napkin   •  Nodes  connected  by  RelaAonships   •  Just  like  your  conceptual  model   •  Implemented  network-­‐database  in  memory   •  Java  API,  fast  Traversals   •  Worked  well,  but  …   •  No  persistence,  No  Transac1ons   •  Long  import  /  export  1me  from  rela1onal  storage  
  7. 7. History  of  Neo4j  -­‐  Solu%on   •  Evolved  to  full  fledged  database  in  Java   •  With  persistence  using  files  +  memory  mapping   •  Transac1ons  with  Transac1on  Log  (WAL)   •  Lucene  for  fast  Node  search   •  Founded  Company  in  2007   •  Neo4j  (REST)-­‐Server   •  Neo4j  Clustering  &  HA     •  Cypher  Query  Language   •  Today  …  
  8. 8. Neo  Technology  Overview   Product   • Neo4j  -­‐  World’s  leading  graph   database   • 1M+  downloads,  adding  50k+     per  month   • 150+  enterprise  subscrip1on   customers  including  over     50  of  the  Global  2000   Company   • Neo  Technology,  Creator  of  Neo4j   • 80  employees  with  HQ  in  Silicon   Valley,  London,  Munich,  Paris  and   Malmö   • $45M  in  funding  from  Fidelity,   Sunstone,  Conor,  Creandum,   Dawn  Capital  
  9. 9. Neo4j  Adop%on  by  Selected  Ver%cals   Financial
 Services Communications Health &
 Life Sciences HR &
 Recruiting Media &
 Publishing Social
 Web Industry 
 & Logistics Entertainment Consumer Retail Information Services Business Services
  10. 10. How  Customers  Use  Neo4j   Network & Data Center Master Data
 Management Social Recom– mendations Identity & Access Search &
 Discovery GEO
  11. 11. “Forrester  es1mates  that  over  25%  of  enterprises  will  be  using   graph  databases  by  2017”   Neo4j  Leads  the  Graph  Database  Revolu%on   “Neo4j  is  the  current  market  leader  in  graph  databases.”   “Graph  analysis  is  possibly  the  single  most  effec%ve  compe%%ve   differen%ator  for  organiza1ons  pursuing  data-­‐driven  opera1ons   and  decisions  aler  the  design  of  data  capture.”   IT  Market  Clock  for  Database  Management  Systems,  2014   hmps://www.gartner.com/doc/2852717/it-­‐market-­‐clock-­‐database-­‐management   TechRadar™:  Enterprise  DBMS,  Q1  2014   hmp://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-­‐/E-­‐RES106801   Graph  Databases  –  and  Their  Poten%al  to  Transform  How  We  Capture  Interdependencies  (Enterprise  Management  Associates)   hmp://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-­‐databasesand-­‐poten1al-­‐transform-­‐capture-­‐interdependencies/  
  12. 12. Largest  Ecosystem  of  Graph  Enthusiasts   •  1,000,000+  downloads   •  20,000+  educated  developers   •  18,000+  Meetup  members   •  100+  technology  and  service  partners   •  150+  enterprise  subscrip1on  customers     including  50+  Global  2000  companies  
  13. 13. High  Business  Value  in  Data  Rela%onships   Data  is  increasing  in  volume…   •  New  digital  processes   •  More  online  transac1ons   •  New  social  networks   •  More  devices   Using  Data  Rela%onships  unlocks  value     •  Real-­‐1me  recommenda1ons   •  Fraud  detec1on   •  Master  data  management   •  Network  and  IT  opera1ons   •  Iden1ty  and  access  management   •  Graph-­‐based  search  …  and  is  ge^ng  more  connected   Customers,  products,  processes,   devices  interact  and  relate  to   each  other     Early  adopters  became  industry  leaders  
