Hibernate SpringBoot
1.0.0
| Si necesita profundizar en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Mejores prácticas de persistencia de Spring Boot". | Si necesita consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia de Java, entonces la "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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Muestras de hibernación y arranque de primavera
Descripción: esta aplicación es un ejemplo de cómo almacenar la fecha, la hora y las marcas de tiempo en la zona horaria UTC. La segunda configuración, useLegacyDatetimeCode solo es necesaria para MySQL. De lo contrario, configure solo hibernate.jdbc.time_zone .
Puntos clave:
spring.jpa.properties.hibernate.jdbc.time_zone=UTCspring.datasource.url=jdbc:mysql://localhost:3306/screenshotdb?useLegacyDatetimeCode=falseDescripción: vea los parámetros vinculantes/extraídos de la declaración preparada a través de la configuración del registrador Log4J 2.
Puntos clave:
pom.xml , excluya el registro predeterminado de Spring Bootpom.xml , agregue la dependencia de Log4j 2log4j2.xml agregue, <Logger name="org.hibernate.type.descriptor.sql" level="trace"/> Ejemplo de salida: 
Descripción: vea los detalles de la consulta (tipo de consulta, parámetros de enlace, tamaño del lote, tiempo de ejecución, etc.) a través de DataSource-Proxy
Puntos clave:
pom.xml la dependencia datasource-proxyDataSourceDataSource a través de ProxyFactory y una implementación de MethodInterceptor Ejemplo de salida: 
saveAll(Iterable<S> entities) en MySQL Descripción: Inserciones por lotes a través del método SimpleJpaRepository#saveAll(Iterable<S> entities) en MySQL
Puntos clave:
application.properties establezca spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties configure spring.jpa.properties.hibernate.generate_statistics (solo para verificar que el procesamiento por lotes esté funcionando)application.properties establezca la URL JDBC con rewriteBatchedStatements=true (optimización para MySQL)application.properties configure la URL JDBC con cachePrepStmts=true (habilite el almacenamiento en caché y es útil si decide configurar prepStmtCacheSize , prepStmtCacheSqlLimit , etc. también; sin esta configuración, el caché está deshabilitado)application.properties establezca la URL JDBC con useServerPrepStmts=true (de esta manera cambia a declaraciones preparadas del lado del servidor (puede conducir a un aumento significativo del rendimiento))spring.jpa.properties.hibernate.order_inserts=true para optimizar el procesamiento por lotes ordenando insercionesIDENTITY hará que se deshabilite el procesamiento por lotes de inserción@Version para evitar declaraciones SELECT adicionales activadas antes del procesamiento por lotes (también evite la pérdida de actualizaciones en transacciones de solicitudes múltiples). Las declaraciones SELECT adicionales son el efecto de usar merge() en lugar de persist() ; detrás de escena, saveAll() usa save() , que en el caso de entidades no nuevas (entidades que tienen ID) llamará merge() , lo que le indica a Hibernate que active una instrucción SELECT para asegurarse de que no haya ningún registro en el base de datos que tiene el mismo identificadorsaveAll() para no "abrumar" el contexto de persistencia; normalmente, el EntityManager debe vaciarse y borrarse de vez en cuando, pero durante la ejecución saveAll() simplemente no puedes hacer eso, por lo que si en saveAll() hay una lista con una gran cantidad de datos, todos esos datos llegarán al Persistencia Contexto (caché de primer nivel) y permanecerá en la memoria hasta el momento del vaciado; usar una cantidad relativamente pequeña de datos debería estar bien (en este ejemplo, cada lote de 30 entidades se ejecuta en una transacción separada y en un contexto persistente)saveAll() devuelve una List<S> que contiene las entidades persistentes; cada entidad persistente se agrega a esta lista; Si simplemente no necesita esta List , se crea para nada.spring.jpa.properties.hibernate.cache.use_second_level_cache=false Descripción: esta aplicación es una muestra de inserciones por lotes a través de EntityManager en MySQL. De esta manera puede controlar fácilmente los ciclos flush() y clear() del contexto de persistencia (caché de primer nivel) dentro de la transacción actual. Esto no es posible a través de Spring Boot, saveAll(Iterable<S> entities) , ya que este método ejecuta una única descarga por transacción. Otra ventaja es que puede llamar persist() en lugar de merge() ; esto lo usan detrás de escena SpringBoot saveAll(Iterable<S> entities) y save(S entity) .
Si desea ejecutar un lote por transacción (recomendado), consulte este ejemplo.
Puntos clave:
application.properties establezca spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties configure spring.jpa.properties.hibernate.generate_statistics (solo para verificar que el procesamiento por lotes esté funcionando)application.properties establezca la URL JDBC con rewriteBatchedStatements=true (optimización para MySQL)application.properties configure la URL JDBC con cachePrepStmts=true (habilite el almacenamiento en caché y es útil si decide configurar prepStmtCacheSize , prepStmtCacheSqlLimit , etc. también; sin esta configuración, el caché está deshabilitado)application.properties establezca la URL JDBC con useServerPrepStmts=true (de esta manera cambia a declaraciones preparadas del lado del servidor (puede conducir a un aumento significativo del rendimiento))spring.jpa.properties.hibernate.order_inserts=true para optimizar el procesamiento por lotes ordenando insercionesIDENTITY hará que se deshabilite el procesamiento por lotes de inserciónspring.jpa.properties.hibernate.cache.use_second_level_cache=false Ejemplo de salida: 
Descripción: Inserciones por lotes a través de JpaContext/EntityManager en MySQL.
Puntos clave:
application.properties establezca spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties configure spring.jpa.properties.hibernate.generate_statistics (solo para verificar que el procesamiento por lotes esté funcionando)application.properties establezca la URL JDBC con rewriteBatchedStatements=true (optimización para MySQL)application.properties configure la URL JDBC con cachePrepStmts=true (habilite el almacenamiento en caché y es útil si decide configurar prepStmtCacheSize , prepStmtCacheSqlLimit , etc. también; sin esta configuración, el caché está deshabilitado)application.properties establezca la URL JDBC con useServerPrepStmts=true (de esta manera cambia a declaraciones preparadas del lado del servidor (puede conducir a un aumento significativo del rendimiento))spring.jpa.properties.hibernate.order_inserts=true para optimizar el procesamiento por lotes ordenando insercionesIDENTITY hará que se deshabilite el procesamiento por lotes de inserciónEntityManager se obtiene por tipo de entidad a través de JpaContext#getEntityManagerByManagedType(Class<?> entity)spring.jpa.properties.hibernate.cache.use_second_level_cache=false Ejemplo de salida: 
Descripción: Inserciones por lotes a través del procesamiento por lotes a nivel de sesión de Hibernate (Hibernate 5.2 o superior) en MySQL.
Puntos clave:
application.properties configure spring.jpa.properties.hibernate.generate_statistics (solo para verificar que el procesamiento por lotes esté funcionando)application.properties establezca la URL JDBC con rewriteBatchedStatements=true (optimización para MySQL)application.properties configure la URL JDBC con cachePrepStmts=true (habilite el almacenamiento en caché y es útil si decide configurar prepStmtCacheSize , prepStmtCacheSqlLimit , etc. también; sin esta configuración, el caché está deshabilitado)application.properties establezca la URL JDBC con useServerPrepStmts=true (de esta manera cambia a declaraciones preparadas del lado del servidor (puede conducir a un aumento significativo del rendimiento))spring.jpa.properties.hibernate.order_inserts=true para optimizar el procesamiento por lotes ordenando insercionesIDENTITY hará que se deshabilite el procesamiento por lotes de inserciónSession de hibernación se obtiene desenvolviéndola mediante EntityManager#unwrap(Session.class)Session#setJdbcBatchSize(Integer size) y se obtiene a través de Session#getJdbcBatchSize()spring.jpa.properties.hibernate.cache.use_second_level_cache=false Ejemplo de salida: 
findById() , JPA EntityManager y Session de Hibernate Descripción: Obtención directa a través de ejemplos de Spring Data, EntityManager e Hibernate Session .
Puntos clave:
findById()EntityManager utiliza find()Session utiliza get()Nota: También puede interesarle leer la receta "Cómo enriquecer los DTO con propiedades virtuales mediante proyecciones de primavera"
Descripción: obtenga solo los datos necesarios de la base de datos a través de Spring Data Projections (DTO).
Puntos clave:
List<projection>LIMIT )Nota: El uso de proyecciones no se limita al uso del mecanismo de creación de consultas integrado en la infraestructura del repositorio de Spring Data. También podemos obtener proyecciones a través de JPQL o consultas nativas. Por ejemplo, en esta aplicación utilizamos un JPQL.
Ejemplo de salida (seleccione las primeras 2 filas; seleccione solo "nombre" y "edad"): 
| Si necesita profundizar en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Mejores prácticas de persistencia de Spring Boot". | Si necesita consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia de Java, entonces la "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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Descripción: De forma predeterminada, los atributos de una entidad se cargan con entusiasmo (todos a la vez). Pero también podemos cargarlos de forma diferida . Esto es útil para tipos de columnas que almacenan grandes cantidades de datos: CLOB , BLOB , VARBINARY , etc. o detalles que deben cargarse según demanda. En esta aplicación, tenemos una entidad llamada Author . Sus propiedades son: id , name , genre , avatar y age . Y queremos cargar el avatar de forma perezosa. Por lo tanto, el avatar debe cargarse según demanda.
Puntos clave:
pom.xml , active la mejora del código de bytes de Hibernate (por ejemplo, utilice el complemento de mejora del código de bytes de Maven)@Basic(fetch = FetchType.LAZY)application.properties , deshabilite Abrir sesión en Ver Comprueba también:
- Valores predeterminados para atributos cargados de forma diferida
- Atributo Lazy Loading y serialización de Jackson
Descripción: Un proxy de Hibernate puede ser útil cuando una entidad secundaria puede persistir con una referencia a su principal (asociación @ManyToOne o @OneToOne ). En tales casos, recuperar la entidad principal de la base de datos (ejecutar la instrucción SELECT ) es una penalización de rendimiento y una acción inútil, porque Hibernate puede establecer el valor de la clave externa subyacente para un proxy no inicializado.
Puntos clave:
EntityManager#getReference()JpaRepository#getOne() -> usado en este ejemploload()Author y Book , involucradas en una asociación @ManyToOne unidireccional ( Author es el lado principal)SELECT ), creamos un nuevo libro, configuramos el proxy como el autor de este libro y guardamos el libro (esto activará un INSERT en la tabla de book )Ejemplo de salida:
INSERT , y no SELECTDescripción: El N+1 es un problema de búsqueda perezosa (pero ansioso no está exento). Esta aplicación reproduce el comportamiento N+1.
