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Melhores práticas de desempenho Hibernate 5/6 e Spring Boot 2

Se você precisar se aprofundar nas receitas de desempenho expostas neste repositório, tenho certeza que você vai adorar meu livro "Spring Boot Persistence Best Practices"	Se você precisar de dicas e ilustrações de mais de 100 problemas de desempenho de persistência Java, o "Guia ilustrado de desempenho de persistência Java" é para você.

Amostras de hibernação e inicialização Spring

Como armazenar o fuso horário UTC no MySQL

Descrição: Este aplicativo é um exemplo de como armazenar data, hora e carimbos de data/hora no fuso horário UTC. A segunda configuração, useLegacyDatetimeCode é necessária apenas para MySQL. Caso contrário, defina apenas hibernate.jdbc.time_zone .

Pontos principais:

spring.jpa.properties.hibernate.jdbc.time_zone=UTC
spring.datasource.url=jdbc:mysql://localhost:3306/screenshotdb?useLegacyDatetimeCode=false

Visualizar parâmetros de ligação/extraídos via Log4J 2

Descrição: Visualize os parâmetros de ligação/extração da instrução preparada por meio da configuração do registrador Log4J 2.

Pontos principais:

para Maven, em pom.xml , exclua o registro padrão do Spring Boot
para Maven, em pom.xml , adicione dependência Log4j 2
em log4j2.xml adicione, <Logger name="org.hibernate.type.descriptor.sql" level="trace"/>

Exemplo de saída:

Como visualizar detalhes da consulta por meio da biblioteca DataSource-Proxy

Descrição: Visualize os detalhes da consulta (tipo de consulta, parâmetros de vinculação, tamanho do lote, tempo de execução, etc.) via DataSource-Proxy

Pontos principais:

para Maven, adicione em pom.xml a dependência datasource-proxy
crie um pós-processador de bean para interceptar o bean DataSource
envolva o bean DataSource via ProxyFactory e uma implementação de MethodInterceptor

Exemplo de saída:

Inserções em lote via saveAll(Iterable<S> entities) no MySQL

Descrição: Inserções em lote por meio do método SimpleJpaRepository#saveAll(Iterable<S> entities) no MySQL

Pontos principais:

em application.properties defina spring.jpa.properties.hibernate.jdbc.batch_size
em application.properties defina spring.jpa.properties.hibernate.generate_statistics (apenas para verificar se o lote está funcionando)
em application.properties defina o URL JDBC com rewriteBatchedStatements=true (otimização para MySQL)
em application.properties defina o URL JDBC com cachePrepStmts=true (habilite o cache e é útil se você decidir definir prepStmtCacheSize , prepStmtCacheSqlLimit , etc também; sem essa configuração o cache será desabilitado)
em application.properties defina o URL JDBC com useServerPrepStmts=true (desta forma você muda para instruções preparadas no lado do servidor (pode levar a um aumento significativo de desempenho))
no caso de usar um relacionamento pai-filho com persistência em cascata (por exemplo, um para muitos, muitos para muitos), considere configurar spring.jpa.properties.hibernate.order_inserts=true para otimizar o lote ordenando inserções
na entidade, use o gerador atribuído, pois o MySQL IDENTITY fará com que o lote de inserção seja desabilitado
na entidade, adicione a propriedade @Version para evitar instruções SELECT extras disparadas antes do processamento em lote (também evita a perda de atualizações em transações de múltiplas solicitações). Instruções extra SELECT são o efeito do uso de merge() em vez de persist() ; nos bastidores, saveAll() usa save() , que no caso de entidades não novas (entidades que possuem IDs) chamará merge() , que instrui o Hibernate a disparar uma instrução SELECT para garantir que não haja nenhum registro no banco de dados com o mesmo identificador
preste atenção na quantidade de inserts passados para saveAll() para não "sobrecarregar" o Contexto de Persistência; normalmente o EntityManager deve ser liberado e limpo de tempos em tempos, mas durante a execução saveAll() você simplesmente não pode fazer isso, então se em saveAll() houver uma lista com uma grande quantidade de dados, todos esses dados atingirão o Persistence Contexto (Cache de 1º Nível) e permanecerá na memória até o momento da liberação; usar uma quantidade relativamente pequena de dados deve ser aceitável (neste exemplo, cada lote de 30 entidades é executado em uma transação separada e em um contexto persistente)
o método saveAll() retorna um List<S> contendo as entidades persistidas; cada entidade persistida é adicionada a esta lista; se você simplesmente não precisa desta List , ela foi criada para nada
se não for necessário, certifique-se de que o cache de segundo nível esteja desabilitado via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Inserções em lote via EntityManager (MySQL)

Descrição: Este aplicativo é um exemplo de inserções em lote via EntityManager no MySQL. Desta forma você pode controlar facilmente os ciclos flush() e clear() do Contexto de Persistência (Cache de 1º Nível) dentro da transação atual. Isso não é possível via Spring Boot, saveAll(Iterable<S> entities) , pois esse método executa uma única liberação por transação. Outra vantagem é que você pode chamar persist() em vez de merge() - isso é usado nos bastidores pelo SpringBoot saveAll(Iterable<S> entities) e save(S entity) .

Se você deseja executar um lote por transação (recomendado), verifique este exemplo.

Pontos principais:

em application.properties defina spring.jpa.properties.hibernate.jdbc.batch_size
em application.properties defina spring.jpa.properties.hibernate.generate_statistics (apenas para verificar se o lote está funcionando)
em application.properties defina o URL JDBC com rewriteBatchedStatements=true (otimização para MySQL)
em application.properties defina o URL JDBC com cachePrepStmts=true (habilite o cache e é útil se você decidir definir prepStmtCacheSize , prepStmtCacheSqlLimit , etc também; sem essa configuração o cache será desabilitado)
em application.properties defina o URL JDBC com useServerPrepStmts=true (desta forma você muda para instruções preparadas no lado do servidor (pode levar a um aumento significativo de desempenho))
no caso de usar um relacionamento pai-filho com persistência em cascata (por exemplo, um para muitos, muitos para muitos), considere configurar spring.jpa.properties.hibernate.order_inserts=true para otimizar o lote ordenando inserções
na entidade, use o gerador atribuído, pois o MySQL IDENTITY fará com que o lote de inserção seja desabilitado
na sua camada DAO, limpe e limpe o contexto de persistência de tempos em tempos (por exemplo, para cada lote); desta forma você evita "sobrecarregar" o Contexto de Persistência
se não for necessário, certifique-se de que o cache de segundo nível esteja desabilitado via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Exemplo de saída:

Como fazer inserções em lote via JpaContext/EntityManager no MySQL

Descrição: inserções em lote via JpaContext/EntityManager no MySQL.

Pontos principais:

em application.properties defina spring.jpa.properties.hibernate.jdbc.batch_size
em application.properties defina spring.jpa.properties.hibernate.generate_statistics (apenas para verificar se o lote está funcionando)
em application.properties defina o URL JDBC com rewriteBatchedStatements=true (otimização para MySQL)
em application.properties defina o URL JDBC com cachePrepStmts=true (habilite o cache e é útil se você decidir definir prepStmtCacheSize , prepStmtCacheSqlLimit , etc também; sem essa configuração o cache será desabilitado)
em application.properties defina o URL JDBC com useServerPrepStmts=true (desta forma você muda para instruções preparadas no lado do servidor (pode levar a um aumento significativo de desempenho))
no caso de usar um relacionamento pai-filho com persistência em cascata (por exemplo, um para muitos, muitos para muitos), considere configurar spring.jpa.properties.hibernate.order_inserts=true para otimizar o lote ordenando inserções
na entidade, use o gerador atribuído, pois o MySQL IDENTITY fará com que o lote de inserção seja desabilitado
o EntityManager é obtido por tipo de entidade por meio de JpaContext#getEntityManagerByManagedType(Class<?> entity)
no DAO, limpe e limpe o contexto de persistência de vez em quando; desta forma você evita "sobrecarregar" o Contexto de Persistência
se não for necessário, certifique-se de que o cache de segundo nível esteja desabilitado via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Exemplo de saída:

Como explorar lotes em nível de sessão (Hibernate 5.2 ou superior) no MySQL

Descrição: inserções em lote por meio de lote em nível de sessão do Hibernate (Hibernate 5.2 ou superior) no MySQL.

Pontos principais:

em application.properties defina spring.jpa.properties.hibernate.generate_statistics (apenas para verificar se o lote está funcionando)
em application.properties defina o URL JDBC com rewriteBatchedStatements=true (otimização para MySQL)
em application.properties defina o URL JDBC com cachePrepStmts=true (habilite o cache e é útil se você decidir definir prepStmtCacheSize , prepStmtCacheSqlLimit , etc também; sem essa configuração o cache será desabilitado)
em application.properties defina o URL JDBC com useServerPrepStmts=true (desta forma você muda para instruções preparadas no lado do servidor (pode levar a um aumento significativo de desempenho))
no caso de usar um relacionamento pai-filho com persistência em cascata (por exemplo, um para muitos, muitos para muitos), considere configurar spring.jpa.properties.hibernate.order_inserts=true para otimizar o lote ordenando inserções
na entidade, use o gerador atribuído, pois o MySQL IDENTITY fará com que o lote de inserção seja desabilitado
a Session do Hibernate é obtida desembrulhando-a via EntityManager#unwrap(Session.class)
o tamanho do lote é definido via Session#setJdbcBatchSize(Integer size) e obtido via Session#getJdbcBatchSize()
no DAO, limpe e limpe o contexto de persistência de vez em quando; desta forma você evita "sobrecarregar" o Contexto de Persistência
se não for necessário, certifique-se de que o cache de segundo nível esteja desabilitado via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Exemplo de saída:

Busca direta via Spring Data findById() , JPA EntityManager e Session de Hibernate

Descrição: busca direta via exemplos de Spring Data, EntityManager e Hibernate Session .

Pontos principais:

busca direta via Spring Data usa findById()
busca direta via JPA EntityManager usa find()
busca direta via Hibernate Session usa get()

DTO por meio de projeções de dados Spring

Observação: você também pode ler a receita "Como enriquecer DTOs com propriedades virtuais por meio de projeções Spring"

Descrição: busque apenas os dados necessários do banco de dados por meio do Spring Data Projections (DTO).

Pontos principais:

escreva uma interface (projeção) contendo getters apenas para as colunas que devem ser buscadas no banco de dados
escreva a consulta apropriada retornando um List<projection>
se for aplicável, limite o número de linhas retornadas (por exemplo, via LIMIT )
neste exemplo, podemos usar o mecanismo de criação de consultas integrado à infraestrutura do repositório Spring Data

Nota: O uso de projeções não se limita ao uso do mecanismo de criação de consultas integrado à infraestrutura do repositório Spring Data. Também podemos buscar projeções via JPQL ou consultas nativas. Por exemplo, nesta aplicação usamos um JPQL.

Exemplo de saída (selecione as 2 primeiras linhas; selecione apenas "nome" e "idade"):

Se você precisar se aprofundar nas receitas de desempenho expostas neste repositório, tenho certeza que você vai adorar meu livro "Spring Boot Persistence Best Practices"	Se você precisar de dicas e ilustrações de mais de 100 problemas de desempenho de persistência Java, o "Guia ilustrado de desempenho de persistência Java" é para você.

Como usar o carregamento lento do atributo Hibernate

Descrição: Por padrão, os atributos de uma entidade são carregados antecipadamente (todos de uma vez). Mas também podemos carregá-los preguiçosamente . Isto é útil para tipos de colunas que armazenam grandes quantidades de dados: CLOB , BLOB , VARBINARY , etc ou detalhes que devem ser carregados sob demanda. Nesta aplicação, temos uma entidade chamada Author . Suas propriedades são: id , name , genre , avatar e age . E queremos carregar o avatar lentamente. Portanto, o avatar deve ser carregado sob demanda.

Pontos principais:

em pom.xml , ative o aprimoramento de bytecode do Hibernate (por exemplo, use o plugin de aprimoramento de bytecode Maven)
na entidade, anote os atributos que devem ser carregados lentamente com @Basic(fetch = FetchType.LAZY)
em application.properties , desative Open Session in View

Verifique também:
- Valores padrão para atributos carregados lentamente
- Atributo Lazy Load e Jackson Serialization

Como preencher uma associação de pais infantis por meio de proxy

Descrição: Um proxy do Hibernate pode ser útil quando uma entidade filha pode ser persistida com uma referência ao seu pai (associação @ManyToOne ou @OneToOne ). Nesses casos, buscar a entidade pai do banco de dados (executar a instrução SELECT ) é uma penalidade de desempenho e uma ação inútil, porque o Hibernate pode definir o valor da chave estrangeira subjacente para um proxy não inicializado.

Pontos principais:

confie em EntityManager#getReference()
no Spring, use JpaRepository#getOne() -> usado neste exemplo
no Hibernate, use load()
assuma duas entidades, Author e Book , envolvidas em uma associação unidirecional @ManyToOne ( Author é o lado pai)
buscamos o autor através de um proxy (isso não acionará um SELECT ), criamos um novo livro, definimos o proxy como o autor deste livro e salvamos o livro (isso acionará um INSERT na tabela book )

Exemplo de saída:

a saída do console revelará que apenas um INSERT é acionado e nenhum SELECT

Como reproduzir rapidamente o problema de desempenho N+1

Descrição: O N+1 é uma questão de busca preguiçosa (mas ansiosa não está isenta). Esta aplicação reproduz o comportamento N+1.

Pontos principais:

defina duas entidades, Author e Book em uma associação @OneToMany bidirecional preguiçosa
buscar todos Book com preguiça, portanto sem Author (resulta em 1 consulta)
faça um loop na coleção Book buscada e para cada entrada busque o Author correspondente (resultados N consultas)
ou busque todos Author com preguiça, portanto sem Book (resulta em 1 consulta)
faça um loop na coleção Author buscada e para cada entrada busque o Book correspondente (resultados N consultas)

Exemplo de saída:

Otimize SELECT DISTINCT via Hibernate HINT_PASS_DISTINCT_THROUGH Dica

Descrição: A partir do Hibernate 5.2.2, podemos otimizar entidades de consulta JPQL (HQL) do tipo SELECT DISTINCT por meio da dica HINT_PASS_DISTINCT_THROUGH . Tenha em mente que esta dica é útil apenas para consultas JPQL (HQL) JOIN FETCH. Não é útil para consultas escalares (por exemplo, List<Integer> ), DTO ou HHH-13280. Nesses casos, a palavra-chave DISTINCT JPQL é necessária para ser passada para a consulta SQL subjacente. Isso instruirá o banco de dados a remover duplicatas do conjunto de resultados.

Pontos principais:

use @QueryHints(value = @QueryHint(name = HINT_PASS_DISTINCT_THROUGH, value = "false"))

Exemplo de saída:

Como ativar o Dirty Tracking em um aplicativo Spring Boot

Nota: O mecanismo Hibernate Dirty Checking é responsável por identificar as modificações das entidades no momento da liberação e acionar as instruções UPDATE correspondentes em nosso nome.

Descrição: Antes do Hibernate versão 5, o mecanismo Dirty Checking dependia da API Java Reflection para verificar todas as propriedades de cada entidade gerenciada. A partir da versão 5 do Hibernate, o mecanismo Dirty Checking pode contar com o mecanismo Dirty Tracking (que é a capacidade de uma entidade rastrear suas próprias alterações de atributos), que requer que o Hibernate Bytecode Enhancement esteja presente na aplicação. O mecanismo Dirty Tracking mantém um melhor desempenho, especialmente quando você tem um número relativamente grande de entidades.

Para Dirty Tracking , durante o processo de aprimoramento de bytecode , o bytecode das classes de entidade é instrumentado pelo Hibernate adicionando um rastreador , $$_hibernate_tracker . No momento da liberação, o Hibernate usará esse rastreador para descobrir as alterações nas entidades (cada rastreador de entidade reportará as alterações). Isto é melhor do que verificar todas as propriedades de todas as entidades gerenciadas.

Normalmente (por padrão), a instrumentação ocorre em tempo de construção, mas também pode ser configurada para ocorrer em tempo de execução ou de implantação. É preferível que ocorra em tempo de construção para evitar sobrecarga no tempo de execução.

Adicionar o aprimoramento de bytecode e ativar o Dirty Tracking pode ser feito por meio de um plug-in adicionado via Maven ou Gradle (o Ant também pode ser usado). Usamos Maven, portanto o adicionamos em pom.xml .

Pontos principais:

O Hibernate vem com plug-ins de aprimoramento de bytecode para Maven, Gradle (o Ant também pode ser usado)
para Maven, adicione o plugin Bytecode Enhancement no arquivo pom.xml

Exemplo de saída:

O efeito Bytecode Enhancement pode ser visto em Author.class aqui. Observe como o bytecode foi instrumentado com $$_hibernate_tracker .

Use Java 8 Optional em entidades e consultas

Descrição: Esta aplicação é um exemplo de como é correto utilizar o Optional Java 8 em entidades e consultas.

Pontos principais:

use os métodos de consulta integrados do Spring Data que retornam Optional (por exemplo, findById() )
escreva suas próprias consultas que retornem Optional
use Optional em getters de entidades
para executar diferentes cenários, verifique o arquivo data-mysql.sql

A melhor maneira de mapear a associação bidirecional @OneToMany

Descrição: Esta aplicação é uma prova de conceito de como é correto implementar a associação bidirecional @OneToMany do ponto de vista de desempenho.

Pontos principais:

sempre cascata de pai para filho
use mappedBy no pai
use orphanRemoval no pai para remover filhos sem referências
use métodos auxiliares no pai para manter ambos os lados da associação sincronizados
use busca lenta em ambos os lados da associação
como identificadores de entidades, use identificadores atribuídos (chave comercial, chave natural ( @NaturalId )) e/ou identificadores gerados pelo banco de dados e substitua (no lado filho) adequadamente os métodos equals() e hashCode() como aqui
se toString() precisar ser substituído, preste atenção para envolver apenas os atributos básicos obtidos quando a entidade for carregada do banco de dados

Nota: Preste atenção às operações de remoção, especialmente à remoção de entidades filhas. O CascadeType.REMOVE e orphanRemoval=true podem produzir muitas consultas. Nesses cenários, confiar em operações em massa é, na maioria das vezes, a melhor maneira de fazer exclusões.

Busca de consulta

Descrição: Esta aplicação é um exemplo de como escrever uma consulta via JpaRepository , EntityManager e Session .

Pontos principais:

para JpaRepository use @Query ou Spring Data Query Creation
para EntityManager e Session use o método createQuery()

Por que e como evitar o tipo de gerador AUTO no Hibernate 5 e no MySQL

Descrição: No MySQL e Hibernate 5, o tipo de gerador GenerationType.AUTO resultará no uso do gerador TABLE . Isso adiciona uma penalidade significativa de desempenho. A transformação desse comportamento para o gerador IDENTITY pode ser obtida usando GenerationType.IDENTITY ou o gerador nativo .

Pontos principais:

use GenerationType.IDENTITY em vez de GenerationType.AUTO
use o gerador nativo - exemplificado neste aplicativo

Exemplo de saída:

Como evitar a chamada save() redundante

Descrição: Este aplicativo é um exemplo de quando a chamada save() para uma entidade é redundante (não é necessária).

Pontos principais:

no momento da liberação, o Hibernate depende do mecanismo de verificação suja para determinar as possíveis modificações nas entidades
para cada modificação, o Hibernate aciona automaticamente a instrução UPDATE correspondente sem a necessidade de chamar explicitamente o método save()
nos bastidores, essa redundância (chamar save() quando não é necessariamente) não afeta o número de consultas acionadas, mas implica uma penalidade de desempenho nos processos subjacentes do Hibernate

Se você precisar se aprofundar nas receitas de desempenho expostas neste repositório, tenho certeza que você vai adorar meu livro "Spring Boot Persistence Best Practices"	Se você precisar de dicas e ilustrações de mais de 100 problemas de desempenho de persistência Java, o "Guia ilustrado de desempenho de persistência Java" é para você.

Por que evitar o PostgreSQL ( BIG ) SERIAL em inserções em lote via Hibernate

Descrição: No PostgreSQL, o uso de GenerationType.IDENTITY desativará o lote de inserção. O (BIG)SERIAL está agindo "quase" como MySQL, AUTO_INCREMENT . Nesta aplicação, usamos o GenerationType.SEQUENCE que permite inserir lotes e o otimizamos por meio do algoritmo de otimização hi/lo .

Pontos principais:

use GenerationType.SEQUENCE em vez de GenerationType.IDENTITY
confie no algoritmo hi/lo para buscar um valor hi em uma viagem de ida e volta do banco de dados (o valor hi é útil para gerar um determinado/dado número de identificadores na memória; até que você não tenha esgotado todos os identificadores na memória, não há necessidade para buscar outro oi )
você pode ir ainda mais longe e usar os geradores de identificadores pooled e pooled-lo do Hibernate (essas são otimizações de hi/lo que permitem que serviços externos usem o banco de dados sem causar erros de chaves de duplicação)
otimizar lotes via spring.datasource.hikari.data-source-properties.reWriteBatchedInserts=true

Exemplo de saída:

Herança JPA - SINGLE_TABLE

Descrição: Este aplicativo é um exemplo de uso da estratégia de herança de tabela única JPA ( SINGLE_TABLE ).

Pontos principais:

esta é a estratégia de herança padrão ( @Inheritance(strategy=InheritanceType.SINGLE_TABLE) )
todas as classes em uma hierarquia de herança são representadas por meio de uma única tabela no banco de dados
a não nulidade dos atributos das subclasses é garantida por meio dos gatilhos @NotNull e MySQL
o espaço de memória da coluna discriminadora padrão foi otimizado declarando-o do tipo TINYINT

Exemplo de saída (abaixo está uma única tabela obtida de 3 entidades):

Contar e afirmar instruções SQL

Descrição: Este aplicativo é um exemplo de contagem e afirmação de instruções SQL acionadas "nos bastidores". É muito útil contar as instruções SQL para garantir que seu código não esteja gerando mais instruções SQL do que você imagina (por exemplo, N+1 pode ser facilmente detectado afirmando o número de instruções esperadas).

Pontos principais:

para Maven, em pom.xml , adicione dependências para a biblioteca DataSource-Proxy e a biblioteca db-util de Vlad Mihalcea
crie o ProxyDataSourceBuilder com countQuery()
redefinir o contador via SQLStatementCountValidator.reset()
assert INSERT , UPDATE , DELETE e SELECT via assertInsert/Update/Delete/Select/Count(long expectedNumberOfSql)

Exemplo de saída (quando o número de SQLs esperados não é igual à realidade, uma exceção é lançada):

Como configurar retornos de chamada JPA

Descrição: Este aplicativo é um exemplo de configuração de retornos de chamada JPA ( Pre/PostPersist , Pre/PostUpdate , Pre/PostRemove e PostLoad ).

Pontos principais:

na entidade, escreva métodos de retorno de chamada e use as anotações adequadas
métodos de retorno de chamada anotados na classe bean devem retornar void e não receber argumentos

Exemplo de saída:

Como usar @MapsId para compartilhar identificador no relacionamento @OneToOne

Descrição: em vez de @OneToOne unidirecional/bidirecional regular , é melhor contar com @OneToOne e @MapsId unidirecionais. Esta aplicação é uma prova de conceito.

Pontos principais:

use @MapsId no lado filho
use @JoinColumn para personalizar o nome da coluna de chave primária
principalmente, para associações @OneToOne , @MapsId compartilhará a chave primária com a tabela pai (a propriedade id atua como chave primária e chave estrangeira)

Observação:

@MapsId também pode ser usado para @ManyToOne

Como buscar DTO via SqlResultSetMapping e EntityManager

Descrição: buscar mais dados do que o necessário está sujeito a penalidades de desempenho. O uso do DTO nos permite extrair apenas os dados necessários. Nesta aplicação contamos com SqlResultSetMapping e EntityManager .

Pontos principais:

use SqlResultSetMapping e EntityManager
para usar projeções de dados Spring, verifique este item

Como buscar DTO via SqlResultSetMapping e NamedNativeQuery

Nota: Se você quiser confiar na convenção de nomenclatura {EntityName}.{RepositoryMethodName} para simplesmente criar na interface do repositório métodos com o mesmo nome da consulta nomeada nativa, pule este aplicativo e verifique este.

