Use of No-SQL databases in web applications is becoming increasingly common. In part this is because they work well with rapid application development due to their schema-less nature. And partly because they scale well in a cloud-based environment without too much effort. MongoDB is one of the many No-SQL database technologies available today. It's schema-less nature works very well with rapid application development nature of ColdFusion. Unlike other No-SQL databases, it offers the ability run arbitrary queries against databases without having to first write map-reduce functions. It also has some other interesting features like capped collections with asynchronous write ability. In this session, Indy takes you through some common use-cases for considering MongoDB with ColdFusion applications, contrasting it with other No-SQL databases like CouchDB. And he shares his experiences of using it with cloud-based ColdFusion applications. The aim of the session is to provide an overview of using MongoDB with ColdFusion so that you have another tool to consider when working on your next web application. This was presented at CFObjective, Melbourne, November 17-18, 2011.

1. NoSQL with ColdFusion
Using MongoDB
Indy Nagpal
CFObjective, Melbourne, 2011

2. About me

3. About me
✴ Straker Translations, New Zealand
✴ Been working with CF for a while now
✴ Cloud-based applications using Railo
✴ Love CFWheels
✴ nagpals.com/blog, @indynagpal

4. NoSQL?

5. NoSQL?
✴ Different things to different people
✴ Non-Relational rather than NoSQL
✴ System for storage/retrieval of data
✴ Persistence layer is not a responsibility of a
single system

6. Why NoSQL?

7. Why NoSQL?
✴ 100s of millions of items in a big table

8. Why NoSQL?
✴ 100s of millions of items in a big table
✴ Dynamic data structure

10. SQL NoSQL
Static Data Structure Dynamic Data Structure
Dynamic Query Static Query (generally)
Consistency Eventual consistency
Transactions Limited Transactions
Difficult to distribute Distributed, fault-tolerant
Limited performance under load Optimized for heavy read-write

11. SQL NoSQL
Static Data Structure Dynamic Data Structure
Dynamic Query Static Query (generally)
Consistency Eventual consistency
Transactions Limited Transactions
Difficult to distribute Distributed, fault-tolerant
Limited performance under load Optimized for heavy read-write

12. SQL NoSQL
Static Data Structure Dynamic Data Structure
Dynamic Query Static Query (generally)
Consistency Eventual consistency
Transactions Limited Transactions
Difficult to distribute Distributed, fault-tolerant
Limited performance under load Optimized for heavy read-write

13. SQL NoSQL
Static Data Structure Dynamic Data Structure
Dynamic Query Static Query (generally)
Consistency Eventual consistency
Transactions Limited Transactions
Difficult to distribute Distributed, fault-tolerant
Limited performance under load Optimized for heavy read-write

14. SQL NoSQL
Static Data Structure Dynamic Data Structure
Dynamic Query Static Query (generally)
Consistency Eventual consistency
Transactions Limited Transactions
Difficult to distribute Distributed, fault-tolerant
Limited performance under load Optimized for heavy read-write

15. SQL NoSQL
Static Data Structure Dynamic Data Structure
Dynamic Query Static Query (generally)
Consistency Eventual consistency
Transactions Limited Transactions
Difficult to distribute Distributed, fault-tolerant
Limited performance under load Optimized for heavy read-write

16. SQL NoSQL
Static Data Structure Dynamic Data Structure
Dynamic Query Static Query (generally)
Consistency Eventual consistency
Transactions Limited Transactions
Difficult to distribute Distributed, fault-tolerant
Limited performance under load Optimized for heavy read-write

17. NoSQL DBs

18. NoSQL DBs
✴ Used with CF
✴ Apache CouchDB
✴ MongoDB
✴ Amazon SimpleDB
✴ CF/Java Wrappers available

19. What is MongoDB

20. What is MongoDB
✴ A Document-oriented database
✴ “Humongous”
✴ Scalable, high-performance, open-source
✴ An alternative to relational databases
✴ One of the NoSQL technologies

21. Key Difference

22. Key Difference
✴ Most No-SQL databases
✴ dynamic data
✴ static queries
✴ MongoDB
✴ dynamic data
✴ dynamic and static queries

23. Basic Concepts

24. Basic Concepts
✴ Database

25. Basic Concepts
✴ Database
✴ Collections (broadly like table)

26. Basic Concepts
✴ Database
✴ Collections (broadly like table)
✴ Documents (like row)

27. Basic Concepts
✴ Database
✴ Collections (broadly like table)
✴ Documents (like row)
✴ Fields (like columns)

28. Basic Concepts
✴ Database
✴ Collections (broadly like table)
✴ Documents (like row)
✴ Fields (like columns)
✴ Collections are indexed

29. Basic Concepts
✴ Database
✴ Collections (broadly like table)
✴ Documents (like row)
✴ Fields (like columns)
✴ Collections are indexed
✴ Cursors (retrieve data as required)

30. Concepts

31. Concepts
✴ New terminology
✴ Similar ideas, but not identical
✴ RDBMS - columns defined for tables
✴ NoSQL - fields defined for documents
✴ Collections are more like containers