Puntos clave:
Author y Book en una asociación @OneToMany bidireccional diferidaBook diferidos, por lo que sin Author (resulta en 1 consulta)Book recuperada y, para cada entrada, obtenga el Author correspondiente (resultados N consultas)Author de forma diferida, por lo que sin Book (resulta en 1 consulta)Author obtenida y para cada entrada obtenga el Book correspondiente (resulta N consultas) Ejemplo de salida: 
SELECT DISTINCT mediante hibernación HINT_PASS_DISTINCT_THROUGH Sugerencia Descripción: A partir de Hibernate 5.2.2, podemos optimizar entidades de consulta JPQL (HQL) de tipo SELECT DISTINCT mediante la sugerencia HINT_PASS_DISTINCT_THROUGH . Tenga en cuenta que esta sugerencia es útil sólo para consultas JPQL (HQL) JOIN FETCH-ing. No es útil para consultas escalares (por ejemplo, List<Integer> ), DTO o HHH-13280. En tales casos, es necesario pasar la palabra clave DISTINCT JPQL a la consulta SQL subyacente. Esto le indicará a la base de datos que elimine los duplicados del conjunto de resultados.
Puntos clave:
@QueryHints(value = @QueryHint(name = HINT_PASS_DISTINCT_THROUGH, value = "false")) Ejemplo de salida: 
Nota: El mecanismo de Hibernate Dirty Checking es responsable de identificar las modificaciones de las entidades en el momento del lavado y de activar las declaraciones UPDATE correspondientes en nuestro nombre.
Descripción: Antes de Hibernate versión 5, el mecanismo de verificación sucia se basa en la API Java Reflection para verificar cada propiedad de cada entidad administrada. A partir de la versión 5 de Hibernate, el mecanismo de verificación sucia puede depender del mecanismo de seguimiento sucio (que es la capacidad de una entidad para rastrear sus propios cambios de atributos) que requiere que Hibernate Bytecode Enhancement esté presente en la aplicación. El mecanismo Dirty Tracking mantiene un mejor rendimiento, especialmente cuando tienes una cantidad relativamente grande de entidades.
Para Dirty Tracking , durante el proceso de mejora del código de bytes , Hibernate instrumenta el código de bytes de las clases de entidad agregando un rastreador , $$_hibernate_tracker . En el momento de la descarga, Hibernate utilizará este rastreador para descubrir los cambios de las entidades (cada rastreador de entidades informará los cambios). Esto es mejor que comprobar todas las propiedades de cada entidad gestionada.
Por lo general (de forma predeterminada), la instrumentación se lleva a cabo en el momento de la compilación, pero también se puede configurar para que se realice en el tiempo de ejecución o en el tiempo de implementación. Es preferible que se lleve a cabo en el momento de la compilación para evitar una sobrecarga en el tiempo de ejecución.
Se puede agregar Bytecode Enhancement y habilitar Dirty Tracking mediante un complemento agregado a través de Maven o Gradle (también se puede usar Ant). Usamos Maven, por lo tanto lo agregamos en pom.xml .
Puntos clave:
pom.xml Ejemplo de salida: 
El efecto de mejora de Bytecode se puede ver en Author.class aquí. Observe cómo el código de bytes se instrumentó con $$_hibernate_tracker .
Optional en entidades y consultas Descripción: Esta aplicación es un ejemplo de cómo es correcto utilizar Java 8 Optional en entidades y consultas.
Puntos clave:
Optional (por ejemplo, findById() )OptionalOptional en captadores de entidadesdata-mysql.sql@OneToMany Descripción: Esta aplicación es una prueba de concepto de cómo es correcto implementar la asociación bidireccional @OneToMany desde la perspectiva del rendimiento.
Puntos clave:
mappedBy en el padreorphanRemoval en el padre para eliminar niños sin referencias@NaturalId )) y/o identificadores generados por la base de datos y anule (en el lado secundario) correctamente los métodos equals() y hashCode() como aquítoString() , entonces preste atención para involucrar solo los atributos básicos obtenidos cuando la entidad se carga desde la base de datos. Nota: preste atención a las operaciones de eliminación, especialmente a la eliminación de entidades secundarias. CascadeType.REMOVE y orphanRemoval=true pueden generar demasiadas consultas. En tales escenarios, confiar en operaciones masivas es la mayor parte del tiempo la mejor manera de realizar eliminaciones.
Descripción: esta aplicación es un ejemplo de cómo escribir una consulta a través de JpaRepository , EntityManager y Session .
Puntos clave:
JpaRepository utilice @Query o Spring Data Query CreationEntityManager y Session utilice el método createQuery()AUTO en Hibernate 5 y MySQL Descripción: En MySQL e Hibernate 5, el tipo de generador GenerationType.AUTO dará como resultado el uso del generador TABLE . Esto añade una importante penalización al rendimiento. Se puede convertir este comportamiento al generador IDENTITY utilizando GenerationType.IDENTITY o el generador nativo .
Puntos clave:
GenerationType.IDENTITY en lugar de GenerationType.AUTO Ejemplo de salida: 
Descripción: Esta aplicación es un ejemplo en el que llamar save() para una entidad es redundante (no es necesario).
Puntos clave:
UPDATE correspondiente sin la necesidad de llamar explícitamente al método save()save() cuando no es necesariamente) no afecta la cantidad de consultas activadas, pero implica una penalización de rendimiento en los procesos subyacentes de Hibernate.| Si necesita profundizar en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Mejores prácticas de persistencia de Spring Boot". | Si necesita consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia de Java, entonces la "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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SERIAL PostgreSQL ( BIG ) en inserciones por lotes a través de Hibernate Descripción: En PostgreSQL, el uso de GenerationType.IDENTITY deshabilitará la inserción por lotes. El (BIG)SERIAL actúa "casi" como MySQL, AUTO_INCREMENT . En esta aplicación, utilizamos GenerationType.SEQUENCE que permite insertar lotes y lo optimizamos mediante el algoritmo de optimización hi/lo .
Puntos clave:
GenerationType.SEQUENCE en lugar de GenerationType.IDENTITYhi/lo para obtener un valor hi en un recorrido de ida y vuelta de la base de datos (el valor hi es útil para generar un número determinado/dado de identificadores en la memoria; hasta que no haya agotado todos los identificadores en la memoria, no es necesario para buscar otro hola )pooled y pooled-lo de Hibernate (estas son optimizaciones de hi/lo que permiten que servicios externos utilicen la base de datos sin causar errores de duplicación de claves)spring.datasource.hikari.data-source-properties.reWriteBatchedInserts=true Ejemplo de salida: 
SINGLE_TABLE Descripción: Esta aplicación es un ejemplo del uso de la estrategia de herencia de tabla única JPA ( SINGLE_TABLE ).
Puntos clave:
@Inheritance(strategy=InheritanceType.SINGLE_TABLE) )@NotNull y MySQL.TINYINT Ejemplo de salida (a continuación se muestra una tabla única obtenida de 3 entidades): 
Descripción: Esta aplicación es un ejemplo de cómo contar y afirmar sentencias SQL activadas "detrás de escena". Es muy útil contar las sentencias SQL para garantizar que su código no genere más sentencias SQL de las que pueda pensar (por ejemplo, N+1 se puede detectar fácilmente afirmando el número de sentencias esperadas).
Puntos clave:
pom.xml , agregue dependencias para la biblioteca DataSource-Proxy y la biblioteca db-util de Vlad MihalceaProxyDataSourceBuilder con countQuery()SQLStatementCountValidator.reset()INSERT , UPDATE , DELETE y SELECT mediante assertInsert/Update/Delete/Select/Count(long expectedNumberOfSql) Ejemplo de salida (cuando el número de SQL esperados no es igual a la realidad, se genera una excepción): 
Descripción: esta aplicación es un ejemplo de cómo configurar las devoluciones de llamada JPA ( Pre/PostPersist , Pre/PostUpdate , Pre/PostRemove y PostLoad ).
Puntos clave:
void y no aceptar argumentos. Ejemplo de salida: 
@MapsId para compartir el identificador en la relación @OneToOne Descripción: En lugar del @OneToOne unidireccional/bidireccional normal , es mejor confiar en un @OneToOne y @MapsId unidireccionales. Esta aplicación es una prueba de concepto.
Puntos clave:
@MapsId en el lado secundario@JoinColumn para personalizar el nombre de la columna de clave principal@OneToOne , @MapsId compartirá la clave principal con la tabla principal (la propiedad id actúa como clave principal y clave externa)Nota:
@MapsId también se puede utilizar para @ManyToOneSqlResultSetMapping y EntityManager Descripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones de rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos basamos en SqlResultSetMapping y EntityManager .
Puntos clave:
SqlResultSetMapping y EntityManagerSqlResultSetMapping y NamedNativeQuery Nota: Si desea confiar en la convención de nomenclatura {EntityName}.{RepositoryMethodName} para simplemente crear en la interfaz del repositorio métodos con el mismo nombre que la consulta con nombre nativo, omita esta aplicación y verifique esta.
Descripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones de rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos basamos en SqlResultSetMapping , NamedNativeQuery .
Puntos clave:
SqlResultSetMapping , NamedNativeQueryjavax.persistence.Tuple y SQL nativo Descripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones de rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos basamos en javax.persistence.Tuple y SQL nativo.
Puntos clave:
java.persistence.Tuple en un repositorio Spring y marque la consulta como nativeQuery = truejavax.persistence.Tuple y JPQL Descripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones de rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos basamos en javax.persistence.Tuple y JPQL.
Puntos clave:
java.persistence.Tuple en un repositorio SpringDescripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones de rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos basamos en Constructor Expression y JPQL.
Puntos clave:
SELECT new com.bookstore.dto.AuthorDto(a.name, a.age) FROM Author a Ver también:
Cómo recuperar DTO a través del constructor y el mecanismo de generación de consultas de datos de Spring
| Si necesita profundizar en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Mejores prácticas de persistencia de Spring Boot". | Si necesita consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia de Java, entonces la "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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ResultTransformer y SQL nativo Descripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones en el rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos apoyamos en Hibernate, ResultTransformer y SQL nativo.
Puntos clave:
AliasToBeanConstructorResultTransformer para DTO sin definidores, pero con constructorTransformers.aliasToBean() para DTO con configuradoresEntityManager.createNativeQuery() y unwrap(org.hibernate.query.NativeQuery.class)ResultTransformer está en desuso, pero hasta que haya un reemplazo disponible (probablemente en Hibernate 6.0) se puede usar (leer más)ResultTransformer y JPQL Descripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones en el rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos apoyamos en Hibernate, ResultTransformer y JPQL.