Pontos principais:

use SqlResultSetMapping , NamedNativeQuery
para usar projeções de dados Spring, verifique este item

Como buscar DTO via javax.persistence.Tuple e SQL nativo

Pontos principais:

use java.persistence.Tuple em um repositório Spring e marque a consulta como nativeQuery = true
para usar projeções de dados Spring, verifique este item

Como buscar DTO via javax.persistence.Tuple e JPQL

Pontos principais:

use java.persistence.Tuple em um repositório Spring
para usar projeções de dados Spring, verifique este item

Como buscar DTO por meio de expressão de construtor e JPQL

Pontos principais:

escreva um construtor adequado na classe DTO
use uma consulta como SELECT new com.bookstore.dto.AuthorDto(a.name, a.age) FROM Author a
para usar projeções de dados Spring, verifique este item

Veja também:
Como buscar DTO por meio do construtor e do mecanismo Spring Data Query Builder

Se você precisar se aprofundar nas receitas de desempenho expostas neste repositório, tenho certeza que você vai adorar meu livro "Spring Boot Persistence Best Practices"	Se você precisar de dicas e ilustrações de mais de 100 problemas de desempenho de persistência Java, o "Guia ilustrado de desempenho de persistência Java" é para você.

Como buscar DTO via ResultTransformer e SQL nativo

Pontos principais:

use AliasToBeanConstructorResultTransformer para DTO sem setters, mas com construtor
use Transformers.aliasToBean() para DTO com setters
use EntityManager.createNativeQuery() e unwrap(org.hibernate.query.NativeQuery.class)
começando com o Hibernate 5.2, ResultTransformer está obsoleto, mas até que um substituto esteja disponível (provavelmente no Hibernate 6.0) ele pode ser usado (leia mais)
para usar projeções de dados Spring, verifique esta receita

Como buscar DTO via ResultTransformer e JPQL

Pontos principais:

use AliasToBeanConstructorResultTransformer para DTO sem setters, com construtor
use Transformers.aliasToBean() para DTO com setters
use EntityManager.createQuery() e unwrap(org.hibernate.query.Query.class)
começando com o Hibernate 5.2, ResultTransformer está obsoleto, mas até que um substituto esteja disponível (no Hibernate 6.0) ele pode ser usado (leia mais)
para usar projeções de dados Spring, verifique este item

Como buscar DTO por meio de visualizações de entidade Blaze-Persistence

Descrição: buscar mais dados do que o necessário está sujeito a penalidades de desempenho. O uso do DTO nos permite extrair apenas os dados necessários. Nesta aplicação, contamos com visualizações de entidade Blaze-Persistence.

Pontos principais:

para Maven, adicione em pom.xml as dependências específicas do Blaze-Persistence
configurar Blaze-Persistence via CriteriaBuilderFactory e EntityViewManager
escrever uma visão de entidade através de uma interface no estilo Blaze-Persistence
escreva um repositório centrado em Spring estendendo EntityViewRepository
método de chamada deste repositório, como findAll() , findOne() , etc
para usar projeções de dados Spring, verifique este item

Como funciona @ElementCollection regular (sem @OrderColumn )

Descrição: Este aplicativo revela as possíveis penalidades de desempenho ao usar @ElementCollection . Neste caso, sem @OrderColumn . Como você pode ver no próximo item (34) adicionar @OrderColumn pode mitigar algumas penalidades de desempenho.

Pontos principais:

um @ElementCollection não possui uma chave primária
um @ElementCollection é mapeado em uma tabela separada
evite @ElementCollection quando você tiver muitas inserções/exclusões nesta coleção; inserções/exclusões farão com que o Hibernate exclua todas as linhas da tabela existentes, processe a coleção na memória e reinsira as linhas restantes da tabela para espelhar a coleção da memória
quanto mais entradas tivermos nesta coleção maior será a penalidade de desempenho

Exemplo de saída:

Como funciona @ElementCollection com @OrderColumn

Descrição: Este aplicativo revela as penalidades de desempenho decorrentes do uso de @ElementCollection . Neste caso, com @OrderColumn . Mas, como você pode ver nesta aplicação (em comparação com o item 33), adicionar @OrderColumn pode mitigar algumas penalidades de desempenho quando as operações ocorrem perto do final da coleção (por exemplo, adicionar/remover no/do final da coleção). Principalmente, todos os elementos situados antes da entrada de adição/remoção permanecem intactos, portanto a penalidade de desempenho pode ser ignorada se afetarmos as linhas próximas ao final da coleção.

Pontos principais:

um @ElementCollection não possui uma chave primária
um @ElementCollection é mapeado em uma tabela separada
prefira @ElementCollection com @OrderColumn quando você tiver muitas inserções e exclusões perto do final da coleção
quanto mais elementos forem inseridos/removidos desde o início da coleção maior será a penalidade de desempenho

Exemplo de saída:

Como evitar problemas de inicialização lenta causados pela desativação da sessão aberta na visualização por meio de valores explícitos (padrão)

Nota: Antes de ler este item tente ver se o Hibernate5Module não é o que você está procurando.

Descrição: O antipadrão Open-Session in View é ativado por padrão no SpringBoot. Agora, imagine uma associação preguiçosa (por exemplo, @OneToMany ) entre duas entidades, Author e Book (um autor associou mais livros). Em seguida, um endpoint do controlador REST busca um Author sem o Book associado. Mas, o View (mais precisamente, Jackson), também força o carregamento lento do Book associado. Como o OSIV fornecerá a Session já aberta, as inicializações dos proxies ocorrerão com sucesso. A solução para evitar esta penalidade de desempenho começa desabilitando o OSIV. Além disso, inicialize explicitamente as associações lentas não buscadas. Dessa forma, o View não forçará o carregamento lento.

Pontos principais:

desabilite o OSIV adicionando em application.properties esta configuração: spring.jpa.open-in-view=false
buscar uma entidade Author e inicializar seu Book associado explicitamente com valores (padrão) (por exemplo, null )
defina @JsonInclude(Include.NON_EMPTY) neste nível de entidade para evitar renderizar null ou o que é considerado vazio no JSON resultante

NOTA: Se o OSIV estiver ativado, o desenvolvedor ainda poderá inicializar manualmente as associações lentas não buscadas, desde que faça isso fora de uma transação para evitar a liberação. Mas, por que isso está funcionando? Como a Session está aberta, por que a inicialização manual das associações de uma entidade gerenciada não aciona a liberação? A resposta pode ser encontrada na documentação do OpenSessionInViewFilter que especifica que: Este filtro, por padrão, não liberará a Session do Hibernate , com o modo de liberação definido como FlushMode.NEVER . Ele pressupõe ser usado em combinação com transações da camada de serviço que cuidam da liberação: O gerenciador de transações ativo alterará temporariamente o modo de liberação para FlushMode.AUTO durante uma transação de leitura e gravação, com o modo de liberação redefinido para FlushMode.NEVER no final de cada transação. Se você pretende utilizar este filtro sem transações, considere alterar o modo de liberação padrão (através da propriedade "flushMode").

Como usar projeções Spring (DTO) e junções internas

Descrição: Este aplicativo é uma prova de conceito para usar Spring Projections (DTO) e inner joins escritos via JPQL e SQL nativo (para MySQL).

Pontos principais:

definir duas entidades (por exemplo, Author e Book em uma associação @OneToMany bidirecional (preguiçosa))
preencher o banco de dados com alguns dados de teste (por exemplo, verifique o arquivo resources/data-mysql.sql )
interfaces de gravação (projeções Spring) que contêm getters para as colunas que devem ser buscadas no banco de dados (por exemplo, verifique AuthorNameBookTitle.java )
escrever consultas de junções internas usando JPQL/SQL

Como usar projeções Spring (DTO) e junções à esquerda

Descrição: Este aplicativo é uma prova de conceito para usar Spring Projections (DTO) e left joins escritos via JPQL e SQL nativo (para MySQL).

Pontos principais:

definir duas entidades (por exemplo, Author e Book em uma associação @OneToMany bidirecional (preguiçosa))
preencher o banco de dados com alguns dados de teste (por exemplo, verifique o arquivo resources/data-mysql.sql )
Escreva interfaces (projeções de primavera) que contém getters para as colunas que devem ser buscadas no banco de dados (por exemplo, check AuthorNameBookTitle.java )
Write Left Juns consultas usando JPQL/SQL

Como usar as projeções da primavera (DTO) e a direita se junta

Descrição: Este aplicativo é uma prova de conceito para o uso de projeções de primavera (DTO) e as junções corretas escritas via JPQL e SQL nativo (para MySQL).

Pontos principais:

Defina duas entidades (por exemplo, Author e Book em uma associação bidirecional (preguiçosa) @OneToMany )
Preencha o banco de dados com alguns dados de teste (por exemplo, verifique os resources/data-mysql.sql )
Escreva interfaces (projeções de primavera) que contém getters para as colunas que devem ser buscadas no banco de dados (por exemplo, check AuthorNameBookTitle.java )
Write Right se junta às consultas usando JPQL/SQL

Como usar as projeções da primavera (DTO) e as junções completas inclusivas (PostgreSQL)

Descrição: Este aplicativo é uma prova de conceito para o uso de projeções de primavera (DTO) e junções completas inclusivas escritas via JPQL e SQL nativo (para PostgreSQL).

Pontos principais:

Defina duas entidades (por exemplo, Author e Book em uma associação bidirecional (preguiçosa) @OneToMany )
Preencha o banco de dados com alguns dados de teste (por exemplo, verifique os resources/data-mysql.sql )
Escreva interfaces (projeções de primavera) que contém getters para as colunas que devem ser buscadas no banco de dados (por exemplo, check AuthorNameBookTitle.java )
Escreva as consultas completas inclusivas usando jpql/sql

Se você precisar de um mergulho profundo nas receitas de desempenho expostas neste repositório, tenho certeza de que você vai adorar o meu livro "Práticas de Melhores Práticas de Persistência de Botas da Primavera"	Se você precisar de uma mão de dicas e ilustrações de mais de 100 problemas de desempenho de persistência de java, "o guia ilustrado de desempenho de persistência Java" é para você.

Como usar as projeções de primavera (DTO) e a esquerda exclusiva

Descrição: Este aplicativo é uma prova de conceito para o uso de projeções de primavera (DTO) e as junções de esquerda exclusivas escritas via JPQL e SQL nativo (para MySQL).

Pontos principais:

Defina duas entidades (por exemplo, Author e Book em uma associação bidirecional (preguiçosa) @OneToMany )
Preencha o banco de dados com alguns dados de teste (por exemplo, verifique os resources/data-mysql.sql )
Escreva interfaces (projeções) que contém getters para as colunas que devem ser buscadas no banco de dados (por exemplo, check AuthorNameBookTitle.java )
Write exclusivo à esquerda junta consultas usando JPQL/SQL

Como usar as projeções de primavera (DTO) e o direito exclusivo se junta

Descrição: Este aplicativo é uma prova de conceito para o uso de projeções de primavera (DTO) e as junções de direito exclusivas escritas via JPQL e SQL nativo (para MySQL).

Pontos principais:

Defina duas entidades (por exemplo, Author e Book em uma associação bidirecional (preguiçosa) @OneToMany )
Preencha o banco de dados com alguns dados de teste (por exemplo, verifique os resources/data-mysql.sql )
Escreva interfaces (projeções de primavera) que contém getters para as colunas que devem ser buscadas no banco de dados (por exemplo, check AuthorNameBookTitle.java )
Write Exclusive Right ingressos consultas usando JPQL/SQL

Como usar as projeções de primavera (DTO) e as junções completas exclusivas (PostgreSQL)

Descrição: Este aplicativo é uma prova de conceito para o uso de projeções de primavera (DTO) e junções completas exclusivas escritas via JPQL e SQL nativo (para PostgreSQL).

Pontos principais:

Defina duas entidades (por exemplo, Author e Book em uma associação bidirecional (preguiçosa) @OneToMany )
Preencha o banco de dados com alguns dados de teste (por exemplo, verifique os resources/data-mysql.sql )
Escreva interfaces (projeções de primavera) que contém getters para as colunas que devem ser buscadas no banco de dados (por exemplo, check AuthorNameBookTitle.java )
Escreva Exclusive Full Kneins consultas usando JPQL/SQL

Por que você deve evitar tarefas demoradas nos ganchos pós-comprometimento da primavera

Descrição: Este aplicativo é uma prova de conceito para o uso de ganchos pós-comprometimento da primavera e como eles podem afetar o desempenho da camada de persistência.

Pontos principais:

Evite tarefas demoradas nos ganchos pós-comprometimento da primavera, pois a conexão do banco de dados permanecerá aberta até que este código finshes

Como explorar as projeções da primavera (DTO) e se juntar a entidades não relacionadas no Hibernate 5.1+

Descrição: Este aplicativo é uma prova de conceito para o uso de projeções de primavera (DTO) e ingressar em entidades não relacionadas. O Hibernate 5.1 introduziu junções explícitas em entidades não relacionadas e a sintaxe e o comportamento são semelhantes às declarações JOIN do SQL.

Pontos principais:

Definir entidades servidores (por exemplo, Author e Book de entidades não relacionadas)
Preencha o banco de dados com alguns dados de teste (por exemplo, verifique os resources/data-mysql.sql )
Escreva interfaces (projeções de primavera) que contém getters para as colunas que devem ser buscadas no banco de dados (por exemplo, BookstoreDto )
Write se junta às consultas usando jpql/sql (por exemplo, consultas todos os nomes de autores e títulos de livros do preço fornecido)

Por que evitar o LOMOBOK @EqualsAndHashCode e @Data em entidades e como substituir equals() e hashCode()

Descrição: As entidades devem implementar equals() e hashCode() como aqui. A idéia principal é que o hibernato exige que uma entidade seja igual a si mesma em todas as suas transições de estado ( transitória , anexada , desapegada e removida ). O uso do LOMBOK @EqualsAndHashCode (ou @Data ) não respeitará esse requisito.

Pontos principais:
Evite essas abordagens

Usando o comportamento padrão do Lombok de @EqualsAndHashCode (entidade: LombokDefaultBook , TEST: LombokDefaultEqualsAndHashCodeTest )
Usando LOMOMPOK @EqualsAndHashCode apenas com chave primária (entidade: LombokIdBook , TEST: LombokEqualsAndHashCodeWithIdOnlyTest )
Confie no padrão equals() e hashCode() (Entidade: DefaultBook , Teste: DefaultEqualsAndHashCodeTest )
Confia no padrão equals() e hashCode() contendo apenas o identificador gerado pelo banco de dados (entidade: IdBook , teste: IdEqualsAndHashCodeTest )

Prefira essas abordagens

Confie na chave de negócios (entidade: BusinessKeyBook , teste: BusinessKeyEqualsAndHashCodeTest )
Confie no @NaturalId (entidade: NaturalIdBook , teste: NaturalIdEqualsAndHashCodeTest )
Confie em identificadores designados manualmente (entidade: IdManBook , teste: IdManEqualsAndHashCodeTest )
Confie em identificadores gerados por banco de dados (Entidade: IdGenBook , teste: IdGenEqualsAndHashCodeTest )

Como evitar LazyInitializationException através JOIN FETCH

Veja também:

Esquerda a adesão a buscar
Junte -se ao vs. Junte -se a buscar

Descrição: Normalmente, quando obtemos uma LazyInitializationException tendemos a modificar o tipo de associação de LAZY a EAGER . Isso é muito ruim! Este é um cheiro de código. A melhor maneira de evitar essa exceção é confiar na JOIN FETCH (se você planeja modificar as entidades buscadas) ou JOIN + dto (se os dados buscados forem lidos apenas). JOIN FETCH permite que as associações sejam inicializadas junto com seus objetos pais usando uma única SELECT . Isso é particularmente útil para buscar coleções associadas.

Este aplicativo é um exemplo JOIN FETCH para evitar LazyInitializationException .

Pontos principais:

Defina duas entidades relacionadas (por exemplo, Author e Book em uma associação @OneToMany Lazy-Bidirectional)
Escreva um JPQL JOIN FETCH para buscar um autor, incluindo seus livros
Escreva um JPQL JOIN FETCH (ou JOIN ) para buscar um livro, incluindo seu autor

Exemplo de saída:

Como mesclar coleções de entidades

Descrição: Este é um exemplo de inicialização da primavera com base no artigo a seguir. É uma implementação funcional do exemplo do VLAD. É altamente recomendável ler esse artigo.

Pontos principais:

Remova as linhas de banco de dados existentes que não são mais encontradas na coleção de entrada
Atualize as linhas de banco de dados existentes que podem ser encontradas na coleção de entrada
Adicione as linhas encontradas na coleção de entrada, que não pode ser encontrada no instantâneo atual do banco de dados

Como atrasar a aquisição de conexão, conforme necessário (Hibernate 5.2.10)

Descrição: Este é um exemplo de inicialização da mola que explora o Hibernate 5.2.10 Capacidade de atrasar a aquisição da conexão, conforme necessário. Por padrão, no modo Resource-Local , uma conexão de banco de dados é AQURIED imediatamente após chamar um método anotado com @Transactional . Se esse método contiver algumas tarefas demoradas antes da primeira instrução SQL, a conexão não será aberta para nada. Mas o Hibernate 5.2.10 nos permite atrasar a aquisição de conexão, conforme necessário. Este exemplo depende do hikaricp como o pool de conexão padrão para a inicialização da mola.

Pontos principais:

set spring.datasource.hikari.auto-commit=false in Application.properties
set spring.jpa.properties.hibernate.connection.provider_disables_autocommit=true no application.properties

Exemplo de saída:

Como gerar sequências de identificadores via hibernato hi/lo algoritmo

NOTA: Se os sistemas externos ao seu aplicativo precisarem inserir linhas em suas tabelas, não confie no algoritmo hi/lo , pois, nesses casos, pode causar erros resultou da geração de identificadores duplicados. Confie em algoritmos pooled ou pooled-lo (otimizações de hi/lo ).

Descrição: Este é um exemplo de inicialização de mola do uso do algoritmo hi/lo para gerar 1000 identificadores em 10 ida e volta para o banco de dados para lotes de 1000 inserções em lotes de 30.

Pontos principais:

Use o tipo de gerador SEQUENCE (por exemplo, no PostgreSQL)
Configure o algoritmo hi/lo Author.java

Exemplo de saída:

Se você precisar de um mergulho profundo nas receitas de desempenho expostas neste repositório, tenho certeza de que você vai adorar o meu livro "Práticas de Melhores Práticas de Persistência de Botas da Primavera"	Se você precisar de uma mão de dicas e ilustrações de mais de 100 problemas de desempenho de persistência de java, "o guia ilustrado de desempenho de persistência Java" é para você.

A melhor maneira de implementar uma associação bidirecional @ManyToMany

Descrição: Este aplicativo é uma prova de conceito de como é correto implementar a Bidirecional @ManyToMany Association da perspectiva de desempenho.

Pontos principais:

Escolha um lado próprio e um mappedBy
materialize as coleções de relacionamentos via Set Not List
Use métodos auxiliares no proprietário do relacionamento para manter os dois lados da associação em sincronia
no proprietário do relacionamento, use CascadeType.PERSIST e CascadeType.MERGE , mas evite CascadeType.REMOVE/ALL
no proprietário do relacionamento configurado na mesa de junção
@ManyToMany é preguiçoso por padrão; Mantenha assim!
Como identificadores de entidades, use identificadores atribuídos (chave de negócios, chave natural ( @NaturalId )) e/ou identificadores gerados por banco de dados e substituição (em ambos os lados) adequadamente os métodos equals() e hashCode() como aqui
Se toString() precisar ser substituído, preste atenção apenas para os atributos básicos buscados quando a entidade é carregada do banco de dados

Preferir Set em vez de List em @ManyToMany associações

Descrição: Este é um exemplo de inicialização de primavera de remoção de linhas em caso de uma List bidirecional @ManyToMany , Set . A conclusão é que Set é muito melhor! Isso também se aplica a unidirecional!

Pontos principais:

Usar Set é muito mais eficaz do que List

Exemplo de saída:

Como visualizar detalhes de consulta via log4jdbc

Descrição: Veja os detalhes da consulta via log4jdbc.

Pontos principais:

Para Maven, em pom.xml , adicione dependência log4jdbc

Amostra de saída:

Como visualizar parâmetros de ligação via rastreamento

Descrição: Veja os parâmetros de ligação/extraídos da instrução preparada via TRACE .

Pontos principais:

no application.properties Add: logging.level.org.hibernate.type.descriptor.sql=TRACE
Ou, ainda melhor (para filtrar os recursos do SQLS), em um arquivo de configuração específico de logback, adicione o logger adequado

Amostra de saída:

Como armazenar java.time.YearMonth como Integer ou Date via Hibernate Types Library

Descrição: Os tipos de hibernato são um conjunto de tipos extras não suportados por padrão no núcleo de hibernato. Um desses tipos é java.time.YearMonth . Este é um aplicativo de inicialização de primavera que usa o tipo Hibernate para armazenar este YearMonth em um banco de dados MySQL como número inteiro ou data.

Pontos principais:

Para maven, adicione os tipos de hibernato como uma dependência em pom.xml
Na entidade, use @TypeDef para mapear typeClass para defaultForType

Exemplo de saída:

Como executar funções SQL na consulta jpql

Nota : Usando as funções SQL na parte WHERE (não na parte SELECT ) da consulta no JPA 2.1 pode ser feita via function() como aqui.

Descrição: Tentar usar as funções SQL (padrão ou definido) em consultas JPQL pode resultar em exceções se o Hibernate não as reconhecerá e não puder analisar a consulta JPQL. Por exemplo, a função MySQL, concat_ws não é reconhecida pelo hibernado. Este aplicativo é um aplicativo de inicialização de primavera com base no Hibernate 5.3, que registra a função concat_ws via MetadataBuilderContributor e informa a propriedade Hibernate sobre ele, metadata_builder_contributor . Este exemplo também usa @Query e EntityManager , para que você possa ver dois casos de uso.

Pontos principais:

Use o Hibernate 5.3 (ou, para ser precisamente, 5.2.18) (por exemplo, use a Botagem de mola 2.1.0.Release)
Implementar MetadataBuilderContributor e registrar a função concat_ws mysql
No application.properties , set spring.jpa.properties.hibernate.metadata_builder_contributor para apontar a implementação do hibernato para MetadataBuilderContributor

Exemplo de saída:

Log de perguntas lentas via DataSource-Proxy

Descrição: Este aplicativo é uma amostra de log apenas consultas lentas via DataSource-Proxy . Uma consulta lenta é uma consulta que tem um tempo de execução maior que um limite específico em milissegundos.

Pontos principais:

Para maven, adicione pom.xml a dependência de próxi de dados de dados
Crie um pós -processador de feijão para interceptar o feijão DataSource
embrulhe o feijão de DataSource via ProxyFactory e uma implementação do MethodInterceptor
Escolha um limiar em milissegundos
Defina um ouvinte e substitua afterQuery()

Exemplo de saída:

Paginação com deslocamento - Trigger SELECT COUNT Subconstração e Retorno Page<dto>

Descrição: Este aplicativo busca dados como Page<dto> via paginação de deslocamento de inicialização da primavera. Na maioria das vezes, os dados que devem ser paginados são dados somente leitura . A busca dos dados em entidades deve ser feita apenas se planejarmos modificar esses dados, portanto, buscar dados somente leitura como Page<entity> não for preferível, pois pode acabar em uma penalidade de desempenho significativa. A SELECT COUNT acionada para contar o número total de registros é uma subconeração da SELECT principal. Portanto, haverá uma única ida e volta ao banco de dados em vez de duas (normalmente, há uma consulta necessária para buscar os dados e outra para contar o número total de registros).

Pontos principais:

Crie uma projeção de primavera (DTO) para contém getters apenas para as colunas que devem ser buscadas
Escreva um repositório que estenda PagingAndSortingRepository
busque dados através de uma consulta JPQL ou nativa (que inclui contagem) em uma List<dto>
Use a List<dto> e o Pageable adequado para criar uma Page<dto>

PAGINAÇÃO DE OFFSET - TIGER SELECT COUNT Subquery and Return List<dto>

Descrição: Este aplicativo busca dados como List<dto> via paginação de deslocamento de inicialização da primavera. Na maioria das vezes, os dados que devem ser paginados são dados somente leitura . A busca dos dados em entidades deve ser feita apenas se planejarmos modificar esses dados, portanto, buscar dados somente leitura como List<entity> não for preferível, pois pode acabar em uma penalidade de desempenho significativa. A SELECT COUNT acionada para contar o número total de registros é uma subconeração da SELECT principal. Portanto, haverá uma única ida e volta ao banco de dados em vez de duas (normalmente, há uma consulta necessária para buscar os dados e outra para contar o número total de registros).

