The document provides an overview of Apache Cassandra, covering its architecture, common use cases, and features like lightweight transactions and load balancing. It includes examples of using the DataStax Java driver and the Cassandra Query Language (CQL) for managing customer event data. Additionally, it discusses the mapping API, reconnection policies, and offers insights into upcoming training and conference events related to Cassandra.

1. Building awesome applications with Apache
Cassandra
Christopher Batey
@chbatey
©2013 DataStax Confidential. Do not distribute without consent.
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2. Who am I?
•Technical Evangelist for Apache Cassandra
• Founder of Stubbed Cassandra
• Help out Apache Cassandra users
• Previous: Cassandra backed apps at BSkyB
@chbatey

3. @chbatey
Overview
• Topics covered
• Cassandra overview
• Customer events example
• DataStax Java Driver
• Java Mapping API
• Other features
• Light weight transactions
• Load balancing
• Reconnection policies

4. @chbatey
Overview
• Topics covered
• Cassandra overview
• Customer events example
• DataStax Java Driver
• Java Mapping API
• Other features
• Light weight transactions
• Load balancing
• Reconnection policies
• Not covered
• Cassandra read and write paths
• Cassandra failure nodes

5. Common use cases
•Ordered data such as time series
•Event stores
•Financial transactions
•Sensor data e.g IoT
@chbatey

6. Common use cases
•Ordered data such as time series
•Event stores
•Financial transactions
•Sensor data e.g IoT
•Non functional requirements:
• Linear scalability
• High throughout durable writes
•Multi datacenter including active-active
•Analytics without ETL
@chbatey

7. Cassandra overview
@chbatey

8. Cassandra
Cassandra
• Distributed master less
database (Dynamo)
• Column family data model
(Google BigTable)

9. Cassandra
Europe
• Distributed master less
database (Dynamo)
• Column family data model
(Google BigTable)
• Multi data centre replication
built in from the start
USA

10. Cassandra
Online
• Distributed master less
database (Dynamo)
• Column family data model
(Google BigTable)
• Multi data centre replication
built in from the start
• Analytics with Apache
Spark
Analytics

11. Replication
WRITE
CL = 1 We have replication!
DC1 DC2
client
C
RC
RF3 RF3

12. Tunable Consistency
• Data is replicated N times
• Every query that you execute you give a consistency
• ALL
• QUORUM
• LOCAL_QUORUM
• ONE
• Christos Kalantzis Eventual Consistency != Hopeful Consistency: http://youtu.be/
A6qzx_HE3EU?list=PLqcm6qE9lgKJzVvwHprow9h7KMpb5hcUU
@chbatey

13. CQL
•Cassandra Query Language
•SQL like query language
•Keyspace – analogous to a schema
• The keyspace determines the RF (replication factor)
•Table – looks like a SQL Table CREATE TABLE scores (
@chbatey
name text,
score int,
date timestamp,
PRIMARY KEY (name, score)
);
INSERT INTO scores (name, score, date)
VALUES ('bob', 42, '2012-06-24');
INSERT INTO scores (name, score, date)
VALUES ('bob', 47, '2012-06-25');
SELECT date, score FROM scores WHERE name='bob' AND score >= 40;

14. Example Time: Customer event store
@chbatey

15. An example: Customer event store
• Customer event
• customer_id - ChrisBatey
• staff_id - Charlie
• store_type Website, PhoneApp, Phone, Retail
• event_type - login, logout, add_to_basket,
remove_from_basket, buy_item
• time
• tags

16. Requirements
• Get all events
• Get all events for a particular customer
• As above for a time slice

17. Modelling in Cassandra
CREATE TABLE customer_events(
customer_id text,
staff_id text,
Partition Key
time timeuuid,
store_type text,
event_type text,
tags map<text, text>,
PRIMARY KEY ((customer_id), time));
Clustering Column(s)

18. How it is stored on disk
customer
_id
time event_type store_type tags
charles 2014-11-18 16:52:04 basket_add online {'item': 'coffee'}
charles 2014-11-18 16:53:00 basket_add online {'item': ‘wine'}
charles 2014-11-18 16:53:09 logout online {}
chbatey 2014-11-18 16:52:21 login online {}
chbatey 2014-11-18 16:53:21 basket_add online {'item': 'coffee'}
chbatey 2014-11-18 16:54:00 basket_add online {'item': 'cheese'}
charles
event_type
basket_add
staff_id
n/a
store_type
online
tags:item
coffee
event_type
basket_add
staff_id
n/a
store_type
online
tags:item
wine
event_type
logout
staff_id
n/a
store_type
online
chbatey
event_type
login
staff_id
n/a
store_type
online
event_type
basket_add
staff_id
n/a
store_type
online
tags:item
coffee
event_type
basket_add
staff_id
n/a
store_type
online
tags:item
cheese