Puntos clave:
AliasToBeanConstructorResultTransformer para DTO sin definidores, con constructorTransformers.aliasToBean() para DTO con configuradoresEntityManager.createQuery() y unwrap(org.hibernate.query.Query.class)ResultTransformer está en desuso, pero hasta que haya un reemplazo disponible (en Hibernate 6.0) se puede usar (leer más)Descripción: Obtener más datos de los necesarios es propenso a sufrir penalizaciones de rendimiento. El uso de DTO nos permite extraer solo los datos necesarios. En esta aplicación nos basamos en las vistas de entidades de Blaze-Persistence.
Puntos clave:
pom.xml las dependencias específicas de Blaze-PersistenceCriteriaBuilderFactory y EntityViewManagerEntityViewRepositoryfindAll() , findOne() , etc.@ElementCollection normal (sin @OrderColumn ) Descripción: Esta aplicación revela las posibles penalizaciones de rendimiento por el uso de @ElementCollection . En este caso, sin @OrderColumn . Como puede ver en el siguiente elemento (34), agregar @OrderColumn puede mitigar algunas penalizaciones de rendimiento.
Puntos clave:
@ElementCollection no tiene una clave principal@ElementCollection se asigna en una tabla separada@ElementCollection cuando tenga muchas inserciones/eliminaciones en esta colección; Las inserciones/eliminaciones harán que Hibernate elimine todas las filas de la tabla existentes, procese la colección en la memoria y vuelva a insertar las filas restantes de la tabla para reflejar la colección desde la memoria. Ejemplo de salida: 
@ElementCollection con @OrderColumn Descripción: esta aplicación revela las penalizaciones de rendimiento derivadas del uso de @ElementCollection . En este caso, con @OrderColumn . Pero, como puede ver en esta aplicación (en comparación con el elemento 33), agregar @OrderColumn puede mitigar algunas penalizaciones de rendimiento cuando las operaciones se realizan cerca del final de la colección (por ejemplo, agregar/eliminar al/desde el final de la colección). Principalmente, todos los elementos situados antes de la entrada de agregar/eliminar se dejan intactos, por lo que la penalización de rendimiento se puede ignorar si afectamos las filas cercanas a la cola de la colección.
Puntos clave:
@ElementCollection no tiene una clave principal@ElementCollection se asigna en una tabla separada@ElementCollection con @OrderColumn cuando tenga muchas inserciones y eliminaciones cerca de la cola de la colección Ejemplo de salida: 
Nota: Antes de leer este artículo, intente ver si Hibernate5Module no es lo que está buscando.
Descripción: El antipatrón Abrir sesión en vista está activado de forma predeterminada en SpringBoot. Ahora, imagine una asociación diferida (por ejemplo, @OneToMany ) entre dos entidades, Author y Book (un autor ha asociado más libros). A continuación, un punto final del controlador REST recupera un Author sin el Book asociado. Pero la Vista (más precisamente, Jackson) también fuerza la carga diferida del Book asociado. Dado que OSIV proporcionará la Session ya abierta, las inicializaciones de los servidores proxy se llevan a cabo con éxito. La solución para evitar esta penalización de rendimiento comienza por desactivar OSIV. Además, inicialice explícitamente las asociaciones diferidas no recuperadas. De esta manera, la Vista no forzará una carga diferida.
Puntos clave:
application.properties esta configuración: spring.jpa.open-in-view=falseAuthor e inicializar su Book asociado explícitamente con valores (predeterminados) (por ejemplo, null )@JsonInclude(Include.NON_EMPTY) en este nivel de entidad para evitar que se muestre null o lo que se considera vacío en el JSON resultante. NOTA: Si OSIV está habilitado, el desarrollador aún puede inicializar manualmente las asociaciones diferidas no recuperadas siempre que lo haga fuera de una transacción para evitar el vaciado. Pero, ¿por qué funciona esto? Dado que la Session está abierta, ¿por qué la inicialización manual de las asociaciones de una entidad administrada no activa la descarga? La respuesta se puede encontrar en la documentación de OpenSessionInViewFilter que especifica que: Este filtro, de forma predeterminada, no vaciará la Session de Hibernación, con el modo de vaciado establecido en FlushMode.NEVER . Se supone que se usará en combinación con transacciones de la capa de servicio que se ocupan del vaciado: el administrador de transacciones activo cambiará temporalmente el modo de vaciado a FlushMode.AUTO durante una transacción de lectura y escritura, y el modo de vaciado se restablecerá a FlushMode.NEVER al final. de cada transacción. Si tiene intención de utilizar este filtro sin transacciones, considere cambiar el modo de descarga predeterminado (a través de la propiedad "flushMode").
Descripción: Esta aplicación es una prueba de concepto para usar Spring Projections (DTO) y uniones internas escritas a través de JPQL y SQL nativo (para MySQL).
Puntos clave:
Author y Book en una asociación bidireccional (perezosa) @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java ).
Descripción: Esta aplicación es una prueba de concepto para usar Spring Projections (DTO) y uniones izquierdas escritas a través de JPQL y SQL nativo (para MySQL).
Puntos clave:
Author y Book en una asociación bidireccional (perezosa) @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Descripción: Esta aplicación es una prueba de concepto para usar proyecciones de primavera (DTO) y uniones correctas escritas a través de JPQL y Native SQL (para MySQL).
Puntos clave:
Author y Book en una asociación (perezosa) bidireccional @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Descripción: Esta aplicación es una prueba de concepto para el uso de proyecciones de primavera (DTO) y juntas completas inclusivas escritas a través de JPQL y SQL nativo (para PostgreSQL).
Puntos clave:
Author y Book en una asociación (perezosa) bidireccional @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )| Si necesita una inmersión profunda en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Prácticas de Persistencia de Boot de primavera" | Si necesita una mano de consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia Java, entonces "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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Descripción: Esta aplicación es una prueba de concepto para usar proyecciones de primavera (DTO) y uniones exclusivas de izquierda escritas a través de JPQL y SQL nativo (para MySQL).
Puntos clave:
Author y Book en una asociación (perezosa) bidireccional @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Descripción: Esta aplicación es una prueba de concepto para el uso de proyecciones de primavera (DTO) y uniones exclusivas de derecha escritos a través de JPQL y SQL nativo (para MySQL).
Puntos clave:
Author y Book en una asociación (perezosa) bidireccional @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Descripción: Esta aplicación es una prueba de concepto para el uso de proyecciones de primavera (DTO) y unión completa exclusiva escritas a través de JPQL y SQL nativo (para PostgreSQL).
Puntos clave:
Author y Book en una asociación (perezosa) bidireccional @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )Descripción: Esta aplicación es una prueba de concepto para usar ganchos de primavera posteriores a la Comunidad y cómo pueden afectar el rendimiento de la capa de persistencia.
Puntos clave:
Descripción: Esta aplicación es una prueba de concepto para usar proyecciones de primavera (DTO) y unirse a entidades no relacionadas. Hibernate 5.1 Introducidos Juntas explícitas en entidades no relacionadas y la sintaxis y el comportamiento son similares a las declaraciones JOIN SQL.
Puntos clave:
Author y Book entidades no relacionadas)resources/data-mysql.sql )BookstoreDto )@EqualsAndHashCode y @Data en entidades y cómo anular equals() y hashCode() Descripción: Las entidades deben implementar equals() y hashCode() como aquí. La idea principal es que Hibernate requiere que una entidad sea igual a sí misma en todas sus transiciones de estado ( transitorias , adjuntas , separadas y eliminadas ). Usar lombok @EqualsAndHashCode (o @Data ) no respetará este requisito.
Puntos clave:
Evite estos enfoques
@EqualsAndHashCode (entidad: LombokDefaultBook , prueba: LombokDefaultEqualsAndHashCodeTest )@EqualsAndHashCode solo con la clave primaria (entidad: LombokIdBook , prueba: LombokEqualsAndHashCodeWithIdOnlyTest )equals() y hashCode() (entidad: DefaultBook , prueba: DefaultEqualsAndHashCodeTest )equals() y hashCode() que contiene solo el identificador generado por la base de datos (entidad: IdBook , prueba: IdEqualsAndHashCodeTest )Prefiere estos enfoques
BusinessKeyBook , Test: BusinessKeyEqualsAndHashCodeTest )@NaturalId (Entity: NaturalIdBook , Test: NaturalIdEqualsAndHashCodeTest )IdManBook , Prueba: IdManEqualsAndHashCodeTest )IdGenBook , Test: IdGenEqualsAndHashCodeTest ) 
LazyInitializationException a través de JOIN FETCHVer también:
Descripción: Por lo general, cuando obtenemos una LazyInitializationException tendemos a modificar el tipo de búsqueda de la asociación de LAZY a EAGER . ¡Eso es muy malo! Este es un olor en código. La mejor manera de evitar esta excepción es confiar en JOIN FETCH (si planea modificar las entidades recuperadas) o JOIN + DTO (si los datos recuperados solo se leen). JOIN FETCH permite que las asociaciones se inicialicen junto con sus objetos principales utilizando una sola SELECT . Esto es particularmente útil para obtener colecciones asociadas.
Esta aplicación es un ejemplo JOIN FETCH para evitar la LazyInitializationException .
Puntos clave:
Author y Book en una asociación @OneToMany Lazy-bidireccional)JOIN FETCH para buscar a un autor que incluya sus librosJOIN FETCH (o JOIN ) para buscar un libro que incluya a su autor Ejemplo de salida: 
Descripción: Este es un ejemplo de arranque de primavera basado en el siguiente artículo. Es una implementación funcional del ejemplo de VLAD. Se recomienda leer ese artículo.
Puntos clave:
Descripción: Este es un ejemplo de arranque de primavera que explota Hibernate 5.2.10 Capacidad de retrasar la adquisición de conexión según sea necesario. De manera predeterminada, en el modo Locos de recursos , una conexión de base de datos se acumula inmediatamente después de llamar a un método anotado con @Transactional . Si este método contiene algunas tareas que requieren mucho tiempo antes de la primera instrucción SQL, la conexión está abierta para nada. Pero, Hibernate 5.2.10 nos permite retrasar la adquisición de conexión según sea necesario. Este ejemplo se basa en Hikaricp como el grupo de conexión predeterminado para el arranque de primavera.
Puntos clave:
spring.datasource.hikari.auto-commit=false en aplicación.propertiesspring.jpa.properties.hibernate.connection.provider_disables_autocommit=true en application.properties Ejemplo de salida: 
hi/lo Nota: Si los sistemas externos a su aplicación necesitan insertar filas en sus tablas, no confíe en el algoritmo hi/lo ya que, en tales casos, puede causar errores resultantes de la generación de identificadores duplicados. Confíe en algoritmos pooled o pooled-lo (optimizaciones de hi/lo ).
Descripción: Este es un ejemplo de arranque de primavera de usar el algoritmo hi/lo para generar 1000 identificadores en 10 Tripas redondas de la base de datos para lotes de 1000 insertos en lotes de 30.