Pontos principais:

Crie uma projeção de primavera (DTO) para contém getters apenas para as colunas que devem ser buscadas
Escreva um repositório que estenda PagingAndSortingRepository
busque dados através de uma consulta JPQL ou nativa (que inclui contagem) em uma List<dto>

Como personalizar as configurações do HikaricP por meio de propriedades

Se você usar o spring-boot-starter-jdbc ou spring-boot-starter-data-jpa "Starters", você recebe automaticamente uma dependência para Hikaricp

Nota: A melhor maneira de ajustar os parâmetros do pool de conexão consiste no uso do pool flexível pelo Vlad Mihalcea. Por meio do Flexy Pool, você pode encontrar as configurações ótimas que sustentam o alto desempenho do seu pool de conexão.

Descrição: Este é um aplicativo de kickoff que configurou apenas o hikaricp via application.properties . O jdbcUrl está configurado para um banco de dados MySQL. Para fins de teste, o aplicativo usa um ExecutorService para simular usuários simultâneos. Verifique o relatório Hickaricp revelando o status do pool de conexões.

Pontos principais:

no application.properties , confie no spring.datasource.hikari.* Para configurar o hikaricp

Amostra de saída:

Se você precisar de um mergulho profundo nas receitas de performance expostas neste repositório, tenho certeza de que você vai adorar meu livro "Práticas de Melhores Práticas de Persistência de Botas da Primavera"	Se você precisar de uma mão de dicas e ilustrações de mais de 100 problemas de desempenho de persistência de java, "o guia ilustrado de desempenho de persistência Java" é para você.

Como personalizar as configurações do Hikaricp por meio de propriedades e DataSourceBuilder

Se você usar o spring-boot-starter-jdbc ou spring-boot-starter-data-jpa "Starters", você recebe automaticamente uma dependência para Hikaricp

Descrição: Este é um aplicativo de kickoff que configurou o Hikaricp via DataSourceBuilder . O jdbcUrl está configurado para um banco de dados MySQL. Para fins de teste, o aplicativo usa um ExecutorService para simular usuários simultâneos. Verifique o relatório Hickaricp revelando o status do pool de conexões.

Pontos principais:

No application.properties , configure o Hikaricp por meio de um prefixo personalizado, por exemplo, app.datasource.*
Escreva um @Bean que retorne o DataSource

Amostra de saída:

Executando um aplicativo Springboot no servidor Payara usando uma fonte de dados Payara (JDBC Resource and Connection Pool)

Este aplicativo é detalhado neste artigo da DZone.

Como personalizar as configurações do BoneCP por meio de propriedades e DataSourceBuilder

Descrição: Este é um aplicativo de kickoff que configurou o Bonecp via DataSourceBuilder . O jdbcUrl está configurado para um banco de dados MySQL. Para fins de teste, o aplicativo usa um ExecutorService para simular usuários simultâneos.

Pontos principais:

em pom.xml Adicione a dependência do Bonecp
No application.properties , configure o BoneCP por meio de um prefixo personalizado, por exemplo, app.datasource.*
Escreva um @Bean que retorne o DataSource

Amostra de saída:

Como personalizar as configurações do ViburdBCP via Propriedades e DataSourceBuilder

Descrição: Este é um aplicativo de kickoff que configurou o viburdbcp via DataSourceBuilder . O jdbcUrl está configurado para um banco de dados MySQL. Para fins de teste, o aplicativo usa um ExecutorService para simular usuários simultâneos.

Pontos principais:

em pom.xml Adicione a dependência do viburdbcp
No application.properties , configure o viburdbcp por meio de um prefixo personalizado, por exemplo, app.datasource.*
Escreva um @Bean que retorne o DataSource

Amostra de saída:

Como personalizar as configurações C3P0 por meio de propriedades e DataSourceBuilder

Descrição: Este é um aplicativo de kickoff que configurou o C3P0 via DataSourceBuilder . O jdbcUrl está configurado para um banco de dados MySQL. Para fins de teste, o aplicativo usa um ExecutorService para simular usuários simultâneos.

Pontos principais:

em pom.xml Adicione a dependência C3P0
No application.properties , configure C3P0 por meio de um prefixo personalizado, por exemplo, app.datasource.*
Escreva um @Bean que retorne o DataSource

Amostra de saída:

Como personalizar as configurações do DBCP2 por meio de propriedades e DataSourceBuilder

Descrição: Este é um aplicativo de kickoff que configurou o DBCP2 via DataSourceBuilder . O jdbcUrl está configurado para um banco de dados MySQL. Para fins de teste, o aplicativo usa um ExecutorService para simular usuários simultâneos.

Pontos principais:

em pom.xml Adicione a dependência do dbcp2
No application.properties , configure o DBCP2 por meio de um prefixo personalizado, por exemplo, app.datasource.*
Escreva um @Bean que retorne o DataSource

Como personalizar as configurações do TomCat por meio de propriedades e DataSourceBuilder

Descrição: Este é um aplicativo de kickoff que configurou o TomCat via DataSourceBuilder . O jdbcUrl está configurado para um banco de dados MySQL. Para fins de teste, o aplicativo usa um ExecutorService para simular usuários simultâneos.

Pontos principais:

em pom.xml Adicione a dependência do tomcat
No application.properties , configure o TomCat por meio de um prefixo personalizado, por exemplo, app.datasource.*
Escreva um @Bean que retorne o DataSource

Amostra de saída:

Como configurar duas fontes de dados com dois pools de conexão

Descrição: Este é um aplicativo de kickoff que usa duas fontes de dados (dois bancos de dados MySQL, um nomeado authorsdb e um chamado booksdb ) com dois pools de conexão (cada banco de dados usa seu próprio pool de conexão Hikaricp com diferentes configurações). Com base nos itens acima, é muito fácil de configurar dois pools de conexão de dois fornecedores diferentes.

Pontos principais:

No application.properties , configure dois pools de conexão Hikaricp por meio de dois prefixos personalizados, por exemplo, app.datasource.ds1 e app.datasource.ds2
Escreva um @Bean que retorne o primeiro DataSource e marque -o como @Primary
Escreva outro @Bean que retorne o segundo DataSource
Configure dois EntityManagerFactory e aponte os pacotes para digitalizar para cada um deles
Coloque os domínios e repositórios para cada EntityManager nos pacotes certos

Amostra de saída:

Como fornecer uma API fluente por meio de criadores de entidades de construção

Nota : Se você deseja que você forneça uma API fluente sem alterar os setters, considere este item.

Descrição: Este é um aplicativo de amostra que altera os métodos de Setters de entidades para capacitar uma API fluente.

Pontos principais:

Com direito, retorne this em vez de void em setters

Exemplo de API fluente:

Como fornecer uma API fluente por meio de métodos adicionais para a construção de entidades

Nota : Se você deseja que você forneça uma API fluente alterando os setters, considere este item.

Descrição: Este é um aplicativo de amostra que adiciona métodos adicionais de entidades (por exemplo, para setName , adicionamos name ), a fim de capacitar uma API fluente.

Pontos principais:

Nas entidades, adicione para cada setter um método adicional que retorne this em vez de void

Exemplo de API fluente:

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Como implementar Slice<T> findAll()

Provavelmente isso é tudo o que você deseja: como buscar Slice<entity> / Slice<dto> via fetchAll / fetchAllDto

Algumas implementações de Slice<T> findAll() :

Esta é uma implementação fina com base em um SQL codificado: "SELECT e FROM " + entityClass.getSimpleName() + " e;"
Esta é apenas mais uma implementação minimalista baseada em CriteriaBuilder em vez de SQL com código duro
Esta é uma implementação que nos permite fornecer um Sort , portanto, a classificação de resultados é possível
Esta é uma implementação que nos permite fornecer uma Sort e uma Specification de dados da primavera
Esta é uma implementação que nos permite fornecer uma Sort , um LockModeType , um QueryHints e uma Specification de dados da primavera
Esta é uma implementação que nos permite fornecer Pageable de dados e/ou Specification da mola, estendendo o SimpleJpaRepository dos dados da mola. Basicamente, essa implementação é a única que retorna Page<T> em vez de Slice<T> , mas não aciona a SELECT COUNT extra, pois foi eliminada substituindo o método da Page<T> readPage(...) do SimpleJpaRepository . A principal desvantagem é que, ao retirar uma Page<T> você não sabe se há uma próxima página ou a atual é a última. No entanto, há soluções alternativas para ter isso também. Nesta implementação, você não pode definir LockModeType ou Dicas de consulta.

História : A Spring Boot fornece um mecanismo de paginação integrado baseado em deslocamento que retorna uma Page ou Slice . Cada uma dessas APIs representa uma página de dados e alguns metadados. A principal diferença é que Page contém o número total de registros, enquanto Slice só pode dizer se há outra página disponível. Para Page , a Spring Boot fornece um método findAll() capaz de Pageable como argumentos uma Specification ou/ou Example . Para criar uma Page que contenha o número total de registros, esse método aciona uma SELECT COUNT extra ao lado da consulta usada para buscar os dados da página atual. Isso pode ser uma penalidade de desempenho, pois a consulta SELECT COUNT é acionada toda vez que solicitarmos uma página. Para evitar essa bota extra-quadra, a Spring Boot fornece uma API mais relaxada, a API Slice . O uso Slice em vez de Page remove a necessidade desta consulta SELECT COUNT extra e retorna a página (registros) e alguns metadados sem o número total de registros. Portanto, embora Slice não saiba o número total de registros, ainda pode dizer se há outra página disponível após a atual ou esta é a última página. O problema é que Slice funciona bem para consultas que contêm o SQL, WHERE a cláusula (incluindo aqueles que usam o mecanismo do construtor de consultas incorporado em dados de mola), mas não funciona para findAll() . Este método ainda retornará uma Page em vez de Slice , portanto, a consulta de SELECT COUNT é acionada para Slice<T> findAll(...); .

Descrição: Este é um conjunto de aplicativos de amostras que fornece versões diferentes de um método de Slice<T> findAll(...) . Temos a partir de uma implementação minimalista que se baseia em uma consulta codificada como: "SELECT e FROM " + entityClass.getSimpleName() + " e"; (Esta receita), a uma implementação personalizada que suporta classificação, especificação, modo de bloqueio e consulta sugere uma implementação que se baseia na extensão SimpleJpaRepository .

Pontos principais:

Escreva uma classe abstract que exponha os métodos Slice<T> findAll(...) ( SlicePagingRepositoryImplementation )
implemente os métodos findAll() para retornar Slice<T> (ou Page<T> , mas sem o número total de elementos)
Retorne um SliceImpl ( Slice<T> ) ou uma PageImpl ( Page<T> ) sem o número total de elementos
Implementar um novo método readSlice() ou substituir a página SimpleJpaRepository#readPage() para evitar SELECT COUNT
Passe a classe de entidade (por exemplo, Author.class ) para esta classe abstract por meio de um repositório de classe ( AuthorRepository )

Paginação offset - COUNT(*) OVER e List<dto>

Descrição: Normalmente, na paginação offset, há uma consulta necessária para buscar os dados e outra para contar o número total de registros. Porém, podemos buscar essas informações em um único banco de dados Rountrip por meio de uma subquery SELECT COUNT aninhada na SELECT principal. Melhor ainda, para fornecedores de bancos de dados que suportam as funções da janela, há uma solução que se baseia na COUNT(*) OVER() como neste aplicativo que usa essa função de janela em uma consulta nativa contra o mysql 8. Portanto, prefira este em vez de SELECT COUNT subcutilizada .

Pontos principais:

Crie uma projeção DTO que contenha getters para as colunas que devem ser buscadas e uma coluna extra para mapear o retorno da COUNT(*) OVER() função da janela
Escreva uma consulta nativa dependendo desta função de janela

Exemplo:

Como implementar a paginação de teclas na bota da primavera

Descrição: Quando contamos com uma paginação de deslocamento , temos a penalidade de desempenho induzida jogando fora os registros N antes de chegar ao deslocamento desejado. N maior leva a uma penalidade de desempenho significativa. Quando temos um N grande, é melhor confiar na paginação de teclas , que mantém um tempo "constante" para grandes conjuntos de dados. Para entender como o desligamento ruim pode ser executado, verifique este artigo:

Captura de tela desse artigo (Paginação Offset ):

Precisa saber se há mais registros?
Por sua natureza, o Keyset não usa uma SELECT COUNT para buscar o número de registros totais. Mas, com um pequeno ajuste, podemos dizer facilmente se houver mais registros, portanto, para mostrar um botão da Next Page do tipo. Principalmente, se você precisar de tal coisa, considere este aplicativo cujo clímax está listado abaixo:

public AuthorView fetchNextPage(long id, int limit) {
List<Author> authors = authorRepository.fetchAll(id, limit + 1);

if (authors.size() == (limit + 1)) {
authors.remove(authors.size() - 1);
return new AuthorView(authors, true);
}

return new AuthorView(authors, false);
}

Ou, assim (depende do método Author.toString() ):

public Map<List<Author>, Boolean> fetchNextPage(long id, int limit) {
List<Author> authors = authorRepository.fetchAll(id, limit + 1);

if(authors.size() == (limit + 1)) {
authors.remove(authors.size() -1);
return Collections.singletonMap(authors, true);
}

return Collections.singletonMap(authors, false);
}

Um botão Previous Page pode ser implementado facilmente com base no primeiro registro.

Pontos principais:

Escolha as colunas para atuar como o último registro visitado (por exemplo, id )
Use as colunas no (s) WHERE e ORDER BY cláusulas do seu SQL

Como implementar a paginação de deslocamento na bota da primavera

Descrição: Este é um exemplo de paginação clássica da bota de inicialização da primavera. No entanto, não é aconselhável usar essa abordagem na produção devido às suas penalidades de desempenho explicadas mais.

Quando confiamos em uma paginação com deslocamento , temos a penalidade de desempenho induzida jogando fora os registros N antes de atingir o deslocamento desejado. N maior leva a uma penalidade de desempenho significativa. Outra penalidade é a SELECT necessária para contar o número total de registros. Para entender o quão ruim a paginação de deslocamento pode executar, verifique este artigo. Uma captura de tela desse artigo está abaixo: No entanto, talvez este exemplo seja um pouco extremo. Para conjuntos de dados relativamente pequenos, a paginação de deslocamento não é tão ruim (é próximo do desempenho da paginação de teclas ) e, como a Spring Boot fornece suporte interno para a paginação com deslocamento através da API Page , é muito fácil usá-lo. No entanto, dependendo do caso, podemos otimizar um pouco a paginação de deslocamento, como nos exemplos a seguir:

Pegue uma página como uma Page :

COUNT(*) OVER Page<dto>
COUNT(*) OVER Page<entity> via coluna extra
Trigger SELECT COUNT subconstração e Page<dto>
Trigger SELECT COUNT Page<entity> Subco
Trigger SELECT COUNT subconstrável e Page<projection> que mapeiam entidades e o número total de registros via projeção

Pegue uma página como uma List :

COUNT(*) OVER List<dto>
COUNT(*) OVER List<entity> via coluna extra
Trigger SELECT COUNT List<dto>
Trigger SELECT COUNT List<entity> Subco
Trigger SELECT COUNT List<projection> Subco

Mas: se a paginação offset estiver causando problemas de desempenho e você decidir ir com a paginação por teclas , verifique aqui (paginação por teclas ).

Pontos -chave da paginação clássica de deslocamento :

Escreva um repositório que estenda PagingAndSortingRepository
Ligue ou escreva métodos que retornem Page<entity>

Exemplos de paginação clássica de deslocamento :

Ligue para o findAll(Pageable) sem classificar:
repository.findAll(PageRequest.of(page, size));
Ligue para o findAll(Pageable) com a classificação:
repository.findAll(PageRequest.of(page, size, new Sort(Sort.Direction.ASC, "name")));
Use a criação de consultas de dados da primavera para definir novos métodos em seu repositório:
Page<Author> findByName(String name, Pageable pageable);
Page<Author> queryFirst10ByName(String name, Pageable pageable);

Como otimizar inserções em lote dos relacionamentos entre pais e filhos em MySQL

Descrição: Vamos supor que tenhamos um relacionamento um para muitos entre as entidades Author e Book . Quando salvamos um autor, salvamos seus livros também, graças a cascata de tudo/persistir. Queremos criar um monte de autores com livros e salvá -los no banco de dados (por exemplo, um banco de dados MySQL) usando a técnica de lote. Por padrão, isso resultará no lote de cada autor e nos livros por autor (um lote para o autor e um lote para os livros, outro lote para o autor e outro lote para os livros, e assim por diante). Para lotes de autores e livros, precisamos solicitar inserções como neste aplicativo.

Key points: Beside all setting specific to batching inserts in MySQL, we need to set up in application.properties the following property: spring.jpa.properties.hibernate.order_inserts=true

Example without ordered inserts:

Example with ordered inserts:

How To Batch Updates In MySQL

Implementations:

Single entity update
Parent-child relationship update

Description: Batch updates in MySQL.

Pontos principais:

in application.properties set spring.jpa.properties.hibernate.jdbc.batch_size
in application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL, statements get rewritten into a single string buffer and sent in a single request)
in application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)
in application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))
in case of using a parent-child relationship with cascade all/persist (eg one-to-many, many-to-many) then consider to set up spring.jpa.properties.hibernate.order_updates=true to optimize the batching by ordering atualizações
before Hibernate 5, we need to set in application.properties a setting for enabling batching for versioned entities during update and delete operations (entities that contains @Version for implicit optimistic locking); this setting is: spring.jpa.properties.hibernate.jdbc.batch_versioned_data=true ; starting with Hibernate 5, this setting should be true by default

Output example for single entity:

Output example for parent-child relationship:

How To Batch Deletes That Don't Involve Associations In MySQL

Description: Batch deletes that don't involve associations in MySQL.

Note: Spring deleteAllInBatch() and deleteInBatch() don't use delete batching and don't take advantage of automatic optimstic locking mechanism to prevent lost updates (eg, @Version is ignored). They rely on Query.executeUpdate() to trigger bulk operations. These operations are fast, but Hibernate doesn't know which entities are removed, therefore, the Persistence Context is not updated accordingly (it's up to you to flush (before delete) and close/clear (after delete) the Persistence Context accordingly to avoid issues created by unflushed (if any) or outdated (if any) entities). The first one ( deleteAllInBatch() ) simply triggers a delete from entity_name statement and is very useful for deleting all records. The second one ( deleteInBatch() ) triggers a delete from entity_name where id=? or id=? or id=? ... statement, therefore, is prone to cause issues if the generated DELETE statement exceedes the maximum accepted size. This issue can be controlled by deleting the data in chunks, relying on IN operator, and so on. Bulk operations are faster than batching which can be achieved via the deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() method. Behind the scene, the two flavors of deleteAll() relies on delete() . The delete() / deleteAll() methods rely on EntityManager.remove() therefore the Persistence Context is synchronized accordingly. Moreover, if automatic optimstic locking mechanism (to prevent lost updates ) is enabled then it will be used.

Key points for regular delete batching:

for deleting in batches rely on deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() method
in application.properties set spring.jpa.properties.hibernate.jdbc.batch_size
in application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL, statements get rewritten into a single string buffer and sent in a single request)
in application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)
in application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))
before Hibernate 5, we need to set in application.properties a setting for enabling batching for versioned entities during update and delete operations (entities that contains @Version for implicit optimistic locking); this setting is: spring.jpa.properties.hibernate.jdbc.batch_versioned_data=true ; starting with Hibernate 5, this setting should be true by default

Exemplo de saída:

How To Batch Deletes In MySQL Via orphanRemoval=true

Description: Batch deletes in MySQL via orphanRemoval=true .

Note: Spring deleteAllInBatch() and deleteInBatch() don't use delete batching and don't take advantage of cascading removal, orphanRemoval and automatic optimstic locking mechanism to prevent lost updates (eg, @Version is ignored). They rely on Query.executeUpdate() to trigger bulk operations. These operations are fast, but Hibernate doesn't know which entities are removed, therefore, the Persistence Context is not updated accordingly (it's up to you to flush (before delete) and close/clear (after delete) the Persistence Context accordingly to avoid issues created by unflushed (if any) or outdated (if any) entities). The first one ( deleteAllInBatch() ) simply triggers a delete from entity_name statement and is very useful for deleting all records. The second one ( deleteInBatch() ) triggers a delete from entity_name where id=? or id=? or id=? ... statement, therefore, is prone to cause issues if the generated DELETE statement exceedes the maximum accepted size. This issue can be controlled by deleting the data in chunks, relying on IN operator, and so on. Bulk operations are faster than batching which can be achieved via the deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() method. Behind the scene, the two flavors of deleteAll() relies on delete() . The delete() / deleteAll() methods rely on EntityManager.remove() therefore the Persistence Context is synchronized accordingly. If automatic optimstic locking mechanism (to prevent lost updates ) is enabled then it will be used. Moreover, cascading removals and orphanRemoval works as well.

Key points for using deleteAll()/delete() :

in this example, we have a Author entity and each author can have several Book ( one-to-many )
first, we use orphanRemoval=true and CascadeType.ALL
second, we dissociate all Book from the corresponding Author
third, we explicitly (manually) flush the Persistent Context; is time for orphanRemoval=true to enter into the scene; thanks to this setting, all disassociated books will be deleted; the generated DELETE statements are batched (if orphanRemoval is set to false , a bunch of updates will be executed instead of deletes)
forth, we delete all Author via the deleteAll() or delete() method (since we have dissaciated all Book , the Author deletion will take advantage of batching as well)

How To Batch Deletes In MySQL Via SQL ON DELETE CASCADE

Description: Batch deletes in MySQL via ON DELETE CASCADE . Auto-generated database schema will contain the ON DELETE CASCADE directive.

Note: Spring deleteAllInBatch() and deleteInBatch() don't use delete batching and don't take advantage of cascading removal, orphanRemoval and automatic optimistic locking mechanism to prevent lost updates (eg, @Version is ignored), but both of them take advantage on ON DELETE CASCADE and are very efficient. They trigger bulk operations via Query.executeUpdate() , therefore, the Persistence Context is not synchronized accordingly (it's up to you to flush (before delete) and close/clear (after delete) the Persistence Context accordingly to avoid issues created by unflushed (if any) or outdated (if any) entities). The first one simply triggers a delete from entity_name statement, while the second one triggers a delete from entity_name where id=? or id=? or id=? ... declaração. For delete in batches rely on deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() method. Behind the scene, the two flavors of deleteAll() relies on delete() . Mixing batching with database automatic actions ( ON DELETE CASCADE ) will result in a partially synchronized Persistent Context.

Pontos principais:

in this application, we have a Author entity and each author can have several Book ( one-to-many )
first, we remove orphanRemoval or set it to false
second, we use only CascadeType.PERSIST and CascadeType.MERGE
third, we set @OnDelete(action = OnDeleteAction.CASCADE) next to @OneToMany
fourth, we set spring.jpa.properties.hibernate.dialect to org.hibernate.dialect.MySQL5InnoDBDialect (or, MySQL8Dialect )
fifth, we run through a set of deleteFoo() methods that uses bulk and batching deletes as well

Exemplo de saída:

How To Use Hibernate @NaturalId In Spring Boot Style

Alternative implementation: In case that you want to avoid extending SimpleJpaRepository check this implementation.

Description: This is a SpringBoot application that maps a natural business key using Hibernate @NaturalId . This implementation allows us to use @NaturalId as it was provided by Spring.

Pontos principais:

in the entity (eg, Book ), mark the properties (business keys) that should act as natural IDs with @NaturalId ; commonly, there is a single such property, but multiple are suppored as well as here
for non-mutable ids, mark the columns as @NaturalId(mutable = false) and @Column(nullable = false, updatable = false, unique = true, ...)
for mutable ids, mark the columns as @NaturalId(mutable = true) and @Column(nullable = false, updatable = true, unique = true, ...)
override the equals() and hashCode() using the natural id(s)
define a @NoRepositoryBean interface ( NaturalRepository ) to define two methods, named findBySimpleNaturalId() and findByNaturalId()
provide an implementation for this interface ( NaturalRepositoryImpl ) relying on Hibernate, Session , bySimpleNaturalId() and byNaturalId() methods
use @EnableJpaRepositories(repositoryBaseClass = NaturalRepositoryImpl.class) to register this implementation as the base class
for the entity, write a classic repository
inject this class in your services and call findBySimpleNaturalId() or findByNaturalId()

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How To Set Up P6Spy in Spring Boot

Description: This is a Spring Boot application that uses P6Spy. P6Spy is a framework that enables database data to be seamlessly intercepted and logged with no code changes to the application.