  14. 14. Rela%onal  Pains  –     Graph  Pleasure  
  15. 15. Rela%onal  DBs  Can’t  Handle  Rela%onships  Well   •  Cannot  model  or  store  data  and  relaAonships   without  complexity   •  Performance  degrades  with  number  and  levels   of  rela1onships,  and  database  size   •  Query  complexity  grows  with  need  for  JOINs   •  Adding  new  types  of    data  and  relaAonships   requires  schema  redesign,  increasing  1me  to   market   …  making  tradi1onal  databases  inappropriate   when  data  rela1onships  are  valuable  in  real-­‐%me     Slow  development   Poor  performance   Low  scalability   Hard  to  maintain  
  16. 16. Why  Rela%onal  DBs  Can’t  Handle  Rela%onships  Well?   •  Data  Model  built  for  tabular  forms  not  JOINS   managing  connec1ons  was  bolted  on  both  in   schema  and  query   •  Strict  schema  not  suitable  for  variable  structured   data  which  is  generated  and  used  by  todays   applica1ons   •  Data  volume  and  JOIN  number  affect  cost  of  query   opera1on  exponen1ally   •  Variable  hierarchies  and  networks  are  hard  to   store  and  query  so  many  “pamerns”  were   developed   …  olen  only  denormaliza1on  makes  complex   rela1onal  queries  fast  but  destroys  the  good   normalized  data-­‐model       Built  for  Forms   Joins  are  expensive   Denormalize  #FTW    
  17. 17. Unlocking  Value  from  Your  Data  Rela%onships   •  Model  your  data  naturally  as  a  graph   of  data  and  rela1onships   •  Drive  graph  model  from  domain  and   use-­‐cases   •  Use  rela1onship  informa1on  in  real-­‐ 1me  to  transform  your  business   •  Add  new  rela1onships  on  the  fly  to   adapt  to  your  changing  requirements  
  18. 18. High  Query  Performance  with  a  Na%ve  Graph  DB   •  Rela1onships  are  first  class  ci1zen   •  No  need  for  joins,  just  follow  pre-­‐ materialized  rela1onships  of  nodes   •  Query  &  Data-­‐locality  –  navigate  out   from  your  star1ng  points   •  Only  load  what’s  needed   •  Aggregate  and  project  results  as  you   go   •  Op1mized  disk  and  memory  model   for  graphs  
  19. 19. High  Query  Performance:  Some  Numbers   •  Traverse  4M+  rela1onships  per   second  and  core   •  Cost  based  query  op1mizer  –   complex  queries  return  in   milliseconds   •  Import  100K-­‐1M  records  per  second   transac1onally   •  Bulk  import  tens  of  billions  of  records   in  a  few  hours  
  20. 20. High  Query  Performance:  Some  Numbers   •  Traverse  4M+  rela1onships  per   second  and  core   •  Cost  based  query  op1mizer  –   complex  queries  return  in   milliseconds   •  Import  100K-­‐1M  records  per  second   transac1onally   •  Bulk  import  tens  of  billions  of  records   in  a  few  hours  
