SlideShare a Scribd company logo
Querydsl
Most popular querying tool for Java
Timo Westkämper
@timowest
www.querydsl.com
What?
● Querydsl is an easy to use unified type-safe query
language
● Compile time query validation
● Instant feedback on query errors
● Compact and intuitive fluent syntax
● Syntactically close to SQL
● Great for dynamic query building
● Supports multiple back-ends and query languages
with consistent query API
● JPA/Hibernate, Mongodb, SQL, Lucene...
Why?
● Querydsl makes you more productive and your code
less errorprone
● Query syntax validation by execution is slow and
breaks the flow
● Each back-end has its own query language and API
● SQL-like for JPA and JDO, but not for MongoDB
and Lucene
● Verbose parameter binding by name or position to
parameter placeholders of a prepared statement
● Or risk injection attack if parameters are directly
concatenated to query
How?
QPerson person = QPerson.person;
JPAQuery query = new JPAQuery(entityManager);
List<Person> persons = query.from(person)
.where(
person.firstName.eq("John"),
person.lastName.eq("Doe"))
.list(person);
is translated into
select person from com.acme.Person person
where person.firstName eq = ?1 and person.lastName = ?2
Before Querydsl
● Queries as strings within code
TypedQuery<Person> query = em.createQuery(
"select person from Person person " +
"where person.firstName = ?1", Person.class);
query.setParameter(1, "Max");
List<Person> persons = query.getResultList();
● Must remember query syntax, domain classes,
properties and relationships
● Syntax reference always at hand
● Domain model/schema reference at hand
● High cognitive overhead
● Error-prone
Before Querydsl
● Dynamic query building by string concatenation
● Very hard with multiple joins, ordering and complex
conditionals depending on actual parameters
StringBuilder where = new StringBuilder();
if (firstName != null)
where.append("person.firstName = :firstName");
...
TypedQuery<Person> query = entityManager.createQuery(
"select person from Person person where " + where,
Person.class);
if (firstName != null) query.setParameter("firstName", firstName);
...
List<Person> persons = query.getResultList();
Before Querydsl
● Hibernate Criteria API as an alternative?
● Better for dynamic queries and has easier
parameter binding, but...
● Lacking expressivity, unintuitive, verbose,
cognitive overhead for schema if not for syntax,
not type-safe, slow validation...
● Hibernate with three query languages to
master with different focuses and expressivity
Querydsl to the rescue!
● Create your variables
QPerson.person // default variable
new QPerson("myPerson") // custom variable
● Create your query
JPAQuery, HibernateQuery, SQLQuery etc
● Populate your query
from, where, groupBy, having, orderBy
● Get the results
count, iterate, list, uniqueResult
Order
// Get persons ordered by last name and first name (desc)
query.from(person)
.orderBy(person.lastName.asc(), person.firstName.desc())
.list(person);
translated into
select person from Person person
order by person.lastname asc, person.firstName desc
Order
// Get persons ordered by women first
query.from(person)
.orderBy(person.gender
.when(Gender.FEMALE).then(0)
.otherwise(1).asc())
.list(person);
translated into
select person from Person person
order by case person.gender = Gender.FEMALE then 0 else 1 end asc
Grouping
// Get person counts grouped by last name
query.from(person)
.groupBy(person.lastName)
.list(person.lastName, person.count());
translated into
select person.lastName, count(person) from Person person
group by person.lastName
Subqueries
//Get persons with max child count
QPerson parent = new QPerson("parent");
query.from(person)
.where(person.children.size().eq(
new JPASubQuery().from(parent)
.uniqueResult(parent.children.size().max())
)).list(person);
translated into
select person from Person person
where person.children.size() = (
select max(parent.children.size()) from Person parent)
Constructor projection
// DTO class with @QueryProjection constructor annotation
public class PersonInfo {
long id;
String name;
@QueryProjection
public PersonInfo(long id, String name) {
this.id = id;
this.name = name;
}
}
// List PersonInfo DTOs
List<PersonInfo> infos = query.from(person)
.list(new QPersonInfo(person.id,
person.lastName.concat(", ”).concat(person.firstName)));
Tuple projection
// List ages of persons
List<Tuple> tuples = query.from(person)
.list(new QTuple(
person.lastName,
person.firstName,
person.yearOfBirth));
for (Tuple tuple : tuples){
// Typed access to mapped query results!
String name = tuple.get(person.firstName) +
" " + tuple.get(person.lastName);
int age = tuple.get(person.yearOfBirth)
- getCurrentYear();
System.out.println(name + " is " + age + " years");
}
BooleanBuilder
● Helper for building complex Boolean expressions
dynamically
BooleanBuilder nameDisjunction = new BooleanBuilder();
for (String name : names) {
nameDisjunction.or(person.firstName.like(name));
nameDisjunction.or(person.lastName.like(name));
}
query.where(nameDisjunction);
Update
// Set firstName of all Does to John
long updatedRowCount =
new JPAUpdateClause(getEntityManager(), person)
.set(person.firstName, "John")
.where(person.lastName.eq("Doe"))
.execute();
translated into
update Person person
set person.firstName = ?1
where person.lastName = ?2
Delete
// Delete all John Does
long updatedRowCount =
new JPADeleteClause(getEntityManager(), person)
.where(person.lastName.eq("Doe"),
person.firstName.eq("John"))
.execute();
translated into
delete Person person
where person.lastName = ?1 and person.firstName = ?2
Querydsl extensions
● Customize the code generation
● @QueryType(PropertyType.NONE)
● Non searchable
● @QueryType(PropertyType.SIMPLE)
