Join Aaron Morton, DataStax MVP for Apache Cassandra and learn the basics of the massively scalable NoSQL database. This webinar is 101 level and will examine C*’s architecture and its strengths for powering mission-critical applications. Aaron will introduce you to concepts such as Cassandra’s data model, multi-datacenter replication, and tunable consistency.

1. DATASTAX C*OLLEGE CREDIT:
AN INTRODUCTION TO
APACHE CASSANDRA
Aaron Morton
Apache Cassandra Committer, Data Stax MVP for Apache Cassandra
@aaronmorton
www.thelastpickle.com
Licensed under a Creative Commons Attribution-NonCommercial 3.0 New Zealand License

2. Overview
The Cluster
The Data Model
The API

3. Cassandra
- Started at Facebook
- Open sourced in 2008
- Top Level Apache project
since 2010.

4. Used by...
Netflix, Twitter,
Reddit, Rackspace...

5. Inspiration
- Google Big Table (2006)
- Amazon Dynamo (2007)

6. Why Cassandra?
- Scale
- Operations
- Data Model

7. Why Cassandra?
Is My App a Good Fit for Apache Cassandra?
Eric Lubow (CTO, SimpleReach)
Wednesday October 24 @ 8:30AM PST
http://www.datastax.com/resources/webinars/collegecredit

8. Overview
The Cluster
The Data Model
The API

9. Store ‘foo’ key with Replication Factor 3.
Node 1 - 'foo'
Node 4 Node 2 - 'foo'
Node 3 - 'foo'

10. Consistent Hashing.
- Evenly map keys to nodes
- Minimise key movements
when nodes join or leave

11. Partitioner.
RandomPartitioner
transforms Keys to Tokens
using MD5.
(Default, there are others.)

12. Keys and Tokens?
key 'fop' 'foo'
token 0 10 90 99

13. Token Ring.
99 0
'foo' 'fop'
token: 90 token: 10

14. Token Ranges.
Node 1
token: 0
76-0 1-25
Node 4 Node 2
token: 75 token: 25
Node 3
token: 50

15. Locate Token Range.
Node 1
token: 0
'foo'
token: 90
Node 4 Node 2
token: 75 token: 25
Node 3
token: 50

16. Replication Strategy selects
Replication Factor number of
nodes for a row.

17. SimpleStrategy with RF 3.
Node 1
token: 0
'foo'
token: 90
Node 4 Node 2
token: 75 token: 25
Node 3
token: 50

18. NetworkTopologyStrategy uses a
Replication Factor per Data
Centre.
(Default.)

19. Multi DC Replication with RF 3 and RF 2.
Node 1 Node 10
token: 0 token: 1
'foo'
token: 90
Node 4 West DC Node 2 Node 40 East DC Node 20
token: 75 token: 25 token: 76 token: 26
Node 3 Node 30
token: 50 token: 51

20. The Snitch knows which Data
Centre and Rack the Node is
in.

21. SimpleSnitch.
Places all nodes in the same
DC and Rack.
(Default, there are others.)

22. PropertyFileSnitch.
DC and Rack is specified per
node via configuration.

23. EC2Snitch.
DC is set to AWS Region and
a Rack to Availability Zone.

24. DynamicSnitch.
Re-orders nodes according to
their observed performance.
(Wraps other snitch.)

25. The Client and the Coordinator.
Node 1
token: 0
'foo'
token: 90
Node 4 Node 2
token: 75 token: 25
Node 3
Client
token: 50

26. Gossip.
Nodes share information with
a small number of neighbours.
Who share information with a
small number of neigh..

27. Multi DC Client and the Coordinator.
Node 1 Node 10
token: 0 token: 1
'foo'
token: 90
Node 4 Node 2 Node 40 Node 20
token: 75 token: 25 token: 76 token: 26
Node 3 Node 30
Client
token: 50 token: 51

28. Consistency Level (CL).
- Specified for each request
- Number of nodes to wait
for.

29. Consistency Level (CL)
- Any*
- One, Two Three
- QUORUM
- LOCAL_QUORUM, EACH_QUOURM*

30. QUOURM at Replication Factor...
Replication
2 or 3 4 or 5 6 or 7
Factor
QUOURM 2 3 4

31. QUOURM at with RF3.
Node 1
token: 0
'foo'
token: 90
Node 4 Node 2
token: 75 token: 25
Node 3
Client
token: 50

32. Write ‘foo’ at QUOURM with Hinted Handoff.
Node 1
'foo'
'foo'
token: 90
Node 4 Node 2
'foo' for #3 'foo'
Node 3
Client

