Hibernate SpringBoot
1.0.0
| Если вам нужно глубоко погрузиться в рецепты повышения производительности, представленные в этом репозитории, я уверен, что вам понравится моя книга «Лучшие практики Spring Boot Persistence». | Если вам нужны советы и иллюстрации более чем 100 проблем с производительностью сохраняемости Java, то «Иллюстрированное руководство по производительности сохраняемости Java» для вас. |
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Образцы Hibernate и Spring Boot
Описание: Это приложение представляет собой образец хранения даты, времени и временных меток в часовом поясе UTC. Второй параметр useLegacyDatetimeCode необходим только для MySQL. В противном случае установите только hibernate.jdbc.time_zone .
Ключевые моменты:
spring.jpa.properties.hibernate.jdbc.time_zone=UTCspring.datasource.url=jdbc:mysql://localhost:3306/screenshotdb?useLegacyDatetimeCode=falseОписание: просмотр подготовленных привязок/извлеченных параметров оператора с помощью настройки регистратора Log4J 2.
Ключевые моменты:
pom.xml исключите ведение журнала Spring Boot по умолчанию.pom.xml добавьте зависимость Log4j 2log4j2.xml добавьте <Logger name="org.hibernate.type.descriptor.sql" level="trace"/> Пример вывода: 
Описание. Просмотр сведений о запросе (тип запроса, параметры привязки, размер пакета, время выполнения и т. д.) через DataSource-Proxy.
Ключевые моменты:
pom.xml зависимость datasource-proxyDataSourceDataSource через ProxyFactory и реализацию MethodInterceptor Пример вывода: 
saveAll(Iterable<S> entities) в MySQL Описание. Пакетная вставка с помощью метода SimpleJpaRepository#saveAll(Iterable<S> entities) в MySQL.
Ключевые моменты:
application.properties установите spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties установите spring.jpa.properties.hibernate.generate_statistics (просто чтобы проверить, что пакетная обработка работает)application.properties установите URL-адрес JDBC с помощью rewriteBatchedStatements=true (оптимизация для MySQL)application.properties установите URL-адрес JDBC с помощью cachePrepStmts=true (включите кеширование и это полезно, если вы также решите установить prepStmtCacheSize , prepStmtCacheSqlLimit и т. д.; без этого параметра кеш отключен)application.properties установите URL-адрес JDBC с помощью useServerPrepStmts=true (таким образом вы переключитесь на подготовленные на стороне сервера операторы (может привести к значительному повышению производительности))spring.jpa.properties.hibernate.order_inserts=true для оптимизации пакетной обработки путем упорядочивания вставокIDENTITY приведет к отключению пакетной обработки вставки@Version , чтобы избежать запуска дополнительных операторов SELECT перед пакетной обработкой (также предотвращайте потерю обновлений в транзакциях с несколькими запросами). Дополнительные инструкции SELECT являются результатом использования merge() вместо persist() ; за сценой saveAll() использует save() , который в случае неновых сущностей (сущностей с идентификаторами) вызывает merge() , который инструктирует Hibernate запустить оператор SELECT , чтобы убедиться, что в хранилище нет записей. база данных с тем же идентификаторомsaveAll() чтобы не «перегрузить» контекст сохранения; обычно EntityManager должен время от времени сбрасываться и очищаться, но во время выполнения saveAll() вы просто не можете этого сделать, поэтому, если в saveAll() есть список с большим объемом данных, все эти данные попадут в Persistence Контекст (кэш 1-го уровня) и останется в памяти до момента очистки; использование относительно небольшого объема данных должно быть приемлемым (в этом примере каждая партия из 30 объектов выполняется в отдельной транзакции и постоянном контексте)saveAll() возвращает List<S> содержащий сохраненные объекты; каждая сохраненная сущность добавляется в этот список; если вам этот List просто не нужен, то он создан зряspring.jpa.properties.hibernate.cache.use_second_level_cache=false Описание: Это приложение представляет собой образец пакетной вставки через EntityManager в MySQL. Таким образом, вы можете легко управлять flush() иclear clear() контекста сохраняемости (кэш 1-го уровня) внутри текущей транзакции. Это невозможно через Spring Boot, saveAll(Iterable<S> entities) , поскольку этот метод выполняет одну очистку для каждой транзакции. Еще одним преимуществом является то, что вы можете вызвать persist() вместо merge() — это используется за сценой SpringBoot saveAll(Iterable<S> entities) и save(S entity) .
Если вы хотите выполнить пакет для каждой транзакции (рекомендуется), проверьте этот пример.
Ключевые моменты:
application.properties установите spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties установите spring.jpa.properties.hibernate.generate_statistics (просто чтобы проверить, что пакетная обработка работает)application.properties установите URL-адрес JDBC с помощью rewriteBatchedStatements=true (оптимизация для MySQL)application.properties установите URL-адрес JDBC с помощью cachePrepStmts=true (включите кеширование и это полезно, если вы также решите установить prepStmtCacheSize , prepStmtCacheSqlLimit и т. д.; без этого параметра кеш отключен)application.properties установите URL-адрес JDBC с помощью useServerPrepStmts=true (таким образом вы переключитесь на подготовленные на стороне сервера операторы (может привести к значительному повышению производительности))spring.jpa.properties.hibernate.order_inserts=true для оптимизации пакетной обработки путем упорядочивания вставокIDENTITY приведет к отключению пакетной обработки вставкиspring.jpa.properties.hibernate.cache.use_second_level_cache=false Пример вывода: 
Описание. Пакетная вставка через JpaContext/EntityManager в MySQL.
Ключевые моменты:
application.properties установите spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties установите spring.jpa.properties.hibernate.generate_statistics (просто чтобы проверить, что пакетная обработка работает)application.properties установите URL-адрес JDBC с помощью rewriteBatchedStatements=true (оптимизация для MySQL)application.properties установите URL-адрес JDBC с помощью cachePrepStmts=true (включите кеширование и это полезно, если вы также решите установить prepStmtCacheSize , prepStmtCacheSqlLimit и т. д.; без этого параметра кеш отключен)application.properties установите URL-адрес JDBC с помощью useServerPrepStmts=true (таким образом вы переключитесь на подготовленные на стороне сервера операторы (может привести к значительному повышению производительности))spring.jpa.properties.hibernate.order_inserts=true для оптимизации пакетной обработки путем упорядочивания вставокIDENTITY приведет к отключению пакетной обработки вставкиEntityManager получается для каждого типа сущности через JpaContext#getEntityManagerByManagedType(Class<?> entity)spring.jpa.properties.hibernate.cache.use_second_level_cache=false Пример вывода: 
Описание. Пакетная вставка через пакетную обработку на уровне сеанса Hibernate (Hibernate 5.2 или выше) в MySQL.