● Equality comparisons only (eq, ne, in)
● Custom query classes
● Extend abstract super classes and preserve fluent
API
● Custom expressions
● Static delegate methods with @QueryDelegate
● Template expressions for e.g. custom SQL
functions
Querydsl extensions
● Query serialization can be customized
● Works for JPA, JDO and SQL
● SQL dialects
● Overriding default templates (e.g.
String#startsWith with like or regexp or...)
● Expression DSL can be replaced
● E.g. Querydsl for Scala
● Custom back-ends
● Lucene (10 classes) + Mongodb (6 classes)
Delegate methods
public class MyQueryExtensions {
@QueryDelegate(Date.class)
public static NumberExpression<Integer> yearAndMonth(DateTimePath<Date> date) {
return date.year().multiply(100).add(date.month());
}
}
causes code generation of
package ext.java.util;
...
public class QDate extends DateTimePath<java.util.Date> {
...
public NumberExpression<Integer> yearAndMonth() {
return MyQueryExtensions.yearAndMonth(this);
}
}
Template expressions
// ilike
query.from(person)
.where(BooleanTemplate.create("{0} ilike {1}”,
person.lastName, ConstantImpl.create("P%")))
.list(person);
translated into
select person from Person person
where person.lastName ilike ?1
Custom query classes
public class PersonQuery extends AbstractJPAQuery<PersonQuery> {
final QPerson person = QPerson.person;
public PersonQuery(EntityManager em) {
super(em);
from(person);
}
public PersonQuery nameMatches(String name) {
return where(person.firstName.like(name)
.or(person.lastName.like(name)));
}
}
JPA 2.0 Criteria vs Querydsl
● JPA 2 Criteria is the standard for type-safe
queries in JPA, but Querydsl is in our opinion
superior in many ways
● Easier and less verbose syntax
● Customizable
● Supports multiple back-ends – not just JPA
● JPA has a difficult to use static query-model
● Verbose property paths
● Operations via builder object
● Inverse order: “equals property value” vs.
“property equals value”
● Broken flow
Criteria example
// All possible pairs of single males and females
CriteriaQuery<Person> query = builder.createQuery(Person.class);
Root<Person> men = query.from( Person.class );
Root<Person> women = query.from( Person.class );
Predicate menRestriction = builder.and(
builder.equal( men.get( Person_.gender ), Gender.MALE ),
builder.equal( men.get( Person_.relationshipStatus ),
RelationshipStatus.SINGLE )
);
Predicate womenRestriction = builder.and(
builder.equal( women.get( Person_.gender ), Gender.FEMALE ),
builder.equal( women.get( Person_.relationshipStatus ),
RelationshipStatus.SINGLE )
);
query.where( builder.and( menRestriction, womenRestriction ) );
Querydsl example
// All possible pairs of single males and females
JPAQuery query = new JPAQuery(entityManager);
QPerson men = new QPerson("men");
QPerson women = new QPerson("women");
query.from(men, women).where(
men.gender.eq(Gender.MALE),
men.relationshipStatus.eq(RelationshipStatus.SINGLE),
women.gender.eq(Gender.FEMALE),
women.relationshipStatus.eq(RelationshipStatus.SINGLE));
SQL
● Pretty similar to JPA/Hibernate
● No deep paths over relations though
● No implicit joins
SQLTemplates templates = new MySQLTemplates();
...
SQLQuery query = new SQLQuery(connection, templates);
query.from(person);
query.innerJoin(parent).on(parent.id.eq(person.parent.id));
● Shortcut for joins with foreign keys
query.innerJoin(person.parentFK, parent);
SQL
● Maven plugin for generating query model
● Support for special SQL constructs and extensions
● Databases supported include
● MySQL
● PostgreSQL
● Oracle
● MS SQL Server
● H2
● HSQLDB
● Derby
● SQLite
● CUBRID
● Teradata
SQL extensions
● Sub class of AbstractSQLQuery
● e.g. OracleQuery with connectByPrior
● Template expressions
● Direct addition of “flags”
SQLInsertClause insert =
new SQLInsertClause(connection, templates, person);
insert.addFlag(Position.START_OVERRIDE, "replace into ");
Collections
● Provides querying functionality over collections of
beans with joins, filtering and sorting
● The same metamodel types can be used like for e.g.
JPA and Mongodb
List<User> users = CollQueryFactory.from(user, users)
.where(user.firstName.eq(“Bob”))
.list(user);
JPA/Hibernate Maven
Integration
<build><plugins><plugin>
<groupId>com.mysema.maven</groupId>
<artifactId>apt-maven-plugin</artifactId>
<version>1.0.9</version>
<executions>
<execution>
<goals><goal>process</goal></goals>
<configuration>
<outputDirectory>target/generated-sources/java</outputDirectory>
<processor>com.mysema.query.apt.jpa.JPAAnnotationProcessor</processor>
</configuration>
</execution>
</executions>
</plugin></plugins></build>
SQL Maven Integration
<build><plugins><plugin>
<groupId>com.mysema.querydsl</groupId>
<artifactId>querydsl-maven-plugin</artifactId>
<version>${querydsl.version}</version>
<executions><execution>
<goals><goal>export</goal></goals>
</execution></executions>
<configuration>
<jdbcDriver>org.apache.derby.jdbc.EmbeddedDriver</jdbcDriver>
<jdbcUrl>jdbc:derby:target/demoDB;create=true</jdbcUrl>
<!—- optional elements : namePrefix, jdbcUser, jdbcPassword, schemaPattern, tableNamePattern -->
<packageName>com.myproject.domain</packageName>
<targetFolder>${project.basedir}/target/generated-sources/java</targetFolder>
</configuration>
<dependencies><dependency>
<!—- jdbc driver dependency -->
<groupId>org.apache.derby</groupId>
<artifactId>derby</artifactId>
<version>${derby.version}</version>
</dependency></dependencies>
</plugin></plugins></build>
What services does Mysema
offer for Querydsl?
● Free public support
● GitHub Issues
● Querydsl Google Group
● Mysema Blog
● Consulting services
● User support
● Custom extensions and integration
● Training
Questions?
Thanks!
Timo Westkämper
@timowest
www.querydsl.com
www.mysema.com
Ad