Puntos clave:
SEQUENCE (por ejemplo, en PostgreSQL)hi/lo como en Author.java Entity Ejemplo de salida: 
| Si necesita una inmersión profunda en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Prácticas de Persistencia de Boot de primavera" | Si necesita una mano de consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia Java, entonces "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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@ManyToMany Descripción: Esta aplicación es una prueba de concepto de cómo es correcto implementar la asociación bidireccional @ManyToMany desde la perspectiva de rendimiento.
Puntos clave:
mappedBySet no ListCascadeType.PERSIST y CascadeType.MERGE , pero evite CascadeType.REMOVE/ALL@ManyToMany es flojo por defecto; ¡Mantenlo de esta manera!@NaturalId )) y/o identificadores generados por la base de datos y anule (en ambos lados) correctamente los métodos equals() y hashCode() como aquítoString() necesita ser anulado, entonces preste atención para involucrar solo a los atributos básicos obtenidos cuando la entidad se carga desde la base de datosSet en lugar de List en @ManyToMany Associations Descripción: Este es un ejemplo de arranque de primavera de eliminación de filas en caso de una @ManyToMany bidireccional usando List , respectivamente Set . ¡La conclusión es que Set es mucho mejor! ¡Esto también se aplica a unidireccional!
Puntos clave:
Set es mucho más eficiente que List Ejemplo de salida: 
log4jdbcDescripción: Vea los detalles de la consulta a través de LOG4JDBC.
Puntos clave:
pom.xml , agregue la dependencia log4jdbc Muestra de salida: 
Descripción: Vea los parámetros de enlace/extraído de instrucción preparada a través de TRACE .
Puntos clave:
application.properties Add: logging.level.org.hibernate.type.descriptor.sql=TRACE Muestra de salida: 
java.time.YearMonth como Integer o Date a través de la biblioteca de tipos hibernados Descripción: Hibernate tipos es un conjunto de tipos adicionales que no se admiten de forma predeterminada en Hibernate Core. Uno de estos tipos es java.time.YearMonth . Esta es una aplicación de arranque de primavera que usa Hibernate Type para almacenar este YearMonth en una base de datos MySQL como entero o fecha.
Puntos clave:
pom.xml@TypeDef para asignar typeClass a defaultForType Ejemplo de salida: 
Nota : El uso de funciones SQL en la parte WHERE (no en la parte SELECT ) de la consulta en JPA 2.1 se puede realizar a través de function() como aquí.
Descripción: Intentar usar funciones SQL (estándar o definidas) en consultas JPQL puede dar lugar a excepciones si Hibernate no las reconocerá y no puede analizar la consulta JPQL. Por ejemplo, la función MySQL, concat_ws no es reconocida por Hibernate. Esta aplicación es una aplicación de arranque de primavera basada en Hibernate 5.3, que registra la función concat_ws a través de MetadataBuilderContributor e informa a Hibernate al respecto a través de la propiedad metadata_builder_contributor . Este ejemplo usa @Query y EntityManager también, por lo que puede ver dos casos de uso.
Puntos clave:
MetadataBuilderContributor y registrar la función concat_ws mysqlapplication.properties , establezca spring.jpa.properties.hibernate.metadata_builder_contributor para señalar Hibernate a MetadataBuilderContributor Implementación Ejemplo de salida: 
Descripción: Esta aplicación es una muestra de registro de consultas lentas solo a través de DataSource-Proxy . Una consulta lenta es una consulta que tiene un tiempo de ejecución más grande que un umbral específico en milisegundos.
Puntos clave:
pom.xml la dependencia de la fuente de datosDataSource BeanDataSource Bean a través de ProxyFactory y una implementación de MethodInterceptorafterQuery() Ejemplo de salida: 
SELECT COUNT de activación y retorno Page<dto> Descripción: Esta aplicación obtiene datos como Page<dto> a través de la paginación de desplazamiento de arranque de primavera. La mayoría de las veces, los datos que deben paginarse son datos de solo lectura . Obtener los datos en las entidades debe realizarse solo si planeamos modificar esos datos, por lo tanto, no es preferible obtener datos de lectura como Page<entity> , ya que puede terminar en una sanción de rendimiento significativa. El SELECT COUNT activado para contar el número total de registros es una subconsulta de la SELECT principal. Por lo tanto, habrá una sola base redonda de base de datos en lugar de dos (generalmente, se necesita una consulta para obtener los datos y uno para contar el número total de registros).
Puntos clave:
PagingAndSortingRepositoryList<dto>List<dto> y el Pageable adecuado para crear una Page<dto>SELECT COUNT List<dto> Descripción: Esta aplicación obtiene datos como List<dto> a través de la paginación de desplazamiento de arranque de primavera. La mayoría de las veces, los datos que deben paginarse son datos de solo lectura . Obtener los datos en entidades solo debe realizarse si planeamos modificar esos datos, por lo tanto, obtener solo datos de lectura como List<entity> no es preferible, ya que puede terminar en una penalización de rendimiento significativa. El SELECT COUNT activado para contar el número total de registros es una subconsulta de la SELECT principal. Por lo tanto, habrá una sola base redonda de base de datos en lugar de dos (generalmente, se necesita una consulta para obtener los datos y uno para contar el número total de registros).
Puntos clave:
PagingAndSortingRepositoryList<dto> Si usa el spring-boot-starter-jdbc o spring-boot-starter-data-jpa "Starters", automáticamente obtiene una dependencia de Hikaricp
Nota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que configuró HIKARICP a través de application.properties solamente. El jdbcUrl está configurado para una base de datos MySQL. Para fines de prueba, la aplicación utiliza un ExecutorService para simular usuarios concurrentes. Consulte el informe Hickaricp que revela el estado del grupo de conexión.
Puntos clave:
application.properties , confíe en spring.datasource.hikari.* Para configurar Hikaricp Muestra de salida: 
| Si necesita una inmersión profunda en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Prácticas de Persistencia de Boot de primavera" | Si necesita una mano de consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia Java, entonces "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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DataSourceBuilder Si usa el spring-boot-starter-jdbc o spring-boot-starter-data-jpa "Starters", automáticamente obtiene una dependencia de Hikaricp
Nota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que configuró HIKARICP a través de DataSourceBuilder . El jdbcUrl está configurado para una base de datos MySQL. Para fines de prueba, la aplicación utiliza un ExecutorService para simular usuarios concurrentes. Consulte el informe Hickaricp que revela el estado del grupo de conexión.
Puntos clave:
application.properties , configure Hikaricp a través de un prefijo personalizado, por ejemplo, app.datasource.*@Bean que devuelva el DataSource Muestra de salida:
Esta aplicación se detalla en este artículo de Dzone.
DataSourceBuilderNota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que configuró Bonecp a través de DataSourceBuilder . El jdbcUrl está configurado para una base de datos MySQL. Para fines de prueba, la aplicación utiliza un ExecutorService para simular usuarios concurrentes.
Puntos clave:
pom.xml agregue la dependencia de Bonecpapplication.properties , configure Bonecp a través de un prefijo personalizado, por ejemplo, app.datasource.*@Bean que devuelva el DataSource Muestra de salida: 
DataSourceBuilderNota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que configuró ViburdBCP a través de DataSourceBuilder . El jdbcUrl está configurado para una base de datos MySQL. Para fines de prueba, la aplicación utiliza un ExecutorService para simular usuarios concurrentes.
Puntos clave:
pom.xml agregue la dependencia VIBURDBCPapplication.properties , configure ViburdBCP a través de un prefijo personalizado, por ejemplo, app.datasource.*@Bean que devuelva el DataSource Muestra de salida: 
DataSourceBuilderNota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que configuró C3P0 a través de DataSourceBuilder . El jdbcUrl está configurado para una base de datos MySQL. Para fines de prueba, la aplicación utiliza un ExecutorService para simular usuarios concurrentes.
Puntos clave:
pom.xml agregue la dependencia C3P0application.properties , configure C3P0 a través de un prefijo personalizado, por ejemplo, app.datasource.*@Bean que devuelva el DataSource Muestra de salida: 
DataSourceBuilderNota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que configuró DBCP2 a través de DataSourceBuilder . El jdbcUrl está configurado para una base de datos MySQL. Para fines de prueba, la aplicación utiliza un ExecutorService para simular usuarios concurrentes.
Puntos clave:
pom.xml agregue la dependencia de DBCP2application.properties , configure DBCP2 a través de un prefijo personalizado, por ejemplo, app.datasource.*@Bean que devuelva el DataSourceDataSourceBuilderNota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que configuró TomCat a través de DataSourceBuilder . El jdbcUrl está configurado para una base de datos MySQL. Para fines de prueba, la aplicación utiliza un ExecutorService para simular usuarios concurrentes.
Puntos clave:
pom.xml agregue la dependencia de Tomcatapplication.properties , configure TomCat a través de un prefijo personalizado, por ejemplo, app.datasource.*@Bean que devuelva el DataSource Muestra de salida: 
Nota: La mejor manera de ajustar los parámetros del grupo de conexión consiste en usar Flexy Pool de Vlad Mihalcea. A través de Flexy Pool, puede encontrar la configuración óptima que mantiene el alto rendimiento de su grupo de conexión.
Descripción: Esta es una aplicación de inicio que utiliza dos fuentes de datos (dos bases de datos MySQL, una llamada authorsdb y otra llamada booksdb ) con dos grupos de conexión (cada base de datos utiliza su propio grupo de conexión Hikaricp con diferentes configuraciones). Basado en los elementos anteriores es bastante fácil de configurar dos grupos de conexión de dos proveedores diferentes también.
Puntos clave:
application.properties , configure dos grupos de conexión Hikaricp a través de dos prefijos personalizados, por ejemplo, app.datasource.ds1 y app.datasource.ds2@Bean que devuelva el primer DataSource y marquelo como @Primary@Bean que devuelva el segundo DataSourceEntityManagerFactory y señale los paquetes para escanear para cada uno de ellosEntityManager en los paquetes correctos Muestra de salida: 
Nota : Si desea que proporcione una API fluida sin alterar a los establecedores, entonces considere este elemento.
Descripción: Esta es una aplicación de muestra que altera los métodos de Setters para capacitar una API fluida.
Puntos clave:
this en lugar de void en los setters Ejemplo de API fluido: 
Nota : Si desea que proporcione una API fluida alterando a los establecedores, entonces considere este elemento.
Descripción: Esta es una aplicación de muestra que agrega métodos adicionales de entidades (por ejemplo, para setName , agregamos name ) métodos para capacitar una API fluida.