Ключевые моменты:
application.properties установите spring.jpa.properties.hibernate.generate_statistics (просто чтобы проверить, что пакетная обработка работает)application.properties установите URL-адрес JDBC с помощью rewriteBatchedStatements=true (оптимизация для MySQL)application.properties установите URL-адрес JDBC с помощью cachePrepStmts=true (включите кеширование и это полезно, если вы также решите установить prepStmtCacheSize , prepStmtCacheSqlLimit и т. д.; без этого параметра кеш отключен)application.properties установите URL-адрес JDBC с помощью useServerPrepStmts=true (таким образом вы переключитесь на подготовленные на стороне сервера операторы (может привести к значительному повышению производительности))spring.jpa.properties.hibernate.order_inserts=true для оптимизации пакетной обработки путем упорядочивания вставокIDENTITY приведет к отключению пакетной обработки вставкиSession Hibernate получается путем его развертывания с помощью EntityManager#unwrap(Session.class)Session#setJdbcBatchSize(Integer size) и получается через Session#getJdbcBatchSize()spring.jpa.properties.hibernate.cache.use_second_level_cache=false Пример вывода: 
findById() , JPA EntityManager и Session Hibernate Описание. Прямая выборка с помощью примеров Spring Data, EntityManager и Hibernate Session .
Ключевые моменты:
findById()EntityManager использует find()Session Hibernate использует get()Примечание. Вам также может быть интересно прочитать рецепт «Как обогатить DTO виртуальными свойствами с помощью Spring Projections».
Описание. Извлекайте из базы данных только необходимые данные с помощью Spring Data Projections (DTO).
Ключевые моменты:
List<projection>LIMIT )Примечание. Использование проекций не ограничивается использованием механизма построения запросов, встроенного в инфраструктуру репозитория данных Spring. Мы также можем получать прогнозы через JPQL или собственные запросы. Например, в этом приложении мы используем JPQL.
Пример вывода (выберите первые 2 строки; выберите только «имя» и «возраст»): 
| Если вам нужно глубоко погрузиться в рецепты повышения производительности, представленные в этом репозитории, я уверен, что вам понравится моя книга «Лучшие практики Spring Boot Persistence». | Если вам нужны советы и иллюстрации более чем 100 проблем с производительностью сохраняемости Java, то «Иллюстрированное руководство по производительности сохраняемости Java» для вас. |
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Описание. По умолчанию атрибуты объекта загружаются быстро (все сразу). Но мы также можем загружать их и лениво . Это полезно для типов столбцов, которые хранят большие объемы данных: CLOB , BLOB , VARBINARY и т. д. или для деталей , которые должны загружаться по требованию. В этом приложении у нас есть сущность с именем Author . Его свойства: id , name , genre , avatar и age . И мы хотим лениво загружать avatar . Итак, avatar должен загружаться по требованию.
Ключевые моменты:
pom.xml активируйте улучшение байт-кода Hibernate (например, используйте плагин улучшения байт-кода Maven)@Basic(fetch = FetchType.LAZY)application.properties отключите открытие сеанса в представлении Проверьте также:
- Значения по умолчанию для ленивых загруженных атрибутов.
- Отложенная загрузка атрибутов и сериализация Джексона
Описание. Прокси-сервер Hibernate может быть полезен, когда дочерний объект можно сохранить со ссылкой на его родительский объект (ассоциация @ManyToOne или @OneToOne ). В таких случаях получение родительского объекта из базы данных (выполнение оператора SELECT ) является потерей производительности и бессмысленным действием, поскольку Hibernate может установить базовое значение внешнего ключа для неинициализированного прокси.
Ключевые моменты:
EntityManager#getReference()JpaRepository#getOne() -> используется в этом примереload()Author и Book , участвуют в однонаправленной ассоциации @ManyToOne ( Author — родительская сторона)SELECT ), мы создаем новую книгу, устанавливаем прокси в качестве автора этой книги и сохраняем книгу (это вызовет INSERT в таблице book )Пример вывода:
INSERT , а не SELECTОписание: N+1 — это проблема ленивой выборки (но нетерпеливость не является исключением). Это приложение воспроизводит поведение N+1.
Ключевые моменты:
Author и Book в ленивой двунаправленной ассоциации @OneToManyBook лениво, поэтому без Author (результат в 1 запросе)Book и для каждой записи получить соответствующего Author (результат N запросов)Author , поэтому без Book (результат в 1 запросе)Author и для каждой записи получить соответствующую Book (результат N запросов) Пример вывода: 
SELECT DISTINCT через Hibernate HINT_PASS_DISTINCT_THROUGH Подсказка Описание. Начиная с Hibernate 5.2.2, мы можем оптимизировать объекты запроса JPQL (HQL) типа SELECT DISTINCT с помощью подсказки HINT_PASS_DISTINCT_THROUGH . Имейте в виду, что эта подсказка полезна только для запросов JPQL (HQL) JOIN FETCH. Бесполезно для скалярных запросов (например, List<Integer> ), DTO или HHH-13280. В таких случаях ключевое слово DISTINCT JPQL необходимо передать базовому SQL-запросу. Это даст указание базе данных удалить дубликаты из набора результатов.
Ключевые моменты:
@QueryHints(value = @QueryHint(name = HINT_PASS_DISTINCT_THROUGH, value = "false")) Пример вывода: 
Примечание. Механизм грязной проверки Hibernate отвечает за идентификацию изменений объектов во время сброса и запуск соответствующих операторов UPDATE от нашего имени.
Описание. До Hibernate версии 5 механизм грязной проверки опирался на Java Reflection API для проверки каждого свойства каждого управляемого объекта. Начиная с версии Hibernate 5, механизм грязной проверки может полагаться на механизм грязного отслеживания (который представляет собой способность объекта отслеживать изменения своих собственных атрибутов), который требует наличия в приложении расширения байт-кода Hibernate. Механизм «грязного отслеживания» обеспечивает более высокую производительность, особенно если у вас относительно большое количество объектов.
Для «грязного отслеживания» во время процесса улучшения байт-кода байт-код классов сущностей инструментируется Hibernate путем добавления трекера $$_hibernate_tracker . Во время сброса Hibernate будет использовать этот трекер для обнаружения изменений сущностей (каждый трекер сущностей будет сообщать об изменениях). Это лучше, чем проверять каждое свойство каждого управляемого объекта.
Обычно (по умолчанию) инструментирование происходит во время сборки, но его также можно настроить для выполнения во время выполнения или во время развертывания. Предпочтительно выполнять это во время сборки, чтобы избежать накладных расходов во время выполнения.
Добавление улучшения байт-кода и включение грязного отслеживания можно выполнить с помощью плагина, добавленного через Maven или Gradle (также можно использовать Ant). Мы используем Maven, поэтому добавляем его в pom.xml .
Ключевые моменты:
pom.xml Пример вывода: 
Эффект улучшения байт-кода можно увидеть в Author.class здесь. Обратите внимание, как байт-код был инструментирован с помощью $$_hibernate_tracker .
