Coding Serbia 2015 conference talk on breaking free from relational databases. This talk covers the reasons why RDBMS is having issues with our current needs and introduces Cassandra as a solution to certain use-cases. There are some details of our learning process and typical failures people make when making this transition.

1. Breaking free from
relational databases
Matija Gobec
@mad_max0204
SmartCat
@SmartCat_io

2. Agenda
What’s wrong with RDBMS
What we need today
Introduction to Cassandra
Use-cases and benefits
Learning process and common mistakes
Migrating to Cassandra

3. What’s wrong with RDBMS
Reliable and we understand it
Been around for ages
Development and administration tools
Worked great for few decades

4. What’s wrong with RDBMS
Requires expensive server hardware
Doesn’t scale
Limited fault tolerance
Can’t handle big amounts of data
Architectural limitations

5. What’s wrong with RDBMS
Master
Slave Slave

6. What’s wrong with RDBMS
Master
Slave Slave
● Network split
● Hardware failure
● Latency

7. What’s wrong with RDBMS
Master
Master Slave

8. What’s wrong with RDBMS
Master
Master Slave
? ?

9. What we need today
90% of world's data generated over last two years
Data impacts business
Systems that can learn and adopt
Personalized experience
We store everything

10. What we need today
Availability
Scalability
Fault tolerance
Performance

11. ACID vs BASE
Atomic
Consistent
Isolated
Durable
Basically Available
Soft state
Eventually Consistent

12. Cassandra - introduction
Row partitioned storage
Fast, scalable and fault tolerant
Share nothing masterless architecture
Active everywhere design
Native multi-datacenter support

13. Cassandra - architecture

14. Cassandra - architecture
Client contact

15. Cassandra - architecture
Client request

16. Cassandra - architecture
Client
response

17. Cassandra - architecture
DC1 DC2
Cluster

18. Cassandra - architecture
DC1 DC2
Cluster

19. Cassandra - data layout
Partition key K:V K:VPartition
Cells
Partition key
K:V K:V
Partition
Cells
Clustering key

20. Cassandra - use cases
IoT applications
Product catalogs and retail apps
Activity tracking
Fraud detection
Messaging
Analytics and recommendation engines
...

21. Cassandra - benefits
Reliable storage
High performance
Easy scaling on commodity hardware
Solves problems by design

22. But at what cost?
An arm
and a leg ?

23. Learning process
CQL looks like SQL
CREATE TABLE songs (
id uuid PRIMARY KEY,
title text,
album text,
artist text,
data blob
);
CREATE TABLE playlists (
id uuid,
song_order int,
song_id uuid,
title text,
album text,
artist text,
PRIMARY KEY (id, song_order )
);

24. Learning process
CQL looks like SQL
CREATE TABLE songs (
id uuid PRIMARY KEY,
title text,
album text,
artist text,
data blob
);
CREATE TABLE playlists (
id uuid,
song_order int,
song_id uuid,
title text,
album text,
artist text,
PRIMARY KEY (id, song_order )
);

25. Learning process
I’ll create a data model based on my relational data model
(especially while migrating)

26. Learning process
I’ll create a data model based on my relational data model
(especially while migrating)

27. Learning process
I can read from database what I just written
(write read antipattern)

28. Learning process
I can read from database what I just written
(write read antipattern)

29. Learning process
I’ll read from database to calculate what I write
(read write antipattern)

30. Learning process
I’ll read from database to calculate what I write
(read write antipattern)

31. Learning process
Secondary indexes are your friend
(at least Mongo didn’t mind)

32. Learning process
Secondary indexes are your friend
(at least Mongo didn’t mind)

33. Migrating from RDBMS
Understand how data is queried
Conceptual model is reusable
Run in parallel (leverage MQ)
Start developing with 3 nodes
Leverage parallel execution
You cannot beat laws of physics

34. MongoDB to Cassandra
MongoDB uses “relational” model
MongoDB is more flexible for R&D
Don’t measure performance of single nodes
Don’t use secondary indexes
Don’t use MongoDB

35. When not to use Cassandra
When you don’t need scalability
When you have a lot of updates
When you need query flexibility
When you don’t know what you need

36. Action points
Data modeling is query based
Understand physical data layout
Respect eventual consistency
Have fun

38. Thank you
Matija Gobec
@mad_max0204
matija.gobec@smartcat.io
SmartCat
www.smartcat.io
@SmartCat_io
https://github.com/smartcat-labs