1. MongoDB
Justin Smestad
@jsmestad
justin@mongomachine.com

2. whoami
Software Engineer (Ruby, Javascript, and Clojure)
User Experience & Design on the side
Passion for DevOps
Chicagoland native

3. whoami
Software Engineer (Ruby, Javascript, and Clojure)
User Experience & Design on the side
Passion for DevOps
Chicagoland native

4. whoami
Software Engineer (Ruby, Javascript, and Clojure)
User Experience & Design on the side
Passion for DevOps
Chicagoland native

5. whoami
Software Engineer (Ruby, Javascript, and Clojure)
User Experience & Design on the side
Passion for DevOps
Chicagoland native

6. Background
Java Engineer @ Orbitz Worldwide
Brought Ruby and RSpec/Cucumber to replace
Fitness Testing
Application Developer @ Factory Design Labs
Brand & Campaign sites for Audi, Oakley, and TNF
Independent Contractor
Development team for TechStars alumni

7. Background
Java Engineer @ Orbitz Worldwide
Brought Ruby and RSpec/Cucumber to replace
Fitness Testing
Application Developer @ Factory Design Labs
Brand & Campaign sites for Audi, Oakley, and TNF
Independent Contractor
Development team for TechStars alumni

8. Background
Java Engineer @ Orbitz Worldwide
Brought Ruby and RSpec/Cucumber to replace
Fitness Testing
Application Developer @ Factory Design Labs
Brand & Campaign sites for Audi, Oakley, and TNF
Independent Contractor
Development team for TechStars alumni

9. mongo machine
hosted MongoDB wherever you need it

10. What’s mongo machine ?

11. mongo machine
hosted MongoDB + data management
http://mongomachine.com

12. mongo machine hosted MongoDB + data management
Our Goals
Educate and promote best practices
Increase customer efficiency
Promote transparency

13. Our Product

14. Hosted MongoDB
Provide the best managed MongoDB
experience, on any platform.

15. mongo machine hosted MongoDB + data management
Managed MongoDB Infrastructure
Our infrastructure on AWS or Rackspace
Automated Deployments to your own infrastructure
Management Console
Instantly create new databases
Track database trends
Scale up & down on-demand

16. Management Console
Analytics data about what your DB is doing.

17. Management Console
Analytics data about what your DB is doing.

19. “NoSQL”
hasn’t this been tried before?
why should I care now?

20. So what is MongoDB?
(10,000ft view)

21. MongoDB (from "humongous") is a
scalable, high-performance, open source,
schema-free, document-oriented
database.
-- mongodb.org

22. Who is using MongoDB?

23. Fortune 500 <=> Startups

24. Github
Bug Tracking & Analytics

25. Foursquare
Check-in System

26. New York Times
Photo Submissions

27. The list is large and growing fast
http://www.mongodb.org/display/DOCS/Production+Deployments

29. Philosophy

30. Philosophy
“One size DOESN’T fit”

31. Philosophy
Non-relational (de-normalized) DBs are easier to scale,
especially horizontally

32. Philosophy
DBs should be an on-demand commodity (cloud-like)

33. Philosophy
Focus on performance, flexibility and scalability (CA)

34. Philosophy
Not concerned with transactional stuff or relational
semantics

35. Philosophy
Mongo aims for the
performance of key-
value stores while
maintaining
functionality of
traditional RDBMS

37. Features

38. Features
Standard database stuff
Indexing
Traditional master-slave
Database References (no concept of JOINS)

39. Features
Standard database stuff
Indexing
Traditional master-slave
Database References (no concept of JOINS)

40. Features
Standard database stuff
Indexing
Traditional master-slave
Database References (no concept of JOINS)

41. Features
Standard database stuff
Indexing
Traditional master-slave
Database References (no concept of JOINS)

42. Features
Speed
in-memory dataset with fsync to disk

43. Features
Durability
solve with replication, or use journaling

44. Features
Document-oriented database

45. Features: Document Storage
Documents are stored in BSON (binary JSON)

46. Features: Document Storage
BSON is a binary serialization of JSON-like objects

47. Features: Document Storage
Large BSON documents are served in chunks (GridFS)

48. Features: Document Storage
This is extremely powerful, b/c it means mongo
understands JSON natively

49. Features: Document Storage
Any valid JSON can be easily imported and queried

50. Features: Document Storage
Documents can contain embedded documents
(nested hashes) without losing any indexing
capabilities.

51. Features
Schema-less; very flexible
no more blocking ALTER TABLE or NULL debates

52. Features
Replica Sets and Sharding make horizontal scaling easy.

53. Features: Replica Set
Traditional Master-Slave, but automatic failover
Each server holds all data (CA)
One is elected master at a given time
Arbitration process detects failover
New election within seconds

54. Features: Replica Set
Traditional Master-Slave, but automatic failover
Each server holds all data (CA)
One is elected master at a given time
Arbitration process detects failover
New election within seconds

55. Features: Replica Set
Traditional Master-Slave, but automatic failover
Each server holds all data (CA)
One is elected master at a given time
Arbitration process detects failover
New election within seconds

56. Features: Replica Set
Traditional Master-Slave, but automatic failover
Each server holds all data (CA)
One is elected master at a given time
Arbitration process detects failover
New election within seconds

57. Features: Replica Set
Traditional Master-Slave, but automatic failover
Each server holds all data (CA)
One is elected master at a given time
Arbitration process detects failover
New election within seconds