Pontos principais:

in pom.xml , add the P6Spy Maven dependency
in application.properties , set up JDBC URL as, jdbc:p6spy:mysql://localhost:3306/db_users
in application.properties , set up driver class name as, com.p6spy.engine.spy.P6SpyDriver
in the application root folder add the file spy.properties (this file contains P6Spy configurations); in this application, the logs will be outputed to console, but you can easy switch to a file; more details about P6Spy configurations can be found in documentation

Amostra de saída:

How To Retry Transactions After OptimisticLockException Exception ( @Version )

Note: Optimistic locking mechanism via @Version works for detached entities as well.

Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. When such exception occur, the application retry the corresponding transaction via db-util library developed by Vlad Mihalcea.

Pontos principais:

for Maven, in pom.xml , add the db-util dependency
configure the OptimisticConcurrencyControlAspect bean
mark the method (not annotated with @Transactional ) that is prone to throw (or that calls a method that is prone to throw (this method can be annotated with @Transactional )) an optimistic locking exception with @Retry(times = 10, on = OptimisticLockingFailureException.class)

Amostra de saída:

How To Retry Transaction After OptimisticLockException Exception (Hibernate Version-less Optimistic Locking Mechanism)

Note: Optimistic locking mechanism via Hibernate version-less doesn't work for detached entities (don't close the Persistent Context).

Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception (eg, in Spring Boot, OptimisticLockingFailureException ) via Hibernate version-less optimistic locking. When such exception occur, the application retry the corresponding transaction via db-util library developed by Vlad Mihalcea.

Pontos principais:

for Maven, in pom.xml , add the db-util library dependency
configure the OptimisticConcurrencyControlAspect bean
annotate the corresponding entity (eg, Inventory ) with @DynamicUpdate and @OptimisticLocking(type = OptimisticLockType.DIRTY)
mark the method (not annotated with @Transactional ) that is prone to throw (or that calls a method that is prone to throw (this method can be annotated with @Transactional )) an optimistic locking exception with @Retry(times = 10, on = OptimisticLockingFailureException.class)

How To Enrich DTO With Virtual Properties Via Spring Projections

Note: You may also like to read the recipe, "How To Create DTO Via Spring Data Projections"

Description: This is an application sample that fetches only the needed columns from the database via Spring Data Projections (DTO) and enrich the result via virtual properties.

Pontos principais:

we fetch from the database only the author name and age
in the projection interface, AuthorNameAge , use the @Value and Spring SpEL to point to a backing property from the domain model (in this case, the domain model property age is exposed via the virtual property years )
in the projection interface, AuthorNameAge , use the @Value and Spring SpEL to enrich the result with two virtual properties that don't have a match in the domain model (in this case, rank and books )

Exemplo de saída:

How To Use Query Creation Mechanism For JPA To Limit Result Size

Description: Spring Data comes with the query creation mechanism for JPA that is capable to interpret a query method name and convert it into a SQL query in the proper dialect. This is possible as long as we respect the naming conventions of this mechanism. This is an application that exploit this mechanism to write queries that limit the result size. Basically, the name of the query method instructs Spring Data how to add the LIMIT (or similar clauses depending on the RDBMS) clause to the generated SQL queries.

Pontos principais:

define a Spring Data regular repository (eg, AuthorRepository )
write query methods respecting the query creation mechanism for JPA naming conventions

Exemplos:
- List<Author> findFirst5ByAge(int age);
- List<Author> findFirst5ByAgeGreaterThanEqual(int age);
- List<Author> findFirst5ByAgeLessThan(int age);
- List<Author> findFirst5ByAgeOrderByNameDesc(int age);
- List<Author> findFirst5ByGenreOrderByAgeAsc(String genre);
- List<Author> findFirst5ByAgeGreaterThanEqualOrderByNameAsc(int age);
- List<Author> findFirst5ByGenreAndAgeLessThanOrderByNameDesc(String genre, int age);
- List<AuthorDto> findFirst5ByOrderByAgeAsc();
- Page<Author> queryFirst10ByName(String name, Pageable p);
- Slice<Author> findFirst10ByName(String name, Pageable p);

The list of supported keywords is listed below:

How To Generate A Schema Via schema-*.sql In MySQL

Note: As a rule, in real applications avoid generating schema via hibernate.ddl-auto or set it to validate . Use schema-*.sql file or better Flyway or Liquibase migration tools.

Description: This application is an example of using schema-*.sql to generate a schema(database) in MySQL.

Pontos principais:

in application.properties , set the JDBC URL (eg, spring.datasource.url=jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true )
in application.properties , disable DDL auto (just don't add explicitly the hibernate.ddl-auto setting)
in application.properties , instruct Spring Boot to initialize the schema from schema-mysql.sql file

How To Generate Two Databases Via schema-*.sql And Match Entities To Them Via @Table In MySQL

Note: As a rule, in real applications avoid generating schema via hibernate.ddl-auto or set it to validate . Use schema-*.sql file or better Flyway or Liquibase .

Description: This application is an example of using schema-*.sql to generate two databases in MySQL. The databases are matched at entity mapping via @Table .

Pontos principais:

in application.properties , set the JDBC URL without the database, eg, spring.datasource.url=jdbc:mysql://localhost:3306
in application.properties , disable DDL auto (just don't specify hibernate.ddl-auto )
in aaplication.properties , instruct Spring Boot to initialize the schema from schema-mysql.sql file
in Author entity, specify that the corresponding table ( author ) is in the database authorsdb via @Table(schema="authorsdb")
in Book entity, specify that the corresponding table ( book ) is in the database booksdb via @Table(schema="booksdb")

Exemplo de saída:

Persisting a Author results in the following SQL: insert into authorsdb.author (age, genre, name) values (?, ?, ?)
Persisting a Book results the following SQL: insert into booksdb.book (isbn, title) values (?, ?)

How To Stream Result Set Via Spring Data In MySQL

Note: For web-applications, pagination should be the way to go, not streaming. But, if you choose streaming then keep in mind the golden rule: keep th result set as small as posible. Also, keep in mind that the Execution Plan might not be as efficient as when using SQL-level pagination.

Description: This application is an example of streaming the result set via Spring Data and MySQL. This example can be adopted for databases that fetches the entire result set in a single roundtrip causing performance penalties.

Pontos principais:

rely on forward-only result set (default in Spring Data)
rely on read-only statement (add @Transactional(readOnly=true) )
set the fetch-size set (eg 30, or row-by-row; Integer.MIN_VALUE (recommended in MySQL))
for MySQL, set Statement fetch-size to Integer.MIN_VALUE , or add useCursorFetch=true to the JDBC URL and set Statement fetch-size to a positive integer (eg, 30)

How To Migrate MySQL Database Using Flyway - MySQL Database Created Via createDatabaseIfNotExist

Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.

Description: This application is an example of migrating a MySQL database via Flyway when the database exists (it is created before migration via MySQL specific parameter, createDatabaseIfNotExist=true ).

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto
in application.properties , set the JDBC URL as follows: jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true
each SQL file containing the schema update add it in classpath:db/migration
each SQL file name it as V1.1__Description.sql , V1.2__Description.sql , ...

How To Migrate MySQL Database Using Flyway - Database Created Via spring.flyway.schemas

Description: This application is an example of migrating a MySQL database when the database is created by Flyway via spring.flyway.schemas . In this case, the entities should be annotated with @Table(schema = "bookstoredb") or @Table(catalog = "bookstoredb") . Here, the database name is bookstoredb .

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto
in application.properties , set the JDBC URL as follows: jdbc:mysql://localhost:3306/
in application.properties , add spring.flyway.schemas=bookstoredb , where bookstoredb is the database that should be created by Flyway (feel free to add your own database name)
each entity that should be stored in this database should be annotated with, @Table(schema/catalog = "bookstoredb")
each SQL file containing the schema update add it in classpath:db/migration
each SQL file name it as V1.1__Description.sql , V1.2__Description.sql , ...

Output of migration history example:

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Auto-Create And Migrate Schemas For Two Data Sources (MySQL and PostgreSQL) Using Flyway

Note: For production don't rely on hibernate.ddl-auto to create your schema. Remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.

Description: This application is an example of auto-creating and migrating schemas for MySQL and PostgreSQL. In addition, each data source uses its own HikariCP connection pool. In case of MySQL, where schema = database , we auto-create the schema ( authorsdb ) based on createDatabaseIfNotExist=true . In case of PostgreSQL, where a database can have multiple schemas, we use the default postgres database and auto-create in it the schema, booksdb . For this we rely on Flyway, which is capable to create a missing schema.

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate
in application.properties , configure the JDBC URL for MySQL as, jdbc:mysql://localhost:3306/authorsdb?createDatabaseIfNotExist=true and for PostgreSQL as, jdbc:postgresql://localhost:5432/postgres?currentSchema=booksdb
in application.properties , set spring.flyway.enabled=false to disable default behavior
programmatically create a DataSource for MySQL and one for PostgreSQL
programmatically create a FlywayDataSource for MySQL and one for PostgreSQL
programmatically create an EntityManagerFactory for MySQL and one for PostgreSQL
for MySQL, place the migration SQLs files in dbmigrationmysql
for PostgreSQL, place the migration SQLs files in dbmigrationpostgresql

How To Auto-Create And Migrate Two Schemas In PostgreSQL Using Flyway

Description: This application is an example of auto-creating and migrating two schemas in PostgreSQL using Flyway. In addition, each data source uses its own HikariCP connection pool. In case of PostgreSQL, where a database can have multiple schemas, we use the default postgres database and auto-create two schemas, authors and books . For this we rely on Flyway, which is capable to create the missing schemas.

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate
in application.properties , configure the JDBC URL for books as jdbc:postgresql://localhost:5432/postgres?currentSchema=books and for authors as jdbc:postgresql://localhost:5432/postgres?currentSchema=authors
in application.properties , set spring.flyway.enabled=false to disable default behavior
programmatically create two DataSource , one for books and one for authors
programmatically create two FlywayDataSource , one for books and one for authors
programmatically create two EntityManagerFactory , one for books and one for authors
for books , place the migration SQLs files in dbmigrationbooks
for authors , place the migration SQLs files in dbmigrationauthors

How To JOIN FETCH an @ElementCollection

Description: This application is an example applying JOIN FETCH to fetch an @ElementCollection .

Pontos principais:

by default, @ElementCollection is loaded lazy, keep it lazy
use JOIN FETCH in the repository

How To Map An Entity To a Query ( @Subselect ) in a Spring Boot Application

Note: Consider using @Subselect only if using DTO, DTO and extra queries, or map a database view to an entity is not a solution.

Description: This application is an example of mapping an entity to a query via Hibernate, @Subselect . Mainly, we have two entities in a bidirectional one-to-many association. An Author has wrote several Book . The idea is to write a read-only query to fetch from Author only some fields (eg, DTO), but to have the posibility to call getBooks() and fetch the Book in a lazy manner as well. As you know, a classic DTO cannot be used, since such DTO is not managed and we cannot navigate the associations (don't support any managed associations to other entities). Via Hibernate @Subselect we can map a read-only and immutable entity to a query. This time, we can lazy navigate the associations.

Pontos principais:

define a new entity that contains only the needed fields from the Author (including association to Book )
for these fields, define only getters
mark the entity as @Immutable since no write operations are allowed
flush pending state transitions for the used entities by @Synchronize
use @Subselect to write the needed query, map an entity to an SQL query

How To Use Hibernate Soft Deletes In A Spring Boot Application

Description: This application is an example of using Hibernate soft deletes in a Spring Boot application.

Pontos principais:

define an abstract class BaseEntity with a field named deleted
the entities (eg, Author and Book entities) that should take advantage of soft deletes should extend BaseEntity
these entities should be marked with Hibernate, @Where annotation like this: @Where(clause = "deleted = false")
these entities should be marked with Hibernate, @SQLDelete annotation to trigger UPDATE SQLs in place of DELETE SQLs, as follows: @SQLDelete(sql = "UPDATE author SET deleted = true WHERE id = ?")
for fetching all entities including those marked as deleted or for fetching only the entities marked as deleted we need to rely on SQL native queries

Exemplo de saída:

How To Programmatically Customize HikariCP Settings Via DataSourceBuilder

If you use the spring-boot-starter-jdbc or spring-boot-starter-data-jpa "starters", you automatically get a dependency to HikariCP

Note: The best way to tune the connection pool parameters consist in using Flexy Pool by Vlad Mihalcea. Via Flexy Pool you can find the optim settings that sustain high-performance of your connection pool.

Description: This is a kickoff application that set up HikariCP via DataSourceBuilder . The jdbcUrl is set up for a MySQL database. For testing purposes, the application uses an ExecutorService for simulating concurrent users. Check the HickariCP report revealing the connection pool status.

Pontos principais:

write a @Bean that returns the DataSource programmatically

How To Setup Spring Data JPA Auditing

Description: Auditing is useful for maintaining history records. This can later help us in tracking user activities.

Pontos principais:

create an abstract base entity (eg, BaseEntity ) and annotate it with @MappedSuperclass and @EntityListeners({AuditingEntityListener.class})
in this base entity, add the following fields that will be automatically persisted:
- @CreatedDate protected LocalDateTime created;
- @LastModifiedDate protected LocalDateTime lastModified;
- @CreatedBy protected U createdBy;
- @LastModifiedBy protected U lastModifiedBy;
enable auditing via @EnableJpaAuditing(auditorAwareRef = "auditorAware")
provide an implementation for AuditorAware (this is needed for persisting the user that performed the modification; use Spring Security to return the currently logged-in user)
expose this implementation via @Bean
entites that should be audited should extend the base entity
store the date-time in database in UTC

Hibernate Envers Auditing ( spring.jpa.hibernate.ddl-auto=create )

Description: Auditing is useful for maintaining history records. This can later help us in tracking user activities.

Pontos principais:

each entity that should be audited should be annotated with @Audited
optionally, annotate entities with @AuditTable to rename the table used for auditing
rely on ValidityAuditStrategy for fast database reads, but slower writes (slower than the default DefaultAuditStrategy )

Attributes Lazy Loading Via Subentities

Description: By default, the attributes of an entity are loaded eager (all at once). This application is an alternative to How To Use Hibernate Attribute Lazy Loading from here. This application uses a base class to isolate the attributes that should be loaded eagerly and subentities (entities that extends the base class) for isolating the attributes that should be loaded on demand.

Pontos principais:

create the base class (this is not an entity), BaseAuthor , and annotate it with @MappedSuperclass
create AuthorShallow subentity of BaseAuthor and don't add any attribute in it (this will inherit the attributes from the superclass)
create AuthorDeep subentity of BaseAuthor and add to it the attributes that should be loaded on demand (eg, avatar )
map both subentities to the same table via @Table(name = "author")
provide the typical repositories, AuthorShallowRepository and AuthorDeepRepository

Run the following requests (via BookstoreController):

fetch all authors shallow (without avatars): localhost:8080/authors/shallow
fetch all authors deep (with avatars): localhost:8080/authors/deep

Check as well:

Attribute Lazy Loading (basic)
Default Values For Lazy Loaded Attributes
Attribute Lazy Loading And Jackson Serialization

DTO Via Constructor And Spring Data Query Builder Mechanism

Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on constructor and Spring Data Query Builder Mechanism.

Pontos principais:

write a proper constructor in the DTO class
rely on Spring Data Query Builder Mechanism to write the SQL
for using Spring Data Projections check this item

Veja também:
Dto Via Constructor Expression and JPQL

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Page The Result Set of a JOIN

Description: Using JOIN is very useful for fetching DTOs (data that is never modified, not in the current or subsequent requests). For example, consider two entities, Author and Book in a lazy-bidirectional @OneToMany association. And, we want to fetch a subset of columns from the parent table ( author ) and a subset of columns from the child table ( book ). This job is a perfect fit for JOIN which can pick up columns from different tables and build a raw result set. This way we fetch only the needed data. Moreover, we may want to serve the result set in pages (eg, via LIMIT ). This application contains several approaches for accomplishing this task with offset pagination.

Pontos principais:

pagination via Page (with SELECT COUNT and COUNT(*) OVER() window function)
pagination via Slice and List
pagination via DENSE_RANK() for avoiding the truncation of the result set (an author can be fetched with only a subset of his books)

LEFT JOIN FETCH

Veja também:

How To Avoid LazyInitializationException Via JOIN FETCH
JOIN VS. JOIN FETCH

Description: Let's assume that we have two entities engaged in a one-to-many (or many-to-many) lazy bidirectional (or unidirectional) relationship (eg, Author has more Book ). And, we want to trigger a single SELECT that fetches all Author and the corresponding Book . This is a job for JOIN FETCH which is converted behind the scene into a INNER JOIN . Being an INNER JOIN , the SQL will return only Author that have Book . If we want to return all Author , including those that doesn't have Book , then we can rely on LEFT JOIN FETCH . Similar, we can fetch all Book , including those with no registered Author . This can be done via LEFT JOIN FETCH or LEFT JOIN .

Pontos principais:

define two related entities (eg, Author and Book in a one-to-many lazy bidirectional relationship)
write a JPQL LEFT JOIN FETCH to fetch all authors and books (fetch authors even if they don't have registered books)
write a JPQL LEFT JOIN FETCH to fetch all books and authors (fetch books even if they don't have registered authors)

JOIN VS. JOIN FETCH

Veja também:

How To Avoid LazyInitializationException Via JOIN FETCH
LEFT JOIN FETCH

Description: This is an application meant to reveal the differences between JOIN and JOIN FETCH . The important thing to keep in mind is that, in case of LAZY fetching, JOIN will not be capable to initialize the associated collections along with their parent objects using a single SQL SELECT . On the other hand, JOIN FETCH is capable to accomplish this kind of task. But, don't underestimate JOIN , because JOIN is the proper choice when we need to combine/join the columns of two (or more) tables in the same query, but we don't need to initialize the associated collections on the returned entity (eg, very useful for fetching DTO).

Pontos principais:

define two related entities (eg, Author and Book in a one-to-many lazy-bidirectional relationship)
write a JPQL JOIN and JOIN FETCH to fetch an author including his books
write a JPQL JOIN to fetch a book (1)
write a JPQL JOIN to fetch a book including its author (2)
write a JOIN FETCH to fetch a book including its author

Notice that:

via JOIN , fetching Book of Author requires additional SELECT statements being prone to N+1 performance penalty
via JOIN (1), fetching Author of Book requires additional SELECT statements being prone to N+1 performance penalty
via JOIN (2), fetching Author of Book works exactly as JOIN FETCH (requires a single SELECT )
via JOIN FETCH , fetching each Author of a Book requires a single SELECT

Entity Inside Spring Projection

Description: If, for some reason, you need an entity in your Spring projection (DTO), then this application shows you how to do it via an example. In this case, there are two entities, Author and Book , involved in a lazy bidirectional one-to-many association (it can be other association as well, or even no materialized association). And, we want to fetch in a Spring projection the authors as entities, Author , and the title of the books.

Pontos principais:

define two related entities (eg, Author and Book in a one-to-many lazy bidirectional relationship)
define the proper Spring projection having public Author getAuthor() and public String getTitle()
write a JPQL to fetch data

Entity Inside Spring Projection (no association)

Description: If, for some reason, you need an entity in your Spring projection (DTO), then this application shows you how to do it via an example. In this case, there are two entities, Author and Book , that have no materialized association between them, but, they share the genre attribute. We use this attribute to join authors with books via JPQL. And, we want to fetch in a Spring projection the authors as entities, Author , and the title of the books.

Pontos principais:

define two unrelated entities (eg, Author and Book )
define the proper Spring projection having public Author getAuthor() and public String getTitle()
write a JPQL to fetch data

Avoid Entity In DTO Via Constructor Expression (no association)

Description: Let's assume that we have two entities, Author and Book . There is no materialized association between them, but, both entities shares an attribute named, genre . We want to use this attribute to join the tables corresponding to Author and Book , and fetch the result in a DTO. The result should contain the Author entity and only the title attribute from Book . Well, when you are in a scenario as here, it is strongly advisable to avoid fetching the DTO via constructor expression . This approach cannot fetch the data in a single SELECT , and is prone to N+1. Way better than this consists of using Spring projections, JPA Tuple or even Hibernate ResultTransformer . These approaches will fetch the data in a single SELECT . This application is a DON'T DO THIS example. Check the number of queries needed for fetching the data. In place, do it as here: Entity Inside Spring Projection (no association).

How To DTO an @ElementCollection

Description: This application is an example of fetching a DTO that includes attributes from an @ElementCollection .

Pontos principais:

by default, @ElementCollection is loaded lazy, keep it lazy
use a Spring projection and JOIN in the repository

Ordering The Set Of Associated Entities In @ManyToMany Association Via @OrderBy

Description: In case of @ManyToMany association, we always should rely on Set (not on List ) for mapping the collection of associated entities (entities of the other parent-side). Por que? Well, please see Prefer Set Instead of List in @ManyToMany Relationships. But, is well-known that HashSet doesn't have a predefined entry order of elements. If this is an issue then this application relies on @OrderBy which adds an ORDER BY clause in the SQL statement. The database will handle the ordering. Further, Hibernate will preserve the order via a LinkedHashSet .

This application uses two entities, Author and Book , involved in a lazy bidirectional many-to-many relationship. First, we fetch a Book by title. Further, we call getAuthors() to fetch the authors of this book. The fetched authors are ordered descending by name. The ordering is done by the database as a result of adding @OrderBy("name DESC") , and is preserved by Hibernate.

Pontos principais:

ask the database to handle ordering and Hibernate to preserve this order via @OrderBy
this works with HashSet , but doesn't provide consistency across all transition states (eg, transient state)
for consistency across the transient state as well, consider using explicitly LinkedHashSet instead of HashSet

Note: Alternatively, we can use @OrderColumn . This gets materialized in an additional column in the junction table. This is needed for maintaining a permanent ordering of the related data.

Versioned Optimistic Locking And Detached Entities Sample

Description: This is a sample application that shows how versioned ( @Version ) optimistic locking and detached entity works. Running the application will result in an optimistic locking specific exception (eg, the Spring Boot specific, OptimisticLockingFailureException ).

Pontos principais:

in a transaction, fetch an entity via findById(1L) ; commit transaction and close the Persistence Context
in a second transaction, fetch another entity via findById(1L) and update it; commit the transaction and close the Persistence Context
outside transactional context, update the detached entity (fetched in the first transaction)
in a third transaction, call save() and pass to it the detached entity; trying to merge ( EntityManager.merge() ) the entity will end up in an optimistic locking exception since the version of the detached and just loaded entity don't match

How To Simulate OptimisticLockException Shaped Via @Version

Note: Optimistic locking via @Version works for detached entities as well.

Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. So, running the application should end up with a Spring specific ObjectOptimisticLockingFailureException exception.

Pontos principais:

set up versioned optimistic locking mechanism
rely on two concurrent threads that call the same @Transactional method used for updating data

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Retry Transaction Via TransactionTemplate After OptimisticLockException Exception ( @Version )

Note: Optimistic locking via @Version works for detached entities as well.

Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. When such exception occurs, the application retry the corresponding transaction via db-util library developed by Vlad Mihalcea.

Pontos principais:

in pom.xml , add the db-util dependency
configure the OptimisticConcurrencyControlAspect bean
rely on TransactionTemplate

How To Simulate OptimisticLockException In Version-less Optimistic Locking

Note: Version-less optimistic locking doesn't work for detached entities (do not close the Persistence Context).

Pontos principais:

set up the version-less optimistic locking mechanism
rely on two concurrent threads that call the same a @Transactional method used for updating data

How To Retry Transaction Via TransactionTemplate After OptimisticLockException Shaped Via Hibernate Version-less Optimistic Locking Mechanism

Note: Version-less optimistic locking doesn't work for detached entities (do not close the Persistence Context).

Pontos principais:

for Maven, in pom.xml , add the db-util dependency
configure the OptimisticConcurrencyControlAspect bean
rely on TransactionTemplate

HTTP Long Conversation Via Versioned Optimistic Locking And Detached Entities In The HTTP Session

Description: This is a sample application that shows how to take advantage of versioned optimistic locking and detached entities in HTTP long conversations. The climax consists of storing the detached entities across multiple HTTP requests. Commonly, this can be accomplished via HTTP session.

Pontos principais:

prepare the entity via @Version
rely on @SessionAttributes for storing the detached entities

Sample output (check the message caused by optimistic locking exception):

Filter Association Via Hibernate @Where

Note: Rely on this approach only if you simply cannot use JOIN FETCH WHERE or @NamedEntityGraph .

Description: This application is a sample of using Hibernate @Where for filtering associations.

