Alternative databases continue to establish their role in the technology stack of the future—and for many, the technology stack of the present. Making mature engineering decisions about when to adopt new products is not easy, and requires that we learn about them both from an abstract perspective and from a very concrete one as well. If you are going to recommend a NoSQL database for a new project, you're going to have to look at code. In this talk, we'll examine three important contenders in the NoSQL space: Cassandra, MongoDB, and Neo4J. We'll review their data models, scaling paradigms, and query idioms. Most importantly, we'll work through the exercise of modeling a real-world problem with each database, and look at the code and queries we'd use to implement real product features. Come to this session for a thorough and thoroughly practical smackdown between three important NoSQL products.

1. NoSQL Smackdown
2012 edition
Tim Berglund
@tlberglund
Wednesday, October 17, 12 1

2. @tlberglund
#nosql
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3. SQL
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4. Wednesday, October 17, 12 4

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13. Metadata
Data model
Scale idiom
Queryability
License
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14. Wednesday, October 17, 12 14

15. name: role: status:
tim
Tim teacher Cool
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16. name: role: status:
tim
Tim teacher Cool
name: role:
kristen marketing
Kristen
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17. name: role: status:
tim
Tim teacher Cool
name: role:
kristen marketing
Kristen
billy role:
CEO
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18. name: role: status:
tim
Tim teacher Cool
name: role:
kristen marketing
Kristen
billy role:
CEO
name: role: status:
matt
Matt founder ubercool
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19. Outer hash key Inner hash key
name: role: status:
tim
Tim teacher Cool
name: role:
kristen marketing
Kristen
billy role:
CEO
name: role: status:
matt
Matt founder ubercool
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20. Scale Idiom
Amazon Dynamo
Horizontal scalability
No distinguished nodes
Fault tolerance
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21. Queries
Nested hash table lookup
Single primary key
Ranged lookups
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22. Queries
Ad-Hoc Query Language:
CQL
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23. Queries
CREATE COLUMNFAMILY Contacts (KEY uuid PRIMARY KEY);
CREATE COLUMNFAMILY Monkey (KEY text PRIMARY KEY,
id_tag long,
emotion text)
WITH comment='Simian Emotional States'
AND read_repair_chance = 0.5;
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24. Queries
UPDATE Contacts SET name='Tim Berglund'
WHERE KEY = B70DE1D0-9908-4AE3-BE34-5573E5B09F14;
UPDATE Monkeys
USING CONSISTENCY EACH_QUORUM
SET emotion = 'Angry',
name = 'Baby Boss'
WHERE KEY = 'OREILLY:8827';
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25. Queries
SELECT * FROM Contacts
WHERE KEY = B70DE1D0-9908-4AE3-BE34-5573E5B09F14;
SELECT FIRST 1000 FROM Temperatures
WHERE KEY = 7627748986;
SELECT emotion, id_tag FROM Monkeys
USING CONSISTENCY ONE
WHERE KEY = 'OREILLY:8827';
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26. License
Apache Public License 2.0
DataStax
http://datastax.com/dev
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27. Overview
The leader at scale
Data model learning curve
Great backing from DataStax
Innovation (Brisk, Solandra)
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28. Wednesday, October 17, 12 28

