NoSQL Databases for Implementing Data Services – Should I Care?

Welcome NoSQL Databases for
Implementing Data
Services – Should I Care ?
Guido Schmutz
UKOUG Conference 2012
04.12.2012

BASEL BERN LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN

2012 © Trivadis
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NoSQL Databases for Implementing Data Services – Should I Care?
10.10.2012

Guido Schmutz

• Working for Trivadis for more than 15 years
• Oracle ACE Director for Fusion Middleware and SOA
• Co-Author of different books
• Consultant, Trainer Software Architect for Java, Oracle, SOA
and EDA
• Member of Trivadis Architecture Board
• Technology Manager @ Trivadis

• More than 20 years of software development
experience

• Contact: guido.schmutz@trivadis.com
• Blog: http://guidoschmutz.wordpress.com
• Twitter: gschmutz

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Agenda

1. Why NoSQL and what is it?
2. NoSQL Database Types
3. Polyglot Persistence
4. NoSQL and Service-Oriented Architecture
5. Summary

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History of Database

1960s File-based, Network (CODASYL) and Hierarchical
Databases
1970s Relational Database
1980 SQL became the standard query language
Early 1990 Object-Databases
Late 1990 XML Databases
2004 NoSQL Databases

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What„s wrong with Relational Databases ? They are great ….

• SQL provides a rich, declarative query language
• Database enforce referential integrity
• ACID semantics
• Well understood by developers, database administrators
• Well supported by different languages, frameworks and tools
• Hibernate, JPA, JDBC, iBATIS, Entity Framework
• Well understood and accepted by operations people (DBAs)
• Configuration
• Monitoring
• Backup and Recovery
• Tuning
• Design

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Relational Databases are great ... But!
ORDER
Order
ID: 1001

Problem: Complex Object graphs
Order Date: 15.9.2012

Customer

 Object/Relational impedance mismatch
CUSTOMER
First Name: Peter
Last Name: Sample

 Complicated to map rich domain model
Billing Address
Street: Somestreet 10

to relational schema
City: Somewhere
Postal Code: 55901 ADDRESS

 Performance issues Line Items

 Many rows in many tables Name

Ipod Touch
Quantity

1
Price

220.95
ORDER_LINES

 Many joins Monster Beat 2 190.00

 Eager vs. lazy loading Apple Mouse 1 69.90

Problem: Schema evolution
 Adding attributes to an object => have to add columns to table
 Expensive, if lots of data in that table
- Holding locks on the tables for long time
- Application downtime …

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ORDER
Order

ID: 1001
Order Date: 15.9.2012

Customer
CUSTOMER
First Name: Peter
Last Name: Sample
Billing Address
City: Somewhere
Postal Code: 55901 ADDRESS

Line Items
Name Quantity Price ORDER_LINES

Ipod Touch 1 220.95

Monster Beat 2 190.00

Apple Mouse 1 69.90

Consumer

REST/SOAP

Service

Repository/DAO

O/R Mapping

SQL

RDBMS

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Problem: Semi-structured data
 Relational schema doesn„t easily handle semi-structured data
 Common solutions
- Name/Value table
- Poor performance
- Lack of constraint
- Serialize as Blob
- Fewer joins, but no query capabilities
Problem: Scaling
 Scaling writes difficult/expensive/impossible => BigData
 Vertical scaling is limited and is expensive
 Horizontal scaling is limited and is expensive

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Solution: NoSQL ?

No standard definition of what NoSQL means
• Not Only SQL
Term began in a workshop organized in 2009
but some common characteristics of NoSQL databases
• They don„t use the relational data model and thus don„t use SQL
• Tend to be designed to run on cluster
RDBMS NoSQL
• Tend to be Open Source Presentation
Tier
User Interface User Interface

• Schema-Less - Don„t have a fixed Key Value Stores

schema, allowing to store any

Services
Caching Search
Middle Tier Object-Relational Relational-Object Lucene
Transactions Batch

data in any record
MapReduce

• Different APIs
Search Blobs

Database Tier Transactions Batch Data

Caching Triggers

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Central vs. Application Databases