Pontos principais:

use @Where(clause = "condition to be met") in entity (check the Author entity)

Batch Inserts In Spring Boot Style

Description: Batch inserts (in MySQL) in Spring Boot style.

Pontos principais:

in application.properties set spring.jpa.properties.hibernate.jdbc.batch_size
in application.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)
in application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)
in application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)
in application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))
in case of using a parent-child relationship with cascade persist (eg one-to-many, many-to-many) then consider to set up spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering inserts
in entity, use the assigned generator since the Hibernate IDENTITY will cause insert batching to be disabled
if is not needed then ensure that Second Level Cache is disabled via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Exemplo de saída:

Offset Pagination - Trigger COUNT(*) OVER And Return Page<entity> Via Extra Column

Description: Typically, in offset pagination, there is one query needed for fetching the data and one for counting the total number of records. But, we can fetch this information in a single database rountrip via a SELECT COUNT subquery nested in the main SELECT . Even better, for databases vendors that support Window Functions there is a solution relying on COUNT(*) OVER() as in this application that uses this window function in a native query against MySQL 8. So, prefer this one instead of SELECT COUNT subquery.This application fetches data as Page<entity> via Spring Boot offset pagination, but, if the fetched data is read-only , then rely on Page<dto> as here.

Pontos principais:

write a repository that extends PagingAndSortingRepository
in the entity, add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)
fetch data via a native query (that includes counting) into a List<entity>
use the fetched List<entity> and Pageable to create a Page<entity>

Offset Pagination - Trigger SELECT COUNT Subquery And Return List<entity> Via Extra Column

Description: This application fetches data as List<entity> via Spring Boot offset pagination. The SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT . Therefore, there will be a single database roundtrip instead of two (typically, one query is needed for fetching the data and one for counting the total number of records).

Pontos principais:

write a repository that extends PagingAndSortingRepository
in the entity , add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)
fetch data via a native query (that includes SELECT COUNT subquery) into a List<entity>

Offset Pagination - Trigger SELECT COUNT Subquery And Return List<projection> That Maps Entities And The Total Number Of Records Via Projection

Description: This application fetches data as List<projection> via Spring Boot offset pagination. The projection maps the entity and the total number of records. This information is fetched in a single database rountrip because the SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT . Therefore, there will be a single database roundtrip instead of two (typically, there is one query needed for fetching the data and one for counting the total number of records). Use this approch only if the fetched data is not read-only . Otherwise, prefer List<dto> as here.

Pontos principais:

write a Spring projection that maps the entity and the total number of records
write a repository that extends PagingAndSortingRepository
fetch data via a JPQL query (that includes SELECT COUNT subquery) into a List<projection>

Offset Pagination - Trigger COUNT(*) OVER And Return List<entity> Via Extra Column

Description: Typically, in offset pagination, there is one query needed for fetching the data and one for counting the total number of records. But, we can fetch this information in a single database rountrip via a SELECT COUNT subquery nested in the main SELECT . Even better, for databases vendors that support Window Functions there is a solution relying on COUNT(*) OVER() as in this application that uses this window function in a native query against MySQL 8. So, prefer this one instead of SELECT COUNT subquery.This application fetches data as List<entity> via Spring Boot offset pagination, but, if the fetched data is read-only , then rely on List<dto> as here.

Pontos principais:

write a repository that extends PagingAndSortingRepository
in the entity , add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)
fetch data via a native query (that includes COUNT(*) OVER subquery) into a List<entity>

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

Offset Pagination - Trigger SELECT COUNT Subquery And Return Page<entity> Via Extra Column

Description: This application fetches data as Page<entity> via Spring Boot offset pagination. Use this only if the fetched data will be modified. Otherwise, fetch Page<dto> as here. The SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT . Therefore, there will be a single database roundtrip instead of two (typically, there is one query needed for fetching the data and one for counting the total number of records).

Pontos principais:

write a repository that extends PagingAndSortingRepository
in the entity, add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)
fetch data via a native query (that includes counting) into a List<entity>
use the fetched List<entity> and Pageable to create a Page<entity>

Offset Pagination - Trigger SELECT COUNT Subquery And Return Page<projection> That Maps Entities And The Total Number Of Records Via Projection

Description: This application fetches data as Page<projection> via Spring Boot offset pagination. The projection maps the entity and the total number of records. This information is fetched in a single database rountrip because the SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT .

Pontos principais:

define a Spring projection that maps the entity and the total number of records
write a repository that extends PagingAndSortingRepository
fetch data via a JPQL query into a List<projection>
use the fetched List<projection> and Pageable to create a Page<projection>

Offset Pagination - Trigger COUNT(*) OVER And Return Page<dto>

Pontos principais:

create a Spring projection (DTO) to contains getters only for the columns that should be fetched
write a repository that extends PagingAndSortingRepository
fetch data via a native query (that includes counting) into a List<dto>
use the fetched List<dto> and Pageable to create a Page<dto>

Exemplo:

How To Fetch Slice<entity> / Slice<dto> Via fetchAll / fetchAllDto

Story : Spring Boot provides an offset based built-in paging mechanism that returns a Page or Slice . Each of these APIs represents a page of data and some metadata. The main difference is that Page contains the total number of records, while Slice can only tell if there is another page available. For Page , Spring Boot provides a findAll() method capable to take as arguments a Pageable and/or a Specification or Example . In order to create a Page that contains the total number of records, this method triggers an SELECT COUNT extra-query next to the query used to fetch the data of the current page . This can be a performance penalty since the SELECT COUNT query is triggered every time we request a page. In order to avoid this extra-query, Spring Boot provides a more relaxed API, the Slice API. Using Slice instead of Page removes the need of this extra SELECT COUNT query and returns the page (records) and some metadata without the total number of records. So, while Slice doesn't know the total number of records, it still can tell if there is another page available after the current one or this is the last page. The problem is that Slice work fine for queries containing the SQL, WHERE clause (including those that uses the query builder mechanism built into Spring Data), but it doesn't work for findAll() . This method will still return a Page instead of Slice therefore the SELECT COUNT query is triggered for Slice<T> findAll(...); .

Workaround: The trick is to simply define a method named fetchAll() that uses JPQL and Pageable to return Slice<entity> , and a method named fetchAllDto() that uses JPQL and Pageable as well to return Slice<dto> . So, avoid naming the method findAll() .

Exemplo de uso:
public Slice<Author> fetchNextSlice(int page, int size) {
return authorRepository.fetchAll(PageRequest.of(page, size, new Sort(Sort.Direction.ASC, "age")));
}

public Slice<AuthorDto> fetchNextSliceDto(int page, int size) {
return authorRepository.fetchAllDto(PageRequest.of(page, size, new Sort(Sort.Direction.ASC, "age")));
}

How To Use Spring Projections(DTOs) And Inclusive Full Joins (MySQL)

Description: This application is a proof of concept for using Spring Projections(DTO) and inclusive full joins written in native SQL (for MySQL).

Pontos principais:

define two entities (eg, Author and Book in a lazy bidirectional @OneToMany relationship)
populate the database with some test data (eg, check the file resources/data-mysql.sql )
write interfaces (projections) that contains getters for the columns that should be fetched from the database (eg, check AuthorNameBookTitle.java )
write inclusive full joins queries using native SQL

How To Declare Immutable Entities And Store Them In Second Level Cache (eg, EhCache )

Description: This application is a sample of declaring an immutable entity. Moreover, the immutable entity will be stored in Second Level Cache via EhCache implementation.

Key points of declaring an immutable entity:

annotate the entity with @Immutable (org.hibernate.annotations.Immutable)
avoid any kind of associations
set hibernate.cache.use_reference_entries configuration to true

How To Programmatically Customize HikariCP Settings Via DataSourceBuilder

If you use the spring-boot-starter-jdbc or spring-boot-starter-data-jpa "starters", you automatically get a dependency to HikariCP

Pontos principais:

write a @Bean that returns the DataSource programmatically

Amostra de saída:

How To Use Hibernate @NaturalIdCache For Skipping The Entity Identifier Retrieval

Description: This is a SpringBoot - MySQL application that maps a natural business key using Hibernate @NaturalId . This implementation allows us to use @NaturalId as it was provided by Spring. Moreover, this application uses Second Level Cache ( EhCache ) and @NaturalIdCache for skipping the entity identifier retrieval from the database.

Pontos principais:

enable Second Level Cache ( EhCache )
annotate entity with @NaturalIdCache for caching natural ids
optionally, annotate entity with @Cache(usage = CacheConcurrencyStrategy.READ_WRITE, region = "Book") for caching entites as well

Output sample (for MySQL with IDENTITY generator, @NaturalIdCache and @Cache ):

How To Calculate Non-Persistent Property via JPA @PostLoad

Description: This application is an example of calculating a non-persistent property of an entity based on the persistent entity attributes. In this case, we will use JPA, @PostLoad .

Pontos principais:

annotate the non-persistent field and property with @Transient
define a method annotated with @PostLoad that calculates this non-persistent property based on the persistent entity attributes

How To Calculate Entity Persistent Property Via Hibernate @Generated

Description: This application is an example of calculating an entity persistent property at INSERT and/or UPDATE time via Hibernate, @Generated .

Pontos principais:

Calculate at INSERT time:

annotate the corresponding persistent field with @Generated(value = GenerationTime.INSERT)
annotate the corresponding persistent field with @Column(insertable = false)

Calculate at INSERT and UPDATE time:

annotate the corresponding persistent field with @Generated(value = GenerationTime.ALWAYS)
annotate the corresponding persistent field with @Column(insertable = false, updatable = false)

Further, apply:

Método 1:

if the database schema is generated via JPA annotations (not recommended) then use columnDefinition element of @Column to specify as an SQL query expression the formula for calculating the persistent property

Método 2:

if the database schema is not generated via JPA annotations (recommended way) then add the formula as part of schema in CREATE TABLE

Note: In production, you should not rely on columnDefinition . You should disable hibernate.ddl-auto (by omitting it) or set it to validate , and add the SQL query expression in CREATE TABLE (in this application, check the discount column in CREATE TABLE , file schema-sql.sql ). Nevertheless, not even schema-sql.sql is ok in production. The best way is to rely on Flyway or Liquibase.

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Calculate Non-Persistent Property via Hibernate @Formula

Description: This application is an example of calculating a non-persistent property of an entity based on the persistent entity attributes. In this case, we will use Hibernate, @Formula .

Pontos principais:

annotate the non-persistent property with @Transient
annotate the non-persistent field with @Formula
as the value of @Formula add the SQL query expression that calculates this non-persistent property based on the persistent entity attributes

How To Add created , createdBy , lastModified And lastModifiedBy In Entities Via Hibernate

Note: The same thing can be obtained via Spring Data JPA auditing as here.

Description: This application is an example of adding in an entity the fields, created , createdBy , lastModified and lastModifiedBy via Hibernate support. These fields will be automatically generated/populated.

Pontos principais:

write an abstract class (eg, BaseEntity ) annotated with @MappedSuperclass
in this abstract class, define a field named created and annotate it with the built-in @CreationTimestamp annotation
in this abstract class, define a field named lastModified and annotate it with the built-in @UpdateTimestamp annotation
in this abstract class, define a field named createdBy and annotate it with the @CreatedBy annotation
in this abstract class, define a field named lastModifiedBy and annotate it with the @ModifiedBy annotation
implement the @CreatedBy annotation via AnnotationValueGeneration
implement the @ModifiedBy annotation via AnnotationValueGeneration
every entity that want to take advantage of created , createdBy , lastModified and lastModifiedBy will extend the BaseEntity
store the date-time in UTC

Hibernate Envers Auditing ( schema-mysql.sql )

Description: Auditing is useful for maintaining history records. This can later help us in tracking user activities.

Pontos principais:

each entity that should be audited should be annotated with @Audited
optionally, annotate entities with @AuditTable to rename the table used for auditing
rely on ValidityAuditStrategy for fast database reads, but slower writes (slower than the default DefaultAuditStrategy )
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate for avoiding schema generated from JPA annotations
create schema-mysql.sql and provide the SQL statements needed by Hibernate Envers
if the schema is not automatically found, then point it via spring.jpa.properties.org.hibernate.envers.default_catalog for MySQL or spring.jpa.properties.org.hibernate.envers.default_schema for the rest

How To Programmatically Setup Flyway And MySQL DataSource

Description: This application is a kickoff for setting Flyway and MySQL DataSource programmatically.

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate
configure DataSource and Flyway programmatically

How To Migrate PostgreSQL Database Using Flyway - Use The Default Database postgres And Schema public

Description: This application is an example of migrating a PostgreSQL database via Flyway for the default database postgres and schema public .

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate
in application.properties , set the JDBC URL as follows: jdbc:postgresql://localhost:5432/postgres
each SQL file containing the schema update add it in classpath:db/migration
each SQL file name it as V1.1__Description.sql , V1.2__Description.sql , ...

How To Migrate Schema Using Flyway In PostgreSQL - Use The Default Database postgres And Schema Created Via spring.flyway.schemas

Description: This application is an example of migrating a schema ( bookstore ) created by Flyway via spring.flyway.schemas in the default postgres database. In this case, the entities should be annotated with @Table(schema = "bookstore") .

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate
in application.properties , set the JDBC URL as follows: jdbc:postgresql://localhost:5432/postgres
in application.properties , add spring.flyway.schemas=bookstore , where bookstore is the schema that should be created by Flyway in the postgres database (feel free to add your own database name)
each entity that should be stored in this database should be annotated with, @Table(schema = "bookstore")
each SQL file containing the schema update add it in classpath:db/migration
each SQL file name it as V1.1__Description.sql , V1.2__Description.sql , ...

How To Programmatically Setup Flyway And PostgreSQL DataSource

Description: This application is a kickoff for setting Flyway and PostgreSQL DataSource programmatically.

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate
configure DataSource and Flyway programmatically

How To Auto-Create And Migrate Two Databases In MySQL Using Flyway

Description: This application is an example of auto-creating and migrating two databases in MySQL using Flyway. In addition, each data source uses its own HikariCP connection pool. In case of MySQL, where a database is the same thing with schema, we create two databases, authorsdb and booksdb .

Pontos principais:

for Maven, in pom.xml , add the Flyway dependency
remove (disable) spring.jpa.hibernate.ddl-auto or set it to validate
in application.properties , configure the JDBC URL for booksdb as jdbc:mysql://localhost:3306/booksdb?createDatabaseIfNotExist=true and for authorsdb as jdbc:mysql://localhost:3306/authorsdb?createDatabaseIfNotExist=true
in application.properties , set spring.flyway.enabled=false to disable default behavior
programmatically create two DataSource , one for booksdb and one for authorsdb
programmatically create two FlywayDataSource , one for booksdb and one for authorsdb
programmatically create two EntityManagerFactory , one for booksdb and one for authorsdb
for booksdb , place the migration SQLs files in dbmigrationbooksdb
for authorsdb , place the migration SQLs files in dbmigrationauthorsdb

Hibernate hi/lo Algorithm And External Systems Issue

Description: This is a Spring Boot sample that exemplifies how the hi/lo algorithm may cause issues when the database is used by external systems as well. Such systems can safely generate non-duplicated identifiers (eg, for inserting new records) only if they know about the hi/lo presence and its internal work. So, better rely on pooled or pooled-lo algorithm which doesn't cause such issues.

Pontos principais:

use the SEQUENCE generator type (eg, in PostgreSQL)
configure the hi/lo algorithm as in Author.java entity
insert a few records via hi/lo
insert a few records natively (this acts as an external system that relies on NEXTVAL('hilo_sequence') and is not aware of hi/lo presence and/or behavior)

Output sample: Running this application should result in the following error:
ERROR: duplicate key value violates unique constraint "author_pkey"
Detail: Key (id)=(2) already exists.

How To Generate Sequences Of Identifiers Via Hibernate pooled Algorithm

Note: Rely on pooled-lo or pooled especially if, beside your application, external systems needs to insert rows in your tables. Don't rely on hi/lo since, in such cases, it may cause errors resulted from generating duplicated identifiers.

Description: This is a Spring Boot example of using the pooled algorithm. The pooled is an optimization of hi/lo . This algorithm fetched from the database the current sequence value as the top boundary identifier (the current sequence value is computed as the previous sequence value + increment_size ). This way, the application will use in-memory identifiers generated between the previous top boundary exclusive (aka, lowest boundary) and the current top boundary inclusive.

Pontos principais:

use the SEQUENCE generator type (eg, in PostgreSQL)
configure the pooled algorithm as in Author.java entity
insert a few records via pooled
insert a few records natively (this acts as an external system that relies on NEXTVAL('hilo_sequence') and is not aware of pooled presence and/or behavior)

Conclusion: In contrast to the classical hi/lo algorithm, the Hibernate pooled algorithm doesn't cause issues to external systems that wants to interact with our tables. In other words, external systems can concurrently insert rows in the tables relying on pooled algorithm. Nevertheless, old versions of Hibernate can raise exceptions caused by INSERT statements triggered by external systems that uses the lowest boundary as identifier. This is a good reason to update to Hibernate latest versions (eg, Hibernate 5.x), which have fixed this issue.

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Generate Sequences Of Identifiers Via Hibernate pooled-lo Algorithm

Description: This is a Spring Boot example of using the pooled-lo algorithm. The pooled-lo is an optimization of hi/lo similar with pooled . Only that, the strategy of this algorithm fetches from the database the current sequence value and use it as the in-memory lowest boundary identifier. The number of in-memory generated identifiers is equal to increment_size .

Pontos principais:

use the SEQUENCE generator type (eg, in PostgreSQL)
configure the pooled-lo algorithm as in Author.java entity
insert a few records via pooled-lo
insert a few records natively (this acts as an external system that relies on NEXTVAL('hilo_sequence') and is not aware of pooled-lo presence and/or behavior)

Fetching Associations In Batches Via @BatchSize

Description: This application uses Hibernate specific @BatchSize at class/entity-level and collection-level. Consider Author and Book entities invovled in a bidirectional-lazy @OneToMany association.

First use case fetches all Author entities via a SELECT query. Further, calling the getBooks() method of the first Author entity will trigger another SELECT query that initializes the collections of the first three Author entities returned by the previous SELECT query. This is the effect of @BatchSize at Author 's collection-level.
Second use case fetches all Book entities via a SELECT query. Further, calling the getAuthor() method of the first Book entity will trigger another SELECT query that initializes the authors of the first three Book entities returned by the previous SELECT query. This is the effect of @BatchSize at Author class-level.

Note: Fetching associated collections in the same query with their parent can be done via JOIN FETCH or entity graphs as well. Fetching children with their parents in the same query can be done via JOIN FETCH , entity graphs and JOIN as well.

Pontos principais:

Author and Book are in a lazy relationship (eg, @OneToMany bidirectional relationship)
Author entity is annotated with @BatchSize(size = 3)
Author 's collection is annotated with @BatchSize(size = 3)

How To Use Entity Graphs ( @NamedEntityGraph ) In Spring Boot

Note: In a nutshell, entity graphs (aka, fetch plans ) is a feature introduced in JPA 2.1 that help us to improve the performance of loading entities. Mainly, we specify the entity's related associations and basic fields that should be loaded in a single SELECT statement. We can define multiple entity graphs for the same entity and chain any number of entities and even use sub-graphs to create complex fetch plans . To override the current FetchType semantics there are properties that can be set:

Fetch Graph (default), javax.persistence.fetchgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated as FetchType.LAZY regardless of the default/explicit FetchType .

Load Graph , javax.persistence.loadgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated according to their specified or default FetchType .

Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. . More details here.

Description: This is a sample application of using entity graphs in Spring Boot.

Pontos principais:

define two entities, Author and Book , involved in a lazy bidirectional @OneToMany association
in Author entity use the @NamedEntityGraph to define the entity graph (eg, load in a single SELECT the authors and the associatated books)
in AuthorRepositry rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous step

How To Use Entity Sub-graphs In Spring Boot

Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. . More details here.

Description: This is a sample application of using entity sub-graphs in Spring Boot. There is one example based on @NamedSubgraph and one based on the dot notation (.) in an ad-hoc entity graph .

Pontos principais:

define three entities, Author , Book and Publisher ( Author and Book are involved in a lazy bidirectional @OneToMany relationship, Book and Publisher are also involved in a lazy bidirectional @OneToMany relationship; between Author and Publisher there is no relationship)

Using @NamedSubgraph

in Author entity define an entity graph via @NamedEntityGraph ; load the authors and the associatated books and use @NamedSubgraph to define a sub-graph for loading the publishers associated with these books
in AuthorRepository rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous step

Using the dot notation (.)

in PublisherRepository define an ad-hoc entity graph that fetches all publishers with associated books, and further, the authors associated with these books (eg, @EntityGraph(attributePaths = {"books.author"}) .

How To Define Ad-Hoc Entity Graphs In Spring Boot

Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. . More details here.

Description: This is a sample application of defining ad-hoc entity graphs in Spring Boot.

Pontos principais:

define two entities, Author and Book , involved in a lazy bidirectional @OneToMany relationship
the entity graph should load in a single SELECT the authors and the associatated books
in AuthorRepository rely on Spring @EntityGraph(attributePaths = {"books"}) annotation to indicate the ad-hoc entity graph

How To Use Entity Graphs For @Basic Attributes In Hibernate And Spring Boot

Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. In other words, by default, attributes are annotated with @Basic which rely on the default fetch policy. The default fetch policy is FetchType.EAGER . These attributes are also loaded in case of fetch graph even if they are not explicitly specified via @NamedAttributeNode . Annotating the basic attributes that should not be fetched with @Basic(fetch = FetchType.LAZY) it is not enough. Both, fetch graph and load graph will ignore these settings as long as we don't add bytecode enhancement as well.

The main drawback consists of the fact the these basic attributes are fetched LAZY by all other queries (eg, findById() ) not only by the queries using the entity graph, and most probably, you will not want this behavior.

Description: This is a sample application of using entity graphs with @Basic attributes in Spring Boot.

Pontos principais:

define two entities, Author and Book , involved in a lazy bidirectional @OneToMany association
in Author entity use the @NamedEntityGraph to define the entity graph (eg, load the authors names (only the name basic attribute; ignore the rest) and the associatated books)
add bytecode enhancement
annotate the basic attributes that should be ignored by the entity graph with @Basic(fetch = FetchType.LAZY)
in AuthorRepository rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous step

How To Implement Soft Deletes Via SoftDeleteRepository In Spring Boot Application

Note: Spring Data built-in support for soft deletes is discussed in DATAJPA-307.

Description: This application is an example of implementing soft deletes in Spring Data style via a repository named, SoftDeleteRepository .

Pontos principais:

define an abstract class, BaseEntity , annotated with @MappedSuperclass
in BaseEntity define a flag-field named deleted (default this field to false or in other words, not deleted)
every entity that wants to take advantage of soft deletes should extend the BaseEntity classs
write a @NoRepositoryBean named SoftDeleteRepository and extend JpaRepository
override and implement the needed methods that provide the logic for soft deletes (check out the source code)
repositories of entities should extend SoftDeleteRepository

Exemplo de saída:

How To Implement Concurrent Table Based Queue Via SKIP_LOCKED In MySQL 8

Description: This application is an example of how to implement concurrent table based queue via SKIP_LOCKED in MySQL 8. SKIP_LOCKED can skip over locks achieved by other concurrent transactions, therefore is a great choice for implementing job queues. In this application, we run two concurrent transactions. The first transaction will lock the records with ids 1, 2 and 3. The second transaction will skip the records with ids 1, 2 and 3 and will lock the records with ids 4, 5 and 6.

Pontos principais:

define an entity that acts as a job queue (eg, see the Book entity)
in BookRepository setup @Lock(LockModeType.PESSIMISTIC_WRITE)
in BookRepository use @QueryHint to setup javax.persistence.lock.timeout to SKIP_LOCKED
rely on org.hibernate.dialect.MySQL8Dialect dialect
run two concurrent transactions to see the effect of SKIP_LOCKED

How To Implement Concurrent Table Based Queue Via SKIP_LOCKED In PostgreSQL

Description: This application is an example of how to implement concurrent table based queue via SKIP_LOCKED in PostgreSQL. SKIP_LOCKED can skip over locks achieved by other concurrent transactions, therefore is a great choice for implementing job queues. In this application, we run two concurrent transactions. The first transaction will lock the records with ids 1, 2 and 3. The second transaction will skip the records with ids 1, 2 and 3 and will lock the records with ids 4, 5 and 6.