More Related Content

What's hot (20)

Monadic Java
Monadic JavaMonadic Java
Monadic Java
Mario Fusco
 
지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016
지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016
지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016
Taehoon Kim
 
Spring Data JPA from 0-100 in 60 minutes
Spring Data JPA from 0-100 in 60 minutesSpring Data JPA from 0-100 in 60 minutes
Spring Data JPA from 0-100 in 60 minutes
VMware Tanzu
 
Testes pythonicos com pytest
Testes pythonicos com pytestTestes pythonicos com pytest
Testes pythonicos com pytest
viniciusban
 
[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호
[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호
[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호
NAVER D2
 
Neo4J 사용
Neo4J 사용Neo4J 사용
Neo4J 사용
홍수 허
 
온톨로지 & 규칙 추론 시스템
온톨로지 & 규칙 추론 시스템온톨로지 & 규칙 추론 시스템
온톨로지 & 규칙 추론 시스템
Sang-Kyun Kim
 
JavaScript - Introdução com Orientação a Objetos
JavaScript - Introdução com Orientação a ObjetosJavaScript - Introdução com Orientação a Objetos
JavaScript - Introdução com Orientação a Objetos
Eduardo Mendes
 
Drools rule Concepts
Drools rule ConceptsDrools rule Concepts
Drools rule Concepts
RaviShankar Mishra
 
Applicative style programming
Applicative style programmingApplicative style programming
Applicative style programming
José Luis García Hernández
 
Spring test mvc 발표자료
Spring test mvc 발표자료Spring test mvc 발표자료
Spring test mvc 발표자료
수홍 이
 
강화 학습 기초 Reinforcement Learning an introduction
강화 학습 기초 Reinforcement Learning an introduction강화 학습 기초 Reinforcement Learning an introduction
강화 학습 기초 Reinforcement Learning an introduction
Taehoon Kim
 
Understanding linq
Understanding linqUnderstanding linq
Understanding linq
Anand Kumar Rajana
 
jQuery - Chapter 1 - Introduction
 jQuery - Chapter 1 - Introduction jQuery - Chapter 1 - Introduction
jQuery - Chapter 1 - Introduction
WebStackAcademy
 
딥러닝을 이용한 자연어처리의 연구동향
딥러닝을 이용한 자연어처리의 연구동향딥러닝을 이용한 자연어처리의 연구동향
딥러닝을 이용한 자연어처리의 연구동향
홍배 김
 
PHP for Adults: Clean Code and Object Calisthenics
PHP for Adults: Clean Code and Object CalisthenicsPHP for Adults: Clean Code and Object Calisthenics
PHP for Adults: Clean Code and Object Calisthenics
Guilherme Blanco
 
Modern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time APIModern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time API
Ganesh Samarthyam
 
미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용
미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용
미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용
NAVER Engineering
 
Lambdas and Streams Master Class Part 2
Lambdas and Streams Master Class Part 2Lambdas and Streams Master Class Part 2
Lambdas and Streams Master Class Part 2
José Paumard
 
Laravel
LaravelLaravel
Laravel
tanveerkhan62
 
지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016
지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016
지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016
Taehoon Kim
 
Spring Data JPA from 0-100 in 60 minutes
Spring Data JPA from 0-100 in 60 minutesSpring Data JPA from 0-100 in 60 minutes
Spring Data JPA from 0-100 in 60 minutes
VMware Tanzu
 
Testes pythonicos com pytest
Testes pythonicos com pytestTestes pythonicos com pytest
Testes pythonicos com pytest
viniciusban
 
[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호
[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호
[221] 딥러닝을 이용한 지역 컨텍스트 검색 김진호
NAVER D2
 
온톨로지 & 규칙 추론 시스템
온톨로지 & 규칙 추론 시스템온톨로지 & 규칙 추론 시스템
온톨로지 & 규칙 추론 시스템
Sang-Kyun Kim
 
JavaScript - Introdução com Orientação a Objetos
JavaScript - Introdução com Orientação a ObjetosJavaScript - Introdução com Orientação a Objetos
JavaScript - Introdução com Orientação a Objetos
Eduardo Mendes
 
Spring test mvc 발표자료
Spring test mvc 발표자료Spring test mvc 발표자료
Spring test mvc 발표자료
수홍 이
 
강화 학습 기초 Reinforcement Learning an introduction
강화 학습 기초 Reinforcement Learning an introduction강화 학습 기초 Reinforcement Learning an introduction
강화 학습 기초 Reinforcement Learning an introduction
Taehoon Kim
 
jQuery - Chapter 1 - Introduction
 jQuery - Chapter 1 - Introduction jQuery - Chapter 1 - Introduction
jQuery - Chapter 1 - Introduction
WebStackAcademy
 
딥러닝을 이용한 자연어처리의 연구동향
딥러닝을 이용한 자연어처리의 연구동향딥러닝을 이용한 자연어처리의 연구동향
딥러닝을 이용한 자연어처리의 연구동향
홍배 김
 
PHP for Adults: Clean Code and Object Calisthenics
PHP for Adults: Clean Code and Object CalisthenicsPHP for Adults: Clean Code and Object Calisthenics
PHP for Adults: Clean Code and Object Calisthenics
Guilherme Blanco
 
Modern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time APIModern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time API
Ganesh Samarthyam
 
미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용
미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용
미등록단어 문제 해결을 위한 비지도학습 기반 한국어자연어처리 방법론 및 응용
NAVER Engineering
 
Lambdas and Streams Master Class Part 2
Lambdas and Streams Master Class Part 2Lambdas and Streams Master Class Part 2
Lambdas and Streams Master Class Part 2
José Paumard
 

Viewers also liked (20)

Query DSL In Elasticsearch
Query DSL In ElasticsearchQuery DSL In Elasticsearch
Query DSL In Elasticsearch
Knoldus Inc.
 