Central Database Application Database
• Using SQL as the integration mechanism • Only accessed by a single application
between applications
• Only the application using the database
• applications store data in common DB needs to know about the structure
• Improves communication, all applications • Easier to maintain and evolve the schema
operate on consistent set of data
• More freedom to choose the database
• Structure ends up to be more complex
• Applicable to SOA (i.e. Data Service/Entity
• Changes need to be coordinated with all Service) with good Service Autonomy
other applications using the database
• Ready for the cloud
• Side-effects (i.e. adding database index)

Application 1 Application 2 Application 3 Application 1 Application 2 Application 3

DB DB DB DB

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Relational vs. Aggregate Data Models

 The relational model takes the  Aggregate is a term that comes
information and divides it into from Domain-Driven Design
tuples (rows) (Evans)
 A tuple is a limited data structure  An aggregate is a collection of
 no nesting of tuples related objects, that should be
 no list of values treated as a unit
 Unit for data manipulation and
management of consistency

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Relational vs. Aggregate Data Model

Relational Instance Aggregate Instance
CUSTOMER PRODUCT
ID NAME ID NAME {
1 Guido 1000 IPod Touch „id“:1,
1020 Monster Beat „name“:“Guido“,
BILLING_ADDRESS „billingAddress“:[{„street“:“Chaumontweg“,“city“:“Spiegel“,“postCode“:“3095“}]
}
ID CUSTOMER_ID ADDRESS_ID
1 1 55 {
„id“:90,
ADDRESS „customerId“:1,
ID STREET CITY POST_CODE „orderItems“:[
{
55 Chaumontweg Spiegel 3095
„productId“:1000,“price“: 250.55, „produtName“: „iPod Touch“
},
ORDER {
ID CUSTOMER_ID SHIPPING_ADDRESS_ID „productId“:1020,“price“: 199.55, „produtName“: „Monster Beat“
90 1 55 }],
„sippingAddress“:[{„street“:“Chaumontweg“,“city“:“Spiegel“,“postCode“:“3095“}]
}
ORDER_ITEM
ID ORDER_ID PRODUCT_ID PRICE
1 90 1000 250.55
1 90 1020 199.55

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Brewer„s CAP Theorem

A distributed system can support only two of the following characteristics
• Consistency
All of the nodes see the same data at the same time, regardless of where the data is stored

• Availability
Node failures do not prevent
survivors from continuing to
operate

• Partition tolerance
The system continues to
operate despite
arbitrary message loss

Source: Making Sense of NoSQL – Manning Publications

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ACID vs. BASE style Transactions

Atomic Basically Available
• All of the work in a transaction Soft State
completes (commit) or none of it
Eventually Consistent
Consistent
• Transaction transforms the database
from one consistent state to another Weak consistency – stale data OK
consistent state
Availability first
Isolated
Best Effort
• results of any changes made during
a transaction are not visible until Approximate answers OK
committed Aggressive (optimistic)
Durable Simpler and Faster
• Results of committed transaction
survive failures
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Agenda

5. Summary

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NoSQL Database Types
Key/Value Column Family Document Graph
 Key/Value Stores
Design Collections of Columns and Key/Value pairs Focus on the
 Ordered Key-Value Stores Colum Families.
Key/Value Pairs but value is connections
Accesses directly interpreted by between data and
 Big Table Stores (map-of-maps-of-maps)
the column values. the database the fast navigation

Document Stores
Scalability/ +++ +++ ++ ++
Performance
Graph Databases
Aggregate- Yes Yes Yes No
oriented
Complexity + ++ ++ +++
Inspiration and Berkley DB, SAP Sybase IQ, Lotus Notes Graph Theory
Relation Memcached, BigTable
Distributed
Hashmaps

NoSQL Voldemort Hbase CouchDB Sones
Products Redis Cassandra MongoDB Neo4J
Riak Hypertable OrientDB InfoGrid
Amazon SimpleDB RavenDB FlockDB
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NoSQL Database Types
Size

Key-value stores

Column Family

Document
Graph

Relational

Complexity

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Key Value Databases

 A key-value store is a simple hash table
 Primarily used when all access to the database is via primary key
 Simplest NoSQL data stores to use (from an API perspective)
 PUT, POST, GET, DELETE (matches REST)