  21. 21. Modeling  as  a  Graph  
  22. 22. The  Whiteboard  Model  Is  the  Physical  Model  
  23. 23. CAR   name:  “Dan”   born:  May  29,  1970   twimer:  “@dan”   name:  “Ann”   born:    Dec  5,  1975   since:     Jan  10,  2011   brand:  “Volvo”   model:  “V70”   Property  Graph  Model  Components   Nodes   •  The  objects  in  the  graph   •  Can  have  name-­‐value  proper&es   •  Can  be  labeled   Rela%onships   •  Relate  nodes  by  type  and  direc1on   •  Can  have  name-­‐value  proper&es   LOVES   LOVES   LIVES  WITH   PERSON   PERSON  
  24. 24. Rela%onal  Versus  Graph  Models   Rela%onal  Model   Graph  Model   KNOWS   ANDREAS   TOBIAS   MICA   DELIA   Person   Friend  Person-­‐Friend   ANDREAS   DELIA   TOBIAS   MICA  
  25. 25. Let’s  Model!     Customer,  Supplier,  and  Product  (Master  Data)   Orders  (Ac%vity)  
  26. 26. The  Domain  Model   Order Product Customer Employee SOLD ORDERS Category Employee REPORTS_TO PART_OF PURCHASED Supplier SUPPLIES
  27. 27. Except…  
  28. 28. The  Requisite   Northwind  Example!     NOT  JUST  ANY  
  29. 29. (Northwind)-­‐[:TO]-­‐>(Graph)   Building  the  Graph  Model  
  30. 30. Building  Rela%onships  in  Graphs   SOLD   Employee   Order  Order  
  31. 31. Locate  Foreign  Keys  
  32. 32. (FKs)-­‐[:BECOME]-­‐>(Rela%onships)   Correct  Direc%ons  
  33. 33. Drop  Foreign  Keys  
  34. 34. Find  the  Join  Tables  
  35. 35. Simple  Join  Tables  Becomes  Rela%onships  
  36. 36. ABributed  Join  Tables  Become   Rela%onships  with  Proper%es  
  37. 37. Working  Subset  (Today’s  Exercise)  
  38. 38. Northwind  Graph  Model   Order Product Customer Employee SOLD ORDERS Category Employee REPORTS_TO PART_OF PURCHASED Supplier SUPPLIES
  39. 39. s   Recap  -­‐  Rules   Model  your  graph  first  and     import  into  that  model.   Alterna%vely  …  
  40. 40. Normalized  ER-­‐Models:  Transforma%on  Rules   •  Tables  become  nodes   •  Table  name  as  node-­‐label   •  Columns  turn  into  proper%es   •  Convert  values  if  needed   •  Foreign  Keys  (1:1,  1:n,  n:1)  into  rela%onships,     column  name  into  rela1onship-­‐type  (or  bemer  verb)   •  JOIN-­‐Tables  represent  rela%onships   •  Also  other  tables  without  domain  iden1ty  (w/o  PK)  and  two  FKs   •  Columns  turn  into  rela%onship  proper%es  
  41. 41. Normalized  ER-­‐Models:  Cleanup  Rules   •  Remove  technical  IDs  (auto-­‐incremen1ng  PKs)   •  Keep  domain  IDs  (e.g.  ISBN)   •  Add  constraints  for  those   •  Add  indexes  for  lookup  fields   •  Adjust  names  for  Label,  REL_TYPE  and  propertyName     Note:  currently  no  composite  constraints  and  indexes  
  42. 42. Impor%ng  Your  Data  
  43. 43. Ge^ng  Data  into  Neo4j   Cypher-­‐Based  “LOAD  CSV”  Capability   •  Transac1onal  (ACID)  writes   •  Ini1al  and  incremental  loads  of  up  to     10  million  nodes  and  rela1onships   Command-­‐Line  Bulk  Loader        neo4j-­‐import   •  For  ini1al  database  popula1on   •  For  loads  up  to  10B+  records   •  Up  to  1M  records  per  second    4.58  million  things   and  their  rela1onships…     Loads  in  100  seconds!   CSV  
