High level introduction to the power of Apache Cassandra.

1. An Introduction to Cassandra
Sydney Tech Day
instaclustr.com
@Instaclustr

2. Who am I and what do I do?
• Adam Zegelin
• Co-founder and Chief Architect of Instaclustr -> www.instaclustr.com
• Instaclustr provides Cassandra-as-a-Service in the cloud.
• Currently in AWS, Azure and Google Cloud in private beta with more to come.
• We currently manage 50+ nodes for various customers, who do various
things with it.

3. Objectives
• A quick history of Databases
• Introducing Cassandra

4. 1980 - Stand Alone
and Mainframes
1990 - 2005 Networked
Computing
2005+ Real Time Web and
Big Data

5. 1980 - Stand Alone
and Mainframes
1990 - 2005 Networked
Computing

6. Early 2000’s
• What happens when you have more data than could
fit on a single server?

7. Throw money away at the problem

8. Lets try a little computer science
instead
• BigTable (2006) - 1 Key: Lots of values, Fast sequential access
• Dynamo (2007) - Reliable, Performant, Always On,
• Cassandra (2008) - Dynamo Architecture, BigTable data model and
storage

9. One database, many servers
• All servers (nodes) participate
in the cluster
• Shared nothing
• Need more capacity add more
servers
• Multiple servers == built in
redundancy
1
3
24

10. What are the benefits to this approach
• Linear scalability

11. Linear scalability

12. What are the benefits to this approach
• Linear scalability
• High Availability (No single point of failure)

13. What are the benefits to this approach
“During Hurricane Sandy, we lost an entire
data center. Completely. Lost. It. Our
application fail-over resulted in us losing just
a few moments of serving requests for a
particular region of the country, but our data
in Cassandra never went offline.”
Nathan Milford, Outbrain’s head of U.S. IT operations management

14. What are the benefits to this approach

15. What are the benefits to this approach
• Linear scalability
• High Availability
• Use commodity hardware

16. What are the benefits to this approach

17. How does it work ?
0
4
28

18. Partitioning
Name Age Postcode Gender
Alice 34 2000 F
Bob 26 2000 M
Eve 25 2004 F
Frank 41 2902 M

19. How does it work ?
client
consistentHash(“Alice”)
0
4
28
Replication Factor = 3

20. How do we keep data consistent ?
client
consistentHash(“Alice”)
0
4
28
CL.ONE
Write
Ack

21. How do we keep data consistent ?
client
consistentHash(“Alice”)
0
4
28
CL.ALL
Write
Ack
Ack
Ack

22. How do we keep data consistent ?
client
consistentHash(“Alice”)
0
4
28
CL.QUORUM
Write
Ack
Ack
X

23. Also supports multi-dc replication
client
0
4
28
0
4
28

24. Add capacity
1
5
37
client
consistentHash(“Alice”)
0
4
2
6

25. Thank you!