Optional в сущностях и запросах Описание: Это приложение является примером того, как правильно использовать Optional Java 8 в сущностях и запросах.
Ключевые моменты:
Optional (например, findById() )OptionalOptional в геттерах сущностейdata-mysql.sql@OneToMany Описание: Это приложение является доказательством того, как правильно реализовать двунаправленную ассоциацию @OneToMany с точки зрения производительности.
Ключевые моменты:
mappedBy для родителяorphanRemoval для родителя, чтобы удалить дочерние элементы без ссылок@NaturalId )) и/или идентификаторы, сгенерированные базой данных, и правильно переопределите (на дочерней стороне) методы equals() и hashCode() , как здесьtoString() необходимо переопределить, обратите внимание на использование только основных атрибутов, извлекаемых при загрузке объекта из базы данных. Примечание. Обратите внимание на операции удаления, особенно на удаление дочерних объектов. CascadeType.REMOVE и orphanRemoval=true могут создавать слишком много запросов. В таких сценариях использование массовых операций в большинстве случаев является лучшим способом удаления.
Описание: Это приложение является примером написания запроса через JpaRepository , EntityManager и Session .
Ключевые моменты:
JpaRepository используйте создание запроса @Query или Spring DataEntityManager и Session используйте метод createQuery()AUTO в Hibernate 5 и MySQL Описание. В MySQL и Hibernate 5 тип генератора GenerationType.AUTO приведет к использованию генератора TABLE . Это приводит к значительному снижению производительности. Превратить это поведение в генератор IDENTITY можно с помощью GenerationType.IDENTITY или собственного генератора.
Ключевые моменты:
GenerationType.IDENTITY вместо GenerationType.AUTO Пример вывода: 
Описание. Это приложение является примером того, как вызов save() для объекта является избыточным (не необходимым).
Ключевые моменты:
UPDATE без необходимости явного вызова метода save() .save() когда это не обязательно) не влияет на количество запускаемых запросов, но подразумевает снижение производительности в базовых процессах Hibernate.| Если вам нужно глубоко погрузиться в рецепты повышения производительности, представленные в этом репозитории, я уверен, что вам понравится моя книга «Лучшие практики Spring Boot Persistence». | Если вам нужны советы и иллюстрации более чем 100 проблем с производительностью сохраняемости Java, то «Иллюстрированное руководство по производительности сохраняемости Java» для вас. |
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BIG ) SERIAL при пакетной вставке через Hibernate Описание. В PostgreSQL использование GenerationType.IDENTITY отключит пакетную вставку. (BIG)SERIAL действует «почти» как MySQL, AUTO_INCREMENT . В этом приложении мы используем GenerationType.SEQUENCE , который разрешает пакетную обработку вставок, и оптимизируем его с помощью алгоритма оптимизации hi/lo .
Ключевые моменты:
GenerationType.SEQUENCE вместо GenerationType.IDENTITYhi/lo для получения значения hi в базе данных туда и обратно (значение hi полезно для генерации определенного/заданного количества идентификаторов в памяти; до тех пор, пока вы не исчерпаете все идентификаторы в памяти, в этом нет необходимости чтобы передать еще один привет )pooled и pooled-lo (это оптимизация hi/lo , которая позволяет внешним службам использовать базу данных, не вызывая ошибок дублирования ключей)spring.datasource.hikari.data-source-properties.reWriteBatchedInserts=true Пример вывода: 
SINGLE_TABLE Описание. Это приложение представляет собой образец использования стратегии наследования одной таблицы JPA ( SINGLE_TABLE ).
Ключевые моменты:
@Inheritance(strategy=InheritanceType.SINGLE_TABLE) )@NotNull и MySQL.TINYINT Пример вывода (ниже представлена одна таблица, полученная из трех объектов): 
Описание: Это приложение представляет собой образец подсчета и утверждения операторов SQL, запускаемых «за сценой». Очень полезно подсчитывать операторы SQL, чтобы гарантировать, что ваш код не генерирует больше операторов SQL, чем вы думаете (например, N+1 можно легко определить, определив количество ожидаемых операторов).
Ключевые моменты:
pom.xml добавьте зависимости для библиотеки DataSource-Proxy и библиотеки db-util Влада Михалчи.ProxyDataSourceBuilder с помощью countQuery()SQLStatementCountValidator.reset()INSERT , UPDATE , DELETE и SELECT через assertInsert/Update/Delete/Select/Count(long expectedNumberOfSql) Пример вывода (когда количество ожидаемых SQL-запросов не соответствует действительности, выдается исключение): 
Описание: Это приложение представляет собой образец настройки обратных вызовов JPA ( Pre/PostPersist , Pre/PostUpdate , Pre/PostRemove и PostLoad ).
Ключевые моменты:
void и не принимать аргументов. Пример вывода: 
@MapsId для совместного использования идентификатора в отношениях @OneToOne Описание. Вместо обычного однонаправленного/двунаправленного @OneToOne лучше использовать однонаправленные @OneToOne и @MapsId . Это приложение является доказательством концепции.
Ключевые моменты:
@MapsId на дочерней стороне@JoinColumn , чтобы настроить имя столбца первичного ключа@OneToOne @MapsId будет использовать первичный ключ совместно с родительской таблицей (свойство id действует как первичный ключ, так и как внешний ключ).Примечание:
@MapsId также можно использовать для @ManyToOneSqlResultSetMapping и EntityManager Описание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на SqlResultSetMapping и EntityManager .
Ключевые моменты:
SqlResultSetMapping и EntityManagerSqlResultSetMapping и NamedNativeQuery Примечание. Если вы хотите использовать соглашение об именовании {EntityName}.{RepositoryMethodName} для простого создания в репозитории методов интерфейса с тем же именем, что и у собственного именованного запроса, пропустите это приложение и проверьте это.
Описание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на SqlResultSetMapping , NamedNativeQuery .
Ключевые моменты:
SqlResultSetMapping , NamedNativeQueryjavax.persistence.Tuple и собственный SQL Описание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на javax.persistence.Tuple и собственный SQL.
Ключевые моменты:
java.persistence.Tuple в репозитории Spring и отметьте запрос как nativeQuery = truejavax.persistence.Tuple и JPQL Описание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на javax.persistence.Tuple и JPQL.
Ключевые моменты:
java.persistence.Tuple в репозитории SpringОписание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на Constructor Expression и JPQL.
Ключевые моменты:
SELECT new com.bookstore.dto.AuthorDto(a.name, a.age) FROM Author a См. также:
Как получить DTO через конструктор и механизм построения запросов данных Spring
| Если вам нужно глубоко погрузиться в рецепты повышения производительности, представленные в этом репозитории, я уверен, что вам понравится моя книга «Лучшие практики Spring Boot Persistence». | Если вам нужны советы и иллюстрации более чем 100 проблем с производительностью сохраняемости Java, то «Иллюстрированное руководство по производительности сохраняемости Java» для вас. |
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ResultTransformer и собственный SQL Описание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на Hibernate, ResultTransformer и собственный SQL.