  44. 44. Ge^ng  Data  into  Neo4j   Custom  Cypher-­‐Based  Loader   •  Uses  transac1onal  Cypher  hmp  endpoint   •  Parametrized,  batched,  concurrent     Cypher  statements   •  Any  programming/script  language  with   driver  or  plain  hmp   JVM  Transac%onal  Loader   •  Use  Neo4j’s  Java-­‐API   •  From  any  JVM  language   •  Up  to  1M  records  per  second   Any     Data     Program   Program   Program  
  45. 45. Data  Import  Demo  
  46. 46. Import  Demo   Cypher-­‐Based  “LOAD  CSV”  Capability   •  Use  to  import  Northwind  CSV  dumps   Command-­‐Line  Bulk  Loader        neo4j-­‐import   •  Chicago  Crimes  Dataset   Rela%onal  Import  Tool        neo4j-­‐rdbms-­‐import   •  Proof  of  Concept   JDBC  +  API   CSV  
  47. 47. RDBMS  Import  Tool  Demo  –  Proof  of  Concept   •  JDBC  for  vendor-­‐independent  database  connec1on   •  SchemaCrawler  to  extract  DB-­‐Meta-­‐Data   •  Use  Rules  to  drive  graph  model  import   •  Op1onal  means  to  override  default  behavior   •  Scales  writes  with  Parallel  Batch  Importer  API   •  Reads  tables  concurrently  for  nodes  &  rela1onships   Demo:  MySQL  -­‐  Employee  Demo  Database     Source:  github.com/jexp/neo4j-­‐rdbms-­‐import   Post   gres   MySQL  Oracle  
  48. 48. Querying  Your  Data  
  49. 49. Basic  Query:  Who  do  people  report  to?   MATCH  (:Employee  {firstName:”Steven”}  )  -­‐[:REPORTS_TO]-­‐>  (:Employee  {firstName:“Andrew”}  )     REPORTS_TO   Steven   Andrew   LABEL   PROPERTY   NODE   NODE   LABEL   PROPERTY  
  50. 50. Basic  Query  Comparison:  Who  do  people  report  to?   SELECT * FROM Employee as e JOIN Employee_Report AS er ON (e.id = er.manager_id) JOIN Employee AS sub ON (er.sub_id = sub.id) MATCH (e:Employee)<-[:REPORTS_TO]-(sub:Employee) RETURN *
  51. 51. Basic  Query:  Who  do  people  report  to?  
  52. 52. Basic  Query:  Who  do  people  report  to?  
  53. 53. MATCH  (sub)-­‐[:REPORTS_TO*0..3]-­‐>(boss),              (report)-­‐[:REPORTS_TO*1..3]-­‐>(sub)   WHERE  boss.firstName  =  'Andrew'   RETURN  sub.firstName  AS  Subordinate,        count(report)  AS  Total;   Express  Complex  Queries  Easily  with  Cypher   Find  all  direct  reports  and  how   many  people  they  manage,     each  up  to  3  levels  down   Cypher  Query   SQL  Query  
  54. 54. “We  found  Neo4j  to  be  literally  thousands  of  %mes  faster   than  our  prior  MySQL  solu1on,  with  queries  that  require   10  to  100  %mes  less  code.  Today,  Neo4j  provides  eBay   with  func1onality  that  was  previously  impossible.”     Volker  Pacher   Senior  Developer  
  55. 55. Who  is  in  Robert’s  (direct,  upwards)  repor%ng  chain?   MATCH path=(e:Employee)<-[:REPORTS_TO*]-(sub:Employee) WHERE sub.firstName = 'Robert' RETURN path;
  56. 56. Who  is  in  Robert’s  (direct,  upwards)  repor%ng  chain?  
  57. 57. Who’s  the  Big  Boss?   MATCH (e:Employee) WHERE NOT (e)-[:REPORTS_TO]->() RETURN e.firstName as bigBoss;
  58. 58. Who’s  the  Big  Boss?  