Fun with windows services
Fun with windows servicesFun with windows services
Fun with windows services
Mike Melusky
 
An evening with querydsl
An evening with querydslAn evening with querydsl
An evening with querydsl
Mike Melusky
 
Elasticsearch logstash kibana meetup
Elasticsearch logstash kibana meetupElasticsearch logstash kibana meetup
Elasticsearch logstash kibana meetup
Bharvi Dixit
 
Delhi elasticsearch meetup
Delhi elasticsearch meetupDelhi elasticsearch meetup
Delhi elasticsearch meetup
Bharvi Dixit
 
Going Reactive with Spring 5 & Project Reactor
Going Reactive with Spring 5 & Project ReactorGoing Reactive with Spring 5 & Project Reactor
Going Reactive with Spring 5 & Project Reactor
Mark Heckler
 
Computer Vision - Artificial Intelligence
Computer Vision - Artificial IntelligenceComputer Vision - Artificial Intelligence
Computer Vision - Artificial Intelligence
ACM-KU
 
Querydsl
QuerydslQuerydsl
Querydsl
Younghan Kim
 
Elasticsearch Query DSL - Not just for wizards...
Elasticsearch Query DSL - Not just for wizards...Elasticsearch Query DSL - Not just for wizards...
Elasticsearch Query DSL - Not just for wizards...
clintongormley
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
International Islamic University
 
Ksug2015 jpa4 객체지향쿼리
Ksug2015 jpa4 객체지향쿼리Ksug2015 jpa4 객체지향쿼리
Ksug2015 jpa4 객체지향쿼리
Younghan Kim
 
Artificial intelligence in software engineering ppt.
Artificial intelligence in software engineering ppt.Artificial intelligence in software engineering ppt.
Artificial intelligence in software engineering ppt.
Pradeep Vishwakarma
 
Elasticsearch in 15 minutes
Elasticsearch in 15 minutesElasticsearch in 15 minutes
Elasticsearch in 15 minutes
David Pilato
 
Artificial intelligence report
Artificial intelligence reportArtificial intelligence report
Artificial intelligence report
Sourabh Sharma
 
10 SQL Tricks that You Didn't Think Were Possible
10 SQL Tricks that You Didn't Think Were Possible10 SQL Tricks that You Didn't Think Were Possible
10 SQL Tricks that You Didn't Think Were Possible
Lukas Eder
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligence
Megha Jain
 
Artificial intelligence in power plants
Artificial intelligence in power plantsArtificial intelligence in power plants
Artificial intelligence in power plants
vivekprajapatiankur
 
Tupperware: Containerized Deployment at FB
Tupperware: Containerized Deployment at FBTupperware: Containerized Deployment at FB
Tupperware: Containerized Deployment at FB
Docker, Inc.
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligence
Girish Naik
 
Artificial Intelligence Presentation
Artificial Intelligence PresentationArtificial Intelligence Presentation
Artificial Intelligence Presentation
lpaviglianiti
 
Query DSL In Elasticsearch
Query DSL In ElasticsearchQuery DSL In Elasticsearch
Query DSL In Elasticsearch
Knoldus Inc.
 
Fun with windows services
Fun with windows servicesFun with windows services
Fun with windows services
Mike Melusky
 
An evening with querydsl
An evening with querydslAn evening with querydsl
An evening with querydsl
Mike Melusky
 
Elasticsearch logstash kibana meetup
Elasticsearch logstash kibana meetupElasticsearch logstash kibana meetup
Elasticsearch logstash kibana meetup
Bharvi Dixit
 
Delhi elasticsearch meetup
Delhi elasticsearch meetupDelhi elasticsearch meetup
Delhi elasticsearch meetup
Bharvi Dixit
 
Going Reactive with Spring 5 & Project Reactor
Going Reactive with Spring 5 & Project ReactorGoing Reactive with Spring 5 & Project Reactor
Going Reactive with Spring 5 & Project Reactor
Mark Heckler
 
Computer Vision - Artificial Intelligence
Computer Vision - Artificial IntelligenceComputer Vision - Artificial Intelligence
Computer Vision - Artificial Intelligence
ACM-KU
 
Elasticsearch Query DSL - Not just for wizards...
Elasticsearch Query DSL - Not just for wizards...Elasticsearch Query DSL - Not just for wizards...
Elasticsearch Query DSL - Not just for wizards...
clintongormley
 
Ksug2015 jpa4 객체지향쿼리
Ksug2015 jpa4 객체지향쿼리Ksug2015 jpa4 객체지향쿼리
Ksug2015 jpa4 객체지향쿼리
Younghan Kim
 
Artificial intelligence in software engineering ppt.
Artificial intelligence in software engineering ppt.Artificial intelligence in software engineering ppt.
Artificial intelligence in software engineering ppt.
Pradeep Vishwakarma
 
Elasticsearch in 15 minutes
Elasticsearch in 15 minutesElasticsearch in 15 minutes
Elasticsearch in 15 minutes
David Pilato
 
Artificial intelligence report
Artificial intelligence reportArtificial intelligence report
Artificial intelligence report
Sourabh Sharma
 
10 SQL Tricks that You Didn't Think Were Possible
10 SQL Tricks that You Didn't Think Were Possible10 SQL Tricks that You Didn't Think Were Possible
10 SQL Tricks that You Didn't Think Were Possible
Lukas Eder
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligence
Megha Jain
 
Artificial intelligence in power plants
Artificial intelligence in power plantsArtificial intelligence in power plants
Artificial intelligence in power plants
vivekprajapatiankur
 
Tupperware: Containerized Deployment at FB
Tupperware: Containerized Deployment at FBTupperware: Containerized Deployment at FB
Tupperware: Containerized Deployment at FB
Docker, Inc.
 