Puntos clave:
this en lugar de void Ejemplo de API fluido: 
| Si necesita una inmersión profunda en las recetas de rendimiento expuestas en este repositorio, estoy seguro de que le encantará mi libro "Prácticas de Persistencia de Boot de primavera" | Si necesita una mano de consejos e ilustraciones de más de 100 problemas de rendimiento de persistencia Java, entonces "Guía ilustrada de rendimiento de persistencia de Java" es para usted. |
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Slice<T> findAll() Lo más probable es que esto sea todo lo que desea: cómo obtener Slice<entity> / Slice<dto> a través de fetchAll / fetchAllDto
Algunas implementaciones de Slice<T> findAll() :
"SELECT e FROM " + entityClass.getSimpleName() + " e;"CriteriaBuilder en lugar de SQL codificadoSort , por lo que es posible clasificar los resultadosSort Specification de datos de Spring y SpringSort , un LockModeType , una QueryHints y una Specification de datos de SpringPageable y/o Specification de datos de Spring al extender el SimpleJpaRepository de los datos de Spring. Bascialmente, esta implementación es la única que devuelve Page<T> en lugar de Slice<T> , pero no se desencadena el SELECT COUNT adicional ya que se eliminó anulando la Page<T> readPage(...) desde SimpleJpaRepository . El principal inconveniente es que al retirar una Page<T> no sabe si hay una página siguiente o la actual es la última. Sin embargo, hay soluciones para tener esto también. En esta implementación no puede establecer LockModeType o consultar sugerencias. Historia : Spring Boot proporciona un mecanismo de paginación incorporado basado en compensación que devuelve una Page o Slice . Cada una de estas API representa una página de datos y algunos metadatos. La principal diferencia es que Page contiene el número total de registros, mientras que Slice solo puede decir si hay otra página disponible. Para Page , Spring Boot proporciona un método findAll() capaz de tomar como argumentos un Pageable y/o una Specification o Example . Para crear una Page que contenga el número total de registros, este método desencadena un SELECT COUNT Extrayery junto a la consulta utilizada para obtener los datos de la página actual. Esta puede ser una penalización de rendimiento ya que la consulta SELECT COUNT se activa cada vez que solicitamos una página. Para evitar esta extraña, Spring Boot proporciona una API más relajada, la API Slice . El uso de Slice en lugar de Page elimina la necesidad de esta consulta SELECT COUNT adicional y devuelve la página (registros) y algunos metadatos sin el número total de registros. Entonces, si bien Slice no sabe el número total de registros, aún puede saber si hay otra página disponible después de la actual o esta es la última página. El problema es que Slice funciona bien para consultas que contienen el SQL, WHERE la cláusula (incluidas las que usan el mecanismo de constructor de consultas integrado en los datos de Spring), pero no funciona para findAll() . Este método aún devolverá una Page en lugar de Slice , por lo tanto, la consulta SELECT COUNT se activa para Slice<T> findAll(...); .
Descripción: Este es un conjunto de aplicaciones de muestras que proporciona diferentes versiones de un método de Slice<T> findAll(...) . Tenemos de una implementación minimalista que se basa en una consulta codificada como: "SELECT e FROM " + entityClass.getSimpleName() + " e"; (esta receta), a una implementación personalizada que admite la clasificación, la especificación, el modo de bloqueo y la consulta, sugiere una implementación que se basa en extender SimpleJpaRepository .
Puntos clave:
abstract que exponga los métodos Slice<T> findAll(...) ( SlicePagingRepositoryImplementation )findAll() para devolver Slice<T> (o Page<T> , pero sin el número total de elementos)SliceImpl ( Slice<T> ) o un PageImpl ( Page<T> ) sin el número total de elementosreadSlice() o anular la página SimpleJpaRepository#readPage() para evitar SELECT COUNTAuthor.class ) a esta clase abstract a través de un repositorio de clases ( AuthorRepository )COUNT(*) OVER List<dto> Descripción: Por lo general, en la paginación compensada, se necesita una consulta para obtener los datos y otra para contar el número total de registros. Pero, podemos obtener esta información en una sola base de datos Rountrip a través de un SELECT COUNT subconsciente anidado en la SELECT principal. Aún mejor, para los proveedores de bases de datos que admiten las funciones de la ventana, hay una solución que depende del COUNT(*) OVER() como en esta aplicación que usa esta función de ventana en una consulta nativa contra MySQL 8. Por lo tanto, prefiera esta en lugar de SELECT COUNT Subquery .
Puntos clave:
COUNT(*) OVER() Ejemplo: 
Descripción: Cuando confiamos en una paginación compensada , tenemos la penalización de rendimiento inducida al desechar n registros antes de alcanzar el desplazamiento deseado. N más grande conduce a una penalización de rendimiento significativa. Cuando tenemos una N grande, es mejor confiar en la paginación de teclas que mantienen un tiempo "constante" para conjuntos de datos grandes. Para comprender cómo puede funcionar de mal compensación, consulte este artículo:
Captura de pantalla de ese artículo (Pagination Offset ): 
¿Necesita saber si hay más registros?
Por su naturaleza, KeySet no usa un SELECT COUNT para obtener el número de registros totales. Pero, con un pequeño ajuste, podemos decir fácilmente si hay más registros, por lo tanto, para mostrar un botón de tipo Next Page . Principalmente, si necesita tal cosa, considere esta aplicación cuyo clímax se enumera a continuación:
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);
}
O, como este (confíe en el 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);
}
Un botón Previous Page se puede implementar fácilmente en función del primer registro.
Puntos clave:
id )WHERE y ORDER BY cláusulas de su SQLDescripción: Este es un ejemplo de paginación de compensación de botas de primavera clásica. Sin embargo, no es aconsejable utilizar este enfoque en la producción debido a sus sanciones de rendimiento explicadas más a fondo.
Cuando confiamos en una paginación compensada , tenemos la penalización de rendimiento inducida al tirar n registros antes de alcanzar la compensación deseada. N más grande conduce a una penalización de rendimiento significativa. Otra penalización es la SELECT extra necesaria para contar el número total de registros. Para comprender cómo puede funcionar la paginación de compensación , consulte este artículo. Una captura de pantalla de ese artículo está a continuación: Sin embargo, tal vez este ejemplo es un poco extremo. Para conjuntos de datos relativamente pequeños, la paginación de compensación no es tan mala (está cerca de rendimiento a la paginación de KeySet ) y, dado que Spring Boot proporciona soporte incorporado para la paginación de compensación a través de la API Page , es muy fácil usarlo. Sin embargo, dependiendo del caso, podemos optimizar un poco la paginación de desplazamiento como en los siguientes ejemplos:
Buscar una página como Page :
COUNT(*) OVER Page<dto>COUNT(*) OVER Page<entity> a través de una columna adicionalSELECT COUNT Subquery y return Page<dto>SELECT COUNT SUBCERY Y RETURN Page<entity> a través de una columna adicionalSELECT COUNT página de conteo y retorno Page<projection> que mapea las entidades y el número total de registros a través de la proyección Obtener una página como List :
COUNT(*) OVER List<dto>COUNT(*) OVER List<entity> a través de una columna adicionalSELECT COUNT Subquery y Return List<dto>SELECT COUNT SUBCERY Y List<entity> a través de una columna adicionalSELECT COUNT List<projection> que mapea las entidades y el número total de registros a través de la proyecciónPero: si la paginación de compensación le causa problemas de rendimiento y usted decide ir con la paginación de KeySet , entonces consulte aquí (Paginación de Keyset ).
Puntos clave de la paginación de compensación clásica:
PagingAndSortingRepositoryPage<entity>Ejemplos de paginación de compensación clásica:
findAll(Pageable) sin clasificar:repository.findAll(PageRequest.of(page, size));findAll(Pageable) con la clasificación:repository.findAll(PageRequest.of(page, size, new Sort(Sort.Direction.ASC, "name")));Page<Author> findByName(String name, Pageable pageable);Page<Author> queryFirst10ByName(String name, Pageable pageable); Descripción: Supongamos que tenemos una relación de uno a muchos entre las entidades Author y Book . Cuando salvamos a un autor, también salvamos sus libros gracias a la cascada de todo/persistir. Queremos crear un grupo de autores con libros y guardarlos en la base de datos (por ejemplo, una base de datos MySQL) utilizando la técnica de lotes. Por defecto, esto dará como resultado un lote de cada autor y los libros por autor (un lote para el autor y un lote para los libros, otro lote para el autor y otro lote para los libros, etc.). Para lanzar a los autores y libros, debemos ordenar inserciones como en esta aplicación.
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: 
Implementations:
Description: Batch updates in MySQL.
Puntos clave:
application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL, statements get rewritten into a single string buffer and sent in a single request)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)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_updates=true to optimize the batching by ordering actualizacionesapplication.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: 
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:
deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() methodapplication.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL, statements get rewritten into a single string buffer and sent in a single request)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)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))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 Ejemplo de salida: 
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() :
Author entity and each author can have several Book ( one-to-many )orphanRemoval=true and CascadeType.ALLBook from the corresponding AuthororphanRemoval=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)Author via the deleteAll() or delete() method (since we have dissaciated all Book , the Author deletion will take advantage of batching as well)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=? ... declaración. 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.
Puntos clave:
Author entity and each author can have several Book ( one-to-many )orphanRemoval or set it to falseCascadeType.PERSIST and CascadeType.MERGE@OnDelete(action = OnDeleteAction.CASCADE) next to @OneToManyspring.jpa.properties.hibernate.dialect to org.hibernate.dialect.MySQL5InnoDBDialect (or, MySQL8Dialect )deleteFoo() methods that uses bulk and batching deletes as wellEjemplo de salida:
@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.
Puntos clave:
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@NaturalId(mutable = false) and @Column(nullable = false, updatable = false, unique = true, ...)@NaturalId(mutable = true) and @Column(nullable = false, updatable = true, unique = true, ...)equals() and hashCode() using the natural id(s)@NoRepositoryBean interface ( NaturalRepository ) to define two methods, named findBySimpleNaturalId() and findByNaturalId()NaturalRepositoryImpl ) relying on Hibernate, Session , bySimpleNaturalId() and byNaturalId() methods@EnableJpaRepositories(repositoryBaseClass = NaturalRepositoryImpl.class) to register this implementation as the base classfindBySimpleNaturalId() or findByNaturalId()| 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. |
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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.
Puntos clave:
pom.xml , add the P6Spy Maven dependencyapplication.properties , set up JDBC URL as, jdbc:p6spy:mysql://localhost:3306/db_usersapplication.properties , set up driver class name as, com.p6spy.engine.spy.P6SpyDriverspy.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 Muestra de salida: 
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.
Puntos clave:
pom.xml , add the db-util dependencyOptimisticConcurrencyControlAspect bean@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) Muestra de salida: 
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.
Puntos clave:
pom.xml , add the db-util library dependencyOptimisticConcurrencyControlAspect beanInventory ) with @DynamicUpdate and @OptimisticLocking(type = OptimisticLockType.DIRTY)@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)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.
