This document discusses using Cassandra for real-time big data applications. It summarizes Cassandra's ring architecture and how it allows for linear scalability. It also describes how Cassandra offers tunable consistency levels and different data models including column families and CQL. Examples are given around using Cassandra for real-time web analytics to provide immediate statistics on website traffic.

1. Real-Time Big Data in
practice with Cassandra
Michaël Figuière
@mfiguiere

2. Speaker
Michaël Figuière
@mfiguiere
©2012 DataStax 2

3. Ring Architecture
Node Node
Node Cassandra Node
Node
Node
©2012 DataStax 3

4. Ring Architecture
Node Replica
Node
Replica
Node
Replica
©2012 DataStax 4

5. Linear Scalability
Client Writes/s by Node Count - Replication Factor = 3
©2012 DataStax 5

6. Client / Server Communication
Client ? Node Replica
Client
Node
Replica
Client
Node
Client Replica
©2012 DataStax 6

7. Client / Server Communication
Client Node Replica
Client
Node
Replica
Client
Node
Client Replica
Coordinator node:
Forwards all R/W requests
to corresponding replicas
©2012 DataStax 7

8. Tunable Consistency
Time
A A A
3 replicas
©2012 DataStax 8

9. Tunable Consistency
Time
A A A
B A A
Write ‘B’
Write and wait for
acknowledge from one node
©2012 DataStax 9

10. Tunable Consistency
Time
R +W < N
A A A
B A A
B A A
Read waiting for one node Write and wait for
to answer acknowledge from one node
©2012 DataStax 10

11. Tunable Consistency
Time
R +W = N
A A A
B B A
B B A
Read waiting for one node Write and wait for
to answer acknowledges from two nodes
©2012 DataStax 11

12. Tunable Consistency
Time
R +W > N
A A A
B B A
B B A
Read waiting for two nodes Write and wait for
to answer acknowledges from two nodes
©2012 DataStax 12

13. Tunable Consistency
Time
R = W = QUORUM
A A A
B B A
B B A
QUORUM = (N / 2) + 1
©2012 DataStax 13

14. Request Path
1
Client Node Replica
2
3
Client
4 2
Node
Replica
3
Client
2
3
Node
Client Replica
Coordinator node
©2012 DataStax 14

15. Column Family Data Model
name email address state
jbellis
Jonathan jb@ds.com 123 main TX
name email address state
dhutch
Daria dh@ds.com 45 2nd st CA
name email
egilmore
Eric eg@ds.com
Row Key Columns
©2012 DataStax 15

16. Column Family Data Model
dhutch egilmore datastax mzcassie
jbellis
egilmore
dhutch
datastax mzcassie
egilmore
Row Key Columns
©2012 DataStax 16

17. CQL3 Data Model
Timeline Table
user_id tweet_id author body
gmason 1765 phenry Give me liberty or give me death
gmason 1742 gwashington I chopped down the cherry tree
ahamilton 1797 jadams A government of laws, not men
ahamilton 1742 gwashington I chopped down the cherry tree
Partition Remaining
Key Key
©2012 DataStax 17

18. CQL3 Data Model
Timeline Table
user_id tweet_id author body
gmason 1765 phenry Give me liberty or give me death
gmason 1742 gwashington I chopped down the cherry tree
ahamilton 1797 jadams A government of laws, not men
ahamilton 1742 gwashington I chopped down the cherry tree
CQL
CREATE TABLE timeline (
user_id varchar,
tweet_id uuid,
author varchar,
body varchar,
PRIMARY KEY (user_id, tweet_id));
©2012 DataStax 18

19. CQL3 Data Model
Timeline Table
user_id tweet_id author body
gmason 1765 phenry Give me liberty or give me death
gmason 1742 gwashington I chopped down the cherry tree
ahamilton 1797 jadams A government of laws, not men
ahamilton 1742 gwashington I chopped down the cherry tree
Timeline Physical Layout
[1742, author] [1742, body] [1765, author] [1765, body]
gmason
gwashington I chopped down the... phenry Give me liberty or give...
[1742, author] [1742, body] [1797, author] [1797, body]
ahamilton
gwashington I chopped down the... jadams A government of laws...
©2012 DataStax 19

20. Real-Time Analytics
Google Analytics gives you
immediate statistics about
your website traffic
©2012 DataStax 20

21. Web Analytics Data Model
Analytics Table
url time views from_search direct from_referrer
/index.html 12:00 354 300 20 34
/index.html 12:01 402 333 25 44
/contacts.html 12:00 23 3 0 20
/contacts.html 12:01 20 4 1 15
CQL
CREATE TABLE analytics (
url varchar,
time timestamp,
views counter,
from_search counter,
direct counter,
from_referrer counter,
PRIMARY KEY (url, time));
©2012 DataStax 21

22. Web Analytics Data Model
Analytics Table
url time views from_search direct from_referrer
/index.html 12:00 354 300 20 34
/index.html 12:01 402 333 25 44
/contacts.html 12:00 23 3 0 20
/contacts.html 12:01 20 4 1 15
CQL
UPDATE analytics
SET views = views + 1,
from_search = from_search + 1
WHERE url = '/index.html'
AND time = '2012-10-06 12:00';
©2012 DataStax 22

23. Web Analytics Data Model
Analytics Table
url time views from_search direct from_referrer
/index.html 12:00 354 300 20 34
/index.html 12:01 402 333 25 44
/contacts.html 12:00 23 3 0 20
/contacts.html 12:01 20 4 1 15
CQL
SELECT * FROM analytics
WHERE url = '/index.html'
©2012 DataStax 23

24. Connect and Write
Cluster cluster = Cluster.builder()
.addContactPoints("127.0.0.1", "127.0.0.2")
.build();
Session session = cluster.connect();
session.execute(
"INSERT INTO user (user_id, name, email)
VALUES (12345, 'johndoe', 'john@doe.com')"
);
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25. Read
ResultSet rs = session.execute("SELECT * FROM user");
List<CQLRow> rows = rs.fetchAll();
for (CQLRow row : rows) {
String userId = row.getString("user_id");
String name = row.getString("name");
String email = row.getString("email");
}
©2012 DataStax 25

26. Object Mapping
@Table("user_and_messages") public enum Gender {
public class User {
@EnumValue("m")
@Column("user_id") MALE,
private String userId;
@EnumValue("f")
private String name; FEMALE;
}
private String email;
private Gender gender;
}
©2012 DataStax 26

27. Aggregation
@Table("user_and_messages") public class Message {
public class User {
private String title;
@Column("user_id")
private String userId; private String body;
}
private String name;
private String email;
@GroupBy("user_id")
private List<Message> messages;
}
©2012 DataStax 27

28. Inheritance
@Table("catalog")
@InheritanceValue("tv")
@Inheritance({Phone.class, TV.class})
public class TV
@InheritanceColumn("product_type")
extends Product {
public abstract class Product {
private float size;
@Column("product_id")
}
private String productId;
private float price;
private String vendor;
private String model;
}
©2012 DataStax 28

29. Online Business Intelligence
Storage for application Distributed batch
in production processing
Application Cassandra Hadoop
Using results in Storage for
production results
©2012 DataStax 29

30. Stay Tuned!
blog.datastax.com
@mfiguiere