Artificial Intelligence
Artificial IntelligenceArtificial Intelligence
Artificial Intelligence
Girish Naik
 
Artificial Intelligence Presentation
Artificial Intelligence PresentationArtificial Intelligence Presentation
Artificial Intelligence Presentation
lpaviglianiti
 
Ad

Similar to Querydsl overview 2014 (20)

Querydsl fin jug - june 2012
Querydsl   fin jug - june 2012Querydsl   fin jug - june 2012
Querydsl fin jug - june 2012
Timo Westkämper
 
Polyglot persistence with Spring Data
Polyglot persistence with Spring DataPolyglot persistence with Spring Data
Polyglot persistence with Spring Data
Corneil du Plessis
 
Data access 2.0? Please welcome: Spring Data!
Data access 2.0? Please welcome: Spring Data!Data access 2.0? Please welcome: Spring Data!
Data access 2.0? Please welcome: Spring Data!
Oliver Gierke
 
Groovy On Trading Desk (2010)
Groovy On Trading Desk (2010)Groovy On Trading Desk (2010)
Groovy On Trading Desk (2010)
Jonathan Felch
 
Introduction to Jooq
Introduction to JooqIntroduction to Jooq
Introduction to Jooq
Kostadin Golev
 
Easy data-with-spring-data-jpa
Easy data-with-spring-data-jpaEasy data-with-spring-data-jpa
Easy data-with-spring-data-jpa
Staples
 
Андрей Слободяник "Test driven development using mockito"
Андрей Слободяник "Test driven development using mockito"Андрей Слободяник "Test driven development using mockito"
Андрей Слободяник "Test driven development using mockito"
Anna Shymchenko
 
JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...
JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...
JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...
Leonardo De Moura Rocha Lima
 
Nodejs functional programming and schema validation lightning talk
Nodejs   functional programming and schema validation lightning talkNodejs   functional programming and schema validation lightning talk
Nodejs functional programming and schema validation lightning talk
Deepank Gupta
 
RESTful web service with JBoss Fuse
RESTful web service with JBoss FuseRESTful web service with JBoss Fuse
RESTful web service with JBoss Fuse
ejlp12
 
ORM JPA
ORM JPAORM JPA
ORM JPA
Rody Middelkoop
 
jQuery & 10,000 Global Functions: Working with Legacy JavaScript
jQuery & 10,000 Global Functions: Working with Legacy JavaScriptjQuery & 10,000 Global Functions: Working with Legacy JavaScript
jQuery & 10,000 Global Functions: Working with Legacy JavaScript
Guy Royse
 
Janos Rusiczki - Backbone.js - Models & views in JavaScript
Janos Rusiczki - Backbone.js - Models & views in JavaScriptJanos Rusiczki - Backbone.js - Models & views in JavaScript
Janos Rusiczki - Backbone.js - Models & views in JavaScript
kitsched
 
JavaScript Fundamentals & JQuery
JavaScript Fundamentals & JQueryJavaScript Fundamentals & JQuery
JavaScript Fundamentals & JQuery
Jamshid Hashimi
 
Groovy - Grails as a modern scripting language for Web applications
Groovy - Grails as a modern scripting language for Web applicationsGroovy - Grails as a modern scripting language for Web applications
Groovy - Grails as a modern scripting language for Web applications
IndicThreads
 
ActiveJDBC - ActiveRecord implementation in Java
ActiveJDBC - ActiveRecord implementation in JavaActiveJDBC - ActiveRecord implementation in Java
ActiveJDBC - ActiveRecord implementation in Java
ipolevoy
 
Http4s, Doobie and Circe: The Functional Web Stack
Http4s, Doobie and Circe: The Functional Web StackHttp4s, Doobie and Circe: The Functional Web Stack
Http4s, Doobie and Circe: The Functional Web Stack
GaryCoady
 
Javascript Ks
Javascript KsJavascript Ks
Javascript Ks
ssetem
 
Intro to php
Intro to phpIntro to php
Intro to php
Sp Singh
 
Functional (web) development with Clojure
Functional (web) development with ClojureFunctional (web) development with Clojure
Functional (web) development with Clojure
Henrik Eneroth
 
Querydsl fin jug - june 2012
Querydsl   fin jug - june 2012Querydsl   fin jug - june 2012
Querydsl fin jug - june 2012
Timo Westkämper
 
Polyglot persistence with Spring Data
Polyglot persistence with Spring DataPolyglot persistence with Spring Data
Polyglot persistence with Spring Data
Corneil du Plessis
 
Data access 2.0? Please welcome: Spring Data!
Data access 2.0? Please welcome: Spring Data!Data access 2.0? Please welcome: Spring Data!
Data access 2.0? Please welcome: Spring Data!
Oliver Gierke
 
Groovy On Trading Desk (2010)
Groovy On Trading Desk (2010)Groovy On Trading Desk (2010)
Groovy On Trading Desk (2010)
Jonathan Felch
 
Easy data-with-spring-data-jpa
Easy data-with-spring-data-jpaEasy data-with-spring-data-jpa
Easy data-with-spring-data-jpa
Staples
 