19. DataStax Java Driver
• Open source
@chbatey

20. @chbatey
Get all the events
public List<CustomerEvent> getAllCustomerEvents() {
return session.execute("select * from customers.customer_events")
.all().stream()
.map(mapCustomerEvent())
.collect(Collectors.toList());
}
private Function<Row, CustomerEvent> mapCustomerEvent() {
return row -> new CustomerEvent(
row.getString("customer_id"),
row.getUUID("time"),
row.getString("staff_id"),
row.getString("store_type"),
row.getString("event_type"),
row.getMap("tags", String.class, String.class));
}

21. All events for a particular customer
private PreparedStatement getEventsForCustomer;
@PostConstruct
public void prepareSatements() {
getEventsForCustomer =
session.prepare("select * from customers.customer_events where customer_id = ?");
}
public List<CustomerEvent> getCustomerEvents(String customerId) {
BoundStatement boundStatement = getEventsForCustomer.bind(customerId);
return session.execute(boundStatement)
.all().stream()
.map(mapCustomerEvent())
.collect(Collectors.toList());
@chbatey
}

22. Customer events for a time slice
public List<CustomerEvent> getCustomerEventsForTime(String customerId, long startTime,
long endTime) {
Select.Where getCustomers = QueryBuilder.select()
.all()
.from("customers", "customer_events")
.where(eq("customer_id", customerId))
.and(gt("time", UUIDs.startOf(startTime)))
.and(lt("time", UUIDs.endOf(endTime)));
return session.execute(getCustomers).all().stream()
.map(mapCustomerEvent())
.collect(Collectors.toList());
@chbatey
}

23. @chbatey
Mapping API
@Table(keyspace = "customers", name = "customer_events")
public class CustomerEvent {
@PartitionKey
@Column(name = "customer_id")
private String customerId;
@ClusteringColumn
private UUID time;
@Column(name = "staff_id")
private String staffId;
@Column(name = "store_type")
private String storeType;
@Column(name = "event_type")
private String eventType;
private Map<String, String> tags;
// ctr / getters etc
}

24. @chbatey
Mapping API
@Accessor
public interface CustomerEventDao {
@Query("select * from customers.customer_events where customer_id = :customerId")
Result<CustomerEvent> getCustomerEvents(String customerId);
@Query("select * from customers.customer_events")
Result<CustomerEvent> getAllCustomerEvents();
@Query("select * from customers.customer_events where customer_id = :customerId
and time > minTimeuuid(:startTime) and time < maxTimeuuid(:endTime)")
Result<CustomerEvent> getCustomerEventsForTime(String customerId, long startTime,
long endTime);
}
@Bean
public CustomerEventDao customerEventDao() {
MappingManager mappingManager = new MappingManager(session);
return mappingManager.createAccessor(CustomerEventDao.class);
}

25. Adding some type safety
public enum StoreType {
ONLINE, RETAIL, FRANCHISE, MOBILE
@chbatey
}
@Table(keyspace = "customers", name = "customer_events")
public class CustomerEvent {
@PartitionKey
@Column(name = "customer_id")
private String customerId;
@ClusteringColumn()
private UUID time;
@Column(name = "staff_id")
private String staffId;
@Column(name = "store_type")
@Enumerated(EnumType.STRING) // could be EnumType.ORDINAL
private StoreType storeType;

26. @chbatey
User defined types
create TYPE store (name text, type text, postcode text) ;
CREATE TABLE customer_events_type(
customer_id text,
staff_id text,
time timeuuid,
store frozen<store>,
event_type text,
tags map<text, text>,
PRIMARY KEY ((customer_id), time));

27. Mapping user defined types
@chbatey
@UDT(keyspace = "customers", name = "store")
public class Store {
private String name;
private StoreType type;
private String postcode;
// getters etc
}
@Table(keyspace = "customers", name = "customer_events_type")
public class CustomerEventType {
@PartitionKey
@Column(name = "customer_id")
private String customerId;
@ClusteringColumn()
private UUID time;
@Column(name = "staff_id")
private String staffId;
@Frozen
private Store store;
@Column(name = "event_type")
private String eventType;
private Map<String, String> tags;