58. Features
Map/Reduce
Map Reduce operations are written in Javascript.

59. Features: Querying
Rich, javascript-based query syntax
Allows us to do deep, nested queries

60. Features: Querying
Rich, javascript-based query syntax
Allows us to do deep, nested queries

61. Features: Querying
Rich, javascript-based query syntax
Allows us to do deep, nested queries
db.order.find( { shipping: { carrier: "usps" } } );

62. Features: Querying
Rich, javascript-based query syntax
Allows us to do deep, nested queries
db.order.find( { shipping: { carrier: "usps" } } );
shipping is an embedded document (object)

63. Features
atomic operations
upserts, $set, $inc, $push, $pull, $pop, $addToSet, ...

64. Features
Official Drivers
.NET, Java, Javascript, Ruby, Node.js, PHP, Haskell, C/C++, Perl

65. Features
Community & Market Growth
MongoDB works alongside your existing technologies

67. Concepts

68. Concepts: Document-oriented
Think of “documents” as objects / database records

69. Concepts: Document-oriented
Think of “documents” as objects / database records
Documents are basically just JSON in binary

70. Concept: Document-oriented
Think of “documents” as objects / database records
Documents are basically just JSON in binary
Ability to store information all together

71. Concept Mapping
RDBMS (mysql, postgres) MongoDB
Tables Collections

72. Concept Mapping
RDBMS (mysql, postgres) MongoDB
Tables Collections
Records/rows Documents/objects

73. Concept Mapping
RDBMS (mysql, postgres) MongoDB
Tables Collections
Records/rows Documents/objects
Queries return record(s) Queries return a cursor

74. Concept Mapping
RDBMS (mysql, postgres) MongoDB
Tables Collections
Records/rows Documents/objects
Queries return record(s) Queries return a cursor
???

75. Concepts: Cursors
Queries return “cursors” instead of collections

76. Concepts: Cursors
Queries return “cursors” instead of collections
A cursor allows you to iterate through the result set

77. Concepts: Cursors
Queries return “cursors” instead of collections
A cursor allows you to iterate through the result set
A big reason for this is performance

78. Concepts: Cursors
Queries return “cursors” instead of collections
A cursor allows you to iterate through the result set
A big reason for this is performance
Much more efficient than loading all objects into
memory

79. Concepts: Cursors
The find() function returns a cursor object

80. Concepts: Cursors
The find() function returns a cursor object
var cursor = db.logged_requests.find({ 'status_code' : 200 })
cursor.hasNext() // "true"
cursor.forEach(
function(item) {
print(tojson(item))
}
);
cursor.hasNext() // "false"

82. Cool Features

83. Cool Features
Capped collections
Fixed-sized, limited operation, auto-LRU age-out
collections
Fixed insertion order
Extremely performant
Ideal for logging and caching

84. Cool Features
Capped collections
Fixed-sized, limited operation, auto-LRU age-out
collections
Fixed insertion order
Extremely performant
Ideal for logging and caching

85. Cool Features
Capped collections
Fixed-sized, limited operation, auto-LRU age-out
collections
Fixed insertion order
Extremely performant
Ideal for logging and caching

86. Cool Features
Capped collections
Fixed-sized, limited operation, auto-LRU age-out
collections
Fixed insertion order
Extremely performant
Ideal for logging and caching

87. Cool Features
Capped collections
Fixed-sized, limited operation, auto-LRU age-out
collections
Fixed insertion order
Extremely performant
Ideal for logging and caching

89. Use Cases

90. Use Cases
Data Warehouse
Mongo understands JSON natively
Very powerful map-reduce for analytics
Nested hashes make roll-up (RRDtool) systems
natural

91. Use Cases
Data Warehouse
Mongo understands JSON natively
Very powerful map-reduce for analytics
Nested hashes make roll-up (RRDtool) systems
natural

92. Use Cases
Data Warehouse
Mongo understands JSON natively
Very powerful map-reduce for analytics
Nested hashes make roll-up (RRDtool) systems
natural

93. Use Cases
Data Warehouse
Mongo understands JSON natively
Very powerful map-reduce for analytics
Nested hashes make roll-up (RRDtool) systems
natural

94. Use Cases
Audit Trails
Nested documents allow you to store audit trails in
context.

95. Use Cases
Audit Trails
Nested documents allow you to store audit trails in
context.
{
‘name’: ‘Justin Smestad’,
‘address’: ‘1441 Central St’,
‘history’: [
{
‘name’: ‘Justin Smestad’,
‘address’: ‘350 S Jackson St’
},....]
}

97. Limitations

98. Limitations
Transaction support
Relational integrity (normalization)
Partition balancing
Map / Reduce is single threaded
Data compaction

99. Limitations
Transaction support
Relational integrity (normalization)
Partition balancing
Map / Reduce is single threaded
Data compaction

100. Limitations
Transaction support
Relational integrity (normalization)
Partition balancing
Map / Reduce is single threaded
Data compaction

101. Limitations
Transaction support
Relational integrity (normalization)
Partition balancing
Map / Reduce is single threaded
Data compaction

102. Limitations
Transaction support
Relational integrity (normalization)
Partition balancing
Map / Reduce is single threaded
Data compaction

103. Why not `other db` ?
CouchDB, Cassandra, Riak, Membase, Redis, ...

104. Why not `other db`?
FLEXIBILITY
MongoDB’s design choices empower developers and
administrators with features that allow you to
implement their own solutions. *

105. Questions?
@jsmestad / @mongo_machine