 Value is a blob with the data store not caring or knowing what is inside
 Aggregate-Oriented

Suitable Use Cases
• Storing Session Information
• User Profiles, Preferences
• Shopping Cart Data

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Column-Family Stores

 Store data in column families as rows that have many columns associated with a
row key
 Column families are groups of related data, often accessed together

Suitable Use Cases
• Event Logging
• Content Management Systems
• Counters
Source: NoSQL Distilled
• Expiring Usage
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Document Databases

 Documents are the main concept
 Stores and retrieves documents, which can be XML, JSON, BSON, …
 Documents are self-describing, hierarchical tree data structures which can consist
of maps, collections and scalar values
 Documents stored are similar to each other but do not have to be exactly the
same

Suitable Use Cases
• Event Logging
• Content Management Systems
• Web Analytics or Real-Time Analytics
• Product Catalog
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Document Database - MongoDB

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Graph Databases

 Allow to store entities and relationships between these entities
 Entities are known as nodes, which have properties
 Relations are known as edges, which also have properties
 A query on the graph is also known as traversing the graph
 Traversing the relationships is very fast

Tag
Customer

Suitable Use Cases
Country
RATED TAG

• Connected Data
ADDRESS
COUNTRY Product

• Routing, Dispatch and Location-Based BILLING_
LINE_ITEM
Services
ADDRESS
Address

Order
• Recommendation Engines DELIVERY_
ADDRESS

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Graph Database – Neo4J

Query through Cypher
START MATCH WHERE RETURN ORDER BY LIMIT
customer=node:Customer(email = "david@dmband.com")
customer-[:ORDERED]->order-[item:LINEITEM]->product
order.date > 20120101
product.name, sum(item.amount) AS product
products DESC 20

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Agenda

5. Summary

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Polyglot Persistence

In 2006, Neal Ford coined the term Polyglot Programming
 Applications should be written in a mix of languages to take advantage
of the fact that different languages are suitable for tackling different
problems
Polyglot Persistence defines a a hybrid approach to persistence
 Using multiple data storage technologies
 Selected based on the way data is being used by individual applications
 Why store binary images in relational databases, when there are better
storage systems?
 Can occur both over the enterprise as well as within a single application

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„Traditional“ Persistence Model
Polyglot Persistence E-commerce Application

Today we use the same
database for all kind of data Shopping cart data User Sessions Completed Order Product Catalog Recomendations

• Business transactions, session
management RDBMS

data, reporting, logging
information, content
information, ...
Polygot Persistence Model
No need for same properties of
availability, consistency or
E-commerce Application

backup requirements
Polyglot Data Storage Usage Shopping cart data User Sessions Completed Order Product Catalog Recomendations

allows to mix and match
Relational and NoSQL data
stores Key-Value RDMBS Document Graph

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Polyglot Persistence – Challenges

• Decisions
• Have to decide what data storage technology to use
• Today it„s easier to go with relational
• New Data Access APIs
• Each data store has its own mechanisms for accessing the data
• Different API‟s
Service-Oriented Polygot Persistence Model
E-commerce Application

• Solution: Wrap the data access
code into services (Data/Entity
Service) exposed to applications
• Will enforce a contract/schema
Shopping cart data User Sessions Completed Order Product Catalog Recomendations

to a schemaless database
Key-Value Graph
RDMBS Document

Shopping Cart User Session Product Catalog Recomendation
Service Service Order Service Service Service

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Polyglot Persistence – Challenges

• Immaturity
• NoSQL tools are still young, full of rough edges that new tools have
• Not much experience, we don„t know how to use them well
• No patterns and best practices exist yet

• Organizational Change
• How will the different data groups in an enterprise react to this new
technology

• Dealing with eventual consistency paradigm
• Reaction of different stakeholders to the fact that data could be stale
• How to enforce rules to sync data across systems

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Agenda

1. What is NoSQL and Big Data
5. Summary

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Data Access Architecture for Polyglot Persistence

well known design patterns are still valid!
some best practices we know in data access are still valid!