Андрей Слободяник "Test driven development using mockito"
Андрей Слободяник "Test driven development using mockito"Андрей Слободяник "Test driven development using mockito"
Андрей Слободяник "Test driven development using mockito"
Anna Shymchenko
 
JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...
JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...
JavaOne 2017 - JNoSQL: The Definitive Solution for Java and NoSQL Database [C...
Leonardo De Moura Rocha Lima
 
Nodejs functional programming and schema validation lightning talk
Nodejs   functional programming and schema validation lightning talkNodejs   functional programming and schema validation lightning talk
Nodejs functional programming and schema validation lightning talk
Deepank Gupta
 
RESTful web service with JBoss Fuse
RESTful web service with JBoss FuseRESTful web service with JBoss Fuse
RESTful web service with JBoss Fuse
ejlp12
 
jQuery & 10,000 Global Functions: Working with Legacy JavaScript
jQuery & 10,000 Global Functions: Working with Legacy JavaScriptjQuery & 10,000 Global Functions: Working with Legacy JavaScript
jQuery & 10,000 Global Functions: Working with Legacy JavaScript
Guy Royse
 
Janos Rusiczki - Backbone.js - Models & views in JavaScript
Janos Rusiczki - Backbone.js - Models & views in JavaScriptJanos Rusiczki - Backbone.js - Models & views in JavaScript
Janos Rusiczki - Backbone.js - Models & views in JavaScript
kitsched
 
JavaScript Fundamentals & JQuery
JavaScript Fundamentals & JQueryJavaScript Fundamentals & JQuery
JavaScript Fundamentals & JQuery
Jamshid Hashimi
 
Groovy - Grails as a modern scripting language for Web applications
Groovy - Grails as a modern scripting language for Web applicationsGroovy - Grails as a modern scripting language for Web applications
Groovy - Grails as a modern scripting language for Web applications
IndicThreads
 
ActiveJDBC - ActiveRecord implementation in Java
ActiveJDBC - ActiveRecord implementation in JavaActiveJDBC - ActiveRecord implementation in Java
ActiveJDBC - ActiveRecord implementation in Java
ipolevoy
 
Http4s, Doobie and Circe: The Functional Web Stack
Http4s, Doobie and Circe: The Functional Web StackHttp4s, Doobie and Circe: The Functional Web Stack
Http4s, Doobie and Circe: The Functional Web Stack
GaryCoady
 
Javascript Ks
Javascript KsJavascript Ks
Javascript Ks
ssetem
 
Intro to php
Intro to phpIntro to php
Intro to php
Sp Singh
 
Functional (web) development with Clojure
Functional (web) development with ClojureFunctional (web) development with Clojure
Functional (web) development with Clojure
Henrik Eneroth
 
Ad

Recently uploaded (20)

DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxDevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
Justin Reock
 
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
organizerofv
 
Electronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploitElectronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploit
niftliyevhuseyn
 
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxIncreasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Anoop Ashok
 
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersLinux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Toradex
 
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...
SOFTTECHHUB
 
Drupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy ConsumptionDrupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy Consumption
Exove
 
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In FranceManifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
chb3
 
Cyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of securityCyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of security
riccardosl1
 
AI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global TrendsAI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global Trends
InData Labs
 
Cybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure ADCybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure AD
VICTOR MAESTRE RAMIREZ
 
Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025
Splunk
 
Build Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For DevsBuild Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For Devs
Brian McKeiver
 
Quantum Computing Quick Research Guide by Arthur Morgan
Quantum Computing Quick Research Guide by Arthur MorganQuantum Computing Quick Research Guide by Arthur Morgan
Quantum Computing Quick Research Guide by Arthur Morgan
Arthur Morgan
 
tecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdftecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdf
fjgm517
 
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
How Can I use the AI Hype in my Business Context?
How Can I use the AI Hype in my Business Context?How Can I use the AI Hype in my Business Context?
How Can I use the AI Hype in my Business Context?
Daniel Lehner
 
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxDevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptx
Justin Reock
 
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...
TrustArc
 
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessGenerative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in Business
Dr. Tathagat Varma
 
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
organizerofv
 
Electronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploitElectronic_Mail_Attacks-1-35.pdf by xploit
Electronic_Mail_Attacks-1-35.pdf by xploit
niftliyevhuseyn
 
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxIncreasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptx
Anoop Ashok
 
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersLinux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Toradex
 
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...
SOFTTECHHUB
 
Drupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy ConsumptionDrupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy Consumption
Exove
 
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In FranceManifest Pre-Seed Update | A Humanoid OEM Deeptech In France
Manifest Pre-Seed Update | A Humanoid OEM Deeptech In France
chb3
 
Cyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of securityCyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of security
riccardosl1
 
AI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global TrendsAI and Data Privacy in 2025: Global Trends
AI and Data Privacy in 2025: Global Trends
InData Labs
 
Cybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure ADCybersecurity Identity and Access Solutions using Azure AD
Cybersecurity Identity and Access Solutions using Azure AD
VICTOR MAESTRE RAMIREZ
 
Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025Splunk Security Update | Public Sector Summit Germany 2025
Splunk Security Update | Public Sector Summit Germany 2025
Splunk
 
Build Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For DevsBuild Your Own Copilot & Agents For Devs
Build Your Own Copilot & Agents For Devs
Brian McKeiver
 
Quantum Computing Quick Research Guide by Arthur Morgan
Quantum Computing Quick Research Guide by Arthur MorganQuantum Computing Quick Research Guide by Arthur Morgan
Quantum Computing Quick Research Guide by Arthur Morgan
Arthur Morgan
 
tecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdftecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdf
fjgm517
 