  59. 59. Product  Cross-­‐Sell   MATCH (choc:Product {productName: 'Chocolade'}) <-[:ORDERS]-(:Order)<-[:SOLD]-(employee), (employee)-[:SOLD]->(o2)-[:ORDERS]->(other:Product) RETURN employee.firstName, other.productName, count(distinct o2) as count ORDER BY count DESC LIMIT 5;
  60. 60. Product  Cross-­‐Sell  
  61. 61. Neo4j  Query  Planner   Cost  based  Query  Planner  since  Neo4j  2.2   •  Uses  database  stats  to  select  best  plan   •  Currently  for  Read  OperaAons   •  Query  Plan  Visualizer,  finds   •  Non  op1mal  queries   •  Cartesian  Product   •  Missing  Indexes,  Global  Scans   •  Typos   •  Massive  Fan-­‐Out    
  62. 62. Query  Planner   Slight  change,  add  an  :Employee  label  -­‐>  more  stats   available  -­‐>  new  plan  with  fewer  database-­‐hits  
  63. 63. Architecture  &  Integra%on   “Polyglot  Persistence”  
  64. 64. Neo4j  Clustering     Architecture  Op%mized  for  Speed  &  Availability  at  Scale   64 Performance  Benefits   •  No  network  hops  within  queries   •  Real-­‐Ame  operaAons  with  fast  and   consistent  response  1mes     •  Cache  sharding  spreads  cache  across   cluster  for  very  large  graphs   Clustering  Features   •  Master-­‐slave  replica1on  with     master  re-­‐elecAon  and  failover     •  Each  instance  has  its  own  local  cache   •  Horizontal  scaling  &  disaster  recovery   Load  Balancer   Neo4j  Neo4j  Neo4j  
  65. 65. MIGRATE     ALL  DATA   MIGRATE     GRAPH  DATA   DUPLICATE   GRAPH  DATA   Non-­‐graph  data   Graph  data   Graph  data  All  data   All  data   Rela%onal   Database   Graph   Database   Applica1on   Applica1on   Applica1on   Three  Ways  to  Migrate  Data  to  Neo4j  
  66. 66. Data  Storage  and   Business  Rules  Execu1on   Data  Mining     and  Aggrega1on   Neo4j  Fits  into  Your  Enterprise  Environment   Applica%on   Graph  Database  Cluster   Neo4j   Neo4j   Neo4j   Ad  Hoc   Analysis   Bulk  Analy%c   Infrastructure   Graph  Compute  Engine   EDW      …   Data   Scien%st   End  User   Databases   Rela1onal   NoSQL   Hadoop  
  67. 67. User  Voice  
  68. 68. Users  Love  Neo4j  
  69. 69. Learn  the  Way  of  the  Graph   Quickly  and  Easily  
  70. 70. Quick  Start:  Plan  Your  Project   1   2   3   4   5   6   7   8   Learn  Neo4j   Decide  on  Architecture   Import  and  Model  Data   Build  Applica%on   Test  Applica%on   Deploy  your  app   in  as  limle  as  8  weeks   PROFESSIONAL  SERVICES  PLAN  
  71. 71. There  Are  Lots  of  Ways  to  Easily  Learn  Neo4j  
  72. 72. GraphConnect,Europe, London,•,May,657,,2015 DATE, LOCATION, ACTIVITIES, Wednesday,,May,6,–,Full,Day,Trainings,(includes,new,Advanced,Deployment,class), Thursday,,May,7,–,Main,Conference, Etc,Venues,in,London,,UK, Training:,4,Norton,Folgate, Conference:,at,155,Bishopsgate,Liverpool,Street, • Customers,and,community,members,such,as,adidas,,Pitney*Bowes,,Orange,,e1 Spirit,,KNMI,and,others,,showcasing,their,Neo4j,solutions, • Neo4j,product,training, • Free,personal,advice,in,Neo4j,GraphClinics, • Opportunity,to,network,with,graph,users,from,across,the,world, • Enjoy,yourself! TICKETS! JAX,Discount,Code, 50%,off, JAX50GCE, www.graphconnect.com www.graphconnect.com GraphConnect,Europe, London,•,May,657,,2015 DATE, LOCATION, ACTIVITIES, Wednesday,,May,6,–,Full,Day,Trainings,(includes,new,Advanced,Deployment,class), Thursday,,May,7,–,Main,Conference, Etc,Venues,in,London,,UK, Training:,4,Norton,Folgate, Conference:,at,155,Bishopsgate,Liverpool,Street, • Customers,and,community,members,such,as,adidas,,Pitney*Bowes,,Orange,,e1 Spirit,,KNMI,and,others,,showcasing,their,Neo4j,solutions, • Neo4j,product,training, • Free,personal,advice,in,Neo4j,GraphClinics, • Opportunity,to,network,with,graph,users,from,across,the,world, • Enjoy,yourself! TICKETS! JAX,Discount,Code, 50%,off, JAX50GCE, www.graphconnect.com www.graphconnect.com
  73. 73. Rela%onal  to  (Big)  Graph   Harnessing  the  Power  of  the  Graph   End  of  PresentaAon  

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