The document discusses various NoSQL databases including Voldemort, MongoDB, CouchDB, Riak, Neo4J, and Redis. It provides information on their origins, licenses, implementation languages, data models, how they scale, APIs, deployments, and support. Key aspects covered are the CAP theorem tradeoffs between consistency, availability, and partition tolerance. The document emphasizes that NoSQL databases take different approaches and have different tradeoffs to consider for each use case.

1. oSQL
N ACKDOWN
SM
Tim
Berglund
1

2. @tlberglund
#nosql
2

3. Voldemort
3

4. via
negativa
4

5. SQL
5

6. relation (n.)
An unordered set of
tuple (n.)the same type.
tuples of
6

7. tuple (n.)
A function that maps
attributes to values.
7

8. tuple (n.)
(A bundle of key-value
pairs—but don’t tell
anyone!)
8

9. id username pwd_hash born_at monkey
1 mluther d8c82af9 Nov 1483 FALSE
2 aaugustine 329b8dae Nov 354 FALSE
3 gnyssa e50ec9e0 Jun 335 FALSE
4 bonzo 330e01f2 Apr 2007 TRUE
9

10. Relations
10

11. Comparing “NoSQL” to
“relational” is a bit of a
shell game.
—Eben Hewitt
author of Cassandra: The Definitive Guide
11

12. Transactions
12

13. CAP Theorem
C
A P
13

14. Tradeoff Between
Consistency
Availability
Partition Tolerance
14

15. Between what?
15

16. consistency (n.)
All clients always have
the same view of the
data.
16

17. availability (n.)
All clients can always
read or write within some
maximum latency.
17

18. partition tolerance (n.)
No set of failures less than
total network failure is
allowed to cause the system
to respond incorrectly.
18

19. Cluster Node
Cluster Node
Cluster Node
Cluster Node
19

20. Cluster Node
Cluster Node
Switch
Cluster Node
Cluster Node
20

21. Cluster Node
Cluster Node
Switch
Cluster Node
Cluster Node
21

22. CAP Theorem
C
A P
22

23. Strongly Consistent
MongoDB
C Cassandra
A P
23

24. Always Available
CouchDB
Riak
C Voldemort
Cassandra
A P
24

25. Partition Intolerant
MySQL C
Oracle
SQL Server
Neo4J
Redis A P
25

26. C
A P
26

27. via
negativa
a way forward
27

28. NoSQL is a set of different
approaches to storing and
retrieving data.
28

29. What’s Different?
Data models
Querying
Approaches to scale
29

30. Tradeoffs
Complex transactions vs. scalability
Consistency vs. availability (often)
Performance vs. durability
Horizontal vs. vertical scale
Cheap writes vs. cheap reads
30

31. Origin
License
I mplementati on language
Data model
How d oes it scale?
API/Qu ery language
Deployments
Support and community
31

32. 32

33. Voldemort
33

34. 34

35. Origin-
Facebook Inbox search
b ack in 2007
License-
Apache Public License 2.0
35

36. Implementa tion Language-
Java 6
Data Model-
It’s a Big-Table-based
“c olumn store.”
36

37. Name Value Timestamp
Column
37

38. email tlberglund@gmail.com 20101011T120502Z
Column
38

39. Key
Column
Column
Column
Column
Column
Row
39

40. Key Column Column
Key Column Column Column
Key Column
Column Family
40

41. bbf77f01d full_name email
050fe74e2 full_name email mobile
8b20d8f6 full_name
“Contacts” Column Family
41

42. Name
key Column
key Column
key Column
SuperColumn
42

43. 4145bfaf15f10c2e6033f8b9c3143297a36f5fe3
full_name full_name Tim Berglund 20101011T120502Z
email email tlberglund@gmail.com 20101011T120503Z
mobile mobile [redacted] 19940217T145637Z
postal_code postal_code 80123 20101011T120452Z
Contact Info SuperColumn
43

