This document provides an introduction to Apache Cassandra, including an overview of key concepts like the cluster, nodes, data model, and data modeling best practices. It discusses Cassandra's origins and popularity. The presentation covers the cluster architecture with consistent hashing and token ranges, replication strategies, consistency levels, and more. It also summarizes the Cassandra data model including tables, columns, SSTables, caching, compaction and discusses building a Twitter-like data model in CQL.

1. CASSANDRA COMMUNITY WEBINARS APRIL 2013
INTRODUCTION TO
APACHE CASSANDRA 1.2
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. Cassandra Summit 2013
June 11 & 12
San Francisco
Use SFSummit25 for 25% off

3. Cassandra Summit 2013
DataStax Ac*ademy
Free certification during the
summit.

4. Overview
The Cluster
The Node
The Data Model

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

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

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

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

9. Overview
The Cluster
The Node
The Data Model

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

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

12. Partitioner.
RandomPartitioner
transforms Keys to Tokens
using MD5.
(Default pre version 1.2.)

13. Partitioner.
Murmur3Partitioner
transforms Keys to Tokens
using Murmur3.
(Default in version 1.2.)

14. Keys andTokens?
token 0 99
key 'fop' 'foo'
10 90

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

16. Token Ranges pre v1.2.
Node 1
token: 0
Node 2
token: 25
Node 4
token: 75
Node 3
token: 50
1-2576-0

17. Token Ranges withVirtual Nodes in v1.2.
Node 1
Node 2
Node 3
Node 4

18. LocateToken Range.
Node 1
Node 2
Node 3
Node 4
'foo'
token: 90

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

20. SimpleStrategy with RF 3.
Node 1
Node 2
Node 3
Node 4
'foo'
token: 90

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

22. Multi DC Replication with RF 3 and RF 2.
Node 1
Node 2
Node 3
Node 4
'foo'
token: 90
Node 1
Node 2
Node 3
Node 4
West DC East DC

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

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

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

26. The Client and the Coordinator.
Node 1
Node 2
Node 3
Node 4
'foo'
token: 90
Client

27. Multi DC Client and the Coordinator.
Node 1
Node 2
Node 3
Node 4
'foo'
token: 90
Client
Node 10
Node 20
Node 30
Node 40

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

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

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

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

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

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

34. Column Timestamps
used to resolve
differences.

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

36. Consistent read for ‘foo’ at QUOURM.
Node 1
Node 2
Node 3
Node 4
Client
cromulent
cromulent
<empty>
Node 1
Node 2
Node 3
Node 4
Client
cromulent cromulent

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

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

39. Achieving Consistency
- Consistency Level
- Hinted Handoff
- Read Repair
- Anti Entropy

40. Overview
The Cluster
The Node
The Data Model

41. Optimised for
Writes.

42. Write path
Append to Write
Ahead Log.
(fsync every 10s by default, other options available)

43. Write path...
Merge Columns
into Memtable.
(Lock free, always in memory.)

44. (Later.)
Asynchronously flush
Memtable to new files.
(May be 10’s or 100’s of MB in size.)

45. Data is stored in
immutable SSTables.
(Sorted String table.)

46. SSTable files.
*-Data.db
*-Index.db
*-Filter.db
(And others)

47. SSTables.
SSTable 1
foo:
dishwasher (ts 10):
tomato
purple (ts 10):
cromulent
SSTable 2
foo:
frink (ts 20):
flayven
monkey (ts 10):
embiggins
SSTable 3 SSTable 4
foo:
dishwasher (ts 15):
tomacco
SSTable 5

48. Read purple, monkey, dishwasher.
SSTable 1-Data.db
foo:
dishwasher (ts 10):
tomato
purple (ts 10):
cromulent
SSTable 2-Data.db
foo:
frink (ts 20):
flayven
monkey (ts 10):
embiggins
SSTable 3-Data.db SSTable 4-Data.db
foo:
dishwasher (ts 15):
tomacco
SSTable 5-Data.db
Bloom Filter
Index Sample
SSTable 1-Index.db
Bloom Filter
Index Sample
SSTable 2-Index.db
Bloom Filter
Index Sample
SSTable 3-Index.db
Bloom Filter
Index Sample
SSTable 4-Index.db
Bloom Filter
Index Sample
SSTable 5-Index.db
Memory
Disk

49. Key Cache caches row key
position in *-Data.db file.
(Removes up to1disk seek per SSTable.)

50. Read with Key Cache.
SSTable 1-Data.db
foo:
dishwasher (ts 10):
tomato
purple (ts 10):
cromulent
SSTable 2-Data.db
foo:
frink (ts 20):
flayven
monkey (ts 10):
embiggins
SSTable 3-Data.db SSTable 4-Data.db
foo:
dishwasher (ts 15):
tomacco
SSTable 5-Data.db
Key Cache
Index Sample
SSTable 1-Index.db
Key Cache
Index Sample
SSTable 2-Index.db
Key Cache
Index Sample
SSTable 3-Index.db
Key Cache
Index Sample
SSTable 4-Index.db
Key Cache
Index Sample
SSTable 5-Index.db
Memory
Disk
Bloom Filter Bloom Filter Bloom Filter Bloom Filter Bloom Filter