Consumer Consumer Consumer

REST/SOAP REST/SOAP

Service Service

REST
Repository/DAO Repository/DAO

O/R Mapping

???
SQL

REST API

RDBMS NoSQL NoSQL

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Middle Tier Architecture for Polyglot Persistence

Resource Tier
Middle Tier
Consumer Integration Service Application Domain Integration

Domain Service Bean

Web Service Exporter

Application Service Bean
REST
Composite Application

Factory Bean

SOAP
O/R Mapping
Domain Objects
NoSQL API
Repository Bean
Aggregate SQL API

DAO Bean

Data Transfer Objects (DTO)

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Polyglot Persistence with Spring Data

makes it easier to build Spring-powered applications that use new data
access technologies
provide improved support for relational database technologies
Commons project supports Polyglot Persistence
Currently support for:
• JPA and JDBC (relational) Consumer

• Apache Hadoop REST/SOAP

• GemFire Service

• REST Repository/DAO

• Redis
• MongoDB ???

• Neo4J
• Hbase NoSQL

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Spring Data – Mapping to Relational Database (using JPA)
Annotations define the mapping:
@Entity, @Id, @Column,
@OneToOne, @OneToMany, @JoinCol
umn,

Consumer

REST/SOAP

Service

Repository/DAO

???

NoSQL

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Spring Data – Mapping to Relational Database
Consumer

REST/SOAP

Service

public interface CustomerRepository extends Repository<Customer, Long> { Repository/DAO

Customer findByEmailAddress(EmailAddress emailAddress);
} ???

@Repository NoSQL

@Profile(“jpa")
class JpaCustomerRepository implements CustomerRepository {
@Override
public Customer findByEmailAddress(EmailAddress emailAddress) {

TypedQuery<Customer> query = em.createQuery(
"select c from Customer c where c.emailAddress = :email“, Customer.class);
query.setParameter("email", emailAddress);
Customer guido= repository.findByEmailAddress(new
return query.getSingleResult(); EmailAddress(“guido@hotmail.com"));
}
Customer anotherCust= new Customer(“Peter", “Sample");
<jpa:repositories base-package="com.oreilly.springdata.jpa" /> anotherCust.setEmailAddress(guido.getEmailAddress());

repository.save(anotherCust);
<bean class="org.springframework.orm.jpa.JpaTransactionManager">
<property name="entityManagerFactory" ref="entityManagerFactory" />
</bean>

<bean id="entityManagerFactory" class="org.springframework.orm.jpa.LocalContainerEntityManagerFactoryBean">
<property name="dataSource" ref="dataSource" />
<property name="packagesToScan" value="com.oreilly.springdata.jpa" />
</bean>

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Spring Data – Mapping to MongoDB
@Document, @Id, @Indexed,
@PersistenceConstructor,
@CompoundIndex, @DBRef,
@GeoSpatialIndex, @Value

Consumer

REST/SOAP

Service

Repository/DAO

???

NoSQL

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Spring Data – Generic Repositories for MongoDB
Consumer

REST/SOAP

Service

public interface CustomerRepository extends Repository<Customer, Long> { Repository/DAO

} ???

@Repository NoSQL

@Profile("mongodb")
class MongoDbCustomerRepository implements CustomerRepository {
@Override
public Customer findByEmailAddress(EmailAddress emailAddress) {

Query query = query(where("emailAddress").is(emailAddress));
return operations.findOne(query, Customer.class);
}

<mongo:db-factory id="mongoDbFactory" dbname="e-store" />

<mongo:mapping-converter id="mongoConverter" base-package="com.oreilly.springdata.mongodb">
<mongo:custom-converters base-package="com.oreilly.springdata.mongodb" />
</mongo:mapping-converter>

<bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
<constructor-arg ref="mongoDbFactory" /> EmailAddress(“guido@hotmail.com"));
<constructor-arg ref="mongoConverter" />
<property name="writeConcern" value="SAFE" /> Customer anotherCust= new Customer(“Peter", “Sample");
</bean> anotherCust.setEmailAddress(guido.getEmailAddress());
<mongo:repositories base-package="com.oreilly.springdata.mongodb" />
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Spring Data – Mapping to Neo4J
@NodeEntity, RelationShipEntity, @Gra
phId, @RelatedTo, @RelatedToVia, @E
ndNode, @Fetch,

Tag
Customer
Country
RATED TAG

ADDRESS
COUNTRY Product

BILLING_
ADDRESS LINE_ITEM
Address

Order
DELIVERY_
ADDRESS

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Spring Data – Generic Repositories for MongoDB
Consumer

REST/SOAP

Service

Repository/DAO

public interface CustomerRepository extends GraphRepository<Customer> {
???