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...
Noah Loul
 
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond SparkRusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Spark
carlyakerly1
 
How Can I use the AI Hype in my Business Context?
How Can I use the AI Hype in my Business Context?How Can I use the AI Hype in my Business Context?
How Can I use the AI Hype in my Business Context?
Daniel Lehner
 

Querydsl overview 2014

  • 1. Querydsl Most popular querying tool for Java Timo Westkämper @timowest www.querydsl.com
  • 2. What? ● Querydsl is an easy to use unified type-safe query language ● Compile time query validation ● Instant feedback on query errors ● Compact and intuitive fluent syntax ● Syntactically close to SQL ● Great for dynamic query building ● Supports multiple back-ends and query languages with consistent query API ● JPA/Hibernate, Mongodb, SQL, Lucene...
  • 3. Why? ● Querydsl makes you more productive and your code less errorprone ● Query syntax validation by execution is slow and breaks the flow ● Each back-end has its own query language and API ● SQL-like for JPA and JDO, but not for MongoDB and Lucene ● Verbose parameter binding by name or position to parameter placeholders of a prepared statement ● Or risk injection attack if parameters are directly concatenated to query
  • 4. How? QPerson person = QPerson.person; JPAQuery query = new JPAQuery(entityManager); List<Person> persons = query.from(person) .where( person.firstName.eq("John"), person.lastName.eq("Doe")) .list(person); is translated into select person from com.acme.Person person where person.firstName eq = ?1 and person.lastName = ?2
  • 5. Before Querydsl ● Queries as strings within code TypedQuery<Person> query = em.createQuery( "select person from Person person " + "where person.firstName = ?1", Person.class); query.setParameter(1, "Max"); List<Person> persons = query.getResultList(); ● Must remember query syntax, domain classes, properties and relationships ● Syntax reference always at hand ● Domain model/schema reference at hand ● High cognitive overhead ● Error-prone
  • 6. Before Querydsl ● Dynamic query building by string concatenation ● Very hard with multiple joins, ordering and complex conditionals depending on actual parameters StringBuilder where = new StringBuilder(); if (firstName != null) where.append("person.firstName = :firstName"); ... TypedQuery<Person> query = entityManager.createQuery( "select person from Person person where " + where, Person.class); if (firstName != null) query.setParameter("firstName", firstName); ... List<Person> persons = query.getResultList();
  • 7. Before Querydsl ● Hibernate Criteria API as an alternative? ● Better for dynamic queries and has easier parameter binding, but... ● Lacking expressivity, unintuitive, verbose, cognitive overhead for schema if not for syntax, not type-safe, slow validation... ● Hibernate with three query languages to master with different focuses and expressivity
  • 8. Querydsl to the rescue! ● Create your variables QPerson.person // default variable new QPerson("myPerson") // custom variable ● Create your query JPAQuery, HibernateQuery, SQLQuery etc ● Populate your query from, where, groupBy, having, orderBy ● Get the results count, iterate, list, uniqueResult
  • 9. Order // Get persons ordered by last name and first name (desc) query.from(person) .orderBy(person.lastName.asc(), person.firstName.desc()) .list(person); translated into select person from Person person order by person.lastname asc, person.firstName desc
  • 10. Order // Get persons ordered by women first query.from(person) .orderBy(person.gender .when(Gender.FEMALE).then(0) .otherwise(1).asc()) .list(person); translated into select person from Person person order by case person.gender = Gender.FEMALE then 0 else 1 end asc
  • 11. Grouping // Get person counts grouped by last name query.from(person) .groupBy(person.lastName) .list(person.lastName, person.count()); translated into select person.lastName, count(person) from Person person group by person.lastName
  • 12. Subqueries //Get persons with max child count QPerson parent = new QPerson("parent"); query.from(person) .where(person.children.size().eq( new JPASubQuery().from(parent) .uniqueResult(parent.children.size().max()) )).list(person); translated into select person from Person person where person.children.size() = ( select max(parent.children.size()) from Person parent)
  • 13. Constructor projection // DTO class with @QueryProjection constructor annotation public class PersonInfo { long id; String name; @QueryProjection public PersonInfo(long id, String name) { this.id = id; this.name = name; } } // List PersonInfo DTOs List<PersonInfo> infos = query.from(person) .list(new QPersonInfo(person.id, person.lastName.concat(", ”).concat(person.firstName)));
  • 14. Tuple projection // List ages of persons List<Tuple> tuples = query.from(person) .list(new QTuple( person.lastName, person.firstName, person.yearOfBirth)); for (Tuple tuple : tuples){ // Typed access to mapped query results! String name = tuple.get(person.firstName) + " " + tuple.get(person.lastName); int age = tuple.get(person.yearOfBirth) - getCurrentYear(); System.out.println(name + " is " + age + " years"); }
  • 15. BooleanBuilder ● Helper for building complex Boolean expressions dynamically BooleanBuilder nameDisjunction = new BooleanBuilder(); for (String name : names) { nameDisjunction.or(person.firstName.like(name)); nameDisjunction.or(person.lastName.like(name)); } query.where(nameDisjunction);
  • 16. Update // Set firstName of all Does to John long updatedRowCount = new JPAUpdateClause(getEntityManager(), person) .set(person.firstName, "John") .where(person.lastName.eq("Doe")) .execute(); translated into update Person person set person.firstName = ?1 where person.lastName = ?2
