This document provides an overview and introduction to Cassandra, an open source distributed database management system designed to handle large amounts of data across many commodity servers. It discusses Cassandra's origins from influential papers on Bigtable and Dynamo, its properties including flexibility, scalability and high availability. The document also covers Cassandra's data model using keyspaces and column families, its consistency options, API including Thrift and language drivers, and provides examples of usage for an address book app and storing timeseries data.

1. Cassandra Explained
Berlin Buzzwords
June 6, 2010
Eric Evans
eevans@rackspace.com
@jericevans
http://blog.sym-link.com

2. Outline
● Background
● Description
● API
● Examples

3. Background

4. Influential Papers
● BigTable
● Strong consistency
● Sparse map data model
● GFS, Chubby, et al
● Dynamo
● O(1) distributed hash table (DHT)
● BASE (aka eventual consistency)
● Client tunable consistency/availability

5. NoSQL
● HBase ● Hypertable
● MongoDB ● HyperGraphDB
● Riak ● Memcached
● Voldemort ● Tokyo Cabinet
● Neo4J ● Redis
● Cassandra ● CouchDB

6. NoSQL Big data
● HBase ● Hypertable
● MongoDB ● HyperGraphDB
● Riak ● Memcached
● Voldemort ● Tokyo Cabinet
● Neo4J ● Redis
● Cassandra ● CouchDB

7. Bigtable / Dynamo
Bigtable Dynamo
● HBase ● Riak
● Hypertable ● Voldemort
Cassandra ??

8. Dynamo-Bigtable Lovechild

9. CAP Theorem “Pick Two”
● CP ● AP
● Bigtable ● Dynamo
● Hypertable ● Voldemort
● HBase ● Cassandra

10. CAP Theorem “Pick Two”
● Consistency
● Availability
● Partition Tolerance

11. Description

12. Properties
● Symmetric
● No single point of failure
● Linearly scalable
● Ease of administration
● Flexible partitioning, replica placement
● Automated provisioning
● High availability (eventual consistency)

13. P2P Routing

14. P2P Routing

15. Partitioning
● Random
● 128bit namespace, (MD5)
● Good distribution
● Order Preserving
● Tokens determine namespace
● Natural order (lexicographical)
● Range / cover queries
● Yours ??

16. Replica Placement
● SimpleSnitch
● Default
● N-1 successive nodes
● RackInferringSnitch
● Infers DC/rack from IP
● PropertyFileSnitch
● Configured w/ a properties file

17. Bootstrap

18. Bootstrap

19. Bootstrap

20. Choosing Consistency
Write Read
Level Description Level Description
ZERO Hail Mary ZERO N/A
ANY 1 replica (HH) ANY N/A
ONE 1 replica ONE 1 replica
QUORUM (N / 2) +1 QUORUM (N / 2) +1
ALL All replicas ALL All replicas
R+W>N

21. Quorum ((N/2) + 1)

22. Quorum ((N/2) + 1)

23. Data Model

24. Overview
● Keyspace
● Uppermost namespace
● Typically one per application
● ColumnFamily
● Associates records of a similar kind
● Record-level Atomicity
● Indexed
● Column
● Basic unit of storage

25. Sparse Table

26. Column
● name
● byte[]
● Queried against (predicates)
● Determines sort order
● value
● byte[]
● Opaque to Cassandra
● timestamp
● long
● Conflict resolution (Last Write Wins)

27. Column Comparators
● Bytes
● UTF8
● TimeUUID
● Long
● LexicalUUID
● Composite (third-party)
http://github.com/edanuff/CassandraCompositeType

28. API

29. Low / High
● Thrift
● Compact binary RPC framework
● 12 different languages
● Idiomatic
● Hector (Java)
● Pycassa (Python)
● Others...
http://wiki.apache.org/cassandra/ClientOptions

30. Thrift Read Methods
● get() → Column
● get_slice() → list<Column>
● mulitget_slice() → map<key, list<Column>>
● get_count() → int
● multiget_count() → map<key, int>
● get_range_slices()

31. Thrift Write Methods
● insert()
● batch_insert()
● remove()
● batch_mutate()

32. Examples

33. Pycassa – Python Client API
● connect() → Thrift proxy
● cf = ColumnFamily(proxy, ksp, cfname)
● cf.insert() → long
● cf.get() → dict
● cf.get_range() → dict
http://github.com/vomjom/pycassa

34. Address Book – Setup
<!-- conf/storage-conf.xml -->
<Keyspace Name=”AddressBook”>
<ColumnFamily Name=”Addresses”
CompareWith=”BytesType”
RowsCached=”10000”
KeysCached=”50%”
Comment=”Too lame” />
</Keyspace>

35. Adding an entry
key = uuid()
columns = {
'first': 'Eric',
'last': 'Evans',
'email': 'eevans@rackspace.com',
'city': 'Austin',
'zip': 78250
}
addresses.insert(key, columns)

36. Fetching a record
# fetching the record by key
record = addresses.get(key)
# accessing columns by name
zipcode = record['zip']
city = record['city']

37. Indexing
<!-- conf/storage-conf.xml -->
<Keyspace Name=”AddressBook”>
<ColumnFamily Name=”Addresses”
CompareWith=”BytesType”
RowsCached=”10000”
KeysCached=”50%”
Comment=”Too lame” />
<ColumnFamily Name=”ByCity”
CompareWith=”UTF8Type” />
</Keyspace>

38. Updating the index
key = uuid()
columns = {
'first': 'Eric',
'last': 'Evans',
'email': 'eevans@rackspace.com',
'city': 'Austin',
'zip': 78250
}
addresses.insert(key, columns)
byCity.insert('Austin', {key: ''})

39. Timeseries
<!-- conf/storage-conf.xml -->
<Keyspace Name=”Sites”>
<ColumnFamily Name=”Stats”
CompareWith=”LongType”/>
</Keyspace>

40. Logging values
# time as a long, binary, network-order
ts = pack('>d', long(time() * 1e6))
stats.insert('org.apache', {ts: value})

41. Slicing
begin = pack('>d', long(s * 1e6))
stats.get_range('org.apache',
column_start=begin)
end = pack('>d', long((s + 86400) * 1e6))
stats.get_range(start='org.apache',
finish='org.debian',
column_start=begin,
column_finish=end)

42. Questions?