My improvised/copied preso for some short talk I gave.

1. Non-relational Databases A new kind of Databases for handling Web Scale

3. The solution

4. Benefits

5. Cost

6. Example: Cassandra

8. Data (peta-bytes generated daily)

9. Processing (all this data needs processing)

10. Exponential growth (surging unpredictable demands)

12. MS SQL

13. Sybase

14. MySQL

17. Existing RDBMS clustering solutions require scale-up, which is limited & not really scalable when dealing with exponential growth

18. Machines have upper limits on capacity, & sharding the data & processing across machines is very complex & app-specific

20. Basically, you end up denormalizing everything & loosing all benefits of relational databases

22. Yahoo!

23. Amazon

24. Facebook

25. Twitter

26. Linked-In

27. & many more

29. Web search result (Google /Yahoo!)

30. Item added to cart (Amazon)

33. A vailability

36. This means, that when writing a record, all replica's must be updated too

38. Don't lock the replicas => be less c onsistent

40. Drop availability (CP)

43. Hbase (developed at Yahoo!)

44. Dynamo (developed at Amazon)

45. Cassandra (developed at FaceBook)

46. Voldemort (developed at LinkedIn)

49. Extremely fast

50. Highly available, decentralized & fault tolerant (no single-point-of-failure)

51. Transparent sharding (consistent hashing)

52. Elasticity

53. Parallel processing

54. Dynamic schema

55. Automatic conflict resolution

56. Consistent hashing

57. Replication

58. Replication – node joining

59. Replication – node leaving

61. Runs on a large number of cheap commodity machines

62. Replication

63. Gossip protocol

64. Transparently handles adding/removing nodes

66. Read your writes consistency

67. Session consistency

68. Monotonic read consistency

70. Quorum

72. Vector clock conflict resolution

75. Record can have several “supercolumns”

77. Brings the workers to the data – excellent fit for non-relational databases

78. Minimizes the programming to 2 simple functions (map & reduce)

79. Example: count appearances of a word in a giant table of large texts

80. Map/Reduce (contd.)

81. Storage

83. Non-standard new API model

84. Non-standard new Schema model

85. New knowledge required to tune/optimize

86. Less mature

88. Put(key, value)

89. Delete(key)

91. a record (list of “columns”: <name, value, timestamp>)

93. No schema

96. Columns / column families

97. Slicing with predicates (queries)

98. PartitionOrderer

102. Use any node (closest to you)

103. Cassandra API

104. Cassandra API (contd.)

106. Simple client

108. Digg

109. Twitter

112. http://prettyprint.me/2010/01/20/introduction-to-nosql-and-cassandra-part-2/