44. Key SuperColumn SuperColumn
Key SuperColumn SuperColumn
Key SuperColumn
SuperColumn Family
44

45. SuperColumn Family
SuperColumn Family
SuperColumn Family
Column Family
Column Family
Keyspace
45

46. A what?
46

47. Nested Hash Table
Cluster.Keyspace.ColumnFamily[key1][key2] = <column>
Cluster.Keyspace.ColumnFamily[key] = <row>
...SuperColumnFamily[key1][key2][key3] = <column>
...SuperColumnFamily[key1][key2] = <row>
...SuperColumnFamily[key] = <map of rows>
47

48. Scalability-
Rock star!
(see Amazo n Dynamo)
0000
2000
E000
4000
C000
6000
A000
8000
48

49. 0000
E000 2000
C000 4000
A000 6000
8000
49

50. Scalability
- Consist ent hashing
- No disting uished nodes
- Add and re move nodes
on a live cluster
50

51. API
- Thrift RPC
- Easy to fet ch columns
by key
- Nat ive clients
- Hadoop integration
51

52. Deployments-
52

53. Sup port/Community-
- www .datastax.com
- Eben H ewitt’s book
- Plus , it is an Apache
project...
53

54. Voldemort
54

55. 55

56. Origin-
Founders of DoubleClick
were tota lly going to
take o ver the Cloud
License-
Database: G NU Affero 3.0
D rivers: APL 2
56

57. Implementatio n Language-
C++
D ata Model-
JSON documen t database
(this is so simple!)
57

58. { "_id" : ObjectId("4cbd00455280f73d395922a4"),
"contact" : {
"tags" : ["man", "", "", ""]
"firstName" : "Myron",
"lastName" : "Dalton",
"address1" : "4322 Maple Street",
"city" : "Santa Ana",
"state" : "CA",
"postalCode" : "92705",
"email" : "Myron.C.Dalton@spambob.com"
},
"occupation" : "Long haul truck driver"
}
58

59. Does it scale?
Well...it shards!
59

60. API
- Native JavaScript
console
- Bin ary drivers
- Ad-hoc q uery language
( but it’s NOT S QL, okay?)
60

61. db.address.find().limit(5)
db.contact.find({ “lastName”: “Berglund” })
db.address.find({ $query: { “stateProvince”: “CO” }, $orderBy:
{ “city”: 1 } })
db.address.find({ “contact.city”: “Chicago” })
db.address.remove({_id: ObjectId("4cbcfd7df72291161b1d1bf2")})
61

62. API
- Can writ e MapReduce
jobs in JavaScript
- Morph ia for Java
- Mongoos e for node.js
62

63. D eployments-
63

64. Communi ty/Support
- www.10gen.com
- Proba bly the NoSQL
minds hare leader
64

65. Concerns
- Write durability?
Journa ling coming
in 1.8!
- Sharding p erformance
- But everyone still wants
to date her
65

66. Voldemort
66

67. 67

68. Origin
-N eo Technolog ies in 2003
-Malmö and San Francisco
68

69. License
- GPL3, f ull-featured
-C ommercial
$49/ mo antiviral
$499/mo advanced
$1,999/mo enterprise
69

70. Maturity
- Productio n since 2003
- 1.0 i n Feb 2010
Implementat ion Language
- Java 6
- Easil y embeddable!
70

71. Da ta Metaphor
- Graph
- Nodes, re lationships
71

72. All nodes and relationships have
arbitrary properties
4CG
7L
CN?
M QCN
B
7I
>CMJ
%HA NCIH -;NNB?Q
LEM
ON;
;A? QCNB
QCNB
M CH
EM
(IFFSQII> ?;
4SJ?M 3J CNB
Q
+HIQM "LC;H
72

73. Query Model
- REST/JSON
- Java traversal API
- Bindings in C lojure, Ruby,
Python, PHP, S cala, Grails
- JTA/JTS XA
73

