You're building the next big thing. It will attract hundreds of thousands of users and make so much cash, Gordon Gecko would blush. You just know that if you build it, they will come. But what happens when all those users do show up? Will you spend your time adding the new features they're clamoring for, or will you be scrambling to make sure your relational database doesn't die hard? In this talk, we'll take a look at some of the risky business we undertake to try and scale our relational databases and the problems we run into. Then we'll talk about how Cassandra is different and some of the knobs you control to turn things up to 11. If you're new to Cassandra and are looking for an introduction, come from a relational database background, or you just want to see how many 80s movie references we can cover in 40 minutes, then don your favorite fedora and come for an excellent adventure.

1. Luke Tillman
Technical Evangelist at DataStax (@LukeTillman)

2. • Evangelist with a focus on Developers
• Long-time Developer on RDBMS (lots of .NET)
Who are you?!
2

3. The Good ol' Relational Database
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First proposed in 1970

4. The Relational Database Makes us Feel Good
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SQL is ubiquitous and allows
flexible querying
Data modeling is well understood
(3NF or higher)
ACID guarantees make us feel good

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Building and Scaling our Applications

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Building and Scaling our Applications

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Building and Scaling our Applications
I'm getting too old for this…

8. Scaling Up
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9. All these JOINs are Killing Us
9
SELECT
array_agg(players),
player_teams
FROM (
SELECT DISTINCT
t1.t1player AS players,
t1.player_teams
FROM (
SELECT
p.playerid AS t1id,
concat(p.playerid, ':', p.playername, ' ') AS t1player,
array_agg (pl.teamid ORDER BY pl.teamid) AS player_teams
FROM player p
LEFT JOIN plays pl
ON p.playerid = pl.playerid
GROUP BY p.playerid, p.playername
) t1
INNER JOIN (
SELECT
p.playerid AS t2id,
array_agg (pl.teamid ORDER BY pl.teamid) AS player_teams
FROM player p
LEFT JOIN plays pl
ON p.playerid = pl.playerid
GROUP BY p.playerid, p.playername
) t2
ON t1.player_teams = t2.player_teams
AND t1.t1id <> t2.t2id
) innerQuery
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10. All these JOINs are Killing Us
9
SELECT * FROM denormalized_view
Let's Denormalize!
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11. All these JOINs are Killing Us
9
But I thought data modeling was 3NF or
higher?! There can be only one!
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12. Read Replication
12
Client
Users Data
Replica 2
Replica 1
Primary
Write Requests
ReadRequests
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Replication Lag
Consistent results? Nope, now
eventually consistent
Replication speed is limited by
the speed of light

13. Sharding
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Client
Router
A-F G-M N-T U-Z
Users Data
Queries that aren't on the shard key
require scatter-gather
Resharding can be a painful, manual process

14. Replication for Availability
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Client
Users Data
Failover
Process
Monitor
Failover
Failover takes time. How long are
you offline while it's happening?

15. And while you're offline...
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16. Putting it All Together
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Client
Users Data

17. Putting it All Together
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Client
Users Data
Sharding
A-F G-M N-T U-Z

18. Putting it All Together
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Client
Users Data
Sharding
Router
A-F G-M N-T U-Z

19. Putting it All Together
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Client
Users Data
Router
A-F G-M N-T U-Z
Sharding and Replication (and probably Denormalization)

20. Putting it All Together
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Client
Users Data
Failover
Process
Router
A-F G-M N-T U-Z
Sharding and Replication (and probably Denormalization)

21. Putting it All Together
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Client
Users Data
Failover
Process
Monitor
Failover
Router
A-F G-M N-T U-Z
Sharding and Replication (and probably Denormalization)

22. Putting it All Together
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Client
Users Data
Failover
Process
Monitor
Failover
Router
A-F G-M N-T U-Z
Sharding and Replication (and probably Denormalization)

23. Putting it All Together
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Client
Users Data
Failover
Process
Monitor
Failover
Router
A-F G-M N-T U-Z
Replication Lag
Sharding and Replication (and probably Denormalization)

24. Putting it All Together
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Client
Users Data
Failover
Process
Monitor
Failover
Router
A-F G-M N-T U-Z
Replication Lag
Sharding and Replication (and probably Denormalization)

25. Putting it All Together
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Client
Users Data
Failover
Process
Monitor
Failover
Router
A-F G-M N-T U-Z
Replication Lag
Sharding and Replication (and probably Denormalization)

26. Putting it All Together
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Client
Users Data
Failover
Process
Monitor
Failover
Router
A-F G-M N-T U-Z
Replication Lag
Sharding and Replication (and probably Denormalization)

27. What is Cassandra?
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A linearly scaling and fault tolerant
distributed database

28. What is Cassandra?
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A linearly scaling and fault tolerant
distributed database
• Data spread over many nodes
• All nodes participate in a cluster
• All nodes are equal
• No SPOF (shared nothing)
• Run on commodity hardware

29. What is Cassandra?
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A linearly scaling and fault tolerant
distributed database
• Have more data? Add more nodes.
• Need more throughput? Add more nodes.

30. What is Cassandra?
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A linearly scaling and fault tolerant
distributed database
• Nodes down != Database Down
• Datacenter down != Database Down
• No middle of the night phone calls

31. Multi Datacenter with Cassandra
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America Zamunda
Client

32. Fault Tolerance in Cassandra
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You have the power to control fault
tolerance in Cassandra

33. Replication Factor
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How many copies of the data should exist?
Client
Write Beetlejuice
RF=3
Beetlejuice Beetlejuice
Beetlejuice

34. Consistency Level
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How many replicas do we need to hear
from before we acknowledge?
CL=ONE
Copy #1 Copy #2
Copy #3
Client

35. Consistency Level
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How many replicas do we need to hear
from before we acknowledge?
CL=QUORUM
Copy #1 Copy #2
Copy #3
Client

36. Consistency Levels and Speed
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Use a lower consistency level like ONE to
get faster reads and writes

37. Consistency Levels and Eventual Consistency
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Use a higher consistency level like
QUORUM if you don’t want to be surprised
by data from the past (stale data)

38. Before you get too excited...
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39. Cassandra is not...
• A Data Ocean, Lake, or Pond
• An In-Memory Database
• A Queue
• A magical database luck dragon
that will solve all your database
use cases and problems
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40. How bad of an idea?
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Actually a 90's movie Actually a 70's movie
Why Arnold?! Why?

41. Cassandra is good when...
• Uptime is a top priority
• You have unpredictable or high
scaling requirements
• The workload is transactional (i.e.
OLTP not OLAP)
• You are willing to put the time and
effort into understanding how it
works and how to use it
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Movie References
(in order of appearance)
Leap of Faith (1992)
Patton (1970)
The Aristocats (1970)
When Harry Met Sally (1989)
Beverly Hills Cop (1984)
Lethal Weapon (1987)
Big (1988)
Trading Places (1983)
Highlander (1986)

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Spaceballs (1987)
Rain Man (1988)
Ghostbusters (1984)
Gremlins (1984)
Star Trek II: The Wrath of Khan (1982)
Star Wars Episode VI: Return of the Jedi (1983)
Weekend at Bernie's (1989)
Coming to America (1988)
Masters of the Universe (1987)
Beetlejuice (1988)
The Goonies (1985)
Top Gun (1986)

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Back to the Future (1985)
Footloose (1984)
The NeverEnding Story (1984)
Batman and Robin (1997)
Star Wars (1977)
Twins (1988)
The Karate Kid (1984)
Find me on Twitter: @LukeTillman

45. Thank You