51. Row Cache caches entire row.
(Removes all disk IO.)

52. Read with Row Cache.
Row Cache
SSTable 1-Data.db
foo:
dishwasher (ts 10):
tomato
purple (ts 10):
cromulent
SSTable 2-Data.db
foo:
frink (ts 20):
flayven
monkey (ts 10):
embiggins
SSTable 3-Data.db SSTable 4-Data.db
foo:
dishwasher (ts 15):
tomacco
SSTable 5-Data.db
Key Cache
Index Sample
SSTable 1-Index.db
Key Cache
Index Sample
SSTable 2-Index.db
Key Cache
Index Sample
SSTable 3-Index.db
Key Cache
Index Sample
SSTable 4-Index.db
Key Cache
Index Sample
SSTable 5-Index.db
Memory
Disk
Bloom Filter Bloom Filter Bloom Filter Bloom Filter Bloom Filter

53. Compaction merges truth from
multiple SSTables into one
SSTable with the same truth.
(Manual and continuous background process.)

54. Compaction.
Column SSTable 1 SSTable 2 SSTable 4 New
purple
cromulent
(timestamp 10)
<tombstone>
(timestamp 15)
<tombstone>
(timestamp 15)
monkey
embiggens
(timestamp 10)
embiggens
(timestamp 10)
dishwasher
tomato
(timestamp 10)
tomacco
(timestamp 15)
tomacco
(timestamp 15)

55. Overview
The Cluster
The Node
The Data Model

56. Cassandra is good at
reading data from a row in the
order it is stored.

57. Typically an efficient data model will
denormalize data and use the
storage engine order.

58. To create a good data model
understand the queries your
application requires.

59. API Choice
Thrift
Original and still fully
supported API.

60. API Choice
CQL3
New and fully supported API.

61. CQL 3
A Table Orientated, Schema
Driven, Data Model and
Query language similar to
SQL.

62. CQL 3
A Table Orientated, Schema
Driven, Data Model and
Query language similar to
SQL.

63. Twitter clone
using CQL 3 via the cqlsh
tool.
bin/cqlsh

64. Queries?
- Post Tweet to Followers
- Get Tweet by ID
- List Tweets by User
- List Tweets in User Timeline
- List Followers

65. Keyspace
A Namespace container.

66. Our Keyspace
CREATE KEYSPACE
cass_community
WITH replication =
{'class':'NetworkTopologyStrategy',
'datacenter1':1};

67. Table
A sparse collection of well
known, ordered columns.

68. FirstTable
CREATE TABLE User
(
user_name text,
password text,
real_name text,
PRIMARY KEY (user_name)
);

69. Some users
cqlsh:cass_community> INSERT INTO User
... (user_name, password, real_name)
... VALUES
... ('fred', 'sekr8t', 'Mr Foo');
cqlsh:cass_community> select * from User;
user_name | password | real_name
-----------+----------+-----------
fred | sekr8t | Mr Foo

70. Some users
cqlsh:cass_community> INSERT INTO User
... (user_name, password)
... VALUES
... ('bob', 'pwd');
cqlsh:cass_community> select * from User where user_name =
'bob';
user_name | password | real_name
-----------+----------+-----------
bob | pwd | null

71. Data Model (so far)
Table /
Value
User
user_name Primary Key

72. TweetTable
CREATE TABLE Tweet
(
tweet_id bigint,
body text,
user_name text,
timestamp timestamp,
PRIMARY KEY (tweet_id)
);

73. TweetTable...
cqlsh:cass_community> INSERT INTO Tweet
... (tweet_id, body, user_name, timestamp)
... VALUES
... (1, 'The Tweet','fred',1352150816917);
cqlsh:cass_community> select * from Tweet where tweet_id = 1;
tweet_id | body | timestamp | user_name
----------+-----------+--------------------------+-----------
1 | The Tweet | 2012-11-06 10:26:56+1300 | fred

74. Data Model (so far)
Table /
Value
User Tweet
user_name Primary Key Field
tweet_id Primary Key

75. UserTweetsTable
CREATE TABLE UserTweets
(
tweet_id bigint,
user_name text,
body text,
timestamp timestamp,
PRIMARY KEY (user_name, tweet_id)
);

76. UserTweetsTable...
cqlsh:cass_community> INSERT INTO UserTweets
... (tweet_id, body, user_name, timestamp)
... VALUES
... (1, 'The Tweet','fred',1352150816917);
cqlsh:cass_community> select * from UserTweets where
user_name='fred';
user_name | tweet_id | body | timestamp
-----------+----------+-----------+--------------------------
fred | 1 | The Tweet | 2012-11-06 10:26:56+1300