Pontos principais:

define an entity that acts as a job queue (eg, see the Book entity)
in BookRepository setup @Lock(LockModeType.PESSIMISTIC_WRITE)
in BookRepository use @QueryHint to setup javax.persistence.lock.timeout to SKIP_LOCKED
rely on org.hibernate.dialect.PostgreSQL95Dialect dialect
run two concurrent transactions to see the effect of SKIP_LOCKED

JPA Inheritance - JOINED

Description: This application is a sample of JPA Join Table inheritance strategy ( JOINED )

Pontos principais:

this inheritance strategy can be employed via @Inheritance(strategy=InheritanceType.JOINED)
all the classes in an inheritance hierarchy (aka, subclasses) are represented via individual tables
by default, subclass-tables contains a primary key column that acts as a foreign key as well - this foreign key references the base class table primary key
customizing this foreign key can be done by annotating the subclasses with @PrimaryKeyJoinColumn

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

JPA Inheritance - TABLE_PER_CLASS

Description: This application is a sample of JPA Table-per-class inheritance strategy ( TABLE_PER_CLASS )

Pontos principais:

this inheritance strategy doesn't allow the usage of the IDENTITY generator
this inheritance strategy can be employed via @Inheritance(strategy=InheritanceType.TABLE_PER_CLASS)
all the classes in an inheritance hierarchy (aka, subclasses) are represented via individual tables
each subclass-table stores the columns inherited from the superclass-table ( base class )

JPA Inheritance - @MappedSuperclass

Description: This application is a sample of using the JPA @MappedSuperclass .

Pontos principais:

the base class is not an entity, it can be abstract , and is annotated with @MappedSuperclass
subclasses of the base class are mapped in tables that contains columns for the inherited attributes and for their own attibutes
when the base class doens't need to be an entity, the @MappedSuperclass is the proper alternative to the JPA table-per-class inheritance strategy

How To Avoid Lazy Initialization Issues Caused By Disabling Open Session In View Via Hibernate5Module

Note: Hibernate5Module is an add-on module for Jackson JSON processor which handles Hibernate datatypes; and specifically aspects of lazy-loading .

Description: By default, in Spring Boot, the Open Session in View anti-pattern is enabled. Now, imagine a lazy relationship (eg, @OneToMany ) between two entities, Author and Book (an author has associated more books). Next, a REST controller endpoint fetches an Author without the associated Book . But, the View (more precisely, Jackson), forces the lazy loading of the associated Book as well. Since OSIV will supply the already opened Session , the Proxy initializations take place successfully.

Of course, the correct decision is to disable OSIV by setting it to false , but this will not stop Jackson to try to force the lazy initialization of the associated Book entities. Running the code again will result in an exception of type: Could not write JSON: failed to lazily initialize a collection of role: com.bookstore.entity.Author.books, could not initialize proxy - no Session; nested exception is com.fasterxml.jackson.databind.JsonMappingException: failed to lazily initialize a collection of role: com.bookstore.entity.Author.books, could not initialize proxy - no Session .

Well, among the Hibernate5Module features we have support for dealing with this aspect of lazy loading and eliminate this exception. Even if OSIV will continue to be enabled (not recommended), Jackson will not use the Session opened via OSIV.

Pontos principais:

for Maven, add the Hibernate5Module dependency in pom.xml
add a @Bean that returns an instance of Hibernate5Module
annotate the Author bean with @JsonInclude(Include.NON_EMPTY) to exclude null or what is considered empty from the returned JSON

Note: The presence of Hibernate5Module instructs Jackson to initialize the lazy associations with default values (eg, a lazy associated collection will be initialized with null ). Hibernate5Module doesn't work for lazy loaded attributes. For such case consider this item.

How To View Binding Params Via profileSQL=true In MySQL

Description: View the prepared statement binding parameters via profileSQL=true in MySQL.

Pontos principais:

in application.properties append logger=Slf4JLogger&profileSQL=true to the JDBC URL (eg, jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true&logger=Slf4JLogger&profileSQL=true )

Amostra de saída:

How To Shuffle Small Result Sets

Description: This application is an example of shuffling small results sets. DO NOT USE this technique for large results sets, since is extremely expensive.

Pontos principais:

write a JPQL SELECT query and append to it ORDER BY RAND()
each RDBMS support a function similar to RAND() (eg, in PostgreSQL is random() )

The Best Way To Remove Parent And Child Entities Via Bulk Deletions

Description: Commonly, deleting a parent and the associated children via CascadeType.REMOVE and/or orphanRemoval=true involved several SQL statements (eg, each child is deleted in a dedicated DELETE statement). When the number of entities is significant, this is far from being efficient, therefore other approaches should be employed.

Consider Author and Book in a bidirectional-lazy @OneToMany association. This application exposes the best way to delete the parent(s) and the associated children in four scenarios listed below. These approaches relies on bulk deletions, therefore they are not useful if you want the deletions to take advantage of automatic optimistic locking mechanisms (eg, via @Version ):

Best way to delete author(s) and the associated books via bulk deletions when:

One Author is in Persistent Context, no Book
More Author are in the Persistent Context, no Book
One Author and the associated Book are in Persistent Context
No Author or Book is in Persistent Context

Note: The most efficient way to delete all entities via a bulk deletion can be done via the built-in deleteAllInBatch() .

How To Bulk Updates

Description: Bulk operations (updates and deletes) are faster than batching, can benefit from indexing, but they have three main drawbacks:

bulk updates/deletes may leave the Persistence Context in an outdated state (it's up to you to prevent this issue by flushing the Persistence Context before update/delete and close/clear it after the update/delete to avoid issues created by potentially unflushed or outdated entities)
bulk updates/deletes don't benefit of automatic optimistic locking mechanisms (eg, @Version is ignored), therefore the lost updates are not prevented (it is advisable to signal these updates by explicitly incrementing version (if any is present))
bulk deletes cannot take advantage of cascading removals ( CascadeType.REMOVE ) and orphanRemoval

This application provides examples of bulk updates for Author and Book entities (between Author and Book there is a bidirectional lazy @OneToMany association). Both, Author and Book , has a version field.

Why You Should Avoid Unidirectional @OneToMany And Prefer Bidirectional @OneToMany Relationship

Description: As a rule of thumb, unidirectional @OneToMany association is less efficient than the bidirectional @OneToMany or the unidirectional @ManyToOne associations. This application is a sample that exposes the DML statements generated for reads, writes and removal operations when the unidirectional @OneToMany mapping is used.

Pontos principais:

regular unidirectional @OneToMany is less efficient than bidirectional @OneToMany association
using @OrderColumn come with some optimizations for removal operations but is still less efficient than bidirectional @OneToMany association
using @JoinColumn eliminates the junction table but is still less efficient than bidirectional @OneToMany association
using Set instead of List or bidirectional @OneToMany with @JoinColumn relationship (eg, @ManyToOne @JoinColumn(name = "author_id", updatable = false, insertable = false) ) still performs worse than bidirectional @OneToMany association

How To Use Subqeries in JPQL WHERE / HAVING Clause

Description: This application is an example of using subqueries in JPQL WHERE clause (you can easily use it in HAVING clause as well).

Pontos principais:
Keep in mind that subqueries and joins queries may or may not be semantically equivalent (joins may returns duplicates that can be removed via DISTINCT ).

Even if the Execution Plan is specific to the database, historically speaking joins are faster than subqueries among different databases, but this is not a rule (eg, the amount of data may significantly influence the results). Of course, do not conclude that subqueries are just a replacement for joins that doesn't deserve attention. Tuning subqueries can increases their performance as well, but this is an SQL wide topic. So, benchmark! Benchmark! Benchmark!

As a rule of thumb, prefer subqueries only if you cannot use joins, or if you can prove that they are faster than the alternative joins.

How To Execute SQL Functions In WHERE Part Of JPQL Query And JPA 2.1

Note: Using SQL functions in SELECT part (not in WHERE part) of the query can be done as here.

Description: Starting with JPA 2.1, a JPQL query can call SQL functions in the WHERE part via function() . This application is an example of calling the MySQL, concat_ws function, but user defined (custom) functions can be used as well.

Pontos principais:

use JPA 2.1, function()

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

Calling Stored Procedure That Returns A Value

Description: This application is an example of calling a MySQL stored procedure that returns a value (eg, an Integer ).

Pontos principais:

rely on @NamedStoredProcedureQuery to shape the stored procedure in the entity
rely on @Procedure in repository

Calling Stored Procedure That Returns A Result Set (Entity And DTO)

Description: This application is an example of calling a MySQL stored procedure that returns a result set. The application fetches entities (eg, List<Author> ) and DTO (eg, List<AuthorDto> ).

Key points:

rely on EntiyManager since Spring Data @Procedure will not work

Calling Stored Procedure That Returns A Result Set Via Native Query

Description: This application is an example of calling a MySQL stored procedure that returns a result set (entity or DTO) via a native query.

Pontos principais:

rely on a native call as @Query(value = "{CALL FETCH_AUTHOR_BY_GENRE (:p_genre)}", nativeQuery = true)

Calling Stored Procedure That Returns A Result Set Via JdbcTemplate

Note: Most probably you'll like to process the result set via BeanPropertyRowMapper as here. This is less verbose than the approach used here. Nevertheless, this approach is useful to understand how the result set looks like.

Description: This application is an example of calling a MySQL stored procedure that returns a result set via JdbcTemplate .

Key points:

rely on JdbcTemplate and SimpleJdbcCall

How To Obtain Auto-Generated Keys

Description: This application is an example of retrieving the database auto-generated primary keys.

Key points:

JPA style, retrieve the auto-generated keys via getId()
JDBC style, retrieve the auto-generated keys via JdbcTemplate
JDBC style, retrieve the auto-generated keys via SimpleJdbcInsert

How To Unproxy A Proxy

Description: A Hibernate proxy can be useful when a child entity can be persisted with a reference to its parent ( @ManyToOne or @OneToOne association). In such cases, fetching the parent entity from the database (execute the SELECT statement) is a performance penalty and a pointless action. Hibernate can set the underlying foreign key value for an uninitialized proxy. This topic is discussed here.

A proxy can be unproxied via Hibernate.unproxy() . This method is available starting with Hibernate 5.2.10.

Key points:

fetch a proxy via JpaRepository#getOne()
unproxy the fetched proxy via Hibernate.unproxy()

How To Convert Boolean To Yes/No Via AttributeConverter

Description: This application is an example of converting a Boolean to Yes / No strings via AttributeConverter . This kind of conversions are needed when we deal with legacy databases that connot be changed. In this case, the legacy database stores the booleans as Yes / No .

Key points:

implement a custom converter via AttributeConverter

How Efficient Is Just @OManyToOne

Note: The @ManyToOne association maps exactly to the one-to-many table relationship. The underlying foreign key is under child-side control in unidirectional or bidirectional relationship.

Description: This application shows that using only @ManyToOne is quite efficient. On the other hand, using only @OneToMany is far away from being efficient. Always, prefer bidirectional @OneToMany or unidirectional @ManyToOne . Consider two entities, Author and Book in a unidirectional @ManyToOne relationship.

Key points:

Adding a new book is efficient
Fetching all books of an author is efficient via a JPQL
Pagination of books is efficient
Remove a book is efficient
Even if the fetched collection is not managed, dirty checking mechanism works as expected

How To Use JOIN FETCH And Pageable Pagination

Description: Trying to combine JOIN FETCH / LEFT JOIN FETCH and Pageable results in an exception of type org.hibernate.QueryException: query specified join fetching, but the owner of the fetched association was not present in the select list . This application is a sample of how to avoid this exception.

Key points:

use countQuery
use entity graph

Note: Fixing the above exception will lead to an warning of type HHH000104, firstResult / maxResults specified with collection fetch; applying in memory! . If this warning is a performance issue, and most probably it is, then follow by reading here.

How To Avoid HHH000104 And Use Pagination Of Parent-Child

Description: HHH000104 is a Hibernate warning that tell us that pagination of a result set is tacking place in memory. For example, consider the Author and Book entities in a lazy-bidirectional @OneToMany association and the following query:

@Transactional
@Query(value = "SELECT a FROM Author a LEFT JOIN FETCH a.books WHERE a.genre = ?1",
countQuery = "SELECT COUNT(a) FROM Author a WHERE a.genre = ?1")
Page<Author> fetchWithBooksByGenre(String genre, Pageable pageable);

Calling fetchWithBooksByGenre() works fine only that the following warning is signaled: HHH000104: firstResult / maxResults specified with collection fetch; applying in memory! Obviously, having pagination in memory cannot be good from performance perspective. This application implement a solution for moving pagination at database-level.

Key points:

use three or two JPQL queries for fetching Page of entities in read-write or read-only mode
use two JPQL queries for fetching Slice or List of entities in read-write or read-only mode

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

What @Transactional(readOnly=true) Actually Do

Description: This application is meant to reveal what is the difference between @Transactional(readOnly = false) and @Transactional(readOnly = true) . In a nuthsell, readOnly = false (default) fetches entites in read-write mode (managed). Before Spring 5.1, readOnly = true just set FlushType.MANUAL/NEVER , therefore the automatic dirty checking mechanism will not take action since there is no flush. In other words, Hibernate keep in the Persistent Context the fetched entities and the hydrated (loaded) state. By comparing the entity state with the hydrated state, the dirty checking mechanism can decide to trigger UPDATE statements in our behalf. But, the dirty checking mechanism take place at flush time, therefore, without a flush, the hydrated state is kept in Persistent Context for nothing, representing a performance penalty. Starting with Spring 5.1, the read-only mode is propagated to Hibernate, therefore the hydrated state is discarded immediately after loading the entities. Even if the read-only mode discards the hydrated state the entities are still loaded in the Persistent Context, therefore, for read-only data, relying on DTO (Spring projection) is better.

Pontos principais:

readOnly = false load data in read-write mode (managed)
readOnly = true discard the hydrated state (starting with Spring 5.1)

Get Transaction Id In MySQL

Description: This application is an example of getting the current database transaction id in MySQL. Only read-write database transactions gets an id in MySQL. Every database has a specific query for getting the transaction id. Here it is a list of these queries.

Key points:

rely on the following query, SELECT tx.trx_id FROM information_schema.innodb_trx tx WHERE tx.trx_mysql_thread_id = connection_id()

Inspect Persistent Context

Description: This application is a sample of inspecting the Persistent Context content via org.hibernate.engine.spi.PersistenceContext .

Key points:

get the current Persistent Context via Hibernate SharedSessionContractImplementor
rely on PersistenceContext API

How To Extract Tables Metadata

Description: This application is an example of using the Hibernate SPI, org.hibernate.integrator.spi.Integrator for extracting tables metadata.

Key points:

implement org.hibernate.integrator.spi.Integrator and override integrate() method to return metadata.getDatabase()
register this Integrator via LocalContainerEntityManagerFactoryBean

How To Map @ManyToOne Relationship To A SQL Query Via The Hibernate @JoinFormula

Description: This application is an example of mapping the JPA @ManyToOne relationship to a SQL query via the Hibernate @JoinFormula annotation. We start with two entities, Author and Book , involved in a unidirectional @ManyToOne relationship. Each book has a price. While we fetch a book by id (let's call it book A ), we want to fetch another book B of the same author whose price is the next smaller price in comparison with book A price.

Key points:

fetching the book B is done via @JoinFormula

How To Fetch Data From A MySQL Database View

Description: This application is an example of fetching a read-only MySQL database view in a JPA immutable entity.

Pontos principais:

the database view is available in data-mysql.sql file
the entity used to map the database view is GenreAndTitleView.java

How To Update/Insert/Delete Data From/In A MySQL Database View

Description: This application is an example of updating, inserting and deleting data in a MySQL database view. Every update/insert/delete will automatically update the contents of the underlying table(s).

Key points:

the database views are available in data-mysql.sql file
respect MySQL requirements for updatable and insertable database views

How To Prevent A MySQL Database View From Updating/Inserting Rows That Are Not Visible Through It Via WITH CHECK OPTION

Description: This application is an example of preventing inserts/updates of a MySQL view that are not visible through this view via WITH CHECK OPTION . In other words, whenever you insert or update a row of the base tables through a view, MySQL ensures that the this operation is conformed with the definition of the view.

Key points:

add WITH CHECK OPTION to the view
this application will throw an exception of type java.sql.SQLException: CHECK OPTION failed 'bookstoredb.author_anthology_view

How To Efficiently Assign A Database Temporary Sequence Of Values To Rows

Description: This application is an example of assigning a database temporary sequence of values to rows via the window function, ROW_NUMBER() . This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.

Key points:

commonly, you don't need to fetch in the result set the temporary sequence of values produced by ROW_NUMBER() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by ROW_NUMBER as well
write several native querys relying on ROW_NUMBER() window function

Amostra de saída:

How To Efficiently Finding Top N Rows Of Every Group

Description: This application is an example of finding top N rows of every group.

Key points:

write a native query relying on ROW_NUMBER() window function

Amostra de saída:

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Implement Pagination Via ROW_NUMBER() Window Function

Description: This application is an example of using ROW_NUMBER() (and COUNT(*) OVER() for counting all elements) window function to implement pagination.

Key points:

use a native query relying on ROW_NUMBER()
we don't return a page as Page or Slice , we return it as List , therefore Pageable is not used

Why the @Transactional annotation is being ignored

Description: This application is an example of fixing the case when @Transactional annotation is ignored. Most of the time, this annotation is ignored in the following scenarios:

@Transactional was added to a private , protected or package-protected method
@Transactional was added to a method defined in the same class where it is invoked

Pontos principais:

write a helper service and move the @Transactional methods there
ensure that these methods are declared as public
call @Transactional methods from other services

How To Generate Custom Sequence IDs

Description: This is a Spring Boot example of using the hi/lo algorithm and a custom implementation of SequenceStyleGenerator for generating custom sequence IDs (eg, A-0000000001 , A-0000000002 , ...).

Key points:

extend SequenceStyleGenerator and override the configure() and generate() methods
set this generator in entities

How To Map Clob And Blob To byte[] And String

Description: This application is an example of mapping Clob and Blob as byte[] and String .

Key points:

this is vey easy to use but the application doesn't take advantage of JDBC driver LOB-specific optimizations

How To Map To JDBC's LOB Locators Clob And Blob

Description: This application is an example of mapping to JDBC's LOB locators Clob and Blob .

Key points:

this takes advantage of JDBC driver LOB-specific optimizations

How To Fetch Certain Subclass From An SINGLE_TABLE Inheritance Hierarchy

Description: This application is a sample of fetching a certain subclass from a SINGLE_TABLE inheritance hierarchy. This is useful when the dedicated repository of the subclass doesn't automatically add in the WHERE clause a dtype based condition for fetching only the needed subclass.

Key points:

explicitly add in the WHERE clause a TYPE check

How To Define An Association That Reference @NaturalId

Description: This is a SpringBoot application that defines a @ManyToOne relationship that doesn't reference a primary key column. It references a Hibernate @NaturalId column.

Key points:

rely on @JoinColumn(referencedColumnName = "natural_id_column")

How To Implement Advanced Search Via Specification

Description: This application is an example of implementing an advanced search via Specification API. Mainly, you can give the search filters to a generic Specification and fetch the result set. Pagination is supported as well. You can chain expressions via logical AND and OR to create compound filters. Nevertheless, there is room for extensions to add brackets support (eg, (x AND y) OR (x AND z) ), more operations, conditions parser and so on and forth.

Key points:

write a generic Specification

How To Create Specification Query Fetch Joins

Description: This application contains two examples of how to define JOIN in Specification to emulate JPQL join-fetch operations.

Pontos principais:

the first approach trigger two SELECT statements and the pagination is done in memory (very bad!)
the second approach trigger three SELECT statements but the pagination is done in the database
in both approaches the JOIN is defined in a Specification implementation

DTO Via Spring Data Projections (Projection Interface In Repository Interface)

Note: You may also like to read the recipe, "How To Enrich DTO With Virtual Properties Via Spring Projections"

Description: Fetch only the needed data from the database via Spring Data Projections (DTO). The projection interface is defined as a static interface (can be non- static as well) in the repository interface.

Key points:

write an interface (projection) containing getters only for the columns that should be fetched from the database
write the proper query returning a List<projection>
if is applicable, limit the number of returned rows (eg, via LIMIT ) - here, we can use query builder mechanism built into Spring Data repository infrastructure

Note: Using projections is not limited to use query builder mechanism built into Spring Data repository infrastructure. We can fetch projections via JPQL or native queries as well. For example, in this application we use a JPQL.

Output example (select first 2 rows; select only "name" and "age"):

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Ensure/Validate That Only One Association Is Non-Null

Description: Consider an entity named Review . This entity defines three @ManyToOne relationships to Book , Article and Magazine . A review can be associated with either a book, a magazine or an article. To validate this constraint, we can rely on Bean Validation as in this application.

Key points:

rely on Bean Validation to validate that only one association is non- null
expose the constraint via a custom annotation ( @JustOneOfMany ) added at class-level to the Review entity
for preventing native query to break our constraint add the validation at database level as well (eg, in MySQL add a TRIGGER )

Quickest Mapping Of Java Enums

Description: This application uses EnumType.ORDINAL and EnumType.STRING for mapping Java enum type to database. As a rule of thumb, strive to keep the data types as small as possible (eg, for EnumType.ORDINAL use TINYINT/SMALLINT , while for EnumType.STRING use VARCHAR(max_needed_bytes) ). Relying on EnumType.ORDINAL should be more efficient but is less expressive than EnumType.STRING .

Key points:

strive for smallest data types (eg, for EnumType.ORDINAL set @Column(columnDefinition = "TINYINT") )

How To Map Java enum To Database Via AttributeConverter

Description: This application maps a Java enum via AttributeConverter . In other words, it maps the enum values HORROR , ANTHOLOGY and HISTORY to the integers 1 , 2 and 3 and viceversa. This allows us to set the column type as TINYINT/SMALLINT which is less space-consuming than VARCHAR(9) needed in this case.

Pontos principais:

define a custom AttributeConverter
annotate with @Converter the corresponding entity field

How To Map Java enum To PostgreSQL enum Type

Description: This application maps a Java enum type to PostgreSQL enum type.

Key points:

define a custom Hibernate EnumType
register this custom EnumType via package-info.java
annotate the corresponding entity field @Type

How To Map Java enum To PostgreSQL enum Type Via Hibernate Types Library

Description: This application maps a Java enum type to PostgreSQL enum type via Hibernate Types library.

Key points:

install Hibernate Types library via pom.xml
use @TypeDef to specify the needed type class
annotate the corresponding entity field with @Type

How To Handle JSON in MySQL

Description: Hibernate Types is a library of extra types not supported by Hibernate Core by default. This is a Spring Boot application that uses this library to persist JSON data (JSON Java Object ) in a MySQL json column and for querying JSON data from the MySQL json column to JSON Java Object . Updates are supported as well.

Key points:

for Maven, add Hibernate Types as a dependency in pom.xml
in entity use @TypeDef to map typeClass to JsonStringType

How To Handle JSON in PostgreSQL

Description: Hibernate Types is a library of extra types not supported by Hibernate Core by default. This is a Spring Boot application that uses this library to persist JSON data (JSON Java Object ) in a PostgreSQL json column and for querying JSON data from the PostgreSQL json column to JSON Java Object . Updates are supported as well.

Key points:

for Maven, add Hibernate Types as a dependency in pom.xml
in entity use @TypeDef to map typeClass to JsonBinaryType

How To Increment The Version Of The Locked Entity Even If This Entity Was Not Modified OPTIMISTIC_FORCE_INCREMENT

Description: This application is a sample of how OPTIMISTIC_FORCE_INCREMENT works in MySQL. This is useful when you want to increment the version of the locked entity even if this entity was not modified. Via OPTIMISTIC_FORCE_INCREMENT the version is updated (incremented) at the end of the currently running transaction.

Key points:

use a root entity, Chapter (which uses @Version )
several editors load a chapter and perform modifications mapped via the Modification entity
between Modification (child-side) and Chapter (parent-side) there is a lazy unidirectional @ManyToOne association
for each modification, Hibernate will trigger an INSERT statement against the modification table, therefore the chapter table will not be modified by editors
but, Chapter entity version is needed to ensure that modifications are applied sequentially (the author and editor are notified if a modificaton was added since the chapter copy was loaded)
the version is forcibly increased at each modification (this is materialized in an UPDATE triggered against the chapter table at the end of the currently running transaction)
set OPTIMISTIC_FORCE_INCREMENT in the corresponding repository
rely on two concurrent transactions to shape the scenario that will lead to an exception of type ObjectOptimisticLockingFailureException

How To Increment The Version Of The Locked Entity Even If This Entity Was Not Modified PESSIMISTIC_FORCE_INCREMENT

Description: This application is a sample of how PESSIMISTIC_FORCE_INCREMENT works in MySQL. This is useful when you want to increment the version of the locked entity even if this entity was not modified. Via PESSIMISTIC_FORCE_INCREMENT the version is updated (incremented) immediately (the entity version update is guaranteed to succeed immediately after acquiring the row-level lock). The incrementation takes place before the entity is returned to the data access layer.