Puntos clave:
name and ageAuthorNameAge , 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 )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 ) Ejemplo de salida: 
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.
Puntos clave:
AuthorRepository ) Ejemplos:
- 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: 
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.
Puntos clave:
application.properties , set the JDBC URL (eg, spring.datasource.url=jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true )application.properties , disable DDL auto (just don't add explicitly the hibernate.ddl-auto setting)application.properties , instruct Spring Boot to initialize the schema from schema-mysql.sql fileschema-*.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 .
Puntos clave:
application.properties , set the JDBC URL without the database, eg, spring.datasource.url=jdbc:mysql://localhost:3306application.properties , disable DDL auto (just don't specify hibernate.ddl-auto )aaplication.properties , instruct Spring Boot to initialize the schema from schema-mysql.sql fileAuthor entity, specify that the corresponding table ( author ) is in the database authorsdb via @Table(schema="authorsdb")Book entity, specify that the corresponding table ( book ) is in the database booksdb via @Table(schema="booksdb")Ejemplo de salida:
Author results in the following SQL: insert into authorsdb.author (age, genre, name) values (?, ?, ?)Book results the following SQL: insert into booksdb.book (isbn, title) values (?, ?)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.
Puntos clave:
@Transactional(readOnly=true) )Integer.MIN_VALUE (recommended in MySQL))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)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 ).
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-autoapplication.properties , set the JDBC URL as follows: jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=trueclasspath:db/migrationV1.1__Description.sql , V1.2__Description.sql , ...spring.flyway.schemas 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 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 .
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-autoapplication.properties , set the JDBC URL as follows: jdbc:mysql://localhost:3306/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)@Table(schema/catalog = "bookstoredb")classpath:db/migrationV1.1__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. |
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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.
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.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=booksdbapplication.properties , set spring.flyway.enabled=false to disable default behaviorDataSource for MySQL and one for PostgreSQLFlywayDataSource for MySQL and one for PostgreSQLEntityManagerFactory for MySQL and one for PostgreSQLdbmigrationmysqldbmigrationpostgresql 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 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.
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.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=authorsapplication.properties , set spring.flyway.enabled=false to disable default behaviorDataSource , one for books and one for authorsFlywayDataSource , one for books and one for authorsEntityManagerFactory , one for books and one for authorsbooks , place the migration SQLs files in dbmigrationbooksauthors , place the migration SQLs files in dbmigrationauthorsJOIN FETCH an @ElementCollection Description: This application is an example applying JOIN FETCH to fetch an @ElementCollection .
Puntos clave:
@ElementCollection is loaded lazy, keep it lazyJOIN FETCH in the repository@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.
Puntos clave:
Author (including association to Book )@Immutable since no write operations are allowed@Synchronize@Subselect to write the needed query, map an entity to an SQL queryDescription: This application is an example of using Hibernate soft deletes in a Spring Boot application.
Puntos clave:
abstract class BaseEntity with a field named deletedAuthor and Book entities) that should take advantage of soft deletes should extend BaseEntity@Where annotation like this: @Where(clause = "deleted = false")@SQLDelete annotation to trigger UPDATE SQLs in place of DELETE SQLs, as follows: @SQLDelete(sql = "UPDATE author SET deleted = true WHERE id = ?") Ejemplo de salida: 
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.
Puntos clave:
@Bean that returns the DataSource programmaticallyDescription: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Puntos clave:
abstract base entity (eg, BaseEntity ) and annotate it with @MappedSuperclass and @EntityListeners({AuditingEntityListener.class})@CreatedDate protected LocalDateTime created;@LastModifiedDate protected LocalDateTime lastModified;@CreatedBy protected U createdBy;@LastModifiedBy protected U lastModifiedBy;@EnableJpaAuditing(auditorAwareRef = "auditorAware")AuditorAware (this is needed for persisting the user that performed the modification; use Spring Security to return the currently logged-in user)@Beanspring.jpa.hibernate.ddl-auto=create )Description: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Puntos clave:
@Audited@AuditTable to rename the table used for auditingValidityAuditStrategy for fast database reads, but slower writes (slower than the default DefaultAuditStrategy )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.
Puntos clave:
BaseAuthor , and annotate it with @MappedSuperclassAuthorShallow subentity of BaseAuthor and don't add any attribute in it (this will inherit the attributes from the superclass)AuthorDeep subentity of BaseAuthor and add to it the attributes that should be loaded on demand (eg, avatar )@Table(name = "author")AuthorShallowRepository and AuthorDeepRepositoryRun the following requests (via BookstoreController):
localhost:8080/authors/shallowlocalhost:8080/authors/deepCheck as well:
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.
Puntos clave:
See also:
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. |
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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.
Puntos clave:
Page (with SELECT COUNT and COUNT(*) OVER() window function)Slice and ListDENSE_RANK() for avoiding the truncation of the result set (an author can be fetched with only a subset of his books)LEFT JOIN FETCHSee also:
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 .
Puntos clave:
Author and Book in a one-to-many lazy bidirectional relationship)LEFT JOIN FETCH to fetch all authors and books (fetch authors even if they don't have registered books)LEFT JOIN FETCH to fetch all books and authors (fetch books even if they don't have registered authors)JOIN VS. JOIN FETCHSee also:
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).
Puntos clave:
Author and Book in a one-to-many lazy-bidirectional relationship)JOIN and JOIN FETCH to fetch an author including his booksJOIN to fetch a book (1)JOIN to fetch a book including its author (2)JOIN FETCH to fetch a book including its authorNotice that:
JOIN , fetching Book of Author requires additional SELECT statements being prone to N+1 performance penaltyJOIN (1), fetching Author of Book requires additional SELECT statements being prone to N+1 performance penaltyJOIN (2), fetching Author of Book works exactly as JOIN FETCH (requires a single SELECT )JOIN FETCH , fetching each Author of a Book requires a single SELECT 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.
Puntos clave:
Author and Book in a one-to-many lazy bidirectional relationship)public Author getAuthor() and public String getTitle() 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.
Puntos clave:
Author and Book )public Author getAuthor() and public String getTitle() 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).
@ElementCollection Description: This application is an example of fetching a DTO that includes attributes from an @ElementCollection .
Puntos clave:
@ElementCollection is loaded lazy, keep it lazyJOIN in the repositorySet 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 qué? 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.
Puntos clave:
@OrderByHashSet , but doesn't provide consistency across all transition states (eg, transient state)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.
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 ).
Puntos clave:
findById(1L) ; commit transaction and close the Persistence ContextfindById(1L) and update it; commit the transaction and close the Persistence Contextsave() 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 matchOptimisticLockException 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.
Puntos clave:
@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. |
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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.
Puntos clave:
pom.xml , add the db-util dependencyOptimisticConcurrencyControlAspect beanTransactionTemplateOptimisticLockException In Version-less Optimistic LockingNote: Version-less optimistic locking doesn't work for detached entities (do not close the Persistence Context).
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.
Puntos clave:
@Transactional method used for updating dataTransactionTemplate After OptimisticLockException Shaped Via Hibernate Version-less Optimistic Locking MechanismNote: Version-less optimistic locking doesn't work for detached entities (do not close the Persistence Context).
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.
Puntos clave:
pom.xml , add the db-util dependencyOptimisticConcurrencyControlAspect beanTransactionTemplateDescription: 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.
Puntos clave:
@Version@SessionAttributes for storing the detached entities Sample output (check the message caused by optimistic locking exception): 
@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.
Puntos clave:
@Where(clause = "condition to be met") in entity (check the Author entity)Description: Batch inserts (in MySQL) in Spring Boot style.
Puntos clave:
application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)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)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledspring.jpa.properties.hibernate.cache.use_second_level_cache=false Ejemplo de salida: 
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.
Puntos clave:
PagingAndSortingRepository@Column(insertable = false, updatable = false)List<entity>List<entity> and Pageable to create a Page<entity>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).
Puntos clave:
PagingAndSortingRepositoryentity , add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)SELECT COUNT subquery) into a List<entity>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.
Puntos clave:
PagingAndSortingRepositorySELECT COUNT subquery) into a List<projection>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.
Puntos clave:
PagingAndSortingRepositoryentity , add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)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. |
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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).
Puntos clave:
PagingAndSortingRepository@Column(insertable = false, updatable = false)List<entity>List<entity> and Pageable to create a Page<entity>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 .
Puntos clave:
PagingAndSortingRepositoryList<projection>List<projection> and Pageable to create a Page<projection>COUNT(*) OVER And Return Page<dto> 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 return a Page<dto> .
Puntos clave:
PagingAndSortingRepositoryList<dto>List<dto> and Pageable to create a Page<dto> Ejemplo:
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() .
Ejemplo 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")));
}
Description: This application is a proof of concept for using Spring Projections(DTO) and inclusive full joins written in native SQL (for MySQL).
Puntos clave:
Author and Book in a lazy bidirectional @OneToMany relationship)resources/data-mysql.sql )AuthorNameBookTitle.java )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:
@Immutable (org.hibernate.annotations.Immutable)hibernate.cache.use_reference_entries configuration to trueDataSourceBuilder 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.
Puntos clave:
@Bean that returns the DataSource programmatically Muestra de salida:
@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.
Puntos clave:
EhCache )@NaturalIdCache for caching natural ids@Cache(usage = CacheConcurrencyStrategy.READ_WRITE, region = "Book") for caching entites as well Output sample (for MySQL with IDENTITY generator, @NaturalIdCache and @Cache ): 
@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 .
Puntos clave:
@Transient@PostLoad that calculates this non-persistent property based on the persistent entity attributes@Generated Description: This application is an example of calculating an entity persistent property at INSERT and/or UPDATE time via Hibernate, @Generated .
Puntos clave:
Calculate at INSERT time:
@Generated(value = GenerationTime.INSERT)@Column(insertable = false) Calculate at INSERT and UPDATE time:
@Generated(value = GenerationTime.ALWAYS)@Column(insertable = false, updatable = false)Further, apply:
Método 1:
columnDefinition element of @Column to specify as an SQL query expression the formula for calculating the persistent propertyMétodo 2:
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. |
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@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 .
Puntos clave:
@Transient@Formula@Formula add the SQL query expression that calculates this non-persistent property based on the persistent entity attributescreated , createdBy , lastModified And lastModifiedBy In Entities Via HibernateNote: 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.