Ключевые моменты:
AliasToBeanConstructorResultTransformer для DTO без установщиков, но с конструкторомTransformers.aliasToBean() для DTO с установщикамиEntityManager.createNativeQuery() и unwrap(org.hibernate.query.NativeQuery.class)ResultTransformer устарел, но пока не появится замена (вероятно, в Hibernate 6.0) его можно использовать (читайте дальше)ResultTransformer и JPQL Описание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на Hibernate, ResultTransformer и JPQL.
Ключевые моменты:
AliasToBeanConstructorResultTransformer для DTO без установщиков, с конструкторомTransformers.aliasToBean() для DTO с установщикамиEntityManager.createQuery() и unwrap(org.hibernate.query.Query.class)ResultTransformer устарел, но пока не появится замена (в Hibernate 6.0) его можно использовать (читайте далее)Описание. Получение большего количества данных, чем необходимо, может привести к снижению производительности. Использование DTO позволяет нам извлекать только необходимые данные. В этом приложении мы полагаемся на представления сущностей Blaze-Persistence.
Ключевые моменты:
pom.xml зависимости, специфичные для Blaze-Persistence.CriteriaBuilderFactory и EntityViewManagerEntityViewRepositoryfindAll() , findOne() и т. д.@ElementCollection (без @OrderColumn ) Описание: Это приложение показывает возможные потери производительности при использовании @ElementCollection . В данном случае без @OrderColumn . Как вы можете видеть в следующем пункте (34), добавление @OrderColumn может смягчить некоторые потери производительности.
Ключевые моменты:
@ElementCollection не имеет первичного ключа@ElementCollection отображается в отдельной таблице@ElementCollection , если в этой коллекции много вставок/удалений; вставки/удаления заставят Hibernate удалить все существующие строки таблицы, обработать коллекцию в памяти и повторно вставить оставшиеся строки таблицы, чтобы отразить коллекцию из памяти. Пример вывода: 
@ElementCollection с @OrderColumn Описание: Это приложение показывает снижение производительности при использовании @ElementCollection . В данном случае с помощью @OrderColumn . Но, как вы можете видеть в этом приложении (по сравнению с пунктом 33), добавление @OrderColumn может смягчить некоторые потери производительности, когда операции происходят вблизи хвоста коллекции (например, добавление/удаление в конце коллекции или из нее). В основном все элементы, расположенные перед записью добавления/удаления, остаются нетронутыми, поэтому снижение производительности можно игнорировать, если мы затрагиваем строки, близкие к хвосту коллекции.
Ключевые моменты:
@ElementCollection не имеет первичного ключа@ElementCollection отображается в отдельной таблице@ElementCollection с @OrderColumn когда у вас много вставок и удалений рядом с хвостом коллекции Пример вывода: 
Примечание. Прежде чем читать этот материал, попробуйте убедиться, что Hibernate5Module — это не то, что вам нужно.
Описание. Антишаблон Open-Session in View активируется в SpringBoot по умолчанию. Теперь представьте себе ленивую ассоциацию (например, @OneToMany ) между двумя объектами, Author и Book (автор связал больше книг). Затем конечная точка контроллера REST извлекает Author без связанной с ним Book . Но View (точнее, Джексон) также вызывает ленивую загрузку связанной Book . Поскольку OSIV предоставит уже открытый Session , инициализация прокси пройдет успешно. Решение, позволяющее избежать этого снижения производительности, начинается с отключения OSIV. Далее явно инициализируйте неизвлеченные ленивые ассоциации. Таким образом, представление не будет вызывать отложенную загрузку.
Ключевые моменты:
application.properties этот параметр: spring.jpa.open-in-view=falseAuthor и явно инициализировать связанную с ним Book со значениями (по умолчанию) (например, null )@JsonInclude(Include.NON_EMPTY) на этом уровне объекта, чтобы избежать отображения null или того, что считается пустым в полученном JSON ПРИМЕЧАНИЕ. Если OSIV включен, разработчик по-прежнему может инициализировать неизвлеченные отложенные ассоциации вручную, если он делает это вне транзакции, чтобы избежать очистки. Но почему это работает? Поскольку Session открыт, почему инициализация ассоциаций управляемого объекта вручную не вызывает сброс? Ответ можно найти в документации OpenSessionInViewFilter , в которой указано, что: Этот фильтр по умолчанию не очищает Session Hibernate, при этом для режима очистки установлено значение FlushMode.NEVER . Предполагается, что он будет использоваться в сочетании с транзакциями уровня обслуживания, которые заботятся о сбросе: активный менеджер транзакций временно изменит режим сброса на FlushMode.AUTO во время транзакции чтения-записи, а в конце режим сброса будет сброшен на FlushMode.NEVER каждой транзакции. Если вы собираетесь использовать этот фильтр без транзакций, рассмотрите возможность изменения режима очистки по умолчанию (с помощью свойства «flushMode»).
Описание: Это приложение является доказательством концепции использования Spring Projections (DTO) и внутренних соединений, написанных с помощью JPQL и собственного SQL (для MySQL).
Ключевые моменты:
Author и Book в (ленивой) двунаправленной ассоциации @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Описание: Это приложение является доказательством концепции использования Spring Projections (DTO) и левых соединений, написанных с помощью JPQL и собственного SQL (для MySQL).
Ключевые моменты:
Author и Book в (ленивой) двунаправленной ассоциации @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Описание: Это приложение является доказательством концепции для использования пружинных проекций (DTO) и правых соединений, написанных через JPQL и Native SQL (для MySQL).
Ключевые моменты:
Author и Book в (ленивой) двунаправленной ассоциации @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Описание: Это приложение является доказательством концепции для использования проекций Spring (DTO) и включающих полных соединений, написанных через JPQL и Native SQL (для PostgreSQL).
Ключевые моменты:
Author и Book в (ленивой) двунаправленной ассоциации @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )| Если вам нужно глубокое погружение в рецепты производительности, выставленные в этом хранилище, я уверен, что вам понравится моя книга «Лучшие практики Spring Boot Stresistence» | Если вам нужна рука советов и иллюстраций 100+ проблем с устойчивости Java, то «Иллюстрированное руководство по устойчивости Java» для вас. |
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Описание: Это приложение является доказательством концепции для использования проекций Spring (DTO) и эксклюзивных левых соединений, написанных через JPQL и Native SQL (для MySQL).
Ключевые моменты:
Author и Book в (ленивой) двунаправленной ассоциации @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Описание: Это приложение является доказательством концепции для использования проекций Spring (DTO) и эксклюзивных правых соединений, написанных через JPQL и Native SQL (для MySQL).
Ключевые моменты:
Author и Book в (ленивой) двунаправленной ассоциации @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )
Описание: Это приложение является доказательством концепции для использования пружинных проекций (DTO) и эксклюзивных полных соединений, написанных через JPQL и Native SQL (для PostgreSQL).