Pontos principais:

use a root entity, Chapter (which uses @Version )
several editors load a chapter and perform modifications mapped via the Modification entity
between Modification (child-side) and Chapter (parent-side) there is a lazy unidirectional @ManyToOne association
for each modification, Hibernate will trigger an INSERT statement against the modification table, therefore the chapter table will not be modified by editors
but, Chapter entity version is needed to ensure that modifications are applied sequentially (each editor is notified if a modificaton was added since his chapter copy was loaded and he must re-load the chapter)
the version is forcibly increased at each modification (this is materialized in an UPDATE triggered against the chapter table immediately after aquiring the row-level lock)
set PESSIMISTIC_FORCE_INCREMENT in the corresponding repository
rely on two concurrent transactions to shape two scenarios: one that will lead to an exception of type OptimisticLockException and one that will lead to QueryTimeoutException

Note: Pay attention to the MySQL dialect: MySQL5Dialect (MyISAM) doesn't support row-level locking, MySQL5InnoDBDialect (InnoDB) acquires row-level lock via FOR UPDATE (timeout can be set), MySQL8Dialect (InnoDB) acquires row-level lock via FOR UPDATE NOWAIT .

How PESSIMISTIC_READ And PESSIMISTIC_WRITE Works In MySQL

Description: This application is an example of using PESSIMISTIC_READ and PESSIMISTIC_WRITE in MySQL. In a nutshell, each database system defines its own syntax for acquiring shared and exclusive locks and not all databases support both types of locks. Depending on Dialect , the syntax can vary for the same database as well (Hibernate relies on Dialect for chosing the proper syntax). In MySQL, MySQL5Dialect doesn't support locking, while InnoDB engine ( MySQL5InnoDBDialect and MySQL8Dialect ) supports shared and exclusive locks as expected.

Key points:

rely on @Lock(LockModeType.PESSIMISTIC_READ) and @Lock(LockModeType.PESSIMISTIC_WRITE) on query-level
for testing, use TransactionTemplate to trigger two concurrent transactions that read and write the same row

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How PESSIMISTIC_WRITE Works With UPDATE / INSERT And DELETE Operations

Description: This application is an example of triggering UPDATE , INSERT and DELETE operations in the context of PESSIMISTIC_WRITE locking against MySQL. While UPDATE and DELETE are blocked until the exclusive lock is released, INSERT depends on the transaction isolation level. Typically, even with exclusive locks, inserts are possible (eg, in PostgreSQL). In MySQL, for the default isolation level, REPEATABLE READ , inserts are prevented against a range of locked entries, but, if we switch to READ_COMMITTED , then MySQL acts as PostgreSQL as well.

Key points:

start Transaction A and trigger a SELECT with PESSIMISTIC_WRITE to acquire an exclusive lock
start a concurrent Transaction B that triggers an UPDATE , INSERT or DELETE on the rows locked by Transaction A
in case of UPDATE , DELETE and INSERT + REPEATABLE_READ , Transaction B is blocked until it timeouts or Transaction A releases the exclusive lock
in case of INSERT + READ_COMMITTED , Transaction B can insert in the range of rows locked by Transaction A even if Transaction A is holding an exclusive lock on this range

How To Check That Transaction Timeout And Rollback At Expiration Works As Expected

Note: Do not test transaction timeout via Thread.sleep() ! This is not working! Rely on two transactions and exclusive locks or even better rely on SQL sleep functions (eg, MySQL, SELECT SLEEP(n) seconds, PostgreSQL, SELECT PG_SLEEP(n) seconds). Most RDBMS supports a sleep function flavor.

Description: This application contains several approaches for setting a timeout period for a transaction or query. The timeout is signaled by a specific timeout exception (eg, .QueryTimeoutException ). After timeout, the transaction is rolled back. You can see this in the database (visually or query) and on log via a message of type: Initiating transaction rollback; Rolling back JPA transaction on EntityManager [SessionImpl(... <open>)] .

Key points:

set global transaction timeout via spring.transaction.default-timeout in seconds (see, application.properties )
set transaction timeout at method-level or class-level via @Transactional(timeout = n) in seconds
set query timeout via JPA javax.persistence.query.timeout hint in milliseconds
set query timeout via Hibrenate org.hibernate.timeout hint in seconds

Note: If you are using TransactionTemplate then the timeout can be set via TransactionTemplate.setTimeout(n) in seconds.

How To Define A Composite Primary Key Via @Embeddable

Description: This application is a proof of concept of how to define a composite key via @Embeddable and @EmbeddedId . This application uses two entities, Author and Book involved in a lazy bidirectional @OneToMany association. The identifier of Author is composed by name and age via AuthorId class. The identifier of Book is just a regular auto-generated numeric value.

Key points:

the composite key class (eg, AuthorId ) is public
the composite key class must implement Serializable
the composite key must define equals() and hashCode()
the composite key must define a no-arguments constructor

How To Define A Composite Primary Key Via @IdClass

Description: This application is a proof of concept of how to define a composite key via @IdClass . This application uses two entities, Author and Book involved in a lazy bidirectional @OneToMany association. The identifier of Author is composed by name and age via AuthorId class. The identifier of Book is just a typical auto-generated numeric value.

Key points:

the composite key class (eg, AuthorId ) is public
the composite key class must implement Serializable
the composite key must define equals() and hashCode()
the composite key must define a no-arguments constructor

Note : The @IdClass can be useful when we cannot modify the compsite key class. Otherwise, rely on @Embeddable .

How To Define A Relationship in an @Embeddable Composite Primary Key

Description: This application is a proof of concept of how to define a relationship in an @Embeddable composite key. The composite key is AuthorId and it belongs to the Author class.

Key points:

the composite key class (eg, AuthorId ) is public
the composite key class must implement Serializable
the composite key must define equals() and hashCode()
the composite key must define a no-arguments constructor

How To Load Multiple Entities By Id

Description: This is a SpringBoot application that loads multiple entities by id via a @Query based on the IN operator and via the Hibernate 5 MultiIdentifierLoadAccess interface.

Pontos principais:

for using the IN operator in a @Query simply add the query in the proper repository
for using Hibernate 5 MultiIdentifierLoadAccess in Spring Data style provide the proper implementation
among its advantages, the MultiIdentifierLoadAccess implementation allows us to load entities by multiple ids in batches and by inspecting or not the current Persistent Context (by default, the Persistent Context is not inspected to see if the entities are already loaded or not)

Fetching All Entity Attributes As Spring Projection (DTO)

Description: This application is a sample of fetching all attributes of an entity ( Author ) as a Spring projection (DTO). Commonly, a DTO contains a subset of attributes, but, sometimes we need to fetch the whole entity as a DTO. In such cases, we have to pay attention to the chosen approach. Choosing wisely can spare us from performance penalties.

Key points:

fetching the result set as a List<Object[]> or List<AuthorDto> via a JPQL of type SELECT a FROM Author a WILL fetch the result set as entities in Persistent Context as well - avoid this approach
fetching the result set as a List<Object[]> or List<AuthorDto> via a JPQL of type SELECT a.id AS id, a.name AS name, ... FROM Author a will NOT fetch the result set in Persistent Context - this is efficient
fetching the result set as a List<Object[]> or List<AuthorDto> via a native SQL of type SELECT id, name, age, ... FROM author will NOT fetch the result set in Persistent Context - but, this approach is pretty slow
fetching the result set as a List<Object[]> via Spring Data query builder mechanism WILL fetch the result set in Persistent Context - avoid this approach
fetching the result set as a List<AuthorDto> via Spring Data query builder mechanism will NOT fetch the result set in Persistent Context
fetching the result set as read-only entitites (eg, via the built-in findAll() method) should be considered after JPQL with explicit list of columns to be fetched and query builder mechanism

How To Efficiently Fetch Spring Projection Including @ManyToOne Or @OneToOne Associations

Description: This application fetches a Spring projection including the @ManyToOne association via different approaches. It can be easily adapted for @OneToOne association as well.

Key points:

fetching raw data is the fastest approach

Pay Attention To Spring Projections That Include Associated Collections

Description: This application inspect the Persistent Context content during fetching Spring projections that includes collections of associations. In this case, we focus on a @OneToMany association. Mainly, we want to fetch only some attributes from the parent-side and some attributes from the child-side.

Reusing Spring projection

Description: This application is a sample of reusing an interface-based Spring projection. This is useful to avoid defining multiple interface-based Spring projections in order to cover a range of queries that fetches different subsets of fields.

Key points:

define an interface-based Spring projection containing getters for the wider case
rely on class-level @JsonInclude(JsonInclude.Include.NON_DEFAULT) annotation to avoid serialization of default fields (eg, fields that are not available in the current projection and are null - these fields haven't been fetched in the current query)
this is useful to Jackson that will not serialize in the resulted JSON the missing fields (eg, null fields)

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

Dynamic Spring projection

Description: This application is a sample of using dynamic Spring projections.

Key points:

declare query-methods in a generic manner (eg, <T> List<T> findByGenre(String genre, Class<T> type); )

Batch Inserts Via EntityManager With Batch Per Transaction (MySQL)

Description: This application is a sample of batching inserts via EntityManager in MySQL. This way you can easily control the flush() and clear() cycles of the Persistence Context (1st Level Cache) inside the current transaction. This is not possible via Spring Boot, saveAll(Iterable<S> entities) , since this method executes a single flush per transaction. Another advantage is that you can call persist() instead of merge() - this is used behind the scene by the SpringBoot saveAll(Iterable<S> entities) and save(S entity) .

Moreover, this example commits the database transaction after each batch excecution. This way we avoid long-running transactions and, in case of a failure, we rollback only the failed batch and don't lose the previous batches. For each batch, the Persistent Context is flushed and cleared, therefore we maintain a thin Persistent Context. This way the code is not prone to memory errors and performance penalties caused by slow flushes.

Key points:

in application.properties set spring.jpa.properties.hibernate.jdbc.batch_size
in application.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)
in application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)
in application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)
in application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))
in case of using a parent-child relationship with cascade persist (eg one-to-many, many-to-many) then consider to set up spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering inserts
in entity, use the assigned generator since MySQL IDENTITY will cause insert batching to be disabled
in your DAO layer, flush and clear the Persistence Context from time to time (eg for each batch); this way you avoid to "overwhelm" the Persistence Context
in your DAO layer, commit the database transaction after each batch execution
if is not needed, then ensure that Second Level Cache is disabled via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Exemplo de saída:

How To JDBC Batch a Big JSON File To MySQL Via ForkJoinPool And HikariCP

Description: This is a Spring Boot application that reads a relatively big JSON file (200000+ lines) and inserts its content in MySQL via batching using ForkJoinPool , JdbcTemplate and HikariCP.

Key points:

using MySQL, json type
read the file content into a List
the list is halved and subtasks are created until the list size is small than the batch size (eg, by default smaller than 30)
for MySQL, in application.properties, you may want to attach to the JDBC URL the following:
- rewriteBatchedStatements=true -> this setting will force sending the batched statements in a single request;
- cachePrepStmts=true -> enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled
- useServerPrepStmts=true -> this way you switch to server-side prepared statements (may lead to signnificant performance boost); moreover, you avoid the PreparedStatement to be emulated at the JDBC Driver level;
- we use the following JDBC URL settings:
  ...?cachePrepStmts=true&useServerPrepStmts=true&rewriteBatchedStatements=true&createDatabaseIfNotExist=true
- Note: Older MySQL versions will not tolerate well to have toghether rewritting and server-side prepared statement activated. For being sure that these statements still valid please check the notes of the Connector/J that you are using
set the HikariCP to provide a number of database connections that ensure that the database achives a minimum context switching (eg, 2 * number of CPU cores)
this application uses StopWatch to measure the time needed to transfer the file into the database
in order to run the application you have to unzip the citylots.zip in the current location; this is the big JSON file collected from Internet;
if you want to see details about the batch process simply activate the DatasourceProxyBeanPostProcessor.java component by uncomment the line, // @Component ; This is needed because this application relies on DataSource-Proxy (for details, see the following item)

Batch Inserts In Spring Boot Style Via CompletableFuture

Description: This application is a sample of using CompletableFuture for batching inserts. This CompletableFuture uses an Executor that has the number of threads equal with the number of your computer cores. Usage is in Spring style.

How To Optimize Batch Inserts of Parent-Child Relationships And Batch Per Transaction (MySQL)

Description: Let's suppose that we have a one-to-many relationship between Author and Book entities. When we save an author, we save his books as well thanks to cascading all/persist. We want to create a bunch of authors with books and save them in the database (eg, a MySQL database) using the batch technique. By default, this will result in batching each author and the books per author (one batch for the author and one batch for the books, another batch for the author and another batch for the books, and so on). In order to batch authors and books, we need to order inserts as in this application.

Key points:

beside all setting specific to batching inserts in MySQL, we need to set up in application.properties the following property: spring.jpa.properties.hibernate.order_inserts=true
in your DAO layer, commit the database transaction after each batch execution

Example without ordered inserts:

Example with ordered inserts:

Batch Inserts In Spring Boot Style And Batch Per Transaction

Description: Batch inserts (in MySQL) in Spring Boot style. This example commits the database transaction after each batch excecution. This way we avoid long-running transactions and, in case of a failure, we rollback only the failed batch and don't lose the previous batches.

Pontos principais:

in application.properties set spring.jpa.properties.hibernate.jdbc.batch_size
in application.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)
in application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)
in application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)
in application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))
in case of using a parent-child relationship with cascade persist (eg one-to-many, many-to-many) then consider to set up spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering inserts
in entity, use the assigned generator since the Hibernate IDENTITY will cause insert batching to be disabled
in your DAO layer, commit the database transaction after each batch execution
if is not needed then ensure that Second Level Cache is disabled via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Exemplo de saída:

IN Clause Parameter Padding

Description: This application is an example of using Hibernate IN cluase parameter padding. This way we can reduce the number of Execution Plans. Mainly, Hibernate is padding parameters as follows:

for 3 and 4 parameters -> it uses 4 bind parameters (2^2)
for 5, 6, 7 and 8 parameters -> it uses 8 bind parameters (2^3)
for 9, 10, 11, 12, 13, 14, 15 and 16 parameters -> it uses 16 parameters (2^4)
...

Key points:

in application.properties set spring.jpa.properties.hibernate.query.in_clause_parameter_padding=true

DTO Via Spring Data Class-Based Projections

Description: Fetch only the needed data from the database via Spring Data Projections (DTO). In this case, via class-based projections.

Key points:

write an class (projection) containing a constructor, getters, setters, equals() and hashCode() only for the columns that should be fetched from the database
write the proper query returning a List<projection>
if it is applicable, limit the number of returned rows (eg, via LIMIT )
in this example, we can use query builder mechanism built into Spring Data repository infrastructure

Output example (select first 2 rows; select only "name" and "age"):

Session-Level Batching (Hibernate 5.2 or Higher) in MySQL

Description: Batch inserts via Hibernate session-level batching (Hibernate 5.2 or higher) in MySQL. This example commits the database transaction after each batch excecution. This way we avoid long-running transactions and, in case of a failure, we rollback only the failed batch and don't lose the previous batches. For each batch, the Persistent Context is flushed and cleared, therefore we maintain a thin Persistent Context. This way the code is not prone to memory errors and performance penalties caused by slow flushes.

Pontos principais:

in application.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)
in application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)
in application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)
in application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))
in case of using a parent-child relationship with cascade persist (eg one-to-many, many-to-many) then consider to set up spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering inserts
in entity, use the assigned generator since MySQL IDENTITY will cause insert batching to be disabled
the Hibernate Session is obtained by un-wrapping it via EntityManager#unwrap(Session.class)
the batching size is set via Session#setJdbcBatchSize(Integer size) and get via Session#getJdbcBatchSize()
in your DAO layer, commit the database transaction after each batch execution
if is not needed, then ensure that Second Level Cache is disabled via spring.jpa.properties.hibernate.cache.use_second_level_cache=false

Exemplo de saída:

Use Read-Only Entity Whenever You Plan To Propagate Entity Changes To The Database In A Future Persistent Context

Description: This application highlights the difference betweeen loading entities in read-write vs. read-only mode. If you plan to modify the entities in a future Persistent Context then fetch them as read-only in the current Persistent Context.

Key points:

in the current Persistent Context, fetch entities in read-only mode
modifiy the entities in the current Persistent Context or in detached state (the potential modifications done in the current Persistent Context will not be propagated to the database at flush time)
in a subsequent Persistent Context, merge the detached entity and propagate changes to the database

Note: If you never plan to modify the fetched result set then use DTO (eg, Spring projection), not read-only entities.

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Publish Domain Events From Aggregate Root

Note: Domain events should be used with extra-caution! The best practices for using them are revealed in my book, Spring Boot Persistence Best Practices.

Description: Starting with Spring Data Ingalls release publishing domain events by aggregate roots becomes easier. Entities managed by repositories are aggregate roots. In a Domain-Driven Design application, these aggregate roots usually publish domain events. Spring Data provides an annotation @DomainEvents you can use on a method of your aggregate root to make that publication as easy as possible. A method annotated with @DomainEvents is automatically invoked by Spring Data whenever an entity is saved using the right repository. Moreover, Spring Data provides the @AfterDomainEventsPublication annotation to indicate the method that should be automatically called for clearing events after publication. Spring Data Commons comes with a convenient template base class ( AbstractAggregateRoot ) to help to register domain events and is using the publication mechanism implied by @DomainEvents and @AfterDomainEventsPublication . The events are registered by calling the AbstractAggregateRoot.registerEvent() method. The registered domain events are published if we call one of the save methods (eg, save() ) of the Spring Data repository and cleared after publication.

This is a sample application that relies on AbstractAggregateRoot and its registerEvent() method. We have two entities, Book and BookReview involved in a lazy-bidirectional @OneToMany association. A new book review is saved in CHECK status and a CheckReviewEvent is published. This event handler is responsible to check the review grammar, content, etc and switch the review status from CHECK to ACCEPT or REJECT and propagate the new status to the database. So, this event is registered before saving the book review in CHECK status and is published automatically after we call the BookReviewRepository.save() method. After publication, the event is cleared.

Key points:

the entity (aggregate root) that publish events should extend AbstractAggregateRoot and provide a method for registering events
here, we register a single event ( CheckReviewEvent ), but more can be registered
event handling take place is CheckReviewEventHandler in an asynchronous manner via @Async

How To Use Hibernate Query Plan Cache

Description: This application is an example of testing the Hibernate Query Plan Cache (QPC). Hibernate QPC is enabled by default and, for entity queries (JPQL and Criteria API), the QPC has a size of 2048, while for native queries it has a size of 128. Pay attention to alter these values to accommodate all queries executed by your aplicativo. If the number of exectued queries is higher than the QPC size (especially for entity queries) then you will start to experiment performance penalties caused by entity compilation time added for each query execution.

In this application, you can adjust the QPC size in application.properties . Mainly, there are 2 JPQL queries and a QPC of size 2. Switching from size 2 to size 1 will cause the compilation of one JPQL query at each execution. Measuring the times for 5000 executions using a QPC of size 2, respectively 1 reveals the importance of QPC in terms of time.

Pontos principais:

for JPQL and Criteria API you can set the QPC via hibernate.query.plan_cache_max_size
for native queries you can set the QPC via hibernate.query.plan_parameter_metadata_max_size

How To Cache Entities And Query Results In Second Level Cache (EhCache)

Description: This is a SpringBoot application that enables Hibernate Second Level Cache and EhCache provider. It contains an example of caching entities and an example of caching a query result.

Key points:

enable Second Level Cache ( EhCache )
rely on @Cache
rely on JPA hint HINT_CACHEABLE

Spring Boot Caching Kickoff

Description: This is a SpringBoot application representing a kickoff application for Spring Boot caching and EhCache .

Key points:

setup EhCache
rely on Spring caching annotations

How To Fetch Entity Via SqlResultSetMapping And NamedNativeQuery

Note: If you want to rely on the {EntityName}.{RepositoryMethodName} naming convention for simply creating in the repository interface methods with the same name as of native named query then skip this application and check this one.

Description: This is a sample application of using SqlResultSetMapping , NamedNativeQuery and EntityResult for fetching single entity and multiple entities as List<Object[]> .

Key points:

use SqlResultSetMapping , NamedNativeQuery and EntityResult

How To Load Multiple Entities By Id Via Specification

Description: This is a SpringBoot application that loads multiple entities by id via a @Query based on the IN operator and via Specification .

Key points:

for using the IN operator in a @Query simply add the query in the proper repository
for using a Specification rely on javax.persistence.criteria.Root.in()

How To Fetch DTO Via A Custom ResultTransformer

Description: Fetching more read-only data than needed is prone to performance penalties. Using DTO allows us to extract only the needed data. Sometimes, we need to fetch a DTO made of a subset of properties (columns) from a parent-child association. For such cases, we can use SQL JOIN that can pick up the desired columns from the involved tables. But, JOIN returns an List<Object[]> and most probably you will need to represent it as a List<ParentDto> , where a ParentDto instance has a List<ChildDto> . For such cases, we can rely on a custom Hibernate ResultTransformer . This application is a sample of writing a custom ResultTransformer .

Key points:

implement the ResultTransformer interface

How To Efficiently Chunk A Java List

Description: Is a common scenario to have a big List and to need to chunk it in multiple smaller List of given size. For example, if we want to employee a concurrent batch implementation we need to give to each thread a sublist of items. Chunking a list can be done via Google Guava, Lists.partition(List list, int size) method or Apache Commons Collections, ListUtils.partition(List list, int size) method. But, it can be implemented in plain Java as well. This application exposes 6 ways to do it. The trade-off is between the speed of implementation and speed of execution. For example, while the implementation relying on grouping collector is not performing very well, it is quite simple and fast to write it.

Key points:

the fastest execution is provided by Chunk.java class which relies on the built-in List.subList() method

Time-performance trend graphic for chunking 500, 1_000_000, 10_000_000 and 20_000_000 items in lists of 5 items:

How To Implement Complex Data Integrity Constraints And Rules

Description: Consider the Book and Chapter entities. A book has a maximum accepted number of pages ( book_pages ) and the author should not exceed this number. When a chapter is ready for review, the author is submitting it. At this point, the publisher should check that the currently total number of pages doesn't exceed the allowed book_pages :

This kind of checks or constraints are easy to implement via database triggers. This application relies on a MySQL trigger to empower our complex contraint ( check_book_pages ).

Key points:

define a MySQL trigger that run after each insert (if you want to run it after each update as well then extract the trigger logic into a function and call it from two triggers - this is specific to MySQL, while is PostgreSQL we have AFTER INSERT OR AFTER UPDATE )

How To Check If A Transient Entity Exists In The Database Via Spring Query By Example (QBE)

Description: This application is an example of using Spring Data Query By Example (QBE) to check if a transient entity exists in the database. Consider the Book entity and a Spring controller that exposes an endpoint as: public String checkBook(@Validated @ModelAttribute Book book, ...) . Beside writting an explicit JPQL, we can rely on Spring Data Query Builder mechanism or, even better, on Query By Example (QBE) API. In this context, QBE API is quite useful if the entity has a significant number of attributes and:

for all attributes, we need a head-to-head comparison of each attribute value to the corresponding column value
for a subset of attributes, we need a head-to-head comparison of each attribute value to the corresponding column value
for a subset of attributes, we return true at first match between an attribute value and the corresponding column value
any other scenario

Key points:

the repository, BookRepository extends QueryByExampleExecutor
the application uses <S extends T> boolean exists(Example<S> exmpl) with the proper probe (an entity instance populated with the desired fields values)
moreover, the probe relies on ExampleMatcher which defines the details on how to match particular fields

Note: Do not conclude that Query By Example (QBE) defines only the exists() method. Check out all methods here.

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

Best Way To Use @Transactional

Description: This application is meant to highlight that the best place to use @Transactional for user defined query-methods is in repository interface, and afterwards, depending on situation, on service-methods level.

Key points:

this application is dissected in my book, Spring Boot Persistence Best Practices .

How To Use JPA JOINED Inheritance Strategy And Visitor Design Pattern

Description: This application is an example of using JPA JOINED inheritance strategy and Visitor pattern.