32. Drivers

33. Drivers
✴ Officially supported drivers
✴ Language-specific drivers (Java, Ruby, etc.)
✴ Framework-specific (MongoMapper)
✴ Wrappers built on top of drivers
✴ cfmongodb built on Java drivers
✴ Unlike Apache CouchDB that uses REST

34. BSON

35. BSON
✴ Binary JSON format
✴ Supports embedding of documents and
arrays within other documents and arrays
✴ Lightweight
✴ Traversable
✴ Efficient
✴ bsonspec.org

36. CF and Mongo

37. CF and Mongo
✴ cfmongodb (on Github) -- excellent
✴ Wrapper around Java drivers
✴ Most functionality encapsulated
✴ Inserts, updates, delete, find
✴ Map-reduce, Geospatial

38. CF and BSON
• Structs/arrays persisted as they are!
people = mongo.getDBCollection(“people”)
person = {
“fname” : “John”,
“lname” : “Doe”
}
people.save(person)

39. Init

40. Init
mongoConfig = createObject(
'component',
'cfmongodb.core.MongoConfig').init(dbName="test")

41. Init
mongoConfig = createObject(
'component',
'cfmongodb.core.MongoConfig').init(dbName="test")
mongo = createObject('component',
'cfmongodb.core.Mongo').init(mongoConfig)

42. Init
mongoConfig = createObject(
'component',
'cfmongodb.core.MongoConfig').init(dbName="test")
mongo = createObject('component',
'cfmongodb.core.Mongo').init(mongoConfig)
people = mongo.getDBCollection('people')

43. Add Data

44. Add Data
// create some data
newPeople = []

45. Add Data
// create some data
newPeople = []
arrayAppend(newPeople, {‘fname’: ‘John’, ‘email’ : ‘john@doe.com’})

46. Add Data
// create some data
newPeople = []
arrayAppend(newPeople, {‘fname’: ‘John’, ‘email’ : ‘john@doe.com’})
arrayAppend(newPeople, {‘fname’: ‘Jane’, ‘lname’ : ‘Doe’})

47. Add Data
// create some data
newPeople = []
arrayAppend(newPeople, {‘fname’: ‘John’, ‘email’ : ‘john@doe.com’})
arrayAppend(newPeople, {‘fname’: ‘Jane’, ‘lname’ : ‘Doe’})
// save everything at once
people.saveAll(newPeople)

48. Find / Update

49. Find / Update
person = people.query().$eq("fname", "Jane").find()

50. Find / Update
person = people.query().$eq("fname", "Jane").find()
// update a record
person[“email”] = “jane@doe.com”
person.update(person)

51. Tools

52. Tools
✴ Terminal console
✴ MongoHub (Mac)
✴ MongoExplorer, MongoVision (Windows)
✴ SSH Tunnel to connect
✴ mongohq.com (hosted MongoDB/admin)

53. MongoDB and Railo

54. MongoDB and Railo
✴ Railo Extension
✴ Cache CF variables/data
✴ Store key-value pairs
✴ cacheGet()
✴ cachePut()

55. Queries and Map-
reduce

56. Queries and Map-
reduce
✴ Queries - rapid development
✴ Map-reduce - advanced querying
✴ Query and Map-reduce together give
MongoDB an edge over other NoSQL
alternatives

57. Data Modeling

58. Data Modeling
✴ No Joins! Scary?
✴ Embedding documents supported via ID
✴ Big mind shift
✴ ‘Fear’ of duplicate data drives design
decisions in relational db world

59. CF ORM

60. CF ORM
✴ No support in CF ORM frameworks
✴ Other language frameworks more
advanced
✴ Ruby on Rails - mongomapper
✴ Need ORM integration for wider adoption

61. Asynchronous

62. Asynchronous
✴ Asynchronous inserts/updates
✴ Journaled
✴ Capped Collections (FIFO)

63. Time taken to add 20,000 records with 5 words in each record
30
25
Time in Seconds
20
15
10
5
0
mysql (cfquery) filesystem (cflog) mongodb (cfmongodb)

64. Compelling features

65. Compelling features
✴ On-demand database and collection
creation
✴ Asynchronous writes
✴ Replication - simple and easy
✴ Large-scale data processing
✴ Sharding - horizontal scaling
✴ Geospatial indexes

66. When to use?

67. When to use?
✴ Relational DBs suffice for most projects
✴ Abstraction via ORM is very useful
✴ MongoDB -- alternative/add-on
✴ Removes reliance on only one data storage
mechanism

68. Learning Curve

69. Learning Curve
✴ Syntax
✴ Concepts like sharding, replication
✴ Well-documented / blogged
✴ Active mailing list
✴ Being used in production

70. So...

71. So...
✴ Another tool to have in one’s kit
✴ Extremely useful and easy to use
✴ Worth spending some time on

72. Thank you!
@indynagpal
indy@nagpals.com
http://nagpals.com/blog