33. Read ‘foo’ at QUOURM.
Node 1
'foo'
'foo'
token: 90
Node 4 Node 2
'foo'
Node 3
Client

34. Consistency Level
nodes must agree.

35. Column Timestamps
used to resolve
differences.

36. Resolving differences.
Column Node 1 Node 2 Node 3
cromulent cromulent
purple <missing>
(timestamp 10) (timestamp 10)
embiggens embiggens debigulator
monkey
(timestamp 10) (timestamp 10) (timestamp 5)
tomato tomato tomacco
dishwasher
(timestamp 10) (timestamp 10) (timestamp 15)

37. Consistent read for ‘foo’ at QUOURM.
Node 1 Node 1
cromulent
cromulent
Node 4 Node 2 Node 4 Node 2
embiggins cromulent
cromulent
Client Client
Node 3 Node 3

38. Strong Consistency
W+R>N
(#Write Nodes + #Read Nodes> Replication Factor)

39. Achieving Strong Consistency.
- QUOURM Read + QUORUM Write
- ALL Read + ONE Write
- ONE Read + ALL Write

40. Eventual Consistency.
W + R <= N

41. Achieving Consistency.
- Hinted Handoff
- Read Repair
- Scheduled nodetool repair

42. Overview
The Cluster
The Data Model
The API

43. Data Model so far.
Row Key: Column Column Column
(Incomplete.)

44. Data Model.
Keyspace
Column Family Column Family Column Family
Column Column Column
Row Key: Column Column Column
Column Column Column
(Column Family and Table mean the same.)

45. Rows are the unit of
replication.

46. The Column Family
is the unit of
storage.

47. Inside the Column Family.
Keyspace
Column Family
Column: name, value, timestamp
Row Key: Column: name, value, timestamp
Column: name, value, timestamp
(Also TTL Columns)

48. Basic Data Types
- ASCII, UTF8
- Integer, Long, Float, Double, Boolean
- Date
- UUID
- Bytes
- Counter*

49. Composite Data Types
- Two or more Basic types
- Ordered by each component
- e.g. (IntegerType, UTF8) to hold
(timestamp, user_name)

50. Data Modelling.
Data Modelling for Apache Cassandra
Aaron Morton (Cassandra Committer)
Wednesday November 7 @ 11AM PST
http://www.datastax.com/resources/webinars/collegecredit

51. Overview
The Cluster
The Data Model
The API

52. The API.
- Original Thrift based RPC
- Declarative Cassandra Query Language
(CQL)

53. RPC via Python pycassa.
# pycassa - Python
>>> col_fam = pycassa.ColumnFamily(connection_pool,
'ColumnFamily1')
>>> col_fam.insert('row_key', {'col_name': 'col_val'})

54. RPC via Python pycassa...
# pycassa - Python
>>> col_fam.get('row_key')
{'col_name': 'col_val', 'col_name2': 'col_val2'}
>>> col_fam.multi_get(['row_key'], [‘col_name’])
{‘row_key’ : {'col_name': 'col_val'}}

55. RPC via Python pycassa...
# pycassa - Python
>>> col_fam.remove('row_key')
>>> col_fam.remove('row_key', [‘col_name’])

56. CQL.
# Cassandra Query Language (CQL)
INSERT INTO ColumnFamily1 (KEY, col_name) VALUES ('row_key',
'col_value');

57. CQL...
# Cassandra Query Language (CQL)
SELECT * FROM ColumnFamily1 IN (‘row_key_1’);
SELECT col_name FROM ColumnFamily1 WHERE KEY IN (‘row_key_1’,
‘row_key_2’);

58. CQL...
# Cassandra Query Language (CQL)
DELETE FROM ColumnFamily1 WHERE key IN ('row_key',);
DELETE col_name FROM ColumnFamily1 WHERE key = 'row_key';

59. Thanks.

60. Aaron Morton
@aaronmorton
www.thelastpickle.com
Licensed under a Creative Commons Attribution-NonCommercial 3.0 New Zealand License