Puntos clave:
abstract class (eg, BaseEntity ) annotated with @MappedSuperclassabstract class, define a field named created and annotate it with the built-in @CreationTimestamp annotationabstract class, define a field named lastModified and annotate it with the built-in @UpdateTimestamp annotationabstract class, define a field named createdBy and annotate it with the @CreatedBy annotationabstract class, define a field named lastModifiedBy and annotate it with the @ModifiedBy annotation@CreatedBy annotation via AnnotationValueGeneration@ModifiedBy annotation via AnnotationValueGenerationcreated , createdBy , lastModified and lastModifiedBy will extend the BaseEntityschema-mysql.sql )Description: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Puntos clave:
@Audited@AuditTable to rename the table used for auditingValidityAuditStrategy for fast database reads, but slower writes (slower than the default DefaultAuditStrategy )spring.jpa.hibernate.ddl-auto or set it to validate for avoiding schema generated from JPA annotationsschema-mysql.sql and provide the SQL statements needed by Hibernate Enversspring.jpa.properties.org.hibernate.envers.default_catalog for MySQL or spring.jpa.properties.org.hibernate.envers.default_schema for the restDataSource 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 a kickoff for setting Flyway and MySQL DataSource programmatically.
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateDataSource and Flyway programmaticallypostgres And Schema public 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 PostgreSQL database via Flyway for the default database postgres and schema public .
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.properties , set the JDBC URL as follows: jdbc:postgresql://localhost:5432/postgresclasspath:db/migrationV1.1__Description.sql , V1.2__Description.sql , ...postgres And Schema Created Via spring.flyway.schemas 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 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") .
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.properties , set the JDBC URL as follows: jdbc:postgresql://localhost:5432/postgresapplication.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)@Table(schema = "bookstore")classpath:db/migrationV1.1__Description.sql , V1.2__Description.sql , ...DataSource 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 a kickoff for setting Flyway and PostgreSQL DataSource programmatically.
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateDataSource and Flyway programmatically 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 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 .
Puntos clave:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.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=trueapplication.properties , set spring.flyway.enabled=false to disable default behaviorDataSource , one for booksdb and one for authorsdbFlywayDataSource , one for booksdb and one for authorsdbEntityManagerFactory , one for booksdb and one for authorsdbbooksdb , place the migration SQLs files in dbmigrationbooksdbauthorsdb , place the migration SQLs files in dbmigrationauthorsdbhi/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.
Puntos clave:
SEQUENCE generator type (eg, in PostgreSQL)hi/lo algorithm as in Author.java entityhi/loNEXTVAL('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.
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.
Puntos clave:
SEQUENCE generator type (eg, in PostgreSQL)pooled algorithm as in Author.java entitypooledNEXTVAL('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. |
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pooled-lo 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-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 .
Puntos clave:
SEQUENCE generator type (eg, in PostgreSQL)pooled-lo algorithm as in Author.java entitypooled-loNEXTVAL('hilo_sequence') and is not aware of pooled-lo presence and/or behavior)@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.
Puntos clave:
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)@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. . Más detalles aquí.
Description: This is a sample application of using entity graphs in Spring Boot.
Puntos clave:
Author and Book , involved in a lazy bidirectional @OneToMany associationAuthor entity use the @NamedEntityGraph to define the entity graph (eg, load in a single SELECT the authors and the associatated books)AuthorRepositry rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous step 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. . Más detalles aquí.
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 .
Puntos clave:
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
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 booksAuthorRepository rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous stepUsing the dot notation (.)
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"}) . 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. . Más detalles aquí.
Description: This is a sample application of defining ad-hoc entity graphs in Spring Boot.
Puntos clave:
Author and Book , involved in a lazy bidirectional @OneToMany relationshipSELECT the authors and the associatated booksAuthorRepository rely on Spring @EntityGraph(attributePaths = {"books"}) annotation to indicate the ad-hoc entity graph@Basic Attributes In Hibernate And 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. 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.
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Author and Book , involved in a lazy bidirectional @OneToMany associationAuthor 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)@Basic(fetch = FetchType.LAZY)AuthorRepository rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous stepSoftDeleteRepository In Spring Boot ApplicationNote: 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 .
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abstract class, BaseEntity , annotated with @MappedSuperclassBaseEntity define a flag-field named deleted (default this field to false or in other words, not deleted)BaseEntity classs@NoRepositoryBean named SoftDeleteRepository and extend JpaRepositorySoftDeleteRepository Ejemplo de salida:
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.
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Book entity)BookRepository setup @Lock(LockModeType.PESSIMISTIC_WRITE)BookRepository use @QueryHint to setup javax.persistence.lock.timeout to SKIP_LOCKEDorg.hibernate.dialect.MySQL8Dialect dialectSKIP_LOCKEDSKIP_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.
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Book entity)BookRepository setup @Lock(LockModeType.PESSIMISTIC_WRITE)BookRepository use @QueryHint to setup javax.persistence.lock.timeout to SKIP_LOCKEDorg.hibernate.dialect.PostgreSQL95Dialect dialectSKIP_LOCKEDJOINED Description: This application is a sample of JPA Join Table inheritance strategy ( JOINED )
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@Inheritance(strategy=InheritanceType.JOINED)@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. |
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TABLE_PER_CLASS Description: This application is a sample of JPA Table-per-class inheritance strategy ( TABLE_PER_CLASS )
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IDENTITY generator@Inheritance(strategy=InheritanceType.TABLE_PER_CLASS)@MappedSuperclass Description: This application is a sample of using the JPA @MappedSuperclass .
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abstract , and is annotated with @MappedSuperclass@MappedSuperclass is the proper alternative to the JPA table-per-class inheritance strategyNote: 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.
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pom.xml@Bean that returns an instance of Hibernate5ModuleAuthor 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.
profileSQL=true In MySQL Description: View the prepared statement binding parameters via profileSQL=true in MySQL.
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application.properties append logger=Slf4JLogger&profileSQL=true to the JDBC URL (eg, jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true&logger=Slf4JLogger&profileSQL=true ) Muestra de salida: 
Description: This application is an example of shuffling small results sets. DO NOT USE this technique for large results sets, since is extremely expensive.
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SELECT query and append to it ORDER BY RAND()RAND() (eg, in PostgreSQL is random() ) 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:
Author is in Persistent Context, no BookAuthor are in the Persistent Context, no BookAuthor and the associated Book are in Persistent ContextAuthor 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() .
Description: Bulk operations (updates and deletes) are faster than batching, can benefit from indexing, but they have three main drawbacks:
@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))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.
@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.
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@OneToMany is less efficient than bidirectional @OneToMany association@OrderColumn come with some optimizations for removal operations but is still less efficient than bidirectional @OneToMany association@JoinColumn eliminates the junction table but is still less efficient than bidirectional @OneToMany associationSet 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 associationWHERE / 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).
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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! ¡Punto de referencia! ¡Punto de referencia!
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.
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.
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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. |
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Description: This application is an example of calling a MySQL stored procedure that returns a value (eg, an Integer ).
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@NamedStoredProcedureQuery to shape the stored procedure in the entity@Procedure in repository 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> ).
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EntiyManager since Spring Data @Procedure will not workDescription: This application is an example of calling a MySQL stored procedure that returns a result set (entity or DTO) via a native query.
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@Query(value = "{CALL FETCH_AUTHOR_BY_GENRE (:p_genre)}", nativeQuery = true)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 .
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JdbcTemplate and SimpleJdbcCallDescription: This application is an example of retrieving the database auto-generated primary keys.
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getId()JdbcTemplateSimpleJdbcInsert 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.
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JpaRepository#getOne()Hibernate.unproxy()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 .
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AttributeConverter@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.
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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.
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countQuery 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.
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.
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Page of entities in read-write or read-only modeSlice 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. |
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@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.
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readOnly = false load data in read-write mode (managed)readOnly = true discard the hydrated state (starting with Spring 5.1)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.
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SELECT tx.trx_id FROM information_schema.innodb_trx tx WHERE tx.trx_mysql_thread_id = connection_id() Description: This application is a sample of inspecting the Persistent Context content via org.hibernate.engine.spi.PersistenceContext .
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SharedSessionContractImplementorPersistenceContext API Description: This application is an example of using the Hibernate SPI, org.hibernate.integrator.spi.Integrator for extracting tables metadata.
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org.hibernate.integrator.spi.Integrator and override integrate() method to return metadata.getDatabase()Integrator via LocalContainerEntityManagerFactoryBean@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.
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B is done via @JoinFormulaDescription: This application is an example of fetching a read-only MySQL database view in a JPA immutable entity.
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data-mysql.sql fileGenreAndTitleView.javaDescription: 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).
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data-mysql.sql fileWITH 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.
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WITH CHECK OPTION to the viewjava.sql.SQLException: CHECK OPTION failed 'bookstoredb.author_anthology_view 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.
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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 wellROW_NUMBER() window function Muestra de salida: 
Description: This application is an example of finding top N rows of every group.
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ROW_NUMBER() window function Muestra de salida: 
| 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. |
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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.
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ROW_NUMBER()Page or Slice , we return it as List , therefore Pageable is not used@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 invokedKey points:
@Transactional methods therepublic@Transactional methods from other services 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 , ...).
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SequenceStyleGenerator and override the configure() and generate() methodsClob And Blob To byte[] And String Description: This application is an example of mapping Clob and Blob as byte[] and String .
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LOB Locators Clob And Blob Description: This application is an example of mapping to JDBC's LOB locators Clob and Blob .
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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.
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WHERE clause a TYPE check@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.
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@JoinColumn(referencedColumnName = "natural_id_column")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.
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SpecificationSpecification Query Fetch Joins Description: This application contains two examples of how to define JOIN in Specification to emulate JPQL join-fetch operations.
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SELECT statements and the pagination is done in memory (very bad!)SELECT statements but the pagination is done in the databaseJOIN is defined in a Specification implementationNote: 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.
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List<projection>LIMIT ) - here, we can use query builder mechanism built into Spring Data repository infrastructureNote: 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. |
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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.
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null@JustOneOfMany ) added at class-level to the Review entityTRIGGER ) 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 .
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EnumType.ORDINAL set @Column(columnDefinition = "TINYINT") )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.
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AttributeConverter@Converter the corresponding entity fieldenum To PostgreSQL enum Type Description: This application maps a Java enum type to PostgreSQL enum type.
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EnumTypeEnumType via package-info.java@Typeenum To PostgreSQL enum Type Via Hibernate Types Library Description: This application maps a Java enum type to PostgreSQL enum type via Hibernate Types library.
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pom.xml@TypeDef to specify the needed type class@Type 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.
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pom.xml@TypeDef to map typeClass to JsonStringType 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.
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pom.xml@TypeDef to map typeClass to JsonBinaryTypeOPTIMISTIC_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.
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Chapter (which uses @Version )Modification entityModification (child-side) and Chapter (parent-side) there is a lazy unidirectional @ManyToOne associationINSERT statement against the modification table, therefore the chapter table will not be modified by editorsChapter 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)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)OPTIMISTIC_FORCE_INCREMENT in the corresponding repositoryObjectOptimisticLockingFailureExceptionPESSIMISTIC_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.
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Chapter (which uses @Version )Modification entityModification (child-side) and Chapter (parent-side) there is a lazy unidirectional @ManyToOne associationINSERT statement against the modification table, therefore the chapter table will not be modified by editorsChapter 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)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)PESSIMISTIC_FORCE_INCREMENT in the corresponding repositoryOptimisticLockException 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 .
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.
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@Lock(LockModeType.PESSIMISTIC_READ) and @Lock(LockModeType.PESSIMISTIC_WRITE) on query-levelTransactionTemplate 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. |
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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.
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SELECT with PESSIMISTIC_WRITE to acquire an exclusive lockUPDATE , INSERT or DELETE on the rows locked by Transaction AUPDATE , DELETE and INSERT + REPEATABLE_READ , Transaction B is blocked until it timeouts or Transaction A releases the exclusive lockINSERT + 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 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>)] .
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spring.transaction.default-timeout in seconds (see, application.properties )@Transactional(timeout = n) in secondsjavax.persistence.query.timeout hint in millisecondsorg.hibernate.timeout hint in seconds Note: If you are using TransactionTemplate then the timeout can be set via TransactionTemplate.setTimeout(n) in seconds.
@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.
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AuthorId ) is publicSerializableequals() and hashCode()@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.
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AuthorId ) is publicSerializableequals() and hashCode() Note : The @IdClass can be useful when we cannot modify the compsite key class. Otherwise, rely on @Embeddable .
@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.
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AuthorId ) is publicSerializableequals() and hashCode() 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.
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IN operator in a @Query simply add the query in the proper repositoryMultiIdentifierLoadAccess in Spring Data style provide the proper implementationMultiIdentifierLoadAccess 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) 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:
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 approachList<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 efficientList<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 slowList<Object[]> via Spring Data query builder mechanism WILL fetch the result set in Persistent Context - avoid this approachList<AuthorDto> via Spring Data query builder mechanism will NOT fetch the result set in Persistent ContextfindAll() method) should be considered after JPQL with explicit list of columns to be fetched and query builder mechanism@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.
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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.
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:
@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)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. |
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Description: This application is a sample of using dynamic Spring projections.
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<T> List<T> findByGenre(String genre, Class<T> type); ) 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:
application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)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)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledspring.jpa.properties.hibernate.cache.use_second_level_cache=false Ejemplo de salida:
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:
json typeListrewriteBatchedStatements=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 disableduseServerPrepStmts=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;...?cachePrepStmts=true&useServerPrepStmts=true&rewriteBatchedStatements=true&createDatabaseIfNotExist=trueStopWatch to measure the time needed to transfer the file into the databasecitylots.zip in the current location; this is the big JSON file collected from Internet;DatasourceProxyBeanPostProcessor.java component by uncomment the line, // @Component ; This is needed because this application relies on DataSource-Proxy (for details, see the following item)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.
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.
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:
application.properties the following property: spring.jpa.properties.hibernate.order_inserts=true Example without ordered inserts:
Example with ordered inserts:
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.
Key points:
application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)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)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledspring.jpa.properties.hibernate.cache.use_second_level_cache=false Ejemplo de salida:
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:
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application.properties set spring.jpa.properties.hibernate.query.in_clause_parameter_padding=trueDescription: Fetch only the needed data from the database via Spring Data Projections (DTO). In this case, via class-based projections.
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equals() and hashCode() only for the columns that should be fetched from the databaseList<projection>LIMIT )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"):
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.
Key points:
application.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)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)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledSession is obtained by un-wrapping it via EntityManager#unwrap(Session.class)Session#setJdbcBatchSize(Integer size) and get via Session#getJdbcBatchSize()spring.jpa.properties.hibernate.cache.use_second_level_cache=false Ejemplo de salida:
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:
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. |
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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.
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AbstractAggregateRoot and provide a method for registering eventsCheckReviewEvent ), but more can be registeredCheckReviewEventHandler in an asynchronous manner via @AsyncDescription: 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 solicitud. 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.
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hibernate.query.plan_cache_max_sizehibernate.query.plan_parameter_metadata_max_sizeDescription: 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.
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EhCache )@CacheHINT_CACHEABLE Description: This is a SpringBoot application representing a kickoff application for Spring Boot caching and EhCache .
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EhCacheSqlResultSetMapping 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[]> .
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SqlResultSetMapping , NamedNativeQuery and EntityResult 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:
IN operator in a @Query simply add the query in the proper repositorySpecification rely on javax.persistence.criteria.Root.in()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:
ResultTransformer interface 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:
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: 
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:
AFTER INSERT OR AFTER UPDATE ) 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:
Key points:
BookRepository extends QueryByExampleExecutor<S extends T> boolean exists(Example<S> exmpl) with the proper probe (an entity instance populated with the desired fields values)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. |
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@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.
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JOINED Inheritance Strategy And Visitor Design Pattern Description: This application is an example of using JPA JOINED inheritance strategy and Visitor pattern.
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JOINED Inheritance Strategy And Strategy Design Pattern Description: This application is an example of using JPA JOINED inheritance strategy and Strategy pattern.
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Description: This folder holds several applications that shows how each Spring transaction propagation works.
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GenerationType.AUTO And UUID Identifiers Description: This application is an example of using the JPA GenerationType.AUTO for assigning automatically UUID identifiers.
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BINARY(16) columnDescription: This application is an example of manually assigning UUID identifiers.
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BINARY(16) columnuuid2 For Generating UUID Identifiers Description: This application is an example of using the Hibernate RFC 4122 compliant UUID generator, uuid2 .
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BINARY(16) columnDescription: 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.
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TransactionTemplateNote: For a detailed explanation of this application consider my book, Spring Boot Persistence Best Practices
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.
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hibernate.enable_lazy_load_no_trans 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:
Author (only the genre ) and associate the corresponding booksAuthor (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. |
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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.
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UPDATE for different subsets of columns via JDBC statements caching (each triggered UPDATE string will be cached and reused accordingly)Description: This application is an example of logging execution time for a repository query-method.
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RepositoryProfiler ) 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.
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TransactionProfiler )TransactionSynchronizationManager.registerSynchronization()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.
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SqlResultSetMapping , NamedNativeQuerySqlResultSetMapping 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.
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SqlResultSetMapping , NamedNativeQuery and EntityResult@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.
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@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.
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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.
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META-INF/jpa-named-queries.properties{EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementationorm.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:
META-INF/orm.xml{EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementation Description: JPA named (native) queries are commonly written via @NamedQuery and @NamedNativeQuery annotations in entity classes. 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. And, you don't need to modify/pollute entitites with the above annotations.
Key points:
@NamedQuery and @NamedNativeQuery annotations in entity classes{EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementationSort 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. |
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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.
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jpa-named-queries.properties ) in a folder named META-INF the application classpath 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.
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jpa-named-queries.properties ) in a folder named META-INF the application classpathorm.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.
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orm.xml file in a folder named META-INF the application classpathorm.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.
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orm.xml file in a folder named META-INF the application classpathorm.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.
Puntos clave:
<named-native-query/> and <sql-result-set-mapping/> to map the native query to AuthorDto class
Description: This application is a proof of concept for using Spring Projections(DTO) and cross joins written via JPQL and native SQL (for MySQL).
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Book and Formatresources/data-mysql.sql )BookTitleAndFormatType.java )JdbcTemplate And BeanPropertyRowMapper Description: This application is an example of calling a MySQL stored procedure that returns a result set via JdbcTemplate and BeanPropertyRowMapper .
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JdbcTemplate , SimpleJdbcCall and BeanPropertyRowMapper@EntityListeners Description: This application is a sample of using the JPA @MappedSuperclass and @EntityListeners with JPA callbacks.
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Book , is not an entity, it can be abstract , and is annotated with @MappedSuperclass and @EntityListeners(BookListener.class)BookListener defines JPA callbacks (eg, @PrePersist )Book is persisted, loaded, updated, etc the corresponding JPA callbacks are called@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.
Puntos clave:
@Fetch(FetchMode.JOIN) doesn't work for query-methods@Fetch(FetchMode.JOIN) works in cases that fetches the entity by id (primary key) like using EntityManager#find() , Spring Data, findById() , findOne() .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:
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 wellRANK() window function Muestra de salida: 
| 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. |
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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:
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 wellDENSE_RANK() window function Muestra de salida: 
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:
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 wellNTILE() window function Muestra de salida: 
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:
count... (eg, long countByGenre(String genre) ) - Spring Data will generate a SELECT COUNT(...) FROM ... querydelete... 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 deleteddelete... 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 deletedDescription: 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.
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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)Review and we want to know the Author of the Book that has received this ReviewAuthorRepository the following query that will be processed by the Spring Data Query Builder mechanism: Author findByBooksReviews(Review review);SELECT with two LEFT JOINbooks.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.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);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:
@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();
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This item is detailed in my book, Spring Boot Persistence Best Practices.
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:
SELECT (merge) and one UPDATESELECT 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) .
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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)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).
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map() )filter() )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)Streamable Wrapper TypesDescription: 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.
Puntos clave:
Streamablestatic factory method named of(…) or valueOf(…) taking Streamable as argumentDescription: 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 .
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application.properties 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.
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default methods and Collectors.toMap() Description: Consider one of the JPA inheritance strategies (eg, JOINED ). Handling entities inheritance With Spring Data repositories can be done as follows:
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:
application.properties add hibernate.session.events.log.LOG_QUERIES_SLOWER_THAN_MS Ejemplo de salida: 
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 {}
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 JDK 14 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 {}
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 .
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:
AuthorDto and BookDtoResultTransformer interfaceJdbcTemplate And ResultSetExtractor 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, JdbcTemplate and ResultSetExtractor .
From Openjdk JEP359:
Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.
Puntos clave:
AuthorDto and BookDtoJdbcTemplate and ResultSetExtractorDescription: This application is a sample of using dynamic Spring projections via DTO classes.
Key points:
<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. |
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CompletableFuture And Return List<S> 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. It returns List<S> :
CompletableFuture And Return List<S> (1)CompletableFuture And Return List<S> (2) 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.
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SELECT with PESSIMISTIC_WRITE to acquire an exclusive lock to table authorauthor genre with success and sleeps for 10sSELECT with PESSIMISTIC_WRITE to acquire an exclusive lock to table bookbook title with success and sleeps for 10s 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).
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@IdClass 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.
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StatementInspector SPIapplication.properties via spring.jpa.properties.hibernate.session_factory.statement_inspector281. 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.
Puntos clave:
application.properties the setting spring.jpa.properties.hibernate.criteria.literal_handling_mode as inlineDescription: 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.
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pom.xml , add the datasource-proxy-spring-boot-starter starterapplication.properties enable DEBUG level for loggingDescription: 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
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Contact )Author ) via @EmbeddedAuthorDto )
