This document summarizes Cassandra, an open source distributed database management system designed to handle large amounts of data across many commodity servers. It discusses Cassandra's history, key features like tunable consistency levels and support for structured and indexed columns. Case studies describe how companies like Digg, Twitter, Facebook and Mahalo use Cassandra to handle terabytes of data and high transaction volumes. The roadmap outlines upcoming releases that will improve features like compaction, management tools, and support for dynamic schema changes.

1. The Cassandra Distributed Database
Eric Evans
eevans@rackspace.com
@jericevans
FOSDEM
February 7, 2010

2. A prophetess in Troy during the Trojan War. Her predictions were
always true, but never believed.

3. A massively scalable, decentralized, structured data store (aka
database).

4. Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap

8. • 7 new committers added
• Dozens of contributors
• 100+ people on IRC
• Hundreds of closed issues (bugs, features, etc)
• 3 major releases, 2 point releases
• Graduation to TLP?

9. Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap

10. Cassandra is...
• O(1) DHT
• Eventual consistency
• Tunable trade-offs, consistency vs. latency

12. But...
• Values are structured, indexed
• Columns / column families
• Slicing w/ predicates (queries)

13. Column families

14. Supercolumn families

15. Querying
• get(): retrieve by column name
• multiget(): by column name for a set of keys
• get slice(): by column name, or a range of names
• returning columns
• returning super columns
• multiget slice(): a subset of columns for a set of keys
• get count: number of columns or sub-columns
• get range slice(): subset of columns for a range of keys

16. Column comparators
• TimeUUID
• LexicalUUID
• UTF8
• Long
• Bytes
• ...

17. Updating
• insert(): add/update column (by key)
• batch insert(): add/update multiple columns (by key)
• remove(): remove a column
• batch mutate(): like batch insert() but can also delete
(new for 0.6, deprecates batch insert())
• Remove key range RSN

18. Consistency
CAP Theorem: choose any two of Consistency, Availability, or
Partition tolerance.
• Zero
• One
• Quorum ((N / 2) + 1)
• All

19. Client API
• Thrift (12 different languages!)
• Ruby
• http://github.com/fauna/cassandra/tree/master
• http://github.com/NZKoz/cassandra object/tree/master
• Python
• http://github.com/digg/lazyboy/tree/master
• http://github.com/driftx/Telephus/tree/master (Twisted)
• Scala
• http://github.com/viktorklang/Cassidy/tree/master
• http://github.com/nodeta/scalandra/tree/master

20. Performance vs MySQL w/ 50GB
• MySQL
• 300ms write
• 350ms read
• Cassandra
• 0.12ms write
• 15ms read

21. Writes

22. About writes...
• No reads
• No seeks
• Sequential disk access
• Atomic within a column family
• Fast
• Any node
• Always writeable (hinted hand-off)

23. Reads

24. About reads...
• Any node
• Read repair
• Usual caching conventions apply

25. Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap

26. Case 1: Digg
Digg is a social news site that allows people to discover and share
content from anywhere on the Internet by submitting stories and
links, and voting and commenting on submitted stories and links.
Ranked 98th by Alexa.com.

27. Digg

28. Problem
• Terabytes of data; high transaction rate (reads dominated)
• Multiple clusters; heavily sharded
• Management nightmare (high effort, error prone)
• Unsatisfied availability requirements (geographic isolation)

29. Solution
• Currently production on ”Green Badges”
• Cassandra as primary data store RSN
• Datacenter and rack-aware replication

30. Case 2: Twitter
Twitter is a social networking and microblogging service that
enables its users to send and read tweets, text-based posts of up to
140 characters.
Ranked 12th by Alexa.com.

31. Twitter

32. MySQL
• Terabytes of data, ˜1,000,000 ops/s
• Calls for heavy sharding, light replication
• Schema changes are very difficult, (if possible at all)
• Manual sharding is very high effort
• Automated sharding and replication is Hard

33. Case 3: Facebook
Facebook is a social networking site where users can create a
profile, add friends, and send them messages. Users can also join
groups organized by location or other points of common interest.
Ranked #2 by Alexa.com.

34. Inbox Search
• 100 TB
• 160 nodes
• 1/2 billion writes per day (2yr old number?)

35. Case 4: Mahalo
Mahalo.com is a web directory and knowledge exchange. It
differentiates itself by tracking and building hand-crafted result
sets for many of the popular search terms.
(it also means ”thank you” in Hawaiian)

36. MySQL
• Partial deployment; 16 million video records (and growing)
• Writes (and storage) rapidly exceeding single box limitations
• Managability suffering (clustering is painful)
• Concerns over availability

37. Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap

38. 0.6
• batch mutate command
• authentication (basic)
• new consistency level, ANY
• fat client
• mmapped i/o reads (default on 64bit jvm)
• improved write concurrency (HH)
• networking optimizations
• row caching
• improved management tools
• per-keyspace replication factor

39. 0.7
• more efficient compactions (row sizes bigger than memory)
• easier (dynamic?) column family changes
• SSTable versioning
• SSTable compression
• support for column family truncation
• improved configuration handling
• remove key range command
• even more improved management tools
• vector clocks w/ server-side conflict resolution

40. THE END