Key points:

this application allows us to define multiple visitors and apply the one that we want

How To Use JPA JOINED Inheritance Strategy And Strategy Design Pattern

Description: This application is an example of using JPA JOINED inheritance strategy and Strategy pattern.

Key points:

this application allows us to define multiple strategies and apply the one that we want

How Spring Transaction Propagation Work

Description: This folder holds several applications that shows how each Spring transaction propagation works.

Key points:

it is strongly recommended to follow these examples in the context of my book, Spring Boot Persistence Best Practices.

How To Use JPA GenerationType.AUTO And UUID Identifiers

Description: This application is an example of using the JPA GenerationType.AUTO for assigning automatically UUID identifiers.

Key points:

store UUID in a BINARY(16) column

How To Manually Assign UUID Identifiers

Description: This application is an example of manually assigning UUID identifiers.

Key points:

store UUID in a BINARY(16) column

How To Use Hibernate uuid2 For Generating UUID Identifiers

Description: This application is an example of using the Hibernate RFC 4122 compliant UUID generator, uuid2 .

Key points:

store UUID in a BINARY(16) column

How Hibernate Session-Level Repeatable Reads Works

Description: This Spring Boot application is a sample that reveals how Hibernate session-level repeatable reads works. Persistence Context guarantees session-level repeatable reads. Check out how it works.

Key points:

rely on two transactions implemented via TransactionTemplate

Note: For a detailed explanation of this application consider my book, Spring Boot Persistence Best Practices

Why To Avoid Hibernate-specific hibernate.enable_lazy_load_no_trans

Description: This application is an example of using Hibernate-specific hibernate.enable_lazy_load_no_trans . Check out the application log to see how transactions and database connections are used.

Key points:

always avoid Hibernate-specific hibernate.enable_lazy_load_no_trans

The Best Way To Clone Entities

Description: This application is an example of cloning entities. The best way to achieve this goal relies on copy-constructors. This way we can control what we copy. Here we use a bidirectional-lazy @ManyToMany association between Author and Book .

Key points:

clone an Author (only the genre ) and associate the corresponding books
clone an Author (only the genre ) and clone the books as well

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Include In The UPDATE Statement Only The Modified Columns Via Hibernate @DynamicUpdate

Description: This application is an example of using the Hibernate-specific, @DynamicUpdate . By default, even if we modify only a subset of columns, the triggered UPDATE statements will include all columns. By simply annotating the corresponding entity at class-level with @DynamicUpdate the generated UPDATE statement will include only the modified columns.

Key points:

pro: avoid updating unmodified indexes (useful for heavy indexing)
con: cannot reuse the same UPDATE for different subsets of columns via JDBC statements caching (each triggered UPDATE string will be cached and reused accordingly)

How To Log Spring Data JPA Repository Query-Method Execution Time

Description: This application is an example of logging execution time for a repository query-method.

Key points:

write an AOP component (see RepositoryProfiler )

How To Take Control Before/After Transaction Commits/Completes Via Callbacks

Description: This application is an example of using the TransactionSynchronizationAdapter for overriding beforeCommit() , beforeCompletion() , afterCommit() and afterCompletion() callbacks globally (application-level) and at method-level.

Key points:

application-level: write an AOP component (see TransactionProfiler )
method-level: use TransactionSynchronizationManager.registerSynchronization()

How To Fetch DTO Via SqlResultSetMapping And NamedNativeQuery Using {EntityName}.{RepositoryMethodName} Naming Convention

Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on SqlResultSetMapping , NamedNativeQuery and the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of native named query.

Key points:

use SqlResultSetMapping , NamedNativeQuery
for using Spring Data Projections check this item

How To Fetch Entity Via SqlResultSetMapping And NamedNativeQuery Using {EntityName}.{RepositoryMethodName} Naming Convention

Description: This is a sample application of using SqlResultSetMapping , NamedNativeQuery and EntityResult for fetching single entity and multiple entities as List<Object[]> . In this application we rely on the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of native named query.

Key points:

use SqlResultSetMapping , NamedNativeQuery and EntityResult

How To Use JPA Named Queries @NamedQuery And Spring Projection (DTO)

Description: This application is an example of combining JPA named queries @NamedQuery and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named query.

Key points:

define the named queries
define the proper Spring projection

How To Use JPA Named Native Queries @NamedNativeQuery And Spring Projection (DTO)

Description: This application is an example of combining JPA named native queries @NamedNativeQuery and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named native query.

Key points:

define the named native queries
define the proper Spring projection

How To Use JPA Named Queries Via a Properties File

Description: JPA named (native) queries are commonly written via @NamedQuery and @NamedNativeQuery annotations in entity classes. Spring Data allows us to write our named (native) queries in a typical *.properties file inside the META-INF folder of your classpath. This way, we avoid modifying our entities. This application shows you how to do it.

Warning: Cannot use native queries with dynamic sorting ( Sort ). Nevertheless, using Sort in named queries works fine. Moreover, using Sort in Pageable works fine for both, named queries and named native queries. At least this is how it behave in Spring Boot 2.2.2. From this point of view, this approach is better than using @NamedQuery / @NamedNativeQuery or orm.xml file.

Key points:

define the named (native) queries in a file, META-INF/jpa-named-queries.properties
follow the Spring {EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementation

How To Use JPA Named Queries Via The orm.xml File

Description: JPA named (native) queries are commonly written via @NamedQuery and @NamedNativeQuery annotations in entity classes. Spring Data allows us to write our named (native) queries in a typical orm.xml file inside the META-INF folder of your classpath. This way, we avoid modifying our entities. This application shows you how to do it.

Warning: Pay attention that, via this approach, we cannot use named (native) queries with dynamic sorting ( Sort ). Using Sort in Pageable is ignored, therefore you need to explicitly add ORDER BY in the queries. At least this is how it behave in Spring Boot 2.2.2. A better approach relies on using a properties file for listing the named (native) queries. In this case, dynamic Sort works for named queries, but not for named native queries. Using Sort in Pageable works as expected in named (native) queries.

Key points:

define the named (native) queries in a file, META-INF/orm.xml
follow the Spring {EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementation

How To Use JPA Named Queries Via Annotations

Description: JPA named (native) queries are commonly written via @NamedQuery and @NamedNativeQuery annotations in entity classes. This application shows you how to do it.

Key points:

use @NamedQuery and @NamedNativeQuery annotations in entity classes
follow the Spring {EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementation
avoid Sort and Pageable

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Use JPA Named Queries Via Properties File And Spring Projection (DTO)

Description: This application is an example of combining JPA named queries listed in a properties file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named query.

Key points:

define the named queries in a properties file (eg, jpa-named-queries.properties ) in a folder named META-INF the application classpath
define the proper Spring projection

How To Use JPA Named Native Queries Via Properties File And Spring Projection (DTO)

Description: This application is an example of combining JPA named native queries listed in a properties file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named native query.

Key points:

define the named native queries in a properties file (eg, jpa-named-queries.properties ) in a folder named META-INF the application classpath
define the proper Spring projection

How To Use JPA Named Queries Via orm.xml File And Spring Projection (DTO)

Description: This application is an example of combining JPA named queries listed in orm.xml file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named query.

Key points:

define the named queries in orm.xml file in a folder named META-INF the application classpath
define the proper Spring projection

How To Use JPA Named Native Queries Via orm.xml File And Spring Projection (DTO)

Description: This application is an example of combining JPA named native queries listed in orm.xml file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named native query.

Key points:

define the named native queries in orm.xml file in a folder named META-INF the application classpath
define the proper Spring projection

How To Dto Via Named Native Query And Result Set Mapping Via orm.xml

Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on named native queries and result set mapping via orm.xml and the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of native named query.

Key points:

use <named-native-query/> and <sql-result-set-mapping/> to map the native query to AuthorDto class

How To Use Spring Projections(DTO) And Cross Joins

Description: This application is a proof of concept for using Spring Projections(DTO) and cross joins written via JPQL and native SQL (for MySQL).

Key points:

define two entities (eg, Book and Format
populate the database with some test data (eg, check the file resources/data-mysql.sql )
write interfaces (Spring projections) that contains getters for the columns that should be fetched from the database (eg, check BookTitleAndFormatType.java )
write cross joins queries using JPQL/SQL

Calling Stored Procedure That Returns A Result Set Via JdbcTemplate And BeanPropertyRowMapper

Description: This application is an example of calling a MySQL stored procedure that returns a result set via JdbcTemplate and BeanPropertyRowMapper .

Key points:

rely on JdbcTemplate , SimpleJdbcCall and BeanPropertyRowMapper

Defining Entity Listener Class Via @EntityListeners

Description: This application is a sample of using the JPA @MappedSuperclass and @EntityListeners with JPA callbacks.

Key points:

the base class , Book , is not an entity, it can be abstract , and is annotated with @MappedSuperclass and @EntityListeners(BookListener.class)
BookListener defines JPA callbacks (eg, @PrePersist )
subclasses of the base class are mapped in tables that contains columns for the inherited attributes and for their own attibutes
when any entity that is a subclass of Book is persisted, loaded, updated, etc the corresponding JPA callbacks are called

Improper Usage Of @Fetch(FetchMode.JOIN) May Causes N+1 Issues

Advice: Always evaluate JOIN FETCH and entities graphs before deciding to use FetchMode.JOIN . The FetchMode.JOIN fetch mode always triggers an EAGER load so the children are loaded when the parents are. Beside this drawback, FetchMode.JOIN may return duplicate results. You'll have to remove the duplicates yourself (eg storing the result in a Set ). But, if you decide to go with FetchMode.JOIN at least pay attention to avoid N+1 issues discussed below.

Note: Let's assume three entities, Author , Book and Publisher . Between Author and Book there is a bidirectional-lazy @OneToMany association. Between Author and Publisher there is a unidirectional-lazy @ManyToOne . Between Book and Publisher there is no association.

Now, we want to fetch a book by id ( BookRepository#findById() ), including its author, and the author's publisher. In such cases, using Hibernate fetch mode, @Fetch(FetchMode.JOIN) works as expected. Using JOIN FETCH or entity graph is also working as expected.

Next, we want to fetch all books ( BookRepository#findAll() ), including their authors, and the authors publishers. In such cases, using Hibernate fetch mode, @Fetch(FetchMode.JOIN) will cause N+1 issues. It will not trigger the expected JOIN . In this case, using JOIN FETCH or entity graph should be used.

Key points:

using Hibernate fetch mode, @Fetch(FetchMode.JOIN) doesn't work for query-methods
Hibernate fetch mode, @Fetch(FetchMode.JOIN) works in cases that fetches the entity by id (primary key) like using EntityManager#find() , Spring Data, findById() , findOne() .

How To Efficiently Assign A Database Temporary Ranking Of Values To Rows via RANK()

Description: This application is an example of assigning a database temporary ranking of values to rows via the window function, RANK() . This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.

Key points:

commonly, you don't need to fetch in the result set the temporary ranking of values produced by RANK() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by RANK() as well
write several native querys relying on RANK() window function

Amostra de saída:

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

How To Efficiently Assign A Database Temporary Ranking Of Values To Rows via DENSE_RANK()

Description: This application is an example of assigning a database temporary ranking of values to rows via the window function, DENSE_RANK() . In comparison with the RANK() window function, DENSE_RANK() avoid gaps within partition. This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.

Key points:

commonly, you don't need to fetch in the result set the temporary ranking of values produced by DENSE_RANK() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by DENSE_RANK() as well
write several native querys relying on DENSE_RANK() window function

Amostra de saída:

How To Efficiently Distribute The Number Of Rows In The Specified (N) Number Of Groups Via NTILE(N)

Description: This application is an example of distributing the number of rows in the specified (N) number of groups via the window function, NTILE(N) . This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.

Key points:

commonly, you don't need to fetch in the result set the temporary ranking of values produced by NTILE() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by NTILE() as well
write several native querys relying on NTILE() window function

Amostra de saída:

How To Write Derived Count And Delete Queries

Description: Spring Data comes with the Query Builder mechanism for JPA that is capable to interpret a query method name (known as a derived query) and convert it into a SQL query in the proper dialect. This is possible as long as we respect the naming conventions of this mechanism. Beside the well-known query of type find... , Spring Data supports derived count queries and derived delete queries.

Key points:

a derived count query starts with count... (eg, long countByGenre(String genre) ) - Spring Data will generate a SELECT COUNT(...) FROM ... query
a derived delete query can return the number of deleted records or the list of the deleted records
a derived delete query that returns the number of deleted records starts with delete... or remove... and returns long (eg, long deleteByGenre(String genre) ) - Spring Data will trigger first a SELECT to fetch entities in the Persistence Context, and, afterwards, it triggers a DELETE for each entity that must be deleted
a derived delete query that returns the list of deleted records starts with delete... or remove... and returns List<entity> (eg, List<Author> removeByGenre(String genre) ) - Spring Data will trigger first a SELECT to fetch entities in the Persistence Context, and, afterwards, it triggers a DELETE for each entity that must be deleted

Working With Spring Data Property Expressions

Description: Property expressions can refer to a direct property of the managed entity. However, you can also define constraints by traversing nested properties. This application is a sample of traversing nested properties for fetching entities and DTOs.

Key points:

Assume an Author has several Book and each book has several Review (between Author and Book there is a bidirectional-lazy @oneToMany association, and, between Book and Review there is also a bidirectional-lazy @OneToMany association)
Assume that we fetched a Review and we want to know the Author of the Book that has received this Review
via property expressions, we can write in AuthorRepository the following query that will be processed by the Spring Data Query Builder mechanism: Author findByBooksReviews(Review review);
Behind the scene Spring Data will produce a SELECT with two LEFT JOIN
In this case, the method creates the property traversal books.reviews . The algorithm starts by interpreting the entire part ( BooksReviews ) as the property and checks the domain class for a property with that name (uncapitalized). If the algorithm succeeds, it uses that property. If not, the algorithm splits up the source at the camel case parts from the right side into a head and a tail and tries to find the corresponding property — in our example, Books and Reviews . If the algorithm finds a property with that head, it takes the tail and continues building the tree down from there, splitting the tail up in the way just described. If the first split does not match, the algorithm moves the split point to the left and continues.
Although this algorithm should work for most cases, it is possible for the algorithm to select the wrong property. Suppose the Author class has an booksReview property as well. The algorithm would match in the first split round already, choose the wrong property, and fail (as the type of booksReview probably has no code property). To resolve this ambiguity you can use _ inside your method name to manually define traversal points. So our method name would be as follows: Author findByBooks_Reviews(Review review);
More examples (including DTOs) are available in the application

The Best Way To Fetch Parent And Children In Different Queries

Note: Fetching read-only data should be done via DTO, not managed entities. But, there is no tragedy to fetch read-only entities in a context as follows:

we need all attributes of the entity (so, a DTO just mirrors an entity)
we manipulate a small number of entities (eg, an author with several books)
we use @Transactional(readOnly = true)

Under these circumstances, let's tackle a common case that I saw quite a lot. There is even an SO answer about it (don't do this):

Description: Let's assume that Author and Book are involved in a bidirectional-lazy @OneToMany association. Imagine an user that loads a certain Author (without the associated Book ). The user may be interested or not in the Book , therefore, we don't load them with the Author . If the user is interested in the Book then he will click a button of type, View books . Now, we have to return the List<Book> associated to this Author .

So, at first request (query), we fetch an Author . The Author is detached. At second request (query), we want to load the Book associated to this Author . But, we don't want to load the Author again (for example, we don't care about lost updates of Author ), we just want to load the associated Book in a single SELECT . A common (not recommended) approach is to load the Author again (eg, via findById(author.getId()) ) and call the author.getBooks() . But, this end up in two SELECT statements. One SELECT for loading the Author , and another SELECT after we force the collection initialization. We force collection initialization because it will not be initialize if we simply return it. In order to trigger the collection initialization the developer call books.size() or he rely on Hibernate.initialize(books); .

But, we can avoid such solution by relying on an explicit JPQL or Query Builder property expressions. This way, there will be a single SELECT and no need to call size() or Hibernate.initialize();

Key points:

use an explicit JPQL
use Query Builder propery expressions

This item is detailed in my book, Spring Boot Persistence Best Practices.

How To Optimize The Merge Operation Using Update

Description: Behind the built-in Spring Data save() there is a call of EntityManager#persist() or EntityManager#merge() . It is important to know this aspect in several cases. Among this cases, we have the entity update case (simple update or update batching).

Consider Author and Book involved in a bidirectional-lazy @OneToMany association. And, we load an Author , detach it, update it in the detached state, and save it to the database via save() method. Calling save() will come with the following two issues resulting from calling merge() behind the scene:

there will be two SQL statements, one SELECT (merge) and one UPDATE
the SELECT will contain a LEFT OUTER JOIN to fetch the associated Book as well (we don't need the books!)

How about triggering only the UPDATE instead of this? The solution relies on calling Session#update() . Calling Session.update() requires to unwrap the Session via entityManager.unwrap(Session.class) .

Key points:

calling Session.update() will trigger only the UPDATE (there is no SELECT )
Session.update() works with versioned optimistic locking mechanism as well (so, lost updates are prevented)

How To NOT Use Spring Data Streamable

Description: This application is a sample of fetching Streamable<entity> and Streamable<dto> . But, more important, this application contains three examples of how to not use Streamable . It is very tempting and comfortable to fetch a Streamable result set and chop it via filter() , map() , flatMap() , and so on until we obtain only the needed data instead of writing a query (eg, JPQL) that fetches exactly the needed result set from the database. Mainly, we just throw away some of the fetched data to keep only the needed data. But, is not advisable to follow such practices because fetching more data than needed can cause significant performance penalties.

Moreover, pay attention to combining two or more Streamable via the and() method. The returned result may be different from what you are expecting to see. Each Streamable produces a separate SQL statement and the final result set is a concatenation of the intermediate results sets (prone to duplicate values).

Key points:

don't fetch more columns than needed just to drop a part of them (eg, via map() )
don't fetch more rows than needed just to throw away a part of it (eg, via filter() )
pay attention on combining Streamable via and() ; each Streamable produces a separate SQL statement and the final result set is a concatenation of the intermediate results sets (prone to duplicate values)

How To Return Custom Streamable Wrapper Types

Description: A common practice consists of exposing dedicated wrappers types for collections resulted after mapping a query result set. This way, on a single query execution, the API can return multiple results. After we call a query-method that return a collection, we can pass it to a wrapper class by manually instantiation of that wrapper-class. But, we can avoid the manually instantiation if the code respects the following key points.

Key points:

the type implements Streamable
the type exposes a constructor (used in this example) or a static factory method named of(…) or valueOf(…) taking Streamable as argument

How To Use In Spring Boot JPA 2.1 Schema Generation And Data Loading

Description: JPA 2.1 come with schema generation features. This feature can setup the database or export the generated commands to a file. The parameters that we should set are:

spring.jpa.properties.javax.persistence.schema-generation.database.action : Instructs the persistence provider how to setup the database. Possible values include: none , create , drop-and-create , drop
javax.persistence.schema-generation.scripts.action : Instruct the persistence provider which scripts to create. Possible values include: none , create , drop-and-create , drop .
javax.persistence.schema-generation.scripts.create-target : Indicate the target location of the create script generated by the persistence provider. This can be as a file URL or a java.IO.Writer .
javax.persistence.schema-generation.scripts.drop-target : Indicate the target location of the drop script generated by the persistence provider. This can be as a file URL or a java.IO.Writer .

Moreover, we can instruct the persistence provider to load data from a file into the database via: spring.jpa.properties.javax.persistence.sql-load-script-source . The value of this property represents the file location and it can be a file URL or a java.IO.Writer .

Key points:

the settings are available in application.properties

How To Return A Map Result From A Spring Data Query Method

Description: Sometimes, we need to write in repositories certain query-methods that return a Map instead of a List or a Set . For example, when we need a Map<Id, Entity> or we use GROUP BY and we need a Map<Group, Count> . This application shows you how to do it via default methods directly in repository.

Key points:

rely on default methods and Collectors.toMap()

How To Handle Entities Inheritance With Spring Data Repositories

Description: Consider one of the JPA inheritance strategies (eg, JOINED ). Handling entities inheritance With Spring Data repositories can be done as follows:

Single Table
Ingressou
Table per class
Mapped Superclass

Log Slow Queries Via Hibernate 5.4.5

Description: This application is a sample of logging only slow queries via Hibernate 5.4.5, hibernate.session.events.log.LOG_QUERIES_SLOWER_THAN_MS property. A slow query is a query that has an execution time bigger than a specificed threshold in milliseconds.

Key points:

in application.properties add hibernate.session.events.log.LOG_QUERIES_SLOWER_THAN_MS

Exemplo de saída:

DTO Via JDK14 Records And Spring Data Query Builder Mechanism

Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on JDK14 Records feature and Spring Data Query Builder Mechanism.

From Openjdk JEP359:

Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.

Key points: Define the AuthorDto as:

public record AuthorDto(String name, int age) implements Serializable {}

How To Fetch DTO Via JDK14 Records, Constructor Expression and JPQL

From Openjdk JEP359:

Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.

Key points:

Define the AuthorDto as:

public record AuthorDto(String name, int age) implements Serializable {}

How To Fetch DTO Via JDK14 Records And A Custom ResultTransformer

As DTO, we rely on JDK 14 Records. From Openjdk JEP359:

Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.

Key points:

define the Java Records as AuthorDto and BookDto
implement the ResultTransformer interface

DTO Via JDK14 Records, JdbcTemplate And ResultSetExtractor

From Openjdk JEP359:

Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.

Key points:

define the Java Records as AuthorDto and BookDto
use JdbcTemplate and ResultSetExtractor

Dynamic Spring projection (DTO class)

Description: This application is a sample of using dynamic Spring projections via DTO classes.

Key points:

declare query-methods in a generic manner (eg, <T> List<T> findByGenre(String genre, Class<T> type); )

If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices"	If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you.

Batch Inserts In Spring Boot Style Via CompletableFuture And Return List<S>

Batch Inserts In Spring Boot Style Via CompletableFuture And Return List<S> (1)
Batch Inserts In Spring Boot Style Via CompletableFuture And Return List<S> (2)

How to simulate a deadlock

Description: This application is an example of causing a database deadlock in MySQL. This application produces an exception of type: com.mysql.cj.jdbc.exceptions.MySQLTransactionRollbackException: Deadlock found when trying to get lock; try restarting transaction . However, the database will retry until transaction (A) succeeds.

Key points:

start Transaction (A) and trigger a SELECT with PESSIMISTIC_WRITE to acquire an exclusive lock to table author
Transaction (A) update author genre with success and sleeps for 10s
after 5s, start a concurrent Transaction B that trigger a SELECT with PESSIMISTIC_WRITE to acquire an exclusive lock to table book
Transaction (B) update book title with success and sleeps for 10s
Transaction (A) wakes up and attempt to update the book but it cannot acquire the lock holded by Transaction (B)
Transaction (B) wakes up and attempt to update the author but it cannot acquire the lock holded by Transaction (A)
IMPASSE
database retry and succeeds after Transaction (B) releases the lock

How To Define A Composite Primary Key Having An Explicit Part and a Generated Part Via Sequence

Description: This application is a proof of concept of how to define a composite key having an explicit part ( name ) and a generated part ( authorId via SEQUENCE generator).

Key points:

use @IdClass

How To Intercept The Generated SQL For Logging Or Altering

Description: Sometimes we need to intercept the generated SQL that originates from Spring Data, EntityManager , Criteria API, JdbcTemplate and so on. This can be done as in this sample application. After interception, you can log, modify or even return a brand new SQL that will be executed in the end.

Key points:

define an implementation of Hibernate StatementInspector SPI
configure this SPI in application.properties via spring.jpa.properties.hibernate.session_factory.statement_inspector

281. Force inline params in Criteria API

NOTE Use this with high precaution since you open the gate for SQL injections.

Description: Sometimes we need to force inline params in Criteria API. By default, numeric parameters are inlined, but string parameters are not.

Key points:

configure in application.properties the setting spring.jpa.properties.hibernate.criteria.literal_handling_mode as inline

Using Arthur Gavlyukovskiy's data source decorator

Description: Arthur Gavlyukovskiy provide a suite of Spring Boot starters for quickly integrate P6Spy, Datasource Proxy, and FlexyPool. In this example, we add Datasource Proxy, but please consider this for more details.

Key points:

for Maven, in pom.xml , add the datasource-proxy-spring-boot-starter starter
in application.properties enable DEBUG level for logging

Using Java records as Hibernate embeddable

Description: This application is an example of using Java records as embeddable. This is available starting with Hibernate 6.0, but it was refined to be more accessible and easy to use in Hibernate 6.2

Key points: