The document discusses different database technologies including SQL, NoSQL, and different types of NoSQL databases like key-value, column, graph and document stores. It provides examples like Redis, Riak, Neo4j and MongoDB. It discusses concepts like CAP theorem and tradeoffs between consistency, availability and partition tolerance. It also provides use cases for different database types and suggests using the right database for the right problem.

1. To SQL
OR
No (T) SQL?
PFCongres 2012 Jeroen van Dijk

2. JEROEN VAN DIJK
∂ Nerd chief @ ENRISE
∂ PHPBenelux board member
∂ Zend Certified Engineer
∂ Web technology freak
∂ Open source addict
JEROEN@ENRISE.COM @NEOREY

3. THE ENRISE RESTAURANT
∂ We want to prepare the best dishes
∂ With the best ingredients
∂ To create a magical client experience!
∂ You engineers are our top chefs!

5. WHAT IS
ACID?

7. EVER HEARD OF
CAP
THEOREM?

8. KNOW
ABC?
∂ Courtesy of Tim Anglade

9. Always
∂ Courtesy of Tim Anglade

10. Always
Be
∂ Courtesy of Tim Anglade

11. Always
Be
Caching
∂ Courtesy of Tim Anglade

12. Always
Be
Caching
∂ Courtesy of Tim Anglade

13. JOIN THE HYPE?
WANT!==HAVE
13

14. HAVE TO USE NOSQL?
SCALABILITY
&
PERFORMANCE
14

15. HAVE TO USE NOSQL?
SCALABILITY
&
PERFORMANCE
15

16. RECAP RDBMS GREATNESS
∂ Standard Query Language
∂ ACID: Atomicity, Consistency, Isolation, Durability
∂ Supported by everyone and everything
∂ Tuning options
∂ Battle tested!
∂ Open source?!
NOT ENOUGH?
16

17. MAKING LOADS OF MONEY?
17

18. BUY A BIGGER BOX
18

19. NOT SO GREAT RDBMS FEATURES
∂ Vertical scalability
19

20. NOT SO GREAT RDBMS FEATURES
∂ Vertical scalability
∂ Horizontal scalability
20

21. NOT SO GREAT RDBMS FEATURES
∂ Vertical scalability
∂ Horizontal scalability
∂ Schema changes!
21

22. SINCE 2004
DATA++++++
22

23. WHO NEEDS
ACID!?

24. CAP THEOREM
AVAILABILITY
A
C P
CONSISTENCY PARTITION TOLERANCE

25. CAP THEOREM
AVAILABILITY
A
CA AP
PICK
TWO
C P
CONSISTENCY PARTITION TOLERANCE
CP

26. CAP THEOREM
AVAILABILITY
MySQL (InnoDB, not MyISAM) A Dynamo Voldemort
PostgreSQL SQL Server Cassandra CouchDB
CA AP
Oracle RAC Neo4J SimpleDB Riak
PICK
TWO
C P
CONSISTENCY PARTITION TOLERANCE
CP
Hypertable Hbase
BigTable MongoDB Terrastore
Couchbase MemcacheDB Redis

27. 4 NOSQL
TYPES

28. 4 NOSQL TYPES
KEY-VALUE
COLUMN
GRAPH
DOCUMENT

29. 4 NOSQL TYPES
KEY-VALUE
COLUMN
GRAPH
DOCUMENT

30. 4 NOSQL TYPES
KEY-VALUE
COLUMN
GRAPH
DOCUMENT

31. 4 NOSQL TYPES
KEY-VALUE
COLUMN
GRAPH
DOCUMENT

32. 4 NOSQL TYPES
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ 122 known NoSQL databases

33. 4 NOSQL TYPES
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Focus from Redis, Riak, Neo4J, MongoDB

34. KEY - VALUE
KEY-VALUE ∂ Schema-less design
∂ Just strings of data
∂ Hard to query
COLUMN
∂ Mostly in memory
GRAPH
DOCUMENT

35. KEY - VALUE
KEY-VALUE
[
COLUMN “key1” => “value1”,
“key2” => “value2”,
GRAPH
“key3” => “value3”,
DOCUMENT
]

36. REDIS
KEY-VALUE
∂ Blazing fast key-value implementation
∂ Master - slave replication
∂ Lots of methods to query data
COLUMN
∂ Notable options
∂ Data types : Strings, hashes, lists, sets
GRAPH ∂ Data expiration
∂ Pub/Sub for messaging
DOCUMENT
∂ Reconsider when using Memcached

37. COLUMN
KEY-VALUE ∂ BigTable or Dynamo style
∂ Consistent hashing
∂ Vector clocks
COLUMN
∂ Hinted hand off
GRAPH
DOCUMENT

38. COLUMN
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Data stored in a ring

39. COLUMN
D
KEY-VALUE
COLUMN
C A
GRAPH
DOCUMENT
B
∂ Consistent hashing with 4 nodes

40. COLUMN
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Partitioning as done by Riak

41. COLUMN
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Read / Write....

42. COLUMN
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Anywhere in the ring

43. COLUMN
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ First node joins the cluster, claims all partitions

44. COLUMN
KEY-VALUE N=3 A
B
C
COLUMN
GRAPH
DOCUMENT
∂ Reading / writing is done to 3 nodes

45. COLUMN
KEY-VALUE N=3 A
B
W=2 C
COLUMN
R=2
GRAPH
DOCUMENT
∂ Reading / writing succeeds with 2 valid responses

46. COLUMN
KEY-VALUE N=3 A
[ Ov1,v2 ]
B
W=2 C [ Ov1 ]
COLUMN
R=2
D
[ Ov1,v2 ]
GRAPH
DOCUMENT
∂ Node C down, while new write action

47. COLUMN
KEY-VALUE N=3 A
[ Ov1,v2 ]
B
W=2 C [ Ov1,v2 ]
COLUMN
R=2
D
[ Ov1,v2 ]
GRAPH
DOCUMENT
∂ Node D hands the new version off

48. RIAK
KEY-VALUE ∂ Dynamo implementation
∂ MapReduce query style
∂ Multiple storage backends
COLUMN
∂ Notable options
§ Link walking (like Graph solutions)
GRAPH
§ Solr-like search interface
§ Secondary indexes
DOCUMENT

49. GRAPH
KEY-VALUE ∂ Relations more important then entities
∂ From RDBMS perspective : SELF JOINS
COLUMN
GRAPH
DOCUMENT

50. GRAPH
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Facebook style

51. GRAPH
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Betweenness centrality

52. GRAPH
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Degree centrality

53. GRAPH
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Closeness centrality

54. GRAPH
KEY-VALUE
COLUMN
GRAPH
DOCUMENT
∂ Twitter style

55. GRAPH
KEY-VALUE
2
COLUMN 1
9 3 2
1 2 1
GRAPH 2
3
3
DOCUMENT
∂ TomTom style

56. GRAPH
KEY-VALUE
2
COLUMN 1
9 3 2
1 2 1
GRAPH 2
3
3
DOCUMENT
∂ TomTom style

57. GRAPH
KEY-VALUE
2
COLUMN 1
9 3 2
1 2 1
GRAPH 2
3
3
DOCUMENT
∂ TomTom style

58. NEO4J
KEY-VALUE ∂ ACID compliant
∂ Enterprise product for HA ($$$)
∂ Custom query language
COLUMN
∂ Notable options
§ Self contained web admin
GRAPH
DOCUMENT

59. DOCUMENT
KEY-VALUE ∂ Largest resemblance with RDBMS
∂ MapReduce
∂ Software architect more important
COLUMN
GRAPH
DOCUMENT

60. MONGODB
KEY-VALUE ∂ MySQL of it’s generation?!
∂ Master - slave structure
∂ MapReduce
COLUMN
∂ Notable options
§ Geo indexes
GRAPH
DOCUMENT
SMALLEST LEARNING CURVE!

61. USE CASES
∂ Rapid changing data which fits in memory
KEY-VALUE
∂ Analytics, logging, real-time data collection
COLUMN
GRAPH
DOCUMENT

62. USE CASES
∂ Rapid changing data which fits in memory
KEY-VALUE
∂ Analytics, logging, real-time data collection
∂ Very good availability & fault tolerance
COLUMN
∂ Applications where seconds of downtime hurt
GRAPH
DOCUMENT

63. USE CASES
∂ Rapid changing data which fits in memory
KEY-VALUE
∂ Analytics, logging, real-time data collection
∂ Very good availability & fault tolerance
COLUMN
∂ Applications where seconds of downtime hurt
∂ For rich interconnected data
GRAPH
∂ Social relational data, geo & maps data
DOCUMENT

64. USE CASES
∂ Rapid changing data which fits in memory
KEY-VALUE
∂ Analytics, logging, real-time data collection
∂ Very good availability & fault tolerance
COLUMN
∂ Applications where seconds of downtime hurt
∂ For rich interconnected data
GRAPH
∂ Social relational data, geo & maps data
∂ MySQL like usage with indexes
DOCUMENT
∂ Any type of data you’d fit in MySQL

65. ONE USEFUL INGREDIENT
65

66. MORE GREAT TASTES
66

67. ∂ KLIK VOOR FOOTER
67

68. Polyglot persistence?
∂ KLIK VOOR FOOTER
68

69. ∂ THANK YOU! FEEDBACK? JOIND.IN/7084

70. ∂ MORE DETAILS? SCAN THIS CODE.