29. Data Model
Document Database
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30. Data Model
{
name: "Hubert Poston",
username: "hposton",
password: "ab87d24bdc7452e55738deb5f868e1f16dea5ace",
active: true,
lastLogin: ISODate('2012-01-19 16:37:03'),
loginCount: 873,
rating: 8.6,
roles: ["ADMIN", "USER", "BOSS"],
address: {
street1: "984 Sunny Glen Lane",
city: "Warrensville Heights",
province: "OH",
postalCode: "44128"
}
}
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31. Data Model
{
name: "Hubert Poston",
username: "hposton",
password: "ab87d24bdc7452e55738deb5f868e1f16dea5ace",
active: true,
lastLogin: ISODate('2012-01-19 16:37:03'),
loginCount: 873,
rating: 8.6,
roles: ["ADMIN", "USER", "BOSS"],
address: {
street1: "984 Sunny Glen Lane",
city: "Warrensville Heights",
province: "OH",
postalCode: "44128"
}
}
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32. Data Model
{
name: "Hubert Poston",
username: "hposton",
password: "ab87d24bdc7452e55738deb5f868e1f16dea5ace",
active: true,
lastLogin: ISODate('2012-01-19 16:37:03'),
loginCount: 873,
rating: 8.6,
roles: ["ADMIN", "USER", "BOSS"],
address: {
street1: "984 Sunny Glen Lane",
city: "Warrensville Heights",
province: "OH",
postalCode: "44128"
}
}
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33. Data Model
{
name: "Hubert Poston",
username: "hposton",
password: "ab87d24bdc7452e55738deb5f868e1f16dea5ace",
active: true,
lastLogin: ISODate('2012-01-19 16:37:03'),
loginCount: 873,
rating: 8.6,
roles: ["ADMIN", "USER", "BOSS"],
address: {
street1: "984 Sunny Glen Lane",
city: "Warrensville Heights",
province: "OH",
postalCode: "44128"
}
}
Wednesday, October 17, 12 33

34. Data Model
{
name: "Hubert Poston",
username: "hposton",
password: "ab87d24bdc7452e55738deb5f868e1f16dea5ace",
active: true,
lastLogin: ISODate('2012-01-19 16:37:03'),
loginCount: 873,
rating: 8.6,
roles: ["ADMIN", "USER", "BOSS"],
address: {
street1: "984 Sunny Glen Lane",
city: "Warrensville Heights",
province: "OH",
postalCode: "44128"
}
}
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35. Scale Idiom
Single-master design
Master/slave replica sets
Auto-sharding
Like a nicer MySQL
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36. Querying
Ad-hoc language
Secondary indexes
Many language bindings
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37. Querying
Creating a Record
db.users.insert({
firstName: "Tim",
lastName: "Berglund",
username: "tlberglund",
password: "ab87d24bdc7452e55738deb5f868e1f16dea5ace"
active: true,
lastLogin: ISODate('2012-01-19 16:37:03'),
loginCount: 873,
roles: ["ADMIN", "USER", "BOSS"]
});
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38. Querying
Finding a Record
db.users.find({
username: "tlberglund",
password: "ab87d24bdc7452e55738deb5f868e1f16dea5ace"
});
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39. Querying
Updating a record
db.users.update(
{ username: "tlberglund" },
{ $inc: { loginCount: 1 } }
);
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40. License
Database: AGPL 3.0
Drivers: APL 2.0
http://www.10gen.com/commercial-licenses
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41. Overview
NoSQL mindshare leader
Easy to adopt
Usually used at smaller scale
Not your father’s database!
(More like your young uncle)
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43. Data Model
Graph Database
Nodes and Relationships
Arbitrary key/value bundles
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44. Data Model
...
How do I use that?
Easily!
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45. Data Model
Graph Database Traversal
manages manages navigates identifies expresses
Indexes Graph Algorithm
map to records data in Paths
map to Relationships records data in order
map from organize
have Nodes
have
Properties
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46. Data Model
Graph Database Traversal
manages manages navigates identifies e
Indexes Graph A
map to records data in Paths
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47. Indexes Graph Algorithm
Data Model
map to records data in Paths
map to Relationships records data in order
map from organize
have Nodes
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48. map to Data Model
Relationships records data in order
map from organize
have Nodes
have
Properties
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49. Scale Idiom
Single-master design
Master/slave replication
High-availability clustering
Always single-master
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51. Querying
REST/JSON
Cypher
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52. License
GPL 3.0 (Community)
AGPL 3.0 (Advanced/Enterprise)
Commercial
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53. Overview
The 95% niche
Usually commercial
Scales like MySQL
Affinity to ROA
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54. Do you need this?
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55. You will.
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56. Tim Berglund
http://timberglund.com
tlberglund@github.com
@tlberglund
Wednesday, October 17, 12
Thank You 56