74. Scale Idiom
- Traditional ly focused on
si ngle-node pe rformance
- Recent HA support
-M aster/slave
- ZK mast er election
- Writeable slaves
74

75. Support
- Neo Technologies
Deployments
- Box.net
-T houghtWorks
75

76. Voldemort
76

77. 77

78. Origin-
Internal datastore for
Basho’s S alesforce.com
apps
(Hey, it seemed like a
good idea at the time!)
78

79. License-
APL 2 fo r OSS version
Clo sed-source
“Enterprise DS” version
79

80. Implementatio n Language-
Erlang, C, S piderMonkey
Ja vaScript VM
Data Model-
Key/va lue store, but
w ith buckets!
80

81. Key Value
That’s it.
81

82. Key Value Key Value
Key Value Key Value
Bucket A
Key Value Key Value
Key Value Key Value
Bucket B
82

83. name Tim birthday 061972
occupation Developer city Littleton
Bucket A
name Aurelius birthday 110354
occupation Bishop city Hippo
Bucket B
83

84. Does it scale?
- Like a boss!
- No distin guished node
- Tunable consistency,
replication
- Add n odes without
taking the cluster down
84

85. API
- HTTP interface (slow,
but featureful)
- Proto col Buffers (a
performa nce beast)
85

86. API
- Key CRUD
- Ma pReduce in
JavaScript
- Grap h traversals
translate t o MapReduce
86

87. D eployments-
87

88. Communi ty/Support
- www.basho.com
88

89. Voldemort
89

90. 90

91. Origin-
Salvatore Sanfilippo
w rote it for his analytics
s ite, llogg.com
License-
Open Sou rce-Brand
open source
91

92. Implementat ion Language
- ANSI C, baby
- Wan ts a POSIX OS
- 340kB download!
92

93. Data Model
-Key/ value store++
-Strings
-Hashes, Sets
-Lists
-Sorted Sets
93

94. Does it scale?
- Ve rtically, sure
- Plus it’s really fast
- Master/sl ave options
- Technically a CA system
94

95. API
- Binary socke t interface
- Dr ivers for 22+
languages
- Comm ands look like
assem bly language
95

96. 96

97. 97

98. 98

99. 99

100. D eployments-
craigslist
100

101. Commun ity/Support
- Offici ally sponsored
by VMware
101

102. Voldemort
102

103. Do you need this?
Maybe.
103

104. 104

105. Thank You
Tim Berglund
www.augusttechgroup.com
tim.berglund@augusttechgroup.com
@tlberglund
105

106. Further Reading
Brewer’s Conjecture
http://www.podc.org/podc2000/
Proof of Brewer’s Conjecture (the “CAP Theorem”)
http://bit.ly/cap-theorem-proof
Amazon Dynamo
http://bit.ly/amazon-dynamo
http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html
Google BigTable
http://bit.ly/big-table
The CAP Theorem Explained
http://www.julianbrowne.com/article/viewer/brewers-cap-theorem
Visualzing NoSQL Databases on the CAP Venn Diagram
http://blog.nahurst.com/visual-guide-to-nosql-systems
Redis
http://redis.io/
Cassandra
http://cassandra.apache.org
MongoDB
http://mongodb.org
106

107. Further Reading
CouchDB
http://couchdb.apache.org
Riak
http://basho.com
Voldemort
http://project-voldemort.com
Neo4J
http://neo4j.org
Pretty Much Everything About NoSQL
http://nosql.mypopescu.com
107

108. Photo Credits
Wrestlers
http://www.flickr.com/photos/stigster/4573851095
Desert Road
http://www.flickr.com/photos/kenlund/2439199670
Kindergarten Graduation
http://www.flickr.com/photos/moyermk/3102262394
Clipboard
http://www.flickr.com/photos/wheatfields/264890076
Winning Wrestler
http://www.flickr.com/photos/jrandallc/2259174414
108