  • 17. Delete // Delete all John Does long updatedRowCount = new JPADeleteClause(getEntityManager(), person) .where(person.lastName.eq("Doe"), person.firstName.eq("John")) .execute(); translated into delete Person person where person.lastName = ?1 and person.firstName = ?2
  • 18. Querydsl extensions ● Customize the code generation ● @QueryType(PropertyType.NONE) ● Non searchable ● @QueryType(PropertyType.SIMPLE) ● Equality comparisons only (eq, ne, in) ● Custom query classes ● Extend abstract super classes and preserve fluent API ● Custom expressions ● Static delegate methods with @QueryDelegate ● Template expressions for e.g. custom SQL functions
  • 19. Querydsl extensions ● Query serialization can be customized ● Works for JPA, JDO and SQL ● SQL dialects ● Overriding default templates (e.g. String#startsWith with like or regexp or...) ● Expression DSL can be replaced ● E.g. Querydsl for Scala ● Custom back-ends ● Lucene (10 classes) + Mongodb (6 classes)
  • 20. Delegate methods public class MyQueryExtensions { @QueryDelegate(Date.class) public static NumberExpression<Integer> yearAndMonth(DateTimePath<Date> date) { return date.year().multiply(100).add(date.month()); } } causes code generation of package ext.java.util; ... public class QDate extends DateTimePath<java.util.Date> { ... public NumberExpression<Integer> yearAndMonth() { return MyQueryExtensions.yearAndMonth(this); } }
  • 21. Template expressions // ilike query.from(person) .where(BooleanTemplate.create("{0} ilike {1}”, person.lastName, ConstantImpl.create("P%"))) .list(person); translated into select person from Person person where person.lastName ilike ?1
  • 22. Custom query classes public class PersonQuery extends AbstractJPAQuery<PersonQuery> { final QPerson person = QPerson.person; public PersonQuery(EntityManager em) { super(em); from(person); } public PersonQuery nameMatches(String name) { return where(person.firstName.like(name) .or(person.lastName.like(name))); } }
  • 23. JPA 2.0 Criteria vs Querydsl ● JPA 2 Criteria is the standard for type-safe queries in JPA, but Querydsl is in our opinion superior in many ways ● Easier and less verbose syntax ● Customizable ● Supports multiple back-ends – not just JPA ● JPA has a difficult to use static query-model ● Verbose property paths ● Operations via builder object ● Inverse order: “equals property value” vs. “property equals value” ● Broken flow
  • 24. Criteria example // All possible pairs of single males and females CriteriaQuery<Person> query = builder.createQuery(Person.class); Root<Person> men = query.from( Person.class ); Root<Person> women = query.from( Person.class ); Predicate menRestriction = builder.and( builder.equal( men.get( Person_.gender ), Gender.MALE ), builder.equal( men.get( Person_.relationshipStatus ), RelationshipStatus.SINGLE ) ); Predicate womenRestriction = builder.and( builder.equal( women.get( Person_.gender ), Gender.FEMALE ), builder.equal( women.get( Person_.relationshipStatus ), RelationshipStatus.SINGLE ) ); query.where( builder.and( menRestriction, womenRestriction ) );
  • 25. Querydsl example // All possible pairs of single males and females JPAQuery query = new JPAQuery(entityManager); QPerson men = new QPerson("men"); QPerson women = new QPerson("women"); query.from(men, women).where( men.gender.eq(Gender.MALE), men.relationshipStatus.eq(RelationshipStatus.SINGLE), women.gender.eq(Gender.FEMALE), women.relationshipStatus.eq(RelationshipStatus.SINGLE));
  • 26. SQL ● Pretty similar to JPA/Hibernate ● No deep paths over relations though ● No implicit joins SQLTemplates templates = new MySQLTemplates(); ... SQLQuery query = new SQLQuery(connection, templates); query.from(person); query.innerJoin(parent).on(parent.id.eq(person.parent.id)); ● Shortcut for joins with foreign keys query.innerJoin(person.parentFK, parent);
  • 27. SQL ● Maven plugin for generating query model ● Support for special SQL constructs and extensions ● Databases supported include ● MySQL ● PostgreSQL ● Oracle ● MS SQL Server ● H2 ● HSQLDB ● Derby ● SQLite ● CUBRID ● Teradata
  • 28. SQL extensions ● Sub class of AbstractSQLQuery ● e.g. OracleQuery with connectByPrior ● Template expressions ● Direct addition of “flags” SQLInsertClause insert = new SQLInsertClause(connection, templates, person); insert.addFlag(Position.START_OVERRIDE, "replace into ");
  • 29. Collections ● Provides querying functionality over collections of beans with joins, filtering and sorting ● The same metamodel types can be used like for e.g. JPA and Mongodb List<User> users = CollQueryFactory.from(user, users) .where(user.firstName.eq(“Bob”)) .list(user);
  • 31. SQL Maven Integration <build><plugins><plugin> <groupId>com.mysema.querydsl</groupId> <artifactId>querydsl-maven-plugin</artifactId> <version>${querydsl.version}</version> <executions><execution> <goals><goal>export</goal></goals> </execution></executions> <configuration> <jdbcDriver>org.apache.derby.jdbc.EmbeddedDriver</jdbcDriver> <jdbcUrl>jdbc:derby:target/demoDB;create=true</jdbcUrl> <!—- optional elements : namePrefix, jdbcUser, jdbcPassword, schemaPattern, tableNamePattern --> <packageName>com.myproject.domain</packageName> <targetFolder>${project.basedir}/target/generated-sources/java</targetFolder> </configuration> <dependencies><dependency> <!—- jdbc driver dependency --> <groupId>org.apache.derby</groupId> <artifactId>derby</artifactId> <version>${derby.version}</version> </dependency></dependencies> </plugin></plugins></build>
  • 32. What services does Mysema offer for Querydsl? ● Free public support ● GitHub Issues ● Querydsl Google Group ● Mysema Blog ● Consulting services ● User support ● Custom extensions and integration ● Training