77. UserTweetsTable...
cqlsh:cass_community> select * from UserTweets where
user_name='fred' and tweet_id=1;
user_name | tweet_id | body | timestamp
-----------+----------+-----------+--------------------------
fred | 1 | The Tweet | 2012-11-06 10:26:56+1300

78. UserTweetsTable...
cqlsh:cass_community> INSERT INTO UserTweets
... (tweet_id, body, user_name, timestamp)
... VALUES
... (2, 'Second Tweet', 'fred', 1352150816918);
cqlsh:cass_community> select * from UserTweets where user_name = 'fred';
user_name | tweet_id | body | timestamp
-----------+----------+--------------+--------------------------
fred | 1 | The Tweet | 2012-11-06 10:26:56+1300
fred | 2 | Second Tweet | 2012-11-06 10:26:56+1300

79. UserTweetsTable...
cqlsh:cass_community> select * from UserTweets where user_name = 'fred' order by
tweet_id desc;
user_name | tweet_id | body | timestamp
-----------+----------+--------------+--------------------------
fred | 2 | Second Tweet | 2012-11-06 10:26:56+1300
fred | 1 | The Tweet | 2012-11-06 10:26:56+1300

80. UserTimeline
CREATE TABLE UserTimeline
(
user_name text,
tweet_id bigint,
tweet_user text,
body text,
timestamp timestamp,
PRIMARY KEY (user_name, tweet_id)
)
WITH CLUSTERING ORDER BY (tweet_id DESC);

81. UserTimeline
cqlsh:cass_community> INSERT INTO UserTimeline
... (user_name, tweet_id, tweet_user, body, timestamp)
... VALUES
... ('fred', 1, 'fred', 'The Tweet',1352150816917);
cqlsh:cass_community> INSERT INTO UserTimeline
... (user_name, tweet_id, tweet_user, body, timestamp)
... VALUES
... ('fred', 100, 'bob', 'My Tweet',1352150846917);

82. UserTimeline
cqlsh:cass_community> select * from UserTimeline where user_name = 'fred';
user_name | tweet_id | body | timestamp | tweet_user
-----------+----------+-----------+--------------------------+------------
fred | 100 | My Tweet | 2012-11-06 10:27:26+1300 | bob
fred | 1 | The Tweet | 2012-11-06 10:26:56+1300 | fred

83. Data Model (so far)
Table /
Value
User Tweet
User
Tweets
User
Timeline
user_name Primary Key Field Primary Key Primary Key
tweet_id Primary Key
Primary Key
Component
Primary Key
Component

84. UserMetricsTable
CREATE TABLE UserMetrics
(
user_name text,
tweets counter,
followers counter,
following counter,
PRIMARY KEY (user_name)
);

85. UserMetricsTable...
cqlsh:cass_community> UPDATE
... UserMetrics
... SET
... tweets = tweets + 1
... WHERE
... user_name = 'fred';
cqlsh:cass_community> select * from UserMetrics where
user_name = 'fred';
user_name | followers | following | tweets
-----------+-----------+-----------+--------
fred | null | null | 1

86. Data Model (so far)
Table /
Value
User Tweet
User
Tweets
User
Timeline
User Metrics
user_name
Primary
Key
Field
Primary
Key
Primary
Key
Primary
Key
tweet_id
Primary
Key
Primary Key
Component
Primary Key
Component

87. Relationships
CREATE TABLE Followers
(
user_name text,
follower text,
timestamp timestamp,
PRIMARY KEY (user_name, follower)
);
CREATE TABLE Following
(
user_name text,
following text,
timestamp timestamp,
PRIMARY KEY (user_name, following)
);

88. Relationships
cqlsh:cass_community> INSERT INTO
... Following
... (user_name, following, timestamp)
... VALUES
... ('bob', 'fred', 1352247749161);
cqlsh:cass_community> INSERT INTO
... Followers
... (user_name, follower, timestamp)
... VALUES
... ('fred', 'bob', 1352247749161);

89. Relationships
cqlsh:cass_community> select * from Following;
user_name | following | timestamp
-----------+-----------+--------------------------
bob | fred | 2012-11-07 13:22:29+1300
cqlsh:cass_community> select * from Followers;
user_name | follower | timestamp
-----------+----------+--------------------------
fred | bob | 2012-11-07 13:22:29+1300

90. Data Model
Table /
Value
User Tweet
User
Tweets
User
Timeline
User
Metrics
Follows
Followers
user_name
Primary
Key
Field
Primary
Key
Primary
Key
Primary
Key
Primary
Key
tweet_id
Primary
Key
Primary Key
Component
Primary Key
Component

91. Cassandra Summit 2013
June 11 & 12
San Francisco
Use SFSummit25 for 25% off

92. Thanks.

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