Ключевые моменты:
Author и Book в (ленивой) двунаправленной ассоциации @OneToMany )resources/data-mysql.sql )AuthorNameBookTitle.java )Описание: Это приложение является доказательством концепции для использования пружинных крючков после торговли и как они могут повлиять на производительность упорства.
Ключевые моменты:
Описание: Это приложение является доказательством концепции использования пружинных прогнозов (DTO) и присоединиться к не связанным объектам. Hibernate 5.1 представил явные соединения на несвязанных объектах, а синтаксис и поведение аналогичны операторам JOIN SQL.
Ключевые моменты:
Author и Book , не связанные с ними)resources/data-mysql.sql )BookstoreDto )@EqualsAndHashCode и @Data в сущностях и как переопределить equals() и hashCode() Описание: объекты должны реализовать equals() и hashCode() как здесь. Основная идея состоит в том, что Hibernate требует, чтобы сущность была равна себе во всех переходах состояния ( переходные , прикрепленные , отстраненные и удаленные ). Использование Lombok @EqualsAndHashCode (или @Data ) не будет уважать это требование.
Ключевые моменты:
Избегайте этих подходов
@EqualsAndHashCode (Entity: LombokDefaultBook , тест: LombokDefaultEqualsAndHashCodeTest )@EqualsAndHashCode только с первичным ключом (Entity: LombokIdBook , тест: LombokEqualsAndHashCodeWithIdOnlyTest )equals() и hashCode() (Entity: DefaultBook , тест: DefaultEqualsAndHashCodeTest )equals() и hashCode() содержащий только идентификатор, сгенерированный базой данных (объект: IdBook , тест: IdEqualsAndHashCodeTest )Предпочитаю эти подходы
BusinessKeyBook , тест: BusinessKeyEqualsAndHashCodeTest )@NaturalId (Entity: NaturalIdBook , тест: NaturalIdEqualsAndHashCodeTest )IdManBook , тест: IdManEqualsAndHashCodeTest )IdGenBook , тест: IdGenEqualsAndHashCodeTest ) 
LazyInitializationException через JOIN FETCHСм. также:
Описание: Как правило, LAZY мы получаем EAGER LazyInitializationException . Это очень плохо! Это запах кода. Лучший способ избежать этого исключения - полагаться на JOIN FETCH (если вы планируете изменять избираемые объекты) или JOIN + DTO (если полученные данные только прочитаны). JOIN FETCH позволяет инициализировать ассоциации вместе с их родительскими объектами, используя один SELECT . Это особенно полезно для извлечения связанных коллекций.
Это приложение является примером JOIN FETCH для избежания LazyInitializationException .
Ключевые моменты:
Author и Book в @OneToMany Lazy-Bideerational Association)JOIN FETCH , чтобы получить автора, включая его книгиJOIN FETCH (или JOIN ), чтобы получить книгу, включая ее автора Пример вывода: 
Описание: Это пример Spring Boot, основанный на следующей статье. Является функциональной реализацией примера VLAD. Настоятельно рекомендуется прочитать эту статью.
Ключевые моменты:
Описание: Это пример Spring Boot, который использует Hibernate 5.2.10 Возможность задержки при необходимости задержки подключения. По умолчанию, в режиме ресурса-локальный , соединение базы данных Aquuried сразу же после вызова метода, аннотированного с @Transactional . Если этот метод содержит несколько трудоемких задач перед первым оператором SQL, то соединение удерживается открытым для ничего. Но Hibernate 5.2.10 позволяет нам задержать получение подключения по мере необходимости. Этот пример полагается на Hikaricp в качестве пула соединений по умолчанию для Spring Boot.
Ключевые моменты:
spring.datasource.hikari.auto-commit=false in application.propertiesspring.jpa.properties.hibernate.connection.provider_disables_autocommit=true в application.properties Пример вывода: 
hi/lo ПРИМЕЧАНИЕ. Если системы, внешние по отношению к вашему приложению, должны вставлять строки в ваши таблицы, не полагаются на алгоритм hi/lo , поскольку в таких случаях это может вызвать ошибки, возникшие в результате создания дублированных идентификаторов. Полагайтесь на pooled или pooled-lo алгоритмы (оптимизации hi/lo ).
Описание: Это пример Spring Boot с использованием алгоритма hi/lo для генерации 1000 идентификаторов в 10 круглых познаниях базы данных для пакета 1000 вставки в партиях 30.
Ключевые моменты:
SEQUENCE (например, в PostgreSQL)hi/lo как в Author.java Entity Пример вывода: 
| Если вам нужно глубокое погружение в рецепты производительности, выставленные в этом хранилище, я уверен, что вам понравится моя книга «Лучшие практики Spring Boot Stresistence» | Если вам нужна рука советов и иллюстраций 100+ проблем с устойчивости Java, то «Иллюстрированное руководство по устойчивости Java» для вас. |
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@ManyToMany Описание: Это приложение является доказательством концепции того, как правильно реализовать двунаправленную ассоциацию @ManyToMany с точки зрения производительности.
Ключевые моменты:
mappedBy сторонуSet не ListCascadeType.PERSIST и CascadeType.MERGE , но избегайте CascadeType.REMOVE/ALL@ManyToMany ленив по умолчанию; Держите это так!@NaturalId )) и/или идентификаторы, сгенерированные базой данных и правильно переопределите (с обеих сторон) методы equals() и hashCode() как здесьtoString() должен быть переопределен, то обратите внимание на то, чтобы включить только для основных атрибутов, полученных при загрузке объекта из базы данныхSet вместо List в ассоциациях @ManyToMany Описание: Это пример Spring Boot снятия строк в случае двунаправленного @ManyToMany с использованием List Set . Вывод в том, что Set намного лучше! Это относится и к однонаправленному!
Ключевые моменты:
Set гораздо более эффективно, чем List Пример вывода: 
log4jdbcОписание: Просмотреть данные запроса через log4jdbc.
Ключевые моменты:
pom.xml , добавьте зависимость log4jdbc Выходной образец: 
Описание: Просмотреть подготовленное привязка оператора/извлеченные параметры через TRACE .
Ключевые моменты:
application.properties добавить: logging.level.org.hibernate.type.descriptor.sql=TRACE Выходной образец: 
java.time.YearMonth как Integer или Date через библиотеку Hibernate Types Описание: Типы Hibernate - это набор дополнительных типов, не поддерживаемых по умолчанию в Hibernate Core. Одним из этих типов является java.time.YearMonth . Это приложение Spring Boot, которое использует тип Hibernate для хранения этого YearMonth в базе данных MySQL в качестве целого числа или даты.
Ключевые моменты:
pom.xml@TypeDef для карты typeClass для defaultForType Пример вывода: 
ПРИМЕЧАНИЕ . Использование функций SQL в части (не в части WHERE ) запроса в JPA 2.1 может быть выполнено через function() SELECT как здесь.
Описание: Попытка использовать функции SQL (стандартные или определенные) в запросах JPQL может привести к исключениям, если Hibernate не распознает их и не может анализировать запрос JPQL. Например, функция mysql, concat_ws не распознается Hibernate. Это приложение представляет собой приложение Spring Boot, основанное на Hibernate 5.3, которое регистрирует функцию concat_ws через MetadataBuilderContributor и информировать о ней через свойство metadata_builder_contributor . В этом примере также используются @Query и EntityManager , так что вы можете увидеть два варианта использования.
Ключевые моменты:
MetadataBuilderContributor и зарегистрировать функцию concat_ws mysqlspring.jpa.properties.hibernate.metadata_builder_contributor application.properties MetadataBuilderContributor Пример вывода: 
Описание: Это приложение представляет собой образец ведения журнала только медленных запросов с помощью данных данных . Медленный запрос - это запрос, который имеет время исполнения, больше, чем специфический порог в миллисекундах.
Ключевые моменты:
pom.xml зависимость от прокси-данных данныхDataSourceDataSource через ProxyFactory и реализацию MethodInterceptorafterQuery() Пример вывода: 
SELECT COUNT подсчет подсчета и возвращаемой Page<dto> Описание: Это приложение получает данные в качестве Page<dto> с помощью пружинной загрузки. В большинстве случаев данные, которые должны быть страстными, представляют собой данные только для чтения . Извлечение данных в сущности должно быть сделано только в том случае, если мы планируем изменить эти данные, поэтому извлечение только данных чтения только в качестве Page<entity> не предпочтительнее, поскольку они могут оказаться в значительном штрафе. SELECT COUNT запускаемый для подсчета общего количества записей, является подразделением основного SELECT . Следовательно, вместо двух (как правило, будет одна база данных об обратном направлении (как правило, есть один запрос, необходимый для получения данных, а один для подсчета общего количества записей).
Ключевые моменты:
PagingAndSortingRepositoryList<dto>List<dto> и подходящую Pageable для создания Page<dto>SELECT COUNT и List<dto> Описание: Это приложение получает данные в качестве List<dto> через пружинную загрузку. В большинстве случаев данные, которые должны быть страстными, представляют собой данные только для чтения . Привлечение данных в объекты должны быть сделаны только в том случае, если мы планируем изменить эти данные, поэтому привлечение только данных чтения только в качестве List<entity> не предпочтительнее, поскольку они могут оказаться в значительном штрафе. SELECT COUNT запускаемый для подсчета общего количества записей, является подразделением основного SELECT . Следовательно, вместо двух (как правило, будет одна база данных об обратном направлении (как правило, есть один запрос, необходимый для получения данных, а один для подсчета общего количества записей).
Ключевые моменты:
PagingAndSortingRepositoryList<dto> Если вы используете «стартеры» spring-boot-starter-jdbc или «стартеров» spring-boot-starter-data-jpa , вы автоматически получаете зависимость от Hikaricp
Примечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение для начала, которое настраивает Hikaricp через application.properties . jdbcUrl настроен для базы данных MySQL. В целях тестирования в приложении используется ExecutorService для моделирования параллельных пользователей. Проверьте отчет HICKARICP, показывающий статус пула соединений.
Ключевые моменты:
application.properties spring.datasource.hikari.* Выходной образец: 
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DataSourceBuilder Если вы используете «стартеры» spring-boot-starter-jdbc или «стартеров» spring-boot-starter-data-jpa , вы автоматически получаете зависимость от Hikaricp
Примечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение для начала, которое настраивает Hikaricp через DataSourceBuilder . jdbcUrl настроен для базы данных MySQL. В целях тестирования в приложении используется ExecutorService для моделирования параллельных пользователей. Проверьте отчет HICKARICP, показывающий статус пула соединений.
Ключевые моменты:
application.properties app.datasource.*@Bean который возвращает DataSource Выходной образец:
Это приложение подробно описано в этой статье Dzone.
DataSourceBuilderПримечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение, которое настраивает BONECP через DataSourceBuilder . jdbcUrl настроен для базы данных MySQL. В целях тестирования в приложении используется ExecutorService для моделирования параллельных пользователей.
Ключевые моменты:
pom.xml добавить зависимость Bonecpapplication.properties app.datasource.*@Bean который возвращает DataSource Выходной образец: 
DataSourceBuilderПримечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение для начала, которое настраивает ViburDBCP через DataSourceBuilder . jdbcUrl настроен для базы данных MySQL. В целях тестирования в приложении используется ExecutorService для моделирования параллельных пользователей.
Ключевые моменты:
pom.xml добавить зависимость viburdbcpapplication.properties app.datasource.*@Bean который возвращает DataSource Выходной образец: 
DataSourceBuilderПримечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение для начала, которое настраивает C3P0 через DataSourceBuilder . jdbcUrl настроен для базы данных MySQL. В целях тестирования в приложении используется ExecutorService для моделирования параллельных пользователей.
Ключевые моменты:
pom.xml добавить зависимость C3P0application.properties app.datasource.*@Bean который возвращает DataSource Выходной образец: 
DataSourceBuilderПримечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение для начала, которое настраивает DBCP2 через DataSourceBuilder . jdbcUrl настроен для базы данных MySQL. В целях тестирования в приложении используется ExecutorService для моделирования параллельных пользователей.
Ключевые моменты:
pom.xml добавить зависимость DBCP2application.properties app.datasource.*@Bean который возвращает DataSourceDataSourceBuilderПримечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение для начала, которое настраивает Tomcat через DataSourceBuilder . jdbcUrl настроен для базы данных MySQL. В целях тестирования в приложении используется ExecutorService для моделирования параллельных пользователей.
Ключевые моменты:
pom.xml добавить зависимость Tomcatapplication.properties app.datasource.*@Bean который возвращает DataSource Выходной образец: 
Примечание. Лучший способ настроить параметры пула соединений состоит в использовании Flexy Pool от VLAD Mihalcea. Через Flexy Pool вы можете найти оптимальные настройки, которые поддерживают высокоэффективность вашего пула соединений.
Описание: Это приложение Kickoff, в котором используются два источника данных (две базы данных MySQL, одну из названных authorsdb и один с именем booksdb ) с двумя пулами соединений (каждая база данных использует свой собственный пул соединений HikaricP с различными настройками). На основе вышеуказанных элементов довольно легко настроить два пула соединений от двух разных поставщиков.
Ключевые моменты:
app.datasource.ds1 application.properties app.datasource.ds2@Bean , который возвращает первый DataSource и отметьте его как @Primary@Bean , который возвращает второй DataSourceEntityManagerFactory и укажите пакеты на сканирование для каждого из нихEntityManager в правильных пакетах Выходной образец: 
ПРИМЕЧАНИЕ . Если вы хотите, чтобы вы предоставили беглый API, не изменяя сеттеры, рассмотрите этот элемент.
Описание: Это образец применения, которое изменяет методы установки объектов, чтобы расширить возможности свободного API.
Ключевые моменты:
this вместо void в сетевых устройствах ПРИМЕЧАНИЕ API. Пример: 
ПРИМЕЧАНИЕ . Если вы хотите, чтобы вы предоставили беглый API, изменяя сеттеры, рассмотрите этот элемент.
Описание: Это образец приложения, которое добавляет в сущности дополнительные методы (например, для setName мы добавляем name ), чтобы расширить возможности Fluent API.
Ключевые моменты:
this вместо void ПРИМЕЧАНИЕ API. Пример: 
| Если вам нужно глубокое погружение в рецепты производительности, выставленные в этом хранилище, я уверен, что вам понравится моя книга «Лучшие практики Spring Boot Stresistence» | Если вам нужна рука советов и иллюстраций 100+ проблем с устойчивости Java, то «Иллюстрированное руководство по устойчивости Java» для вас. |
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Slice<T> findAll() Скорее всего, это все, что вам нужно: как принести Slice<entity> / Slice<dto> через fetchAll / fetchAllDto
Некоторые реализации Slice<T> findAll() :
"SELECT e FROM " + entityClass.getSimpleName() + " e;"CriteriaBuilder вместо твердого кодируемого SQLSort , поэтому возможна результаты сортировкиSort и Specification данных пружиныSort , LockModeType , QueryHints и Specification пружиныPageable и/или Specification путем расширения SimpleJpaRepository из данных Spring. В основном эта реализация является единственной, которая возвращает Page<T> вместо Slice<T> , но она не запускает дополнительное SELECT COUNT , поскольку он был исключен, переопределив метод Page<T> readPage(...) из SimpleJpaRepository . Основной недостаток заключается в том, что, возвращая Page<T> вы не знаете, есть ли следующая страница или текущая является последней. Тем не менее, есть обходные пути, чтобы иметь это. В этой реализации вы не можете установить подсказки LockModeType или запроса. История : Spring Boot обеспечивает встроенный механизм пьесы, основанный на смещении , который возвращает Page или Slice . Каждый из этих API представляет страницу данных и некоторые метаданные. Основное отличие состоит в том, что Page содержит общее количество записей, в то время как Slice может сказать, только если есть еще одна страница. Для Page Spring Boot предоставляет метод findAll() способный принимать в качестве аргументов, а Pageable Example Specification Чтобы создать Page , которая содержит общее количество записей, этот метод запускает SELECT COUNT экш -веды рядом с запросом, используемым для получения данных текущей страницы. Это может быть штрафом на производительность, поскольку запрос SELECT COUNT запускается каждый раз, когда мы запрашиваем страницу. Чтобы избежать этого экстраквета, Spring Boot обеспечивает более расслабленный API, Slice API. Использование Slice вместо Page удаляет необходимость этого дополнительного запроса SELECT COUNT и возвращает страницу (записи) и некоторые метаданные без общего количества записей. Таким образом, в то время как Slice не знает общего количества записей, он все еще может сказать, есть ли еще одна страница, доступная после текущей или это последняя страница. Проблема заключается в том, что Slice отлично работает для запросов, содержащих SQL, WHERE пункт (включая те, которые используют механизм строителя запросов, встроенный в данные Spring), но он не работает для findAll() . Этот метод все равно будет возвращать Page вместо Slice , поэтому запрос SELECT COUNT запускается для Slice<T> findAll(...); .
Описание: Это набор приложений для образцов, которые предоставляют различные версии метода Slice<T> findAll(...) . У нас есть минималистская реализация, которая опирается на жесткий запрос как: "SELECT e FROM " + entityClass.getSimpleName() + " e"; (Этот рецепт), для пользовательской реализации, которая поддерживает сортировку, спецификацию, режим блокировки и запросы намекает на реализацию, которая опирается на расширение SimpleJpaRepository .
Ключевые моменты:
abstract класс, который обнажает Slice<T> findAll(...) Методы ( SlicePagingRepositoryImplementation )findAll() для возврата Slice<T> (или Page<T> , но без общего количества элементов)SliceImpl ( Slice<T> ) или PageImpl ( Page<T> ) без общего количества элементовreadSlice() или переопределить страницу SimpleJpaRepository#readPage() чтобы избежать SELECT COUNTAuthor.class ) в этот abstract класс через репозиторий класса ( AuthorRepository )COUNT(*) OVER и возвращаемого List<dto> Описание: Как правило, при смещении страниц есть один запрос, необходимый для получения данных, а один для подсчета общего количества записей. Но мы можем получить эту информацию в одной базе данных Rountrip через подразделение SELECT COUNT вложенное в основной SELECT . Более того, для поставщиков баз данных, которые поддерживают функции оконного окна, существует решение, полагаемое на COUNT(*) OVER() как в этом приложении, которое использует эту функцию окна в собственном запросе против MySQL 8. Итак, предпочитайте это вместо SELECT COUNT Subquery .
Ключевые моменты:
COUNT(*) OVER() Пример: 
Описание: Когда мы полагаемся на подключение к смещению , у нас есть штраф за производительность, вызванную отбрасыванием n записей, прежде чем достигли желаемого смещения . Большой N приводит к значительному штрафу производительности. Когда у нас есть большой N, лучше полагаться на странификацию Keyset , которая поддерживает «постоянное» время для больших наборов данных. Чтобы понять, как плохое смещение может выполнить, проверьте эту статью:
Скриншот из этой статьи ( смещенная страница): 
Нужно знать, есть ли записи больше записей?
По своей природе, Keyset не использует SELECT COUNT чтобы получить количество общих записей. Но, с небольшим настроением, мы можем легко сказать, есть ли записи, поэтому для показа Next Page типа. Главным образом, если вам нужна такая вещь, рассмотрите это приложение, кульминация которого указана ниже:
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);
}
Или, как это (полагайтесь на метод 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);
}
Кнопка Previous Page может быть легко реализована на основе первой записи.
Ключевые моменты:
id )WHERE и ORDER BY предложениям вашего SQLОписание: Это классический пример пружинного ботинка . Тем не менее, не рекомендуется использовать этот подход в производстве из -за его наказания на производительность, объясненные далее.
Когда мы полагаемся на смещенную странику, у нас есть штраф за производительность, вызванную отбрасыванием n записей, прежде чем достичь желаемого смещения . Большой N приводит к значительному штрафу производительности. Еще одним штрафом является дополнительное SELECT необходимый для подсчета общего количества записей. Чтобы понять, как плохое смещение страниц может выполнить, пожалуйста, проверьте эту статью. Скриншот из этой статьи ниже: Тем не менее, может быть, этот пример немного экстремальный. Для относительно небольших наборов данных офсетная страница не так уж и плох (она близок к производительности к клавишной странице), и, поскольку Spring Boot обеспечивает встроенную поддержку для смещения страниц через API Page , он очень прост в его использовании. Однако, в зависимости от случая, мы можем немного оптимизировать офсетную страницу, как в следующих примерах:
Принесите страницу в качестве Page :
COUNT(*) OVER and Return Page<dto>COUNT(*) OVER and Return Page<entity> через дополнительный столбецSELECT COUNT и возвращаемой Page<dto>SELECT COUNT подсчет подсчета и возвращаемой Page<entity> через дополнительный столбецSELECT COUNT подсчет подсчета и возвращаемой Page<projection> , что карты объектов и общее количество записей через проекцию Принесите страницу в качестве List :
COUNT(*) OVER and Return List<dto>COUNT(*) OVER и RETURN List<entity> С помощью дополнительного столбцаSELECT COUNT и List<dto>SELECT COUNT и List<entity> через дополнительный столбецSELECT COUNT и List<projection> , которые карты объектов и общее количество записей с помощью проекцииНо: если офсетная страница вызывает у вас проблемы с производительностью, и вы решите пойти с ключевой страницей, пожалуйста, проверьте здесь (Pagination Keyset Pagination).
Ключевые моменты классической офсетной страниц:
PagingAndSortingRepositoryPage<entity>Примеры классической офсетной страниц:
findAll(Pageable) без сортировки:repository.findAll(PageRequest.of(page, size));findAll(Pageable) с сортировкой: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); Описание: Предположим, что у нас есть отношения между Author и Book сущностями. Когда мы спасаем автора, мы также сохраняем его книги благодаря каскаде все/упорствуют. Мы хотим создать кучу авторов с книгами и сохранить их в базе данных (например, база данных MySQL), используя методику пакета. По умолчанию это приведет к тому, что выпадение каждого автора и книг на одного автора (одна партия для автора и одна партия для книг, еще одна партия для автора и другая партия для книг и так далее). In order to batch authors and books, we need to order inserts as in this application.
Key points: Beside all setting specific to batching inserts in MySQL, we need to set up in application.properties the following property: spring.jpa.properties.hibernate.order_inserts=true
Example without ordered inserts: 
Example with ordered inserts: 
Implementations:
Description: Batch updates in MySQL.
Ключевые моменты:
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 updatesapplication.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 Пример вывода: 
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=? ... заявление. 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.
Ключевые моменты:
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 wellПример вывода:
@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.
Ключевые моменты:
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.
Ключевые моменты:
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 Output sample: 
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.
Ключевые моменты:
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) Output sample: 
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.
Ключевые моменты:
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.
Ключевые моменты:
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 ) Пример вывода: 
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.
Ключевые моменты:
AuthorRepository ) Примеры:
- 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.
Ключевые моменты:
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 .
Ключевые моменты:
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")Пример вывода:
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.
Ключевые моменты:
@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 ).
Ключевые моменты:
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 .
Ключевые моменты:
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.
Ключевые моменты:
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.
Ключевые моменты:
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 .
Ключевые моменты:
@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.
Ключевые моменты:
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.
Ключевые моменты:
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 = ?") Пример вывода: 
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.
Ключевые моменты:
@Bean that returns the DataSource programmaticallyDescription: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Ключевые моменты:
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.
Ключевые моменты:
@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.
Ключевые моменты:
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.
Ключевые моменты:
См. также:
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.
Ключевые моменты:
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 FETCHСм. также:
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 .
Ключевые моменты:
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 FETCHСм. также:
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).
Ключевые моменты:
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.
Ключевые моменты:
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.
Ключевые моменты:
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 .
Ключевые моменты:
@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). Почему? 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.
Ключевые моменты:
@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 ).
Ключевые моменты:
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.
Ключевые моменты:
@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.
Ключевые моменты:
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.
Ключевые моменты:
@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.
Ключевые моменты:
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.
Ключевые моменты:
@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.
Ключевые моменты:
@Where(clause = "condition to be met") in entity (check the Author entity)Description: Batch inserts (in MySQL) in Spring Boot style.
Ключевые моменты:
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 Пример вывода: 
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.
Ключевые моменты:
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).
Ключевые моменты:
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.
Ключевые моменты:
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.
Ключевые моменты:
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).
Ключевые моменты:
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 .
Ключевые моменты:
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> .
Ключевые моменты:
PagingAndSortingRepositoryList<dto>List<dto> and Pageable to create a Page<dto> Пример:
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() .
Пример использования:
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).
Ключевые моменты:
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.
Ключевые моменты:
@Bean that returns the DataSource programmatically Output sample:
@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.
Ключевые моменты:
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 .
Ключевые моменты:
@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 .
Ключевые моменты:
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:
Метод 1:
columnDefinition element of @Column to specify as an SQL query expression the formula for calculating the persistent propertyМетод 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 .
Ключевые моменты:
@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.
Ключевые моменты:
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.
Ключевые моменты:
@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.
Ключевые моменты:
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 .
Ключевые моменты:
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") .
Ключевые моменты:
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.
Ключевые моменты:
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 .
Ключевые моменты:
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.
Ключевые моменты:
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.
Ключевые моменты:
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 .
Ключевые моменты:
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.
Ключевые моменты:
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. . Более подробная информация здесь.
Description: This is a sample application of using entity graphs in Spring Boot.
Ключевые моменты:
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. . Более подробная информация здесь.
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 .
Ключевые моменты:
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. . Более подробная информация здесь.
Description: This is a sample application of defining ad-hoc entity graphs in Spring Boot.
Ключевые моменты:
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.
Key points:
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 .
Key points:
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 Пример вывода:
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.
Ключевые моменты:
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.
Key points:
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 )
Key points:
@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 )
Key points:
IDENTITY generator@Inheritance(strategy=InheritanceType.TABLE_PER_CLASS)@MappedSuperclass Description: This application is a sample of using the JPA @MappedSuperclass .
Key points:
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 ) Output sample: 
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! Benchmark! Benchmark!
As a rule of thumb, prefer subqueries only if you cannot use joins, or if you can prove that they are faster than the alternative joins.
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.
Key points:
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 Output sample: 
Description: This application is an example of finding top N rows of every group.
Ключевые моменты:
ROW_NUMBER() window function Output sample: 
| 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.
Key points:
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>)] .
Key points:
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.
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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.
Key points:
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.
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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 Пример вывода:
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.
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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.
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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.
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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 Пример вывода:
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.
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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 Пример вывода:
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.
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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 приложение. 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 .
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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 .
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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.
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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 ).
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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:
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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 .
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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.
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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.
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@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.
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<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.
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@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.
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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 Output sample: 
| 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.
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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 Output sample: 
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.
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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 Output sample: 
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.
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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) .
Key points:
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).
Key points:
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.
Key points:
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 .
Key points:
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.
Key points:
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 Пример вывода: 
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.
Ключевые моменты:
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.
Key points:
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).
Key points:
@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.
Key points:
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.
Key points:
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.
Key points:
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
Key points:
Contact )Author ) via @EmbeddedAuthorDto )