}
NoSQL

<neo4j:config graphDatabaseService="graphDatabaseService" />
<neo4j:repositories base-package="com.oreilly.springdata.neo4j" />

<bean id="graphDatabaseService" class="org.neo4j.kernel.EmbeddedGraphDatabase" destroy-method="shutdown">
<constructor-arg value="target/graph.db" />
</bean>

EmailAddress(“guido@hotmail.com"));

Customer anotherCust= new Customer(“Peter", “Sample");
anotherCust.setEmailAddress(guido.getEmailAddress());


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Expose contract-first Web service
Consumer

REST/SOAP

Service

Repository/DAO

Use any Java Web Service Framework which supports
Contract-First approach ???

Can be SOAP or can be REST NoSQL

Maps the data contract (WSDL or WADL) to the schemaless
database
Uses the different Repository implementations
Must handle data migration issues together with the
Repository

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NoSQL databases as
Caching in a SOA

Results are returned from cache rather than invoking always
the external service
 Product data is rather static, so ideal candidate for caching

Proxy Business 1 Product DB
Service Service
2 3

Result
Cache
Oracle Service
Bus

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Schemaless – We still have to migrate the data!

With RDMBS we are used to keep DDL scripts together Customer

with DML scripts for each single data model change
Name: Peter Sample
First Name: Peter
Last Name: Sample
BillingAddress
Billing Address

• Has to be in sync with the data access code Street: Somestreet 10 Version 1.0
City: Somewhere
Postal Code:55901
PostalCode: 55901

RDBMS has to be changed before the application
is changed => possible application downtime Customer
• This is what the schemaless approach of most NoSQL Name: Peter Sample
FirstName: Peter
DB tries to avoid LastName: Sample
Transition
Billing Address
Version 1.0 => 2.0

Schemaless DBs still need careful migration, due to
City: Somewhere

implicit schema in any data access code
PostalCode: 55901

But a more “on-demand” approach is possible Customer

• Code can read data in a way that it tolerant to First Name: Peter
Last Name: Sample

changes in the data‟s implicit schema and migrate Billing Address
Version 2.0

the data on the next update City: Somewhere
PostalCode: 55901

• Similar to service versioning => gradual change

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Pros & Cons of NoSQL compared to RDBMS

Pros Cons
• No O/R impedance mismatch • Lacks in tool and framework
support
• Can easily evolve schemas
• Few other implementations =>
• Can represent semi-structured
potential lock in
info
• No support for ad-hoc queries
• Can represent graphs/networks
(with performance) • Another/A new database in
production to take care of

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Summary

Relational databases are here to stay but NoSQL offers new persistence
model
Polyglot Persistence will be the future
Schemaless does not mean there is no data migration! => but a more on-
demand model might be possible
Encapsulate data access code to be able to switch databases
Service-orientation provides the data contract to a NoSQL database => to
make information reusable
Don„t commit to a NoSQL until you have done a significant PoC
Make sure that Operations people (DBAs) are on board early enough
Non-relational is not new in an enterprise (OLTP vs. OLAP)

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VIELEN DANK.
Trivadis

Guido Schmutz
guido.schmutz@trivadis.com

info@trivadis.com
www.trivadis.com

BASEL BERN LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN

Five Cool use cases for the Spring component of the Oracle SOA Suite
Monday 3th December at 11:50 - 12:35 – Executive 7

Where and When Should I Use the Oracle Service Bus
Tuesday 4th December at 09:55 - 10:55 - Hall 8b

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NoSQL Databases for Implementing Data Services – Should I Care?

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