28. Mapping user defined types
@chbatey
@UDT(keyspace = "customers", name = "store")
public class Store {
private String name;
private StoreType type;
private String postcode;
// getters etc
}
@Table(keyspace = "customers", name = "customer_events_type")
public class CustomerEventType {
@PartitionKey
@Column(name = "customer_id")
private String customerId;
@ClusteringColumn()
private UUID time;
@Column(name = "staff_id")
private String staffId;
@Frozen
private Store store;
@Column(name = "event_type")
private String eventType;
private Map<String, String> tags;
@Query("select * from customers.customer_events_type")
Result<CustomerEventType> getAllCustomerEventsWithStoreType();

29. What else can I do?
@chbatey

30. Lightweight Transactions (LWT)
Consequences of Lightweight Transactions
4 round trips vs. 1 for normal updates (uses Paxos algorithm)
Operations are done on a per-partition basis
Will be going across data centres to obtain consensus (unless you use
LOCAL_SERIAL consistency)
Cassandra user will need read and write access i.e. you get back the row!
Great for 1% your app, but eventual consistency is still your friend!
@chbatey

31. Company Confidential
@chbatey
Batch Statements
BEGIN BATCH
INSERT INTO users (userID, password, name) VALUES ('user2', 'ch@ngem3b', 'second user')
UPDATE users SET password = 'ps22dhds' WHERE userID = 'user2'
INSERT INTO users (userID, password) VALUES ('user3', 'ch@ngem3c')
DELETE name FROM users WHERE userID = 'user2’
APPLY BATCH;
BATCH statement combines multiple INSERT, UPDATE, and DELETE statements into a single logical
operation
Atomic operation
If any statement in the batch succeeds, all will
No batch isolation
Other “transactions” can read and write data being affected by a partially executed batch
© 2014 DataStax, All Rights Reserved.

32. Batch Statements with LWT
BEGIN BATCH
UPDATE foo SET z = 1 WHERE x = 'a' AND y = 1;
UPDATE foo SET z = 2 WHERE x = 'a' AND y = 2 IF t = 4;
Company Confidential
@chbatey
APPLY BATCH;
Allows you to group multiple conditional updates in a batch as long as all
those updates apply to the same partition
© 2014 DataStax, All Rights Reserved.

33. Load balancing
• Data centre aware policy
• Token aware policy
• Latency aware policy
• Whitelist policy APP APP
DC1 DC2
@chbatey
Async Replication

34. Load balancing
• Data centre aware policy
• Token aware policy
• Latency aware policy
• Whitelist policy APP APP
DC1 DC2
@chbatey
Async Replication

35. Reconnection Policies
• Policy that decides how often the reconnection to a dead node is
attempted.
Cluster cluster = Cluster.builder()
.addContactPoints("127.0.0.1", "127.0.0.2")
.withReconnectionPolicy(new ConstantReconnectionPolicy(1000))
.withLoadBalancingPolicy(new TokenAwarePolicy())
.build();
• ConstantReconnectionPolicy
• ExponentialReconnectionPolicy (Default)
@chbatey ©2014 DataStax. Do not distribute without consent.

36. Reconnection Policies
• Policy that decides how often the reconnection to a dead node is
attempted.
Cluster cluster = Cluster.builder()
.addContactPoints("127.0.0.1", "127.0.0.2")
.withReconnectionPolicy(new ConstantReconnectionPolicy(1000))
.withLoadBalancingPolicy(new TokenAwarePolicy())
.build();
• ConstantReconnectionPolicy
• ExponentialReconnectionPolicy (Default)
@chbatey ©2014 DataStax. Do not distribute without consent.

37. @chbatey
Summary
• Cassandra overview
• Customer events example
• DataStax Java Driver
• Java Mapping API
• Other features
• Light weight transactions
• Load balancing
• Reconnection policies

38. Thanks for listening
• Badger me on twitter @chbatey
• https://github.com/chbatey/cassandra-customer-events
• https://academy.datastax.com/
• http://christopher-batey.blogspot.co.uk/
@chbatey

39. © 2014 DataStax, All Rights Reserved. Company Confidential
Training Day | December 3rd
Beginner Track
• Introduction to Cassandra
• Introduction to Spark, Shark, Scala and Cassandra
Advanced Track
• Data Modeling
• Performance Tuning
Conference Day | December 4th
Cassandra Summit Europe 2014 will be the single
largest gathering of Cassandra users in Europe. Learn
how the world's most successful companies are
transforming their businesses and growing faster than
ever using Apache Cassandra.
http://bit.ly/cassandrasummit2014
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