1. Cassandra Jahangir Mohammed md.jahangir27@gmail.com

2. What is Cassandra? Distributed data store O(1) DHT Column-oriented Dynamo + Big Table

3. Why not RDBMS? Many-to-many relationships -> Joins -> Denormalization -> Multiple copies of data or redundancy Rigid schema Vertical scaling is easier than horizontal ACID, Distributed transaction, Two-phase commit Slower writes

4. CAP Theorem Consistency – All clients will read the same data at same time. Availability – Service always up and running. Partition tolerance – System on whole operates despite network issues.

6. Features Proven Rich data model Scalable Distributed & Decentralized Cross datacenter support High performance writes/reads No SPOF Schema free Tunable consistency

7. Limitations No ACID transactions(if needed) Eventually consistent(Tunable consistency, trade-off with performance)

8. ARCHITECTURE Ring Each node – unique token Tokens range from 0 to 2**127 Keys MD5 hash to determine node

9. RING h(key1) 0 1 N=3 B h(key2) A C F E D 1/2 9

10. ARCHITECTURE P2P: All nodes are identical No “master” node Gossip: Protocol for intra-ring communication Each node have state information about other nodes Anti-entropy & Read Repair: Replica synchronization mechanism Occurs during major compaction Uses Merkle trees

11. READ REPAIR Client Result Query Cassandra Cluster Read repair if digests differ Closest replica Result Replica A Digest Query Digest Response Digest Response Replica B Replica C

12. WRITE PATH Commit log: Responsible for all writes Memtable: In-memory data structure, written after commit log. SSTable: Immutable table Memtable flushed to disk

14. Number of Objects

15. LifetimeMemtable ( CF1) Commit Log Binary serialized Key ( CF1 , CF2 , CF3 ) Memtable ( CF2) FLUSH Memtable ( CF2) Data file on disk <Key name><Size of key Data><Index of columns/supercolumns>< Serialized column family> --- --- --- --- <Key name><Size of key Data><Index of columns/supercolumns>< Serialized column family> Dedicated Disk

16. READ PATH

17. ARCHITECTURE Bloom filter: Performance booster Fast, nondeterministic algorithms In memory Used during read operation Tombstones: Deletion marker Soft delete Marker older than a set time, GC’ed

18. HINTED HANDOFF & COMPACTION Hinted Handoff: Node responsible down Coordinator creates hint Compaction: Merge SSTables. Keys merged Columns combined Tombstones discarded New index created

19. PARTITIONER Decides where row key(data) finds place in ring. Random Partitioner: MD5 hash Spreads keys evenly Inefficient range queries Order-Preserving Partitioner: Rows sorted

20. DATA MODEL Keyspace: Like Database. Container for CFs. Column Family: Like Table(But, not exactly a relational database table). Container of rows. Row: Sorted collection of columns. Column: Basic unit of data structure. Triplet of name, value and timestamp

21. DATA MODEL Super Column: Special column. Sorted associative array of columns. Map of maps. Only one level deep. Super Column Family: Container of rows having super columns. 4-D DHT = Standard CF: [Keyspace][ColumnFamily][Key][Column]. 5-D DHT = Super CF: [Keyspace][ColumnFamily][Key][SuperColumn][SubColumn].

22. REPLICATION & CONSISTENCY Replication: No. of copies of data in the system. Consistency level: No. of replicas to respond.

23. WRITE

24. READ

25. REPLICA PLACEMENT STRATEGY Simple Strategy: Rack-Unaware Fast Single D.C.

26. SIMPLE STRATEGY

27. OLD NTS Rack-aware Same D.C.

28. NTS Rack-aware D.C. aware

29. IMAGE REFERENCES Nathan Hurst’s Blog http://2.bp.blogspot.com/_YGilJHLjrrI/TJy3K0wshLI/AAAAAAAAAOI/ogAvf8Ckq3k/s1600/cassandra-ring2.png Sigmod presentation: Avinash et. al, Facebook Datastax http://answers.oreilly.com/topic/2408-replica-placement-strategies-when-using-cassandra/

30. QUESTIONS?