This document provides an introduction to Cassandra, including key details about its history, supported versions, scalability, data model, and use cases. Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers. It provides high availability with no single points of failure and linear scalability across commodity hardware. Cassandra is optimized for fast reads on large datasets based on predefined keys or indexes and is well-suited for applications with heavy write loads like time series data, messaging, and fraud detection.

1. Introduction
to
Cassandra
Michał Matłoka @mmatloka

2. CASSANDRA
Cassandra by Evelyn De Morgan (1898, London)

3. • First release in 2008
• Supported versions: 2.1.x, 2.2.x, 3.0.x,
3.x
• Used by Apple, Netflix, eBay, Reddit, …

4. • Big Data (Volume, Velocity, Variety)
• (Near-linear) horizontal scalability
• Fully distributed, no SPoF
• Data Replication
• Modelling include data duplication

5. –ThoughtWorks Technology Radar
“It’s a great tool and we like it, but too often we see
teams run into trouble using it. We recommend
using Cassandra carefully. Teams often
misunderstand the use case for Cassandra,
attempting to use it as a general-purpose data
store when in fact it is optimized for fast reads
on large data sets based on predefined keys or
indexes. (…)”

6. VERTICAL SCALING

7. CAP
theorem
In distributed
system you can
only have two of
three guarantees:
• Consistency
• Availability
• Partition
Tolerance
(Brewer’s theorem)

8. Source: http://blog.nahurst.com/visual-guide-to-nosql-systems

9. Relational scaling
(horizontal)
Example limitations:
• Max 48 nodes
• Read-only nodes
• Cross-shard joins…
• Auto-increments
• Distributed transactions,
possible, but…

10. NoSQL (Not Only SQL)
• Key-value (Redis, Dynamo, ...)
• Column (Cassandra, HBase, ...)
• Document (MongoDB, … )
• Graph (Neo4J, … )
• Multi-model (OrientDB, …)

11. Atomicity
Consistency
Isolation
Durability

12. Basically
Available
Soft state
Eventual consistency

13. • Keyspace
• Table
• Partition (set of rows)
• Row (columns & cells)

14. Table

15. CLUSTER STRUCTURE

16. -2^63
2^63
-1
Murmur3
Token ring

17. Write

18. Read

19. Write

20. Read

21. CL => Tunable Consistency
• ONE
• TWO
• THREE
• ALL
• QUORUM
• LOCAL_QUORUM
• …

22. Immediate consistency
Read consistency + Write consistency > Replication Factor
• QUORUM + QUORUM
• ONE + ALL
• ALL + ONE

23. WRITE DETAILS

24. Write path

25. Compaction (SizeTiered)

26. MODELING METHODOLOGY

27. Modeling Methodology

28. Chebotko Diagram

29. CREATE TABLE potatoes_by_group (
group text,
name text,
photo_id uuid,
PRIMARY KEY (group, name)
);
INSERT INTO potatoes_by_group (group, name, photo_id) values
('RED', 'Red Bliss', d4b2732b-d035-4034-8e13-a7c17af25c36);
SELECT * FROM potatoes_by_group WHERE group = 'RED';
CQL

30. • Secondary index
• Materialized view (>= 3.0)
• SASI Index (>= 3.4)
• Table per query

31. • Hinted handoff
• Read repair
• Repair
• Tombstones
Other mechanisms

32. When to use Cassandra?
• Heavy writes
• Time series - sensor data/IoT
• Play list (Spotify)
• Messaging
• Fraud detection
• …

33. Learning materials
• https://www.thoughtworks.com/radar/
platforms/cassandra-carefully
• https://www.infoq.com/articles/cap-
twelve-years-later-how-the-rules-have-
changed
• https://academy.datastax.com/courses/

34. Thank you, Q&A?
@mmatloka
mmatloka
softwaremill.com/blog